Patent Application
20200105367
The present invention relates to methods of combining chromosomal site of transcription information with RNA expression for clinical biomarker discovery. The identified biomarkers include coding transcripts and their expression products, as well as non-coding transcripts, and are useful for predicting the likelihood of breast cancer recurrence in a breast cancer patient
Tumor levels of certain individual RNA species serve as clinically useful prognostic (Paik S, et al. (2004). N Engl J Med 351: 2817-2826; Habel L A, et al. (2006). Breast Cancer Res 8: R25; Van't Veer L J, et al. (2002) Nature 415: 530-536) and predictive (Paik S, et al. (2006). J Clin Oncol 24: 3726-3734; Gianni L, et al. (2005) J Clin Oncol 23: 7265-7277) biomarkers in several cancers. Discovery and development of these RNA biomarkers has been based on use of RT-PCR or DNA microarrays to screen hundreds to thousands of tumor tissue mRNAs to identify relatively small subsets of mRNAs that repeatedly associate with disease outcome in multiple patient cohorts. The most useful tests are based on building multi-gene classifiers that incorporate on the order of a dozen to several dozen different mRNA species. Widespread clinical adoption requires that prospective tests be validated in one or more clinical studies.
RNA-Seq represents a technology advance over RT-PCR and DNA microarrays for initial discovery of RNAs that associate with clinical outcomes (Tucker et al., The American J. Human Genetics 85:142-154, 2009; Sinicropi et al. PLoS One. 7:e4009, 2012; Levin et al. Genome Biology 2009, 10:R115), due to the combined sensitivity, precision and high throughput of massively parallel sequencing. For example, we recently reported the discovery of more than a thousand mRNA species that associate with risk of recurrence of breast cancer, based on whole transcriptome RNA-Seq analysis of tumors from 136 early breast cancer patients (“Providence patient cohort”). We showed a high level of concordance between measured RNA levels derived from RNA-Seq and RT-PCR, which has been considered the gold standard for quantification of RNAs (M. Cronin, et al., Am J Pathol 164:35-42, 2004). In addition, whole transcriptome RNA-Seq quantifies greater numbers of mRNA species than DNA microarrays (heretofore the platform capable of evaluating the largest numbers of RNA species), notably intronic and intragenic RNA species. It is noteworthy that in this RNA-Seq study of the Providence cohort a greater number of intronic versus exonic RNA species associate with recurrence risk (Sinicropi et al. PLoS One. 7:e4009, 2012).
RNA-Seq and DNA microarray capabilities to evaluate thousands of different RNA species are useful for biomarker discovery, but their utility can be diminished due to the large numbers of RNAs that have little or no association with clinical outcome, decreasing the statistical power of false discovery rate (FDR) controlling analyses to identify biomarkers (Crager, Genetic Epidemiology 36:839-847, 2012). This problem is compounded because typically the sizes of available patient cohorts are no more than several hundred patients. The identification power can be improved by prospectively nominating classes of RNAs considered to be most promising, based on biological or biomedical prior knowledge, and carrying out a separate class analysis (Efron B Annals of Applied Statistics 2:197-223, 2008; Crager, Genetic Epidemiology 36:839-847, 2012).
This application discloses methods for identifying prognostic features based on RNAs or proteins. The method can be used to analyze RNAs or proteins in various diseases, including cancer and non-cancer-based diseases. The disclosed methods can use RNA-Seq, RT-PCR, expression arrays, and/or proteomic methods, such as Western blots and mass spectrometry.
In the one approach (termed “mapped strings”), the expression of a population of RNAs can be evaluated to identify individual RNA species associated with breast recurrence rate, as is conventional practice. Each member of the subset of genes that is identified at a given false discovery rate as associating with recurrence risk is then graphically placed on its chromosomal locus. Remarkably, this demonstrates that genes associated with good prognosis tend to distribute in long strings uninterrupted by genes associated with bad prognosis, and the reverse is true for genes associated with bad prognosis. Each identified string is then evaluated as a unit (“metagene”) candidate biomarker for risk of disease recurrence.
