Methods and systems for assessment of clinical infertility

This application is a divisional of U.S. patent application Ser. No. 12/496,493, filed Jul. 1, 2009, which application claims the benefit of U.S. Provisional Application Nos. 61/077,439, filed Jul. 1, 2008, and 61/081,596, filed Jul. 17, 2008, which applications are incorporated by reference herein in their entirety.

This invention was made with government support under federal grant nos. R01 GM067250 and R01 HD057970 awarded by the National Institutes of Health. The United States Government has certain rights in this invention.

Reproductive failure is a serious problem that has been addressed clinically by various assisted reproductive technologies, including in vitro fertilization (IVF) and embryo transfer (ET). These procedures might be expected to yield exceptionally high conception rates as in vitro fertilization provides embryos that appear normal at a morphological level for transfer into a fully primed recipient. Despite these efforts the success rate of IVF/ET is less than ideal. In the published data for IVF/ET in the United States and Canada in 1994, there were 26,961 initiated cycles of standard IVF. Of these, 86.2% led to retrieval and of these 90.2% led to a transfer. However, the overall success rate in terms of clinical pregnancies was 22.7% per initiated cycle and a 29.1% pregnancy rate per transfer.

Additionally, there appears to be a high incidence of early pregnancy loss after in vitro fertilization with a biochemical pregnancy rate of 18% and a spontaneous abortion rate of 27%. Thus, it appears that the IVF technique has been well optimized but implantation failure may be the cause for a large number of losses with ET and this peri-implantational loss is an area of potential improvement. A major factor in the success rate of various assisted reproductive technologies is endometrial receptivity, a transient state that must be coordinated with embryo development to implantation-competent blastocysts.

IVF is an expensive procedure and can be psychologically traumatic for a patient. Surgical procedures are required to collect eggs from a female for IVF and, following fertilization, further surgery is required to implant the fertilized eggs in the womb. The recipient must then wait for a period of time before it can be determined whether or not pregnancy has been established. In some cases, pregnancy may never be achieved despite repeated attempts, and these cases can represent a considerable expense to the patient and society, both in financial and human terms.

Therefore, until success rates of IVF can be improved, it would be desirable to be able to identify recipients for whom IVF is unlikely to be successful prior to treatment, so that such patients may avoid the above mentioned costs and trauma of the IVF procedure.

The present invention addresses these needs.

Methods and computer-based systems for facilitating assessment of clinical infertility are provided. The methods and systems can be implemented to, for example, facilitate assessment of a subject for an in vitro fertilization treatment cycle, including determining probability of a live birth event. The methods and systems can be implemented to, for example, facilitate a determination of success of implantation of embryos, selection of an optimal number of embryos to transfer, and determination of success in subsequent in vitro fertilization treatment cycles following an unsuccessful treatment cycle.

These and other objects, advantages, and features of the invention will become apparent to those persons skilled in the art upon reading the details of the invention as more fully described below.

The invention is best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity. Included in the drawings are the following figures.

FIG. 1 shows the source of the data. Panel A shows the IVF cycles performed in 2005. Panel B shows the utilization of oocytes and embryos in 665 fresh, non-donor IVF cycles. All numbers in Panel A indicate the number of cycles and numbers in Panel B indicate the number of oocytes or embryos. Fresh cycles are defined by ovarian stimulation of gonadotropins and embryo transfer performed within the same cycle; cryopreserved cycles utilize embryos that were obtained and cryopreserved from a previous cycle; “freeze-all” are cycles in which ovarian stimulation was performed, but embryos were cryopreserved instead of being transferred back within the same cycle for medical or non-medical reasons. 157 cycles were removed from analysis for a variety of medical and non-medical reasons that did not result in fresh embryo transfer.

FIG. 2 shows the distribution of all embryos from 665 fresh, non-donor IVF cases according to their cell number on Day 3.

FIG. 3 shows variables and their relative importance in determining A) number of 8-cell embryos (Panel A), day 3 FSH (Panel B), and the total number of embryos (Panel C).

FIG. 4 shows that translational block of cyclin A2 by Ccna2-MO causes embryos to arrest at the 2-cell stage. Panel A shows Ccna2 expression in 1- to 2-cell embryos by RT-PCR. Panel B shows nuclear cyclin A2 localization was absent in 83.4±6.0% of Ccna2-MO-injected embryos but present in all uninjected embryos; p<0.01). Panel C shows that ˜50 kD cyclin A2 protein was not detected in Ccna2-MO-injected embryos.

FIG. 5 , panel A shows that Ccna2-MO induced higher rates of 2-cell stage arrest compared to controls (p<0.01). Panel B shows that only 1.8±1.8% of Ccna2-MO-injected embryos reached blastocyst stage (p=0.06 compared to Ccna2-MM). Panel C shows that the rates of 2-cell stage arrest decreased with the concentration of Ccna2-MO (p=0.05 for 0.5 mM; p=0.01 for 0.25 mM). Panel D shows that the rate of blastocyst development at were higher at 0.25 mM than at 0.75 mM (p<0.001). All columns and error bars represent mean±s.e.m., respectively, from at least three independent sets of experiments. Scale bar 40 μm. See Table 1 for the targeted sequence of all MOs used, and Table 7 for the total number of embryos tested in each set of experiments.

FIG. 6 shows the mechanisms that regulate mammalian embryo development at the earliest stages. In wild type (+/−) embryos, maternal transcripts are present before embryonic genome activation (EGA), while maternal and embryonic transcripts are present at maternal-embryonic transition stage, both resulting in production of a normal gene product (Panel A). In homozygous null mutant (−/−) embryos generated from a mother that is heterozygous (+/−) for the null mutation, persistent maternal transcripts and/or proteins may “rescue” or delay the phenotype onset (Panel B). In contrast, homozygous null mutant embryos generated from a homozygous mutant female, or a female with oocyte-specific gene deletion, the observed defects may reflect oocyte defects, rather than specific gene requirement in the early embryo (Panel C). Therefore, these strategies do not address the precise roles of specific genes at the cusp of EGA or during EGA, when both maternal and early embryonic transcripts may be present simultaneously. Cytoplasmic microinjection of antisense morpholino oligonucleotides (MOs) into wild type embryo just at or before EGA results in specific translational block of both maternal and embryonic gene transcripts (Panel D). Since MOs persist for at least a few cycles of cell division, gene-specific translational block is presumably effective until the morula-blastocyst stages. The absence of gene product during these developmental stages would reveal critical gene function and unmask early phenotypes that may not be detectable in conventional gene-targeting strategies by homologous recombination and transgenesis. While this model is well established in other species, it shifts the paradigm from investigating function of embryonic genes to that of gene products regardless of their maternal or embryonic origin.

FIG. 7 shows the experimental strategy. Embryos at the 2 pronuclei (2PN) or 1-cell stage were collected from wild type matings, and injected with an antisense morpholino oligomer (MO) that was designed to target a specific gene. MO binds to 5′ UTR or transcription start site and blocks translation by steric hindrance. Microinjected embryos and uninjected control embryos were cultured in vitro and observed for developmental phenotypes such as fragmentation, or arrest at the 2-cell, 4-cell, multicell, or morula stages. If a gene-specific MO produces the same phenotype consistently, while the mismatch control MO allows normal development, then knockdown of the gene of interest was validated by immunocytochemistry and/or immunoblotting. Mechanism of gene function was further investigated by obtaining global gene expression profiles from injected and control embryos at the mid-2-cell stage (43 hours post-HCG). Candidate downstream genes were tested for differential expression, and gene function in the early embryo.

FIG. 8 shows Oct4 expression in mouse zygote by single embryo RT-PCR.

FIG. 9 shows that Oct4 is required for early embryo development prior to formation of the blastocyst. Panel A shows that Oct4-MO-injected embryos arrested at multicell stage, while controls reached blastocyst stage. Panel B shows that Oct4 knockdown induced higher arrest rates at the 1- to multicell stages; p<0.01. Panel C shows that None of the Oct4-MO-injected embryos developed to blastocysts. Panel D shows that the rates of arrest at the 1- to multicell stages decreased with concentration of Oct4-MO (p<0.01). Panel E shows that there was a non-significant trend for higher rates of blastocyst development with decreasing concentrations of Oct4-MO.

FIG. 10 shows that decreased Oct4 expression was evident by the 4-cell stage in Oct4-MO-injected embryos. Oct4 signal was absent in embryos injected with Oct4-MO, but its nuclear localization was present in uninjected and mismatch controls. Scale bar 40 μm.

FIG. 11 , Panel A, shows that nuclear Oct4 expression is absent in Oct4-MO-injected embryos (top panel) but present in Oct4-MM-injected embryos and uninjected. Panel B shows that compared to no coinjection or mEYFP mRNA coinjection, coinjection of 36 or 3.6 ng/μL of Oct4 mRNA resulted in partial rescue of the Oct4-MO-induced phenotype by specifically decreasing the arrest rates at the 1- to multicell stages, which resulted in higher rates blastocyst development (p<0.01). Panel C shows that overexpression of Oct4 mRNA induced higher developmental arrest in a dose-dependent manner, such that blastocyst rates are significantly lower after injection of Oct4 mRNA at 36 or 90 ng/μL, compared to overexpression of mEYFP (p<0.01). Gene overexpression was validated by q-PCR or immunocytochemistry (data not shown). (Scale bar=40 μm).

FIG. 12 shows decreased Oct4 expression at the multicell stage in Oct4-MO-injected embryos. Only 6.4±3.2% of Oct4-MO-injected embryos showed nuclear Oct4 signal, while 88.9±11.1% of Oct4-MM-injected embryos and 82.7±10.9% of uninjected control embryos showed unequivocal nuclear Oct4 expression at the multicell stage; p<0.05.

FIG. 13 shows confirmation of the requirement of Oct4 in early embryo development by Oct4E4-MO, an antisense morpholino that targets the splice site of exon 4 of Oct4. Panel A shows sites targeted by the two morpholinos, Oct4-MO and Oct4E4-MO. Oct4-MO targets the 25 nucleotides starting at the ATG start site, while Oct4E4-MO targets the splice site at the intron (I)-exon (E) boundary of the 4 th exon (E4). Removal of E4 is expected to result in a protein product that lacks the DNA-binding and activation domains. Panel B shows that 64.6±19.9% of embryos injected with Oct4E4-MO, while none that were injected with the mismatch control, Oct4E4-MM, arrested at the 2-cell stage. Panel C shows that blastocyst development is severely compromised after injection of Oct4E4-MO compared to the mismatch control, Oct4E4-MM.

FIGS. 14 and 15 show gene regulation by Oct4. FIG. 14 , Panel A shows unsupervised clustering of 3 Oct4 knockdown, 3 Ccna2 knockdown, and 6 uninjected (NI) pooled embryo samples, and increased (red) and decreased (blue) gene expression. Scale is standard deviation FIG. 14 , Panel B shows intersection of differentially expressed genes in Oct4 and Ccna2 knockdown embryos. FIG. 14 , Panel C and FIG. 15 show relative expression of Oct4 knockdown by single-embryo q-PCR for FIG. 14 , Panel C overexpressed genes (p<0.05 for Hes5) and FIG. 15 downregulated genes (*p<<0.001, **p<0.05, ***p<0.1). TR translational repression. Error bars indicate s.e.m.

