Patent Grant
10438686
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.
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 (CtRef). The normalized expression value for a gene (CtGene) is then calculated as CtGene−Ct CtRef. 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., 32P, 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% O2, 5% CO2 and 90% N2. 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 (CT) 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 CT˜β0+β1*Condition+β2*CT[Gapdh]+β3*CT[beta-actin], where “Condition” referred to no injection or Oct4 knockdown. CT values were directly used in data analysis, as gene expression at the single-cell level has been shown to follow a lognormal distribution.
Of all 1117 IVF treatments performed at Stanford University in 2005, 822 were fresh IVF cycles that used the patients' own oocytes (
The 157 cycles that were excluded consisted of: cancelled oocyte retrieval due to poor ovarian stimulation (63 cycles), cancelled embryo transfer due to complete lack of embryo development (8 cycles), cancelled embryo transfer due to unexpected medical or non-medical reasons (35 cycles), cycles that were not treated with gonadotropins (3 cycles), missing outcomes (8 cycles), and women being ≥45 years of age based on age alone (29 cycles). In our study, 160 patients underwent subsequent repeat cycles after a previously failed attempt for a total of 368 cycles. Of these, 126 patients had 2 IVF cycles performed, 25 patients had 3 cycles, 6 patients had 4 cycles, 1 patient had 5 cycles and 2 patients had 6 cycles performed in the same year at the same institution. 511 cycles (76.8%) with Day 3 embryo transfer and 154 cycles (23.2%) with Day 5 transfer, or a total of 665 IVF cycles, fulfilled the inclusion and exclusion criteria for analyses.
Clinical and embryology data on the remaining 665 cycles that satisfied inclusion and exclusion criteria, and their 4144 embryos, respectively, were analyzed to test the hypothesis that cohort-specific variables predict IVF cycle outcomes (
We systematically examined the association of each variable with IVF outcomes, and the correlation of each pair of variables. Pair-wise logistic regression tests confirmed many known prognostic variables, including female age, day 3 FSH, and the number of 8-cell embryos. However, in addition to these known prognostic variables, we observed that cohort-specific variables such as fertilization rate and the rate of cleavage arrest were also significantly associated with IVF cycle outcome (p<0.001; Table 3). In contrast, except for male factor infertility (p<0.05), none of the conventional clinical infertility diagnoses were significantly associated with IVF outcomes. Notably, despite a high degree of correlation between many variables and age or day 3 FSH level, which estimates ovarian aging, neither age nor day 3 FSH level was correlated to cohort-specific embryo parameters (Table 4). Collectively, these results suggest that determinants other than age-related mechanisms and clinical diagnoses impact cohort-specific embryo developmental competence.
Sequential Multiple Additive Regression Tree (MART®) and Classification and Regression Tree (CART) analyses of all 30 variables (listed in Table 3 and its legend) determined that IVF cycle outcomes were most accurately predicted at ˜70% by using only four non-redundant variables: total number of embryos, rate of cleavage arrest in an embryo cohort, the number of 8-cell embryos in a cohort, and day 3 FSH level. Remarkably, these four variables all describe the embryo cohort rather than individual embryos, and were more informative than age, clinical diagnoses, or any measures of the transferred embryos. Interestingly, the total number of embryos, day 3 FSH, and the number of 8-cell embryos depended on and thus captured the effects of many other variables. In contrast, the rate of cleavage arrest was independent of any of those known variables. (Details on MART® and CART analyses are reported in Table 5, and
†Clinical infertility diagnoses that were not significantly associated with pregnancy outcome (p-value > 0.05) were not listed: uterine factor, polycystic ovarian syndrome, endometriosis, tubal ligation, tubal disease, hydrosalpinges, unexplained infertility, and “other diagnoses”. Each IVF case may have more than one clinical infertility diagnosis.
‡Cleavage arrest rate is defined as the percentage of embryos with 4 or fewer cells on Day 3 of in vitro culture.
§Day 5 embryo transfer is arbitrarily listed under Parameters of Transferred Embryos. It can also be considered an Embryo Cohort Parameter, as it depends on the total number of embryos and the number of 8-cell embryos.
