USPatent applicationPatented

Non-coding RNAS and uses thereof

Granted 12 Jan 2021 · 6 office actions

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Abstract

Provided herein are compositions and methods for cancer diagnosis, research and therapy, including but not limited to, cancer markers. In particular, provided herein are non-coding RNAs as diagnostic markers and clinical targets for cancer.

Description

42 parts
›The present application claims priority to U.S. Provisional…

The present application claims priority to U.S. Provisional Patent Application Ser. No. 62/088,772 filed Dec. 8, 2014, the disclosure of which is herein incorporated by reference in its entirety.

›STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under CA111275, CA154365 and CA069568 awarded by the National Institutes of Health and support under W81XWH-11-1-0337 awarded by the Army/MRMC. The Government has certain rights in the invention.

›FIELD OF THE DISCLOSURE

Provided herein are compositions and methods for cancer diagnosis, research and therapy, including but not limited to, cancer markers. In particular, provided herein are non-coding RNAs as diagnostic markers and clinical targets for cancer.

›BACKGROUND OF THE DISCLOSURE

Afflicting one out of nine men over age 65, prostate cancer (PCA) is a leading cause of male cancer-related death, second only to lung cancer (Abate-Shen and Shen, Genes Dev 14:2410 [2000]; Ruijter et al., Endocr Rev, 20:22 [1999]). The American Cancer Society estimates that about 184,500 American men will be diagnosed with prostate cancer and 39,200 will die in 2001.

Prostate cancer is typically diagnosed with a digital rectal exam and/or prostate specific antigen (PSA) screening. An elevated serum PSA level can indicate the presence of PCA. PSA is used as a marker for prostate cancer because it is secreted only by prostate cells. A healthy prostate will produce a stable amount—typically below 4 nanograms per milliliter, or a PSA reading of “4” or less—whereas cancer cells produce escalating amounts that correspond with the severity of the cancer. A level between 4 and 10 may raise a doctor's suspicion that a patient has prostate cancer, while amounts above 50 may show that the tumor has spread elsewhere in the body.

When PSA or digital tests indicate a strong likelihood that cancer is present, a transrectal ultrasound (TRUS) is used to map the prostate and show any suspicious areas. Biopsies of various sectors of the prostate are used to determine if prostate cancer is present. Treatment options depend on the stage of the cancer. Men with a 10-year life expectancy or less who have a low Gleason number and whose tumor has not spread beyond the prostate are often treated with watchful waiting (no treatment). Treatment options for more aggressive cancers include surgical treatments such as radical prostatectomy (RP), in which the prostate is completely removed (with or without nerve sparing techniques) and radiation, applied through an external beam that directs the dose to the prostate from outside the body or via low-dose radioactive seeds that are implanted within the prostate to kill cancer cells locally. Anti-androgen hormone therapy is also used, alone or in conjunction with surgery or radiation. Hormone therapy uses luteinizing hormone-releasing hormones (LH-RH) analogs, which block the pituitary from producing hormones that stimulate testosterone production. Patients must have injections of LH-RH analogs for the rest of their lives.

While surgical and hormonal treatments are often effective for localized PCA, advanced disease remains essentially incurable. Androgen ablation is the most common therapy for advanced PCA, leading to massive apoptosis of androgen-dependent malignant cells and temporary tumor regression. In most cases, however, the tumor reemerges with a vengeance and can proliferate independent of androgen signals.

The advent of prostate specific antigen (PSA) screening has led to earlier detection of PCA and significantly reduced PCA-associated fatalities. However, the impact of PSA screening on cancer-specific mortality is still unknown pending the results of prospective randomized screening studies (Etzioni et al., J. Natl. Cancer Inst., 91:1033 [1999]; Maattanen et al., Br. J. Cancer 79:1210 [1999]; Schroder et al., J. Natl. Cancer Inst., 90:1817 [1998]). A major limitation of the serum PSA test is a lack of prostate cancer sensitivity and specificity especially in the intermediate range of PSA detection (4-10 ng/ml). Elevated serum PSA levels are often detected in patients with non-malignant conditions such as benign prostatic hyperplasia (BPH) and prostatitis, and provide little information about the aggressiveness of the cancer detected. Coincident with increased serum PSA testing, there has been a dramatic increase in the number of prostate needle biopsies performed (Jacobsen et al., JAMA 274:1445 [1995]). This has resulted in a surge of equivocal prostate needle biopsies (Epstein and Potter J. Urol., 166:402 [2001]). Thus, development of additional serum and tissue biomarkers to supplement PSA screening is needed.

›SUMMARY OF THE DISCLOSURE

Provided herein are compositions and methods for cancer diagnosis, research and therapy, including but not limited to, cancer markers. In particular, provided herein are non-coding RNAs as diagnostic markers and clinical targets for cancer.

In some embodiments, the present disclosure provides a method of screening for the presence of cancer in a subject, comprising (a) contacting a biological sample from a subject with a gene expression detection assay, wherein said gene expression detection assay comprises a gene expression informative reagent for identification of the level of expression of one or more non-coding RNAs selected from the group consisting of those described by SEQ ID NOs: 1-2309; (b) detecting the level of expression of said non-coding in said sample using an in vitro assay; and (c) diagnosing cancer in said subject when an increased level of expression of said non-coding RNAs in said sample relative to the level in normal cells is detected. In some embodiments, the RNAs are converted to cDNA prior to or during detection. In some embodiments, the sample is selected from, for example, tissue, blood, plasma, serum, urine, urine supernatant, urine cell pellet, semen, prostatic secretions or prostate cells. In some embodiments, the detection is carried out utilizing a method selected from, for example, a sequencing technique, a nucleic acid hybridization technique, or a nucleic acid amplification technique. In some embodiments, the nucleic acid amplification technique is selected from, for example, polymerase chain reaction, reverse transcription polymerase chain reaction, transcription-mediated amplification, ligase chain reaction, strand displacement amplification, or nucleic acid sequence based amplification. The present disclosure is not limited to a particular cancer. Examples include, but are not limited to, prostate cancer, breast cancer, acute myeloid leukemia (AML), chronic myeloid leukemia (CML), myeloproliferative neoplasia (MPN)), lower grade glioma (LGG), glioblastome multiforme (GBM)), cervical cancer, head and neck cancer, lung squamous cell cancer, lung adenocarcinoma, kidney cancer, papillary cell carcimona, or bladder cancer. In some embodiments, the reagent is a pair of amplification oligonucleotides, a sequencing primer, or an oligonucleotide probe. In some embodiments, the reagent comprises one or more labels. In some embodiments, the one or more non-coding RNAs is two or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 50, 75, 100, or more).

Further embodiments provide a method of identifying gene expression (e.g., gene expression associated with cancer), comprising (a) contacting a biological sample from a subject with a gene expression detection assay, wherein said gene expression detection assay comprises a gene expression informative reagent for identification of the level of expression of one or more non-coding RNAs selected from the group consisting of those described by SEQ ID NOs: 1-2309; (b) detecting the level of expression of said non-coding RNA in said sample using an in vitro assay; and optionally (c) identifying gene expression subjects at risk of prostate cancer metastasis when an increased level of expression of said non-coding RNA said sample relative to the level in normal prostate cells is detected.

Additional embodiments provide a system for analyzing a cancer, comprising: a probe set comprising a plurality of probes, wherein the plurality of probes comprises a sequence that hybridizes to at least a portion of one or more non-coding RNAs selected from the group consisting of those described by SEQ ID NOs: 1-2309 or the corresponding cDNA; and a computer model or algorithm for analyzing an expression level and/or expression profile of said non-coding RNA hybridized to the probe in a sample from a subject. In some embodiments, the system further comprises one or more of computer memory for capturing and storing an expression profile, a computer-processing device, optionally connected to a computer network, a software module executed by the computer-processing device to analyze an expression profile, a software module executed by the computer-processing device to compare the expression profile to a standard or control, a software module executed by the computer-processing device to determine the expression level of the non-coding RNA, a software module executed by the computer-processing device to transmit an analysis of the expression profile to the subject or a medical professional treating the subject or a software module executed by the computer-processing device to transmit a diagnosis or prognosis to the subject or a medical professional treating the subject.

Also provided is a probe set for assessing a cancer status of a subject comprising a plurality of probes, wherein the probes in the probe set are capable of detecting an expression level of one or more non-coding RNAs selected from the group consisting of those described by SEQ ID NOs: 1-2309 or the corresponding cDNA.

Yet other embodiments provide a composition comprising one or more reaction mixtures, wherein each reaction mixture comprises a complex of a non-coding RNAs selected from those described by SEQ ID NOs: 1-2309 or the corresponding cDNA and a probe that binds to said non-coding RNA.

Additionally provided herein are methods of treating cancer, comprising contacting a cancer cell with a compound (e.g., siRNA or antisense oligonucleotide) that specifically targets one or more non-coding RNAs selected from those described by SEQ ID NOs: 1-2309. In some embodiments, the cell is in a subject.

Additional embodiments are described herein.

›DESCRIPTION OF THE FIGURES · 1 of 2

FIG. 1 shows that Ab initio transcriptome assembly reveals an expansive landscape of human transcription. (a) Pie chart showing composition and cohort sizes for RNA-Seq transcriptome reconstruction. (b) Workflow diagram for transcriptome reconstruction. (c) Bar chart comparing exons, splice sites, transcripts, and genes in the MiTranscriptome assembly with the RefSeq (December, 2013), UCSC (December, 2013) and GENCODE (release 19) catalogs.

FIG. 2 shows characterization of the MiTranscriptome assembly. (a) Pie chart of composition and quantities of lncRNA, transcripts of unknown coding potential (TUCP), expressed pseudogene, read-through, and protein-coding genes in the MiTranscriptome assembly. (b) Pie charts of number of lncRNAs and TUCP genes (top) unannotated versus annotated relative to reference catalogs and (bottom) intragenic versus intergenic. (c) Genomic view of the chromosome 16p13.3 locus. (d) Empirical cumulative distribution plot comparing the maximum expression (FPKM) of the major isoform of each gene across gene categories. (e, f, and g) Plots of enrichment of 10 kb intervals surrounding expressed transcription start sites (TSSs with RPM>0.1) with aggregated ENCODE data from 13 cell lines for (e) H3K4me3 ChIP-Seq, PolII transcription factor binding sites, and (g) DNase hypersensitivity. (h) Scatter plot with marginal histograms depicting the distribution of full transcript conservation levels (x axis) and maximal 200 bp window conservation levels (y axis) for lncRNA and TUCP transcripts.

FIG. 3 shows exemplary methodology for discovering cancer-associated lncRNAs. (a) Transcript expression analysis workflow. (b) Heatmap showing concordance of SSEA algorithm with cancer gene signatures obtained from the Oncomine database. (c) Sample set enrichment density plots showing spectrum of transcript enrichment scores (ES) obtained from SSEA analysis of breast carcinomas versus corresponding normal samples. (d and e) SSEA enrichment plots and expression boxplots for the lncRNAs (d) HOTAIR and (e) MEG3. (f) Sample set enrichment density plots showing spectrum of transcript enrichment scores (ES) obtained from SSEA analysis of prostate carcinomas versus corresponding normal samples. (g and h) Transcript enrichment bar plots for prostate cancer-specific lncRNAs (g) PCA3 and (h) SChLAP1 across Cancer vs. Normal, Cancer Type and Normal Type sample sets.

FIG. 4 shows discovery of lineage-associated and cancer-associated lncRNAs in the MiTranscriptome compendia. (a) Heatmap of lineage-specific lncRNAs. (b) Heatmap of cancer-specific lncRNAs nominated by SSEA Cancer vs. Normal analysis of 12 cancer types (columns). (c) Scatter plots showing enrichment score for Cancer vs. Normal (x axis) and Cancer Lineage (y axis) for all lineage-specific and cancer-associated lncRNA transcripts across 12 cancer types. (d) Boxplot comparing the performance of cancer- and lineage-associated lncRNAs corresponding to unannotated or annotated lncRNAs or protein-coding transcripts (including readthroughs) across 12 cancer types. (e) Expression data for MEAT6 across all MiTranscriptome cancer and normal tissue type cohorts. (f) Genomic view of chromosome 2q35 locus. (g) Expression data for BRCAT49 across all MiTranscriptome cancer and normal tissue type cohorts.

FIG. 5 shows curation and processing of samples in the MiTrascriptome compendia. A. Pie chart showing the number of studies curated from TCGA, ENCODE, MCTP, and other datasets.

FIG. 6 shows transfrag filtering. a) Pie chart showing the number of studies curated from datasets. b) workflow for bioinformatics processing of individual RNA-SEQ libraries. C) scatter plot showing total fragments (x-axis) and the fraction of aligned fragments (y-axis for each RNA-SEQ library. D) dot plot showing the fraction of aligned bases corresponding to mRNA, intronic regions, or intergenic regions. E0pie chart showing numbers of primare tumors, metastatic tumors, benign adjacent tissues or healthy tissues, or cell lines for RNA-SEQ libraries.

FIG. 7 shows meta assembly. a, schematic of transcriptome meta-assembly algorithm using a simplified example with three transtrags transcribed from left to right. b, The pruned splice graph from panel a is subjected to meta-assembly. c, Genome view showing an example of the meta-assembly procedure for breast cohort transfrags in a chromosome 12q13.3 locus containing the LncRNA HOTAIR and the protein-coding gene HOXC11 on opposite strands (chr12:54,349,995-54,377,376, hg19).

FIG. 8 shows characterization of unannotated transcripts. a, Bar plots comparing numbers of unannolaled versus different dasses of annotated transcripts for each of the 18 cohorts. b, 001 plots depicting comparison of MiTransriptome with reference transcripts from RefSeq, UCSC, or GENCODE. c, Dot plots comparing the basewise, splice site, and splicing pattern precision and sensitivity of MiTranscriptome and GENCODE using RefSeq (left) or Cabili et al. LncRNAs (right)

FIG. 9 shows classification of transcripts of unknown coding potential. a, Decision tree showing categorization of ab initio transcripts. b, ROC curve comparing false positive rate (x axis) with true positive rate (yaxis) for CPAT coding potential predictions of ncRNAs versus protein-coding genes. c, Curve comparing probability cutoff (x axis) with balanced accuracy (yaxis). d, Scatter plot comparing frequencies of Pfam domain occurrences in non-transcribed intergenic space versus transcribed regions. e, Three-dimensional scatter plot comparing Fickett score (x axis), ORF size (yaxis), and Hexamer score (z axis) for all transcripts. f,g,h Boxplots comparing ORF size (f), Hexamer score (g), and Fickett score (h).

FIG. 10 shows Mitranscriptome characterization. a, Comparison of the relationship of maximum number of exons per gene to the number of isoforms per gene. b, Density histogram depicting the confidence scores for annotated and unannotated IncRNAs. c, Cumulative distribulion plot for basewise conservation fraction of proteins, read-throughs, pseudogenes, TUCPs, lncRNAs. d, Bar plot showing KS test statistics for classes of transcripts versus random intergenic controls. e, Cumulative dislribution plot for promoter conservation (legend shared with a). f, Bar plot showing KS tests for promoter conservation versus random intergenic regions. g, ROC curve for predicting conservation of protein coding genes versus random intergenic controls.

›DESCRIPTION OF THE FIGURES · 2 of 2

FIG. 11 shows validation of lncRNA transcripts.

FIG. 12 shows validation of lncRNA transcripts. a, b, Representative example of two of twenty previously unannotated IncRNA transcripts that were analyzed by Sanger sequencing to ensure primer specificity with their associated chromatograms. c, Heatmap representation of the correlation between qPCR (fold change over median) with RNA-seq (FPKM) of 100 selected transcripts in cell lines A549, LNCaP, and MCF7.

FIG. 13 shows enrichment of MiTranscriptome assembly for disease-associated regions. a, Venn diagram comparing coverage of disease or trait associated genomic regions for the MiTranscriptome assembly in comparison to reference catalog. b, Pie charts comparing distributions of intronic and exonic GWAS SNP coverage of the MiTranscriptome assembly (left) and reference catalogs (right). c, Dot plot showing enrichment of GWAS SNPs (cirde) versus random SNPs (diamond) for novel intergenic lncRNAs and TUCPs.

FIG. 14 shows discovery of lineage associated and cancer associated transcripts. a, Heatmap of lineage-specific transcripts (LATs) nominated by SSEA. b, Heatmap of cancer-specific transcripts (CATS) nominated by SSEA.

FIG. 15 shows lineage-specific and cancer-specific transcripts. a, Scatter plot grid showing lineage-specific and cancer-specific transcripts (CLATs) nominated by SSEA. b and c, Boxplots comparing the performance of (b) positively enriched CLATs and c) negatively enriched CLATs for each transcript category across 12 cancer types.

FIG. 16 shows examples of cancer and/or lineage associated transcripts). a, Genomic view of chromosome 6q26-q27locus. b, Expression data for MEAT6 (demarcated by asterisk in a). Expression profile for cancer and lineage assodated transcripts across all MiTraoscriptome tissue cohorts are shown for c, lung adenocarcinoma, and d, thyroid cancer

›DEFINITIONS · 1 of 3

To facilitate an understanding of the present disclosure, a number of terms and phrases are defined below:

As used herein, the terms “detect”, “detecting” or “detection” may describe either the general act of discovering or discerning or the specific observation of a composition. Detecting a composition may comprise determining the presence or absence of a composition. Detecting may comprise quantifying a composition. For example, detecting comprises determining the expression level of a composition. The composition may comprise a nucleic acid molecule. For example, the composition may comprise at least a portion of the ncRNAs disclosed herein. Alternatively, or additionally, the composition may be a detectably labeled composition.

As used herein, the term “subject” refers to any organisms that are screened using the diagnostic methods described herein. Such organisms preferably include, but are not limited to, mammals (e.g., murines, simians, equines, bovines, porcines, canines, felines, and the like), and most preferably includes humans. Alternatively, the organism is an avian, amphibian, reptile or fish.

The term “diagnosed,” as used herein, refers to the recognition of a disease by its signs and symptoms, or genetic analysis, pathological analysis, histological analysis, and the like.

As used herein, the term “characterizing cancer in a subject” refers to the identification of one or more properties of a cancer sample in a subject, including but not limited to, the presence of benign, pre-cancerous or cancerous tissue, the stage of the cancer, and the subject's prognosis. Cancers may be characterized by the identification of the expression of one or more cancer marker genes, including but not limited to, the ncRNAs disclosed herein.

As used herein, the term “stage of cancer” refers to a qualitative or quantitative assessment of the level of advancement of a cancer. Criteria used to determine the stage of a cancer include, but are not limited to, the size of the tumor and the extent of metastases (e.g., localized or distant).

As used herein, the term “nucleic acid molecule” refers to any nucleic acid containing molecule, including but not limited to, DNA or RNA. The nucleic acid molecule may comprise one or more nucleotides. The term encompasses sequences that include any of the known base analogs of DNA and RNA including, but not limited to, 4-acetylcytosine, 8-hydroxy-N6-methyladenosine, aziridinylcytosine, pseudoisocytosine, 5-(carboxyhydroxylmethyl) uracil, 5-fluorouracil, 5-bromouracil, 5-carboxymethylaminomethyl-2-thiouracil, 5-carboxymethylaminomethyluracil, dihydrouracil, inosine, N6-isopentenyladenine, 1-methyladenine, 1-methylpseudouracil, 1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-methyladenine, 7-methylguanine, 5-methylaminomethyluracil, 5-methoxyaminomethyl-2-thiouracil, beta-D-mannosylqueosine, 5′-methoxycarbonylmethyluracil, 5-methoxyuracil, 2-methylthio-N6-isopentenyladenine, uracil-5-oxyacetic acid methylester, uracil-5-oxyacetic acid, oxybutoxosine, pseudouracil, queosine, 2-thiocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, 5-methyluracil, N-uracil-5-oxyacetic acid methylester, uracil-5-oxyacetic acid, pseudouracil, queosine, 2-thiocytosine, and 2,6-diaminopurine.

The term “gene” refers to a nucleic acid (e.g., DNA) sequence that comprises coding sequences necessary for the production of a polypeptide, precursor, or RNA (e.g., rRNA, tRNA). The polypeptide can be encoded by a full length coding sequence or by any portion of the coding sequence so long as the desired activity or functional properties (e.g., enzymatic activity, ligand binding, signal transduction, immunogenicity, etc.) of the full-length or fragments are retained. The term also encompasses the coding region of a structural gene and the sequences located adjacent to the coding region on both the 5′ and 3′ ends for a distance of about 1 kb or more on either end such that the gene corresponds to the length of the full-length mRNA. Sequences located 5′ of the coding region and present on the mRNA are referred to as 5′ non-translated sequences. Sequences located 3′ or downstream of the coding region and present on the mRNA are referred to as 3′ non-translated sequences. The term “gene” encompasses both cDNA and genomic forms of a gene. A genomic form or clone of a gene contains the coding region interrupted with non-coding sequences termed “introns” or “intervening regions” or “intervening sequences.” Introns are segments of a gene that are transcribed into nuclear RNA (hnRNA); introns may contain regulatory elements such as enhancers. Introns are removed or “spliced out” from the nuclear or primary transcript; introns therefore are absent in the messenger RNA (mRNA) transcript. The mRNA functions during translation to specify the sequence or order of amino acids in a nascent polypeptide.

As used herein, the term “oligonucleotide,” refers to a short length of single-stranded polynucleotide chain. Oligonucleotides are typically less than 200 residues long (e.g., between 15 and 100), however, as used herein, the term is also intended to encompass longer polynucleotide chains. Oligonucleotides are often referred to by their length. For example a 24 residue oligonucleotide is referred to as a “24-mer”. Oligonucleotides can form secondary and tertiary structures by self-hybridizing or by hybridizing to other polynucleotides. Such structures can include, but are not limited to, duplexes, hairpins, cruciforms, bends, and triplexes.

As used herein, the terms “complementary” or “complementarity” are used in reference to polynucleotides (i.e., a sequence of nucleotides) related by the base-pairing rules. For example, the sequence “5′-A-G-T-3′,” is complementary to the sequence “3′-T-C-A-5′.” Complementarity may be “partial,” in which only some of the nucleic acids' bases are matched according to the base pairing rules. Or, there may be “complete” or “total” complementarity between the nucleic acids. The degree of complementarity between nucleic acid strands has significant effects on the efficiency and strength of hybridization between nucleic acid strands. This is of particular importance in amplification reactions, as well as detection methods that depend upon binding between nucleic acids.

›DEFINITIONS · 2 of 3

The term “homology” refers to a degree of complementarity. There may be partial homology or complete homology (i.e., identity). A partially complementary sequence is a nucleic acid molecule that at least partially inhibits a completely complementary nucleic acid molecule from hybridizing to a target nucleic acid is “substantially homologous.” The inhibition of hybridization of the completely complementary sequence to the target sequence may be examined using a hybridization assay (Southern or Northern blot, solution hybridization and the like) under conditions of low stringency. A substantially homologous sequence or probe will compete for and inhibit the binding (i.e., the hybridization) of a completely homologous nucleic acid molecule to a target under conditions of low stringency. This is not to say that conditions of low stringency are such that non-specific binding is permitted; low stringency conditions require that the binding of two sequences to one another be a specific (i.e., selective) interaction. The absence of non-specific binding may be tested by the use of a second target that is substantially non-complementary (e.g., less than about 30% identity); in the absence of non-specific binding the probe will not hybridize to the second non-complementary target.

As used herein, the term “hybridization” is used in reference to the pairing of complementary nucleic acids. Hybridization and the strength of hybridization (i.e., the strength of the association between the nucleic acids) is impacted by such factors as the degree of complementary between the nucleic acids, stringency of the conditions involved, the T m of the formed hybrid, and the G:C ratio within the nucleic acids. A single molecule that contains pairing of complementary nucleic acids within its structure is said to be “self-hybridized.”

As used herein the term “stringency” is used in reference to the conditions of temperature, ionic strength, and the presence of other compounds such as organic solvents, under which nucleic acid hybridizations are conducted. Under “low stringency conditions” a nucleic acid sequence of interest will hybridize to its exact complement, sequences with single base mismatches, closely related sequences (e.g., sequences with 90% or greater homology), and sequences having only partial homology (e.g., sequences with 50-90% homology). Under ‘medium stringency conditions,” a nucleic acid sequence of interest will hybridize only to its exact complement, sequences with single base mismatches, and closely relation sequences (e.g., 90% or greater homology). Under “high stringency conditions,” a nucleic acid sequence of interest will hybridize only to its exact complement, and (depending on conditions such a temperature) sequences with single base mismatches. In other words, under conditions of high stringency the temperature can be raised so as to exclude hybridization to sequences with single base mismatches.

The term “isolated” when used in relation to a nucleic acid, as in “an isolated oligonucleotide” or “isolated polynucleotide” refers to a nucleic acid sequence that is identified and separated from at least one component or contaminant with which it is ordinarily associated in its natural source. Isolated nucleic acid is such present in a form or setting that is different from that in which it is found in nature. In contrast, non-isolated nucleic acids as nucleic acids such as DNA and RNA found in the state they exist in nature. For example, a given DNA sequence (e.g., a gene) is found on the host cell chromosome in proximity to neighboring genes; RNA sequences, such as a specific mRNA sequence encoding a specific protein, are found in the cell as a mixture with numerous other mRNAs that encode a multitude of proteins. However, isolated nucleic acid encoding a given protein includes, by way of example, such nucleic acid in cells ordinarily expressing the given protein where the nucleic acid is in a chromosomal location different from that of natural cells, or is otherwise flanked by a different nucleic acid sequence than that found in nature. The isolated nucleic acid, oligonucleotide, or polynucleotide may be present in single-stranded or double-stranded form. When an isolated nucleic acid, oligonucleotide or polynucleotide is to be utilized to express a protein, the oligonucleotide or polynucleotide will contain at a minimum the sense or coding strand (i.e., the oligonucleotide or polynucleotide may be single-stranded), but may contain both the sense and anti-sense strands (i.e., the oligonucleotide or polynucleotide may be double-stranded).

As used herein, the term “purified” or “to purify” refers to the removal of components (e.g., contaminants) from a sample. For example, antibodies are purified by removal of contaminating non-immunoglobulin proteins; they are also purified by the removal of immunoglobulin that does not bind to the target molecule. The removal of non-immunoglobulin proteins and/or the removal of immunoglobulins that do not bind to the target molecule results in an increase in the percent of target-reactive immunoglobulins in the sample. In another example, recombinant polypeptides are expressed in bacterial host cells and the polypeptides are purified by the removal of host cell proteins; the percent of recombinant polypeptides is thereby increased in the sample.

The term “label” as used herein refers to any atom or molecule that can be used to provide a detectable (preferably quantifiable) effect, and that can be attached to a nucleic acid or protein. Labels include but are not limited to dyes; radiolabels such as 32 P; binding moieties such as biotin; haptens such as digoxgenin; luminogenic, phosphorescent or fluorogenic moieties; and fluorescent dyes alone or in combination with moieties that can suppress or shift emission spectra by fluorescence resonance energy transfer (FRET). Labels may provide signals detectable by fluorescence, radioactivity, colorimetry, gravimetry, X-ray diffraction or absorption, magnetism, enzymatic activity, and the like. A label may be a charged moiety (positive or negative charge) or alternatively, may be charge neutral. Labels can include or consist of nucleic acid or protein sequence, so long as the sequence comprising the label is detectable. In some embodiments, nucleic acids are detected directly without a label (e.g., directly reading a sequence).

›DEFINITIONS · 3 of 3

As used herein, the term “sample” is used in its broadest sense. In one sense, it is meant to include a specimen or culture obtained from any source, as well as biological and environmental samples. Biological samples may be obtained from animals (including humans) and encompass fluids, solids, tissues, and gases. Biological samples include blood products, such as plasma, serum and the like. Such examples are not however to be construed as limiting the sample types applicable to the present disclosure.

›DETAILED DESCRIPTION OF THE DISCLOSURE · 1 of 20

Provided herein are compositions and methods for cancer diagnosis, research and therapy, including but not limited to, cancer markers. In particular, provided herein are non-coding RNAs as diagnostic markers and clinical targets for cancer.

Many RNA transcripts are not classical protein-coding genes. There is an abundance of unknown, uncharacterized RNA species in the human transcriptome (e.g., lncRNA or long non-coding RNAs). Provided herein are compositions and methods for utilizing such non-coding RNAs in diagnostic, research, and screening methods.

I. Diagnostic and Screening Methods

As described herein, embodiments of the present disclosure provide diagnostic and screening methods that utilize the detection of one or more non-coding RNAs. Exemplary non-coding RNAs include, but are not limited to, those described in SEQ ID NOs: 1-2309. Exemplary, non-limiting methods are described herein.

Any patient sample suspected of containing the non-coding RNAs may be tested according to methods of embodiments of the present disclosure. By way of non-limiting examples, the sample may be tissue (e.g., a biopsy sample, a prostate biopsy sample or a tissue sample obtained by prostatectomy), blood, urine, semen, prostatic secretions or a fraction thereof (e.g., plasma, serum, urine supernatant, urine cell pellet, cells or prostate cells). A urine sample may be collected immediately following an attentive digital rectal examination (DRE), which causes prostate cells from the prostate gland to shed into the urinary tract.

In some embodiments, the patient sample is subjected to preliminary processing designed to isolate or enrich the sample for the non-coding RNAs or cells that contain the non-coding RNAs. A variety of techniques known to those of ordinary skill in the art may be used for this purpose, including but not limited to: centrifugation; immunocapture; cell lysis; nucleic acid amplification; and, nucleic acid target capture (See, e.g., EP Pat. No. 1 409 727, herein incorporated by reference in its entirety). The non-coding RNAs may be detected along with other markers in a multiplex or panel format.

Markers may be selected for their predictive value alone or in combination with non-coding RNAs described herein (e.g., one or more of SEQ ID NOs: 1-2309). Exemplary prostate cancer markers include, but are not limited to: AMACR/P504S (U.S. Pat. No. 6,262,245); PCA3 (U.S. Pat. No. 7,008,765); PCGEM1 (U.S. Pat. No. 6,828,429); prostein/P501S, P503S, P504S, P509S, P510S, prostase/P703P, P710P (U.S. Publication No. 20030185830); RAS/KRAS (Bos, Cancer Res. 49:4682-89 (1989); Kranenburg, Biochimica et Biophysica Acta 1756:81-82 (2005)); and, those disclosed in U.S. Pat. Nos. 5,854,206 and 6,034,218, 7,229,774, each of which is herein incorporated by reference in its entirety. Markers for other cancers, diseases, infections, and metabolic conditions are also contemplated for inclusion in a multiplex or panel format.

In some embodiments, multiplex or array formats are utilized to detect multiple markers in combination. For example, in some embodiments, the level of expression of one or more, 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more 45 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more non-coding RNAs (ncRNAs) is utilized in the research, screening, diagnostic and prognositic compositions and methods described herein. The one or more ncRNAs may be selected from the group comprising.

i. DNA and RNA Detection

The non-coding RNAs of the present disclosure are detected using a variety of nucleic acid techniques known to those of ordinary skill in the art, including but not limited to: nucleic acid sequencing; nucleic acid hybridization; and, nucleic acid amplification.

The methods, compositions and kits may comprise one or more ncRNAs. The methods, compositions and kits may comprise 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 15 or more, 20 or more, 25 or more, 30 or more, 40 or more, 45 or more, 50 or more, 55 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 110 or more, 120 or more, 130 or more, 140 or more, 150 or more ncRNAs.

The one or more ncRNAs may be selected from, for example, those described in SEQ ID NOs: 1-2309.

1. Sequencing

In some embodiments, nucleic acid sequencing methods are utilized (e.g., for detection of amplified nucleic acids). In some embodiments, the technology provided herein finds use in a Second Generation (a.k.a. Next Generation or Next-Gen), Third Generation (a.k.a. Next-Next-Gen), or Fourth Generation (a.k.a. N3-Gen) sequencing technology including, but not limited to, pyrosequencing, sequencing-by-ligation, single molecule sequencing, sequence-by-synthesis (SBS), semiconductor sequencing, massive parallel clonal, massive parallel single molecule SBS, massive parallel single molecule real-time, massive parallel single molecule real-time nanopore technology, etc. Morozova and Marra provide a review of some such technologies in Genomics, 92: 255 (2008), herein incorporated by reference in its entirety. Those of ordinary skill in the art will recognize that because RNA is less stable in the cell and more prone to nuclease attack experimentally RNA is usually reverse transcribed to DNA before sequencing.

A number of DNA sequencing techniques are suitable, including fluorescence-based sequencing methodologies (See, e.g., Birren et al., Genome Analysis: Analyzing DNA, 1, Cold Spring Harbor, N.Y.; herein incorporated by reference in its entirety). In some embodiments, the technology finds use in automated sequencing techniques understood in that art. In some embodiments, the present technology finds use in parallel sequencing of partitioned amplicons (PCT Publication No: WO2006084132 to Kevin McKernan et al., herein incorporated by reference in its entirety). In some embodiments, the technology finds use in DNA sequencing by parallel oligonucleotide extension (See, e.g., U.S. Pat. No. 5,750,341 to Macevicz et al., and U.S. Pat. No. 6,306,597 to Macevicz et al., both of which are herein incorporated by reference in their entireties). Additional examples of sequencing techniques in which the technology finds use include the Church polony technology (Mitra et al., 2003, Analytical Biochemistry 320, 55-65; Shendure et al., 2005 Science 309, 1728-1732; U.S. Pat. Nos. 6,432,360, 6,485,944, 6,511,803; herein incorporated by reference in their entireties), the 454 picotiter pyrosequencing technology (Margulies et al., 2005 Nature 437, 376-380; US 20050130173; herein incorporated by reference in their entireties), the Solexa single base addition technology (Bennett et al., 2005, Pharmacogenomics, 6, 373-382; U.S. Pat. Nos. 6,787,308; 6,833,246; herein incorporated by reference in their entireties), the Lynx massively parallel signature sequencing technology (Brenner et al. (2000). Nat. Biotechnol. 18:630-634; U.S. Pat. Nos. 5,695,934; 5,714,330; herein incorporated by reference in their entireties), and the Adessi PCR colony technology (Adessi et al. (2000). Nucleic Acid Res. 28, E87; WO 00018957; herein incorporated by reference in its entirety).

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Next-generation sequencing (NGS) methods share the common feature of massively parallel, high-throughput strategies, with the goal of lower costs in comparison to older sequencing methods (see, e.g., Voelkerding et al., Clinical Chem., 55: 641-658, 2009; MacLean et al., Nature Rev. Microbiol., 7: 287-296; each herein incorporated by reference in their entirety). NGS methods can be broadly divided into those that typically use template amplification and those that do not. Amplification-requiring methods include pyrosequencing commercialized by Roche as the 454 technology platforms (e.g., GS 20 and GS FLX), Life Technologies/Ion Torrent, the Solexa platform commercialized by Illumina, GnuBio, and the Supported Oligonucleotide Ligation and Detection (SOLiD) platform commercialized by Applied Biosystems. Non-amplification approaches, also known as single-molecule sequencing, are exemplified by the Heli Scope platform commercialized by Helicos BioSciences, and emerging platforms commercialized by VisiGen, Oxford Nanopore Technologies Ltd., and Pacific Biosciences, respectively.

In pyrosequencing (Voelkerding et al., Clinical Chem., 55: 641-658, 2009; MacLean et al., Nature Rev. Microbiol., 7: 287-296; U.S. Pat. Nos. 6,210,891; 6,258,568; each herein incorporated by reference in its entirety), template DNA is fragmented, end-repaired, ligated to adaptors, and clonally amplified in-situ by capturing single template molecules with beads bearing oligonucleotides complementary to the adaptors. Each bead bearing a single template type is compartmentalized into a water-in-oil microvesicle, and the template is clonally amplified using a technique referred to as emulsion PCR. The emulsion is disrupted after amplification and beads are deposited into individual wells of a picotitre plate functioning as a flow cell during the sequencing reactions. Ordered, iterative introduction of each of the four dNTP reagents occurs in the flow cell in the presence of sequencing enzymes and luminescent reporter such as luciferase. In the event that an appropriate dNTP is added to the 3′ end of the sequencing primer, the resulting production of ATP causes a burst of luminescence within the well, which is recorded using a CCD camera. It is possible to achieve read lengths greater than or equal to 400 bases, and 10 6 sequence reads can be achieved, resulting in up to 500 million base pairs (Mb) of sequence.

In the Solexa/Illumina platform (Voelkerding et al., Clinical Chem., 55: 641-658, 2009; MacLean et al., Nature Rev. Microbiol., 7: 287-296; U.S. Pat. Nos. 6,833,246; 7,115,400; 6,969,488; each herein incorporated by reference in its entirety), sequencing data are produced in the form of shorter-length reads. In this method, single-stranded fragmented DNA is end-repaired to generate 5′-phosphorylated blunt ends, followed by Klenow-mediated addition of a single A base to the 3′ end of the fragments. A-addition facilitates addition of T-overhang adaptor oligonucleotides, which are subsequently used to capture the template-adaptor molecules on the surface of a flow cell that is studded with oligonucleotide anchors. The anchor is used as a PCR primer, but because of the length of the template and its proximity to other nearby anchor oligonucleotides, extension by PCR results in the “arching over” of the molecule to hybridize with an adjacent anchor oligonucleotide to form a bridge structure on the surface of the flow cell. These loops of DNA are denatured and cleaved. Forward strands are then sequenced with reversible dye terminators. The sequence of incorporated nucleotides is determined by detection of post-incorporation fluorescence, with each fluor and block removed prior to the next cycle of dNTP addition. Sequence read length ranges from 36 nucleotides to over 250 nucleotides, with overall output exceeding 1 billion nucleotide pairs per analytical run.