FIG. 16 shows models of Oct4 function at the 1- to 2-cell stage. Panel A shows genes whose Absolute (ΔC T ) (red) is greater than its s.e.m. (blue), are ordered clockwise based on increasing s.e.m. (Log scale.) Panel B shows proposed regulation of various modules by Oct4 via transcriptional and post-transcriptional mechanisms.

FIG. 17 shows a summary of the data source for the live birth analysis. The numbers in the boxes indicate the number of fresh cycles performed in the Stanford IVF center through 2003-06. The cycles that were not included were related to: gestational carriers, Cryo. ET or cryopreserved embryo transfer (cycles which utilized frozen embryos, irrespective of whether the cycle was performed at Stanford or outside), donor oocytes (non-self oocytes), recipient cycles and cycles in which no gonadotropin was injected. 9 cycles were excluded from analysis for duplicated entries and unknown outcomes. The cycles analyzed were further classified into Cycles 1, 2, 3 or beyond, depending on the number of times the patient returned for a fresh IVF cycle.

FIG. 18 shows a summary of live birth outcomes. Different boxes represented C1 (first-IVF cycle), C2 (second-IVF cycle), and C3 (third-IVF cycle). Each box shows the number of cycles with live birth (LB+) or no live birth (LB−) outcomes, the percentage of the total number (N) of cycles analyzed and the number of patients that dropped out and did not return.

FIG. 19 is a graph showing distribution of embryos with respect to developmental stage. The distribution of 2969 fresh, non-donor IVF cases from all three cycles according to their cell number on day 3 of in vitro culture. The number of cells per embryo and the maximum number of embryos (˜40%) that had 8 cells were similar amongst cycles C1 (no fill pattern), C2 (horizontal fill pattern), and C3 (vertical fill pattern).

FIG. 20 shows prediction models and their temporal relationship with a typical IVF cycle. An IVF cycle comprises of down-regulation of endogenous gonadotropins and the menstrual cycle using oral contraceptive pill (OCP), periodic FSH injections and cycle monitoring by ultrasound with or without serum estradiol, followed by hCG injection to simulate endogenous LH surge, oocyte retrieval, fertilization by IVF or ICSI, in vitro embryo culture, embryo transfer, and cryopreservation of excess embryos. The three black stars represent the points at which patients can take an informed decision about further treatment options and whether to continue treatment. The Pre-IVF model extends until the treatment start date. The Pre-OR (oocyte retrieval) model extends until the hCG injection. The Post-IVF model includes all variables until embryo transfer.

FIGS. 21A, 21B, and 21C show tree models for three analysis groups: Pre-IVF ( 21 A), Pre-OR ( 21 B) and Post-IVF ( 21 C). The pie-charts show the percentage of live births (LB) among the total number (N) of cycles analyzed. Terminal populations are labeled as population 1 (Pop 1), etc., with its live birth rate indicated.

FIG. 22 shows inclusion and exclusion criteria for 2007 data set.

FIG. 23 shows that Pre-IVF, Pre-OR and Post-IVF models identify distinct populations with differential live birth rates.

FIGS. 24A, 24B, and 24C show comparison of live birth rates between Pre-IVF ( FIG. 23A ), Pre-OR- ( FIG. 23 B), Post-IVF models and an age-based control model.

FIG. 25 shows that Post-IVF model C1 population assignment predicts differential live birth rates in C2 and cumulative live birth rates in subsequent cycles (C2 and C3).

FIG. 26 shows results of gene chip experiments performed at the 1-cell stage.

FIG. 27 shows results of gene chip experiments performed at the 4-cell stage.

FIG. 28 shows results of gene chip experiments performed at the 8-cell stage.

FIG. 29 shows a correlation of two gene chip experiments showing expression of a panel of genes at various cell stages.

FIG. 30 shows a correlation of two gene chip experiments showing expression of a panel of genes at various cell stages.

Before the present embodiments are described, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range, and each range where either, neither or both limits are included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, some potential and preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. It is understood that the present disclosure supercedes any disclosure of an incorporated publication to the extent there is a contradiction.

It must be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a cell” includes a plurality of such cells and reference to “the compound” includes reference to one or more compounds and equivalents thereof known to those skilled in the art, and so forth.

It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as “solely”, “only” and the like in connection with the recitation of claim elements, or the use of a “negative” limitation.

The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.

The terms “subject,” “individual,” and “patient” are used interchangeably herein to refer to a mammal being assessed for treatment and/or being treated. In an embodiment, the mammal is a human, such as a female human. The terms “subject,” “individual,” and “patient” thus encompass individuals in need of assessment of clinical infertility, including those who have undergone or are candidates for an in vitro fertilization cycle.

As used herein, the term “correlates,” or “correlates with,” and like terms, refers to a statistical association between instances of two events, where events include numbers, data sets, and the like. For example, when the events involve numbers, a positive correlation (also referred to herein as a “direct correlation”) means that as one increases, the other increases as well. A negative correlation (also referred to herein as an “inverse correlation”) means that as one increases, the other decreases.

A “biological sample” encompasses a variety of sample types obtained from an individual. The definition encompasses blood and other liquid samples of biological origin, solid tissue samples such as a biopsy specimen or tissue cultures or cells derived therefrom and the progeny thereof. The definition also includes samples that have been manipulated in any way after their procurement, such as by treatment with reagents; washed; or enrichment for certain cell populations, such as cancer cells. The definition also includes sample that have been enriched for particular types of molecules, e.g., nucleic acids, polypeptides, etc. The term “biological sample” encompasses a clinical sample, and also includes tissue obtained by surgical resection, tissue obtained by biopsy, cells in culture, cell supernatants, cell lysates, tissue samples, organs, bone marrow, blood, plasma, serum, and the like. A “biological sample” includes a sample obtained from a patient's uterus, including embryo cultures.

The terms “gene product” and “expression product” are used interchangeably herein in reference to a gene, to refer to the RNA transcription products (transcripts) of the gene, including mRNA and the polypeptide translation products of such RNA transcripts, whether such product is modified post-translationally or not. The terms “gene product” and “expression product” are used interchangeably herein, in reference to an RNA, particularly an mRNA, to refer to the polypeptide translation products of such RNA, whether such product is modified post-translationally or not. A gene product can be, for example, an unspliced RNA, an mRNA, a splice variant mRNA, a polypeptide, a post-translationally modified polypeptide, a splice variant polypeptide, etc.

As used herein, the term “normalized expression level” refers to an expression level of a response indicator gene relative to the level of an expression product of a reference gene(s).

As used herein, the terms “label” and “detectable label” refer to a molecule capable of being detected, where such molecules include, but are not limited to, radioactive isotopes, fluorescers (fluorophores), chemiluminescers, chromophores, enzymes, enzyme substrates, enzyme cofactors, enzyme inhibitors, chromophores, dyes, metal ions, metal sols, ligands (e.g., biotin, avidin, strepavidin or haptens), intercalating dyes and the like. The term “fluorescer” or “fluorophore” refers to a substance or a portion thereof which is capable of exhibiting fluorescence in a detectable range.

As used herein, the term “target nucleic acid region” or “target nucleic acid” refers to a nucleic acid with a “target sequence” to be detected (e.g., in a method involving nucleic acid hybridization and/or amplification). The target nucleic acid may be either single-stranded or double-stranded and may or may not include other sequences besides the target sequence (e.g., the target nucleic acid may or may not include nucleic acid sequences upstream or 5′ flanking sequence, and may or may not include downstream or 3′ flanking sequence. Where detection is by amplification, these other sequences in addition to the target sequence may or may not be amplified with the target sequence.

The term “primer” or “oligonucleotide primer” as used herein, refers to an oligonucleotide which acts to initiate synthesis of a complementary nucleic acid strand when placed under conditions in which synthesis of a primer extension product is induced, e.g., in the presence of nucleotides and a polymerization-inducing agent such as a DNA or RNA polymerase and at suitable temperature, pH, metal ion concentration, and salt concentration. Primers are generally of a length compatible with their use in synthesis of primer extension products, and can be in the range of between about 8 nucleotides and about 100 nucleotides (nt) in length, such as about 10 nt to about 75 nt, about 15 nt to about 60 nt, about 15 nt to about 40 nt, about 18 nt to about 30 nt, about 20 nt to about 40 nt, about 21 nt to about 50 nt, about 22 nt to about 45 nt, about 25 nt to about 40 nt, and so on, e.g., in the range of between about 18 nt and about 40 nt, between about 20 nt and about 35 nt, between about 21 and about 30 nt in length, inclusive, and any length between the stated ranges. Primers can be in the range of between about 10-50 nucleotides long, such as about 15-45, about 18-40, about 20-30, about 21-25 nt and so on, and any length between the stated ranges. In some embodiments, the primers are not more than about 10, 12, 15, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 55, 60, 65, or 70 nucleotides in length. In this context, the term “about” may be construed to mean 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 more nucleotides either 5′ or 3′ from either termini or from both termini.

Primers are in many embodiments single-stranded for maximum efficiency in amplification, but may alternatively be double-stranded. If double-stranded, the primer is in many embodiments first treated to separate its strands before being used to prepare extension products. This denaturation step is typically effected by heat, but may alternatively be carried out using alkali, followed by neutralization. Thus, a “primer” is complementary to a template, and complexes by hydrogen bonding or hybridization with the template to give a primer/template complex for initiation of synthesis by a polymerase, which is extended by the covalent addition of bases at its 3′ end.

A “primer pair” as used herein refers to first and second primers having nucleic acid sequence suitable for nucleic acid-based amplification of a target nucleic acid. Such primer pairs generally include a first primer having a sequence that is the same or similar to that of a first portion of a target nucleic acid, and a second primer having a sequence that is complementary to a second portion of a target nucleic acid to provide for amplification of the target nucleic acid or a fragment thereof. Reference to “first” and “second” primers herein is arbitrary, unless specifically indicated otherwise. For example, the first primer can be designed as a “forward primer” (which initiates nucleic acid synthesis from a 5′ end of the target nucleic acid) or as a “reverse primer” (which initiates nucleic acid synthesis from a 5′ end of the extension product produced from synthesis initiated from the forward primer). Likewise, the second primer can be designed as a forward primer or a reverse primer.