†Number of trees constructed by MART ®
††10-fold cross validation error
Of the prognostic thresholds identified, the most robust phenotypes are A1 and A2, and B1 and B2 (Table 6). Number of embryos <6 or ≥6 is used by all 5 top CART models, defines all other phenotypes (B to F), and can be applied to all cases. Specifically, the phenotype defined by having fewer than 6 embryos, has an odds ratio of 3.9 for no pregnancy compared to cases with ≥6 embryos (95% Confidence Interval [CI], 2.8 to 5.5). Similarly, the next most robust phenotypes are defined by the number of embryos and cleavage arrest rate, such that for cases with ≥6 embryos, those with cleavage arrest rate >14.6% are 3.0 times more likely to result in no pregnancy than those with cleavage arrest rate ≤14.6% (95% CI, 1.9 to 4.9).
In contrast, the rest of the thresholds listed in Table 6 are used by only 1 CART model each, and is applicable to fewer cases. However, as some of those phenotypes describe very specific subset of cases and have odds ratios that are highly discriminatory, they may be extremely useful depending on the clinical or translational research context. For example, for cases with ≥6 embryos, having cleavage arrest rates of 14.6-52.8% and >52.8% increase the odds of no pregnancy by 2.6 (95% CI 1.6 to 4.3) and 10.6 (95% CI 3.2 to 49.6), respectively, when compared to cases with cleavage rates of ≤14.6%.
To overcome the challenges presented by the highly interactive nature and potentially non-linear dependence amongst variables, and their multicollinearity, all 30 variables listed in Table 5 and its legend were analyzed by MART® to generate models that utilize non-redundant prognostic variables. Eight models were generated by MART® with increasing complexity of individual trees for boosting, while the learning rate was chosen to produce a minimum test error for the given complexity. Among them, the 3 top-ranking models had cross-validated (CV) prediction error rates of 0.301 to 0.308, which separated them well from the other five models, whose CV error rates ranged from 0.315 to 0.331 (Table 5). The third model, however, was based on very complex individual trees yet showed a larger CV error than the two preceding models; thus it was excluded from the analysis. The resulting two top models, consistent with characteristics of meaningful regression tree models, used very few trees and shared common features, such that the two models collectively used only 5 trees containing 5 variables, while some of the other models used up to 16 to 30 trees each.
These 5 non-redundant prognostic variables were: total number of embryos, the rate of cleavage arrest, the number of 8-cell embryos, day 3 FSH level, and the number of 8-cell embryos transferred. In each model, removal of each of the first four variables while keeping all other parameters constant, increased the error rate of the model, thus confirming their significant contribution. However, removal of the number of 8-cell embryos transferred, did not alter the error rate (data not shown), which suggested that this variable was less relevant than others.
24 models representing all possible combinations of these 5 variables were analyzed by CART to further define thresholds that have prognostic significance. The top 5 trees generated by CART had superior prediction scores (0.6828 to 0.6950) compared to the rest of the models (0.565 to 0.6700). As there were shared features, these top 5 tree models utilize mostly the same threshold conditions for 4 variables, while the variable, number of 8-cell embryos transferred, was not utilized by any of these top models. Therefore, IVF cycle outcomes could be most accurately predicted at ˜70% by using only four non-redundant variables that are more informative than age, clinical diagnoses, or any measures of the transferred embryos.