Sequencing nucleic acid molecules using SOLiD technology (Voelkerding et al., Clinical Chem., 55: 641-658, 2009; MacLean et al., Nature Rev. Microbiol., 7: 287-296; U.S. Pat. Nos. 5,912,148; 6,130,073; each herein incorporated by reference in their entirety) also involves fragmentation of the template, ligation to oligonucleotide adaptors, attachment to beads, and clonal amplification by emulsion PCR. Following this, beads bearing template are immobilized on a derivatized surface of a glass flow-cell, and a primer complementary to the adaptor oligonucleotide is annealed. However, rather than utilizing this primer for 3′ extension, it is instead used to provide a 5′ phosphate group for ligation to interrogation probes containing two probe-specific bases followed by 6 degenerate bases and one of four fluorescent labels. In the SOLiD system, interrogation probes have 16 possible combinations of the two bases at the 3′ end of each probe, and one of four fluors at the 5′ end. Fluor color, and thus identity of each probe, corresponds to specified color-space coding schemes. Multiple rounds (usually 7) of probe annealing, ligation, and fluor detection are followed by denaturation, and then a second round of sequencing using a primer that is offset by one base relative to the initial primer. In this manner, the template sequence can be computationally re-constructed, and template bases are interrogated twice, resulting in increased accuracy. Sequence read length averages 35 nucleotides, and overall output exceeds 4 billion bases per sequencing run.

In certain embodiments, the technology finds use in nanopore sequencing (see, e.g., Astier et al., J. Am. Chem. Soc. 2006 Feb. 8; 128(5):1705-10, herein incorporated by reference). The theory behind nanopore sequencing has to do with what occurs when a nanopore is immersed in a conducting fluid and a potential (voltage) is applied across it. Under these conditions a slight electric current due to conduction of ions through the nanopore can be observed, and the amount of current is exceedingly sensitive to the size of the nanopore. As each base of a nucleic acid passes through the nanopore, this causes a change in the magnitude of the current through the nanopore that is distinct for each of the four bases, thereby allowing the sequence of the DNA molecule to be determined.

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In certain embodiments, the technology finds use in HeliScope by Helicos BioSciences (Voelkerding et al., Clinical Chem., 55: 641-658, 2009; MacLean et al., Nature Rev. Microbiol., 7: 287-296; U.S. Pat. Nos. 7,169,560; 7,282,337; 7,482,120; 7,501,245; 6,818,395; 6,911,345; 7,501,245; each herein incorporated by reference in their entirety). Template DNA is fragmented and polyadenylated at the 3′ end, with the final adenosine bearing a fluorescent label. Denatured polyadenylated template fragments are ligated to poly(dT) oligonucleotides on the surface of a flow cell. Initial physical locations of captured template molecules are recorded by a CCD camera, and then label is cleaved and washed away. Sequencing is achieved by addition of polymerase and serial addition of fluorescently-labeled dNTP reagents. Incorporation events result in fluor signal corresponding to the dNTP, and signal is captured by a CCD camera before each round of dNTP addition. Sequence read length ranges from 25-50 nucleotides, with overall output exceeding 1 billion nucleotide pairs per analytical run.

The Ion Torrent technology is a method of DNA sequencing based on the detection of hydrogen ions that are released during the polymerization of DNA (see, e.g., Science 327(5970): 1190 (2010); U. S. Pat. Appl. Pub. Nos. 20090026082, 20090127589, 20100301398, 20100197507, 20100188073, and 20100137143, incorporated by reference in their entireties for all purposes). A microwell contains a template DNA strand to be sequenced. Beneath the layer of microwells is a hypersensitive ISFET ion sensor. All layers are contained within a CMOS semiconductor chip, similar to that used in the electronics industry. When a dNTP is incorporated into the growing complementary strand a hydrogen ion is released, which triggers a hypersensitive ion sensor. If homopolymer repeats are present in the template sequence, multiple dNTP molecules will be incorporated in a single cycle. This leads to a corresponding number of released hydrogens and a proportionally higher electronic signal. This technology differs from other sequencing technologies in that no modified nucleotides or optics are used. The per-base accuracy of the Ion Torrent sequencer is ˜99.6% for 50 base reads, with ˜100 Mb to 100 Gb generated per run. The read-length is 100-300 base pairs. The accuracy for homopolymer repeats of 5 repeats in length is ˜98%. The benefits of ion semiconductor sequencing are rapid sequencing speed and low upfront and operating costs.

The technology finds use in another nucleic acid sequencing approach developed by Stratos Genomics, Inc. and involves the use of Xpandomers. This sequencing process typically includes providing a daughter strand produced by a template-directed synthesis. The daughter strand generally includes a plurality of subunits coupled in a sequence corresponding to a contiguous nucleotide sequence of all or a portion of a target nucleic acid in which the individual subunits comprise a tether, at least one probe or nucleobase residue, and at least one selectively cleavable bond. The selectively cleavable bond(s) is/are cleaved to yield an Xpandomer of a length longer than the plurality of the subunits of the daughter strand. The Xpandomer typically includes the tethers and reporter elements for parsing genetic information in a sequence corresponding to the contiguous nucleotide sequence of all or a portion of the target nucleic acid. Reporter elements of the Xpandomer are then detected. Additional details relating to Xpandomer-based approaches are described in, for example, U.S. Pat. Pub No. 20090035777, entitled “High Throughput Nucleic Acid Sequencing by Expansion,” filed Jun. 19, 2008, which is incorporated herein in its entirety.

Other emerging single molecule sequencing methods include real-time sequencing by synthesis using a VisiGen platform (Voelkerding et al., Clinical Chem., 55: 641-58, 2009; U.S. Pat. No. 7,329,492; U.S. patent application Ser. No. 11/671,956; U.S. patent application Ser. No. 11/781,166; each herein incorporated by reference in their entirety) in which immobilized, primed DNA template is subjected to strand extension using a fluorescently-modified polymerase and florescent acceptor molecules, resulting in detectible fluorescence resonance energy transfer (FRET) upon nucleotide addition.

2. Hybridization

Illustrative non-limiting examples of nucleic acid hybridization techniques include, but are not limited to, in situ hybridization (ISH), microarray, and Southern or Northern blot.

In situ hybridization (ISH) is a type of hybridization that uses a labeled complementary DNA or RNA strand as a probe to localize a specific DNA or RNA sequence in a portion or section of tissue (in situ), or, if the tissue is small enough, the entire tissue (whole mount ISH). DNA ISH can be used to determine the structure of chromosomes. RNA ISH is used to measure and localize mRNAs and other transcripts (e.g., ncRNAs) within tissue sections or whole mounts. Sample cells and tissues are usually treated to fix the target transcripts in place and to increase access of the probe. The probe hybridizes to the target sequence at elevated temperature, and then the excess probe is washed away. The probe that was labeled with either radio-, fluorescent- or antigen-labeled bases is localized and quantitated in the tissue using either autoradiography, fluorescence microscopy or immunohistochemistry, respectively. ISH can also use two or more probes, labeled with radioactivity or the other non-radioactive labels, to simultaneously detect two or more transcripts.

In some embodiments, ncRNAs are detected using fluorescence in situ hybridization (FISH). In some embodiments, FISH assays utilize bacterial artificial chromosomes (BACs). These have been used extensively in the human genome sequencing project (see Nature 409: 953-958 (2001)) and clones containing specific BACs are available through distributors that can be located through many sources, e.g., NCBI. Each BAC clone from the human genome has been given a reference name that unambiguously identifies it. These names can be used to find a corresponding GenBank sequence and to order copies of the clone from a distributor.

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The present disclosure further provides a method of performing a FISH assay on the patient sample. The methods disclosed herein may comprise performing a FISH assay on one or more cells, tissues, organs, or fluids surrounding such cells, tissues and organs. In some instances, the methods disclosed herein further comprise performing a FISH assy on human prostate cells, human prostate tissue or on the fluid surrounding said human prostate cells or human prostate tissue. Alternatively, or additionally, the methods disclosed herien comprise performing a FISH assay on breast cells, lung cells, pancreatic cells, liver cells, breast tissue, lung tissue, pancreatic tissue, liver tissue, or on the fluid surrounding the cells or tissues. Specific protocols are well known in the art and can be readily adapted for the present disclosure. Guidance regarding methodology may be obtained from many references including: In situ Hybridization: Medical Applications (eds. G. R. Coulton and J. de Belleroche), Kluwer Academic Publishers, Boston (1992); In situ Hybridization: In Neurobiology; Advances in Methodology (eds. J. H. Eberwine, K. L. Valentino, and J. D. Barchas), Oxford University Press Inc., England (1994); In situ Hybridization: A Practical Approach (ed. D. G. Wilkinson), Oxford University Press Inc., England (1992)); Kuo, et al., Am. J. Hum. Genet. 49:112-119 (1991); Klinger, et al., Am. J. Hum. Genet. 51:55-65 (1992); and Ward, et al., Am. J. Hum. Genet. 52:854-865 (1993)). There are also kits that are commercially available and that provide protocols for performing FISH assays (available from e.g., Oncor, Inc., Gaithersburg, Md.). Patents providing guidance on methodology include U.S. Pat. Nos. 5,225,326; 5,545,524; 6,121,489 and 6,573,043. All of these references are hereby incorporated by reference in their entirety and may be used along with similar references in the art and with the information provided in the Examples section herein to establish procedural steps convenient for a particular laboratory.

The one or more ncRNAs may be detected by conducting one or more hybridization reactions. The one or more hybridization reactions may comprise one or more hybridization arrays, hybridization reactions, hybridization chain reactions, isothermal hybridization reactions, nucleic acid hybridization reactions, or a combination thereof. The one or more hybridization arrays may comprise hybridization array genotyping, hybridization array proportional sensing, DNA hybridization arrays, macroarrays, microarrays, high-density oligonucleotide arrays, genomic hybridization arrays, comparative hybridization arrays, or a combination thereof.

3. Microarrays

Different kinds of biological assays are called microarrays including, but not limited to: DNA microarrays (e.g., cDNA microarrays and oligonucleotide microarrays); protein microarrays; tissue microarrays; transfection or cell microarrays; chemical compound microarrays; and, antibody microarrays. A DNA microarray, commonly known as gene chip, DNA chip, or biochip, is a collection of microscopic DNA spots attached to a solid surface (e.g., glass, plastic or silicon chip) forming an array for the purpose of expression profiling or monitoring expression levels for thousands of genes simultaneously. The affixed DNA segments are known as probes, thousands of which can be used in a single DNA microarray. Microarrays can be used to identify disease genes or transcripts (e.g., ncRNAs) by comparing gene expression in disease and normal cells. Microarrays can be fabricated using a variety of technologies, including but not limiting: printing with fine-pointed pins onto glass slides; photolithography using pre-made masks; photolithography using dynamic micromirror devices; ink-jet printing; or, electrochemistry on microelectrode arrays.

3. Amplification

The methods disclosed herein may comprise conducting one or more amplification reactions. Nucleic acids (e.g., ncRNAs) may be amplified prior to or simultaneous with detection. Conducting one or more amplification reactions may comprise one or more PCR-based amplifications, non-PCR based amplifications, or a combination thereof. Illustrative non-limiting examples of nucleic acid amplification techniques include, but are not limited to, polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), nested PCR, linear amplification, multiple displacement amplification (MDA), real-time SDA, rolling circle amplification, circle-to-circle amplification transcription-mediated amplification (TMA), ligase chain reaction (LCR), strand displacement amplification (SDA), and nucleic acid sequence based amplification (NASBA). Those of ordinary skill in the art will recognize that certain amplification techniques (e.g., PCR) require that RNA be reversed transcribed to DNA prior to amplification (e.g., RT-PCR), whereas other amplification techniques directly amplify RNA (e.g., TMA and NASBA).

The polymerase chain reaction (U.S. Pat. Nos. 4,683,195, 4,683,202, 4,800,159 and 4,965,188, each of which is herein incorporated by reference in its entirety), commonly referred to as PCR, uses multiple cycles of denaturation, annealing of primer pairs to opposite strands, and primer extension to exponentially increase copy numbers of a target nucleic acid sequence. In a variation called RT-PCR, reverse transcriptase (RT) is used to make a complementary DNA (cDNA) from mRNA, and the cDNA is then amplified by PCR to produce multiple copies of DNA. For other various permutations of PCR see, e.g., U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159; Mullis et al., Meth. Enzymol. 155: 335 (1987); and, Murakawa et al., DNA 7: 287 (1988), each of which is herein incorporated by reference in its entirety.

Transcription mediated amplification (U.S. Pat. Nos. 5,480,784 and 5,399,491, each of which is herein incorporated by reference in its entirety), commonly referred to as TMA, synthesizes multiple copies of a target nucleic acid sequence autocatalytically under conditions of substantially constant temperature, ionic strength, and pH in which multiple RNA copies of the target sequence autocatalytically generate additional copies. See, e.g., U.S. Pat. Nos. 5,399,491 and 5,824,518, each of which is herein incorporated by reference in its entirety. In a variation described in U.S. Publ. No. 20060046265 (herein incorporated by reference in its entirety), TMA optionally incorporates the use of blocking moieties, terminating moieties, and other modifying moieties to improve TMA process sensitivity and accuracy.

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The ligase chain reaction (Weiss, R., Science 254: 1292 (1991), herein incorporated by reference in its entirety), commonly referred to as LCR, uses two sets of complementary DNA oligonucleotides that hybridize to adjacent regions of the target nucleic acid. The DNA oligonucleotides are covalently linked by a DNA ligase in repeated cycles of thermal denaturation, hybridization and ligation to produce a detectable double-stranded ligated oligonucleotide product.

Strand displacement amplification (Walker, G. et al., Proc. Natl. Acad. Sci. USA 89: 392-396 (1992); U. S. Pat. Nos. 5,270,184 and 5,455,166, each of which is herein incorporated by reference in its entirety), commonly referred to as SDA, uses cycles of annealing pairs of primer sequences to opposite strands of a target sequence, primer extension in the presence of a dNTPαS to produce a duplex hemiphosphorothioated primer extension product, endonuclease-mediated nicking of a hemimodified restriction endonuclease recognition site, and polymerase-mediated primer extension from the 3′ end of the nick to displace an existing strand and produce a strand for the next round of primer annealing, nicking and strand displacement, resulting in geometric amplification of product. Thermophilic SDA (tSDA) uses thermophilic endonucleases and polymerases at higher temperatures in essentially the same method (EP Pat. No. 0 684 315).

Other amplification methods include, for example: nucleic acid sequence based amplification (U.S. Pat. No. 5,130,238, herein incorporated by reference in its entirety), commonly referred to as NASBA; one that uses an RNA replicase to amplify the probe molecule itself (Lizardi et al., BioTechnol. 6: 1197 (1988), herein incorporated by reference in its entirety), commonly referred to as Qβ replicase; a transcription based amplification method (Kwoh et al., Proc. Natl. Acad. Sci. USA 86:1173 (1989)); and, self-sustained sequence replication (Guatelli et al., Proc. Natl. Acad. Sci. USA 87: 1874 (1990), each of which is herein incorporated by reference in its entirety). For further discussion of known amplification methods see Persing, David H., “In Vitro Nucleic Acid Amplification Techniques” in Diagnostic Medical Microbiology: Principles and Applications (Persing et al., Eds.), pp. 51-87 (American Society for Microbiology, Washington, D.C. (1993)).

ii. Data Analysis

In some embodiments, a computer-based analysis program is used to translate the raw data generated by the detection assay (e.g., the presence, absence, or amount of a given marker or markers) into data of predictive value for a clinician. The clinician can access the predictive data using any suitable means. Thus, in some preferred embodiments, the present disclosure provides the further benefit that the clinician, who is not likely to be trained in genetics or molecular biology, need not understand the raw data. The data is presented directly to the clinician in its most useful form. The clinician is then able to immediately utilize the information in order to optimize the care of the subject.

The present disclosure contemplates any method capable of receiving, processing, and transmitting the information to and from laboratories conducting the assays, information providers, medical personnel, and subjects. For example, in some embodiments of the present disclosure, a sample (e.g., a biopsy or a serum or urine sample) is obtained from a subject and submitted to a profiling service (e.g., clinical lab at a medical facility, genomic profiling business, etc.), located in any part of the world (e.g., in a country different than the country where the subject resides or where the information is ultimately used) to generate raw data. Where the sample comprises a tissue or other biological sample, the subject may visit a medical center to have the sample obtained and sent to the profiling center, or subjects may collect the sample themselves (e.g., a urine sample) and directly send it to a profiling center. Where the sample comprises previously determined biological information, the information may be directly sent to the profiling service by the subject (e.g., an information card containing the information may be scanned by a computer and the data transmitted to a computer of the profiling center using an electronic communication systems). Once received by the profiling service, the sample is processed and a profile is produced (i.e., expression data), specific for the diagnostic or prognostic information desired for the subject.

The profile data is then prepared in a format suitable for interpretation by one or more medical personnel (e.g., a treating clinician, physician assistant, nurse, or pharmacist). For example, rather than providing raw expression data, the prepared format may represent a diagnosis or risk assessment (e.g., presence or absence of a ncRNA) for the subject, along with recommendations for particular treatment options. The data may be displayed to the medical personnel by any suitable method. For example, in some embodiments, the profiling service generates a report that can be printed for the medical personnel (e.g., at the point of care) or displayed to the medical personnel on a computer monitor.

In some embodiments, the information is first analyzed at the point of care or at a regional facility. The raw data is then sent to a central processing facility for further analysis and/or to convert the raw data to information useful for medical personnel or patient. The central processing facility provides the advantage of privacy (all data is stored in a central facility with uniform security protocols), speed, and uniformity of data analysis. The central processing facility can then control the fate of the data following treatment of the subject. For example, using an electronic communication system, the central facility can provide data to the medical personnel, the subject, or researchers.

In some embodiments, the subject is able to directly access the data using the electronic communication system. The subject may chose further intervention or counseling based on the results.

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In some embodiments, the data is used for research use. For example, the data may be used to further optimize the inclusion or elimination of markers as useful indicators of a particular condition or stage of disease or as a companion diagnostic to determine a treatment course of action.

iii. Compositions & Kits

Compositions for use in the diagnostic methods described herein include, but are not limited to, probes, amplification oligonucleotides, and the like.

The compositions and kits may comprise 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more, 45 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 110 or more, 120 or more probes.

The probes may hybridize to 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more, 45 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 110 or more, 120 or more target molecules. The target molecules may be a ncRNA, RNA, DNA, cDNA, mRNA, a portion or fragment thereof or a combination thereof. In some instances, at least a portion of the target molecules are ncRNAs. The probes may hybridize to 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more, 45 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 110 or more, 120 or more ncRNAs disclosed herein (e.g., SEQ ID NOs: 1-2309).

Typically, the probes comprise a target specific sequence. The target specific sequence may be complementary to at least a portion of the target molecule. The target specific sequence may be at least about 50% or more, 55% or more, 60% or more, 65% or more, 70% or more, 75% or more, 80% or more, 85% or more, 90% or more, 95% or more, 97% or more, 98% or more, or 100% complementary to at leat a portion of the target molecule.

The target specific sequence may be at least about 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more nucleotides in length. In some instances, the target specific sequence is between about 8 to about 20 nucleotides, 10 to about 18 nucleotides, or 12 to about 16 nucleotides in length.

The compositions and kits may comprise a plurality of probes, wherein the two or more probes of the plurality of probes comprise identical target specific sequences. The compositions and kits may comprise a plurality of probes, wherein the two or more probes of the plurality of probes comprise different target specific sequences.

The probes may further comprise a unique sequence. The unique sequence is noncomplementary to the ncRNA. The unique sequence may comprise a label, barcode, or unique identifier. The unique sequence may comprise a random sequence, nonrandom sequence, or a combination thereof. The unique sequence may be at least about 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 22 or more, 24 or more, 26 or more, 28 or more, 30 or more nucleotides in length. In some instances, the unique sequence is between about 8 to about 20 nucleotides, 10 to about 18 nucleotides, or 12 to about 16 nucleotides in length.

The unique sequence may allow differentiation of two or more target molecules. The two or more target molecules may have identical sequences. Thus, the unique sequence may allow quantification of a target molecule. Alternatively, the two or more target molecules may have different sequences. Thus, the unique sequence may allow detection of the target molecules. The compositions and kits may comprise a plurality of probes for quantifying one or more target molecules. The compositions and kits may comprise a plurality of probes for detecting one or more target molecules.

The unique sequence may allow differentiation of two or more samples. The compositions and kits may comprise 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, 30 or more probe sets for differentiating two or more samples from one or more subjects. The two or more samples may be from two or more different subjects. For example, the compositions and kits comprise a first set of probes comprising a first unique sequence that is specific for a first subject and a second set of probes comprising a second unique sequence that is specific for a second subject. The compositions and kits may further comprise one or more sets of probes with one or more unique sequences to differentiate one or more additional subjects.

The compositions and kits may comprise 2 or more probe sets for differentiating from 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, 30 or more samples from 1 or more subjects.

The compositions and kits may comprise 2 or more probe sets for differentiating 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, 30 or more samples from one or more cells, tissues, organs, bodily fluid, or a combination thereof.

The compositions and kits may comprise 2 or more probe sets for differentiating samples from 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, 30 or more subjects.

›DETAILED DESCRIPTION OF THE DISCLOSURE · 7 of 20

Alternatively, or additionally, the two or more samples may be from two or more different timepoints from the same subject or different subjects. For example, the compositions and kits comprise a first set of probes comprising a first unique sequence that is specific for a first subject and a second set of probes comprising a second unique sequence that is specific for a second subject. The compositions and kits may comprise 2 or more probe sets for differentiating samples from 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, 30 or more timepoints. The timepoints may be every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 or more hours. The timepoints may be every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 or more days. The timepoints may be every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 or more weeks. The timepoints may be every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 or more months. The timepoints may be every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 or more years. The timepoints may be before diagnosis, after diagnosis, before treatment, during treatment, after treatment, before metastasis, after metastatis, before remission, during remission, or a combination thereof.

The compositions and kits may comprise a first probe comprising a first target-specific sequence and a first unique sequence and a second probe comprising a second target-specific sequence and a second unique sequence, wherein the first target specific sequence and the second target specific sequence are identical and the first unique sequence and the second unique sequence are different. The compositions and kits may comprise a first probe comprising a first target-specific sequence and a first unique sequence and a second probe comprising a second target-specific sequence and a second unique sequence, wherein the first target specific sequence and the second target specific sequence are different and the first unique sequence and the second unique sequence are different. The compositions and kits may comprise a first probe comprising a first target-specific sequence and a first unique sequence and a second probe comprising a second target-specific sequence and a second unique sequence, wherein the first target specific sequence and the second target specific sequence are identical and the first unique sequence and the second unique sequence are identical. The compositions and kits may comprise a first probe comprising a first target-specific sequence and a first unique sequence and a second probe comprising a second target-specific sequence and a second unique sequence, wherein the first target specific sequence and the second target specific sequence are different and the first unique sequence and the second unique sequence are identical.

The probes may further comprise a universal sequence. The universal sequence may comprise a primer binding site. The universal sequence may enable detection of the target sequence. The universal sequence may enable amplification of the target sequence. The universal sequence may enable transcription or reverse transcription of the target sequence. The universal sequence may enable sequencing of the target sequence.

The probe and antibody compositions of the present disclosure may also be provided on a solid support. The solid support may comprise one or more beads, plates, solid surfaces, wells, chips, or a combination thereof. The beads may be magnetic, antibody coated, protein A crosslinked, protein G crosslinked, streptavidin coated, oligonucleotide conjugated, silica coated, or a combination thereof. Examples of beads include, but are not limited to, Ampure beads, AMPure XP beads, streptavidin beads, agarose beads, magnetic beads, Dynabeads®, MACS® microbeads, antibody conjugated beads (e.g., anti-immunoglobulin microbead), protein A conjugated beads, protein G conjugated beads, protein A/G conjugated beads, protein L conjugated beads, oligo-dT conjugated beads, silica beads, silica-like beads, anti-biotin microbead, anti-fluorochrome microbead, and BcMag™ Carboxy-Terminated Magnetic Beads.

The compositions and kits may comprise primers and primer pairs capable of amplifying target molecules, or fragments or subsequences or complements thereof. The nucleotide sequences of the target molecules may be provided in computer-readable media for in silico applications and as a basis for the design of appropriate primers for amplification of one or more target molecules.

Primers based on the nucleotide sequences of target molecules can be designed for use in amplification of the target molecules. For use in amplification reactions such as PCR, a pair of primers can be used. The exact composition of the primer sequences is not critical to the disclosure, but for most applications the primers may hybridize to specific sequences of the target molecules or the universal sequence of the probe under stringent conditions, particularly under conditions of high stringency, as known in the art. The pairs of primers are usually chosen so as to generate an amplification product of at least about 15 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 125 or more, 150 or more, 175 or more, 200 or more, 250 or more, 300 or more, 350 or more, 400 or more, 450 or more, 500 or more, 600 or more, 700 or more, 800 or more, 900 or more, or 1000 or more nucleotides. Algorithms for the selection of primer sequences are generally known, and are available in commercial software packages. These primers may be used in standard quantitative or qualitative PCR-based assays to assess transcript expression levels of target molecules. Alternatively, these primers may be used in combination with probes, such as molecular beacons in amplifications using real-time PCR.

›DETAILED DESCRIPTION OF THE DISCLOSURE · 8 of 20

One skilled in the art also appreciates that the nucleotide sequence of the entire length of the primer does not need to be derived from the target sequence. Thus, for example, the primer may comprise nucleotide sequences at the 5′ and/or 3′ termini that are not derived from the target molecule. Nucleotide sequences which are not derived from the nucleotide sequence of the target molecule may provide additional functionality to the primer. For example, they may provide a restriction enzyme recognition sequence or a “tag” that facilitates detection, isolation, purification or immobilization onto a solid support. Alternatively, the additional nucleotides may provide a self-complementary sequence that allows the primer to adopt a hairpin configuration. Such configurations may be necessary for certain primers, for example, molecular beacon and Scorpion primers, which can be used in solution hybridization techniques.

The probes or primers can incorporate moieties useful in detection, isolation, purification, or immobilization, if desired. Such moieties are well-known in the art (see, for example, Ausubel et al., (1997 & updates) Current Protocols in Molecular Biology , Wiley & Sons, New York) and are chosen such that the ability of the probe to hybridize with its target molecule is not affected.

Examples of suitable moieties are detectable labels, such as radioisotopes, fluorophores, chemiluminophores, enzymes, colloidal particles, and fluorescent microparticles, as well as antigens, antibodies, haptens, avidin/streptavidin, biotin, haptens, enzyme cofactors/substrates, enzymes, and the like.

A label can optionally be attached to or incorporated into a probe or primer to allow detection and/or quantitation of a target polynucleotide representing the target molecule of interest. The target polynucleotide may be the expressed target molecule RNA itself, a cDNA copy thereof, or an amplification product derived therefrom, and may be the positive or negative strand, so long as it can be specifically detected in the assay being used. Similarly, an antibody may be labeled.

In certain multiplex formats, labels used for detecting different target molecules may be distinguishable. The label can be attached directly (e.g., via covalent linkage) or indirectly, e.g., via a bridging molecule or series of molecules (e.g., a molecule or complex that can bind to an assay component, or via members of a binding pair that can be incorporated into assay components, e.g. biotin-avidin or streptavidin). Many labels are commercially available in activated forms which can readily be used for such conjugation (for example through amine acylation), or labels may be attached through known or determinable conjugation schemes, many of which are known in the art.

Labels useful in the disclosure described herein include any substance which can be detected when bound to or incorporated into the target molecule. Any effective detection method can be used, including optical, spectroscopic, electrical, piezoelectrical, magnetic, Raman scattering, surface plasmon resonance, colorimetric, calorimetric, etc. A label is typically selected from a chromophore, a lumiphore, a fluorophore, one member of a quenching system, a chromogen, a hapten, an antigen, a magnetic particle, a material exhibiting nonlinear optics, a semiconductor nanocrystal, a metal nanoparticle, an enzyme, an antibody or binding portion or equivalent thereof, an aptamer, and one member of a binding pair, and combinations thereof. Quenching schemes may be used, wherein a quencher and a fluorophore as members of a quenching pair may be used on a probe, such that a change in optical parameters occurs upon binding to the target introduce or quench the signal from the fluorophore. One example of such a system is a molecular beacon. Suitable quencher/fluorophore systems are known in the art. The label may be bound through a variety of intermediate linkages. For example, a target polynucleotide may comprise a biotin-binding species, and an optically detectable label may be conjugated to biotin and then bound to the labeled target polynucleotide. Similarly, a polynucleotide sensor may comprise an immunological species such as an antibody or fragment, and a secondary antibody containing an optically detectable label may be added.

Chromophores useful in the methods described herein include any substance which can absorb energy and emit light. For multiplexed assays, a plurality of different signaling chromophores can be used with detectably different emission spectra. The chromophore can be a lumophore or a fluorophore. Typical fluorophores include fluorescent dyes, semiconductor nanocrystals, lanthanide chelates, polynucleotide-specific dyes and green fluorescent protein.

Coding schemes may optionally be used, comprising encoded particles and/or encoded tags associated with different polynucleotides of the disclosure. A variety of different coding schemes are known in the art, including fluorophores, including SCNCs, deposited metals, and RF tags.

Polynucleotides from the described target molecules may be employed as probes for detecting target molecules expression, for ligation amplification schemes, or may be used as primers for amplification schemes of all or a portion of a target molecule. When amplified, either strand produced by amplification may be provided in purified and/or isolated form.

In some instances, the compositions and kits comprise a biomarker library. The biomarker library may comprise 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more, 45 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 110 or more, 120 or more target molecules. The target molecules may be a ncRNA, RNA, DNA, cDNA, mRNA, a portion or fragment thereof or a combination thereof. In some instances, at least a portion of the target molecules are ncRNAs. The biomarker library may comprise 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more, 45 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 110 or more, 120 or more ncRNAs disclosed herein.

›DETAILED DESCRIPTION OF THE DISCLOSURE · 9 of 20

In some embodiments, is a kit for analyzing a cancer comprising (a) a probe set comprising a plurality of probes comprising target specific sequences complementary to one or more target molecules, wherein the one or more target molecules comprise one or more ncRNAs; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the one or more target molecules in a sample. The target molecules may comprose one or more of those described by SEQ ID NOs:1-2309, or a combination thereof. In some embodiments, is a kit for analyzing a cancer comprising (a) a probe set comprising a plurality of probes comprising target specific sequences complementary to one or more target molecules of a biomarker library; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the one or more target molecules in a sample. Control samples and/or nucleic acids may optionally be provided in the kit. Control samples may include tissue and/or nucleic acids obtained from or representative of tumor samples from a healthy subject, as well as tissue and/or nucleic acids obtained from or representative of tumor samples from subjects diagnosed with a cancer.

Instructions for using the kit to perform one or more methods of the disclosure can be provided, and can be provided in any fixed medium. The instructions may be located inside or outside a container or housing, and/or may be printed on the interior or exterior of any surface thereof. A kit may be in multiplex form for concurrently detecting and/or quantitating one or more different target polynucleotides representing the expressed target molecules.

iv. Devices

Devices useful for performing methods of the disclosure are also provided. The devices can comprise means for characterizing the expression level of a target molecule of the disclosure, for example components for performing one or more methods of nucleic acid extraction, amplification, and/or detection. Such components may include one or more of an amplification chamber (for example a thermal cycler), a plate reader, a spectrophotometer, capillary electrophoresis apparatus, a chip reader, and or robotic sample handling components. These components ultimately can obtain data that reflects the expression level of the target molecules used in the assay being employed.

The devices may include an excitation and/or a detection means. Any instrument that provides a wavelength that can excite a species of interest and is shorter than the emission wavelength(s) to be detected can be used for excitation. Commercially available devices can provide suitable excitation wavelengths as well as suitable detection component.

Exemplary excitation sources include a broadband UV light source such as a deuterium lamp with an appropriate filter, the output of a white light source such as a xenon lamp or a deuterium lamp after passing through a monochromator to extract out the desired wavelength(s), a continuous wave (cw) gas laser, a solid state diode laser, or any of the pulsed lasers. Emitted light can be detected through any suitable device or technique; many suitable approaches are known in the art. For example, a fluorimeter or spectrophotometer may be used to detect whether the test sample emits light of a wavelength characteristic of a label used in an assay.

The devices typically comprise a means for identifying a given sample, and of linking the results obtained to that sample. Such means can include manual labels, barcodes, and other indicators which can be linked to a sample vessel, and/or may optionally be included in the sample itself, for example where an encoded particle is added to the sample. The results may be linked to the sample, for example in a computer memory that contains a sample designation and a record of expression levels obtained from the sample. Linkage of the results to the sample can also include a linkage to a particular sample receptacle in the device, which is also linked to the sample identity.

The devices also comprise a means for correlating the expression levels of the target molecules being studied with a prognosis of disease outcome. Such means may comprise one or more of a variety of correlative techniques, including lookup tables, algorithms, multivariate models, and linear or nonlinear combinations of expression models or algorithms. The expression levels may be converted to one or more likelihood scores, reflecting a likelihood that the patient providing the sample may exhibit a particular disease outcome. The models and/or algorithms can be provided in machine readable format and can optionally further designate a treatment modality for a patient or class of patients.

The device also comprises output means for outputting the disease status, prognosis and/or a treatment modality. Such output means can take any form which transmits the results to a patient and/or a healthcare provider, and may include a monitor, a printed format, or both. The device may use a computer system for performing one or more of the steps provided.

The methods disclosed herein may also comprise the transmission of data/information. For example, data/information derived from the detection and/or quantification of the target may be transmitted to another device and/or instrument. In some instances, the information obtained from an algorithm may also be transmitted to another device and/or instrument. Transmission of the data/information may comprise the transfer of data/information from a first source to a second source. The first and second sources may be in the same approximate location (e.g., within the same room, building, block, campus). Alternatively, first and second sources may be in multiple locations (e.g., multiple cities, states, countries, continents, etc).

Transmission of the data/information may comprise digital transmission or analog transmission. Digital transmission may comprise the physical transfer of data (a digital bit stream) over a point-to-point or point-to-multipoint communication channel. Examples of such channels are copper wires, optical fibres, wireless communication channels, and storage media. The data may be represented as an electromagnetic signal, such as an electrical voltage, radiowave, microwave, or infrared signal.

›DETAILED DESCRIPTION OF THE DISCLOSURE · 10 of 20

Analog transmission may comprise the transfer of a continuously varying analog signal. The messages can either be represented by a sequence of pulses by means of a line code (baseband transmission), or by a limited set of continuously varying wave forms (passband transmission), using a digital modulation method. The passband modulation and corresponding demodulation (also known as detection) can be carried out by modem equipment. According to the most common definition of digital signal, both baseband and passband signals representing bit-streams are considered as digital transmission, while an alternative definition only considers the baseband signal as digital, and passband transmission of digital data as a form of digital-to-analog conversion.

v. Samples

Samples for use with the compositions and kits and in the methods of the present disclosure comprise nucleic acids suitable for providing RNA expression information. In principle, the biological sample from which the expressed RNA is obtained and analyzed for target molecule expression can be any material suspected of comprising cancer tissue or cells. The sample can be a biological sample used directly in a method of the disclosure. Alternatively, the sample can be a sample prepared from a biological sample.

In one embodiment, the sample or portion of the sample comprising or suspected of comprising cancer tissue or cells can be any source of biological material, including cells, tissue, secretions, or fluid, including bodily fluids. Non-limiting examples of the source of the sample include an aspirate, a needle biopsy, a cytology pellet, a bulk tissue preparation or a section thereof obtained for example by surgery or autopsy, lymph fluid, blood, plasma, serum, tumors, and organs. Alternatively, or additionally, the source of the sample can be urine, bile, excrement, sweat, tears, vaginal fluids, spinal fluid, and stool. In some instances, the sources of the sample are secretions. In some instances, the secretions are exosomes.

The samples may be archival samples, having a known and documented medical outcome, or may be samples from current patients whose ultimate medical outcome is not yet known.

In some embodiments, the sample may be dissected prior to molecular analysis. The sample may be prepared via macrodissection of a bulk tumor specimen or portion thereof, or may be treated via microdissection, for example via Laser Capture Microdissection (LCM).

The sample may initially be provided in a variety of states, as fresh tissue, fresh frozen tissue, fine needle aspirates, and may be fixed or unfixed. Frequently, medical laboratories routinely prepare medical samples in a fixed state, which facilitates tissue storage. A variety of fixatives can be used to fix tissue to stabilize the morphology of cells, and may be used alone or in combination with other agents. Exemplary fixatives include crosslinking agents, alcohols, acetone, Bouin's solution, Zenker solution, Hely solution, osmic acid solution and Carnoy solution.

Crosslinking fixatives can comprise any agent suitable for forming two or more covalent bonds, for example, an aldehyde. Sources of aldehydes typically used for fixation include formaldehyde, paraformaldehyde, glutaraldehyde or formalin. Preferably, the crosslinking agent comprises formaldehyde, which may be included in its native form or in the form of paraformaldehyde or formalin. One of skill in the art would appreciate that for samples in which crosslinking fixatives have been used special preparatory steps may be necessary including for example heating steps and proteinase-k digestion.

One or more alcohols may be used to fix tissue, alone or in combination with other fixatives. Exemplary alcohols used for fixation include methanol, ethanol and isopropanol.

Formalin fixation is frequently used in medical laboratories. Formalin comprises both an alcohol, typically methanol, and formaldehyde, both of which can act to fix a biological sample.

Whether fixed or unfixed, the biological sample may optionally be embedded in an embedding medium. Exemplary embedding media used in histology including paraffin, Tissue-Tek® V.I.P.™, Paramat, Paramat Extra, Paraplast, Paraplast X-tra, Paraplast Plus, Peel Away Paraffin Embedding Wax, Polyester Wax, Carbowax Polyethylene Glycol, Polyfin™, Tissue Freezing Medium TFMFM, Cryo-Gef™, and OCT Compound (Electron Microscopy Sciences, Hatfield, Pa.). Prior to molecular analysis, the embedding material may be removed via any suitable techniques, as known in the art. For example, where the sample is embedded in wax, the embedding material may be removed by extraction with organic solvent(s), for example xylenes. Kits are commercially available for removing embedding media from tissues. Samples or sections thereof may be subjected to further processing steps as needed, for example serial hydration or dehydration steps.