As used herein, the term “probe” or “oligonucleotide probe”, used interchangeable herein, refers to a structure comprised of a polynucleotide, as defined above, which contains a nucleic acid sequence complementary to a nucleic acid sequence present in the target nucleic acid analyte (e.g., a nucleic acid amplification product). The polynucleotide regions of probes may be composed of DNA, and/or RNA, and/or synthetic nucleotide analogs. Probes are generally of a length compatible with their use in specific detection of all or a portion of a target sequence of a target nucleic acid, and are in many embodiments in the range of between about 8 nt and about 100 nt in length, such as about 8 to about 75 nt, about 10 to about 74 nt, about 12 to about 72 nt, about 15 to about 60 nt, about 15 to about 40 nt, about 18 to about 30 nt, about 20 to about 40 nt, about 21 to about 50 nt, about 22 to about 45 nt, about 25 to about 40 nt in length, and so on, e.g., in the range of between about 18-40 nt, about 20-35 nt, or about 21-30 nt in length, and any length between the stated ranges. In some embodiments, a probe is in the range of between about 10-50 nucleotides long, such as about 15-45, about 18-40, about 20-30, about 21-28, about 22-25 and so on, and any length between the stated ranges. In some embodiments, the primers are not more than about 10, 12, 15, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 55, 60, 65, or 70 nucleotides in length. In this context, the term “about” may be construed to mean 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 more nucleotides either 5′ or 3′ from either termini or from both termini.

Where a nucleic acid is said to hybridize to a recited nucleic acid sequence, hybridization is under stringent conditions. An example of stringent hybridization conditions is hybridization at 50° C. or higher and 0.1×SSC (15 mM sodium chloride/1.5 mM sodium citrate). Another example of stringent hybridization conditions is overnight incubation at 42° C. in a solution: 50% formamide, 5×SSC (150 mM NaCl, 15 mM trisodium citrate), 50 mM sodium phosphate (pH7.6), 5×Denhardt's solution, 10% dextran sulfate, and 20 μg/ml denatured, sheared salmon sperm DNA, followed by washing the filters in 0.1×SSC at about 65° C. Stringent hybridization conditions are hybridization conditions that are at least as stringent as the above representative conditions, where conditions are considered to be at least as stringent if they are at least about 80% as stringent, e.g., at least about 90% as stringent as the above specific stringent conditions.

As noted above, methods and computer-based systems for facilitating assessment of clinical infertility are provided. The methods and systems can be implemented to, for example, facilitate assessment of a subject for an in vitro fertilization treatment cycle. The methods and systems can be implemented to, for example, facilitate a determination of success for implantation of embryos, selection of an optimal number of embryos to transfer, and determination of success in subsequent in vitro fertilization treatment cycles following an unsuccessful treatment cycle.

In certain embodiments, the method includes obtaining items of information from a female subject to provide a profile for the female subject, wherein each item of information relate to preselected patient variables, comparing the profile for the female subject to a library of known profile patterns known to be indicative of responsiveness to an in vitro fertilization procedure using an algorithm based upon the preselected patient variables, wherein the comparing provides an evaluation of the female subject for an in vitro fertilization procedure. In certain embodiments, the in vitro fertilization procedure is at least a second vitro fertilization procedure for said female subject.

The items of information may be provided by the female subject based on a written or electronic questionnaire or may be requested, transcribed, or otherwise logged, by a health care practitioner, such as a doctor, nurse, technician, or the like, during or concurrent with a medical evaluation that may optionally be associated with a determination to undergo a first or subsequent in vitro fertilization cycle.

Exemplary items of information relating to preselected patient variables include, but are not limited to: patient characteristics, such as age, previous infertility history, clinical diagnosis; clinical treatment information, such as type of medication, number of days of stimulation, number of oocytes, etc.; conventional embryo morphology data, such as number of embryos, developmental stage, grade, and the like. In some embodiments, the information includes age, total number of embryos, rate of cleavage arrest, number of 8-cell embryos, day 3 follicle stimulating hormone (FSH) level, and number of 8-cell embryos transferred.

In certain embodiments, the in vitro fertilization procedure provides for a live birth event following the in vitro fertilization procedure. In such embodiments, the method provides a probability of a live birth event occurring resulting from the first or subsequent in vitro fertilization cycle based at least in part on items of information from the female subjects.

In some embodiments, the female subject is a pre-in vitro fertilization (pre-IVF) procedure patient. In certain embodiments, the items of information relating to preselected patient variables for determining the probability of a live birth event for a pre-IVF procedure patient may include age, diminished ovarian reserve, 3 follicle stimulating hormone (FSH) level, body mass index, polycystic ovarian disease, season, unexplained female infertility, number of spontaneous miscarriages, year, other causes of female infertility, number of previous pregnancies, number of previous term deliveries, endometriosis, tubal disease, tubal ligation, male infertility, uterine fibroids, hydrosalpinx, and male infertility causes.

In some embodiments, the female subject is a pre-surgical (pre-OR) procedure patient (pre-OR is also referred to herein as pre-oocyte retrieval). In certain embodiments, the items of information relating to preselected patient variables for determining the probability of a live birth event for a pre-OR procedure patient may include age, endometrial thickness, total number of oocytes, total amount of gonatropins administered, number of total motile sperm after wash, number of total motile sperm before wash, day 3 follicle stimulating hormone (FSH) level, body mass index, sperm collection, age of spouse, season number of spontaneous miscarriages, unexplained female infertility, number of previous term deliveries, year, number of previous pregnancies, other causes of female infertility, endometriosis, male infertility, tubal ligation, polycystic ovarian disease, tubal disease, sperm from donor, hydrosalpinx, uterine fibroids, and male infertility causes.

In some embodiments, the female subject is a post-in vitro fertilization (post-IVF) procedure patient. In certain embodiments, the items of information relating to preselected patient variables for determining the probability of a live birth event for a post-IVF procedure patient may include blastocyst development rate, total number of embryos, total amount of gonatropins administered, endometrial thickness, flare protocol, average number of cells per embryo, type of catheter used, percentage of 8-cell embryos transferred, day 3 follicle stimulating hormone (FSH) level, body mass index, number of motile sperm before wash, number of motile sperm after wash, average grade of embryos, day of embryo transfer, season, number of spontaneous miscarriages, number of previous term deliveries, oral contraceptive pills, sperm collection, percent of unfertilized eggs, number of embryos arrested at 4-cell stage, compaction on day 3 after transfer, percent of normal fertilization, percent of abnormally fertilized eggs, percent of normal and mature oocytes, number of previous pregnancies, year, polycystic ovarian disease, unexplained female infertility, tubal disease, male infertility only, male infertility causes, endometriosis, other causes of female infertility, uterine fibroids, tubal ligation, sperm from donor, hydrosalpinx, performance of ICSI, or assisted hatching.

Additional examples of parameters are provided in the examples section, including, for example, Tables 13 and 15.

In certain embodiments, the method includes obtaining items of information relating to at least two preselected patient variables, or more. As such, in other embodiments, the method includes obtaining items of information relating to at 3 or more preselected patient variables, including 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 12 or more, 15 or more, 17 or more, 20 or more, and the like.

In certain embodiments, the method includes assigning a weighted relative importance to each preselected patient variable in relation to other preselected patient variables. For example, in an analysis of a Pre-IVF model subject, the preselected patient variables of age and diminished ovarian reserve are given a higher relative importance value over other preselected patient variables, such as, for example, body mass index, tubular disease, and endometriosis. In such embodiments, the sum of the relative importance of each preselected patient variables will equal 100.

In another embodiment, in an analysis of a Pre-OR model subject, the preselected patient variables of total amount of gonoatropins, endometrial thickness and age are given a higher relative importance value over other preselected patient variables, such as, for example, number of previous pregnancies, tubal ligation and number of spontaneous miscarriages. In such embodiments, the sum of the relative importance of each preselected patient variables will equal 100.

In yet another embodiment, in an analysis of a Post-IVF model subject, the preselected patient variables of blastocyst development rate, total number of embryos, total amount of gonatropins administered, and endometrial thickness are given a higher relative importance value over other preselected patient variables, such as, for example, body mass index, number of motile sperm before wash, number of motile sperm after wash, average grade of embryos, and day of embryo transfer. In such embodiments, the sum of the relative importance of each preselected patient variables will equal 100.

In some embodiments, the comparing includes applying a decision rule. In some embodiments, the data analysis algorithm comprises the use of a classification tree. In other embodiments, the data analysis algorithm is nonparametric, such as using a Wilcoxon Signed Rank Test. In certain embodiments, the data analysis algorithm detects differences in a distribution of feature values. In some embodiments, the data analysis algorithm includes using a multiple additive regression tree. In some embodiments, the data analysis algorithm is a logistic regression.

In further embodiments, the method includes assessment of a gene expression profile of an arrested embryo from the female subject. For example, embryos that have arrested, for example embryos that have fewer than about 5 cells on day 3 following in vitro fertilization, are tested for a relative expression level of a panel of genes that are critical to embryo development. The gene expression profile is then compared to a control gene expression profile.

Any gene that for which normalized expression level is correlated (either positively or negatively) with infertility or likelihood of success or failure of an in vitro fertilization cycle is suitable for use with the methods of the invention. Exemplary genes include, but are not limited to, Oct4, Eif3c, Papola, Piwil2, Eif3b, Eif4b, Rbm3, Cpsf4 and other genes found to be down- or upregulated upon Oct4 knockdown, or the knockdown of another gene encoding a pluripotency regulator (e.g. Sal14). Other exemplary genes include those listed in Tables 8A, 8B, 9A, and 9B in attached Appendixes A-D, as well as Table 12. These gene products are referred to herein as “infertility indicator gene products”; and genes encoding the response indicator gene products are referred to as “infertility indicator genes.” Normalized expression levels of one or more of these infertility indicator genes can be determined to assess a female patient for an in vitro fertilization treatment cycle. Infertility indicator genes were identified as described in detail below in the Examples. Other genes that are suitable for use in the analysis can be identified using the methods described here in the Examples section.

In carrying out a subject assessment, a sample comprising an infertility indicator gene is assayed for a level of an infertility indicator gene product(s). Where the gene product being assayed is a nucleic acid, a nucleic acid sample (e.g., a sample comprising nucleic acid) is obtained from an embryo cell. Where the gene product being assayed is a protein, a polypeptide sample (e.g., a sample comprising polypeptides) is obtained from an embryo.

Nucleic acid (including mRNA) and/or polypeptide infertility indicator gene products can be obtained from an embryo, including an oocyte, such as an arrested embryo or oocyte (e.g., an embryo or oocyte having less than about 8 cells on about day 3 following fertilization, including about 7 cells, about 6 cells, about 5 cells, about 4 cells, about 3 cells, etc.), using standard methods. Levels of nucleic acid and/or polypeptide gene products can be measured using any of a variety of well-known methods, including those described in the Examples below.

An expression level of a response indicator gene is normalized relative to the level of an expression product of a reference gene(s). Assessing the infertility likelihood is conducted by comparing the normalized expression level to a range of values of normalized expression levels of the gene product in an embryo cell.

Normalized expression level of one or more infertility indicator genes can be carried out to assess the likelihood that a patient will respond positively or negatively to an in vitro fertilization treatment cycle. Normalized expression level of a single infertility indicator gene can be carried out to assess the likelihood that a patient will respond positively or negatively to an in vitro fertilization treatment cycle. In addition, normalized expression level of two or more infertility indicator genes can be carried out to assess the likelihood that a patient will respond positively or negatively to an in vitro fertilization treatment cycle. The analysis can be more stringent, e.g., the optimal number of embryos to transfer to the female patient to maximize the likelihood of a live birth outcome while minimizing the likelihood of multiple gestations. The analysis can be less stringent, e.g., the likelihood that a patient will exhibit a beneficial response to an in vitro fertilization treatment cycle.