In the second approach the average measured normalized quantity of each transcript species is calculated for the entire patient cohort being studied, standardizing the level of each species to produce a centered scaled robust z-score. This is used to construct for each patient a transcriptome profile physical map (TPM) where the X-axis represents the chromosomal locus for each RNA species and the Y-axis represents, for each RNA species, the individual patient's standardized normalized abundance value. These graphs reveal striking patient to patient differences, for example various sizes and numbers of spike-like features (segments) and global clouds of data points that vary considerably in their dispersion. These features, or summaries thereof, are then evaluated as candidate biomarkers for disease recurrence.
Data in this application demonstrate that these approaches yield novel breast cancer recurrence risk markers that prove to be stably prognostic when tested across breast cancer patient cohorts/datasets.
Before the present invention and specific exemplary embodiments of the invention 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 limit of that 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 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 both of those included limits are also included in the invention.
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 “an RNA transcript” includes a plurality of such RNA transcripts.
Unless defined otherwise, 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. Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), and March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992), provide one skilled in the art with a general guide to many of the terms used in the present application.
Additionally, the practice of the present invention will employ, unless otherwise indicated, conventional techniques of molecular biology (including recombinant techniques), microbiology, cell biology, and biochemistry, which are within the skill of the art. Such techniques are explained fully in the literature, such as, “Molecular Cloning: A Laboratory Manual”, 2nd edition (Sambrook et al., 1989); “Oligonucleotide Synthesis” (M. J. Gait, ed., 1984); “Animal Cell Culture” (R. I. Freshney, ed., 1987); “Methods in Enzymology” (Academic Press, Inc.); “Handbook of Experimental Immunology”, 4th edition (D. M. Weir & C. C. Blackwell, eds., Blackwell Science Inc., 1987); “Gene Transfer Vectors for Mammalian Cells” (J. M. Miller & M. P. Calos, eds., 1987); “Current Protocols in Molecular Biology” (F. M. Ausubel et al., eds., 1987); and “PCR: The Polymerase Chain Reaction”, (Mullis et al., eds., 1994).
One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention. Indeed, the present invention is in no way limited to the methods and materials described herein. For purposes of the invention, the following terms are defined below.
The terms “tumor” and “lesion” as used herein, refer to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues. Those skilled in the art will realize that a tumor tissue sample may comprise multiple biological elements, such as one or more cancer cells, partial or fragmented cells, tumors in various stages, surrounding histologically normal-appearing tissue, and/or macro or micro-dissected tissue.
The terms “cancer” and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Examples of cancer in the present disclosure include breast cancer, prostate cancer, colon cancer, bladder cancer, melanoma, leukemias, etc.
As used herein, the term “breast cancer” is used in the broadest sense and refers to all stages and all forms of cancer arising from the tissue of the breast.
As used herein, the term “exon” refers to any segment of an interrupted gene that is represented in the mature RNA product (B. Lewin. Genes IV Cell Press, Cambridge Mass. 1990). As used herein, the terms “intron” and “intronic sequence” refer to any non-coding region found within genes.
The term “expression product” as used herein refers to an expression product of a coding RNA transcript. Thus, the term refers to a polypeptide or protein.
As used herein, the term “intergenic region” refers to a stretch of DNA or RNA sequences located between clusters of genes that contain few or no genes. Intergenic regions are different from intragenic regions (or “introns”), which are non-coding regions that are found between exons within genes. An intergenic region may be comprised of one or more “intergenic sequences.”
As used herein, the terms “long intergenic non-coding RNAs” and “lincRNAs” are used interchangeably and refer to non-coding transcripts that are typically longer than 200 nucleotides.
As used herein, the term “level” as used herein refers to qualitative or quantitative determination of the number of copies of a coding or non-coding RNA transcript or a polypeptide/protein. An RNA transcript or a polypeptide/protein exhibits an “increased level” when the level of the RNA transcript or polypeptide/protein is higher in a first sample, such as in a clinically relevant subpopulation of patients (e.g., patients who have experienced cancer recurrence), than in a second sample, such as in a related subpopulation (e.g., patients who did not experience cancer recurrence). In the context of an analysis of a level of an RNA transcript or a polypeptide/protein in a tumor sample obtained from an individual patient, an RNA transcript or polypeptide/protein exhibits “increased level” when the level of the RNA transcript or polypeptide/protein in the subject trends toward, or more closely approximates, the level characteristic of a clinically relevant subpopulation of patients.