In some embodiments, the sample is a fixed, wax-embedded biological sample. Frequently, samples from medical laboratories are provided as fixed, wax-embedded samples, most commonly as formalin-fixed, paraffin embedded (FFPE) tissues.

Whatever the source of the biological sample, the target polynucleotide that is ultimately assayed can be prepared synthetically (in the case of control sequences), but typically is purified from the biological source and subjected to one or more preparative steps. The RNA may be purified to remove or diminish one or more undesired components from the biological sample or to concentrate it. Conversely, where the RNA is too concentrated for the particular assay, it may be diluted.

II. Drug Screening Applications

In some embodiments, the present disclosure provides drug screening assays (e.g., to screen for anticancer drugs). The screening methods of the present disclosure utilize ncRNAs. For example, in some embodiments, the present disclosure provides methods of screening for compounds that alter the expression or activity of ncRNAs. The compounds may increase the expression or activity of the ncRNAs. The compounds may decrease the expression or activity of the ncRNAs. The compounds or agents may interfere with transcription, by interacting, for example, with the promoter region. The compounds or agents may interfere with mRNA (e.g., by RNA interference, antisense technologies, etc.). The compounds or agents may interfere with pathways that are upstream or downstream of the biological activity of ncRNAs. In some embodiments, candidate compounds are antisense or interfering RNA agents (e.g., oligonucleotides) directed against ncRNAs. In other embodiments, candidate compounds are antibodies or small molecules that specifically bind to a ncRNA regulator. Alternatively, or additionally, the candidate compounds are expression products that inhibit the biological function of the ncRNAs.

›DETAILED DESCRIPTION OF THE DISCLOSURE · 11 of 20

In one screening method, candidate compounds are evaluated for their ability to alter ncRNAs expression by contacting a compound with a cell expressing a ncRNA and then assaying for the effect of the candidate compounds on expression. In some embodiments, the effect of candidate compounds on expression of ncRNAs is assayed for by detecting the level ncRNA expressed by the cell. mRNA expression can be detected by any suitable method.

III. Diagnosis, Prognosis, and Monitoring

The methods, compositions, and kits disclosed herein may be used for the diagnosis, prognosis, and/or monitoring the status or outcome of a cancer in a subject. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the malignancy or malignant potential of the cancer or tumor. Alternatively, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the stage of the cancer. The diagnosing, predicting, and/or monitoring the status or outcome of a cancer can comprise determining the tumor grade. Alternatively, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises assessing the risk of developing a cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes assessing the risk of cancer recurrence. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining the efficacy of treatment.

In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen.

In some instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 50%. In other instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 60%. The methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 65%. Alternatively, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 70%. In some instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 75%. In other instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 80%. The methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 85%. Alternatively, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 90%. The methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 95%.

The disclosure also encompasses any of the methods disclosed herein where the sensitivity is at least about 45%. In some embodiments, the sensitivity is at least about 50%. In some embodiments, the sensitivity is at least about 55%. In some embodiments, the sensitivity is at least about 60%. In some embodiments, the sensitivity is at least about 65%. In some embodiments, the sensitivity is at least about 70%. In some embodiments, the sensitivity is at least about 75%. In some embodiments, the sensitivity is at least about 80%. In some embodiments, the sensitivity is at least about 85%. In some embodiments, the sensitivity is at least about 90%. In some embodiments, the sensitivity is at least about 95%.

The disclosure also encompasses any of the methods disclosed herein where the expression level determines the status or outcome of a cancer in the subject with at least about 45% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 50% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 55% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 60% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 65% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 70% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 75% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 80% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 85% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 90% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 95% specificity.

Cancer

The systems, compositions and methods disclosed herein may be used to diagnosis, monitor and/or predict the status or outcome of a cancer. Generally, a cancer is characterized by the uncontrolled growth of abnormal cells anywhere in a body. The abnormal cells may be termed cancer cells, malignant cells, or tumor cells. Many cancers and the abnormal cells that compose the cancer tissue are further identified by the name of the tissue that the abnormal cells originated from (for example, breast cancer, lung cancer, colon cancer, prostate cancer, pancreatic cancer, thyroid cancer). Cancer is not confined to humans; animals and other living organisms can get cancer.

›DETAILED DESCRIPTION OF THE DISCLOSURE · 12 of 20

In some instances, the cancer may be malignant. Alternatively, the cancer may be benign. The cancer may be a recurrent and/or refractory cancer. Most cancers can be classified as a carcinoma, sarcoma, leukemia, lymphoma, myeloma, or a central nervous system cancer.

The cancer may be a sarcoma. Sarcomas are cancers of the bone, cartilage, fat, muscle, blood vessels, or other connective or supportive tissue. Sarcomas include, but are not limited to, bone cancer, fibrosarcoma, chondrosarcoma, Ewing's sarcoma, malignant hemangioendothelioma, malignant schwannoma, bilateral vestibular schwannoma, osteosarcoma, soft tissue sarcomas (e.g. alveolar soft part sarcoma, angiosarcoma, cystosarcoma phylloides, dermatofibrosarcoma, desmoid tumor, epithelioid sarcoma, extraskeletal osteosarcoma, fibrosarcoma, hemangiopericytoma, hemangiosarcoma, Kaposi's sarcoma, leiomyosarcoma, liposarcoma, lymphangiosarcoma, lymphosarcoma, malignant fibrous histiocytoma, neurofibrosarcoma, rhabdomyosarcoma, and synovial sarcoma).

Alternatively, the cancer may be a carcinoma. Carcinomas are cancers that begin in the epithelial cells, which are cells that cover the surface of the body, produce hormones, and make up glands. By way of non-limiting example, carcinomas include breast cancer, pancreatic cancer, lung cancer, colon cancer, colorectal cancer, rectal cancer, kidney cancer, bladder cancer, stomach cancer, prostate cancer, liver cancer, ovarian cancer, brain cancer, vaginal cancer, vulvar cancer, uterine cancer, oral cancer, penic cancer, testicular cancer, esophageal cancer, skin cancer, cancer of the fallopian tubes, head and neck cancer, gastrointestinal stromal cancer, adenocarcinoma, cutaneous or intraocular melanoma, cancer of the anal region, cancer of the small intestine, cancer of the endocrine system, cancer of the thyroid gland, cancer of the parathyroid gland, cancer of the adrenal gland, cancer of the urethra, cancer of the renal pelvis, cancer of the ureter, cancer of the endometrium, cancer of the cervix, cancer of the pituitary gland, neoplasms of the central nervous system (CNS), primary CNS lymphoma, brain stem glioma, and spinal axis tumors. In some instances, the cancer is a skin cancer, such as a basal cell carcinoma, squamous, melanoma, nonmelanoma, or actinic (solar) keratosis. Preferably, the cancer is a prostate cancer. Alternatively, the cancer may be a thyroid cancer. The cancer can be a pancreatic cancer. In some instances, the cancer is a bladder cancer.

In some instances, the cancer is a lung cancer. Lung cancer can start in the airways that branch off the trachea to supply the lungs (bronchi) or the small air sacs of the lung (the alveoli). Lung cancers include non-small cell lung carcinoma (NSCLC), small cell lung carcinoma, and mesotheliomia. Examples of NSCLC include squamous cell carcinoma, adenocarcinoma, and large cell carcinoma. The mesothelioma may be a cancerous tumor of the lining of the lung and chest cavity (pleura) or lining of the abdomen (peritoneum). The mesothelioma may be due to asbestos exposure. The cancer may be a brain cancer, such as a glioblastoma.

Alternatively, the cancer may be a central nervous system (CNS) tumor. CNS tumors may be classified as gliomas or nongliomas. The glioma may be malignant glioma, high grade glioma, diffuse intrinsic pontine glioma. Examples of gliomas include astrocytomas, oligodendrogliomas (or mixtures of oligodendroglioma and astocytoma elements), and ependymomas. Astrocytomas include, but are not limited to, low-grade astrocytomas, anaplastic astrocytomas, glioblastoma multiforme, pilocytic astrocytoma, pleomorphic xanthoastrocytoma, and subependymal giant cell astrocytoma. Oligodendrogliomas include low-grade oligodendrogliomas (or oligoastrocytomas) and anaplastic oligodendriogliomas. Nongliomas include meningiomas, pituitary adenomas, primary CNS lymphomas, and medulloblastomas. In some instances, the cancer is a meningioma.

The cancer may be leukemia. The leukemia may be an acute lymphocytic leukemia, acute myelocytic leukemia, chronic lymphocytic leukemia, or chronic myelocytic leukemia. Additional types of leukemias include hairy cell leukemia, chronic myelomonocytic leukemia, and juvenile myelomonocytic-leukemia.

In some instances, the cancer is a lymphoma. Lymphomas are cancers of the lymphocytes and may develop from either B or T lymphocytes. The two major types of lymphoma are Hodgkin's lymphoma, previously known as Hodgkin's disease, and non-Hodgkin's lymphoma. Hodgkin's lymphoma is marked by the presence of the Reed-Sternberg cell. Non-Hodgkin's lymphomas are all lymphomas which are not Hodgkin's lymphoma. Non-Hodgkin lymphomas may be indolent lymphomas and aggressive lymphomas. Non-Hodgkin's lymphomas include, but are not limited to, diffuse large B cell lymphoma, follicular lymphoma, mucosa-associated lymphatic tissue lymphoma (MALT), small cell lymphocytic lymphoma, mantle cell lymphoma, Burkitt's lymphoma, mediastinal large B cell lymphoma, Waldenström macroglobulinemia, nodal marginal zone B cell lymphoma (NMZL), splenic marginal zone lymphoma (SMZL), extranodal marginal zone B cell lymphoma, intravascular large B cell lymphoma, primary effusion lymphoma, and lymphomatoid granulomatosis.

Cancer Staging

Diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise determining the stage of the cancer. Generally, the stage of a cancer is a description (usually numbers I to IV with IV having more progression) of the extent the cancer has spread. The stage often takes into account the size of a tumor, how deeply it has penetrated, whether it has invaded adjacent organs, how many lymph nodes it has metastasized to (if any), and whether it has spread to distant organs. Staging of cancer can be used as a predictor of survival, and cancer treatment may be determined by staging. Determining the stage of the cancer may occur before, during, or after treatment. The stage of the cancer may also be determined at the time of diagnosis.

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Cancer staging can be divided into a clinical stage and a pathologic stage. Cancer staging may comprise the TNM classification. Generally, the TNM Classification of Malignant Tumours (TNM) is a cancer staging system that describes the extent of cancer in a patient's body. T may describe the size of the tumor and whether it has invaded nearby tissue, N may describe regional lymph nodes that are involved, and M may describe distant metastasis (spread of cancer from one body part to another). In the TNM (Tumor, Node, Metastasis) system, clinical stage and pathologic stage are denoted by a small “c” or “p” before the stage (e.g., cT3N1M0 or pT2N0).

Often, clinical stage and pathologic stage may differ. Clinical stage may be based on all of the available information obtained before a surgery to remove the tumor. Thus, it may include information about the tumor obtained by physical examination, radiologic examination, and endoscopy. Pathologic stage can add additional information gained by examination of the tumor microscopically by a pathologist. Pathologic staging can allow direct examination of the tumor and its spread, contrasted with clinical staging which may be limited by the fact that the information is obtained by making indirect observations at a tumor which is still in the body. The TNM staging system can be used for most forms of cancer.

Alternatively, staging may comprise Ann Arbor staging. Generally, Ann Arbor staging is the staging system for lymphomas, both in Hodgkin's lymphoma (previously called Hodgkin's disease) and Non-Hodgkin lymphoma (abbreviated NHL). The stage may depend on both the place where the malignant tissue is located (as located with biopsy, CT scanning and increasingly positron emission tomography) and on systemic symptoms due to the lymphoma (“B symptoms”: night sweats, weight loss of >10% or fevers). The principal stage may be determined by location of the tumor. Stage I may indicate that the cancer is located in a single region, usually one lymph node and the surrounding area. Stage I often may not have outward symptoms. Stage II can indicate that the cancer is located in two separate regions, an affected lymph node or organ and a second affected area, and that both affected areas are confined to one side of the diaphragm—that is, both are above the diaphragm, or both are below the diaphragm. Stage III often indicates that the cancer has spread to both sides of the diaphragm, including one organ or area near the lymph nodes or the spleen. Stage IV may indicate diffuse or disseminated involvement of one or more extralymphatic organs, including any involvement of the liver, bone marrow, or nodular involvement of the lungs.

Modifiers may also be appended to some stages. For example, the letters A, B, E, X, or S can be appended to some stages. Generally, A or B may indicate the absence of constitutional (B-type) symptoms is denoted by adding an “A” to the stage; the presence is denoted by adding a “B” to the stage. E can be used if the disease is “extranodal” (not in the lymph nodes) or has spread from lymph nodes to adjacent tissue. X is often used if the largest deposit is >10 cm large (“bulky disease”), or whether the mediastinum is wider than ⅓ of the chest on a chest X-ray. S may be used if the disease has spread to the spleen.

The nature of the staging may be expressed with CS or PS. CS may denote that the clinical stage as obtained by doctor's examinations and tests. PS may denote that the pathological stage as obtained by exploratory laparotomy (surgery performed through an abdominal incision) with splenectomy (surgical removal of the spleen).

Therapeutic Regimens

Diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise treating a cancer or preventing a cancer progression. In addition, diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise identifying or predicting responders to an anti-cancer therapy. In some instances, diagnosing, predicting, or monitoring may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapy. Alternatively, determining a therapeutic regimen may comprise modifying, recommending, continuing or discontinuing an anti-cancer regimen. In some instances, if the sample expression patterns are consistent with the expression pattern for a known disease or disease outcome, the expression patterns can be used to designate one or more treatment modalities (e.g., therapeutic regimens, anti-cancer regimen). An anti-cancer regimen may comprise one or more anti-cancer therapies. Examples of anti-cancer therapies include targeting cancer therapy (e.g., targeting the non-coding RNAs described herein), surgery, chemotherapy, radiation therapy, immunotherapy/biological therapy, photodynamic therapy. In some embodiments, the present disclsoure targets the expression of cancer markers. For example, in some embodiments, the present disclsoure employs compositions comprising oligomeric antisense or RNAi compounds, particularly oligonucleotides (e.g., those identified in the drug screening methods described above), for use in modulating the function of nucleic acid molecules encoding cancer markers of the present disclsoure, ultimately modulating the amount of cancer marker expressed.

In some embodiments, RNAi is utilized to target non-coding RNAs (e.g., one or more of SEQ ID NOs: 1-2309). RNAi represents an evolutionary conserved cellular defense for controlling the expression of foreign genes in most eukaryotes, including humans. RNAi is typically triggered by double-stranded RNA (dsRNA) and causes sequence-specific mRNA degradation of single-stranded target RNAs homologous in response to dsRNA. The mediators of mRNA degradation are small interfering RNA duplexes (siRNAs), which are normally produced from long dsRNA by enzymatic cleavage in the cell. siRNAs are generally approximately twenty-one nucleotides in length (e.g. 21-23 nucleotides in length), and have a base-paired structure characterized by two nucleotide 3′-overhangs. Following the introduction of a small RNA, or RNAi, into the cell, it is believed the sequence is delivered to an enzyme complex called RISC (RNA-induced silencing complex). RISC recognizes the target and cleaves it with an endonuclease. It is noted that if larger RNA sequences are delivered to a cell, RNase III enzyme (Dicer) converts longer dsRNA into 21-23 nt ds siRNA fragments.

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Chemically synthesized siRNAs have become powerful reagents for genome-wide analysis of mammalian gene function in cultured somatic cells. Beyond their value for validation of gene function, siRNAs also hold great potential as gene-specific therapeutic agents (Tuschl and Borkhardt, Molecular Intervent. 2002; 2(3):158-67, herein incorporated by reference).

The transfection of siRNAs into animal cells results in the potent, long-lasting post-transcriptional silencing of specific genes (Caplen et al, Proc Natl Acad Sci U.S.A. 2001; 98: 9742-7; Elbashir et al., Nature. 2001; 411:494-8; Elbashir et al., Genes Dev. 2001; 15: 188-200; and Elbashir et al., EMBO J. 2001; 20: 6877-88, all of which are herein incorporated by reference). Methods and compositions for performing RNAi with siRNAs are described, for example, in U.S. Pat. No. 6,506,559, herein incorporated by reference.

siRNAs are extraordinarily effective at lowering the amounts of targeted RNA, and by extension proteins, frequently to undetectable levels. The silencing effect can last several months, and is extraordinarily specific, because one nucleotide mismatch between the target RNA and the central region of the siRNA is frequently sufficient to prevent silencing (Brummelkamp et al, Science 2002; 296:550-3; and Holen et al, Nucleic Acids Res. 2002; 30:1757-66, both of which are herein incorporated by reference).

An important factor in the design of siRNAs is the presence of accessible sites for siRNA binding. Bahoia et al., (J. Biol. Chem., 2003; 278: 15991-15997; herein incorporated by reference) describe the use of a type of DNA array called a scanning array to find accessible sites in mRNAs for designing effective siRNAs. These arrays comprise oligonucleotides ranging in size from monomers to a certain maximum, usually Comers, synthesized using a physical barrier (mask) by stepwise addition of each base in the sequence. Thus the arrays represent a full oligonucleotide complement of a region of the target gene. Hybridization of the target mRNA to these arrays provides an exhaustive accessibility profile of this region of the target mRNA. Such data are useful in the design of antisense oligonucleotides (ranging from 7mers to 25mers), where it is important to achieve a compromise between oligonucleotide length and binding affinity, to retain efficacy and target specificity (Sohail et al, Nucleic Acids Res., 2001; 29(10): 2041-2045). Additional methods and concerns for selecting siRNAs are described for example, in WO 05054270, WO05038054A1, WO03070966A2, J Mol Biol. 2005 May 13; 348(4):883-93, J Mol Biol. 2005 May 13; 348(4):871-81, and Nucleic Acids Res. 2003 Aug. 1; 31(15):4417-24, each of which is herein incorporated by reference in its entirety. In addition, software (e.g., the MWG online siMAX siRNA design tool) is commercially or publicly available for use in the selection of siRNAs.

In other embodiments, expression of non-coding RNAs (e.g., one or more of SEQ ID NOs: 1-2309) is modulated using antisense compounds that specifically hybridize with one or more nucleic acids encoding the RNAs. The specific hybridization of an oligomeric compound with its target nucleic acid interferes with the normal function of the nucleic acid. This modulation of function of a target nucleic acid by compounds that specifically hybridize to it is generally referred to as “antisense.” The functions of DNA to be interfered with include replication and transcription. The functions of RNA to be interfered with include all vital functions such as, for example, translocation of the RNA to the site of protein translation, translation of protein from the RNA, splicing of the RNA to yield one or more mRNA species, and catalytic activity that may be engaged in or facilitated by the RNA. The overall effect of such interference with target nucleic acid function is modulation of the expression of cancer markers of the present disclsoure. In the context of the present disclsoure, “modulation” means either an increase (stimulation) or a decrease (inhibition) in the expression of a gene. For example, expression may be inhibited to potentially prevent tumor proliferation.

It is preferred to target specific nucleic acids for antisense. “Targeting” an antisense compound to a particular nucleic acid, in the context of the present disclsoure, is a multistep process. The process usually begins with the identification of a nucleic acid sequence whose function is to be modulated. This may be, for example, a cellular gene (or mRNA transcribed from the gene) whose expression is associated with a particular disorder or disease state, or a nucleic acid molecule from an infectious agent. In the present disclsoure, the target is a nucleic acid molecule encoding a cancer marker of the present disclsoure. The targeting process also includes determination of a site or sites within this gene for the antisense interaction to occur such that the desired effect, e.g., detection or modulation of expression of the protein, will result. Within the context of the present disclsoure, a preferred intragenic site is the region encompassing the translation initiation or termination codon of the open reading frame (ORF) of the gene. Since the translation initiation codon is typically 5′-AUG (in transcribed mRNA molecules; 5′-ATG in the corresponding DNA molecule), the translation initiation codon is also referred to as the “AUG codon,” the “start codon” or the “AUG start codon”. A minority of genes have a translation initiation codon having the RNA sequence 5′-GUG, 5′-UUG or 5′-CUG, and 5′-AUA, 5′-ACG and 5′-CUG have been shown to function in vivo. Thus, the terms “translation initiation codon” and “start codon” can encompass many codon sequences, even though the initiator amino acid in each instance is typically methionine (in eukaryotes) or formylmethionine (in prokaryotes). Eukaryotic and prokaryotic genes may have two or more alternative start codons, any one of which may be preferentially utilized for translation initiation in a particular cell type or tissue, or under a particular set of conditions. In the context of the present disclsoure, “start codon” and “translation initiation codon” refer to the codon or codons that are used in vivo to initiate translation of an RNA (e.g., one or more of SEQ ID NOs: 1-2309).

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Translation termination codon (or “stop codon”) of a gene may have one of three sequences (i.e., 5′-UAA, 5′-UAG and 5′-UGA; the corresponding DNA sequences are 5′-TAA, 5′-TAG and 5′-TGA, respectively). The terms “start codon region” and “translation initiation codon region” refer to a portion of such an mRNA or gene that encompasses from about 25 to about 50 contiguous nucleotides in either direction (i.e., 5′ or 3′) from a translation initiation codon. Similarly, the terms “stop codon region” and “translation termination codon region” refer to a portion of such an mRNA or gene that encompasses from about 25 to about 50 contiguous nucleotides in either direction (i.e., 5′ or 3′) from a translation termination codon.

The open reading frame (ORF) or “coding region,” which refers to the region between the translation initiation codon and the translation termination codon, is also a region that may be targeted effectively. Other target regions include the 5′ untranslated region (5′ UTR), referring to the portion of an mRNA in the 5′ direction from the translation initiation codon, and thus including nucleotides between the 5′ cap site and the translation initiation codon of an mRNA or corresponding nucleotides on the gene, and the 3′ untranslated region (3′ UTR), referring to the portion of an mRNA in the 3′ direction from the translation termination codon, and thus including nucleotides between the translation termination codon and 3′ end of an mRNA or corresponding nucleotides on the gene. The 5′ cap of an mRNA comprises an N7-methylated guanosine residue joined to the 5′-most residue of the mRNA via a 5′-5′ triphosphate linkage. The 5′ cap region of an mRNA is considered to include the 5′ cap structure itself as well as the first 50 nucleotides adjacent to the cap. The cap region may also be a preferred target region.

Although some eukaryotic mRNA transcripts are directly translated, many contain one or more regions, known as “introns,” that are excised from a transcript before it is translated. The remaining (and therefore translated) regions are known as “exons” and are spliced together to form a continuous mRNA sequence. mRNA splice sites (i.e., intron-exon junctions) may also be preferred target regions, and are particularly useful in situations where aberrant splicing is implicated in disease, or where an overproduction of a particular mRNA splice product is implicated in disease. It has also been found that introns can also be effective, and therefore preferred, target regions for antisense compounds targeted, for example, to DNA or pre-mRNA.

In some embodiments, target sites for antisense inhibition are identified using commercially available software programs (e.g., Biognostik, Gottingen, Germany; SysArris Software, Bangalore, India; Antisense Research Group, University of Liverpool, Liverpool, England; GeneTrove, Carlsbad, Calif.). In other embodiments, target sites for antisense inhibition are identified using the accessible site method described in PCT Publ. No. WO0198537A2, herein incorporated by reference.

Once one or more target sites have been identified, oligonucleotides are chosen that are sufficiently complementary to the target (i.e., hybridize sufficiently well and with sufficient specificity) to give the desired effect. For example, in preferred embodiments of the present disclsoure, antisense oligonucleotides are targeted to or near the start codon.

In the context of this disclsoure, “hybridization,” with respect to antisense compositions and methods, means hydrogen bonding, which may be Watson-Crick, Hoogsteen or reversed Hoogsteen hydrogen bonding, between complementary nucleoside or nucleotide bases. For example, adenine and thymine are complementary nucleobases that pair through the formation of hydrogen bonds. It is understood that the sequence of an antisense compound need not be 100% complementary to that of its target nucleic acid to be specifically hybridizable. An antisense compound is specifically hybridizable when binding of the compound to the target DNA or RNA molecule interferes with the normal function of the target DNA or RNA to cause a loss of utility, and there is a sufficient degree of complementarity to avoid non-specific binding of the antisense compound to non-target sequences under conditions in which specific binding is desired (i.e., under physiological conditions in the case of in vivo assays or therapeutic treatment, and in the case of in vitro assays, under conditions in which the assays are performed).

The specificity and sensitivity of antisense is also applied for therapeutic uses. For example, antisense oligonucleotides have been employed as therapeutic moieties in the treatment of disease states in animals and man. Antisense oligonucleotides have been safely and effectively administered to humans and numerous clinical trials are presently underway. It is thus established that oligonucleotides are useful therapeutic modalities that can be configured to be useful in treatment regimes for treatment of cells, tissues, and animals, especially humans.

While antisense oligonucleotides are a preferred form of antisense compound, the present disclsoure comprehends other oligomeric antisense compounds, including but not limited to oligonucleotide mimetics such as are described below. The antisense compounds in accordance with this disclsoure preferably comprise from about 8 to about 30 nucleobases (i.e., from about 8 to about 30 linked bases), although both longer and shorter sequences may find use with the present disclsoure. Particularly preferred antisense compounds are antisense oligonucleotides, even more preferably those comprising from about 12 to about 25 nucleobases.

Specific examples of preferred antisense compounds useful with the present disclsoure include oligonucleotides containing modified backbones or non-natural internucleoside linkages. As defined in this specification, oligonucleotides having modified backbones include those that retain a phosphorus atom in the backbone and those that do not have a phosphorus atom in the backbone. For the purposes of this specification, modified oligonucleotides that do not have a phosphorus atom in their internucleoside backbone can also be considered to be oligonucleosides.

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Preferred modified oligonucleotide backbones include, for example, phosphorothioates, chiral phosphorothioates, phosphorodithioates, phosphotriesters, aminoalkylphosphotriesters, methyl and other alkyl phosphonates including 3′-alkylene phosphonates and chiral phosphonates, phosphinates, phosphoramidates including 3′-amino phosphoramidate and aminoalkylphosphoramidates, thionophosphoramidates, thionoalkylphosphonates, thionoalkylphosphotriesters, and boranophosphates having normal 3′-5′ linkages, 2′-5′ linked analogs of these, and those having inverted polarity wherein the adjacent pairs of nucleoside units are linked 3′-5′ to 5′-3′ or 2′-5′ to 5′-2′. Various salts, mixed salts and free acid forms are also included.

The present disclsoure contemplates the use of any genetic manipulation for use in modulating the expression of non-coding RNAs (e.g., one or more of SEQ ID NOs: 1-2309). Examples of genetic manipulation include, but are not limited to, gene knockout (e.g., removing the gene encoding the RNA from the chromosome using, for example, recombination), expression of antisense constructs with or without inducible promoters, and the like. Delivery of nucleic acid construct to cells in vitro or in vivo may be conducted using any suitable method. A suitable method is one that introduces the nucleic acid construct into the cell such that the desired event occurs (e.g., expression of an antisense construct). Genetic therapy may also be used to deliver siRNA or other interfering molecules that are expressed in vivo (e.g., upon stimulation by an inducible promoter (e.g., an androgen-responsive promoter)).

Introduction of molecules carrying genetic information into cells is achieved by any of various methods including, but not limited to, directed injection of naked DNA constructs, bombardment with gold particles loaded with said constructs, and macromolecule mediated gene transfer using, for example, liposomes, biopolymers, and the like. Preferred methods use gene delivery vehicles derived from viruses, including, but not limited to, adenoviruses, retroviruses, vaccinia viruses, and adeno-associated viruses. Because of the higher efficiency as compared to retroviruses, vectors derived from adenoviruses are the preferred gene delivery vehicles for transferring nucleic acid molecules into host cells in vivo. Adenoviral vectors have been shown to provide very efficient in vivo gene transfer into a variety of solid tumors in animal models and into human solid tumor xenografts in immune-deficient mice. Examples of adenoviral vectors and methods for gene transfer are described in PCT publications WO 00/12738 and WO 00/09675 and U.S. Pat. Nos. 6,033,908, 6,019,978, 6,001,557, 5,994,132, 5,994,128, 5,994,106, 5,981,225, 5,885,808, 5,872,154, 5,830,730, and 5,824,544, each of which is herein incorporated by reference in its entirety.

Vectors may be administered to subject in a variety of ways. For example, in some embodiments of the present disclsoure, vectors are administered into tumors or tissue associated with tumors using direct injection. In other embodiments, administration is via the blood or lymphatic circulation (See e.g., PCT publication 99/02685 herein incorporated by reference in its entirety). Exemplary dose levels of adenoviral vector are preferably 10 8 to 10 11 vector particles added to the perfusate.

Surgical oncology uses surgical methods to diagnose, stage, and treat cancer, and to relieve certain cancer-related symptoms. Surgery may be used to remove the tumor (e.g., excisions, resections, debulking surgery), reconstruct a part of the body (e.g., restorative surgery), and/or to relieve symptoms such as pain (e.g., palliative surgery). Surgery may also include cryosurgery. Cryosurgery (also called cryotherapy) may use extreme cold produced by liquid nitrogen (or argon gas) to destroy abnormal tissue. Cryosurgery can be used to treat external tumors, such as those on the skin. For external tumors, liquid nitrogen can be applied directly to the cancer cells with a cotton swab or spraying device. Cryosurgery may also be used to treat tumors inside the body (internal tumors and tumors in the bone). For internal tumors, liquid nitrogen or argon gas may be circulated through a hollow instrument called a cryoprobe, which is placed in contact with the tumor. An ultrasound or MRI may be used to guide the cryoprobe and monitor the freezing of the cells, thus limiting damage to nearby healthy tissue. A ball of ice crystals may form around the probe, freezing nearby cells. Sometimes more than one probe is used to deliver the liquid nitrogen to various parts of the tumor. The probes may be put into the tumor during surgery or through the skin (percutaneously). After cryosurgery, the frozen tissue thaws and may be naturally absorbed by the body (for internal tumors), or may dissolve and form a scab (for external tumors).

Chemotherapeutic agents may also be used for the treatment of cancer. Examples of chemotherapeutic agents include alkylating agents, anti-metabolites, plant alkaloids and terpenoids, vinca alkaloids, podophyllotoxin, taxanes, topoisomerase inhibitors, and cytotoxic antibiotics. Cisplatin, carboplatin, and oxaliplatin are examples of alkylating agents. Other alkylating agents include mechlorethamine, cyclophosphamide, chlorambucil, ifosfamide. Alkylating agens may impair cell function by forming covalent bonds with the amino, carboxyl, sulfhydryl, and phosphate groups in biologically important molecules. Alternatively, alkylating agents may chemically modify a cell's DNA.

Anti-metabolites are another example of chemotherapeutic agents. Anti-metabolites may masquerade as purines or pyrimidines and may prevent purines and pyrimidines from becoming incorporated in to DNA during the “S” phase (of the cell cycle), thereby stopping normal development and division. Antimetabolites may also affect RNA synthesis. Examples of metabolites include azathioprine and mercaptopurine.

Alkaloids may be derived from plants and block cell division may also be used for the treatment of cancer. Alkyloids may prevent microtubule function. Examples of alkaloids are vinca alkaloids and taxanes. Vinca alkaloids may bind to specific sites on tubulin and inhibit the assembly of tubulin into microtubules (M phase of the cell cycle). The vinca alkaloids may be derived from the Madagascar periwinkle, Catharanthus roseus (formerly known as Vinca rosea ). Examples of vinca alkaloids include, but are not limited to, vincristine, vinblastine, vinorelbine, or vindesine. Taxanes are diterpenes produced by the plants of the genus Taxus (yews). Taxanes may be derived from natural sources or synthesized artificially. Taxanes include paclitaxel (Taxol) and docetaxel (Taxotere). Taxanes may disrupt microtubule function. Microtubules are essential to cell division, and taxanes may stabilize GDP-bound tubulin in the microtubule, thereby inhibiting the process of cell division. Thus, in essence, taxanes may be mitotic inhibitors. Taxanes may also be radiosensitizing and often contain numerous chiral centers.

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Alternative chemotherapeutic agents include podophyllotoxin. Podophyllotoxin is a plant-derived compound that may help with digestion and may be used to produce cytostatic drugs such as etoposide and teniposide. They may prevent the cell from entering the G1 phase (the start of DNA replication) and the replication of DNA (the S phase).

Topoisomerases are essential enzymes that maintain the topology of DNA. Inhibition of type I or type II topoisomerases may interfere with both transcription and replication of DNA by upsetting proper DNA supercoiling. Some chemotherapeutic agents may inhibit topoisomerases. For example, some type I topoisomerase inhibitors include camptothecins : irinotecan and topotecan. Examples of type II inhibitors include amsacrine, etoposide, etoposide phosphate, and teniposide.

Another example of chemotherapeutic agents is cytotoxic antibiotics. Cytotoxic antibiotics are a group of antibiotics that are used for the treatment of cancer because they may interfere with DNA replication and/or protein synthesis. Cytotoxic antiobiotics include, but are not limited to, actinomycin, anthracyclines, doxorubicin, daunorubicin, valrubicin, idarubicin, epirubicin, bleomycin, plicamycin, and mitomycin.

In some instances, the anti-cancer treatment may comprise radiation therapy. Radiation can come from a machine outside the body (external-beam radiation therapy) or from radioactive material placed in the body near cancer cells (internal radiation therapy, more commonly called brachytherapy). Systemic radiation therapy uses a radioactive substance, given by mouth or into a vein that travels in the blood to tissues throughout the body.

External-beam radiation therapy may be delivered in the form of photon beams (either x-rays or gamma rays). A photon is the basic unit of light and other forms of electromagnetic radiation. An example of external-beam radiation therapy is called 3-dimensional conformal radiation therapy (3D-CRT). 3D-CRT may use computer software and advanced treatment machines to deliver radiation to very precisely shaped target areas. Many other methods of external-beam radiation therapy are currently being tested and used in cancer treatment. These methods include, but are not limited to, intensity-modulated radiation therapy (IMRT), image-guided radiation therapy (IGRT), Stereotactic radiosurgery (SRS), Stereotactic body radiation therapy (SBRT), and proton therapy.

Intensity-modulated radiation therapy (IMRT) is an example of external-beam radiation and may use hundreds of tiny radiation beam-shaping devices, called collimators, to deliver a single dose of radiation. The collimators can be stationary or can move during treatment, allowing the intensity of the radiation beams to change during treatment sessions. This kind of dose modulation allows different areas of a tumor or nearby tissues to receive different doses of radiation. IMRT is planned in reverse (called inverse treatment planning). In inverse treatment planning, the radiation doses to different areas of the tumor and surrounding tissue are planned in advance, and then a high-powered computer program calculates the required number of beams and angles of the radiation treatment. In contrast, during traditional (forward) treatment planning, the number and angles of the radiation beams are chosen in advance and computers calculate how much dose may be delivered from each of the planned beams. The goal of IMRT is to increase the radiation dose to the areas that need it and reduce radiation exposure to specific sensitive areas of surrounding normal tissue.

Another example of external-beam radiation is image-guided radiation therapy (IGRT). In IGRT, repeated imaging scans (CT, MRI, or PET) may be performed during treatment. These imaging scans may be processed by computers to identify changes in a tumor's size and location due to treatment and to allow the position of the patient or the planned radiation dose to be adjusted during treatment as needed. Repeated imaging can increase the accuracy of radiation treatment and may allow reductions in the planned volume of tissue to be treated, thereby decreasing the total radiation dose to normal tissue.

Tomotherapy is a type of image-guided IMRT. A tomotherapy machine is a hybrid between a CT imaging scanner and an external-beam radiation therapy machine. The part of the tomotherapy machine that delivers radiation for both imaging and treatment can rotate completely around the patient in the same manner as a normal CT scanner. Tomotherapy machines can capture CT images of the patient's tumor immediately before treatment sessions, to allow for very precise tumor targeting and sparing of normal tissue.

Stereotactic radiosurgery (SRS) can deliver one or more high doses of radiation to a small tumor. SRS uses extremely accurate image-guided tumor targeting and patient positioning. Therefore, a high dose of radiation can be given without excess damage to normal tissue. SRS can be used to treat small tumors with well-defined edges. It is most commonly used in the treatment of brain or spinal tumors and brain metastases from other cancer types. For the treatment of some brain metastases, patients may receive radiation therapy to the entire brain (called whole-brain radiation therapy) in addition to SRS. SRS requires the use of a head frame or other device to immobilize the patient during treatment to ensure that the high dose of radiation is delivered accurately.

Stereotactic body radiation therapy (SBRT) delivers radiation therapy in fewer sessions, using smaller radiation fields and higher doses than 3D-CRT in most cases. SBRT may treat tumors that lie outside the brain and spinal cord. Because these tumors are more likely to move with the normal motion of the body, and therefore cannot be targeted as accurately as tumors within the brain or spine, SBRT is usually given in more than one dose. SBRT can be used to treat small, isolated tumors, including cancers in the lung and liver. SBRT systems may be known by their brand names, such as the CyberKnife®.

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In proton therapy, external-beam radiation therapy may be delivered by proton. Protons are a type of charged particle. Proton beams differ from photon beams mainly in the way they deposit energy in living tissue. Whereas photons deposit energy in small packets all along their path through tissue, protons deposit much of their energy at the end of their path (called the Bragg peak) and deposit less energy along the way. Use of protons may reduce the exposure of normal tissue to radiation, possibly allowing the delivery of higher doses of radiation to a tumor.