In some embodiments, the analysis includes determining the optimal number of embryos to transfer in order to minimize the probability of multiple gestation events in a subject. In such embodiments, the subject is first identified as a subject having a high probability of having multiple gestation events. The subject is then analyzed to determine the optimal number of embryos to transfer in order to provide for a single live birth event following the in vitro fertilization cycle.

It will be appreciated that assessment of likelihood that a patient will respond positively or negatively in vitro fertilization treatment cycle can be conducted by determining normalized expression levels of two or more infertility indicator genes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more infertility indicator genes), or any combination of one or more sets of infertility indicator genes. The assessment can involve analyzing expression levels of a combination of infertility indicator genes, and determining normalized expression levels of the combination of infertility indicator genes, where the infertility indicator gene products can include gene products that are positively correlated with an in vitro fertilization treatment cycle and gene products that are negatively correlated with an in vitro fertilization treatment cycle. For example, a normalized level of a first gene that positively correlates with an in vitro fertilization treatment cycle, and a normalized level of a second gene that negatively correlates with an in vitro fertilization treatment cycle, can be determined.

Determining a Normalized Level of a Gene Product

As discussed above, the expression level of an infertility indicator gene is normalized, thereby providing a normalized value. The expression level of an infertility indicator gene is normalized relative to the level of an expression product of a reference gene(s).

For example, the expression level of an infertility indicator gene can be normalized relative to the mean level of gene products of two or more reference genes. As an example, the expression level of an infertility indicator gene can be normalized relative to the mean level of gene products of all assayed genes, or a subset of the assayed genes, where a subset of the assayed genes can include 3, 4, 5, 6, 7, 8, 9, or more assayed genes.

Suitable reference genes include, but are not limited to, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (see, e.g., GenBank Accession No. NM_002046; phosphoglycerate kinase 1 (see, e.g., GenBank Accession No. NM_000291); lactate dehydrogenase A (see, e.g., GenBank Accession No. NM_005566); ribosomal protein L32 (see, e.g., GenBank Accession No. NM_000994); ribosomal protein S18 (see, e.g., GenBank Accession No. NM_022551); tubulin, beta polypeptide (TUBB) (see, e.g., GenBank Accession No. NM_001069); and beta actin (see, e.g., GenBank Accession No. NM_001101). See, e.g., Eisenberg and Levanon (2003) Trends in Genetics 19:362, for a list of additional suitable reference genes.

The level of an RNA transcript as measured by TaqMan® RT-PCR refers to the cycle threshold (Ct) value. The lower the Ct, the greater the amount of mRNA present in the sample. The expression value of a RNA transcript in a sample is normalized, e.g., by first determining the mean expression value in Ct of designated reference genes in a sample (Ct Ref ). The normalized expression value for a gene (Ct Gene ) is then calculated as Ct Gene −Ct Ct Ref . Optionally, the normalized expression values for all genes can be adjusted, e.g., so that all adjusted normalized Ct have a value >0.

Determining a Probability of Beneficial Response

A normalized level of an infertility indicator gene product determined for an individual patient can be compared to normalized expression level values for the infertility indication gene determined in a population of patients for which the clinical outcome is already known in order to determine an individual patient's probability of beneficial response to an in vitro fertilization treatment cycle. Normalized expression level values (e.g., expressed as Ct) correlated with a probability can be used. For example, a normalized level of a response indicator gene product can be compared graphically, to determine the probability of beneficial response to an in vitro fertilization treatment cycle.

The analyses and determinations described herein in connection with a subject method for assessing likelihood of response can be made without the need for assessing any change in the level of a response indicator gene over time.

Classification Tree Analysis

One approach to analyze this data is to use a classification tree algorithm that searches for patterns and relationships in large datasets. A “classification tree” is a recursive partition to evaluate a female subject for an in vitro fertilization procedure using a series of questions that are designed to accurately place the patient into one of the classes. Each question asks whether a patient's condition satisfies a given predictor, with each answer being used to guide the user down the classification tree until a class into which the patient falls can be determined. As used herein, a “predictor” is the range of values of the features—such as, for example, age, total number of embryos, rate of cleavage arrest, number of 8-cell embryos, day 3 follicle stimulating hormone (FSH) level, and number of 8-cell embryos transferred.

Multiple Additive Regression Trees

An automated, flexible modeling technique that uses multiple additive regression trees (MART) may also be used to classify sets of features as belonging to one of two populations. A MART model uses an initial offset, which specifies a constant that applies to all predictions, followed by a series of regression trees. Its fitting is specified by the number of decision points in each tree, the number of trees to fit, and a “granularity constant” that specifies how radically a particular tree can influence the MART model. For each iteration, a regression tree is fitted to estimate the direction of steepest descent of the fitting criterion. A step having a length specified by the granularity constant is taken in that direction. The MART model then consists of the initial offset plus the step provided by the regression tree. The differences between the observed and predicted values are recalculated, and the cycle proceeds again, leading to a progressive refinement of the prediction. The process continues either for a predetermined number of cycles or until some stopping rule is triggered.

The number of splits in each tree is a particularly meaningful fitting parameter. If each tree has only one split, the model looks only at one feature and has no capability for combining two predictors. If each tree has two splits, the model can accommodate two-way interactions among features. With three trees, the model can accommodate three-way interactions, and so forth.

The value of sets of features in predicting class status was determined for data sets with features and known class status. MART provides a measure of the contribution or importance of individual features to the classification decision rule. Specifically, the degree to which a single feature contributes to the decision rule upon its selection at a given tree split can be measured to provide a ranking of features by their importance in determining the final decision rule. Repeating the MART analysis on the same data set may yield a slightly different ranking of features, especially with respect to those features that are less important in establishing the decision rule. Sets of predictive features and their corresponding biomarkers that are useful for the present invention, therefore, may vary slightly from those set forth herein.

One exemplary implementation of the MART technology is found in a module, or “package,” for the R statistical programming environment (see Venables et al., in Modern Applied Statistics with S, 4th ed. (Springer, 2002); www.r-project.org). Results reported in this document were calculated using R versions 1.7.0 and 1.7.1. The module implementing MART, written by Dr. Greg Ridgeway, is called “gbm” and is also available for download (see www.r-project.org). The MART algorithm is amenable to ten-fold cross-validation. The granularity parameter was set to 0.05, and the gbm package's internal stopping rule was based on leaving out 20% of the data cases at each marked iteration. The degree of interaction was set to one, so no interactions among features were considered. The gbm package estimates the relative importance of each feature on a percentage basis, which cumulatively equals 100% for all the features of the biomarker profile. The features with highest importance, which together account for at least 90% of total importance, are reported as potentially having predictive value. Note that the stopping rule in the fitting of every MART model contributes a stochastic component to model fitting and feature selection. Consequently, multiple MART modeling runs based on the same data may choose slightly, or possibly even completely, different sets of features. Such different sets convey the same predictive information; therefore, all the sets are useful in the present invention. Fitting MART models a sufficient number of times is expected to produce all the possible sets of predictive features within a profile. Accordingly, the disclosed sets of predictors are merely representative of those sets of features that can be used to classify individuals into populations.

Wilcoxon Signed Rank Test Analysis

In yet another method, a nonparametric test such as a Wilcoxon Signed Rank Test can be used to identify individual biomarkers of interest. The features in a biomarker profile are assigned a “p-value,” which indicates the degree of certainty with which the biomarker can be used to classify individuals as belonging to a particular reference population. Generally, a p-value having predictive value is lower than about 0.05. Biomarkers having a low p-value can be used by themselves to classify individuals. Alternatively, combinations of two or more biomarkers can be used to classify individuals, where the combinations are chosen on the basis of the relative p-value of a biomarker. In general, those biomarkers with lower p-values are preferred for a given combination of items of information. Combinations of at least three, four, five, six, 10, 20 or 30 or more biomarkers also can be used to classify individuals in this manner. The artisan will understand that the relative p-value of any given biomarker may vary, depending on the size of the reference population.

Analysis Results Reporting

As discussed above, evaluation of a female subject for an in vitro fertilization procedure, including determining probability of a live birth event, is done by obtaining and comparing items of information from the female subject to a library of known profile patterns known to be indicative of responsiveness to an in vitro fertilization procedure using an algorithm based upon said preselected patient variables and optionally evaluating the normalized expression level of one or more fertility response genes. In some embodiments, a patient's evaluation is provided in a report. Thus, in some embodiments, the method further includes a step of preparing or generating a report that includes information regarding the patient's likelihood of successes for an in vitro fertilization procedure. For example, a subject method can further include a step of generating or outputting a report providing the results of a patient's evaluation, which report can be provided in the form of an electronic medium (e.g., an electronic display on a computer monitor), or in the form of a tangible medium (e.g., a report printed on paper or other tangible medium).

A report that includes information regarding the patient's evaluation is provided to a user. The evaluation may include, for example, a determination of success for implantation of embryos, selection of an optimal number of embryos to transfer, and determination of success in subsequent in vitro fertilization treatment cycles following an unsuccessful treatment cycle.

The report generator may also perform one or more of sample gathering, sample processing, and data generation, e.g., the report generator may also perform one or more of: a) sample gathering; b) sample processing; c) measuring a level of an infertility indicator gene product(s); d) measuring a level of a reference gene product(s); and e) determining a normalized level of a infertility indicator gene product(s). Alternatively, an entity other than the report generator can perform one or more sample gathering, sample processing, and data generation.

For clarity, it should be noted that the term “user,” which is used interchangeably with “client,” is meant to refer to a person or entity to whom a report is transmitted, and may be the same person or entity who does one or more of the following: a) collects a sample; b) processes a sample; c) provides a sample or a processed sample; and d) generates data (e.g., level of a response indicator gene product(s); level of a reference gene product(s); normalized level of a response indicator gene product(s)) for use in the likelihood assessment. In some cases, the person(s) or entity(ies) who provides sample collection and/or sample processing and/or data generation, and the person who receives the results and/or report may be different persons, but are both referred to as “users” or “clients” herein to avoid confusion. In certain embodiments, e.g., where the methods are completely executed on a single computer, the user or client provides for data input and review of data output. A “user” can be a health professional (e.g., a clinician, a laboratory technician, a physician (e.g., a Reproductive Endocrinologist), etc.).

In embodiments where the user only executes a portion of the method, the individual who, after computerized data processing according to the methods of the invention, reviews data output (e.g., results prior to release to provide a complete report, a complete, or reviews an “incomplete” report and provides for manual intervention and completion of an interpretive report) is referred to herein as a “reviewer.” The reviewer may be located at a location remote to the user (e.g., at a service provided separate from a healthcare facility where a user may be located).