In order to understand which factors amongst patient characteristics, diagnoses, and IVF treatment characteristics, in turn determined these four non-redundant, cohort-specific prognostic variables, tree models were constructed by MART to represent the dependence of each of these four prognostic variables. The total number of embryos, day 3 FSH, and the number of 8-cell embryos depended on and thus captured the effects of many other variables (
Here, we provide proof-of-concept of the efficiency and specificity of MO-mediated gene knockdown by testing the procedure on the Ccna2 gene. We then report the novel role of Oct4 that was revealed by MO-mediated gene knockdown. Ccna2, the gene encoding cell cycle regulator cyclin A2, has been suggested as an important transcriptional regulator in embryonic genome activation (Hara K T, et al. (2005) Dev Biol 286, 102-113), a critical developmental milestone at the 1- to 2-cell stages for which few clear mechanisms or regulators have emerged. Consistent with the literature, MO-mediated Ccna2 knockdown decreased cyclin A2 protein expression. In addition, our results showed that cyclin A2 is required for development beyond the 2-cell stage (
By combining MO-mediated gene knockdown with global gene expression profiling and single-embryo level quantitative RT-PCR (q-PCR), we determined the influence of Oct4 on gene expression, and analyzed the Oct4-regulated gene network in the early embryo (
The critical function of Oct4 at the 1- to 2-cell transition was embryo-autonomous. The effects of Oct4 knockdown could not be rescued by media conditioned by uninjected embryos or the in vivo environment provided by transferring injected embryos to oviducts of appropriately timed surrogate mothers (data not shown). Co-injection of low (3.6 ng/μL) or high (3.6 ng/μL) concentrations of unaltered, full-length Oct4 mRNA with 0.6 mM Oct4-MO resulted in a decrease in the percentage of embryos arresting at the 1- to multicell stages, compared to co-injection of control mRNA encoding a modified enhanced yellow fluorescent protein (mEYFP) (
Injection of Oct4 mRNA resulted in over-expression as quantified by q-PCR, but did not cause ectopic expression of Oct4 protein (data not shown). Oct4 over-expression indeed induced developmental arrest in a dosage-dependent manner, while injection of comparable or greater amounts of mEYFP mRNA interfered minimally with blastocyst development (
To dissect the mechanisms of Oct4 function, we compared the global gene expression profile of Oct4 knockdown embryos to the effects of Ccna2 knockdown and to uninjected controls at the mid-2-cell stage. The goal was to identify differential gene expression that coincides with the first major wave of embryonic genome activation at the mid-2-cell stage (Hamatani T, Carter M G, Sharov A A, & Ko M S (2004) Dev Cell 6, 117-1317; Wang Q T, et al. (2004) Dev Cell 6, 133-1448) (Tables 8, A and B, 9, A and B). Analysis by an unsupervised algorithm showed that the embryo samples clustered according to the experimental conditions, which further supported the specific and non-random effects of gene knockdown (
The list of cyclin A2-regulated genes was rich in genes encoding factors for chromatin modification and remodelling (p=0.005), nucleotide metabolism (p=0.01), and chromosome organization (p=0.01; Tables 11 A and B). Oct4-regulated genes were significantly enriched for translation (p=1.1×10−4) and RNA processing functions (p=3.0×10−5) (Tables 11, C and D). Comparison of our data with published Oct4-regulated networks in mouse ESCs indicates that Oct4 has distinct and specific post-transcriptional and translational regulatory functions mediated by its control of genes encoding subunits in eukaryotic translation initiation factors (Eif), including Eif3c, and Eif3b. Interestingly, these two Eif subunits are evolutionarily conserved from yeast to human, and are amongst the six subunits comprising the functional core of mammalian Eif3, the largest of the Eif complexes (Masutani M, Sonenberg N, Yokoyama S, & Imataka H (2007) Embo J 26, 3373-3383). In addition to its embryo-specific function, Oct4 also controls the expression of genes encoding post-transcriptional regulators Dppa5 and Piwil2 (also known as Mili), as it does in ESCs (data not shown). Dppa5 is an embryo-, germ cell- and ESC-specific RNA-binding protein whose role in maternal-embryonic transition is not known. However, Piwil2 and its bound pi-RNAs, are known for their role in regulating retrotransposons in the fully-grown mouse oocyte (Watanabe T, et al. (2008) Nature 453, 539-543.26). Collectively, our data indicate that the Oct4 has a distinct, developmental stage-specific role in controlling genes encoding post-transcriptional regulators, in addition to its conserved functions shared amongst pluripotent cell types.
Table 8A: Genes that have Higher Expression Levels in Oct4-MO-Injected Compared to Uninjected Embryos (Following the Examples Section)
Table 8B: Genes that have Lower Expression Levels in Oct4-MO-Injected Compared to Uninjected Embryos (Following the Examples Section)
Table 9A: Genes that have Higher Expression Levels in Ccna2-MO-Injected Compared to Uninjected Embryos (Following the Examples Section
Table 9B: Genes that have Lower Expression Levels in Ccna2-MO-Injected Compared to Uninjected Embryos (Following the Examples Section)
In order to understand the role of Oct4 in reprogramming the early embryo, we examined its role in embryonic genome activation and maternal transcript degradation. Overall, Oct4 regulates gene expression pertinent to basic machinery required for the entire spectrum of gene regulation, including transcription involving all three RNA polymerases, translation, RNA processing such as regulation of polyadenylation, and mRNA degradation proteins (Table 12). High levels of mRNA from developmental genes, such as Six1, Nestin, and Hoxa3, indicated that Oct4 was required for their repression, while excessive levels of maternal transcripts that would normally be rapidly degraded, such as Zar1 and Nobox1, indicated that Oct4 knockdown interfered with the mRNA degradation machinery. Thus, Oct4 has developmental stage- and cell-specific functions, and has an important role in the processes that mark maternal-embryonic transition.