Other charged particle beams such as electron beams may be used to irradiate superficial tumors, such as skin cancer or tumors near the surface of the body, but they cannot travel very far through tissue.

Internal radiation therapy (brachytherapy) is radiation delivered from radiation sources (radioactive materials) placed inside or on the body. Several brachytherapy techniques are used in cancer treatment. Interstitial brachytherapy may use a radiation source placed within tumor tissue, such as within a prostate tumor. Intracavitary brachytherapy may use a source placed within a surgical cavity or a body cavity, such as the chest cavity, near a tumor. Episcleral brachytherapy, which may be used to treat melanoma inside the eye, may use a source that is attached to the eye. In brachytherapy, radioactive isotopes can be sealed in tiny pellets or “seeds.” These seeds may be placed in patients using delivery devices, such as needles, catheters, or some other type of carrier. As the isotopes decay naturally, they give off radiation that may damage nearby cancer cells. Brachytherapy may be able to deliver higher doses of radiation to some cancers than external-beam radiation therapy while causing less damage to normal tissue.

Brachytherapy can be given as a low-dose-rate or a high-dose-rate treatment. In low-dose-rate treatment, cancer cells receive continuous low-dose radiation from the source over a period of several days. In high-dose-rate treatment, a robotic machine attached to delivery tubes placed inside the body may guide one or more radioactive sources into or near a tumor, and then removes the sources at the end of each treatment session. High-dose-rate treatment can be given in one or more treatment sessions. An example of a high-dose-rate treatment is the MammoSite® system. Bracytherapy may be used to treat patients with breast cancer who have undergone breast-conserving surgery.

The placement of brachytherapy sources can be temporary or permanent. For permament brachytherapy, the sources may be surgically sealed within the body and left there, even after all of the radiation has been given off. In some instances, the remaining material (in which the radioactive isotopes were sealed) does not cause any discomfort or harm to the patient. Permanent brachytherapy is a type of low-dose-rate brachytherapy. For temporary brachytherapy, tubes (catheters) or other carriers are used to deliver the radiation sources, and both the carriers and the radiation sources are removed after treatment. Temporary brachytherapy can be either low-dose-rate or high-dose-rate treatment. Brachytherapy may be used alone or in addition to external-beam radiation therapy to provide a “boost” of radiation to a tumor while sparing surrounding normal tissue.

In systemic radiation therapy, a patient may swallow or receive an injection of a radioactive substance, such as radioactive iodine or a radioactive substance bound to a monoclonal antibody. Radioactive iodine ( 131 I) is a type of systemic radiation therapy commonly used to help treat cancer, such as thyroid cancer. Thyroid cells naturally take up radioactive iodine. For systemic radiation therapy for some other types of cancer, a monoclonal antibody may help target the radioactive substance to the right place. The antibody joined to the radioactive substance travels through the blood, locating and killing tumor cells. For example, the drug ibritumomab tiuxetan (Zevalin®) may be used for the treatment of certain types of B-cell non-Hodgkin lymphoma (NHL). The antibody part of this drug recognizes and binds to a protein found on the surface of B lymphocytes. The combination drug regimen of tositumomab and iodine 131 I tositumomab (Bexxar®) may be used for the treatment of certain types of cancer, such as NHL. In this regimen, nonradioactive tositumomab antibodies may be given to patients first, followed by treatment with tositumomab antibodies that have 131 I attached. Tositumomab may recognize and bind to the same protein on B lymphocytes as ibritumomab. The nonradioactive form of the antibody may help protect normal B lymphocytes from being damaged by radiation from 131I.

Some systemic radiation therapy drugs relieve pain from cancer that has spread to the bone (bone metastases). This is a type of palliative radiation therapy. The radioactive drugs samarium-153-lexidronam (Quadramet®) and strontium-89 chloride (Metastron®) are examples of radiopharmaceuticals may be used to treat pain from bone metastases.

Biological therapy (sometimes called immunotherapy, biotherapy, or biological response modifier (BRM) therapy) uses the body's immune system, either directly or indirectly, to fight cancer or to lessen the side effects that may be caused by some cancer treatments. Biological therapies include interferons, interleukins, colony-stimulating factors, monoclonal antibodies, vaccines, gene therapy, and nonspecific immunomodulating agents.

Interferons (IFNs) are types of cytokines that occur naturally in the body. Interferon alpha, interferon beta, and interferon gamma are examples of interferons that may be used in cancer treatment.

Like interferons, interleukins (ILs) are cytokines that occur naturally in the body and can be made in the laboratory. Many interleukins have been identified for the treatment of cancer. For example, interleukin-2 (IL-2 or aldesleukin), interleukin 7, and interleukin 12 have may be used as an anti-cancer treatment. IL-2 may stimulate the growth and activity of many immune cells, such as lymphocytes, that can destroy cancer cells. Interleukins may be used to treat a number of cancers, including leukemia, lymphoma, and brain, colorectal, ovarian, breast, kidney and prostate cancers.

›DETAILED DESCRIPTION OF THE DISCLOSURE · 19 of 20

Colony-stimulating factors (CSFs) (sometimes called hematopoietic growth factors) may also be used for the treatment of cancer. Some examples of CSFs include, but are not limited to, G-CSF (filgrastim) and GM-CSF (sargramostim). CSFs may promote the division of bone marrow stem cells and their development into white blood cells, platelets, and red blood cells. Bone marrow is critical to the body's immune system because it is the source of all blood cells. Because anticancer drugs can damage the body's ability to make white blood cells, red blood cells, and platelets, stimulation of the immune system by CSFs may benefit patients undergoing other anti-cancer treatment, thus CSFs may be combined with other anti-cancer therapies, such as chemotherapy. CSFs may be used to treat a large variety of cancers, including lymphoma, leukemia, multiple myeloma, melanoma, and cancers of the brain, lung, esophagus, breast, uterus, ovary, prostate, kidney, colon, and rectum.

Another type of biological therapy includes monoclonal antibodies (MOABs or MoABs). These antibodies may be produced by a single type of cell and may be specific for a particular antigen. To create MOABs, human cancer cells may be injected into mice. In response, the mouse immune system can make antibodies against these cancer cells. The mouse plasma cells that produce antibodies may be isolated and fused with laboratory-grown cells to create “hybrid” cells called hybridomas. Hybridomas can indefinitely produce large quantities of these pure antibodies, or MOABs. MOABs may be used in cancer treatment in a number of ways. For instance, MOABs that react with specific types of cancer may enhance a patient's immune response to the cancer. MOABs can be programmed to act against cell growth factors, thus interfering with the growth of cancer cells.

MOABs may be linked to other anti-cancer therapies such as chemotherapeutics, radioisotopes (radioactive substances), other biological therapies, or other toxins. When the antibodies latch onto cancer cells, they deliver these anti-cancer therapies directly to the tumor, helping to destroy it. MOABs carrying radioisotopes may also prove useful in diagnosing certain cancers, such as colorectal, ovarian, and prostate.

Rituxan® (rituximab) and Herceptin® (trastuzumab) are examples of MOABs that may be used as a biological therapy. Rituxan may be used for the treatment of non-Hodgkin lymphoma. Herceptin can be used to treat metastatic breast cancer in patients with tumors that produce excess amounts of a protein called HER2. Alternatively, MOABs may be used to treat lymphoma, leukemia, melanoma, and cancers of the brain, breast, lung, kidney, colon, rectum, ovary, prostate, and other areas.

Cancer vaccines are another form of biological therapy. Cancer vaccines may be designed to encourage the patient's immune system to recognize cancer cells. Cancer vaccines may be designed to treat existing cancers (therapeutic vaccines) or to prevent the development of cancer (prophylactic vaccines). Therapeutic vaccines may be injected in a person after cancer is diagnosed. These vaccines may stop the growth of existing tumors, prevent cancer from recurring, or eliminate cancer cells not killed by prior treatments. Cancer vaccines given when the tumor is small may be able to eradicate the cancer. On the other hand, prophylactic vaccines are given to healthy individuals before cancer develops. These vaccines are designed to stimulate the immune system to attack viruses that can cause cancer. By targeting these cancer-causing viruses, development of certain cancers may be prevented. For example, cervarix and gardasil are vaccines to treat human papilloma virus and may prevent cervical cancer. Therapeutic vaccines may be used to treat melanoma, lymphoma, leukemia, and cancers of the brain, breast, lung, kidney, ovary, prostate, pancreas, colon, and rectum. Cancer vaccines can be used in combination with other anti-cancer therapies.

Gene therapy is another example of a biological therapy. Gene therapy may involve introducing genetic material into a person's cells to fight disease. Gene therapy methods may improve a patient's immune response to cancer. For example, a gene may be inserted into an immune cell to enhance its ability to recognize and attack cancer cells. In another approach, cancer cells may be injected with genes that cause the cancer cells to produce cytokines and stimulate the immune system.

In some instances, biological therapy includes nonspecific immunomodulating agents. Nonspecific immunomodulating agents are substances that stimulate or indirectly augment the immune system. Often, these agents target key immune system cells and may cause secondary responses such as increased production of cytokines and immunoglobulins. Two nonspecific immunomodulating agents used in cancer treatment are bacillus Calmette-Guerin (BCG) and levamisole. BCG may be used in the treatment of superficial bladder cancer following surgery. BCG may work by stimulating an inflammatory, and possibly an immune, response. A solution of BCG may be instilled in the bladder. Levamisole is sometimes used along with fluorouracil (5-FU) chemotherapy in the treatment of stage III (Dukes' C) colon cancer following surgery. Levamisole may act to restore depressed immune function.

Photodynamic therapy (PDT) is an anti-cancer treatment that may use a drug, called a photosensitizer or photosensitizing agent, and a particular type of light. When photosensitizers are exposed to a specific wavelength of light, they may produce a form of oxygen that kills nearby cells. A photosensitizer may be activated by light of a specific wavelength. This wavelength determines how far the light can travel into the body. Thus, photosensitizers and wavelengths of light may be used to treat different areas of the body with PDT.

In the first step of PDT for cancer treatment, a photosensitizing agent may be injected into the bloodstream. The agent may be absorbed by cells all over the body but may stay in cancer cells longer than it does in normal cells. Approximately 24 to 72 hours after injection, when most of the agent has left normal cells but remains in cancer cells, the tumor can be exposed to light. The photosensitizer in the tumor can absorb the light and produces an active form of oxygen that destroys nearby cancer cells. In addition to directly killing cancer cells, PDT may shrink or destroy tumors in two other ways. The photosensitizer can damage blood vessels in the tumor, thereby preventing the cancer from receiving necessary nutrients. PDT may also activate the immune system to attack the tumor cells.

›DETAILED DESCRIPTION OF THE DISCLOSURE · 20 of 20

The light used for PDT can come from a laser or other sources. Laser light can be directed through fiber optic cables (thin fibers that transmit light) to deliver light to areas inside the body. For example, a fiber optic cable can be inserted through an endoscope (a thin, lighted tube used to look at tissues inside the body) into the lungs or esophagus to treat cancer in these organs. Other light sources include light-emitting diodes (LEDs), which may be used for surface tumors, such as skin cancer. PDT is usually performed as an outpatient procedure. PDT may also be repeated and may be used with other therapies, such as surgery, radiation, or chemotherapy.

Extracorporeal photopheresis (ECP) is a type of PDT in which a machine may be used to collect the patient's blood cells. The patient's blood cells may be treated outside the body with a photosensitizing agent, exposed to light, and then returned to the patient. ECP may be used to help lessen the severity of skin symptoms of cutaneous T-cell lymphoma that has not responded to other therapies. ECP may be used to treat other blood cancers, and may also help reduce rejection after transplants.

Additionally, photosensitizing agent, such as porfimer sodium or Photofrin®, may be used in PDT to treat or relieve the symptoms of esophageal cancer and non-small cell lung cancer. Porfimer sodium may relieve symptoms of esophageal cancer when the cancer obstructs the esophagus or when the cancer cannot be satisfactorily treated with laser therapy alone. Porfimer sodium may be used to treat non-small cell lung cancer in patients for whom the usual treatments are not appropriate, and to relieve symptoms in patients with non-small cell lung cancer that obstructs the airways. Porfimer sodium may also be used for the treatment of precancerous lesions in patients with Barrett esophagus, a condition that can lead to esophageal cancer.

Laser therapy may use high-intensity light to treat cancer and other illnesses. Lasers can be used to shrink or destroy tumors or precancerous growths. Lasers are most commonly used to treat superficial cancers (cancers on the surface of the body or the lining of internal organs) such as basal cell skin cancer and the very early stages of some cancers, such as cervical, penile, vaginal, vulvar, and non-small cell lung cancer.

Lasers may also be used to relieve certain symptoms of cancer, such as bleeding or obstruction. For example, lasers can be used to shrink or destroy a tumor that is blocking a patient's trachea (windpipe) or esophagus. Lasers also can be used to remove colon polyps or tumors that are blocking the colon or stomach.

Laser therapy is often given through a flexible endoscope (a thin, lighted tube used to look at tissues inside the body). The endoscope is fitted with optical fibers (thin fibers that transmit light). It is inserted through an opening in the body, such as the mouth, nose, anus, or vagina. Laser light is then precisely aimed to cut or destroy a tumor.

Laser-induced interstitial thermotherapy (LITT), or interstitial laser photocoagulation, also uses lasers to treat some cancers. LITT is similar to a cancer treatment called hyperthermia, which uses heat to shrink tumors by damaging or killing cancer cells. During LITT, an optical fiber is inserted into a tumor. Laser light at the tip of the fiber raises the temperature of the tumor cells and damages or destroys them. LITT is sometimes used to shrink tumors in the liver.

Laser therapy can be used alone, but most often it is combined with other treatments, such as surgery, chemotherapy, or radiation therapy. In addition, lasers can seal nerve endings to reduce pain after surgery and seal lymph vessels to reduce swelling and limit the spread of tumor cells.

Lasers used to treat cancer may include carbon dioxide (CO2) lasers, argon lasers, and neodymium:yttrium-aluminum-garnet (Nd:YAG) lasers. Each of these can shrink or destroy tumors and can be used with endoscopes. CO2 and argon lasers can cut the skin's surface without going into deeper layers. Thus, they can be used to remove superficial cancers, such as skin cancer. In contrast, the Nd:YAG laser is more commonly applied through an endoscope to treat internal organs, such as the uterus, esophagus, and colon. Nd:YAG laser light can also travel through optical fibers into specific areas of the body during LITT. Argon lasers are often used to activate the drugs used in PDT.

Experimental

The following examples are provided in order to demonstrate and further illustrate certain preferred embodiments and aspects of the present disclosure and are not to be construed as limiting the scope thereof.

›Examples12
›Example 1 · 1 of 12

Methods

High Performance Computing

Computational analysis was performed using the Flux high-performance computer cluster hosted by the Advanced Research Computing (ARC) at the University of Michigan.

RNA-Seq Data Processing

A comprehensive RNA-Seq analysis pipeline was employed on all samples ( FIG. 5 b ). The analysis pipeline provided sequence quality metrics, filtering of contaminant reads, fragment size estimation, strand-specific library type estimation, spliced alignment of reads to the human reference genome (version hg19/GrCh37), alignment performance metrics, generation of visualization tracks for genome browsers, and ab initio transcript assembly. The third-party tools used to process RNA-Seq data were selected based on computational performance, ease-of-use, user and community support, and experience.

Software versions were managed effectively using the Modules Environment Management system. Computational analysis was performed in a 64-bit Linux environment (Red Hat Enterprise Linux 6). Pre-compiled 64-bit Linux binaries were downloaded when available.

Initial sequence quality control metrics were calculated using FASTQC. Next, filtering was performed to remove reads mapping to mitochondrial DNA, ribosomal RNA, poly-A, poly-C, Illumina sequencing adaptors, and the spiked-in phiX174 viral genome. Sequences were downloaded from the Illumina iGenomes server (2012 Mar. 9). Mapping was performed using bowtie2 (2.0.2).

The fragment size distribution (for paired-end libraries) and fragment layout of each library was determined automatically by mapping a subset of the reads to a reference consisting of the 15,868 unique Ensembl v69 exons larger than 500 bp that had no other overlapping features on either strand. These exons represent contiguous genomic regions where both paired-end reads from a single fragment could confidently be aligned. An alignment index was prepared from this reference using the bowtie-build utility.

Reads were mapped using Tophat2 (2.0.6 and 2.0.8) using default parameters 1. Reference genome annotation files were downloaded from the Illumina iGenomes FTP server. A human genome reference was constructed from UCSC version hg19 chromosomes 1-22, X, Y, and mitochondrial DNA. References from alternate haplotype alleles were omitted. Alignment index files for Bowtie versions 0.12.8 and 2.0.2 were built from this reference using the bowtie-build and bowtie2-build programs, respectively. The Ensembl version 69 transcriptome reference gene set was downloaded from the Ensembl FTP server. Chromosome names were converted from GRCh37 format to UCSC format (e.g. “1” converted to “chr1”). Genes found on alternate haplotype alleles were omitted. The cuffcompare utility was used as specified in the Cufflinks user's manual to assign promoter and transcription start site attributes to the gene features in the Ensembl reference. Alignment index files for Bowtie versions 0.12.8 and 2.0.2 were prepared from this reference using the—transcriptome index option in Tophat version 2.0.6.

Sequence alignment metrics were computed using the Picard tools CollectMultipleMetrics and CollectRnaSeqMetrics. The Picard CollectRnaSeqMetrics diagnostic utility required gene annotation and ribosomal interval files as input. The “refFlat” table provided by the Illumina iGenomes download package (2012 Mar. 9) was used as the gene annotation reference. Ribosomal DNA intervals were curated from the RepeatMasker table downloaded from the UCSC table browser (Karolchik, D. et al. Nucleic acids research 32, D493-496, (2004)). This table of repeat elements was originally provided for hg19 by UCSC on May 27, 2009. Tracks for visualization on genome browers were generated using the BEDTools ‘genomecov’ utility and the UCSC bedGraphToBigWig utility (Kent, et al., Bioinformatics 26, 2204-2207, (2010); Quinlan, A. R. & Hall, I. M. Bioinformatics 26, 841-842, (2010)).

Ab initio assembly was performed using Cufflinks (2.0.2) with multi-read correction Enabled (Trapnell, C. et al. Nature biotechnology 28, 511-515, (2010)). Gene features with the ribosomal RNA biotype ‘rRNA’ were added to a mask file for use with the --mask-file option in Cufflinks.

Overview of Transcriptome Reconstruction

To merge ab initio assembled transcript fragments (transfrags) into a consensus transcriptome a bioinformatics method that (1) classifies and filters sources of background noise in individual libraries and (2) reassembles transfrags weighted by their expression levels from multiple libraries into a consensus transcriptome was utiized.

Quality Control for Ab Initio Assembled Transcripts

Ab initio assembly yielded 312,883,292 transcript fragments (transfrags) across all libraries average of 46,810 transfrags per library). Alignment artifacts and poorly assembled transcripts were controlled for by clipping very short first or last exons 15 bp) and excluding short transfrags (<250 bp). These thresholds filtered out an average of 2.0% of the transfrags from each library, but in rare cases up to 67% of all transfrags in a library were excluded ( FIG. 4 a ). After implementing these measures, 304,397,840 transfrags (97.2% of input) were maintained.

Assessment of Genomic DNA and Incompletely Processed RNA Levels

RNA sequencing experiments that isolate poly-adenylated RNA from whole cells inadvertently capture variable amounts of incompletely processed RNA and genomic DNA4. These noise sequences manifest within ab initio transcript assemblies as intron retentions, mono-exonic intronic transfrags in the sense orientation, and relatively lowly expressed transfrags dispersed throughout intergenic regions (Cabili, M. N. et al. Genes & development 25, 1915-1927, (2011)). Thus, background noise complicates the correct assembly of mono-exonic transcripts, intronic transcripts, or both. To characterize noise, the total unannotated sense-oriented intronic (intronic-like) transfrag population was used as a surrogate measure of both genomic and incompletely processed RNA levels, and the unannotated intergenic or antisense-oriented (intergenic-like) transfrag population as a surrogate measure of only genomic DNA levels. Comparing the transfrags in each category across all 6,503 libraries revealed significant variability in both the number and abundance of transfrags corresponding to noise ( FIG. 6 b ). On average, intergenic-like transfrags constituted 8.6% of all transfrags (min: 0.65%, max: 43%), but only 0.88% of total FPKM per library (min: 0.16%, max: 16.8%). Intronic-like transfrags constituted 17% (min: 0.56%, max: 64%) of all transfrags and 2.0% (min: 0.18%, max: 54%) of total FPKM per library. These results implicate genomic DNA contamination and incompletely processed RNA as approximately equal contributors to total noise levels; however, these two sources of noise were not necessarily correlated. Furthermore, individual libraries contain variable amounts of incompletely processed RNA and genomic DNA contamination. Thus, a filtering strategy that discriminated true unannotated transcription from background noise in a library-specific manner was utilized.

›Example 1 · 2 of 12

Filtering Genomic DNA Contamination Artifacts from Ab Initio Assemblies

To discriminate genomic DNA contamination from robust transcription a classification method that utilizes both relative transcript abundance and recurrence across independent biological samples was developed. The method requires a known transcript catalogue (Ensembl version 69) to determine the annotation status of ab initio transfrags (Flicek, P. et al. Ensembl 2014. Nucleic acids research 42, D749-755, (2014)). Transfrags that overlapped known transcripts in the sense orientation were denoted “annotated”, and the remaining transfrags were categorized as either “Sense Intronic” or “Antisense/Intergenic” based on their relationship to annotated transcripts ( FIG. 6 c,d ). Relative abundance was determined by using the empirical distribution of FPKM values to converting transcript FPKM values into quantiles. Recurrence levels were first computed per base by counting independent biological samples with evidence of transcription (replicates of identical cell lines or tumor tissues from the same patient were not counted towards recurrence). A single recurrence value was then computed for each transfrag by averaging the recurrence values of all bases of the transfrag. After computing relative abundance and recurrence for all transfrags, a classifier was trained to discriminate annotated from unannotated transfrags as a surrogate for classifying true transcription from background noise. Specifically, bivariate kernel density estimates were converted using the abundance-recurrence axes separately for annotated and unannotated transfrags. These densities were mapped onto a square grid (50×50). The annotated density was then divided by the unannotated density at each grid point after adding a nominal value to avoid floating point overflow errors. This resulted in a new grid containing likelihood ratios for annotated versus unannotated transfrags along the abundance-recurrence axes. To account for the total noise present in the library, the likelihood estimates were weighted by the relative ratio of unannotated versus annotated transfrags in the library being classified. This weight equaled the ratio of the fraction of known to unannotated transcripts in a library divided by the ratio of the medians of these fractions in all libraries. Finally, for each transfrag in an ab initio assembly, the weighted log-likelihood of the transfrag being annotated was calculated by linearly interpolating the transfrag abundance and recurrence onto the grid. For each library, a likelihood ratio cutoff was calculated by optimizing the balanced accuracy (average of sensitivity and specificity) of the classifier performance ( FIG. 6 e ). Transfrags with likelihood below this cutoff were labeled ‘background’ and the remainder ‘expressed’. Results from individual libraries were then concatenated to produce separate background and expressed transfrag catalogues as output. Transcripts classified as background noise were discarded and meta-assembly was carried out on the expressed fraction. To assess the sensitivity of the classification method, the filtering approach was calculated after leaving out 10% of annotated transfrags as ‘test’ data. The ability to detect these genes was then assessed using likelihood cutoffs determined without the test data included ( FIG. 10 f ).

Transcriptome Meta-Assembly

A meta-assembly algorithm that produces isoforms from splicing pattern graphs after pruning sources of incompletely processed RNA that manifest as intron retentions and inappropriately long exons is provided. Studies of alternative splicing have revealed a tightly controlled system where often only a small number of possible isoforms is observed from loci with innumerable splicing possibilities (Pickrell, J. K. et al. Nature 464, 768-772, (2010); Barash, Y. et al. Nature 465, 53-59, (2010)). To incorporate these biological observations, a greedy dynamic programming approach that reports the most highly abundant transcripts and discards minor isoforms was used.

To begin, directed acyclic splicing graphs where nodes in the graph reflect contiguous exonic regions and edges correspond to splicing possibilities were generated ( FIG. 7 a ). Nodes in the splicing graph are then pruned according to several criteria. First, low scoring ends in the graph that correspond to extraneously long exons or overhanging exons that extend into introns are removed. Second, nodes within introns are trimmed when their scores are less than a fraction of neighboring exons. Weakly connected components of the pruned splicing graphs are then extracted and processed independently.

A splicing graph encompasses the milieu of possible isoforms that could be transcribed. Enumerating all possible paths through splicing graphs is impractical; many graphs have millions of paths of which only minute fractions are observed in vivo. The initial input transfrags provide partial paths through the splicing graph and also indicate which parts of the graph are more abundant. The approach described herein incorporates this partial path information by building a splicing pattern graph that subsumes the original splice graph ( FIG. 7 b ). The splicing pattern graph is a type of De Bruijn graph where each node represents a contiguous path of length k through the splice graph, and edges connect paths with k−1 nodes in common. As k increases so does the amount of correlative path information retained in the graph at the cost of losing short transfrags with length less than k. Each node in the graph carries a weight equal to the summed weights from all transcripts that share the node. Thus for each splice graph the partial path length k is optimized to maximize the number of nodes in the path graph with the constraint that the summed node weights of transfrags with path length greater than or equal to k is above a userspecified fraction of the total score of all transfrags. After the path graph has been constructed, every partial path transfrag is extended into a full-length transcript by transmitting the transfrag's weight along incoming and outgoing edges. This weight is allocated proportionally at nodes with multiple incoming or outgoing edges. This approach effectively extends all partial transcript fragments into full-length transcripts and assures that the sum of incoming and outgoing node weights at equivalent. Finally, a set of isoforms is predicted from the graph using a greedy algorithm. The algorithm finds and reports the highest abundance transcript by traversing the graph using dynamic programming. The weight of the transcript equals the minimum weight of all nodes in the path. The transcript weight is then subtracted from every node in the path and the dynamic programming procedure is repeated. Suboptimal transcripts are enumerated until a path weight falls below a fraction of the highest weighted transcript (e.g. the major isoform). The total number of isoforms produced from each gene can also be explicitly constrained. The meta-assembled isoforms are then reported in GTF and/or BED format. A genome track with summed node weights can optionally be reported in BedGraph format as well.

›Example 1 · 3 of 12

AssemblyLine was developed as a software package written in Python and R to

(1) characterize and filter sources of background noise in RNA-Seq assemblies and (2) perform meta-assembly to coalesce large-scale RNA-Seq datasets. AssemblyLine accepts as input a set GTF files containing transfrags assembled from individual libraries. Transfrags of length less than 250 bp were omitted from meta-assembly, and the remaining transfrags were labeled as ‘annotated’ or ‘unannotated’ relative to a reference GTF file (GENCODE version 16). An ab initio transfrag was considered ‘annotated’ if its exons overlapping any reference transcript exons on the identical strand. A recurrence score for each ab initio transfrag was computed as the average number of samples (replicate libraries from a single cell line or tissue were considered a single sample) per nucleotide with same-stranded transcription.

Classification and filtering of ‘background’ and ‘expressed’ transfrags was performed by modeling the abundance (FPKM) and recurrence of ‘annotated’ and ‘unannotated’ transcripts using bivariate kernel density estimation on a square grid (grid size 50×50, bandwidth determined by Silverman's rule of thumb). A grid of likelihood ratios was derived from the ‘annotated’ and ‘unannotated’ grids by element-wise division at each grid point. The probability of each transfrag being ‘annotated’ was then determined by linearly interpolation onto this grid, and this probability was used as a surrogate measure for the probability that a transcript represented background noise. A likelihood ratio of less than or equal to one was used as a cutoff for filtering ‘background’ transcripts.

Filtered transcripts were subjected to the AssemblyLine meta-assembly algorithm. To limit transcript output for complex loci, isoforms with abundance less than 10% of the major transcript isoform were excluded (--fraction-major-isoform 0.10), a maximum of 20 isoforms were allowed for each gene (--maxpaths 20). During splicing pattern graph creation an optimal De Bruijn graph parameter k was determine to maximize the number of graph nodes. A maximum value of k was limited to 20 to improve the computational tractability of the optimization approach (--kmax 20). The output of meta-assembly was a GTF-formatted file as well as BED and BEDGraph-formatted files (--gtf-bed--bedgraph).

Merging of Meta-Assemblies

To merge meta-assemblies from 18 cohorts, the Cuffmerge tool (Trapnell, C. et al. Nature protocols 7, 562-578, (2012)), which produced a final transcriptome GTF file, was used.

Comparisons of MiTranscriptome with Reference Catalogs

The exons, splice sites, and splicing patterns of all assembled transcripts were compared to RefSeq, UCSC, GENCODE (version 19), and the merged union of all three reference catalogs using custom python scripts. Sensitivity and precision values were computed using the number of shared strand-specific transcribed bases, introns, and splicing patterns. Precision was also computed for the subset of ab initio transcripts that overlapped any part of a reference transcript.

Transcripts that overlapped a reference transcript on the same strand were designated annotated. When an ab initio transcript matched multiple reference transcripts, a best match was chosen using the following criteria: (1) matching splicing pattern, (2) fraction of shared introns, and (3) fraction of shared transcribed bases. The biotype (protein, read-through, pseudogene, or lncRNA) for annotated transcripts was imputed from the best matching reference transcript. Annotated lncRNAs and unannotated transcripts were reclassified as either lncRNAs or TUCPs.

Prediction of Transcripts of Unknown Coding Potential (TUCP)

Coding potential as predicted by integrating two sources of evidence: (1) predictions from the alignment-free Coding Potential Assessment Tool (CPAT) (Wang, L. et al. Nucleic acids research 41, e74, (2013)) and (2) searches for Pfam 27.0 matches (Finn, R. D. et al. Nucleic acids research 42, D222-230, (2014)). CPAT determines the coding probability of transcript sequences using a logistic regression model built from ORF size, Fickett TESTCODE statistic55, and hexamer usage bias. A CPAT probability cutoff was chosen by repeatedly randomly sampling 100,000 each of putative non-coding and protein-coding transcripts and optimizing on the balanced accuracy (average of sensitivity and specificity) metric ( FIG. 9 b,c ). The average area-under-the-curve (AUC) across 100 iterations was 0.9310 (minimum 0.9302, maximum 0.9320), and the average optimal probability cutoff was 0.5242 (minimum 0.5090, maximum 0.5482). This cutoff value achieved accurate discrimination of lncRNAs and protein-coding genes (sensitivity: 0.84, specificity: 0.95, FDR: 0.076). Of the putative non-coding transcripts 9,903 (5.3%) exceeded the CPAT cutoff and met the criteria for TUCP.

As additional evidence of coding potential, all transcripts were scanned for Pfam A or B domains across the three translated reading frames for stranded transcripts and six frames for monoexonic transcripts of unknown strand. To control for false positives, non-transcribed intergenic regions were scanned in the same manner. 3,781,935 hits to 12,430 unique Pfam domains in transcribed regions were observed compared with 1,774,937 hits to 1,277 unique domains in non-transcribed intergenic space. The occurrences of each Pfam domain in transcribed versus non-transcribed regions were compared using Fisher's Exact Test and 750 domains with an odds ratio of less than 10.0 or p-value greater than 0.05 as likely artifacts were flagged ( FIG. 9 d ). The remaining 11,726 Pfam domains were considered valid. This procedure filtered 2,972,629 artifact hits and retained 809,306 valid hits. Putative non-coding transcripts harbored only 4,674 (0.40%) of the valid Pfam domains.

The presence of Pfam domains provided strong support for CPAT coding predictions. The presence or absence of a Pfam domain stratified transcripts by the three features modeled by CPAT as well as overall coding probability ( FIG. 9 e ). Transcripts possessing Pfam domains were much more likely to be predicted positive by CPAT than those lacking a Pfam domain (p-value<2.2e-16, odds ratio=90.3, Fisher's Exact Test). Given the complementary aspects of Pfam domain and CPAT prediction, putative non-coding transcripts with either a Pfam domain or a positive CPAT prediction as TUCP were designed. In total 11,603 uncharacterized transcripts were flagged as TUCPs, including 5,248 transcripts previously annotated as lncRNAs. There were 2,729 uncharacterized transcripts with at least one Pfam domain, including 1,700 that did meet the CPAT criteria. By contrast, 8,874 CPAT positive transcripts lacked a valid Pfam domain. Transcripts predicted by CPAT that also harbored valid Pfam domains had longer ORFs, higher hexamer scores, and higher Fickett TESTCODE scores than other TUCPs, indicating that the Pfam and CPAT calls may be complementary ( FIG. 9 f - h ).

›Example 1 · 4 of 12

Coding Potential Assessment Tool (CPAT) version 1.2.1 was performed with default parameters and used the human hexamer table and logit model (Wang, L. et al. Nucleic acids research 41, e74, (2013)). Results were scanned for Pfam 27.0 (March 2013) A and B hits using the pfam_scan.pl utility built on HMMER 3.1b (Eddy, S. R. PLoS computational biology 7, e1002195, (2011); Finn, R. D. et al. Nucleic acids research 42, D222-230, (2014)). Receiver operating characteristic (ROC) analysis was performed using the ROCR package (Sing, et al., Bioinformatics 21, 3940-3941, (2005)).

Proteomics Analysis

Thermo .raw files were obtained from the PRIDE database. Adult_Kidney_Gel_Elite_55, Adult_Liver_Gel_Elite_56, Adult_Pancreas_Gel_Elite_60, Adult_Rectum_Gel_Elite_63, Adult_Urinarybladder_Gel_Elite_40, Fetal_Brain_Gel_Velos_16, Adult_Lung_Gel_Elite_56, and Adult_Prostate_Gel_Elite_62. The Thermo .raw fiels were transformed into mzXML using MSConverter and interrogated against human UniProt database V.15.11 using X!tandem search engine. The database was concatenated with all possible open reading frames longer than 7 amino acids from lncRNA database and with reversed sequences for determination of FDR. The X!Tandem search parameters were: fully tryptic cleavage, parent mass error 5 ppm, fragment mass error 0.5 Da, 2 allowed missed cleavages. Fixed modifications: Cys carbamidomethylation. Variable modifications: Met oxidation. X! Tandem output files were processed by PeptideProphet and ProteinProphet and for final output the data was filtered at peptide probability 0.5 and protein probability 0.9 to ensure protein FDR<1%.

Confidence Scoring System

After assembly of the MiTranscriptome, transcripts were subjected to an additional confidence evaluation. lncRNAs in the MiTranscriptome were categorized into tiers based on their annotation status and the degree of matching of splice junctions to the reference annotation. Tier 1 transcripts are all annotated and tier 2 transcripts are unannotated. An empirical cumulative distribution function was developed by profiling the second highest expression value (across all 6,503 samples) for each tier 1 transcript. The second highest value was used to control for outlier expression. The second highest expression value for each tier 2 transcript was then fed into the distribution function to produce the confidence score.

Validation of lncRNA Transcript by qRT-PCR

150 lncRNAs with at least 1 FPKM expression in either A549, LNCaP, or MCF7 cells were chosed for biological validation. For each transcript, primer pairs were designed using the Primer-BLAST tool. Primer pairs with the following parameters were selected: (1) amplicon length between 80-140 bp (2) primer GC content between 35-65%, and (3) primer length greater than 20 bp. Primers were blasted against the human genome to ensure specificity to the target gene, and primers designed against multiexonic transcripts spanned exon junctions. Regions of any transcript that directly overlapped an exon on the antisense strand were avoided. Primer pairs meeting these criteria could be designed for 100 out of 150 lncRNAs (38 monoexonic and 62 multiexonic). All oligonucleotide primers were obtained from Integrated DNA Technologies (Coralville, Iowa).

RNA was isolated from A549, LNCaP and MCF7 cells in Trizol (Invitrogen) using the RNeasy Mini Kit (Qiagen). Equal amount of RNA was converted into cDNA using random primer's and the Superscript III reverse transcription system (Invitrogen). Quantitative real-time PCR (qPCR) was performed using Power SYBR Green Mastermix (Applied Biosystems, Foster City, Calif.) on an Applied Biosystems 7900HT Real-Time PCR System. The housekeeping genes, CHMP2A, EMC7, GPI, PSMB2, PSMB4, RAB7A, REEP5, SNRPD3 were used as loading controls56. Data was normalized first to housekeeping genes and then to the median value of all samples using the delta-delta Ct method and plotted as fold change over median. To ensure the specificity of the primers, 20 amplicons were further analyzed by Sanger sequencing.

Cell Lines and Reagents:

All cell lines were obtained from the American Type Culture Collection (Manassas, Va.). Cell lines were maintained using standard conditions. Specifically, A549 were grown in F-12K plus 10% fetal bovine serum (FBS), LNCaP in RMPI1640 (Invitrogen) plus 10% FBS and 1% penicillin-streptomycin, and MCF7 in Eagle's Minimum Essential Media (EMEM) plus 10% FBS. All of the cell lines were grown at 37° C. degrees in a 5% CO2 cell culture incubator. To ensure identity, cell lines were genotyped at the University of Michigan Sequencing Core using

Profiler Plus (Applied Biosystems) and compared with the short tandem repeat (STR) profiles of respective cell lines available in the STR Profile Database (ATCC). All of the cell lines were routinely tested and found to be free of Mycoplasma contamination.