Report

A “report,” as described herein, is an electronic or tangible document which includes report elements that provide information of interest relating to a subject likelihood assessment and its results. A subject report includes at least an evaluation for an in vitro fertilization treatment cycle, e.g., facilitate a determination of success for implantation of embryos, selection of an optimal number of embryos to transfer, and determination of success in subsequent in vitro fertilization treatment cycles following an unsuccessful treatment cycle. A subject report can be completely or partially electronically generated. A subject report can further include one or more of: 1) information regarding the testing facility; 2) service provider information; 3) patient data; 4) sample data; 5) an interpretive report, which can include various information including: a) indication; b) test data, where test data can optionally include normalized level of one or more infertility indicator gene products; and 6) other features.

Where government regulations or other restrictions apply (e.g., requirements by health, malpractice, or liability insurance), all results, whether generated wholly or partially electronically, are subjected to a quality control routine prior to release to the user.

Testing Facility Information

The report can include information about the testing facility, which information is relevant to the hospital, clinic, or laboratory in which sample gathering and/or data generation was conducted. Data generation can include one or more of: a) measuring a level of a gene product(s) (e.g., an infertility indicator gene product(s), a reference gene product(s)); b) determination of a normalized level of an infertility indicator gene product. This information can include one or more details relating to, for example, the name and location of the testing facility, the identity of the lab technician who conducted the assay and/or who entered the input data, the date and time the assay was conducted and/or analyzed, the location where the sample and/or result data is stored, the lot number of the reagents (e.g., kit, etc.) used in the assay, and the like. Report fields with this information can generally be populated using information provided by the user.

Service Provider Information

The report can include information about the service provider, which may be located outside the healthcare facility at which the user is located, or within the healthcare facility. Examples of such information can include the name and location of the service provider, the name of the reviewer, and where necessary or desired the name of the individual who conducted sample gathering and/or data generation. Report fields with this information can generally be populated using data entered by the user, which can be selected from among pre-scripted selections (e.g., using a drop-down menu). Other service provider information in the report can include contact information for technical information about the result and/or about the interpretive report.

Patient Data

The patient data can include patient medical history (which can include, e.g., data about prior or current in vitro fertilization treatment cycles), personal history; administrative patient data (that is, data that are not essential to the likelihood assessment), such as information to identify the patient (e.g., name, patient date of birth (DOB), gender, mailing and/or residence address, medical record number (MRN), room and/or bed number in a healthcare facility), insurance information, and the like), the name of the patient's physician or other health professional who ordered the response likelihood assessment and, if different from the ordering physician, the name of a staff physician who is responsible for the patient's care (e.g., primary care physician). Report fields with this information can generally be populated using data entered by the user.

Exemplary items of information include, but are not limited to: patient characteristics, such as age, previous infertility history, clinical diagnosis; clinical treatment information, such as type of medication, number of days of stimulation, number of oocytes, etc.; conventional embryo morphology data, such as number of embryos, developmental stage, grade, and the like. In some embodiments, the information includes age, total number of embryos, rate of cleavage arrest, number of 8-cell embryos, day 3 follicle stimulating hormone (FSH) level, number of 8-cell embryos transferred, age, diminished ovarian reserve, endometrial thickness, blastocyst rate, total number of embryos, total number of oocytes, total amount of gonatropins administered, and number of total motile sperm.

Sample Data

The sample data can provide information about the embryo analyzed in the likelihood assessment, such as the number of days following fertilization resulting in arrest, and the date and time collected. Report fields with this information can generally be populated using data entered by the user, some of which may be provided as pre-scripted selections (e.g., using a drop-down menu).

Interpretive Report

The interpretive report portion of the report includes information generated after processing of the data as described herein. The interpretive report can include an evaluation of a female subject for an in vitro fertilization procedure. The interpretive report can include, for example, Result of analysis comparing the profile for the female subject to a library of known profile patterns known to be indicative of responsiveness to an in vitro fertilization procedure using an algorithm based upon said preselected patient variables, and optionally Result of normalized level of infertility indicator gene(s) (e.g., “normalized level of infertility indicator gene(s)”); Interpretation; and, optionally, Recommendation(s).

The Interpretation portion of the report can include a Recommendation(s). Where the results indicate a determination of success for implantation of embryos, selection of an optimal number of embryos to transfer, and determination of success in subsequent in vitro fertilization treatment cycles following an unsuccessful treatment cycle.

It will be readily appreciated that the report can include all or some of the elements above, with the proviso that the report generally includes at least the elements sufficient to provide the analysis requested by the user (e.g., likelihood assessment).

Additional Features

It will also be readily appreciated that the reports can include additional elements or modified elements. For example, where electronic, the report can contain hyperlinks which point to internal or external databases which provide more detailed information about selected elements of the report. For example, the patient data element of the report can include a hyperlink to an electronic patient record, or a site for accessing such a patient record, which patient record is maintained in a confidential database. This latter embodiment may be of interest in an in-hospital system or in-clinic setting.

Computer-Based Systems and Methods

The methods and systems described herein can be implemented in numerous ways. In one embodiment of particular interest, the methods involve use of a communications infrastructure, for example the internet. Several embodiments of the invention are discussed below. It is also to be understood that the present invention may be implemented in various forms of hardware, software, firmware, processors, or a combination thereof. The methods and systems described herein can be implemented as a combination of hardware and software. The software can be implemented as an application program tangibly embodied on a program storage device, or different portions of the software implemented in the user's computing environment (e.g., as an applet) and on the reviewer's computing environment, where the reviewer may be located at a remote site associated (e.g., at a service provider's facility).

The various elements of the computing device, such as a the input device, may be associated with other elements of the system via a wired connection or a wireless connection, including, for example, a wireless LAN connection, Bluetooth connection protocol, ZigBee connection protocol, radio-frequency connection protocol, or a cellular phone connection protocol, including code derived multiple access (CDMA) or via a global system for mobile communication (GSM).

For example, during or after data input by the user, portions of the data processing can be performed in the user-side computing environment. For example, the user-side computing environment can be programmed to provide for defined test codes to denote a likelihood “score,” where the score is transmitted as processed or partially processed responses to the reviewer's computing environment in the form of test code for subsequent execution of one or more algorithms to provide a results and/or generate a report in the reviewer's computing environment.

The application program for executing the algorithms described herein may be uploaded to, and executed by, a machine comprising any suitable architecture. In general, the machine involves a computer platform having hardware such as one or more central processing units (CPU), a random access memory (RAM), and input/output (I/O) interface(s). The computer platform also includes an operating system and microinstruction code. The various processes and functions described herein may either be part of the microinstruction code or part of the application program (or a combination thereof) which is executed via the operating system. In addition, various other peripheral devices may be connected to the computer platform such as an additional data storage device and a printing device.

As a computer system, the system generally includes a processor unit. The processor unit operates to receive information, which generally includes subject information data, such as age, total number of embryos, rate of cleavage arrest, number of 8-cell embryos, day 3 follicle stimulating hormone (FSH) level, number of 8-cell embryos transferred, age, diminished ovarian reserve, endometrial thickness, blastocyst rate, total number of embryos, total number of oocytes, total amount of gonatropins administered, and number of total motile sperm. This information received can be stored at least temporarily in a database, and data analyzed to generate a report as described above.

Part or all of the input and output data can also be sent electronically; certain output data (e.g., reports) can be sent electronically or telephonically (e.g., by facsimile, e.g., using devices such as fax back). Exemplary output receiving devices can include a display element, a printer, a facsimile device and the like. Electronic forms of transmission and/or display can include email, interactive television, and the like. In an embodiment of particular interest, all or a portion of the input data and/or all or a portion of the output data (e.g., usually at least the final report) are maintained on a web server for access, preferably confidential access, with typical browsers. The data may be accessed or sent to health professionals as desired. The input and output data, including all or a portion of the final report, can be used to populate a patient's medical record which may exist in a confidential database at the healthcare facility.

A system for use in the methods described herein generally includes at least one computer processor (e.g., where the method is carried out in its entirety at a single site) or at least two networked computer processors (e.g., where data is to be input by a user (also referred to herein as a “client”) and transmitted to a remote site to a second computer processor for analysis, where the first and second computer processors are connected by a network, e.g., via an intranet or internet). The system can also include a user component(s) for input; and a reviewer component(s) for review of data, generated reports, and manual intervention. Additional components of the system can include a server component(s); and a database(s) for storing data (e.g., as in a database of report elements, e.g., interpretive report elements, or a relational database (RDB) which can include data input by the user and data output. The computer processors can be processors that are typically found in personal desktop computers (e.g., IBM, Dell, Macintosh), portable computers, mainframes, minicomputers, or other computing devices, such as Smartphone devices, including, for example, an Apple® iPhone® device.

The networked client/server architecture can be selected as desired, and can be, for example, a classic two or three tier client server model. A relational database management system (RDMS), either as part of an application server component or as a separate component (RDB machine) provides the interface to the database.

In one embodiment, the architecture is provided as a database-centric client/server architecture, in which the client application generally requests services from the application server which makes requests to the database (or the database server) to populate the report with the various report elements as required, particularly the interpretive report elements, especially the interpretation text and alerts. The server(s) (e.g., either as part of the application server machine or a separate RDB/relational database machine) responds to the client's requests.

The input client components can be complete, stand-alone personal computers offering a full range of power and features to run applications. The client component usually operates under any desired operating system and includes a communication element (e.g., a modem or other hardware for connecting to a network), one or more input devices (e.g., a keyboard, mouse, keypad, or other device used to transfer information or commands), a storage element (e.g., a hard drive or other computer-readable, computer-writable storage medium), and a display element (e.g., a monitor, television, LCD, LED, or other display device that conveys information to the user). The user enters input commands into the computer processor through an input device. Generally, the user interface is a graphical user interface (GUI) written for web browser applications.

The server component(s) can be a personal computer, a minicomputer, or a mainframe and offers data management, information sharing between clients, network administration and security. The application and any databases used can be on the same or different servers.

Other computing arrangements for the client and server(s), including processing on a single machine such as a mainframe, a collection of machines, or other suitable configuration are contemplated. In general, the client and server machines work together to accomplish the processing of the present invention.

Where used, the database(s) is usually connected to the database server component and can be any device which will hold data. For example, the database can be a any magnetic or optical storing device for a computer (e.g., CDROM, internal hard drive, tape drive). The database can be located remote to the server component (with access via a network, modem, etc.) or locally to the server component.

Where used in the system and methods, the database can be a relational database that is organized and accessed according to relationships between data items. The relational database is generally composed of a plurality of tables (entities). The rows of a table represent records (collections of information about separate items) and the columns represent fields (particular attributes of a record). In its simplest conception, the relational database is a collection of data entries that “relate” to each other through at least one common field.

Additional workstations equipped with computers and printers may be used at point of service to enter data and, in some embodiments, generate appropriate reports, if desired. The computer(s) can have a shortcut (e.g., on the desktop) to launch the application to facilitate initiation of data entry, transmission, analysis, report receipt, etc. as desired.