Thus, for example, when the RNA transcript analyzed is an RNA transcript that shows an increased level in subjects that experienced long-term survival without cancer recurrence as compared to subjects that did not experience long-term survival without cancer recurrence, then an “increased” level of a given RNA transcript can be described as being positively correlated with a likelihood of long-term survival without cancer recurrence. If the level of the RNA transcript in an individual patient being assessed trends toward a level characteristic of a subject who experienced long-term survival without cancer recurrence, the level of the RNA transcript supports a determination that the individual patient is more likely to experience long-term survival without cancer recurrence. If the level of the RNA transcript in the individual patient trends toward a level characteristic of a subject who experienced cancer recurrence, then the level of the RNA transcript supports a determination that the individual patient is more likely to experience cancer recurrence.
The term “likelihood score” is an arithmetically or mathematically calculated numerical value for aiding in simplifying or disclosing or informing the analysis of more complex quantitative information, such as the correlation of certain levels of the disclosed RNA transcripts, their expression products, or gene networks to a likelihood of a certain clinical outcome in a breast cancer patient, such as likelihood of long-term survival without breast cancer recurrence. A likelihood score may be determined by the application of a specific algorithm. The algorithm used to calculate the likelihood score may group the RNA transcripts, or their expression products, into gene networks. A likelihood score may be determined for a gene network by determining the level of one or more RNA transcripts, or an expression product thereof, and weighting their contributions to a certain clinical outcome such as recurrence. A likelihood score may also be determined for a patient. In an embodiment, a likelihood score is a recurrence score, wherein an increase in the recurrence score negatively correlates with an increased likelihood of long-term survival without breast cancer recurrence. In other words, an increase in the recurrence score correlates with bad prognosis. Examples of methods for determining the likelihood score or recurrence score are disclosed in U.S. Pat. No. 7,526,387.
The term “long-term” survival as used herein refers to survival for at least 3 years. In other embodiments, it may refer to survival for at least 5 years, or for at least 10 years following surgery or other treatment.
As used herein, the term “normalized” with regard to a coding or non-coding RNA transcript, or an expression product of the coding RNA transcript, refers to the level of the RNA transcript, or its expression product, relative to the mean levels of transcript/product of a set of reference RNA transcripts, or their expression products. The reference RNA transcripts, or their expression products, are based on their minimal variation across patients, tissues, or treatments. Alternatively, the coding or non-coding RNA transcript, or its expression product, may be normalized to the totality of tested RNA transcripts, or a subset of such tested RNA transcripts.
As used herein, the term “pathology” of cancer includes all phenomena that comprise the well-being of the patient. This includes, without limitation, abnormal or uncontrollable cell growth, metastasis, interference with the normal functioning of neighboring cells, release of cytokines or other secretory products at abnormal levels, suppression or aggravation of inflammatory or immunological response, neoplasia, premalignancy, malignancy, invasion of surrounding or distant tissues or organs, such as lymph nodes.
A “patient response” may be assessed using any endpoint indicating a benefit to the patient, including, without limitation, (1) inhibition, to some extent, of tumor growth, including slowing down and complete growth arrest; (2) reduction in the number of tumor cells; (3) reduction in tumor size; (4) inhibition (i.e., reduction, slowing down or complete stopping) of tumor cell infiltration into adjacent peripheral organs and/or tissues; (5) inhibition (i.e. reduction, slowing down or complete stopping) of metastasis; (6) enhancement of anti-tumor immune response, which may, but does not have to, result in the regression or rejection of the tumor; (7) relief, to some extent, of one or more symptoms associated with the cancer; (8) increase in the length of survival following treatment; and/or (9) decreased mortality at a given point of time following treatment.
The term “prognosis” as used herein, refers to the prediction of the likelihood of cancer-attributable death or progression, including recurrence, metastatic spread, and drug resistance, of neoplastic disease, such as breast cancer. The term “prediction” is used herein to refer to the likelihood that a patient will respond either favorably or unfavorably to a drug or set of drugs, and also the extent of those responses, or that a patient will survive, following surgical removal of the primary tumor and/or chemotherapy for a certain period of time without cancer recurrence. The methods of the present invention can be used clinically to make treatment decisions by choosing the most appropriate treatment modalities for any particular patient. The methods of the present invention are tools in predicting if a patient is likely to respond favorably to a treatment regimen, such as surgical intervention, chemotherapy with a given drug or drug combination, and/or radiation therapy, or whether long-term survival of the patient without cancer recurrence is likely, following surgery and/or termination of chemotherapy or other treatment modalities.