To further define the Oct4-regulated gene network, we selected 42 genes representing transcriptional, post-transcriptional and signaling functions for q-PCR assays. We analyzed RNA from single Oct4-MO-injected and control embryos and focused on genes that were under-expressed in Oct4 knockdown. After removing data related to 3 genes for which there were technical difficulties, expression changes of 39 genes were appropriately measured based on analysis using a linear model (See Methods and Materials). Of those, 34 or ˜87% showed altered expression levels in Oct4 knockdown in the expected directions (
Our single-embryo data allowed us to go beyond simply validating our gene chip data. Methods using samples comprised of pooled cells or embryos, generate relative gene expression that represents an average of all cells assayed, but they cannot discern between genes that are consistently differentially regulated versus those with a tendency towards stochastic changes; similarly, rare outlier embryos expressing unique transcriptomes are not recognized (Bengtsson M, Stahlberg A, Rorsman P, & Kubista M (2005) Genome Res 15, 1388-1392; Chang H H, et al. (2008) Nature 453, 544-547; Warren L, Bryder D, Weissman I L, & Quake S R (2006) Proc Natl Acad Sci USA 103, 17807-17812). By analyzing quantitative expression data at the single-embryo level, we were able to make this discrimination. We presume genes whose relative expression is consistent amongst single embryos have a higher likelihood to be essential nodes in a gene regulatory network, which is expected to respond to perturbations in a consistent and predictable manner. The gene set was restricted to genes whose differential expression (represented by the difference in threshold cycles, ΔCT) ΔCT is greater than expression differences amongst single embryos (represented by standard error of the mean, s.e.m.). We propose a hierarchy in the Oct4-regulated gene network in which 29 genes are ordered based on their increasing s.e.m., or inter-embryo variation and presumed decreasing biological significance in this network (
The data shows that in the unique developmental context of maternal-embryonic transition, concomitant with massive mRNA degradation and dramatic reprogramming, Oct4 controls the expression of many transcriptional regulators. Oct4 also maintains the expression of many genes, such as Eif3c, Papola, Piwil2, Eif3b, Eif4e, Rbm3 and Cpsf4, that are involved in the post-transcriptional control. Through its influence on both the transcriptional and post-transcriptional regulators, Oct4 can directly or indirectly affect many essential processes, such as chromatin remodelling, epigenetic regulation, apoptosis, cell cycle regulation, and signalling, during early developmental program (
In the US, 7.3 million couples suffer from clinical infertility, for which more than ˜120,000 IVF treatment cycles are performed per year. IVF is the most effective treatment for many infertility couples, but it is largely empiric, and may not result in a live birth for some couples despite multiple attempts. IVF treatment has revolutionized how physicians can help subfertile patients, but success rates with the use of autologous oocytes appear to have reached a plateau in the past decade. At an average cost of $12,000 per cycle, IVF treatment alone costs health care payers, most of whom are the couples themselves, more than $1 billion per year. However, the decision-making process for IVF can be daunting because IVF counseling and management decisions are often made based on the woman's chronological age, with non-standardized ways to adjust for various estimates of ovarian reserve and embryo quality. Further, quality of care is thought to vary amongst IVF clinics, but head-to-head comparisons are not possible in the absence of scientifically and rigorously defined prognostic stratification of patients. For a treatment that is physically, emotionally, and financially demanding, patients may feel that they are rolling the dice when deciding on whether to start or repeat IVF treatment. Consequently, there may be incongruence in patient- or self-selection for IVF, such that some patients with truly poor prognosis may develop unrealistic expectations, while others with truly good prognosis may miss opportunities for effective treatment. Although numerous publications have reported variables, including chronological age of the female patient, that are significantly associated with IVF outcomes, their contribution relative to outcomes is not clear, and we are even less certain how to directly apply those findings to counsel patients.