Computer-Readable Storage Media

The invention also contemplates a computer-readable storage medium (e.g. CD-ROM, memory key, flash memory card, diskette, etc.) having stored thereon a program which, when executed in a computing environment, provides for implementation of algorithms to carry out all or a portion of the methods of analysis of evaluating a subject for an in vitro fertilization procedure as described herein. Where the computer-readable medium contains a complete program for carrying out the methods described herein, the program includes program instructions for collecting, analyzing and generating output, and generally includes computer readable code devices for interacting with a user as described herein, processing that data in conjunction with analytical information, and generating unique printed or electronic media for that user.

Where the storage medium provides a program which provides for implementation of a portion of the methods described herein (e.g., the user-side aspect of the methods (e.g., data input, report receipt capabilities, etc.)), the program provides for transmission of data input by the user (e.g., via the internet, via an intranet, etc.) to a computing environment at a remote site. Processing or completion of processing of the data is carried out at the remote site to generate a report. After review of the report, and completion of any needed manual intervention, to provide a complete report, the complete report is then transmitted back to the user as an electronic document or printed document (e.g., fax or mailed paper report). The storage medium containing a program according to the invention can be packaged with instructions (e.g., for program installation, use, etc.) recorded on a suitable substrate or a web address where such instructions may be obtained. The computer-readable storage medium can also be provided in combination with one or more reagents for carrying determining subject information data, e.g., material for determining rate of cleavage arrest, number of 8-cell embryos, day 3 follicle stimulating hormone (FSH) level, and number of 8-cell embryos transferred, and the like.

Kits

The materials for use in the methods of the present invention are suited for preparation of kits produced in accordance with well known procedures. The invention thus provides kits comprising reagents, which may include gene-specific or gene-selective probes and/or primers useful for assaying the expression of genes disclosed herein and for assessing the likelihood of response to an in vitro treatment cycle.

For example, a subject kit can include one or more nucleic acid probes that hybridize specifically to nucleic acid infertility indicator gene products. A subject kit can include, e.g., one or more nucleic acid probes, where each of the one or more probes hybridizes specifically to a different response indicator gene product. For example, a subject kit can include probes that hybridize specifically to nucleic acid products of infertility indicator genes including, but are not limited to, Oct4, Eif3c, Papola, Piwil2, Eif3b, Eif4b, Rbm3, and Cpsf4. As another example, a subject kit can include a set of two or more nucleic acid probes, where each probe of the set hybridizes to a nucleic acid product of a different infertility indicator gene. For example, a subject kit can include a set of two, three, four, five, six, seven, or more, nucleic acid probes, where each probe of the set hybridizes to a nucleic acid product of a different member of a set of infertility indicator genes.

In some cases, a subject kit will include, in addition to a probe that hybridizes specifically to a nucleic acid product of an infertility indicator gene, one or more probes that hybridize specifically to a reference gene product. Such probes can be used in determining normalized expression levels of an infertility indicator gene.

A subject kit can include one or more nucleic acid primer pairs, where the primer pairs, when used as primers in a polymerase chain reaction, amplify a target nucleic acid response indicator gene product, or a target region of a nucleic acid response indicator gene product. A subject kit can include primer pairs for multiple infertility indicator genes.

Exemplary sequences of nucleic acid primers and probes are provided in the Examples described herein. Those skilled in the art will readily appreciate that other probe and primer sequences are also possible, and are readily obtained based on known nucleotide sequences of infertility indicator genes, and/or based on known nucleotide sequences of reference genes.

In addition to the above-mentioned probes and primers, a subject kit can comprise reagents for the extraction and/or isolation of RNA from single cell embryos, in particular fixed paraffin-embedded tissue samples and/or reagents for preparing a cDNA copy of an mRNA, and/or reagents for nucleic acid amplification. Exemplary reagents include those required for use of a FLUIDIGM® BIOMARK® 48.48 Dynamic array system, comparable single-cell gene expression analysis platform (RT-PCR), or emerging technology such as next-generation, whole-transcriptome sequencing at the single-cell level.

Primers and probes can be designed based on known sequences of infertility indicator genes, and are readily synthesized by standard techniques, e.g., solid phase synthesis via phosphoramidite chemistry, as disclosed in U.S. Pat. Nos. 4,458,066 and 4,415,732, incorporated herein by reference; Beaucage et al. (1992) Tetrahedron 48:2223-2311; and Applied Biosystems User Bulletin No. 13 (1 Apr. 1987). Other chemical synthesis methods include, for example, the phosphotriester method described by Narang et al., Meth. Enzymol. (1979) 68:90 and the phosphodiester method disclosed by Brown et al., Meth. Enzymol. (1979) 68:109. Poly(A) or poly(C), or other non-complementary nucleotide extensions may be incorporated into probes using these same methods. Hexaethylene oxide extensions may be coupled to probes by methods known in the art. Cload et al. (1991) J. Am. Chem. Soc. 113:6324-6326; U.S. Pat. No. 4,914,210 to Levenson et al.; Durand et al. (1990) Nucleic Acids Res. 18:6353-6359; and Horn et al. (1986) Tet. Lett. 27:4705-4708.

A probe or a primer can include a detectable label. Exemplary labels include fluorochromes, e.g. fluorescein isothiocyanate (FITC), rhodamine, Texas Red, phycoerythrin, allophycocyanin, 6-carboxyfluorescein (6-FAM), 2′,7′-dimethoxy-4′,5′-dichloro-6-carboxyfluorescein (JOE), 6-carboxy-X-rhodamine (ROX), 6-carboxy-2′,4′,7′,4,7-hexachlorofluorescein (HEX), 5-carboxyfluorescein (5-FAM), N,N,N′,N′-tetramethyl-6-carboxyrhodamine (TAMRA), Cy5, Cy3, and the like; and radioactive labels (e.g., 32 P, etc.).

Probes and primers for inclusion in a subject kit include those useful in various amplification and/or detection systems. Exemplary amplification and/or detection systems include Sunrise primer-based systems, Molecular Beacons, the Taqman™ system, an Amplifluor™ hairpin primer-based system, a Scorpions technology (e.g., bi-functional molecules containing a PCR primer element covalently linked to a probe element), a Light Upon Extension or LUX™-based system, and a FLUIDIGM® BIOMARK® 48.48 Dynamic array system. Further exemplary detection systems include those based on a melt-curve analysis, and using intercalating dyes such as the fluorescent dye SYBR Green.

The kits may optionally comprise reagent(s) with an identifying description or label or instructions relating to their use in the methods of the present invention. The kits may comprise containers (including microtiter plates suitable for use in an automated implementation of the method), each with one or more of the various reagents (typically in concentrated form) utilized in the methods of the invention, including, for example, pre-fabricated microarrays, buffers, the appropriate nucleotide triphosphates (e.g., dATP, dCTP, dGTP and dTTP; or rATP, rCTP, rGTP and UTP), reverse transcriptase, DNA polymerase, RNA polymerase, and one or more probes and primers of the present invention (e.g., appropriate length poly(T) or random primers linked to a promoter reactive with the RNA polymerase).

Instructions for the use of mathematical algorithms used to evaluate a female subject for an in vitro fertilization treatment cycle, including determining a probability of a live birth event, can also be included in a subject kit. In such embodiments, the kits will further include a written or electronic medium, or instructions to access a remote database, as described above, to provide and/or receive information, which generally includes subject information data, such as age, total number of embryos, rate of cleavage arrest, number of 8-cell embryos, day 3 follicle stimulating hormone (FSH) level, number of 8-cell embryos transferred, age, diminished ovarian reserve, endometrial thickness, blastocyst rate, total number of embryos, total number of oocytes, total amount of gonatropins administered, and number of total motile sperm, in order to carry out the methods as described above.

The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g. amounts, temperature, etc.) but some experimental errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees Centigrade, and pressure is at or near atmospheric.

Materials and Methods

The following methods and materials were used in the examples below.

Data Collection, Inclusion and Exclusion Criteria

Data related to clinical diagnoses, IVF treatment protocol and monitoring, embryology data and treatment outcomes for all IVF cycles performed between Jan. 1, 2003 and Dec. 31, 2006 at Stanford University Medical Center were retrieved from BabySentryPro (BabySentry Ltd, Limassol, Cyprus), a widely used fertility database management system, or obtained from medical and embryology records as necessary. Retrospective data collection, de-identification, and analysis were performed according to a Stanford University Institutional Review Board-approved protocol. The inclusion criteria for data analysis were fresh, stimulated, non-donor oocyte IVF cycles.

In certain experiments, cycles that did not result in embryo transfer for any reason, cycles performed for women aged over 45, and those performed for preimplantation genetic screening were excluded.

In other experiments, we did not exclude cycles that were canceled at any point due to medical or non-medical reasons after the IVF treatment had started. We excluded cryopreserved embryo transfer cycles which used embryos that had been conceived in a different IVF clinic.

Defining Cycle Numbers

We renumbered all IVF cycles according to the following method. For each patient, the first fresh IVF cycle appearing in this four-year data set is called “Cycle 1”. The final outcome of all embryo transfer treatments that utilize fresh or cryopreserved embryos conceived from Cycle 1 is defined as the outcome for Cycle 1. The second fresh IVF cycle that appears is defined “Cycle 2”, and so on. Our database could not accommodate data on IVF cycles that were performed elsewhere.

Assessment of Embryo Development

The standard clinical protocols for IVF treatment, fertilization, embryo culture, embryo assessment, cryopreservation criteria, and clinical outcomes are described below. The normal progression of human embryo development in vitro is characterized by the appearance of 2 pronuclei at 16-20 hours after insemination as evidence of fertilization on Day 1, with Day 0 as the day of oocyte retrieval. By late Day 1, embryo development has reached the 2-cell stage, followed by the 4-cell and 8-cell stages on Days 2 and 3, respectively. On Days 4 and 5, embryo development is characterized by the establishment of the morula and blastocyst stages, respectively. All embryos were available for evaluation on Day 3. The day of embryo transfer was determined by the number of blastomeres on Day 3. In general, if 4 or more 8-cell embryos were present, we would recommend extended embryo culture until Day 5, when blastocyst transfer, which has been associated with higher pregnancy rates, would be performed. If fewer than four 8-cell embryos were present, embryo transfer would be performed on Day 3.

Patient, IVF Cycle, and Embryo Parameters

30 variables for association with IVF treatment outcomes were analyzed, as listed in Table 1, under four main categories: patient characteristics and clinical diagnoses, IVF cycle characteristics, embryo cohort parameters, and parameters of transferred embryos. The cleavage arrest rate was defined as the percentage of embryos within a cohort with 4 or fewer cells on Day 3 of in vitro culture. All other variables were self-explanatory.

Definition of Outcomes

Statistical analyses were performed based on the dichotomous outcomes of live birth versus no live birth. No live birth encompassed all other outcomes such as negative serum β-hCG, or positive serum β-hCG followed by biochemical pregnancy, spontaneous abortion, and ectopic pregnancy.

Statistical Analysis

Since some patients underwent more than one IVF cycle during the study period, the analyses were performed based on treatment cycles rather than patients. Statistical analyses were performed based on the dichotomous outcomes of no pregnancy, as defined by negative serum β-hCG, and pregnancy, as defined by positive serum β-hCG, and included biochemical pregnancy, clinical pregnancy, spontaneous abortion, and ectopic pregnancy. We performed pair-wise logistic regression of each variable to the outcome and determined the Pearson correlation coefficient between each pair of continuous variables.