Evidence for Active Regulation of Transcriptional Start Sites

To conduct analysis of TSS intervals ENCODE project datasets were downloaded from the UCSC Genome Browser (Karolchik, D. et al. Nucleic acids research 42, D764-770, (2014)). For H3K4me3 analysis the Encode Project Broad Institute H3K4me3 ChIP-Seq peaks for cell lines GM12878, H1-hESC, HeLa-S3, HepG2, HMEC, HSMM, HSMMtube, HUVEC, K562, NH-A, NHDF-Ad, NHEK, and NHLF57 were used. For RNA polymerase II analysis POL2RA binding sites from the Encode Project Uniform TFBS master file version 3 for any of the cell lines with H3k4me3 data were used (Consortium, E. P. et al. Nature 489, 57-74, (2012)). Finally, for DNase hypersensitivity analysis the Encode Project combined UW and Duke DNaseI hypersensitivity regions were downloaded as a master file from EMBL-EBI, and filtered for any of the cell lines with H3k4me3 data. Peak enrichment files (BED format) were aggregated across all cell lines.

Intervals of +/−10 kilobases surrounding unique MiTranscriptome TSSs were generated using BEDTools ‘slop’ tool (Quinlan, A. R. & Hall, I. M. Bioinformatics 26, 841-842, (2010)). TSSs were filtered for expression in each cell line at RPKM>0.1. Basewise peak coverage was generated for each TSS interval using the BEDTools ‘coverage’ function and summarized across subsets of TSSs. Summed per-base coverage histograms were normalized by dividing by the number of expressed TSSs.

›Example 1 · 5 of 12

Conservation Analysis

The evolutionary conservation of transcripts in the assembly was studied using two metrics: (1) the fraction of significantly conserved bases (p<0.01, phyloP algorithm), and (2) the maximally conserved 200nt sliding window (phastCons scores averaged within each window). The former captures independently conserved elements within a transcript regardless of position, and the latter captures contiguous regions of high conservation. The 200nt sliding window size was chosen to aid in discovery of putative ultraconserved elements (Bejerano, G. et al. Science 304, 1321-1325, (2004)). As a negative control the conservation of non-transcribed regions was measured using these metrics by randomly sampling contiguous length-matched intervals from intergenic and intronic space. Non-transcribed interval sampling was restricted to regions with valid 46-way conservation data. The fractional basewise conservation and contiguous window conservation metrics were used to nominate highly conserved and ultraconserved transcripts, respectively. In both cases cutoffs for significant transcripts were determined by controlling the rate of observing elements with similar conservation levels within non-transcribed intergenic space at a level of 0.01. For fractional basewise conservation a score of 0.0947 (9.5% of transcript bases conserved at phyloP p-value<0.01) corresponded to a false discovery rate<0.01. At this cutoff the sensitivity for detecting protein-coding transcripts was 0.67. For contiguous sliding window conservation an average PhastCons probability of 0.9986 corresponded to a false discovery rate<0.01. At this cutoff the sensitivity for detecting true positive ultraconserved non-coding elements downloaded from UCNEbase was 0.6926. Applying these criteria to the assembly yielded 6,034 lncRNAs (3.4%) and 541 TUCPs (4.7%) with significant basewise conservation levels. Additionally, 1,686 lncRNAs (0.96%) and 121 TUCPs (0.01%) harbored contiguous ultraconserved regions.

GWAS Analysis

A list of GWAS SNPs was obtained from the National Human Genome Research Institute's GWAS catalog (Welter, D. et al. Nucleic acids research 42, D1001-1006, (2014)). SNP haplotypes were excluded from the SNP overlap analysis, and a list of 11,194 unique SNPs was obtained. The merged union of the RefSeq, UCSC, and GENCODE catalogs was used as a reference for comparison with MiTranscriptome.

Genomic conservation profiles generated by the phyloP (phylogenetic p-values) and PhastCons algorithms for multiple alignments of 45 vertebrate genomes to the human genome were downloaded from the UCSC genome browser (Karolchik, D. et al. Nucleic acids research 42, D764-770, (2014); Pollard, et al., Genome research 20, 110-121, (2010); Siepel, A. et al. Genome research 15, 1034-1050, (2005)). The ‘wigFix’ formatted files were converting into ‘bigwig’ formatted files using the ‘wigToBigWig’ binary utility program provided by the UCSC genome browser (Karolchik et al., supra). For each transcript a vector of conservation scores for each exon was extracted using the ‘bigWigToBedGraph’ utility and concatenated into a single vector. Conservation metrics were then computed from these vectors.

Intersections of GWAS SNPs with transcripts or exons was performed using the BEDtools ‘intersect’ tool, with the ‘-split’ option invoked for quantification of exonic Overlap (4. The number of GWAS SNPs overlapping the entire assembly and individual transcript categories (lncRNA,TUCP, pseudogene, protein-coding, and read-through) was determined by BEDTools ‘intersect’ for both the whole transcript and for exonic regions (nGWAS). Subsequently, a set of all the SNPs from two popular SNP arrays (Illumina HumanHap550 and Affymetrix SNP6) was created, which was termed the “SNP background”. The amount of SNPs from the SNP background overlapping the MiTranscriptome was calculated (nbackground), and the fraction of the number of overlapping GWAS SNPs to the number of overlapping SNPs from the SNP background (fracGWAS=nGWAS/nbackground) was then reported for each category. This fraction was also calculated using random shuffling of the MiTranscriptome and its components into noncoding regions of the genome (fracshuffle). One hundred shuffles were performed for each condition, and an odds ratio (ORGWAS=fracGWAS/fracshuffle) was determined for each shuffle. The purpose of using fracGWAS instead of simply using nGWAS in this analysis is to control for the possibility that during the shuffle, transcripts could be shuffled into regions not represented on SNP arrays (e.g., regions unable to possess GWAS SNPs), falsely lowering the amount of GWAS SNP overlap by the shuffle. If transcripts are shuffled into regions that are not represented by the SNP background, both nGWAS and nbackground will decrease together, with fracGWAS relatively unchanged.

Shuffling was performed using the BEDTools ‘shuffle’ tool. MiTranscriptome transcripts were grouped by transcription locus (e.g., regions of the genome that have contiguous transcription) prior to shuffling. Shuffling of transcript loci was performed to control for the fact that transcripts within a locus are spatially linked to one another. Shuffling without locus clustering would falsely elevate the amount of genome covered by transcripts, and subsequently elevate the number of SNPs overlapping the shuffled regions. A concatenation of the UCSC hg19 gaps file and the MiTranscriptome protien-coding transcripts was used as an exclusion file for these shuffles.

As a negative control, the entire above analysis was repeated using and equal number randomly selected SNPs (chosen from the Illumina HumanHap550 and Affymetrix SNP6 background) in place of the GWAS SNPs. The significance of enrichment for GWAS SNPs versus random SNPs was measured across identical shuffles of the transcript loci using paired Student's t-tests comparing the set of odds ratios for all shuffles. Similar analysis to determination of compendia enrichment was performed to identify enrichment of novel intergenic lncRNAs and TUCPs. The intergenic space was defined as all regions not covered by the merged reference. For this analysis, the shuffles were performed into the intergenic space, instead of all non-coding space. The exclusion file used by BEDtools ‘shuffle’ was a concatenation of the UCSC gaps file and the merged reference.

›Example 1 · 6 of 12

Transcript Expression Estimation

Expression levels (FPKM) of the transcripts in the assembly were determined using Cufflinks (version 2.02 and 2.1.1)60. Normalized abundance estimates (FPKM) were computed for all MiTranscriptome transcripts, converted into approximate fragment count values, and aggregated into a matrix of expression data ( FIG. 3 a ). Library size factors for expression normalization were computed by applying the geometric normalization method described by Anders and Huber (Genome biology 11, R106 (2010)).

The transcript abundances for all transcripts in the MiTranscriptome assembly were estimated using Cufflinks version 2.1.15 with the following parameters: ‘-max-fragmultihits=1’, ‘-no-effective-length-correction’, ‘-max-bundle-length 5000000’, ‘-maxbundle-frags 20000000’. To convert normalized transcript abundance estimates (FPKM) to approximate fragment count values each FPKM is multiplied by the transcript length (in kilobases) and by the “Map Mass” value (divided by 1.0e6) reported in the Cufflinks log files. By some reverse engineering and assistance from the seqanswers online forum (seqanswers.com), it was determined that this factor was utilized in the normalization process. Abundance estimation for 28 libraries failed for technical reasons (corrupt BAM files) and these libraries were discarded from the expression analysis. Expression estimation for 2,246 transcripts yielded errors and/or zero-valued counts and were discarded.

Transcript Expression Enrichment Analysis

To analyze differential expression of transcripts relative to sample phenotypes a method called Sample Set Enrichment Analysis (SSEA) was developed. The source code for this software is available online. The method adapts the weighted Kolmorgorov-Smirnoff (KS) tests proposed by Gene Set Enrichment Analysis (GSEA). In contrast to GSEA, which tests for associations with gene sets, SSEA tests for associations between individual gene expression observations (which could be transcript or gene expression) and sample sets. Thus, SSEA is analogous to performing GSEA on a ‘transposed’ input dataset. However, SSEA incorporates important features not provided by GSEA: (1) methodology for non-parametric analysis of discrete count data (e.g. RNA-Seq count datasets), (2) engineering improvements to enable analysis of big datasets (here, a matrix of 381,731 rows and 6,475 columns was analyzed using less than 1 Gb of RAM), and (3) parallelization of the algorithm for use in high performance computing environments.

Differential expression testing was performed using the Sample Set Enrichment Analysis method developed as part of this study. SSEA was performed with 100 iterations of count resampling and 1,000 null permutations for each transcript (--resampling-iterations=100, --perms=1000). These parameters yielded a minimum FDR resolution of approximately 1e-7 for all sample sets. Weights for the KS-test were log(x+1)−transformed normalized count values (--weight-hit=log, --weight-miss=log, --weightparam=1).

KS-Tests Using Normalized Count Data Vectors as Weights.

To convert count values into weights for a single KS-test the following steps are performed: (1) raw count values are normalized by library-specific size factors, (2) normalized count values are “resampled” from a Poisson distribution (lambda equals the observed count value) to mimic the effect of technical replication, and (3) random Poisson noise (by default, lambda equals 1) is added to the normalized, resampled count values to destabilize zero-valued counts and break ties. A power transform (exponential or logarithmic) is then applied to the weights (by default, a logtransformation is applied after incrementing normalized count values by 1). The choice of power transformation influences the relative importance of precision versus recall during enrichment testing. For example, users aiming to discover genes new in molecular subtypes of a disease would prioritize precision over sensitivity, whereas a user aiming to discover ideal biomarkers may value sensitivity over precision. Following count data normalization and power transformation, SSEA performs the weighted KS-test procedure described in GSEA28. The resulting enrichment score (ES) statistic describes the strength of association between the weights and the sample set.

To control for random sampling bias in count values (e.g. “shot noise”) SSEA performs repeated enrichment tests using resampled count values to mimic observations from technical replicates and uses the median enrichment score (by default, 100 tests are performed). The basis for Poisson resampling as a legitimate model for technical replication was established by Marioni et al. 62 To test for significance, SSEA performs enrichment tests using randomly shuffled sample labels to derive a set of null enrichment scores with the same sign as the observed score (by default, 1000 null enrichment scores are computed). The nominal p value reported is the relative rank of the observed enrichment score within the null enrichment scores. To control for multiple hypothesis testing, SSEA maintains the null normalized enrichment score (NES) distributions for all transcripts in a sample set, and uses the null NES distribution to compute FDR q values in the same manner as proposed by Subramanian et al. (Proceedings of the National Academy of Sciences of the United States of America 102, 15545-15550, (2005)).

Benchmarking SSEA performance using microarray gene signatures

Gene signatures for the top 1% of overexpressed and underexpressed genes from three prostate cancer (Grasso, C. S. et al. Nature 487, 239-243, (2012); Taylor, B. S. et al. Cancer cell 18, 11-22, (2010); Yu, Y. P. et al. Journal of clinical oncology: official journal of the American Society of Clinical Oncology 22, 2790-2799, (2004)) and three breast cancer (Cancer Genome Atlas, N. Nature 490, 61-70, (2012); Curtis, C. et al. Nature 486, 346-352, (2012); Gluck, S. et al. Breast cancer research and treatment 132, 781-791, (2012).) microarray studies were obtained using Oncomine (Rhodes, D. R. et al. Neoplasia 9, 166-180 (2007).). The top 1% gene signatures as detected by SSEA in the MiTranscriptome breast and prostate cohorts were determined using prostate cancer versus normal and breast cancer versus normal sample sets ( FIG. 3 a ). Given that the MiTranscriptome was produced from an ab initio assembly, transcript identity was assigned to the annotated reference gene with the greatest degree of concordance, where degree of splicing agreement was prioritized over degree of exonic same-stranded overlap. The most-enriched isoform for each gene was used to produce a gene signature.

›Example 1 · 7 of 12

Degree of overlap for all combinations of the 16 gene sets tested (3 published breast up-regulated sets, 3 published breast down-regulated sets, 3 published prostate up-regulated sets, 3 published prostate down-regulated sets, 1 SSEA-determined prostate up-regulated set, 1 SSEA-determined prostate down-regulated set, 1 SSEA-determined breast up-regulated and 1 SSEA-determined breast down-regulated set) was determined by calculating an odds ratio and performing a Fisher's exact test for each gene set pair. Each comparison was restricted to the set of genes assessed by both profiling platforms. Microarray chip annotation files were downloaded from the Molecular Signatures Database (MSigDB) web site (Subramanian, A. et al, supra). The set of all annotated genes (relative to RefSeq, UCSC, and GENCODE) was used as the annotation file for MiTranscriptome.

Discovery of Lineage-Specific and Cancer-Specific Transcripts

To generate enrichment test data for unsupervised clustering, transcripts were ranked within each SSEA sample set by normalized enrichment score (NES) and assigned fractional ranks (e.g. a fractional rank of 0.95 implies the transcript ranked in the top 5th percentile of all transcripts in the sample set). Only significant results (FDR<1e-7 for lineage analysis and FDR<1e-3 for cancer versus normal analysis) were used. Unsupervised clustering was performed using Pearson correlation of log-transformed fractional ranks as a distance metric and Ward's method. Transcripts that were significantly associated with multiple sample sets were grouped with the most strongly associated sample set. Heatmaps were produced using the ‘heatmap.2’ function from the ‘gplots’ package in R.

Guilt-by-Association GSEA Analysis

For each cancer and/or lineage associated lncRNA, expression levels of the target lncRNA were correlated to the expression of all protein-coding genes across all samples in the associated tissue cohort. For cancer cohorts (e.g. breast, prostate), correlations were performed (Spearman) using only the cancer samples (normal samples were excluded). To account for multiple isoforms of each The protein-coding genes were then ranked by the Rho value, and used in a weighted, pre-ranked GSEA analysis against a collection of cancer associated gene sets from MSigDB. Significant associations were determined for any gene set having an FWER p-value below 0.001.

Results

An Expanded Landscape of Human Transcription

The spectrum of human transcriptional diversity was investigated by curating 7,256 poly-A+ RNA-Seq libraries from 25 independent studies, including 5,847 from TCGA, 928 from the Michigan Center for Translational Pathology (MCTP), 67 libraries from the Encyclopedia of DNA Elements (ENCODE), and 414 samples from other public datasets ( FIG. 5 a ). An automated transcriptome assembly pipeline was developed and employed to process the raw sequencing datasets into ab initio transcriptome assemblies ( FIG. 5 b ). This bioinformatics pipeline utilized approximately 1,870 core-months (average 0.26 core-months per library) on high-performance computing environments.

Collectively the RNA-Seq data constituted 493 billion fragments; individual libraries averaged 67.9M total fragments and 55.5M successful alignments to human chromosomes. On average 86% of aligned bases from individual libraries corresponded to annotated RefSeq exons, while the remaining 14% fell within introns or intergenic space15. Coarse quality control measures were used to account for variations in sequencing throughput, run quality, and RNA content by removing 753 libraries with (1) fewer than 20 million total fragments, (2) fewer than

20 million total aligned reads, (3) read length less than 48 bp, or (4) fewer than 50% of aligned bases corresponding to RefSeq genes ( FIGS. 5 c,d ). After coarse filtration, approximately 391 billion aligned fragments (43.69 terabases of sequence) were identified for subsequent analysis. The set of 6,503 libraries passing quality control filters included 6,280 datasets from human tissues and 223 samples from cell lines. Of the tissue libraries, 5,298 originated from primary tumor specimens, 281 from metastases, and 701 from normal or benign adjacent tissues ( FIG. 5 e ). This set of samples is referred to as the MiTranscriptome compendium.

Upon processing the MiTranscriptome libraries, ab initio transcriptome were obtained reconstructions from 6,503 individual tumors, normal tissues, or cell lines. A computational methodology was developed to coalesce individual transcriptomes into a consensus transcriptome, a procedure known as ‘meta-assembly’. Unlike previous methods for meta-assembly of expressed sequence tag (EST) data or small numbers of RNA-Seq experiments, the meta-assembly utilized in this study addressed computational and scalability challenges stemming from the magnitude of this study (Haas, B. J. et al. Nucleic acids research 31, 5654-5666 (2003); Trapnell, C. et al. Nature protocols 7, 562-578, (2012)).

To permit sensitive detection of lineage-specific transcription the libraries were partitioned into 18 cohorts by organ system ( FIG. 1 a ), performed filtering and meta-assembly separately for each cohort, and re-merged the cohorts ( FIG. 1 b ). The individual ab initio assemblies collectively totaled ˜312M transcript predictions (transfrags) across all libraries. To perform filtering, short transfrags (<250 bp) and clipped short flanking exons (<15 bp) were removed, leaving ˜304M transfrags ( FIG. 6 a ). Whereas levels of annotated transfrags were relatively constant, fractions of unannotated intragenic and intergenic transcripts varied considerably across libraries ( FIG. 6 b ). Almost one-third of all transfrags were unannotated (29.3%, or 89M), including 86.2M mono-exonic and 2.8M multiexonic transfrags. Two sources of background noise in RNA-Seq experiments that could give rise to unannotated mono-exonic transfrags are incompletely processed RNA and genomic DNA contamination ( FIG. 6 c ). To minimize this noise, a conservative filtering scheme was used ( FIG. 6 d ). 60M mono-exonic transfrags within introns that could have arisen from incompletely processed RNA were discarded. A machine learning method was developed to discriminate recurrent antisense and intergenic transcription from possible genomic DNA contamination. The approach models the empirical distributions of relative transcript abundance and recurrence (number of independent samples in which the transcript was observed) to determine optimal library-specific thresholds for distinguishing annotated from unannotated transcription. The classifier achieved remarkable

›Example 1 · 8 of 12

performance (average AUC of 0.89, range 0.77-0.96) and displayed no bias for cancer versus normal samples ( FIG. 6 e ). Moreover, the classifier recovered test transcripts left out of the training process with 80% mean sensitivity (range 0.64-0.95, FIG. 6 f ). Ultimately 3.2M of the 86.2M (3.7%) mono-exonic intergenic or antisense transfrags were retained for a total of 6.0M unannotated transfrags (6.75% of the original 89M). The filtered collection of 221M annotated and unannotated transfrags was subjected to meta-assembly. The meta-assembly algorithm first collapses transfrags into a splice graph and utilizes transcript abundance information to prune intron-retentions and trim long first or last exons ( FIG. 7 a ). Furthermore, the algorithm integrates splicing pattern information by constructing a splicing pattern graph and traverses the graph using a greedy dynamic programming algorithm to generate full-length transcript predictions ( FIG. 7 b ). For example, meta-assembly of 7,471 transfrags in the chromosome 12 locus containing HOTAIR and HOXC11 produced just 17 transcripts, including transcripts that accurately matched annotated HOTAIR and HOXC11 isoforms ( FIG. 7 c ). After merging meta-assemblies from 18 cohorts, a consensus set of 384,066 predicted transcripts designated as the MiTranscriptome assembly was identified.

To begin characterizing the assembly, comparisons with reference catalogs from RefSeq (December, 2013) (Pruitt, K. D. et al. Nucleic acids research 42, D756-763, (2014)), UCSC (December, 2013) (Karolchik, D. et al, supra), and GENCODE (Release 19) (Harrow, J. et al. Genome

research 22, 1760-1774, (2012)). ( FIG. 1 c ) were performed. In particular, increases in numbers of exons, splice sites, transcripts, and genes of 29%, 52%, 95%, and 57%, respectively, were observed relative to GENCODE, the most expansive of the three reference catalogs. To understand the source of the increases, the assembly was overlapped with a merged union of the three reference catalogs and the fraction of unannotated versus annotated transcripts were delineated for each cohort ( FIG. 8 a ).

Analysis of the assemblies on the cohort level reveals that the majority of transcripts assembled within each lineage cohort overlapped annotated genes (range 62-88%, mean 75%). However, the fraction of annotated genes within the entire MiTranscriptome (a merger of the 18 individual cohorts) was just 46%, indicating the presence of much unannotated transcription unique to specific lineages. The sensitivity and precision for detecting annotated nucleotides, splice sites, and splicing patterns in the three reference catalogs and intergenic lncRNA predictions from the previous cataloguing study by Cabili et al. (Cabili, M. N. et al. Genes & development 25, 1915-1927, (2011)) were quantitated ( FIG. 8 b,c ). The MiTranscriptome assembly was very sensitive to detection of annotated transcribed bases and splice sites. For example, the MiTranscriptome detected 94% and 93% of annotated RefSeq bases and splice sites, respectively. Detection of precise splicing patterns remains an ongoing challenge for in silico transcriptome reconstruction methods (Steijger, T. et al. Nature methods 10, 1177-1184, (2013)).

Coding Potential Assessment of Long RNA Transcripts

To facilitate further study of the assembly, transcripts were classified into one of five categories: (1) Protein-coding, (2) Read-through (implying a transcript overlapped multiple

separate annotated genes), (3) Pseudogene, (4) lncRNA, and (5) Transcript of Unknown Coding Potential (TUCP) ( FIG. 9 a ). The TUCP classification was originally described by Cabili et al. (supra) and pertains to long RNAs with features indicative of coding potential but not already annotated as protein coding. The ability to predict coding potential in silico using sequence features alone has important implications for ab initio transcript annotation studies. Here, TUCPs were predicted by incorporating two methods: (1) predictions from the Coding Potential Assessment Tool (CPAT) (Wang, L. et al. Nucleic acids research 41, e74, (2013)), which analyzes the sequence features of transcript open reading frames (ORFs), and (2) presence of a known Pfam domain (Finn, R. D. et al. Nucleic acids research 42, D222-230, (2014)) within a transcript ORF ( FIG. 9 b - h ). Over sixty percent of all MiTranscriptome genes were classified as either lncRNAs or TUCPs (59% lncRNAs, 3.5% TUCPs, FIG. 2 a ). The majority of lncRNAs and TUCPs were unannotated relative to RefSeq, UCSC, and GENCODE genes (79% and 66%, respectively) and located within intergenic regions (72% and 60%, respectively) ( FIG. 2 b ). 5,248 transcripts overlapping annotated lncRNAs were flagged as TUCPs, indicating that previous annotation attempts may have identified incomplete ostensibly noncoding fragments that may actually comprise transcripts possessing robust ORFs. For example, in a chromosome 16 intergenic locus, transcripts harboring a 418 amino acid ORF spanning 29 exons that overlapped three independent genes annotated by GENCODE as lncRNAs (LINC00514, LA16c-380H5.3, LA16c-380H5.4) were identified, indicating that the annotated GENCODE lncRNAs may be incomplete partial annotations of a larger protein-coding gene ( FIG. 2 c ).

To further investigate the coding potential of these TUCP transcripts, a proteomics analysis was performed to search for reported peptides that may map to ORFs in the TUCPs. Recent proteomics studies have produced the most comprehensive analysis of the human proteome to date (Kim, M. S. et al. Nature 509, 575-581, (2014)). Using these data, it was assessed whether any novel, uniquely mapping peptides map to an ORF in any of the TUCP transcripts. Many novel and uniquely mapping peptides in various tissue types mapped to ORFs in the TUCP transcripts, with a total of 268 TUCP genes possessing matching peptides. These and other TUCP predictions exemplify the potential for MiTranscriptome to enhance reference transcript catalogs.

›Example 1 · 9 of 12

Characterization and Validation of Long RNA Transcripts

LncRNA and TUCP genes tended to have fewer exons than read-through or protein coding genes, but appreciable alternative splicing was observed for all classes of transcripts (Cabili et al, supra; Derrien, T. et al. Genome research 22, 1775-1789, (2012).) ( FIG. 10 a ). Furthermore, it was observed that lncRNAs and TUCPs were expressed at lower levels than read-through or protein-coding transcripts, which is also consistent with previous studies (Prensner, J. R. et al. Nature biotechnology 29, 742-749, (2011)); Cabili et al., supra; Derrien et al., supra; Guttman, M. et al. Nature biotechnology 28, 503-510, (2010)) ( FIG. 2 d ).

To characterize transcription start sites (TSS), intervals surrounding TSSs with ENCODE histone 3 lysine 4 trimethylation (H3K4me3) ChIP-Seq, RNA polymerase II (PolII) binding sites, and DNase hypersensitivity data from 13 cell lines were compared. To control for expression, binding was only assessed for transcripts expressed in the cell lines being assayed, filtered TSSs for expression before intersection at a level of FPKM>0.1. LncRNA and TUCP promoters were enriched for these marks relative to randomly shuffled control regions, with maximal enrichment at the TSS ( FIG. 2 e - g ). Enrichment was lower for lncRNA and TUCP promoters than for protein-coding genes, but much more enriched than pseudogenes, which may reflect their overall lower expression levels. These chromatin modification and polymerase binding data indicate that the assembled lncRNA and TUCP transcripts possess actively regulated promoters.

During assembly of the MiTranscriptome, a first-pass filtering of low-confidence transfrags was performed via a machine-learning algorithm built using the expression level and recurrence of the transfrag ( FIG. 6 d ). Millions of transfrags were removed at this step, and the resultant MiTranscriptome contains only transcripts that have met this first-pass confidence evaluation. To further stratify the confidence transcripts, a confidence score (CS) system was developed. lncRNAs were classified into two tiers based on their annotation status and the matching of splice junctions, and a cumulative distribution function was built using the expression levels for the annotated lncRNAs (tier 1). The expression level of each unannotated lncRNA (tier 2) was then fed into the cumulative distribution function to calculate a CS for each lncRNA ( FIG. 10 b ). The CS profile of the tier 1 and tier 2 transcripts was largely similar, with a slight enrichment in low confidence transcripts among the unannotated transcripts (e.g. 32% of unannotated lncRNAs have CSs lower than the bottom 12.5th percentile of annotated lncRNAs). This phenomenon, however, can be explained by a discovery bias given that the confidence metric is expression based. To further strengthen confidence in the assembly transcripts, the predicted lncRNA expression was validated by qRT-PCR. qPCR primers were developed for 100 candidate lncRNAs. Three cell lines were selected representing lung cancer, prostate cancer and breast cancer (A549, LNCaP, MCF7, respectively), and lncRNAs with expression of at least 1 FPKM by RNAseq in at least one of the cell lines were selected for validation (38 monoexonic, 62 polyexonic). Given that genomic contamination can produce spurious monoexonic reads during assembly, an absence of reverse transcriptase (−RT) was used as a control for this study. Of the 100 lncRNAs tested, 95 had significantly higher expression with reverse transcriptase when compared to −RT (Student's t-test, p-value<0.05) in cell lines for which expression was expected via RNA-Seq (>1 FPKM) ( FIG. 11 ). DSCAM-AS1 and PCAT130 are two examples of lncRNAs nominated by SSEA analysis to have cancer specificity (in breast and prostate, respectively) whose cell line expression profile by qRT-PCR reflects what is expected from the tissue SSEA analysis ( FIG. 12 , boxed genes).

To further ensure that the amplicon was from the expected gene, twenty of the most expressed transcripts across the three cell lines (according to the qRT-PCR data) were selected and their identity confirmed by Sanger sequencing. In eighteen of the twenty cases, the sequence of the exact gene of interest was amplified ( FIG. 12 a,b ). Additionally, the expression values identified by qRT-PCR for each cell line were correlated to the RNA-seq FPKM values in each cell line. qRT-PCR was correlated best with RNA-seq expression from the same cell line ( FIG. 12 c ).

LncRNAs Harboring Conserved Elements

The evolutionary conservation of lncRNAs has been a topic of ongoing conversation, with several reports indicating that lncRNAs are modestly conserved (Cabili et al, supra; Derrien et al. supra; Necsulea, A. et al. Nature 505, 635-640 (2014)). In agreement with previous reports, increases in both transcript and promoter conservation levels for lncRNAs and TUCPs relative to random control regions were observed ( FIG. 10 c - f ). Shifts in the cumulative distributions of lncRNA and TUCP transcripts were greater for annotated transcripts relative to unannotated transcripts. This difference may reflect discovery bias favoring highly conserved genes detectable across multiple model systems. Despite observing increased conservation within the entire class of lncRNAs, the results indicated that human lncRNA conservation may be an exceptional phenomenon rather than a general one; therefore, lncRNAs harboring higher than expected basewise conservation were selected for focused study ( FIG. 2 h ). 3,309 lncRNA genes (5.6% of all lncRNAs) that were highly conserved relative to random intergenic regions were selected ( FIG. 10 e ). In addition, part of the noncoding genome includes ultraconserved elements (UCE), which are stretches of DNA>200nt with nearly perfect sequence identity across multiple organisms (Bejerano, G. et al. Science 304, 1321-1325, (2004); Dimitrieva, S. & Bucher, P.

Nucleic acids research 41, D101-109, (2013)). 597 intergenic lncRNAs (1.2% of all intergenic lncRNAs) harboring UCEs were designated as Highly Conserved Long Intergenic Non-Coding RNAs (HICLINCs) to promote further study of transcribed UCEs as a class ( FIG. 10 h ). For example, THCAT126, a previously unannotated intergenic lncRNA on chromosome 2q24, contains elements in its final exons that are conserved in nearly all vertebrates including zebrafish ( FIG. 2 i ). Moreover, THCAT126 is expressed widely across many tissue types, and is expressed in multiple cancers, with a significant association in the thyroid cancer versus normal analysis ( FIG. 2 j ). Highly conserved lncRNAs such as THCAT126 (and many other cancer-associated HICLINCs described below) provide an avenue for in vivo study of the role of lncRNAs in development and cancer.

›Example 1 · 10 of 12

LncRNAs Overlapping Disease-Associated SNPs

To investigate the relationship of the MiTranscriptome assembly with disease-associated regions of the genome, overlap of transcripts in the assembly was compared with 11,194 unique disease associated single nucleotide polymorphisms (SNPs) from a catalog of genome-wide association studies (GWAS) (Welter, D. et al. Nucleic acids research 42, D1001-1006, (2014)). MiTranscriptome transcripts overlapped 9,770 GWAS SNPs compared to just 7,050 SNPs overlapping GENCODE, UCSC, or RefSeq transcripts. Exonic overlap was 2,586 and 1,096 GWAS SNPs for the MiTranscriptome and aggregated reference catalogs, respectively ( FIG. 13 a,b ). Altogether transcripts in the assembly coincided with 2,881 formerly intergenic SNPs located within ‘gene deserts’, and only missed 161 GWAS SNPs overlapping annotated genes. It was observed that the increased overlap with GWAS SNPs for MiTranscriptome transcripts and exons were significantly enriched for GWAS SNPs relative to random SNPs chosen from the same chip platform (paired t-test, p-value, 5.25e-135 and 1.15e-199, respectively, FIG. 2 k ). Moreover, unannotated intergenic lncRNAs and TUCPs were also significantly enriched for disease-associated regions, with exons more highly enriched than full-length transcripts (paired t-test, p-value, 9.90e-78 and 5.50e-50, for whole transcript and exon, respectively, FIG. 13 c ). These data indicate that a rigorous reevaluation of allele-specific gene expression regulation in regions proximal to GWAS SNPs yields informative biological associations with the new lncRNA transcripts identified in this study.

Detection of Cancer-Associated Transcription by Enrichment Analysis

The large-scale transcriptome reconstruction process unveiled tremendous transcriptional complexity highlighted by the presence of thousands of uncharacterized lncRNAs and TUCPs. To prioritize disease-associated and lineage-specific transcription, a nonparametric method for differential expression testing called Sample Set Enrichment Analysis (SSEA) was used. SSEA adapts the weighted Kolmorgorov-Smirnoff-like tests used by Gene Set Enrichment Analysis (GSEA) (Subramanian, A. et al. Proceedings of the National Academy of Sciences of the United States of America 102, 15545-15550, (2005)) to discover transcript expression changes associated with predefined sample sets. This method permits sensitive detection of differential expression within heterogeneous sample populations (e.g., tumor sub-types). Prior to running SSEA, isoform-level expression data for the entire MiTranscriptome assembly was re-computed and samples from the compendia were grouped into fifty sample sets. A sample set represents a single condition for evaluating differential transcript expression. The sets in the present study included various cancer types (e.g., prostate cancers versus all other MiTranscriptome samples), normal tissues or cell types, and cancer versus normal comparisons within a single tissue type (e.g., prostate cancers versus benign prostate samples) ( FIG. 3 a ). All MiTranscriptome transcripts were tested against the fifty samples sets, and collectively, SSEA detected over two million significant associations (FDR<1e-3 for cancer versus normal analyses and FDR<1e-7 for lineage analyses) involving 267,726 of the 381,821 MiTranscriptome transcripts for which enrichment analysis was possible.

To validate the enrichment testing approach, its ability to rediscover known proteins up-regulated and down-regulated in prostate cancers and breast cancers was assessed by assessing the concordance between the top 1% positively and negatively enriched genes from each cancer type with cancer gene signatures obtained from the Oncomine database of microarray studies (Rhodes, D. R. et al. Neoplasia 9, 166-180 (2007); Cancer Genome Atlas, N. Nature 490, 61-70, (2012); Curtis, C. et al. Nature 486, 346-352, (2012); Gluck, S. et al. Breast cancer research and treatment 132, 781-791, (2012); Grasso, C. S. et al. Nature 487, 239-243, (2012); Taylor, B. S. et al. Cancer cell 18, 11-22, (2010); Yu, Y. P. et al. Journal of clinical oncology: official journal of the American Society of Clinical Oncology 22, 2790-2799, (2004)). A heatmap of the odds ratios of the gene signature associations revealed striking agreement between SSEA and the other studies for both cancer types, with SSEA often demonstrating equal or better concordance to each microarray study than comparison of the microarray studies to each other ( FIG. 3 b ). Thus, isoform-level differential expression testing from the MiTranscritpome ab initio assembly of RNA-Seq data recapitulated the results from cancer microarray gene expression studies, supporting the SSEA method as a viable tool for detection of differential expression. To further credential the enrichment testing approach, the ability to detect positive control lncRNAs and protein-coding genes in breast cancers and prostate cancers was assesed. For example, SSEA correctly identified the oncogenic lncRNA HOTAIR7, estrogen receptor 1 (ESR1), and GATA binding protein 3 (GATA3) as highly positively enriched in breast cancers (Rhodes et al., 2007, supra; Cancer Genome Atlas, supra), and accurately nominated the tumor suppressor lncRNA MEG3 (Rhodes et al., 2007, supra; Cancer Genome Atlas, supra) and the metastasis suppressor LIFR (Chen, D. et al. Nature medicine 18, 1511-1517, (2012)) as highly negatively enriched ( FIG. 3 c - e ). Similarly, in the prostate cancer set SSEA detected differential expression of lncRNAs and protein-coding genes consistent with the literature ( FIG. 3 f ). Notably, the known prostate cancer lncRNAs Prostate Cancer Antigen-3 (PCA3) and SChLAP1 were strikingly enriched in a cancer-specific and prostate-specific manner relative to all other sample set analyses ( FIG. 3 g,h ) (Taylor et al., supra; Presner et al., 2013, supra). Overall the ability of the enrichment testing approach to rediscover known cancer genes in an unbiased fashion indicates its utility for the analysis of the cancer association and lineage specificity within the panorama of uncharacterized transcription unveiled by MiTranscriptome.

›Example 1 · 11 of 12

Characterization of Lineage-Specific and Cancer-Specific lncRNA Transcription

To extend the study beyond known cancer genes, the enrichment test results for lineage-specific and cancer-specific transcripts were mined in an unbiased manner. Lineage specificity was assayed using sample sets for each cancer or tissue type compared to all other samples in the MiTranscriptome compendium ( FIG. 3 a , “Cancer Types/Normal Types”), and SSEA results were utilized to determine the degree of enrichment for each transcript in the various cancer and tissue types. Unsupervised clustering of transcript percentile ranks for the top 1% of transcripts in each lineage demonstrated distinct signatures for each lineage while also described relationships among lineages and between cancer and normal sets from the same lineage ( FIG. 14 a ). Examples of closely related lineage clusters include blood cancers (acute myeloid leukemia (AML), chronic myeloid leukemia (CML), and myeloproliferative neoplasia (MPN)), brain cancers (lower grade glioma (LGG) and glioblastome multiforme (GBM)), and muscle tissue (cardiac and skeletal). Additionally, a cluster comprising cervical cancer, head and neck cancer and normal lineages, lung squamous cell cancer, and bladder cancer emerged and indicated that primarily squamous (and transitional) cell carcinomas from distant primary sites share important gene expression relationships. Unsupervised clustering of only the lncRNAs in the top 1% of the SSEA analysis for lineage association recapitulated all of these relationships, indicating the capacity for lncRNAs to independently identify cancer and normal lineages ( FIG. 4 a ).

Next, the dimension of cancer-specific transcriptional dynamics was investigated in twelve tissues with ample numbers of both cancer and normal samples ( FIG. 3 a , “Cancer vs. Normal”). Similar to above, unsupervised clustering of the top 1% cancer-associated lncRNAs demonstrated highly specific signatures for each cancer type, with the exception of lung cancers and kidney cancers ( FIG. 4 b and FIG. 14 b ). Lung squamous cell carcinomas (LUSC) and adenocarcinomas (LUAD) clustered together and shared numerous transcripts with cancer association. Similarly, renal clear cell (KIRC) and papillary cell (KIRP) carcinomas exhibited highly overlapping signatures, while renal chromophobe carcinomas (KICH) remained distinct from KIRC and KIRP.