Accordingly, in the example we applied MART to analyze dichotomous live birth outcomes of a larger, four-year data set on 3338 fresh, non-oocyte donor IVF cycles and their associated embryos, including subsequent transfer of cryopreserved embryos from 2003 to 2006. Importantly, 57 variables pertaining to clinical diagnoses, treatment response, and embryo developmental parameters from 1,879 fresh, first-cycles were used to generate prediction models in an unbiased manner, with no pre-selection of variables based on previous literature. We identified four prognostic factors—the percentage of blastocysts (Blast Rate), total number of embryos (Embryo Number), total amount of gonadotropins required (TG), and endometrial thickness (EndoTh)—that determine live birth outcomes. The prognostic contribution of other variables, such as age and the number of 8-cell stage embryos on day 3, were optional once these four prognostic variables were known. Finally, the results show how our approach and findings can be applied to immediately improve patient counseling and management protocols, and how these rigorously defined prognostic criteria can fuel our concerted effort to improve quality assurance and decrease rates of multiple gestation.
Between Jan. 1, 2003 to Dec. 31, 2006, 5037 IVF treatments were performed in total, with annual volumes that were comparable across years. Of those, 3347 were fresh IVF cycles that used the patients' own oocytes and that fulfilled inclusion criteria. After applying exclusion criteria, 3338 cycles remained, of which 1879 were first-IVF cycles (C1), 778 were second-IVF cycles performed for patients who did not have live birth outcomes in C1 and who returned for their second treatment (C2); 312 were third-IVF cycles (C3); 369 cycles were fourth, fifth and sixth cycles, which were not analyzed (
Data from C1, C2, and C3 were subsequently analyzed separately. Overall, 41% % of embryos had 8 blastomeres, the developmentally appropriate cell number, while 17% % of embryos had <4 cells, and 33% % had 5-7 cells, and 9% % had more than 8 cells (
We used C1 data only to generate our prediction models, because patients dropped out for a variety of reasons for which there were no controls (
Variables Associated with Live Birth Outcomes
We first focused on 1879 C1 cycles and their associated 10,687 embryos that satisfied all inclusion and exclusion criteria. We systematically examined the association of each of 57 variables with live birth outcomes. These variables were chosen based on the quality and completeness of data entry, rather than prior scientific or clinical knowledge. Pair-wise logistic regression confirmed significant association of many variables with live birth outcomes, including patient's age, male partner's age, Day 3 FSH, and the number of prior clinical pregnancy losses (p<0.05) (Table 13). As expected, diminished ovarian reserve (DOR), a clinical diagnosis based on poor ovarian response in previous non-IVF infertility treatments or clomid challenge test, was highly negatively associated with live birth outcomes (p<0.0001), while the diagnosis of polycystic ovarian syndrome, which tends to increase ovarian response, was positively associated with live births (p<0.0001).
1Positive and negative estimates indicate association with positive and negative pregnancy outcomes, respectively.
2Number of previous clinical pregnancies as defined by positive serum human chorionic gonadotropin (hCG) or pregnancy test.
3Number of previous deliveries carried to term, 37 wks.
4Miscarriages refer to developmental arrest or clinical pregnancy loss at or after 5 wks gestation. Season, seemingly irrelevant, was included as a negative control.
We noted that many pairs of variables, such as age and total amount of gonadotropins used, were highly correlated with one another (Table 14). Although these findings are consistent with the presumed complex and poorly understood biological mechanisms amongst ovarian aging, ovarian hormone production and egg quality, those interactions could not be studied well by ANOVA using Chi-square statistics tests (data not shown), presumably because they do not interact in a linear fashion, or multiple conditions may affect the nature of the interactions.
We chose to analyze data and generate prediction models by constructing boosted classification trees by MART® to identify non-redundant prognostic 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. Finally, MART does not presume the absence or presence of interactions, or the nature of the interactions.
Table 14a and 14B. Correlation Between Every Pair of Variables.
IVF Treatment Time Point-Specific Prognostic Factors and Models
Consistent with our goal to facilitate real life decision-making, we used boosted tree from MART to generate three prediction models, each of which utilized variables that would be available at a specific time point during IVF planning and treatment. As shown by the schematics in
Pre-IVF Model.