For the main analyses, boosted classification trees were constructed by MART® to identify non-redundant prognostic variables, which were then further analyzed by CART to identify thresholds that would define them as categorical variables. MART® is a robust method used to identify interactive structure of variables that are predictive of outcomes. The use of cross-validation and boosting in parameter selection and model assessment in MART® also preserve parsimony and prevent over-fitting. In the MART® tree constructions, the whole data set is divided into 10 subsets to achieve 10 fold cross validation for model assessment. The same 10 fold cross validation was repeated 1000 times to perform a robust prediction rate estimation and identify tree models with the highest prediction rates in the CART. While MART® is powerful in selecting non-redundant prognostic variables from a large set of highly interactive variables, CART analysis results in simple algorithms, and more easily understood “decision trees”, that are used in the medical literature (Guznick et al., N Engl J Med 345: 1388-1393. (2001)). Thus non-redundant, prognostic variables identified by MART® to confer prediction were analyzed by CART to further define prognostic thresholds.

In some experiments, if patients underwent multiple fresh IVF cycles during the study period, only the first three cycles were analyzed. We generated the models based on cycle 1 (C1) data only. Data from C2 and C3 were used to test inter-cycle validation of the pre-transfer model only.

In such experiments, we performed pair-wise logistic regression of each variable to the outcome and determined the Pearson correlation coefficient between each pair of continuous variables. We generated three models based on data that were available at three different time points prior to IVF treatment (pre-IVF), oocyte retrieval (pre-OR), and after embryo transfer (post-IVF). We generated each model by ranking variables according to their relative influence using gradient boosting machine (GBM), the R-software implementation of MART®, followed by construction of regression tree models using top-ranked variables with Rpart software. MART® is a robust method used to identify interactive structure of variables that are predictive of outcomes. The use of cross-validation and boosting in parameter selection and model assessment in MART® also preserve parsimony and prevent over-fitting. In the MART® tree constructions, the whole data set is divided into 10 subsets to achieve 10 fold cross validation for model assessment. The same 10 fold cross validation was repeated 1000 times to perform a robust prediction rate estimation and identify tree models with the highest prediction rates. While MART® is powerful in selecting non-redundant prognostic variables from a large set of highly interactive variables.

Treatment Protocols Used for Assisted Reproductive Technologies (ART)

The majority of our analyzed IVF cycles were performed in patients with poor ovarian reserve, severe male factor infertility, tubal infertility, anovulatory disorders, or unexplained etiology. In general, one of three stimulation protocols was used in each treatment cycle: luteal downregulation (long) was used for most patients, and microdose lupron (flare) and antagonist protocols were used primarily for patients with presumed diminished ovarian reserve or with a history of previously failed IVF cycles. The long protocol consisted of luteal downregulation using 0.5 mg leuprolide acetate, which was decreased to 0.25 mg with stimulation. In the flare protocol, microdose lupron (0.04 mg s.c. bid) was started after 2-4 weeks of oral contraceptive pills. In the antagonist protocol, GnRH antagonist was initiated when the lead follicle reached 14 mm in size. In all three protocols, baseline ultrasound testing was performed to document that no cysts >1.5 cm were present in the ovaries. When baseline criteria were met, gonadotropin therapy using recombinant FSH with human menopausal gonadotropin was begun. Stimulation was generally achieved using daily dosing of a total of 150-600 IU per day in order to maximize follicular recruitment. Ultrasound monitoring of follicular growth was performed starting on cycle day 7 and then every 1-3 days as indicated. Serum estradiol levels were monitored as necessary.

A dose of 10,000 IU of human chorionic gonadotropin was administered when at least two follicles reached an average diameter of >17 mm. Transvaginal ultrasound-guided oocyte retrieval was performed 34-36 hours after hCG administration in the standard fashion with monitored anesthetic care.

Oocyte Fertilization and Embryo Culture

Oocytes were cultured in groups before fertilization under mineral oil in approximately 125 μl droplets of Sage Cleavage Medium (Cooper Surgical, Inc, Trumbull, Conn.) with 10% Serum Protein Substitute (SPS, Irvine Scientific, Santa Ana, Calif.). Oocytes destined for conventional IVF were cultured in groups of 5 and oocytes destined for intracytoplasmic sperm injection (ICSI) were cultured in groups of up to 20 after stripping the cumulus cells. Oocytes were inseminated with sperm if the semen analysis was normal and fertilization was expected to be normal. Oocytes were inseminated conventionally between 4-6 hours after retrieval. If the semen analysis was abnormal or poor fertilization was expected, then the oocytes were injected with sperm using ICSI. The fertilized oocytes were cultured in groups of up to 20 under mineral oil in approximately 125 μl droplets of Sage Cleavage Medium (Cooper Surgical, Inc, Trumbull, Conn.) with 10% SPS at 37° C. in a humidified atmosphere of 5% O 2 , 5% CO 2 and 90% N 2 . Fertilization check was performed 16-18 hours after insemination or ICSI. The zygotes with clear two pronuclei were cultured for another 48 hours in Sage Cleavage Medium with 10% SPS. Oocytes with single pronucleus (1PN) or three or more pronuclei were considered abnormally fertilized. If Day 5 blastocyst transfer was indicated, extended embryo culture was be performed in Quinn's Advantage Blastocyst medium (Cooper Surgical) with 10% SPS for 48 hours before transfer. Of note, the same culture media was used during the study time period.

Cleavage and Grading of Embryos

A single team of experienced embryologists evaluated the embryos on post-retrieval day 3, 68 to 72 hours after oocyte harvest. Embryos were examined for cleavage (cell number) and grade, which includes cytoplastmic fragmentation. Embryos were graded as follows on Day 3: Grade 1, blastomeres have equal size and no cytoplasmic fragmentation; Grade 2, blastomeres have equal size and minor cytoplasmic fragmentation involving <10% of the embryo; Grade 3, blastomeres have unequal size and fragmentation involving 10-20% of the embryo; Grade 4, blastomeres have equal or unequal size, and moderate to significant cytoplasmic fragmentation covering 20-50% of the embryo; and Grade 5, few blastomeres and severe fragmentation covering ≥50% of the embryo (Veeck et al., (1999) An Atlas of Human Gametes and Conceptuses. New York: Parthenon Publishing. 47-50). Of note, cytoplasmic fragmentation in embryos was easily differentiated from cleavage based on the size of the fragments, their location within the embryo, and the absence of a nucleus. In contrast, cytoplasmic fragmentation of oocytes was not included as a variable because it was extremely rare. The presence or absence of compaction was routinely noted by our embryologists on Day 3. As compaction was observed in <10% of embryos, we did not include this variable in the analysis.

Assisted Hatching

Indications for assisted hatching (AH) in our center were advanced maternal age, elevated FSH level, and/or a history of multiple failed assisted reproduction cycles. On the day of embryo transfer, embryos for hatching were placed in phosphate-buffered saline (PBS) with 10% SPS. AH was accomplished by using the ZILOS-tk laser (Hamilton Thorne Biosciences, Beverly, Mass.) to make a hole in an area of the zona pellucida that was between blastomeres. Embryos were then rinsed and returned to the media until transfer.

Embryo Transfer and Cryopreservation

Ultrasound-guided embryo transfer was performed using a Tefcat or Echotip Softpass catheter (Cook Ob/Gyn, Spencer, Ind.). Progesterone supplementation with vaginal suppositories was performed in all patients. For patients having embryo transfer on Day 3, any remaining embryos with more than five blastomeres were placed in extended culture for another 2 or 3 days. Any expanding, expanded and hatching blastocysts with good inner cell mass and trophectoderm were frozen on day 5 or day 6. In addition, cryopreservation was performed on excess embryos, severe ovarian hyperstimulation syndrome, and fertility preservation due to medical or social reasons. Excess embryos that were not transferred were commonly discarded if they were not of sufficient quality for cryopreservation or if patients did not opt for embryo cryopreservation due to non-medical reasons. In cases of pre-implantation genetic diagnosis, embryos that tested positive for genetic diseases or aneuploidy were also discarded.

Clinical Outcomes

Serum quantitative β-hCG levels were obtained at 8-10 days after embryo transfer, and followed serially until the diagnosis of clinical pregnancy was made by the presence of a gestational sac on transvaginal ultrasound. Outcomes other than clinical pregnancy included: 1) no pregnancy if serum quantitative B-hCG was negative; 2) biochemical pregnancy as defined by decreasing serum quantitative B-hCG levels before a gestational sac could be visualized by transvaginal ultrasound; 3) spontaneous abortion as defined by pregnancy loss after a gestational sac was visualized by transvaiginal ultrasound; and 4) ectopic pregnancy; and 5) other abnormal gestations such as gestational trophoblastic disease. Live birth outcomes were obtained by follow-up contact with patients as part of routine clinical care, but they were not used in this study.

Regression Trees

Clinical IVF data, especially when considering oocyte and embryo parameters, often do not lend themselves to meaningful analysis by multivariate logistic regression. The high degree of interaction and multicollinearity of many relevant variables interfere with conventional multivariate regression. In these situations, the regression and classification tree models (CART), has been widely used in clinical research (Fonarow G C, Jama 293: 572-580 (2005); Friedman J (1999) Greedy function approximation: A stochastic boosting machine. Technical Report, Department of Statistics, Stanford University; Friedman J (1999) Stochastic gradient boosting. Technical Report, Department of Statistics, Stanford University; Friedman J (2002) Tutorial: Getting started with MART in R. Department of Statistics, Stanford University; Friedman et al., Stat Med 22: 1365-1381 (2003); Guzick et al., N Engl J Med 345: 1388-1393 (2001); Pilote et al., N Engl J Med 335: 1198-1205 (1996)). Here, we used Multiple Additive Regression Tree (MART®), a more powerful statistical method that combines “boosting” with CART to “boost” or increase accuracy in the CART method, to identify non-redundant prognostic variables.

In general, regression trees have several key advantages: 1) ability to consider all types of clinical IVF and embryology data, including numeric, ordinal, binary, and categorical variables; 2) ability to handle missing values well based on a “surrogate” splitting technique without the need for imputation; 3) ability to generate results that are invariant to monotonic data transformation and thus eliminate the need to test different methods of data transformation or metrics; 4) ability to generate trees that are immune to the effects of extreme outliers; 5) ability to generate trees that inherently explore and identify interactions of variables that would otherwise need to be explicitly stated in a multiple logistic regression. Most importantly, regression trees can consider a large number of variables, including ones that may turn out to be irrelevant, even if only a small number of variables have significant statistical impact on outcomes. This ability to consider many variables is critical for analysis of IVF outcomes, as many variables, such as percentage of 8-cell embryos, number of 8-cell embryos, percentage of 8-cell embryos transferred, and number of 8-cell embryos transferred, may be highly interactive; thus, arbitrarily selecting one of them may compromise completeness of data and introduce bias, while including all of them may cause the conventional multivariate regression to breakdown.