Finally, results from lineage and cancer analyses were intersected. Such transcripts have translational potential for use in non-invasive clinical tests, particularly for cancers that lack reliable biomarkers. Notable examples included the prostatespecific lncRNAs PCA3 and SChLAP1 presented earlier ( FIG. 3 g,h ). A myriad of lncRNAs were detected as being lineage and cancer associated (i.e. in the top 5% of both analyses) for each of the cancer types analyzed ( FIG. 4 c , FIG. 15 a ). A direct comparison of lncRNAs and protein-coding transcripts revealed that both annotated and unannotated lncRNAs have the perform at a comparable level to protein-coding genes in lineage and cancer association and support a role for lncRNAs as cancer specificity markers ( FIG. 4 d and FIG. 15 b,c ). After applying stringent statistical cutoffs to nominate the most compelling associations, a cohort of 7,942 lncRNA or TUCP genes (11,478 transcripts) were nominated as cancer associated, lineage associated, or both. Many of these lncRNAs also possessed base-wise conservation or ultraconserved elements ( FIG. 2 , Table 1). Transcripts meeting the stringent cutoffs in the cancer versus normal analyses (“Cancer vs. Normal”, FIG. 3 a ) were designated as having “cancer association”. Those transcripts meeting stringent cutoffs for linage specificity in non-cancerous tissue (e.g. heart, skeletal muscle, embryonic stem cells) and in cancers lacking RNA-Seq data for benign tissue were designated as “lineage associated”. Moreover, transcripts meeting the cutoffs for both the cancer versus normal and lineage specificity analyses were designated as having “cancer and lineage association” (Table 1). Transcripts with significant association in just one tissue type were given names according to that tissue type (Table 1), and transcripts with associations in multiple tissues were named “Cancer Associated Transcripts” (CATs). An additional 545 lncRNA genes (1634 transcripts) that possessed ultraconserved elements but did not meet the stringent lineage and cancer association cutoffs were designated as HICLINCs (Highly Conserved Long Intergenic Non-Coding RNA). Taken together, the cancer and/or lineage lncRNAs and HICLINCs comprise a set of 8,487 lncRNAs that bear strong functional potential. 7,804 of these lncRNAs did not possess an official gene name according to the HUGO Gene Nomenclature Committee, and were thus given names according to the convention described above and in Table 1.

Additional analyses were performed to provide more information about these transcripts for use in selecting candidates for subsequent experimentation. A comprehensive assessment of transcription factor binding to the promoters of these lncRNAs was performed using the ENCODE dataset for 161 transcription factors. Additionally, statistics describing the expression of each lncRNA in the different tissue cohorts is reported. For each TUCP transcript, the longest ORF, coding potential score, and presence of any pfam domain were identified.

Further interrogation of the relationship with GWAS SNPs was also performed, and all transcripts within 50 kb of a GWAS SNP implicated in disease of the same cancer or tissue as the transcript awere identified. These lncRNAs provide candidates for intergenic expression quantitative trait loci (eQTLs) analysis. For example, the lncRNA named Breast Cancer Associated Transcript-85, BRCAT49 is a breast cancer- and lineageassociated lncRNA ( FIG. 4 d ) located ˜45 kb downstream of a breast cancer SNP (rs13387042) that has been implicated by six independent GWAS studies ( FIG. 4 f ) (Li, J. et al. Breast cancer research and treatment 126, 717-727, (2011); Michailidou, K. et al. Nature genetics 45, 353-361, 361e351-352, (2013); Stacey, S. N. et al. Nature genetics 39, 865-869, (2007); Thomas, G. et al. Nature genetics 41, 579-584, doi:10.1038/ng.353 (2009); Turnbull, C. et al. Nature genetics 42, 504-507, (2010)). The NHGRI GWAS catalog describes rs13387042 as an intergenic SNP with no reported associated gene (Welter, D. et al. Nucleic acids research 42, D1001-1006, (2014)). Given its breast cancer specificity ( FIG. 4 g ), BRCAT49 provides a target for explaining the breast cancer association of this genomic region. Moreover, with further investigation and analysis, its cancer and lineage specificity support a role for BRCAT49 (and other similar cancer and lineage-specific lncRNAs) as a cancer specific transcriptional marker. Additional representative expression profiles for cancer- or lineage-specific lncRNAs in other tissue types are displayed in FIG. 16 c,d.

›Example 1 · 12 of 12

Because the MiTranscriptome represents such a comprehensive array of tissues and cancers ( FIG. 1 a ), it is able to uncover an abundance of lineage and cancer specific transcription that has biological and clinical impact. A representative example of one such lineage specific lncRNA is a transcript was termed Melanoma Associated Transcript-7, MEAT6, which was found to be in the 99.8th percentile in the melanoma lineage SSEA analysis ( FIG. 4 a ). Genomic investigation delineated MEAT6 as a partially annotated transcriptional variant of the UCSC lncRNA AK090788 lncRNA on chromosome 6q26 ( FIG. 16 a ). However, MEAT6 utilizes an alternative start site and upstream exons absent from reference catalogs, highlighting the breadth and depth of transcriptome reconstruction effort. Expression of MEAT6 isoforms using the novel start site were highly specific to the melanoma samples in the MiTranscriptome cohort ( FIG. 4 e ); however, isoforms lacking the MEAT6 start site had a dramatically different pan-cancer expression profile with almost no expression in melanoma ( FIG. 16 b ). These findings manifest the ability of the assembly to provide a clear and consummate portrayal of the transcriptional activity that distinguishes disease types.

To further corroborate the differential expression analysis, a high-throughput “guilt-by association” analysis was performed for all of the lncRNAs meeting the stringent cutoffs. Expression of each transcript isoform was correlated to all annotated protein-coding genes for each relevant tissue cohort, and various cancer signatures were tested for enrichment with the most correlated or anti-correlated genes using the GSEA method. The gene sets were curated and categorized into cancer relevant categories: angiogenesis/hypoxia associated, metastasis associated, proliferation/cell-cycle associated, adhesion associated, DNA damage/repair associate, oncogenic association, and miscellaneous cancer association. In total over 14 thousand transcripts were analyzed with this method, and the significantly associated cancer gene sets are reported (Tables 2 and 3).

All publications, patents, patent applications and accession numbers mentioned in the above specification are herein incorporated by reference in their entirety. Although the disclosure has been described in connection with specific embodiments, it should be understood that the disclosure as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications and variations of the described compositions and methods of the disclosure will be apparent to those of ordinary skill in the art and are intended to be within the scope of the following claims.