MART analysis of 1879 C1 patients with respect to 21 continuous and categorical variables that are known prior to starting IVF treatment (see Table 13), showed that each of patient's age and the diagnosis of diminished ovarian reserve (DOR) alone, as well as their interaction, predicted live birth outcomes. At our center, DOR has been routinely used to describe patients who showed a history of poor response to ovarian stimulation by gonadotropins in controlled ovarian hyperstimulation/intrauterine insemination (COH/IUI) treatment. While the presence of DOR is a negative predictor for LB, it only pertained to ˜12% of the population. The rest of the patients were stratified by age. However, the model identified age thresholds (i.e. 40.5, 42.5, and 46.5) that were vastly different from thresholds (i.e. <35, 36-37, 38-40, 41-42, >42) that are arbitrarily and commonly used in the literature (ref). Overall, 5 patient subsets, hereafter populations, were discerned by this model with LB rates of 21%, 39%, 27%, 17%, and 4% for 222 (12% of all C1 patients), 690 (37%), 176 (9%), 581 (31%), and 199 (11%) patients, respectively (
Pre-OR Model.
In this model, 30 variables known by the time of oocyte retrieval, from the same population comprising 1879 C1 patients, were analyzed by MART. Three independent prognostic factors were identified—total amount of gonadotropins required (TG), age, and endometrial thickness (EndoTh). Again, although each of these prognostic factors had been reported to be associated with LB outcomes, for the first time, we are objectively defining their thresholds and the nature of their potential interaction, which are critical for clinical application. Overall, this model identified 7 distinct populations with respect to their LB rates spanning 2%, 51%, 28%, 32%, 18%, 37%, and 8% for 26 (1.4% of all C1 patients), 387 (21%), 58 (3%), 200 (11%), 662 (35%), 141 (8%), and 405 (22%) patients in Populations (Pops) 1 to 7 (
Post-IVF Model.
The Post-IVF model was generated by MART based on all 57 variables listed in Table 1. Of note, this analysis was restricted to 1664 of the 1879 C1 patients because 215 patients did not complete their IVF treatment and had “cycle cancellation” primarily due to suboptimal ovarian response. Four significant prognostic factors—Blast Rate, Embryo Number, TG, and EndoTh sufficed to differentiate 6 populations with LB rates ranging from 7%, 39%, 17%, 54%, 36%, and 72% for 361 (19% of patients analyzed), 96 (5.5%), 643 (40%), 316 (19%), 198 (12%), and 95 (6%) patients in Populations 1 to 6 (
Model Validation in Returning Patients and an Independent Data Set.
We validated our models by testing whether live birth rates amongst populations are significantly different in related data sets comprised of patients whose C1 did not result in a live birth and who returned for repeat treatment in 778 2nd cycles (C2) and 312 3rd-cycles (C3). To further validate our results, we also tested an independent data set comprised of C1 data from 343 non-redundant patients who had IVF treatment in 2007. (See
We tested differential live birth rates amongst populations by ANOVA Chi-square test, which revealed that differential LB outcomes amongst populations remained significantly different in C2 and C3 from the 2003-2006 data set as well as C1 from the independent data set from 2007, for each of the Pre-IVF, Pre-OR, and Post-IVF models with p-values at 1.17E-50, 8.69E-71, and 1.64E-96, respectively (
Then we validated our models in a different way, by testing whether the live birth rates of each prognostically-defined population was stable and unaltered amongst C1, C2, C3 of 2003-2006 data (inter-cycle, hereafter), and between C1 of 2007 and C1 of 2003-2006 data sets. The inter-cycle comparison showed that all populations remained similar for the Pre-OR and Post-IVF models; in the Pre-IVF model, only Pop 2 and Pop 4 showed that their live birth rates altered amongst cycles (p=0.02). (See Table 15 for p-values of inter-cycle comparisons.) Further, comparison of populations between the 2003-2006 and 2007 data sets indicated that the Pre-IVF and Post-IVF models, and most of the Pre-OR models have remained constant and highly reproducible (p≥0.5); Pops 1 and 6 of the Pre-OR model had differential live birth outcomes between the two data sets.
Collectively, our results showed that all three models were effective in stratifying patients with differential live birth rates, and this ability to stratify by prognosis is reproducible in subsequent cycles and in an independent data set, despite changes in the composition of the general IVF patient population. Further, we confirmed that the prognostic stratification and live birth prediction used in each model were reproducible and validated in an independent data set, as well as in repeat cycles of returning patients.
Models Predicted Live Birth Outcomes More Specifically than Age.