The results from MART® may help to identify variables that could be re-analyzed by multivariate logistic regression. Often, they may identify thresholds, or “cut-offs”, that will be used to create categorical variables to segregate cases into subgroups for further inter-group comparison of characteristics by conventional methods such as t-tests, chi-square analysis, or Wilcoxon rank sum test. For example, CART analysis was used by Guzick et al. to classify men as subfertile, of indeterminate fertile status, or fertile based on threshold values for sperm concentration, motility, and morphology, exemplifies the power of this strategy in infertility research (Guzick et al., N Engl J Med 345: 1388-1393 (2001)).

Embryo Culture

3-5 week old wild type F1 (C57BL6×DBA/2) females (Charles River) were superovulated by intraperitonial injections of 5 IU of pregnant mare's serum gonadotropin (Sigma) followed by 5 IU of human chorionic gonadotropin (Sigma) 48 hours later, and mated overnight with wild type males. Mice were sacrificed by cervical dislocation 17 hours after hCG injection, and 1-cell embryos were released from oviducts. Cumulus cells were removed by hyaluronidase (Sigma) treatment and pipetting. Pre-implantation embryos at the two pronuclei stage were recovered, pooled from 3-6 females in M2 media (Chemicon International), followed by immediate cytoplasmic microinjection and culture in Human Tubal Fluid with 10% serum supplement (In-Vitro Fertilization, Inc.) microdrops under mineral oil (Sigma) in mixed gas (90% nitrogen, 5% oxygen, 5% carbon dioxide; Praxair) at 37° C., and cultured at ten embryos per 20 μL drop.

Microinjection of Antisense Morpholino Oligonucleotides

25-nt, antisense morpholino oligonucleotides (MOs) that specifically target the 5′UTR or translational start site, or controls mismatched at 5 nts were purchased from Gene Tools, LLC. (See Table 1 for sequence details). We had determined 0.6-0.75 mM to be the maximal concentration that would allow normal rates of blastocyst development (data not shown). Hence, unless otherwise specified, 5-10 pL of 0.75 mM Ccna2-MO (0.60 mM for Oct4-MO) was injected into the cytoplasm of each embryo on an inverted microscope (Olympus IX70) equipped with hydraulic micromanipulation system (IM300 Microinjector, Narishige, Japan). 10 uninjected control embryos were used in each experiment, which was performed at least three times. The mean percentage and standard error of the mean (mean±s.e.m.) of embryos progressing to, or arresting at, each developmental stage were calculated, and statistical significance was determined by calculating the p-value using 2-tailed Student's t-test.

Immunoblot and Immunocytochemistry

Injected and control embryos were collected at 2-cell stage (43 hours post human chorionic gonadotropin (hCG) administration) and washed in PBS containing 3 mg/mL polyvinylpyrrolidone (PVP). For immunoblot, injected and control embryos were lysed in RIPA buffer (50 mM Tris-HCl, pH7.5, 150 mM NaCl, 1 mM EDTA, 1% Nonident P-40, 2 mg/ml aprotonin, 2 mg/ml leupeptin, 1 mg/ml pepstain, and 20 mg/ml phenylmethylsulfonyl) containing a phosphatase inhibitor cocktail (Roche), boiled in Laemli buffer, and stored at −80° C. until lysates from 75 embryos were collected for each condition. Samples were loaded and analyzed by electrophoresis on 10% Tris-HCl polyacrylamide gel, semi-dry transferred onto nitrocellulose membrane (Bio-Rad), blocked in 0.1% Tween-20-1% Casein TBS Blocking Solution (Bio-Rad), incubated overnight in 1:250 diluted primary rabbit polyclonal anti-cyclin A2 antibody (Santa Cruz Biotechnologies, sc-751) at 4° C. and in 1:2000 diluted secondary donkey anti-rabbit horseradish peroxidase-linked antibody (Amersham, NA934V) for one hour at room temperature, and visualized using ECL Blotting Detection Reagent (Amersham). Immunocytochemistry was performed according to standard protocol. Briefly, embryos were fixed in 4% paraformaldehyde-PBS solution, permeabilized in 0.1% Triton X-PBS and treated with ImageIT FX Signal Enhancer solution (Invitrogen) at RT, incubated in 1:100 diluted primary antibody overnight at 4° C., in 1:10,000 diluted secondary antibody for one hour, followed by 3 μM DAPI for 10 min., and were mounted in VectaShield Mounting Medium (Vector Laboratories, H-1000). Controls were performed in parallel with normal rabbit or mouse serum control. All antibodies were diluted in 1% BSA. Embryos were imaged by confocal microscopy using LSM 510 Confocal Laser Scanning Microscope or epifluorescence microscopy or the Axiovert 200 microscope equipped with an Axiocam digital camera (Zeiss) using fixed parameters and exposure times. Primary antibodies were purchased from Santa Cruz Biotechnologies (anti-cyclin A2 rabbit polyclonal (sc-751), normal rabbit IgG (sc-2027), mouse monoclonal anti-Oct4 (sc-5279), and normal mouse IgG (sc-2025). Secondary antibodies were purchased from Molecular Probes (Alexa Fluor 594 goat anti-rabbit IgG (A-11012) and goat-anti mouse IgG (A-11001).

mRNA Synthesis by In Vitro Transcription.

Full-length mouse cDNA clone, Oct4-pSPORT (clone ID 30019896) (Open Biosystems), and a plasmid encoding the fluorescence mitotic biosensor, a modified enhanced yellow fluorescence protein (mEYFP), were sequence-verified, linearized by restriction enzyme digest, and used as templates. 5′ capped and polyadenylated mRNA transcripts were transcribed in vitro (mMessage and PolyA-Tail kits, Ambion), which were then quantitated by UV spectroscopy, and analyzed by electrophoresis to confirm size.

RNA Sample Preparation for Gene Chip Experiments

Samples containing 20 pooled injected or control mouse embryos were washed through 3 drops of PBS/PVP and collected for total RNA extraction and isolation (Picopure Total RNA Isolation Kit, Molecular Devices Corp.), to yield 10 μL of total RNA. 5 μL of total RNA, or the equivalent of 10 mouse embryos, was subjected to two rounds of amplification (WT-Ovation Pico system, Nugen) according to manufacturer's instructions. The quality of ssDNA resulting from the second round amplification was tested on the Bioanalyzer 2100 (Agilent), and a typical yield of 5-8 μg per sample was quantitated by the ND-1000 UV spectrophotometer (Nanodrop Technologies). Direct biotin labeling and fragmentation were performed (FL-Ovation cDNA Biotin Module (Nugen). Fragmented, labeled, ssDNA samples were submitted to Stanford University PAN Core Facility for hybridization to the GeneChip® Mouse Genome 430 2.0 Array (Affymetrix), and laser scanning.

Statistical Analysis for Gene Chip Experiments

Raw data from a total of 12 gene chips (3 Ccna2-MO and their uninjected controls, and 3 Oct4-MO and their uninjected controls) and were normalized by dChip (Li C & Wong W H (2003) in The analysis of gene expression data: methods and software . (Springer, New York), pp. 120-141). Unsupervised clustering analysis was performed for genes that have: 1) expression level greater than 500 Signal Intensity (SI) in at least 10 percent of the samples; 2) standard deviation to mean ratio >0.4 and <1000 across the samples. Subsequent analyses were not restricted to these criteria. The lists of differentially expressed genes and their ranking were generated by the method proposed by Johnson and Wong, which is based on fold change, logged fold change and unpaired t-statistic (Nicholas Johnson and W. H. W. (2007) Combining scientific and statistical significance in gene ranking. Unpublished.). Differential expression was defined by a threshold of 5 percent median false discovery rate (FDR) estimated from 300 random permutations across the samples. To find significantly enriched gene ontology (GO) terms (Ashburner M, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25, 25-29), each gene list was mapped to Entrez gene identification numbers (NCBI) and tested by GOSTAT PACKAGE (Beissbarth T & Speed T P (2004) GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 20, 1464-1465) against a set of “universe” genes defined by “P” call in at least 2 out of 3 treated samples or 4 out of 6 control samples by (MAS, Affymetrix). To estimate the FDR, 50 tests were performed with randomly generated lists of genes from the “universe” gene set, and the average number of enriched GO terms were calculated (Ashburner et al.; Beissbarth et al.). The cut off for p-value was chosen to reflect FDR of ˜10 percent. Differentially expressed genes with possible Oct4-binding sites were identified by comparison with putative Oct4-regulated genes previously identified by Zhou et. al. (Zhou Q, Chipperfield H, Melton D A, & Wong W H (2007) A gene regulatory network in mouse embryonic stem cells. Proc Natl Acad Sci USA 104, 16438-16443), followed by mapping of probe sets to Refseq using the BIOCONDUCTOR ANNOT PACKAGE (R. C. Gentleman VJC, D. M. Bates, B. Bolstad, M. Dettling, S. Dudoit, B. Ellis, L. Gautier, Y. Ge, J. Gentry, K. Hornik, T. Hothorn, W. Huber, S. Iacus, R. Irizarry, F. Leisch, C. Li, M. Maechler, A. J. Rossini, G. Sawitzki, C. Smith, G. Smyth, L. Tierney, J. Y. H. Yang, J. Zhang. (2004) Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology 5, R80).

RT-PCR, q-PCR, and Single Embryo q-PCR Analysis

Lysis buffer was added to PBS/PVP washed embryos (Cells-to-cDNA kit, Ambion) and samples were treated with 1 μL of DNAse I. Reverse transcription (RT) was performed by using 1.0 μL of SuperScript III RT enzyme (200 U/μL) as per protocol (Invitrogen). Amplification of gene-specific product by TaqPolymerase High Fidelity Kit (Invitrogen) was performed on the thermocycler (Mastercycler gradient 5331, Eppendorf) as follows: 94.0° C. for 2 min., 94.0° C. for 15 sec., 60.0° C. for 30 sec., 68.0° C. for 45 sec, 68.0° C. for 7 minutes for 50 cycles. (See Table 2 for all primer sequence and TaqMan probes). Single embryo qRT-PCR was performed using the Biomark 48.48 Dynamic Array system (Fluidigm, South San Francisco, Calif.). Single embryos were treated with acid tyrode, and collected in 10 μl reaction buffer, followed by preamplification as per manufacturer's instructions (TaqMan Gene Expression Assay, Applied Biosystems; Table S8). Amplified cDNA was loaded onto a 48.48 Dynamic Array using the NanoFlex IFC controller (Fluidigm). Threshold cycle (C T ) as a measurement of relative fluorescence intensity was extracted from the BioMark PCR analysis software (Fluidigm). All reactions were performed in duplicates or triplicates along with negative RT, PBS, and positive controls in at least three to five independent experiments. Data for each gene assayed were tested with a linear model (ANCOVA) in which C T ˜β 0 +β 1 *Condition+β 2 *C T [Gapdh]+β 3 *C T [beta-actin], where “Condition” referred to no injection or Oct4 knockdown. C T values were directly used in data analysis, as gene expression at the single-cell level has been shown to follow a lognormal distribution.