›Tables in the description — 2
TABLE 1 — Summary of lineage and/or cancer-specific lncRNAs nominated in this study. Total
Associated# Cancer- &# Containing
Tissue/Cancer TypeNon-CodingTissue-#Ultraconserved# Classified
(Naming Convention)TranscriptsSpecificConservedElementas TUCP
Acute Myelogenous Leukemia Associated373NA291326
Transcripts (AMATs)
Bladder Cancer Associated Transcripts610925
(BLCATs)
Breast Cancer Associated Transcripts1115134822776
(BRCATs)
Cervical Cancer Associated Transcripts162NA12213
(CVATs)
Chronic Myelogenous Leukemia Associated157NA16311
Transcripts (CMATs)
Colorectal Cancer Associated Transcripts163NA29417
(CRATs)
Glioblastoma Multiforme Associated161NA11222
Transcripts (GBATs)
Head and Neck Cancer Associated Transcripts7665451568
(HNCATs)
Heart Tissue Associated Transcripts (HRATs)170NA16112
Human Embryonic Stem Cells Associated205NA10020
Transcripts (ESATs)
Chromophobe Renal Cell Carcinoma Associated105052642092
Transcripts (KCHCATs)
Renal Clear Cell Carcinoma Associated14292158426123
Transcripts (KCCATs)
Renal Papillary Cell Carcinoma Associated474041838
Transcripts (KPCATs)
Low Grade Glioma Associated Transcripts265NA311023
(LGATs)
Liver Cancer Associated Transcripts (LVCATs)250018120
Lung Adenocarcinoma Associated Transcripts95319641961
(LACATs)
Lung Squamous Cell Carcinoma Associated101410702358
Transcripts (LSCATs)
Medulloblastoma Associated Transcripts312NA26333
(MBATs)
Melanoma Associated Transcripts (MEATs)339NA24234
Myeloproliferative Neoplasia Associated101NA1218
Transcripts (MPATs)
Ovarian Cancer Associated Transcripts163NA371230
(OVATs)
Pancreatic Cancer Associated Transcripts247NA27422
(PNATs)
Prostate Cancer Associated Transcripts72738491462
(PCATs)
Skeletal Muscle Tissue Associated Transcripts123NA5111
(SMATs)
Stomach Cancer Associated Transcripts95010110
(STCATs)
Thyroid Cancer Associated Transcripts1289807321111
(THCATs)
Uterine Endometrial Carcinoma Associated183NA31116
Transcripts (UTATs)
TABLE 2
func_name_finaltissuetranscript_lengthtcatSEQ ID NO
OVAT106.2ovarian1425lncrna1
OVAT106.1ovarian2622lncrna2
CAT14.1prostate2156lncrna3
CAT14.1kich2156lncrna4
CAT14.2luad996lncrna5
CAT14.2kich996lncrna6
CAT15uterine372lncrna7
CAT15medulloblastoma372lncrna8
THCAT66thyroid6340lncrna9
THCAT66thyroid6340lncrna10
CMAT37cml369lncrna11
UTAT19Uterine3914tucp12
SNHG12.1Kirc1478lncrna13
SNHG12.1Uterine1478lncrna14
SNHG12.2Kirc2738lncrna15
SNHG12.3cervical1508tucp16
SNHG12.3Kirc1508tucp17
CAT33Gbm2241lncrna18
CAT33Breast2241lncrna19
CAT34Thyroid496lncrna20
CAT34Lusc496lncrna21
CAT34prostate496lncrna22
THCAT20Thyroid2421lncrna23
CAT36head_neck2956lncrna24
CAT36stomach2956lncrna25
CAT36Breast2956lncrna26
CAT36Luad2956lncrna27
CAT36Liver2956lncrna28
CAT36Thyroid2956lncrna29
CAT44Kirc3336lncrna30
CAT44Kirp3336lncrna31
CAT57.1melanoma1816lncrna32
CAT57.2ovarian2148lncrna33
CAT57.2melanoma2148lncrna34
CAT62.1Luad580lncrna35
CAT62.1Lusc580lncrna36
MBAT23medulloblastoma1714lncrna37
LAMTOR5-AS1.2colorectal853lncrna38
LAMTOR5-AS1.2Kirp853lncrna39
LAMTOR5-AS1.2Kich853lncrna40
ESAT85.1embryonic_stem_cells1082lncrna41
ESAT85.2embryonic_stem_cells1336lncrna42
ESAT40embryonic_stem_cells787tucp43
THCAT22.1Thyroid2848lncrna44
THCAT22.1Thyroid2848lncrna45
THCAT22.4Thyroid2315lncrna46
THCAT22.4Thyroid2315lncrna47
THCAT22.3Thyroid3056lncrna48
THCAT22.3Thyroid3056lncrna49
CAT99.1colorectal3188lncrna50
CAT99.1medulloblastoma3188lncrna51
CAT99.2Gbm3526lncrna52
CAT99.2Luad3526lncrna53
CAT99.2Lusc3526lncrna54
OVAT12ovarian653lncrna55
BRCAT23Breast2635lncrna56
CAT112.1Luad5210lncrna57
CAT112.2cervical6470lncrna58
CAT112.2Luad6470lncrna59
CAT112.2skeletal_muscle6470lncrna60
CAT115.1head_neck1032lncrna61
CAT115.1Lusc1032lncrna62
CAT115.2prostate1599lncrna63
CAT118.1Lgg1658tucp64
RUSC1-AS1.1Uterine1852tucp65
RUSC1-AS1.1Aml1852tucp66
RUSC1-AS1.2colorectal7499tucp67
CAT122head_neck9412lncrna68
CAT122Kirp9412lncrna69
CAT122Liver9412lncrna70
CAT122Luad9412lncrna71
CAT122Kirc9412lncrna72
PNAT1.2pancreatic902lncrna73
CAT147.1medulloblastoma1533lncrna74
MIR205HG.1Lusc4404lncrna75
MIR205HG.1prostate4404lncrna76
MIR205HG.2Lusc2753tucp77
MIR205HG.2prostate2753tucp78
MIR205HG.3Lusc2336lncrna79
MIR205HG.3prostate2336lncrna80
CAT171.1Breast741lncrna81
CAT171.1Luad741lncrna82
CAT179Thyroid475lncrna83
CAT179Lusc475lncrna84
CAT179Kirp475lncrna85
CAT179Breast475lncrna86
CAT179Luad475lncrna87
CAT179prostate475lncrna88
CAT179Kich475lncrna89
CAT186.1Luad3863lncrna90
CAT186.2prostate1661lncrna91
CAT186.2Breast1661lncrna92
CAT186.2Luad1661lncrna93
CAT186.2Lusc1661lncrna94
CAT186.2Lgg1661lncrna95
CAT187.1Heart4840lncrna96
CAT187.2Aml9637lncrna97
CAT187.2Kirc9637lncrna98
ESAT33.2embryonic_stem_cells13412lncrna99
ESAT33.1embryonic_stem_cells2024lncrna100
CAT1224.1head_neck2502lncrna101
CALML3-AS1.1prostate11669lncrna102
CALML3-AS1.1Breast11669lncrna103
CALML3-AS1.2Lusc8709tucp104
GATA3-AS1.1Kirc7280lncrna105
GATA3-AS1.2Breast9735lncrna106
BRCAT1.1Breast6529lncrna107
BRCAT1.1Breast6529lncrna108
BRCAT1.4Breast26917lncrna109
BRCAT1.4Breast26917lncrna110
BRCAT1.3Breast1676lncrna111
BRCAT1.3Breast1676lncrna112
BRCAT1.5Breast8975lncrna113
BRCAT1.5Breast8975lncrna114
BRCAT1.2Breast32732lncrna115
BRCAT1.2Breast32732lncrna116
CAT1233Thyroid1709lncrna117
CAT1233pancreatic1709lncrna118
CAT1235.1melanoma3656lncrna119
CAT1235.1Kirc3656lncrna120
CAT1235.1Kirp3656lncrna121
CAT1235.1ovarian3656lncrna122
CAT1235.1prostate3656lncrna123
CAT1235.1Breast3656lncrna124
CAT1235.1Lusc3656lncrna125
CAT1235.2Uterine4480lncrna126
CAT1235.2Kirc4480lncrna127
CAT1235.2cervical4480lncrna128
CAT1235.2Breast4480lncrna129
CAT1235.2Luad4480lncrna130
CAT1235.2Lusc4480lncrna131
ST8SIA6-AS1.1prostate1250lncrna132
ST8SIA6-AS1.1Liver1250lncrna133
ST8SIA6-AS1.2prostate10110lncrna134
ST8SIA6-AS1.2Liver10110lncrna135
LINC00948.1pancreatic1205lncrna136
LINC00948.1Kirp1205lncrna137
CAT1269Mpn9975lncrna138
CAT1269Cml9975lncrna139
CAT1269medulloblastoma9975lncrna140
UNC5B-AS1.1ovarian1602lncrna141
UNC5B-AS1.1Thyroid1602lncrna142
UNC5B-AS1.2Thyroid915lncrna143
UNC5B-AS1.2prostate915lncrna144
KCCAT243Kirc1647lncrna145
KCCAT243Kirc1647lncrna146
OVAT44ovarian1283lncrna147
CAT1284.1Kirc17882lncrna148
CAT1284.1Kirp17882lncrna149
CAT1284.1Lusc17882lncrna150
CAT1284.1pancreatic17882lncrna151
CAT1284.1prostate17882lncrna152
CAT1284.1Kich17882lncrna153
MEAT20.3melanoma1252lncrna154
MEAT20.1melanoma1222lncrna155
MEAT20.2melanoma1028lncrna156
CAT1324colorectal1329tucp157
CAT1324Uterine1329tucp158
CAT1324head_neck1329tucp159
CMAT6Cml540lncrna160
CAT1337.1Lusc41069lncrna161
CAT1337.1pancreatic41069lncrna162
CAT1337.1prostate41069lncrna163
LINC00958.1Lusc42272lncrna164
LINC00958.1prostate42272lncrna165
LINC00958.2Lusc38359lncrna166
CAT1337.2head_neck2101lncrna167
CAT1337.2Lusc2101lncrna168
CAT1337.2pancreatic2101lncrna169
CAT1337.2medulloblastoma2101lncrna170
CAT1337.2prostate2101lncrna171
LINC00958.3Lusc30635lncrna172
LINC00958.3pancreatic30635lncrna173
LINC00958.4Lusc14161lncrna174
LINC00958.4pancreatic14161lncrna175
LINC00958.5Luad8665lncrna176
CAT1345.1Luad1080lncrna177
CAT1345.1Lusc1080lncrna178
LINC00678.1embryonic_stem_cells471lncrna179
WT1-AS.1ovarian4079lncrna180
WT1-AS.2ovarian3132lncrna181
WT1-AS.3ovarian3618lncrna182
WT1-AS.3Kich3618lncrna183
CAT1363.1Uterine2421lncrna184
CAT1363.2Uterine2263lncrna185
CAT1363.2Kich2263lncrna186
CAT1363.2Lusc2263lncrna187
UTAT10Uterine1287tucp188
NEAT1Kich26332lncrna189
MALAT1.2Heart7744lncrna190
MALAT1.2Kich7744lncrna191
CAT1373Breast3975lncrna192
CAT1373Luad3975lncrna193
CAT1373Lusc3975lncrna194
CAT1373skeletal_muscle3975lncrna195
CAT1373Gbm3975lncrna196
CAT1373Lgg3975lncrna197
CAT1373medulloblastoma3975lncrna198
CAT1373Kirc3975lncrna199
CAT1376Uterine8904tucp200
CAT1376Kich8904tucp201
CAT1376medulloblastoma8904tucp202
OVAT47ovarian833lncrna203
ANO1-AS1head_neck10628tucp204
ANO1-AS1Kirc10628tucp205
ANO1-AS1Cml10628tucp206
ANO1-AS1Gbm10628tucp207
ANO1-AS1Lgg10628tucp208
ANO1-AS1medulloblastoma10628tucp209
ANO1-AS1prostate10628tucp210
CAT1385.1Thyroid8545lncrna211
CAT1385.1head_neck8545lncrna212
CAT1385.2Thyroid3095lncrna213
CAT1385.2head_neck3095lncrna214
CAT1385.2Breast3095lncrna215
CAT1385.2Luad3095lncrna216
CAT1385.2Aml3095lncrna217
CAT1385.3medulloblastoma4221lncrna218
CAT1391Cml7090lncrna219
CAT1391Aml7090lncrna220
CAT1391Kirc7090lncrna221
CAT1399Thyroid6255lncrna222
CAT1399stomach6255lncrna223
CAT1399head_neck6255lncrna224
CAT1399Kirp6255lncrna225
CAT1399Luad6255lncrna226
CAT1399Kirc6255lncrna227
CAT1399Mpn6255lncrna228
CAT1399Aml6255lncrna229
CAT1399Lgg6255lncrna230
CAT1425.1Kirc1151lncrna231
CAT1425.1Lusc1151lncrna232
CAT1425.1Breast1151lncrna233
CAT1425.1Liver1151lncrna234
CAT1425.2Kirc5627lncrna235
CAT1425.2Breast5627lncrna236
CAT1425.2Liver5627lncrna237
CAT1434ovarian637lncrna238
CAT1434Uterine637lncrna239
CAT1434Kich637lncrna240
ESAT80embryonic_stem_cells4806lncrna241
CAT1452.1Kich3332tucp242
CAT1452.1prostate3332tucp243
CAT1452.1Luad3332tucp244
CAT1452.1Lusc3332tucp245
CAT1464Kich7157lncrna246
CAT1464Gbm7157lncrna247
CAT1464melanoma7157lncrna248
CAT1464Lgg7157lncrna249
ESAT3embryonic_stem_cells2056lncrna250
MEAT11melanoma1006lncrna251
CAT1468Kirc2243lncrna252
CAT1468medulloblastoma2243lncrna253
CAT1469head_neck2125lncrna254
CAT1469Lusc2125lncrna255
CAT1472.1Lusc1709lncrna256
DDX11-AS1.1Kirp2011lncrna257
DDX11-AS1.1Liver2011lncrna258
DDX11-AS1.1Lusc2011lncrna259
CAT1501.1Kich3920lncrna260
CAT1501.1Thyroid3920lncrna261
CAT1501.2ovarian4652lncrna262
CAT1528Heart2528lncrna263
CAT1528Kich2528lncrna264
CAT1528Thyroid2528lncrna265
MEAT10melanoma1589lncrna266
CRAT16colorectal1650lncrna267
HNCAT4head_neck764lncrna268
CAT1547Mpn3302lncrna269
CAT1547Cml3302lncrna270
CAT1547Kirc3302lncrna271
CAT1547embryonic_stem_cells3302lncrna272
CAT1547skeletal_muscle3302lncrna273
CAT1564Aml4645tucp274
CAT1564Kirc4645tucp275
ESAT56.3embryonic_stem_cells8304lncrna276
ESAT56.2embryonic_stem_cells7622lncrna277
ESAT56.1embryonic_stem_cells7755lncrna278
LINC00428embryonic_stem_cells7715lncrna279
LINC00458.2embryonic_stem_cells2657lncrna280
LINC00458.1embryonic_stem_cells1021lncrna281
ESAT23embryonic_stem_cells1747lncrna282
LINC00458.3embryonic_stem_cells2948lncrna283
ESAT13.3embryonic_stem_cells1855lncrna284
ESAT13.1embryonic_stem_cells878lncrna285
ESAT34embryonic_stem_cells923lncrna286
ESAT36.1embryonic_stem_cells10586lncrna287
ESAT36.4embryonic_stem_cells11133lncrna288
ESAT36.2embryonic_stem_cells11103lncrna289
ESAT36.3embryonic_stem_cells13928lncrna290
MEAT69melanoma2031lncrna291
DOCK9-AS2thyroid2091lncrna292
DOCK9-AS2thyroid2091lncrna293
CAT1629.1medulloblastoma5852lncrna294
CAT1629.2luad3834lncrna295
CAT1629.2lusc3834lncrna296
CAT1629.2pancreatic3834lncrna297
CAT1629.2kirc3834lncrna298
CAT1631kirc1210lncrna299
CAT1631medulloblastoma1210lncrna300
GBAT25.1gbm3520lncrna301
SMAT25skeletal_muscle6343tucp302
CAT1636.1head_neck1465lncrna303
CAT1636.1uterine1465lncrna304
CAT1641colorectal3474lncrna305
CAT1641stomach3474lncrna306
CAT1641kich3474lncrna307
CAT1641liver3474lncrna308
UTAT29uterine322lncrna309
OVAT48ovarian650lncrna310
ESAT39.3embryonic_stem_cells8927lncrna311
CAT1658head_neck835lncrna312
CAT1658lusc835lncrna313
CAT1659.1breast1250lncrna314
CAT1659.2embryonic_stem_cells15089lncrna315
CAT1659.2breast15089lncrna316
CAT1659.3embryonic_stem_cells1255lncrna317
CAT1659.4embryonic_stem_cells13866lncrna318
CAT1683.1kich1006lncrna319
CAT1683.1embryonic_stem_cells1006lncrna320
OVAT5ovarian288lncrna321
CAT1723aml2435tucp322
CAT1723head_neck2435tucp323
CAT1723luad2435tucp324
CAT1723lusc2435tucp325
CAT1723gbm2435tucp326
CAT1728heart3209tucp327
CAT1728thyroid3209tucp328
CAT1728breast3209tucp329
CAT1728luad3209tucp330
CAT1728lusc3209tucp331
CAT1735breast850lncrna332
CAT1735luad850lncrna333
CAT1735lusc850lncrna334
CAT1735thyroid850lncrna335
CAT1735kirp850lncrna336
CAT1736.1thyroid1229lncrna337
CAT1736.1luad1229lncrna338
CAT1760.1melanoma6162lncrna339
CAT1760.1lgg6162lncrna340
CAT1760.1prostate6162lncrna341
CAT1760.2kirc7244lncrna342
CAT1760.2melanoma7244lncrna343
USP3-AS1.3colorectal477lncrna344
USP3-AS1.3uterine477lncrna345
USP3-AS1.3melanoma477lncrna346
CAT1768.1prostate6771lncrna347
CAT1768.1kirp6771lncrna348
CAT1773melanoma834lncrna349
CAT1773thyroid834lncrna350
CAT1773kirc834lncrna351
CAT1773liver834lncrna352
CAT1773cml834lncrna353
CAT1777thyroid11399tucp354
CAT1777cervical11399tucp355
CAT1777luad11399tucp356
CAT1777lusc11399tucp357
CAT1784lgg2795lncrna358
CAT1784head_neck2795lncrna359
CAT1784kirp2795lncrna360
CAT1784breast2795lncrna361
CAT1784kirc2795lncrna362
UTAT4uterine650lncrna363
CAT1785heart852lncrna364
CAT1785embryonic_stem_cells852lncrna365
CAT1796thyroid3587lncrna366
CAT1796prostate3587lncrna367
CAT1796breast3587lncrna368
KCCAT209kirc3196lncrna369
KCCAT11.2kirc2905lncrna370
KCCAT11.2kirc2905lncrna371
KCCAT11.1kirc1577lncrna372
KCCAT11.1kirc1577lncrna373
AMAT59aml4948lncrna374
KCCAT71kirc1442lncrna375
CAT1825thyroid1672lncrna376
CAT1825kich1672lncrna377
CAT1825lgg1672lncrna378
CAT1825embryonic_stem_cells1672lncrna379
CAT1837.1cervical605lncrna380
CAT1837.2aml1853lncrna381
CAT1841.1kirc2864lncrna382
CAT1841.1kirp2864lncrna383
CAT1841.1breast2864lncrna384
CAT1841.1ovarian2864lncrna385
CAT1841.1skeletal_muscle2864lncrna386
CAT1841.1uterine2864lncrna387
CAT1843kirc1519lncrna388
CAT1843liver1519lncrna389
CAT1844lgg2653lncrna390
CAT1844thyroid2653lncrna391
CRNDE.1kirp10743lncrna392
CRNDE.2colorectal10057lncrna393
CAT1871.1gbm5165lncrna394
CAT1871.1lgg5165lncrna395
CAT1871.2lgg7095lncrna396
VPS9D1-AS1.1prostate2428lncrna397
VPS9D1-AS1.1lusc2428lncrna398
VPS9D1-AS1.2prostate3100lncrna399
VPS9D1-AS1.2luad3100lncrna400
VPS9D1-AS1.2lusc3100lncrna401
VPS9D1-AS1.3prostate2710lncrna402
VPS9D1-AS1.3lusc2710lncrna403
CAT1889melanoma925lncrna404
CAT1889head_neck925lncrna405
CAT1889kirp925lncrna406
CAT1889breast925lncrna407
CAT1889luad925lncrna408
PITPNA-AS1gbm356lncrna409
CAT1892.1uterine2087lncrna410
CAT1893ovarian990lncrna411
CAT1893kich990lncrna412
CAT1909.1gbm2569tucp413
CAT1909.1colorectal2569tucp414
CAT1909.1uterine2569tucp415
CAT1909.1liver2569tucp416
CAT1909.2pancreatic3180tucp417
CAT1909.2cervical3180tucp418
CAT1909.2kirc3180tucp419
CAT1915uterine1314lncrna420
CAT1915cml1314lncrna421
CAT1928colorectal4160tucp422
CAT1928thyroid4160tucp423
CAT1928head_neck4160tucp424
CAT1928kirp4160tucp425
CAT1940gbm578lncrna426
CAT1940kirp578lncrna427
CAT1940kich578lncrna428
CAT1944uterine749lncrna429
CAT1944luad749lncrna430
CAT1949colorectal2817lncrna431
CAT1949uterine2817lncrna432
CAT1949mpn2817lncrna433
CAT1949kirc2817lncrna434
MPAT8mpn952lncrna435
CAT1957.1lusc1013lncrna436
CAT1957.1head_neck1013lncrna437
CAT1957.1breast1013lncrna438
CAT1957.1luad1013lncrna439
CAT1957.1kirc1013lncrna440
CAT1957.1liver1013lncrna441
CAT1964.1kirc9319lncrna442
CAT1964.1cervical9319lncrna443
CAT1964.1medulloblastoma9319lncrna444
CAT1964.1breast9319lncrna445
CAT1964.1luad9319lncrna446
CAT1964.1lusc9319lncrna447
CAT1964.2kirc3949lncrna448
CAT1964.2embryonic_stem_cells3949lncrna449
CAT1964.2ovarian3949lncrna450
CAT1964.2medulloblastoma3949lncrna451
CAT1964.2breast3949lncrna452
CAT1964.2luad3949lncrna453
CAT1964.2lusc3949lncrna454
CAT1967.1lusc2747lncrna455
CAT1967.1mpn2747lncrna456
CAT1967.1thyroid2747lncrna457
CAT1967.1prostate2747lncrna458
CAT1968.1kirc17122tucp459
CAT1968.2gbm3663tucp460
CAT1968.2lgg3663tucp461
CAT1968.2colorectal3663tucp462
CAT1968.2prostate3663tucp463
LINC00511.1luad13352tucp464
LINC00511.2lusc7531lncrna465
LINC00511.3thyroid5973lncrna466
LINC00511.3luad5973lncrna467
LINC00511.3lusc5973lncrna468
CAT1977gbm1001lncrna469
CAT1977melanoma1001lncrna470
CAT1977lgg1001lncrna471
CAT1977kirc1001lncrna472
MEAT77melanoma1767lncrna473
MEAT75melanoma4785tucp474
CAT1984uterine3024lncrna475
CAT1984thyroid3024lncrna476
CAT1984breast3024lncrna477
CAT1984liver3024lncrna478
UTAT3uterine2710tucp479
MAFG-AS1.1lusc4227tucp480
MAFG-AS1.1kirp4227tucp481
MAFG-AS1.1breast4227tucp482
MAFG-AS1.1luad4227tucp483
MAFG-AS1.1prostate4227tucp484
MAFG-AS1.1liver4227tucp485
MAFG-AS1.1mpn4227tucp486
MAFG-AS1.2breast4434tucp487
MAFG-AS1.2luad4434tucp488
MAFG-AS1.2lusc4434tucp489
LINC00668.1head_neck3547lncrna490
LINC00668.1lusc3547lncrna491
LINC00668.2lusc3153lncrna492
UTAT6uterine532lncrna493
LINC00669luad439lncrna494
LINC00669lusc439lncrna495
ESAT59embryonic_stem_cells954lncrna496
CAT2045.1lusc4504lncrna497
CAT2045.2colorectal825lncrna498
CAT2045.2kich825lncrna499
CAT2045.2cml825lncrna500
CAT2045.2aml825lncrna501
CAT2045.2kirp825lncrna502
CAT2045.2luad825lncrna503
MIR7-3HG.7pancreatic3602lncrna504
MIR7-3HG.10pancreatic3029lncrna505
MIR7-3HG.22pancreatic3219lncrna506
MIR7-3HG.6pancreatic2963lncrna507
MIR7-3HG.19pancreatic2698lncrna508
MIR7-3HG.16pancreatic3067lncrna509
MIR7-3HG.20pancreatic2737lncrna510
MIR7-3HG.23pancreatic3021lncrna511
MIR7-3HG.3pancreatic2895lncrna512
MIR7-3HG.1pancreatic3596lncrna513
MIR7-3HG.2pancreatic3598lncrna514
MIR7-3HG.18pancreatic3581lncrna515
CAT2059kirc8226lncrna516
CAT2059kirc8226lncrna517
CAT2059breast8226lncrna518
CAT2069.1colorectal786tucp519
UTAT37uterine1498lncrna520
CAT2082.1embryonic_stem_cells8633tucp521
CAT2082.1breast8633tucp522
CAT2082.1cervical8633tucp523
LINC00906.1kich27059lncrna524
CAT2092.1kich3077lncrna525
CAT2092.1thyroid3077lncrna526
CAT2092.2kich2061lncrna527
CAT2092.2thyroid2061lncrna528
LINC00906.2kich36082tucp529
CAT2095kirc11872tucp530
CAT2095kich11872tucp531
MBAT15medulloblastoma5173lncrna532
LINC00665.1prostate1615lncrna533
LINC00665.1luad1615lncrna534
LINC00665.1colorectal1615lncrna535
LINC00665.2prostate6207lncrna536
LINC00665.2colorectal6207lncrna537
LINC00665.3embryonic_stem_cells1519lncrna538
LINC00665.4prostate4249lncrna539
LINC00665.5prostate10952lncrna540
LINC00665.5colorectal10952lncrna541
HRAT8heart333lncrna542
CAT2118.1thyroid1437lncrna543
CAT2118.1breast1437lncrna544
CAT2118.1breast1437lncrna545
CAT2118.1ovarian1437lncrna546
CAT2118.1medulloblastoma1437lncrna547
CAT2118.2thyroid2401lncrna548
CAT2118.2breast2401lncrna549
CAT2120cml2146lncrna550
CAT2120kirc2146lncrna551
CAT2120embryonic_stem_cells2146lncrna552
CAT2120luad2146lncrna553
CAT2120lusc2146lncrna554
HNCAT3.1head_neck6262lncrna555
CAT201.1embryonic_stem_cells3565lncrna556
CAT201.2kich1898lncrna557
MEAT48.1melanoma2063lncrna558
CAT219.1colorectal4360lncrna559
CAT219.1uterine4360lncrna560
CAT219.2head_neck4284lncrna561
CAT219.2lusc4284lncrna562
CAT219.3colorectal7435lncrna563
CAT219.3uterine7435lncrna564
CAT226.1melanoma1445lncrna565
CAT226.2melanoma2173lncrna566
CAT226.3melanoma2427lncrna567
CAT226.4kich12408lncrna568
CAT226.5kirc7403lncrna569
CAT226.5kirc7403lncrna570
CAT226.6kirc27140lncrna571
CAT226.6kirc27140lncrna572
CAT227.1kirc2216lncrna573
CAT227.1kirc2216lncrna574
CAT227.2kirc8010lncrna575
CAT227.2kirc8010lncrna576
CAT227.3kirc21694lncrna577
CAT227.3kirc21694lncrna578
CAT227.3kich21694lncrna579
OVAT114.6ovarian1428lncrna580
CAT249.1skeletal_muscle1566lncrna581
CAT249.1kich1566lncrna582
CAT249.1breast1566lncrna583
CAT252.1liver660lncrna584
CAT255.1head_neck2682tucp585
CAT255.1luad2682tucp586
CAT255.1lusc2682tucp587
CAT255.1gbm2682tucp588
CAT255.2lusc10836lncrna589
LINC00152.1head_neck17432lncrna590
LINC00152.1stomach17432lncrna591
LINC00152.2luad3471lncrna592
GBAT19gbm736lncrna593
MIR4435-1HG.1stomach7049lncrna594
MIR4435-1HG.2kirc7670lncrna595
CAT313.1kirc4601lncrna596
CAT313.2colorectal414lncrna597
CAT313.3kirc1782lncrna598
CAT313.4kirc3674lncrna599
CAT313.5kirc3554lncrna600
CAT313.5lusc3554lncrna601
CERS6-AS1breast3097lncrna602
CERS6-AS1luad3097lncrna603
CERS6-AS1lusc3097lncrna604
CERS6-AS1breast3097lncrna605
PRCAT44prostate1046lncrna606
PRCAT44prostate1046lncrna607
HOXD-AS1liver3808lncrna608
HOXD-AS1kirc3808lncrna609
HOXD-AS1kirp3808lncrna610
HOXD-AS1lusc3808lncrna611
TTN-AS1.2heart1538lncrna612
TTN-AS1.3skeletal_muscle1559lncrna613
PRCAT122prostate4816lncrna614
PRCAT122prostate4816lncrna615
ESAT86embryonic_stem_cells8125lncrna616
CAT350kirc2633lncrna617
CAT350medulloblastoma2633lncrna618
CAT350luad2633lncrna619
CAT355.1colorectal5090lncrna620
CAT355.1uterine5090lncrna621
CAT355.1head_neck5090lncrna622
CAT355.2heart4143lncrna623
CAT355.2pancreatic4143lncrna624
CAT355.2head_neck4143lncrna625
CAT359.1kich774lncrna626
CAT359.1kich774lncrna627
CAT359.1kirp774lncrna628
CAT366.1thyroid1533lncrna629
PNAT34pancreatic1634lncrna630
ESAT94.1embryonic_stem_cells7212lncrna631
OVAT30ovarian2756lncrna632
CAT2158embryonic_stem_cells5319lncrna633
CAT2158head_neck5319lncrna634
CAT2158stomach5319lncrna635
CAT2160.1head_neck478lncrna636
CAT2160.2kich511lncrna637
CAT2160.2pancreatic511lncrna638
CAT2160.2medulloblastoma511lncrna639
CAT2160.2lusc511lncrna640
PNAT11.3pancreatic10140lncrna641
PNAT11.2pancreatic7906lncrna642
PNAT11.1pancreatic8471lncrna643
PNAT11.4pancreatic8912lncrna644
OSER1-AS1.1gbm2198lncrna645
OSER1-AS1.1lgg2198lncrna646
OSER1-AS1.2uterine4658lncrna647
OSER1-AS1.3mpn4342lncrna648
CAT2176.1head_neck1893lncrna649
ZFAS1.1thyroid2921lncrna650
ZFAS1.1kirc2921lncrna651
ZFAS1.2kirc3006lncrna652
ZFAS1.2kirp3006lncrna653
ZFAS1.3thyroid4202lncrna654
ZFAS1.3kirc4202lncrna655
ZFAS1.3heart4202lncrna656
ZFAS1.3uterine4202lncrna657
ZFAS1.4embryonic_stem_cells5507lncrna658
ZFAS1.4kirc5507lncrna659
CAT2180.1luad1833lncrna660
CAT2180.2luad1960tucp661
CAT2180.2lusc1960tucp662
MEAT44melanoma1645lncrna663
CAT2186.1melanoma1000lncrna664
OVAT65.1ovarian1284lncrna665
OVAT65.2ovarian1052lncrna666
OVAT65.4ovarian867lncrna667
IL10RB-AS1.2kirc3951lncrna668
PRCAT38prostate2598lncrna669
PRCAT38prostate2598lncrna670
PRCAT23prostate98lncrna671
PRCAT23prostate98lncrna672
CAT2215.1melanoma10845lncrna673
CAT2215.2prostate9282lncrna674
UTAT36uterine732lncrna675
DGCR5.1kirc12734tucp676
DGCR10.1kirc19778tucp677
DGCR10.1kirc19778tucp678
DGCR5.2kirc6204tucp679
DGCR5.2kirp6204tucp680
DGCR5.2kirc6204tucp681
DGCR5.3kirc1170lncrna682
DGCR5.3kirc1170lncrna683
DGCR10.2kirc18135tucp684
DGCR5.4kirc2794tucp685
DGCR5.4kirc2794tucp686
DGCR5.5kirc13853tucp687
DGCR5.5kirc13853tucp688
DGCR5.6kirc1384lncrna689
DGCR5.6kirc1384lncrna690
PRCAT104.4prostate6446tucp691
PRCAT104.4prostate6446tucp692
PRCAT104.3prostate11062tucp693
PRCAT104.3prostate11062tucp694
PRCAT104.6prostate2283lncrna695
PRCAT104.6prostate2283lncrna696
TUG1.1thyroid8722lncrna697
TUG1.2thyroid8736lncrna698
TUG1.3head_neck8597lncrna699
KHCAT21.2kich4229lncrna700
KHCAT21.1kich4310lncrna701
ESAT27embryonic_stem_cells908lncrna702
CAT2248kirc1515lncrna703
CAT2248kirp1515lncrna704
CAT2248prostate1515lncrna705
CAT2248breast1515lncrna706
CAT2251.1luad6597lncrna707
HRAT18heart546lncrna708
FGD5-AS1.2gbm3826tucp709
FGD5-AS1.2lgg3826tucp710
FGD5-AS1.2kirc3826tucp711
FGD5-AS1.2prostate3826tucp712
FGD5-AS1.2luad3826tucp713
FGD5-AS1.2lusc3826tucp714
FGD5-AS1.3kirc3840tucp715
FGD5-AS1.4luad3882tucp716
LVCAT12liver4522lncrna717
GBAT1gbm1849lncrna718
ESRG.1embryonic_stem_cells4629lncrna719
ESRG.1lusc4629lncrna720
ESRG.2embryonic_stem_cells7300lncrna721
ESRG.3ovarian5685lncrna722
ESRG.4ovarian10753lncrna723
ESRG.5ovarian6373lncrna724
H1FX-AS1.1ovarian3535lncrna725
H1FX-AS1.1uterine3535lncrna726
ESAT16.4embryonic_stem_cells3076lncrna727
CAT474.1embryonic_stem_cells5036tucp728
ESAT19.2embryonic_stem_cells1639lncrna729
ESAT19.3embryonic_stem_cells870lncrna730
ESAT19.1embryonic_stem_cells996lncrna731
MFI2-AS1.1lusc1268lncrna732
MFI2-AS1.2lusc1504lncrna733
KPCAT2.1kirp6514tucp734
LINC00504aml673lncrna735
LINC00504head_neck673lncrna736
LINC00504luad673lncrna737
LINC00504lusc673lncrna738
KCCAT215kirc6566lncrna739
UGDH-AS1.3aml5875tucp740
UGDH-AS1.3lusc5875tucp741
CAT558.1luad837lncrna742
CAT558.1lusc837lncrna743
CAT558.1prostate837lncrna744
CAT558.2lusc757lncrna745
CAT565thyroid1574lncrna746
CAT565luad1574lncrna747
CAT565cml1574lncrna748
CAT565gbm1574lncrna749
CAT565melanoma1574lncrna750
CAT565skeletal_muscle1574lncrna751
CAT565medulloblastoma1574lncrna752
CAT565liver1574lncrna753
LINC01094gbm10936lncrna754
LINC01094lgg10936lncrna755
LINC01094kirc10936lncrna756
LINC01094kirp10936lncrna757
LINC01094breast10936lncrna758
CAT566.1gbm11030lncrna759
CAT566.1kirc11030lncrna760
CAT566.1kirp11030lncrna761
ESAT72.2embryonic_stem_cells7755lncrna762
ESAT72.1embryonic_stem_cells8076lncrna763
CAT573.1kich3527lncrna764
CAT573.1ovarian3527lncrna765
CAT573.2lusc3652lncrna766
ESAT31.3embryonic_stem_cells3369tucp767
ESAT31.4embryonic_stem_cells3469lncrna768
ESAT83embryonic_stem_cells319lncrna769
CAT576head_neck450lncrna770
CAT576lusc450lncrna771
CAT577luad522tucp772
CAT577lusc522tucp773
ESAT2embryonic_stem_cells1293tucp774
PRCAT42.2prostate2702lncrna775
UTAT51.1uterine2030lncrna776
CAT605kirc1119lncrna777
CAT605thyroid1119lncrna778
LSCAT1.4lusc1983lncrna779
LSCAT1.3lusc4287lncrna780
ESAT32.3embryonic_stem_cells9967lncrna781
ESAT22.2embryonic_stem_cells1094lncrna782
ESAT22.1embryonic_stem_cells1442lncrna783
ESAT22.3embryonic_stem_cells4028lncrna784
ESAT22.5embryonic_stem_cells11693lncrna785
ESAT22.4embryonic_stem_cells749lncrna786
CAT659kirc2559lncrna787
CAT659breast2559lncrna788
CAT664.1thyroid2750lncrna789
CAT664.1skeletal_muscle2750lncrna790
CAT664.1medulloblastoma2750lncrna791
CAT681thyroid12569lncrna792
CAT681prostate12569lncrna793
CAT681medulloblastoma12569lncrna794
CAT682.1breast4267lncrna795
EPB41L4A-AS1.1kirc827lncrna796
EPB41L4A-AS1.1head_neck827lncrna797
CAT713thyroid1282lncrna798
CAT713kirc1282lncrna799
CAT713mpn1282lncrna800
CAT713embryonic_stem_cells1282lncrna801
PNAT54pancreatic2598tucp802
CAT721prostate2544tucp803
UTAT1uterine949lncrna804
ESAT17embryonic_stem_cells1601lncrna805
MEAT2melanoma8677lncrna806
CAT742.1kich5635lncrna807
CAT742.1kich5635lncrna808
CAT742.1kirp5635lncrna809
CAT742.2kich4481lncrna810
CAT742.2kirp4481lncrna811
CAT742.3kich6941lncrna812
CAT742.3kich6941lncrna813
CAT743aml6575lncrna814
CAT743kirc6575lncrna815
CAT743medulloblastoma6575lncrna816
CAT749.1kirc10675lncrna817
LINC00518.1melanoma2922lncrna818
LINC00518.7melanoma3083lncrna819
LINC00518.2melanoma3019lncrna820
LINC00518.3melanoma3188lncrna821
LINC00518.6melanoma2806lncrna822
LINC00518.5melanoma3289lncrna823
CAT773.1embryonic_stem_cells399lncrna824
ZSCAN16-AS1heart2914lncrna825
ZSCAN16-AS1kich2914lncrna826
CAT789mpn1491lncrna827
CAT789cml1491lncrna828
CAT789kirc1491lncrna829
CAT789embryonic_stem_cells1491lncrna830
CAT789skeletal_muscle1491lncrna831
PRCAT30.2prostate5371lncrna832
PRCAT30.1prostate5396lncrna833
PRCAT15.1prostate1663lncrna834
PRCAT15.2prostate2719lncrna835
CAT793.1lgg9738lncrna836
CAT793.1cervical9738lncrna837
CAT793.1kirp9738lncrna838
CAT793.1breast9738lncrna839
CAT793.1luad9738lncrna840
AMAT92aml7843lncrna841
CAT800.1prostate561lncrna842
CAT800.2prostate650lncrna843
CAT828.1head_neck5239lncrna844
CAT828.1luad5239lncrna845
CAT828.1lusc5239lncrna846
CAT837cml3351lncrna847
CAT837breast3351lncrna848
CAT840.1kich9492lncrna849
CAT840.2kich9162lncrna850
CAT840.3embryonic_stem_cells5951lncrna851
CAT840.4kich1448lncrna852
CAT840.5kich8137lncrna853
CMAT40cml6761lncrna854
KCCAT167kirc8975lncrna855
CAT862.1embryonic_stem_cells21097lncrna856
CAT862.2embryonic_stem_cells602lncrna857
CAT862.2breast602lncrna858
CAT862.3embryonic_stem_cells6909lncrna859
CAT870.1lusc2047tucp860
CAT870.2gbm1381lncrna861
CAT870.2lusc1381lncrna862
CAT878.1prostate2322lncrna863
CAT878.2prostate2286lncrna864
CAT878.3ovarian1126lncrna865
CAT878.3colorectal1126lncrna866
PNAT3pancreatic3222lncrna867
CAT905head_neck912lncrna868
CAT905lusc912lncrna869
CAT906head_neck1465lncrna870
CAT906lusc1465lncrna871
LINC00265.1kich5240tucp872
CAT932.1kirp2798lncrna873
CAT932.1medulloblastoma2798lncrna874
CAT932.2head_neck2795lncrna875
PNAT63.1pancreatic1608lncrna876
PNAT63.2pancreatic1717lncrna877
HNCAT59head_neck3151lncrna878
ESAT63.2embryonic_stem_cells1968lncrna879
CAT962embryonic_stem_cells3877lncrna880
CAT962kich3877lncrna881
CAT962luad3877lncrna882
CAT962lusc3877lncrna883
HRAT17.4heart1064lncrna884
OVAT20.3ovarian695lncrna885
OVAT20.4ovarian5210lncrna886
OVAT20.1ovarian4908lncrna887
OVAT20.5ovarian5017lncrna888
CAT972.1kirc18548tucp889
CAT972.1kirp18548tucp890
CAT972.1kirc18548tucp891
CAT972.1kich18548tucp892
CAT979kirc1053lncrna893
CAT979kich1053lncrna894
CAT979lusc1053lncrna895
CAT989gbm1262tucp896
CAT989prostate1262tucp897
ESAT10.4embryonic_stem_cells2160lncrna898
ESAT10.1embryonic_stem_cells1879lncrna899
ESAT10.2embryonic_stem_cells3150lncrna900
ESAT10.3embryonic_stem_cells3206lncrna901
CAT1012.1thyroid4788lncrna902
CAT1012.1lusc4788lncrna903
CAT1015cml2519lncrna904
CAT1015kirc2519lncrna905
CAT1015kirp2519lncrna906
MEAT62.2melanoma4917lncrna907
CAT1022thyroid2380lncrna908
CAT1022kirc2380lncrna909
CAT1022luad2380lncrna910
CAT1043.1uterine2970lncrna911
CAT1043.2uterine3416lncrna912
CAT1043.3ovarian1582lncrna913
CAT1043.4ovarian1246lncrna914
ESAT18.1embryonic_stem_cells7795lncrna915
ESAT18.2embryonic_stem_cells14742lncrna916
ESAT18.3embryonic_stem_cells8468lncrna917
CAT1055luad460lncrna918
CAT1055lusc460lncrna919
CASC9.1head_neck715lncrna920
CASC9.1lusc715lncrna921
CASC9.2head_neck6268lncrna922
CASC9.2lusc6268lncrna923
CASC9.3head_neck5793lncrna924
CASC9.3luad5793lncrna925
CASC9.3lusc5793lncrna926
CAT1060kich5618lncrna927
CAT1060kich5618lncrna928
CAT1060kirp5618lncrna929
CAT1060prostate5618lncrna930
CAT1070gbm1299lncrna931
CAT1070kirc1299lncrna932
CAT1079.1gbm1843lncrna933
CAT1079.2gbm1009lncrna934
AMAT6aml4515tucp935
ESAT52.4embryonic_stem_cells5974lncrna936
ESAT52.1embryonic_stem_cells13333lncrna937
ESAT52.5embryonic_stem_cells16180lncrna938
ESAT52.3embryonic_stem_cells19387lncrna939
ESAT52.2embryonic_stem_cells8455lncrna940
CAT1089.1gbm12863lncrna941
CAT1089.1melanoma12863lncrna942
CAT1089.1lgg12863lncrna943
CAT1089.1skeletal_muscle12863lncrna944
CAT1089.2melanoma4663lncrna945
CAT1089.3colorectal676lncrna946
CAT1089.3breast676lncrna947
CAT1089.3cml676lncrna948
CAT1089.3melanoma676lncrna949
CAT1089.3aml676lncrna950
CAT1089.3skeletal_muscle676lncrna951
CAT1089.3gbm676lncrna952
CAT1089.3lgg676lncrna953
CAT1089.3medulloblastoma676lncrna954
CAT1089.3kirc676lncrna955
THCAT4.1thyroid1631lncrna956
RNF139-AS1.1thyroid15042lncrna957
RNF139-AS1.2head_neck1914lncrna958
RNF139-AS1.2breast1914lncrna959
ESAT82.1embryonic_stem_cells3858lncrna960
CAT1109head_neck1243lncrna961
CAT1109skeletal_muscle1243lncrna962
CAT1109colorectal1243lncrna963
CAT1115.1lusc5792lncrna964
CAT1115.2ovarian1126lncrna965
HNCAT56head_neck621lncrna966
FAM83H-AS1.1prostate5530tucp967
FAM83H-AS1.1breast5530tucp968
FAM83H-AS1.1luad5530tucp969
FAM83H-AS1.1lusc5530tucp970
FAM83H-AS1.1heart5530tucp971
FAM83H-AS1.1melanoma5530tucp972
FAM83H-AS1.1mpn5530tucp973
FAM83H-AS1.1gbm5530tucp974
FAM83H-AS1.1lgg5530tucp975
FAM83H-AS1.1medulloblastoma5530tucp976
FAM83H-AS1.2lusc6435tucp977
FAM83H-AS1.2gbm6435tucp978
FAM83H-AS1.2melanoma6435tucp979
FAM83H-AS1.3gbm6637tucp980
FAM83H-AS1.3melanoma6637tucp981
FAM83H-AS1.3medulloblastoma6637tucp982
FAM83H-AS1.4prostate3087lncrna983
FAM83H-AS1.4breast3087lncrna984
FAM83H-AS1.4luad3087lncrna985
FAM83H-AS1.4lusc3087lncrna986
FAM83H-AS1.4gbm3087lncrna987
FAM83H-AS1.4melanoma3087lncrna988
FAM83H-AS1.4lgg3087lncrna989
FAM83H-AS1.4medulloblastoma3087lncrna990
CAT1129aml7255lncrna991
CAT1129embryonic_stem_cells7255lncrna992
CAT1129thyroid7255lncrna993
CAT1129kirp7255lncrna994
CAT1129breast7255lncrna995
CAT1129luad7255lncrna996
CAT1129liver7255lncrna997
CAT1141.1heart4172tucp998
CAT1141.2medulloblastoma4670tucp999
CAT1147.1prostate1890lncrna1000
CAT1147.1luad1890lncrna1001
CAT1147.1lusc1890lncrna1002
CAT1147.2heart1112lncrna1003
CAT1147.2prostate1112lncrna1004
CAT1147.2luad1112lncrna1005
CAT1147.2lusc1112lncrna1006
CAT1147.3heart15070lncrna1007
CAT1147.3prostate15070lncrna1008
CAT1147.3luad15070lncrna1009
CAT1147.3lusc15070lncrna1010
CAT1160head_neck2191lncrna1011
CAT1160lusc2191lncrna1012
CAT1162kirc1292lncrna1013
CAT1162melanoma1292lncrna1014
CAT1162thyroid1292lncrna1015
THCAT39.7thyroid8539lncrna1016
THCAT39.7thyroid8539lncrna1017
THCAT39.1thyroid6408lncrna1018
THCAT39.9thyroid1265lncrna1019
THCAT39.9thyroid1265lncrna1020
THCAT39.17thyroid13258lncrna1021
THCAT39.14thyroid3327lncrna1022
MIR181A2HG.2thyroid849lncrna1023
MIR181A2HG.1thyroid922lncrna1024
MIR181A2HG.1thyroid922lncrna1025
CAT1195.1kich10727lncrna1026
CAT1195.1kirp10727lncrna1027
CAT1195.2kirp6079lncrna1028
CAT1201cervical868lncrna1029
CAT1201lgg868lncrna1030
CAT1204.1ovarian1049lncrna1031
CAT1204.1kirc1049lncrna1032
CAT1204.1kirp1049lncrna1033
CAT1204.1prostate1049lncrna1034
CAT1204.1kich1049lncrna1035
CAT1204.2cervical4425lncrna1036
CAT1204.3lusc1795lncrna1037
CAT1204.3prostate1795lncrna1038
CAT1204.4lusc1313lncrna1039
CAT1204.4prostate1313lncrna1040
CAT1212colorectal877lncrna1041
CAT1212lusc877lncrna1042
CAT1212liver877lncrna1043
CAT1212luad877lncrna1044
CAT1212prostate877lncrna1045
CAT1212kich877lncrna1046
CAT1212breast877lncrna1047
CAT2275.1prostate5925lncrna1048
CAT2275.2prostate2443lncrna1049
CAT2275.2kich2443lncrna1050
CMAT1cml3108lncrna1051
MBAT1medulloblastoma7147lncrna1052
ESAT92.2embryonic_stem_cells386lncrna1053
ESAT92.1embryonic_stem_cells26348tucp1054
ESAT92.3embryonic_stem_cells32499tucp1055
MEAT16melanoma662lncrna1056
CAT2277.1melanoma1373lncrna1057
CAT2277.2uterine1238lncrna1058
CAT2277.3breast504lncrna1059
CAT2277.3melanoma504lncrna1060
MPAT1mpn569lncrna1061
ATP6V0E2-AS1.1thyroid3969lncrna1062
ATP6V0E2-AS1.2kich2679lncrna1063
ATP6V0E2-AS1.2thyroid2679lncrna1064
HNCAT1head_neck1495lncrna1065
CAT566.2kirc11102lncrna1066
CAT264.1prostate5674lncrna1067
CAT264.1medulloblastoma5674lncrna1068
CAT264.2thyroid5252lncrna1069
CAT264.2kirc5252lncrna1070
CAT264.2prostate5252lncrna1071
CAT264.2breast5252lncrna1072
CAT1495.1thyroid8833lncrna1073
CAT1495.1kirp8833lncrna1074
CAT1495.2thyroid7437lncrna1075
CAT1495.3thyroid7646lncrna1076
CAT1496.1thyroid5209lncrna1077
CAT1496.1kirp5209lncrna1078
CAT1496.1head_neck5209lncrna1079
CAT1496.2thyroid4778lncrna1080
CAT1496.2kirp4778lncrna1081
CAT1496.3thyroid5016lncrna1082
CAT1496.3kirp5016lncrna1083
CAT1496.3thyroid5016lncrna1084
CAT1496.3head_neck5016lncrna1085
CAT1383.1kirc1507lncrna1086
CAT1382.1kirc1663lncrna1087
CAT1382.1breast1663lncrna1088
CAT1382.2kirc930lncrna1089
CAT1382.2breast930lncrna1090
GBAT2gbm666lncrna1091
KCCAT148.1kirc2950lncrna1092
KCCAT148.1kirc2950lncrna1093
CAT329kirc1343lncrna1094
CAT329luad1343lncrna1095
CAT329lusc1343lncrna1096
LINC00511.4lusc12087lncrna1097
SBF2-AS1.2kich955lncrna1098
SBF2-AS1.3kich1210lncrna1099
SBF2-AS1.4liver1216lncrna1100
SBF2-AS1.4luad1216lncrna1101
SBF2-AS1.4lusc1216lncrna1102
VCAN-AS1pancreatic522lncrna1103
VCAN-AS1kirp522lncrna1104
VCAN-AS1stomach522lncrna1105
VCAN-AS1breast522lncrna1106
GATA3-AS1.3breast1059lncrna1107
GATA3-AS1.3breast1059lncrna1108
GATA3-AS1.3kirc1059lncrna1109
GATA3-AS1.3prostate1059lncrna1110
GATA3-AS1.4breast4620lncrna1111
GATA3-AS1.4kirc4620lncrna1112
GATA3-AS1.5breast4613lncrna1113
GATA3-AS1.5breast4613lncrna1114
GATA3-AS1.5prostate4613lncrna1115
CAT800.3kich638lncrna1116
HRAT70heart674lncrna1117
CAT162.1skeletal_muscle8558lncrna1118
CAT162.1aml8558lncrna1119
CAT162.1colorectal8558lncrna1120
CAT162.1prostate8558lncrna1121
CAT162.1kich8558lncrna1122
CAT162.1luad8558lncrna1123
CAT162.1breast8558lncrna1124
CAT162.2skeletal_muscle8749lncrna1125
CAT162.2cml8749lncrna1126
CAT162.2prostate8749lncrna1127
CAT162.2kich8749lncrna1128
CAT162.2luad8749lncrna1129
CAT162.2breast8749lncrna1130
CAT1852.1thyroid4821lncrna1131
CAT1852.2thyroid9499tucp1132
CAT1852.2head_neck9499tucp1133
CAT1852.2liver9499tucp1134
CAT1852.2kirc9499tucp1135
CAT1852.2prostate9499tucp1136
CAT1852.2kich9499tucp1137
CAT1852.2breast9499tucp1138
LINC00545embryonic_stem_cells677lncrna1139
CAT664.2kich4397lncrna1140
CAT2064thyroid2859lncrna1141
CAT2064kirp2859lncrna1142
CAT2064prostate2859lncrna1143
CAT2064breast2859lncrna1144
CAT2064gbm2859lncrna1145
CAT2064lgg2859lncrna1146
CAT2064skeletal_muscle2859lncrna1147
CAT1591aml16567lncrna1148
CAT1591kirc16567lncrna1149
CAT793.2ovarian8428lncrna1150