We tested whether these prediction models defined patient populations better and more specifically than an age-based control model (hereafter, control). (See Methods for age-based control model details.) We show the population-specific, observed live birth outcomes compared to live birth rates predicted by the control for each five age categories, <35, 35-37, 38-40, 41-42, and >43 (
We compared each of Pre-IVF, Pre-OR and Post-IVF models to the control with respect to the live birth rates in each of five age categories. Both the Pre-OR and Post-IVF models predicted live birth rates significantly better than the control model across populations in all five age groups (p-values range from 0.04 to 0.5E-16, see Table 15 for all p-values of these comparisons). In the Pre-IVF model, in which age is a key prognostic factor, the Pre-IVF model predicted outcomes better than the control for the age group 38-40, presumably because it also considers the diagnosis of diminished ovarian reserve (p=0.01). In summary, amongst patients who are undergoing their first fresh IVF cycle 17.7% of patients would receive more accurate and personalized live birth outcomes prediction from using the Pre-IVF model and 76% of patients would benefit from using the Pre-OR model. The utility of the Post-IVF model is to facilitate decision-making regarding subsequent IVF treatment in the event that live birth does not result from the first treatment, its use as a prediction tool for subsequent cycles is analyzed more in-depth later.
Prognostic Stratification Predicts Subsequent and Cumulative Live Birth Rates
We asked whether population assignment in C1 predicts live birth rates in C2 and the cumulative live birth rates in C2 and C3, for patients who do not have a live birth from C1. These questions precisely address the frequent scenario in which a couple needs to decide whether to repeat IVF treatment after an unsuccessful cycle. We addressed these questions by assigning patients to populations based on the Post-IVF model, followed by tracking patients' outcomes in C2 and C3. We considered both observed and hypothetical cumulative live birth rates. The observed cumulative rates were conservatively calculated based on the actual patients who returned for C2 and C3; the nominator was number of live births in C2 and C3, and the denominator was the total number of C2 cycles. The hypothetical cumulative rates were calculated based on the probability of having live birth outcomes in C2 or C3, and assumed that the patients who dropped out were no different from the ones who returned; therefore, they are not affected by the limited number of patients who chose to have C3. Specifically, C1 assignment to Pops 1 and 3 resulted in observed cumulative live birth rates of 15.1% and 18.4%, respectively, and 27.5% for Pop 5. In contrast, Pops 2, 4 and 6 had much higher cumulative live birth rates of 56.7%, 42.9%, and 75%, respectively; the hypothetical cumulative live birth rates had a trend towards being slightly higher (
Gradient Boost Analysis to Identify Relative Importance of Preselected Patient Variables
We used gradient boosted machine (GBM) to perform analyses to determine the “relative influence” of each variable on live birth outcomes for all three models. Briefly, we performed 10-fold cross validation for 1879, 1879, and 1664 fresh cycles for Pre-IVF, Pre-OR, and Post-IVF models, respectively. The optimal numbers of trees were 4677, 8680, and 10,745 for Pre-IVF, Pre-OR, and Post-IVF models, respectively; 25,000 trees (i.e. more than the minimum required) were analyzed for each model. The results are shown in Tables 16A, 16B, and 16C. The variables are ranked according to decreasing relative influence/importance such that the most important variables are ranked at the top. The numbers for relative influence add up to 100 in each model.
These GBM outputs contain thousands of trees which cannot be visualized and are conceptually too abstract for most scientific and clinical discussions. Therefore, we chose the top variables for further analysis to construct simpler tree models that could be visualized with Rpart. The advantages of simpler tree models were that they were conceptually more easily understood by scientists, clinicians, and lay people; distinct sub-populations can be characterized and subjected to further analyses to address specific questions, and to explore the utility of the models.
Tables 16a, 16B, and 16C. List of Variables and their Relative Importance in Determining Live Birth Outcomes in the Pre-IVF, Pre-OR and Post-IVF Model Respectively.
We successfully tested the expression of a panel of genes in dissociated blastomeres of control cryopreserved-thawed human embryos that are at the 1-cell, 4-cell, and 8-cell stages. (Patients donated those control embryos to research because they had completed their families.) We analyzed the data by considering both standard deviation and mean. The results of gene chip experiments on the 1-cell, 4-cell, and 8-cell stages are provided in
The preceding merely illustrates the principles of the invention. It will be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventors to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. The scope of the present invention, therefore, is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of present invention is embodied by the appended claims.
Drosophila E(spl)
musculus adult male testis
musculus, hypothetical protein
Mus musculus, clone
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.
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