THCAT36.1thyroid1195lncrna1151
THCAT36.1thyroid1195lncrna1152
MIR205HG.4lusc2515lncrna1153
MIR205HG.4gbm2515lncrna1154
MIR205HG.4prostate2515lncrna1155
MIR205HG.4breast2515lncrna1156
THCAT36.4thyroid1355lncrna1157
THCAT36.4thyroid1355lncrna1158
MIR205HG.5lusc2829lncrna1159
CAT969.1thyroid1371lncrna1160
MIR205HG.6lusc3963lncrna1161
CAT1664thyroid605lncrna1162
CAT1664lusc605lncrna1163
CAT1664kirp605lncrna1164
CAT1664luad605lncrna1165
CAT1664kirc605lncrna1166
CAT1664kich605lncrna1167
SMAT24skeletal_muscle531lncrna1168
CAT2176.2breast2212lncrna1169
CAT2176.3breast671lncrna1170
CAT2176.3breast671lncrna1171
CAT2157head_neck2017lncrna1172
CAT2157lusc2017lncrna1173
CAT1546kirc860lncrna1174
CAT1546kirp860lncrna1175
MYO16-AS1luad4439lncrna1176
WT1-AS.4ovarian10678lncrna1177
WT1-AS.5ovarian9901lncrna1178
WT1-AS.6ovarian10532lncrna1179
LINC00087kich5585tucp1180
LINC00087colorectal5585tucp1181
LINC00087thyroid5585tucp1182
LINC00087kirp5585tucp1183
LINC00087bladder5585tucp1184
LINC00087breast5585tucp1185
LINC00087luad5585tucp1186
LINC00087kirc5585tucp1187
LINC00087prostate5585tucp1188
LINC00152.3thyroid3019lncrna1189
LINC00152.3kirc3019lncrna1190
LINC00152.3liver3019lncrna1191
LINC00152.3luad3019lncrna1192
ESAT15.2embryonic_stem_cells1906lncrna1193
LSCAT1.2lusc6456tucp1194
CAT171.2stomach36752lncrna1195
CAT171.3thyroid6660lncrna1196
THCAT39.3thyroid695lncrna1197
THCAT39.3thyroid695lncrna1198
THCAT39.11thyroid1445lncrna1199
CAT2071mpn4868lncrna1200
CAT2071colorectal4868lncrna1201
CAT2071kirc4868lncrna1202
CAT2071luad4868lncrna1203
CAT2071lusc4868lncrna1204
PRCAT102.2prostate667lncrna1205
MEAT1.1melanoma1700lncrna1206
CAT1325kich6235lncrna1207
CAT1325medulloblastoma6235lncrna1208
CAT1947.1head_neck2165lncrna1209
CAT1947.1prostate2165lncrna1210
CAT1947.2head_neck3123tucp1211
CAT742.4kirp4914lncrna1212
CAT742.5kich4909lncrna1213
CAT742.5kich4909lncrna1214
HNCAT25.1head_neck1733lncrna1215
HNCAT25.3head_neck1168lncrna1216
HNCAT25.2head_neck784lncrna1217
PRCAT101prostate2254lncrna1218
GBAT18gbm2013lncrna1219
THCAT3thyroid2016lncrna1220
CAT1768.2prostate1209lncrna1221
TRPC7-AS1pancreatic5009lncrna1222
CAT828.2head_neck15379lncrna1223
CAT828.2luad15379lncrna1224
CAT828.2lusc15379lncrna1225
CAT1284.2kich5397lncrna1226
CAT1284.3kirc2413lncrna1227
CAT1284.3lusc2413lncrna1228
CAT1284.3pancreatic2413lncrna1229
CAT1284.3prostate2413lncrna1230
LBX2-AS1.1head_neck4528lncrna1231
LBX2-AS1.1breast4528lncrna1232
LBX2-AS1.1kirc4528lncrna1233
LBX2-AS1.2thyroid1086lncrna1234
LBX2-AS1.2kirc1086lncrna1235
LBX2-AS1.2kirp1086lncrna1236
LBX2-AS1.2luad1086lncrna1237
ESAT51embryonic_stem_cells7090lncrna1238
CAT2176.4breast2564lncrna1239
CAT2176.4breast2564lncrna1240
CAT1946uterine700lncrna1241
CAT1946ovarian700lncrna1242
ESAT4embryonic_stem_cells1072lncrna1243
HRAT1heart323lncrna1244
PNAT13.2pancreatic1430lncrna1245
CAT1489head_neck277lncrna1246
CAT1489lusc277lncrna1247
CAT682.2breast4292lncrna1248
CAT682.2luad4292lncrna1249
CAT682.2breast4292lncrna1250
CAT682.2kirc4292lncrna1251
CAT682.2kirp4292lncrna1252
CAT682.2kich4292lncrna1253
LSCAT1.5lusc4306lncrna1254
GBAT8gbm1276lncrna1255
CAT1919uterine655lncrna1256
CAT1919luad655lncrna1257
ESAT31.2embryonic_stem_cells5275lncrna1258
ESAT31.5embryonic_stem_cells5977lncrna1259
ESAT31.1embryonic_stem_cells6396lncrna1260
CAT1858colorectal567lncrna1261
CAT1858kirc567lncrna1262
CAT1393colorectal816lncrna1263
CAT1393kirc816lncrna1264
CAT1393skeletal_muscle816lncrna1265
CAT1393prostate816lncrna1266
CMAT2cml1201lncrna1267
CAT1957.2head_neck494lncrna1268
CAT1957.2breast494lncrna1269
CAT1957.2luad494lncrna1270
CAT1957.2lusc494lncrna1271
CMAT28cml1214lncrna1272
CAT1195.3kich8390lncrna1273
CAT2186.2head_neck4333lncrna1274
PRCAT104.7prostate7804tucp1275
PRCAT104.7prostate7804tucp1276
PRCAT104.1prostate7781tucp1277
PRCAT104.1prostate7781tucp1278
CAT2227prostate9019lncrna1279
CAT2227prostate9019lncrna1280
CAT2227thyroid9019lncrna1281
PRCAT104.2prostate4575lncrna1282
PRCAT104.2prostate4575lncrna1283
PRCAT104.5prostate10052tucp1284
PRCAT104.5prostate10052tucp1285
UTAT18uterine557lncrna1286
MIR205HG.7lusc2018lncrna1287
MIR205HG.7prostate2018lncrna1288
CAT1472.2kirp6055lncrna1289
CAT1472.2lusc6055lncrna1290
CAT1897uterine622lncrna1291
CAT1897kirc622lncrna1292
CAT366.2thyroid1512lncrna1293
CAT366.3thyroid2528lncrna1294
CAT366.3stomach2528lncrna1295
CAT366.3lusc2528lncrna1296
CAT366.3head_neck2528lncrna1297
CAT366.3breast2528lncrna1298
CAT366.3luad2528lncrna1299
THCAT39.4thyroid1747lncrna1300
THCAT39.4thyroid1747lncrna1301
THCAT39.6thyroid18125lncrna1302
THCAT39.6thyroid18125lncrna1303
THCAT39.13thyroid1507lncrna1304
THCAT39.13thyroid1507lncrna1305
THCAT39.8thyroid8126lncrna1306
THCAT39.8thyroid8126lncrna1307
THCAT39.16thyroid1140lncrna1308
THCAT39.12thyroid1375lncrna1309
UTAT40uterine4336tucp1310
UTAT2uterine535lncrna1311
CAT1023.1breast1144lncrna1312
CAT1023.1lusc1144lncrna1313
TTN-AS1.4heart1146lncrna1314
CAT612.1lusc5363lncrna1315
CAT612.2head_neck9926tucp1316
CAT612.2lusc9926tucp1317
CAT932.3stomach4148lncrna1318
CAT2267head_neck2709lncrna1319
CAT2267kirp2709lncrna1320
CAT2267breast2709lncrna1321
CAT2267luad2709lncrna1322
CAT2267kirc2709lncrna1323
HRAT40.1heart3737lncrna1324
HRAT40.2heart4209lncrna1325
ESAT63.3embryonic_stem_cells3502lncrna1326
ESAT63.1embryonic_stem_cells996lncrna1327
LINC00678.6embryonic_stem_cells5446lncrna1328
CAT1860cml4554lncrna1329
CAT1860kirc4554lncrna1330
CAT1860kich4554lncrna1331
CAT229kirc1780lncrna1332
CAT229kirc1780lncrna1333
CAT229kich1780lncrna1334
CAT2268prostate6181lncrna1335
CAT2268cervical6181lncrna1336
CAT2268ovarian6181lncrna1337
CAT1370breast642lncrna1338
CAT1370luad642lncrna1339
CAT1370lusc642lncrna1340
PCAT6luad1849lncrna1341
PCAT6lusc1849lncrna1342
CAT152.1thyroid1963lncrna1343
CAT152.1breast1963lncrna1344
CAT152.1luad1963lncrna1345
CAT152.1lusc1963lncrna1346
CAT152.2kirp2167lncrna1347
CAT152.2prostate2167lncrna1348
CAT152.2breast2167lncrna1349
CAT152.2luad2167lncrna1350
CAT152.2lusc2167lncrna1351
CAT76.1kirc1119lncrna1352
CAT2067uterine1391lncrna1353
CAT2067head_neck1391lncrna1354
CAT2067breast1391lncrna1355
CAT2067medulloblastoma1391lncrna1356
HNCAT13head_neck1471lncrna1357
MIR22HG.1mpn2041lncrna1358
MIR22HG.1heart2041lncrna1359
MIR22HG.1embryonic_stem_cells2041lncrna1360
MIR22HG.1thyroid2041lncrna1361
MIR22HG.1stomach2041lncrna1362
MIR22HG.1lusc2041lncrna1363
MIR22HG.1kirp2041lncrna1364
MIR22HG.1bladder2041lncrna1365
MIR22HG.1liver2041lncrna1366
MIR22HG.1luad2041lncrna1367
MIR22HG.1prostate2041lncrna1368
MIR22HG.1breast2041lncrna1369
MIR22HG.2prostate2158lncrna1370
KCCAT40kirc56307lncrna1371
ESAT13.2embryonic_stem_cells515lncrna1372
SNHG12.4kirc1934lncrna1373
CAT2082.2thyroid3487lncrna1374
CAT342.1prostate1241lncrna1375
CAT342.2lusc107308lncrna1376
THCAT39.2thyroid1199lncrna1377
THCAT39.2thyroid1199lncrna1378
THCAT39.15thyroid5673lncrna1379
LINC00518.4melanoma2704lncrna1380
ESAT66embryonic_stem_cells11196lncrna1381
LINC00371.1embryonic_stem_cells823tucp1382
LINC00371.2embryonic_stem_cells1126lncrna1383
TTN-AS1.5heart1744lncrna1384
CAT2039.1breast1624lncrna1385
BRCAT19breast4385tucp1386
CAT2039.2breast1464lncrna1387
KCCAT4kirc5015lncrna1388
CAT1967.2thyroid1099lncrna1389
CAT1967.2prostate1099lncrna1390
CAT655.1head_neck7282lncrna1391
CAT655.1kirc7282lncrna1392
CAT655.2head_neck21099lncrna1393
CAT655.2kirp21099lncrna1394
CAT655.2luad21099lncrna1395
MIR7-3HG.12pancreatic3802lncrna1396
MIR7-3HG.21pancreatic1964lncrna1397
MIR7-3HG.8pancreatic1967lncrna1398
MIR7-3HG.4pancreatic1627lncrna1399
MIR7-3HG.5pancreatic1572lncrna1400
MIR7-3HG.13pancreatic3273lncrna1401
MEAT1.3melanoma15524tucp1402
MEAT1.2melanoma1006lncrna1403
UTAT51.2uterine3938lncrna1404
CAT2218kirp630lncrna1405
CAT2218medulloblastoma630lncrna1406
MBAT8medulloblastoma2035lncrna1407
CAT313.6kirc3210lncrna1408
CAT313.7kirc2984lncrna1409
MEAT20.4melanoma875lncrna1410
KHCAT98kich2408lncrna1411
THCAT39.5thyroid6854lncrna1412
THCAT39.5thyroid6854lncrna1413
CAT1272mpn8184lncrna1414
CAT1272cml8184lncrna1415
CAT1272kirc8184lncrna1416
CAT1272breast8184lncrna1417
CAT773.2breast1293lncrna1418
CAT773.2breast1293lncrna1419
CAT868thyroid1970lncrna1420
CAT868lusc1970lncrna1421
CAT868gbm1970lncrna1422
CAT868lgg1970lncrna1423
CAT868prostate1970lncrna1424
CAT868liver1970lncrna1425
CAT1485kirc1501lncrna1426
CAT1485mpn1501lncrna1427
CAT1485cml1501lncrna1428
CAT1485embryonic_stem_cells1501lncrna1429
CAT1485medulloblastoma1501lncrna1430
CAT1485prostate1501lncrna1431
CAT1485breast1501lncrna1432
CAT1580ovarian515lncrna1433
CAT1580medulloblastoma515lncrna1434
ESAT37embryonic_stem_cells4865lncrna1435
LINC00668.3head_neck2625lncrna1436
LINC00668.3lusc2625lncrna1437
KCCAT200kirc2339lncrna1438
CAT1828kirc2309lncrna1439
CAT1828prostate2309lncrna1440
CAT1828breast2309lncrna1441
CAT1828luad2309lncrna1442
MEAT48.2melanoma24636lncrna1443
MEAT48.3melanoma24396lncrna1444
CAT969.2breast3312lncrna1445
CAT969.2luad3312lncrna1446
KCCAT131.1kirc2547lncrna1447
KCCAT131.1kirc2547lncrna1448
KCCAT131.3kirc2221lncrna1449
KCCAT131.2kirc3651lncrna1450
KCCAT131.2kirc3651lncrna1451
CAT1113.1colorectal1852lncrna1452
FAM83H-AS1.5prostate10522tucp1453
FAM83H-AS1.5breast10522tucp1454
FAM83H-AS1.5luad10522tucp1455
FAM83H-AS1.5lusc10522tucp1456
FAM83H-AS1.5heart10522tucp1457
FAM83H-AS1.5gbm10522tucp1458
FAM83H-AS1.5melanoma10522tucp1459
LINC01003gbm3779lncrna1460
CAT1023.2breast13105lncrna1461
CAT1456colorectal1916lncrna1462
CAT1456uterine1916lncrna1463
CAT1456breast1916lncrna1464
CAT1456lusc1916lncrna1465
THCAT36.9thyroid1259lncrna1466
CAT1576kirc1579lncrna1467
CAT1576medulloblastoma1579lncrna1468
MIR7-3HG.14pancreatic3421lncrna1469
MIR7-3HG.11pancreatic3252lncrna1470
MIR7-3HG.15pancreatic3304lncrna1471
MIR7-3HG.17pancreatic3560lncrna1472
MIR7-3HG.9pancreatic3491lncrna1473
ESAT39.1embryonic_stem_cells3310lncrna1474
ESAT39.2embryonic_stem_cells12934tucp1475
MIR4435-1HG.3breast903lncrna1476
MIR4435-1HG.3luad903lncrna1477
MIR4435-1HG.3liver903lncrna1478
MIR4435-1HG.3lgg903lncrna1479
CAT271luad1212lncrna1480
CAT271lusc1212lncrna1481
MEAT51.2melanoma2746lncrna1482
MEAT51.1melanoma2650lncrna1483
CAT249.2embryonic_stem_cells11296tucp1484
CAT249.2kich11296tucp1485
BRCAT24.2breast1938lncrna1486
CAT2164.1prostate5670lncrna1487
CAT2164.1breast5670lncrna1488
CAT2164.1luad5670lncrna1489
CAT2164.2heart3981lncrna1490
CAT2164.2cml3981lncrna1491
CAT2164.2aml3981lncrna1492
CAT2164.2lgg3981lncrna1493
CAT2164.2lusc3981lncrna1494
CAT2164.2prostate3981lncrna1495
CAT2164.2kich3981lncrna1496
CAT2164.2luad3981lncrna1497
CAT2164.2breast3981lncrna1498
KPCAT19kirp632lncrna1499
MEAT38melanoma568lncrna1500
MIR31HG.1thyroid2183lncrna1501
MIR31HG.1prostate2183lncrna1502
MIR31HG.2thyroid2442lncrna1503
MIR31HG.2prostate2442lncrna1504
THCAT22.2thyroid7479lncrna1505
THCAT22.2thyroid7479lncrna1506
THCAT22.5thyroid3138lncrna1507
THCAT22.5thyroid3138lncrna1508
CAT226.7melanoma1278lncrna1509
CAT226.8melanoma599lncrna1510
LINC00665.6ovarian5220lncrna1511
LINC00665.6prostate5220lncrna1512
LINC00665.6breast5220lncrna1513
LINC00665.6luad5220lncrna1514
LINC00665.6lusc5220lncrna1515
LINC00665.6colorectal5220lncrna1516
LINC00665.7breast5165lncrna1517
CAT944ovarian553lncrna1518
CAT944colorectal553lncrna1519
CAT944uterine553lncrna1520
CAT944breast553lncrna1521
CAT944melanoma553lncrna1522
LINC00665.8ovarian3759lncrna1523
LINC00665.8prostate3759lncrna1524
LINC00665.8breast3759lncrna1525
LINC00665.8luad3759lncrna1526
LINC00665.8lusc3759lncrna1527
LINC00665.8colorectal3759lncrna1528
DDX11-AS1.2lusc3230lncrna1529
KCCAT162kirc16275lncrna1530
CAT2251.2mpn2297lncrna1531
CAT2251.2aml2297lncrna1532
LINC00265.3kich4722tucp1533
LINC00265.2kich4851tucp1534
CAT1326aml1076lncrna1535
CAT1326kirc1076lncrna1536
CAT1326lusc1076lncrna1537
CAT529thyroid1995lncrna1538
CAT529lusc1995lncrna1539
THCAT57thyroid938lncrna1540
CAT1683.2thyroid5074lncrna1541
CAT1683.2lusc5074lncrna1542
CAT1683.2kirp5074lncrna1543
CAT1683.2bladder5074lncrna1544
CAT1683.2luad5074lncrna1545
CAT1683.2kirc5074lncrna1546
CAT1345.2luad822lncrna1547
CAT1345.2lusc822lncrna1548
KCCAT279.2kirc2100lncrna1549
KCCAT279.1kirc2224lncrna1550
KCCAT279.1kirc2224lncrna1551
BRCAT24.1breast1593lncrna1552
BRCAT24.1breast1593lncrna1553
BRCAT24.3breast2882lncrna1554
MPAT2mpn6029lncrna1555
CAT1435liver404lncrna1556
CAT1435mpn404lncrna1557
CAT1435cml404lncrna1558
CAT1435aml404lncrna1559
CAT1435kirp404lncrna1560
CAT1435kich404lncrna1561
CAT226.9kirc1604lncrna1562
CAT99.3colorectal4273lncrna1563
CAT99.3uterine4273lncrna1564
CAT99.3kich4273lncrna1565
CAT99.3luad4273lncrna1566
CAT99.3lusc4273lncrna1567
ESAT33.3embryonic_stem_cells78505lncrna1568
MIR31HG.3thyroid4972lncrna1569
CAT1807kich1795lncrna1570
CAT1807breast1795lncrna1571
HOXA11-AS.1head_neck9419lncrna1572
HOXA11-AS.1lusc9419lncrna1573
HOXA11-AS.2kirc7671lncrna1574
HOXA11-AS.3lusc7195tucp1575
CAT1768.3prostate5769lncrna1576
CAT1768.3luad5769lncrna1577
CAT1768.4prostate4744lncrna1578
PRCAT121.2prostate2141lncrna1579
PRCAT121.1prostate6441lncrna1580
CAT359.2kich1706lncrna1581
CAT359.2kich1706lncrna1582
CAT359.3kich3119lncrna1583
CAT359.3kich3119lncrna1584
CAT359.3kirp3119lncrna1585
LIMD1-AS1.1kich1252lncrna1586
LIMD1-AS1.1luad1252lncrna1587
LIMD1-AS1.1lusc1252lncrna1588
CAT226.10kich12650lncrna1589
CAT227.4kirc11959lncrna1590
CAT227.4kirc11959lncrna1591
CAT227.5kirc2100lncrna1592
CAT227.5kirc2100lncrna1593
CAT226.11kich28708lncrna1594
CAT227.6kirc14049lncrna1595
CAT474.2embryonic_stem_cells18915tucp1596
CAT474.3ovarian11888lncrna1597
ESAT9embryonic_stem_cells1048lncrna1598
CMAT7cml724lncrna1599
CAT1532gbm3711lncrna1600
CAT1532lgg3711lncrna1601
CAT1532kirp3711lncrna1602
CAT1532liver3711lncrna1603
CAT1532colorectal3711lncrna1604
CAT1532thyroid3711lncrna1605
AMAT47aml1525tucp1606
CAT1439medulloblastoma770lncrna1607
CAT1439kirc770lncrna1608
CAT1439mpn770lncrna1609
CAT1439cml770lncrna1610
CAT1439thyroid770lncrna1611
CAT1439head_neck770lncrna1612
ESRG.6ovarian10002lncrna1613
GBAT14gbm1042lncrna1614
KHCAT3.2kich1083lncrna1615
GBAT5gbm459lncrna1616
CAT1736.2luad2444lncrna1617
TINCR.1thyroid4657tucp1618
TINCR.1breast4657tucp1619
TINCR.1kirc4657tucp1620
TINCR.1luad4657tucp1621
THCAT63thyroid2818tucp1622
KHCAT1kich609lncrna1623
AFAP1-AS1luad6729lncrna1624
PRCAT47.3prostate2543lncrna1625
PRCAT47.3prostate2543lncrna1626
PRCAT47.2prostate2261lncrna1627
PRCAT47.2prostate2261lncrna1628
PRCAT47.4prostate2375lncrna1629
PRCAT47.4prostate2375lncrna1630
WT1-AS.7ovarian2477lncrna1631
THCAT4.2thyroid1860lncrna1632
CAT252.2breast11636lncrna1633
CAT252.2luad11636lncrna1634
CAT742.6kich4836lncrna1635
CAT742.6kich4836lncrna1636
CAT788aml837lncrna1637
CAT788kirc837lncrna1638
LSCAT5lusc1505lncrna1639
IDI2-AS1melanoma615lncrna1640
IDI2-AS1head_neck615lncrna1641
KCCAT6kirc2565lncrna1642
KCCAT6kirc2565lncrna1643
GBAT25.2gbm6794lncrna1644
CAT2062thyroid1317lncrna1645
CAT2062kirc1317lncrna1646
CAT2062lusc1317lncrna1647
ESAT82.2embryonic_stem_cells3839lncrna1648
PNAT1.1pancreatic863lncrna1649
ESAT1.2embryonic_stem_cells1686lncrna1650
ESAT1.1embryonic_stem_cells1196lncrna1651
TUG1.4head_neck5572lncrna1652
PNAT13.1pancreatic1846lncrna1653
LINC00152.4kirc2305lncrna1654
LINC00152.4stomach2305lncrna1655
LINC00958.6lusc38472lncrna1656
LINC00958.6pancreatic38472lncrna1657
LINC00958.7lusc22404lncrna1658
LINC00958.7pancreatic22404lncrna1659
LINC00958.7prostate22404lncrna1660
LINC00958.8lusc38382lncrna1661
LINC00958.8pancreatic38382lncrna1662
LINC00958.9thyroid16426lncrna1663
LINC00958.9head_neck16426lncrna1664
LINC00958.9lusc16426lncrna1665
LINC00958.9pancreatic16426lncrna1666
LINC00958.9prostate16426lncrna1667
CAT1012.2thyroid2674lncrna1668
CAT1012.2thyroid2674lncrna1669
CAT1846colorectal1948lncrna1670
CAT1846uterine1948lncrna1671
CAT1381head_neck6977lncrna1672
CAT1381stomach6977lncrna1673
CAT1381lusc6977lncrna1674
CAT1768.5prostate2715lncrna1675
CAT1768.5luad2715lncrna1676
CAT1768.5cervical2715lncrna1677
CAT1768.5melanoma2715lncrna1678
CAT1768.5kirp2715lncrna1679
CAT1768.6prostate16798lncrna1680
CAT1575.1colorectal1254lncrna1681
CAT1575.1cervical1254lncrna1682
CAT1575.2kich2362lncrna1683
CAT1575.2thyroid2362lncrna1684
CAT591gbm4547lncrna1685
CAT591lgg4547lncrna1686
CAT591kirc4547lncrna1687
CAT591kich4547lncrna1688
CAT2082.3thyroid11813tucp1689
OVAT92ovarian2661lncrna1690
OVAT131ovarian2875lncrna1691
CAT2052breast891lncrna1692
CAT2052heart891lncrna1693
CAT2052melanoma891lncrna1694
CAT2052aml891lncrna1695
CAT2052skeletal_muscle891lncrna1696
CAT2052gbm891lncrna1697
CAT2052lgg891lncrna1698
CAT2052medulloblastoma891lncrna1699
CAT2052head_neck891lncrna1700
LACAT23luad2080lncrna1701
CAT1363.3uterine5104lncrna1702
CAT2168.1breast4445lncrna1703
CAT227.7kirc2301lncrna1704
CAT227.7kirc2301lncrna1705
ESAT32.1embryonic_stem_cells3723lncrna1706
CAT969.3thyroid871lncrna1707
CAT458aml13405lncrna1708
CAT458breast13405lncrna1709
PRCAT47.1prostate11599lncrna1710
PRCAT47.1prostate11599lncrna1711
CAT260.1kirc7684lncrna1712
LINC00938medulloblastoma2932lncrna1713
LINC00938head_neck2932lncrna1714
HNCAT39.2head_neck792lncrna1715
HNCAT39.1head_neck489lncrna1716
CAT1137aml734lncrna1717
CAT1137kirc734lncrna1718
CAT1137gbm734lncrna1719
CAT1137ovarian734lncrna1720
EPB41L4A-AS1.2lgg1557lncrna1721
EPB41L4A-AS1.2head_neck1557lncrna1722
EPB41L4A-AS1.2breast1557lncrna1723
EPB41L4A-AS1.3kirc856lncrna1724
EPB41L4A-AS1.3head_neck856lncrna1725
CAT715lgg1872lncrna1726
CAT715uterine1872lncrna1727
GBAT25.3gbm5608lncrna1728
CAT1237.1liver23796lncrna1729
CAT1237.1pancreatic23796lncrna1730
CAT1237.2prostate1508lncrna1731
ST8SIA6-AS1.3prostate10062lncrna1732
ST8SIA6-AS1.3liver10062lncrna1733
ST8SIA6-AS1.4liver7702lncrna1734
ST8SIA6-AS1.5prostate6276lncrna1735
ST8SIA6-AS1.5liver6276lncrna1736
LINC00678.3embryonic_stem_cells5513lncrna1737
LINC00678.2embryonic_stem_cells5593lncrna1738
LINC00678.5embryonic_stem_cells5362lncrna1739
LINC00678.4embryonic_stem_cells5517lncrna1740
SNHG12.5kirc1470lncrna1741
CAT2039.3prostate1813lncrna1742
CAT2039.3breast1813lncrna1743
CAT2039.3luad1813lncrna1744
CAT2180.3kirc2859lncrna1745
CAT2180.3kirp2859lncrna1746
CAT2180.3stomach2859lncrna1747
CAT2180.3medulloblastoma2859lncrna1748
CAT2180.3luad2859lncrna1749
CAT2180.3lusc2859lncrna1750
CAT2180.4cml3044lncrna1751
CAT2180.4liver3044lncrna1752
CAT2180.4luad3044lncrna1753
CAT2180.4lusc3044lncrna1754
ESAT75embryonic_stem_cells4294lncrna1755
CAT184head_neck1098lncrna1756
CAT184luad1098lncrna1757
CAT821.1ovarian9000lncrna1758
CAT821.1colorectal9000lncrna1759
CAT821.1uterine9000lncrna1760
CAT821.1head_neck9000lncrna1761
CAT821.1breast9000lncrna1762
CAT821.2cml5562lncrna1763
CAT821.2lgg5562lncrna1764
CAT821.2ovarian5562lncrna1765
CAT821.2head_neck5562lncrna1766
CAT821.2prostate5562lncrna1767
CAT821.2breast5562lncrna1768
CAT821.2luad5562lncrna1769
BRCAT1.6breast27927lncrna1770
BRCAT1.6breast27927lncrna1771
CAT148heart2920lncrna1772
CAT148kich2920lncrna1773
MIR4435-1HG.4kirc2901lncrna1774
MIR4435-1HG.4kirp2901lncrna1775
MIR4435-1HG.4liver2901lncrna1776
MIR4435-1HG.5kirc11167lncrna1777
MIR4435-1HG.5stomach11167lncrna1778
CAT294thyroid3095lncrna1779
CAT294kirc3095lncrna1780
CAT294liver3095lncrna1781
MIR4435-1HG.6head_neck4054lncrna1782
MIR4435-1HG.6stomach4054lncrna1783
MIR4435-1HG.6kirc4054lncrna1784
MIR4435-1HG.7kirc3676lncrna1785
MIR4435-1HG.8kirc4586lncrna1786
OVAT65.3ovarian1423lncrna1787
CAT969.4thyroid984lncrna1788
IL10RB-AS1.1kirc2299lncrna1789
PRCAT119prostate10026lncrna1790
PRCAT119prostate10026lncrna1791
CAT972.2kirc1161lncrna1792
CAT972.2kirp1161lncrna1793
CAT972.2kirc1161lncrna1794
CAT972.2kich1161lncrna1795
CAT972.3kirc1282lncrna1796
AMAT24aml2993lncrna1797
KHCAT3.1kich975lncrna1798
ESAT76.2embryonic_stem_cells12107lncrna1799
ESAT76.1embryonic_stem_cells12103lncrna1800
CAT1354cml649lncrna1801
CAT1354kirc649lncrna1802
CAT252.3medulloblastoma11801lncrna1803
CAT1966.1cml5697lncrna1804
CAT1966.1kirc5697lncrna1805
SMAT14skeletal_muscle456lncrna1806
LSCAT1.1lusc1196lncrna1807
CAT1501.3kirc656lncrna1808
CAT1501.3kirp656lncrna1809
CAT1501.3kich656lncrna1810
CAT1501.3pancreatic656lncrna1811
CAT1501.4kich513lncrna1812
CAT1501.4medulloblastoma513lncrna1813
CAT1501.5medulloblastoma2898lncrna1814
CAT119colorectal1750lncrna1815
CAT119uterine1750lncrna1816
LVCAT6liver4958lncrna1817
CRAT4colorectal809lncrna1818
MEAT29.2melanoma3541lncrna1819
MEAT29.1melanoma739lncrna1820
LINC00948.2pancreatic1327lncrna1821
LINC00948.2kirp1327lncrna1822
CAT1410pancreatic626lncrna1823
CAT1410colorectal626lncrna1824
PRCAT42.3prostate2607lncrna1825
PRCAT42.1prostate4241lncrna1826
OVAT148ovarian612lncrna1827
CAT1345.3head_neck885lncrna1828
CAT1345.3luad885lncrna1829
CAT1345.3lusc885lncrna1830
CAT1345.4luad525lncrna1831
CAT1345.4lusc525lncrna1832
CAT1345.5head_neck2239lncrna1833
CAT1345.5luad2239lncrna1834
CAT1345.5lusc2239lncrna1835
CAT2040uterine1952lncrna1836
CAT2040kich1952lncrna1837
CAT2040liver1952lncrna1838
CAT1914gbm1746lncrna1839
CAT1914lgg1746lncrna1840
CAT1914mpn1746lncrna1841
CAT1914aml1746lncrna1842
CAT1914colorectal1746lncrna1843
CAT1914luad1746lncrna1844
CD27-AS1.1kirp1880lncrna1845
CD27-AS1.1prostate1880lncrna1846
CAT1687thyroid1043lncrna1847
CAT1687head_neck1043lncrna1848
CAT1687breast1043lncrna1849
CAT1687luad1043lncrna1850
CAT1079.3gbm583lncrna1851
CAT1079.3lgg583lncrna1852
CAT1079.3kich583lncrna1853
CAT1011aml5218tucp1854
CAT1011thyroid5218tucp1855
CAT1011breast5218tucp1856
H1FX-AS1.2colorectal6093lncrna1857
CAT366.4thyroid1568lncrna1858
CAT366.4thyroid1568lncrna1859
CAT505.1prostate4300tucp1860
CAT505.1breast4300tucp1861
CAT793.3head_neck10130tucp1862
DGCR10.3kirc21073tucp1863
CAT1855.1breast2092lncrna1864
CAT1855.2breast1987lncrna1865
CAT1855.2breast1987lncrna1866
PRC1-AS1colorectal333lncrna1867
PRC1-AS1uterine333lncrna1868
PRC1-AS1lusc333lncrna1869
PRC1-AS1breast333lncrna1870
PRC1-AS1luad333lncrna1871
PRC1-AS1liver333lncrna1872
LACAT16.1luad4814tucp1873
CAT2010melanoma4289lncrna1874
CAT2010thyroid4289lncrna1875
CAT2010lusc4289lncrna1876
CAT773.3embryonic_stem_cells527lncrna1877
ESAT42.3embryonic_stem_cells2715lncrna1878
ESAT42.2embryonic_stem_cells2712lncrna1879
CAT1892.2bladder5614lncrna1880
CAT221ovarian949lncrna1881
CAT221lusc949lncrna1882
CAT2275.3colorectal1176lncrna1883
HRAT4heart3659lncrna1884
KCCAT41kirc616lncrna1885
CAT2082.4head_neck2265lncrna1886
OVAT114.2ovarian5819lncrna1887
OVAT114.4ovarian3619lncrna1888
OVAT114.3ovarian8783lncrna1889
CAT260.2cml22722lncrna1890
CAT260.2aml22722lncrna1891
CAT260.2kirc22722lncrna1892
CAT1595aml8430lncrna1893
CAT1595kirc8430lncrna1894
MEAT62.1melanoma3376lncrna1895
MEAT62.3melanoma3304lncrna1896
ESAT25embryonic_stem_cells842lncrna1897
CAT1841.2kirc3261lncrna1898
CAT1841.3colorectal2718lncrna1899
CAT1841.3breast2718lncrna1900
CAT1841.3pancreatic2718lncrna1901
CAT1841.3medulloblastoma2718lncrna1902
CAT1841.4lusc2433lncrna1903
CAT1841.4head_neck2433lncrna1904
CAT1841.4kirp2433lncrna1905
CAT1841.4breast2433lncrna1906
CAT1841.4luad2433lncrna1907
CAT1841.4kirc2433lncrna1908
CAT1841.4liver2433lncrna1909
CAT1117thyroid1230lncrna1910
CAT1806kich1496lncrna1911
CAT1806thyroid1496lncrna1912
CAT1806kirc1496lncrna1913
CAT1806kirp1496lncrna1914
CAT1710.1uterine1902tucp1915
CAT1195.4kich8189lncrna1916
CAT1195.4kich8189lncrna1917
CAT1195.4kirp8189lncrna1918
CAT1300mpn3661lncrna1919
CAT1300liver3661lncrna1920
CAT1300luad3661lncrna1921
CD27-AS1.2kirp1758lncrna1922
CD27-AS1.2embryonic_stem_cells1758lncrna1923
CAT2023kich821lncrna1924
CAT2023cervical821lncrna1925
CAT2023kirc821lncrna1926
CAT2023kirp821lncrna1927
CAT2023luad821lncrna1928
CAT2023lusc821lncrna1929
PNAT23.2pancreatic7024lncrna1930
PNAT23.5pancreatic1409lncrna1931
PNAT23.4pancreatic13418tucp1932
PNAT23.3pancreatic13621tucp1933
PNAT23.1pancreatic1282lncrna1934
CAT1701kich1055lncrna1935
CAT1701mpn1055lncrna1936
CAT1701cml1055lncrna1937
CAT1701aml1055lncrna1938
DGCR5.7kirc4192tucp1939
DGCR5.7kirp4192tucp1940
DGCR5.7luad4192tucp1941
DGCR5.8kirc12465tucp1942
CAT2051uterine1174lncrna1943
CAT2051pancreatic1174lncrna1944
DGCR5.9kirc4293tucp1945
DGCR5.9luad4293tucp1946
DGCR5.9kirc4293tucp1947
CAT742.7kich6946lncrna1948
CAT742.7kich6946lncrna1949
CAT1069ovarian1412lncrna1950
CAT1069colorectal1412lncrna1951
CAT1069uterine1412lncrna1952
CAT1069kirc1412lncrna1953
CAT2068ovarian713lncrna1954
CAT2068uterine713lncrna1955
CAT2068thyroid713lncrna1956
CAT2068kich713lncrna1957
CAT2068luad713lncrna1958
CAT2068breast713lncrna1959
CAT2068pancreatic713lncrna1960
CAT2012embryonic_stem_cells2410lncrna1961
CAT2012head_neck2410lncrna1962
CAT2012liver2410lncrna1963
CAT2012luad2410lncrna1964
MPAT11mpn17500lncrna1965
CAT1966.2mpn5823lncrna1966
CAT1966.2cml5823lncrna1967
CAT1966.2aml5823lncrna1968
CAT1966.2kirc5823lncrna1969
CAT1966.2embryonic_stem_cells5823lncrna1970
ESAT8.1embryonic_stem_cells4074lncrna1971
THCAT50.1thyroid1675lncrna1972
THCAT50.1thyroid1675lncrna1973
THCAT50.2thyroid1639lncrna1974
CAT1224.2prostate15239lncrna1975
CAT1224.2breast15239lncrna1976
CAT1224.3breast22412lncrna1977
CAT120thyroid4940lncrna1978
CAT120prostate4940lncrna1979
CAT120breast4940lncrna1980
CAT120luad4940lncrna1981
CAT120lusc4940lncrna1982
CAT120pancreatic4940lncrna1983
CAT270heart423lncrna1984
CAT270embryonic_stem_cells423lncrna1985
CAT270head_neck423lncrna1986
CAT655.3head_neck895lncrna1987
LACAT3luad814lncrna1988
ESAT32.2embryonic_stem_cells2299lncrna1989
CAT405.1heart842lncrna1990
CAT405.1uterine842lncrna1991
CAT405.1lusc842lncrna1992
HCP5.1head_neck16291lncrna1993
HCP5.1kich16291lncrna1994
HCP5.1kirc16291lncrna1995
HCP5.1skeletal_muscle16291lncrna1996
HCP5.2kirc13672lncrna1997
HCP5.2skeletal_muscle13672lncrna1998
CAT1202.1ovarian2314tucp1999
CAT1202.2embryonic_stem_cells2066tucp2000
CAT1202.3ovarian1079tucp2001
CAT1202.3thyroid1079tucp2002
CAT1202.3stomach1079tucp2003
CAT1202.3breast1079tucp2004
ESAT53embryonic_stem_cells3328lncrna2005
MIR31HG.4thyroid4713lncrna2006
CASC9.4head_neck10480lncrna2007
CASC9.4lusc10480lncrna2008
HRAT17.2heart818lncrna2009
HRAT17.1heart867lncrna2010
CASC9.5head_neck1316lncrna2011
CASC9.5luad1316lncrna2012
CASC9.5lusc1316lncrna2013
HRAT17.5heart1579lncrna2014
CAT2024kirc8337lncrna2015
CAT2024kirc8337lncrna2016
CAT2024kich8337lncrna2017
UGDH-AS1.4cervical8132tucp2018
UGDH-AS1.4thyroid8132tucp2019
CAT505.2prostate4033tucp2020
CAT505.2breast4033tucp2021
KDM4A-AS1.2lusc5308lncrna2022
KDM4A-AS1.3uterine3382lncrna2023
OVAT150ovarian271lncrna2024
CAT2168.2prostate3715lncrna2025
CAT2168.2breast3715lncrna2026
CAT2168.3breast831lncrna2027
CAT2168.4breast1058lncrna2028
CAT2168.5breast1098lncrna2029
CAT2168.6breast976lncrna2030
CAT505.3breast4286tucp2031
CAT784.1kich1094lncrna2032
CAT1141.3thyroid4764tucp2033
CAT1141.4medulloblastoma4002tucp2034
PRCAT55prostate1063lncrna2035
PRCAT55prostate1063lncrna2036
KCCAT10kirc825lncrna2037
KCCAT10kirc825lncrna2038
CAT1636.2kirc484lncrna2039
CAT1636.2liver484lncrna2040
CAT2069.2thyroid1070tucp2041
CAT2069.2head_neck1070tucp2042
CAT2069.2kirp1070tucp2043
CAT2069.2luad1070tucp2044
MIAT.2embryonic_stem_cells4074lncrna2045
MIAT.2head_neck4074lncrna2046
MIAT.2breast4074lncrna2047
MIAT.2kirc4074lncrna2048
THCAT36.6thyroid1180lncrna2049
THCAT36.6thyroid1180lncrna2050
PRCAT102.1prostate3656lncrna2051
THCAT36.7thyroid1170lncrna2052
THCAT36.7thyroid1170lncrna2053
CAT2176.5head_neck12721lncrna2054
LINC00511.5luad13108lncrna2055
LINC00511.5lusc13108lncrna2056
LINC00673luad7751lncrna2057
LINC00673lusc7751lncrna2058
CALML3-AS1.3lusc13476tucp2059
CALML3-AS1.4lusc9287tucp2060
CALML3-AS1.5lusc7874tucp2061
CALML3-AS1.5pancreatic7874tucp2062
CALML3-AS1.5prostate7874tucp2063
CALML3-AS1.6lusc9117tucp2064
CALML3-AS1.7lusc11578tucp2065
ESAT73.2embryonic_stem_cells11801lncrna2066
ESAT73.3embryonic_stem_cells19092lncrna2067
ESAT73.1embryonic_stem_cells11767lncrna2068
CAT678uterine392lncrna2069
CAT678lusc392lncrna2070
LSCAT8lusc894lncrna2071
ESAT16.1embryonic_stem_cells3069lncrna2072
ESAT16.2embryonic_stem_cells5948lncrna2073
ESAT16.3embryonic_stem_cells3319lncrna2074
CAT808skeletal_muscle3066lncrna2075
CAT808medulloblastoma3066lncrna2076
CAT808lusc3066lncrna2077
CAT808kirp3066lncrna2078
CAT808breast3066lncrna2079
CAT808luad3066lncrna2080
CAT808kirc3066lncrna2081
CAT808kich3066lncrna2082
CAT2045.3kirc3770lncrna2083
CAT2045.4mpn6943tucp2084
CAT2045.4kirc6943tucp2085
CAT1204.5lusc1366lncrna2086
CAT1204.5prostate1366lncrna2087
CAT1204.6thyroid1329lncrna2088
CAT1204.6prostate1329lncrna2089
ESAT94.2embryonic_stem_cells8025lncrna2090
PRCAT89prostate2337lncrna2091
CAT201.3kich1789lncrna2092
AMAT7aml411lncrna2093
CAT1452.2kich1906tucp2094
OVAT20.2ovarian8749tucp2095
KCCAT63kirc1938tucp2096
KCCAT63kirc1938tucp2097
CAT955head_neck4596lncrna2098
CAT955lusc4596lncrna2099
CAT955luad4596lncrna2100
CAT955kirc4596lncrna2101
CAT369luad2102lncrna2102
CAT369melanoma2102lncrna2103
CAT369skeletal_muscle2102lncrna2104
CAT369medulloblastoma2102lncrna2105
ESAT89embryonic_stem_cells10862lncrna2106
LINC00957kich3721tucp2107
LINC00957embryonic_stem_cells3721tucp2108
HNCAT99head_neck2534lncrna2109
HNCAT3.2head_neck6556lncrna2110
OVAT19ovarian341lncrna2111
CAT1916kirc971lncrna2112
CAT1916head_neck971lncrna2113
CAT1916prostate971lncrna2114
CAT877mpn1598lncrna2115
CAT877kirc1598lncrna2116
CAT877medulloblastoma1598lncrna2117
CAT877thyroid1598lncrna2118
CAT877breast1598lncrna2119
CAT877luad1598lncrna2120
CAT877lusc1598lncrna2121
CAT1429kich932lncrna2122
CAT1429thyroid932lncrna2123
CAT1429kirc932lncrna2124
CAT1429kirp932lncrna2125
CAT57.3thyroid3492tucp2126
CAT57.3kich3492tucp2127
CAT57.4thyroid3093tucp2128
TRAF3IP2-AS1.1gbm2387lncrna2129
TRAF3IP2-AS1.1lgg2387lncrna2130
TRAF3IP2-AS1.1prostate2387lncrna2131
TRAF3IP2-AS1.1breast2387lncrna2132
TRAF3IP2-AS1.2lgg5721lncrna2133
TRAF3IP2-AS1.2breast5721lncrna2134
TRAF3IP2-AS1.3gbm5841lncrna2135
TRAF3IP2-AS1.3lgg5841lncrna2136
TRAF3IP2-AS1.3kich5841lncrna2137
CAT781head_neck8619lncrna2138
CAT781breast8619lncrna2139
CAT781luad8619lncrna2140
MIR205HG.8lusc3923lncrna2141
MIR205HG.9lusc4139lncrna2142
CAT2044colorectal356lncrna2143
CAT2044uterine356lncrna2144
AMAT31aml1230lncrna2145
KPCAT2.2kirp1958lncrna2146
LINC00511.6luad11120lncrna2147
SMAT5skeletal_muscle3550lncrna2148
CAT1918colorectal808lncrna2149
CAT1918uterine808lncrna2150
CAT1918kirc808lncrna2151
CAT1918pancreatic808lncrna2152
ESAT54.2embryonic_stem_cells2476lncrna2153
ESAT54.3embryonic_stem_cells6385lncrna2154
ESAT54.4embryonic_stem_cells5819lncrna2155
ESAT54.6embryonic_stem_cells7677lncrna2156
ESAT54.1embryonic_stem_cells10005lncrna2157
CAT275ovarian644lncrna2158
CAT275breast644lncrna2159
CAT275cml644lncrna2160
CAT1113.2lusc9018lncrna2161
CAT1113.3cervical6154lncrna2162
CAT1113.3head_neck6154lncrna2163
CAT1113.3luad6154lncrna2164
CAT1113.3lusc6154lncrna2165
CAT118.2gbm1883tucp2166
CAT2038uterine2445tucp2167
CAT2038thyroid2445tucp2168
DDX11-AS1.3lusc920lncrna2169
CAT1822aml2450lncrna2170
CAT1822kirc2450lncrna2171
CAT708embryonic_stem_cells8558lncrna2172
CAT708stomach8558lncrna2173
ESAT43embryonic_stem_cells764lncrna2174
KCCAT101kirc3308lncrna2175
KCCAT101kirc3308lncrna2176
SMAT17skeletal_muscle915lncrna2177
LIMD1-AS1.2thyroid1896lncrna2178
LIMD1-AS1.2lusc1896lncrna2179
CAT784.2embryonic_stem_cells3134lncrna2180
OVAT21ovarian1169tucp2181
CAT1632head_neck5529lncrna2182
HRAT17.3heart531lncrna2183
CAT512lgg12274tucp2184
CAT512kich12274tucp2185
CAT512kich12274tucp2186
CAT512kirc12274tucp2187
HRAT17.6heart1647lncrna2188
KCCAT148.2kirc7257lncrna2189
KCCAT148.2kirc7257lncrna2190
KCCAT148.3kirc6805lncrna2191
PRCAT106prostate2039lncrna2192
CAT405.2melanoma3058lncrna2193
CAT62.2embryonic_stem_cells1319lncrna2194
CAT62.3breast750lncrna2195
CAT62.3luad750lncrna2196
CAT62.3lusc750lncrna2197
CAT62.4breast1508lncrna2198
CAT62.4luad1508lncrna2199
CAT62.4lusc1508lncrna2200
CAT62.5embryonic_stem_cells1024lncrna2201
CAT749.2kirc2368lncrna2202
CAT749.2medulloblastoma2368lncrna2203
CAT749.2stomach2368lncrna2204
CAT749.2prostate2368lncrna2205
UTAT7uterine465lncrna2206
MIR205HG.10lusc2112lncrna2207
MIR205HG.10breast2112lncrna2208
MIR205HG.11lusc1994lncrna2209
MIR205HG.11prostate1994lncrna2210
GATA3-AS1.6breast2672lncrna2211
GATA3-AS1.6breast2672lncrna2212
GATA3-AS1.6kirc2672lncrna2213
ESAT42.4embryonic_stem_cells960lncrna2214
TINCR.2lusc5928tucp2215
ESAT42.1embryonic_stem_cells957lncrna2216
CAT1710.2heart1269tucp2217
CAT1710.2uterine1269tucp2218
PXN-AS1embryonic_stem_cells707lncrna2219
OVAT175ovarian993lncrna2220
CAT1115.3lusc3501lncrna2221
CAT1115.4colorectal2839lncrna2222
CAT1115.4uterine2839lncrna2223
ESAT54.5embryonic_stem_cells6980lncrna2224
ESAT8.2embryonic_stem_cells3913lncrna2225
KCCAT192kirc15256lncrna2226
LVCAT7liver1843lncrna2227
CAT82cervical912lncrna2228
CAT82lgg912lncrna2229
CAT82medulloblastoma912lncrna2230
PRCAT71prostate1039lncrna2231
PRCAT71prostate1039lncrna2232
THCAT39.10thyroid1098lncrna2233
THCAT39.10thyroid1098lncrna2234
THCAT36.5thyroid1421lncrna2235
CAT62.6melanoma693lncrna2236
LINC00511.7thyroid3816lncrna2237
LINC00511.7luad3816lncrna2238
LINC00511.8luad4626lncrna2239
LINC00511.8lusc4626lncrna2240
CAT1193kirc3537lncrna2241
CAT1193heart3537lncrna2242
LINC00035thyroid745tucp2243
LINC00035kirp745tucp2244
LINC00035head_neck745tucp2245
LACAT16.2luad10365lncrna2246
LACAT16.3luad6960lncrna2247
CAT1508colorectal1081lncrna2248
CAT1508uterine1081lncrna2249
CAT1508breast1081lncrna2250
CAT1508luad1081lncrna2251
CAT1508lusc1081lncrna2252
CAT1508lgg1081lncrna2253
CAT76.2thyroid2232lncrna2254
CAT1383.2kirc1109lncrna2255
CAT1383.2breast1109lncrna2256
CAT1383.3kirc3577lncrna2257
CAT1383.3breast3577lncrna2258
CAT1383.3breast3577lncrna2259
CAT147.2head_neck1660lncrna2260
CAT147.2prostate1660lncrna2261
CAT147.2breast1660lncrna2262
CAT147.2luad1660lncrna2263
CAT147.2medulloblastoma1660lncrna2264
ESAT24.2embryonic_stem_cells2847lncrna2265
ESAT24.1embryonic_stem_cells373lncrna2266
OVAT114.5ovarian6360lncrna2267
OVAT114.1ovarian4598lncrna2268
WT1-AS.8ovarian10131lncrna2269
WT1-AS.9ovarian9999lncrna2270
WT1-AS.10ovarian10357lncrna2271
WT1-AS.11ovarian7286lncrna2272
WT1-AS.11kich7286lncrna2273
CAT669luad678lncrna2274
CAT669lusc678lncrna2275
ESAT15.1embryonic_stem_cells368lncrna2276
LBX2-AS1.3thyroid2478tucp2277
LBX2-AS1.3head_neck2478tucp2278
THCAT36.3thyroid3966lncrna2279
THCAT36.3thyroid3966lncrna2280
THCAT36.2thyroid1552lncrna2281
THCAT36.2thyroid1552lncrna2282
THCAT36.8thyroid1668lncrna2283
THCAT36.8thyroid1668lncrna2284
LINC00511.9luad3918lncrna2285
LINC00511.9lusc3918lncrna2286
LINC00511.10thyroid3907lncrna2287
LINC00511.10breast3907lncrna2288
LINC00511.10luad3907lncrna2289
LINC00511.10lusc3907lncrna2290
LINC00511.11lusc7803lncrna2291
CAT249.3embryonic_stem_cells1097lncrna2292
BRCAT431.1breast1250lncrna2293
BRCAT431.1breast1250lncrna2294
BRCAT431.2breast1804lncrna2295
BRCAT431.2breast1804lncrna2296
BRCAT431.3breast1333lncrna2297
BRCAT431.3breast1333lncrna2298
BRCAT431.4breast2359lncrna2299
BRCAT431.4breast2359lncrna2300
HICLINC62.1NA970lncrna2301
HICLINC62.2NA680Lncrna2302
HICLINC62.3NA796lncrna2303
HICLINC62.4NA1273lncrna2304
HICLINC62.5NA1220lncrna2305
HICLINC62.6NA1941lncrna2306
HICLINC62.7NA2041lncrna2307
HICLINC62.8NA2043lncrna2308
HICLINC62.9NA2100lncrna2309
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IPC · International Patent Classification
Section C — Chemistry; metallurgy
  • C12Q1/6886

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⤢ drag to zoomJan 2016Jul 2016Jan 2017Jul 2017Jan 2018Jul 2018Jan 2019Jul 2019Jan 2020Jul 2020Jan 2021USPTOApplicantRestriction requirementNon-final rejectionFinal rejectionNon-final rejectionResponse after non-finalApplicant-initiated interviewResponse after non-final
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Pendency
5.1 y
1,862 days filing → grant
Office actions
5
after a restriction
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3
2 RCE
Interviews
2
examiner interview summaries
Examiner
Brian Whiteman
art unit 1635 · TC 1600
Citations: 113 back · 0 forward

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