The term “breast cancer prognostic biomarker” refers to an RNA transcript, or an expression product thereof, intronic RNA, lincRNA, intergenic sequence, and/or intergenic region found to be associated with long term survival without breast cancer recurrence.
The term “reference” RNA transcript or an expression product thereof, as used herein, refers to an RNA transcript or an expression product thereof, whose level can be used to compare the level of an RNA transcript or its expression product in a test sample. In an embodiment of the invention, reference RNA transcripts include housekeeping genes, such as beta-globin, alcohol dehydrogenase, or any other RNA transcript, the level or expression of which does not vary depending on the disease status of the cell containing the RNA transcript or its expression product. In another embodiment, all of the assayed RNA transcripts, or their expression products, or a subset thereof, may serve as reference RNA transcripts or reference RNA expression products.
As used herein, the term “RefSeq RNA” refers to an RNA that can be found in the Reference Sequence (RefSeq) database, a collection of publicly available nucleotide sequences and their protein products built by the National Center for Biotechnology Information (NCBI). The RefSeq database provides an annotated, non-redundant record for each natural biological molecule (i.e. DNA, RNA or protein) included in the database. Thus, a sequence of a RefSeq RNA is well-known and can be found in the RefSeq database at http://www.ncbi.nlm.nih.gov/RefSeq/. See also Pruitt et al., Nucl. Acids Res. 33(Supp 1):D501-D504 (2005).
As used herein, the term “RNA transcript” refers to the RNA transcription product of DNA and includes coding and non-coding RNA transcripts. RNA transcripts include, for example, mRNA, an unspliced RNA, a splice variant mRNA, a microRNA, fragmented RNA, long intergenic non-coding RNAs (lincRNAs), intergenic RNA sequences or regions, and intronic RNAs.
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. The terms “subject,” “individual,” and “patient” thus encompass individuals having cancer (e.g., breast cancer), including those who have undergone or are candidates for resection (surgery) to remove cancerous tissue.
As used herein, the term “surgery” applies to surgical methods undertaken for removal of cancerous tissue, including mastectomy, lumpectomy, lymph node removal, sentinel lymph node dissection, prophylactic mastectomy, prophylactic ovary removal, cryotherapy, and tumor biopsy. The tumor samples used for the methods of the present invention may have been obtained from any of these methods.
The term “tumor” as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.
The term “tumor sample” as used herein refers to a sample comprising tumor material obtained from a cancer patient. The term encompasses tumor tissue samples, for example, tissue obtained by surgical resection and tissue obtained by biopsy, such as for example, a core biopsy or a fine needle biopsy. In a particular embodiment, the tumor sample is a fixed, wax-embedded tissue sample, such as a formalin-fixed, paraffin-embedded tissue sample. Additionally, the term “tumor sample” encompasses a sample comprising tumor cells obtained from sites other than the primary tumor, e.g., circulating tumor cells. The term also encompasses cells that are the progeny of the patient's tumor cells, e.g. cell culture samples derived from primary tumor cells or circulating tumor cells. The term further encompasses samples that may comprise protein or nucleic acid material shed from tumor cells in vivo, e.g., bone marrow, blood, plasma, serum, and the like. The term also encompasses samples that have been enriched for tumor cells or otherwise manipulated after their procurement and samples comprising polynucleotides and/or polypeptides that are obtained from a patient's tumor material.
As used herein, “whole transcriptome sequencing” refers to the use of high throughput sequencing technologies to sequence the entire transcriptome in order to get information about a sample's RNA content. Whole transcriptome sequencing can be done with a variety of platforms for example, the Genome Analyzer (Illumina, Inc., San Diego, Calif.), the SOLiD™ Sequencing System (Life Technologies, Carlsbad, Calif.), Ion Torrent (Life Technologies, Carlsbad, Calif.), and GS FLX and GS Junior Systems (454 Life Sciences, Roche, Branford, Conn.). However, any platform useful for whole transcriptome sequencing may be used.
The term “RNA-Seq” or “transcriptome sequencing” refers to sequencing performed on RNA (or cDNA) instead of DNA, where typically, the primary goal is to measure expression levels, detect fusion transcripts, alternative splicing, and other genomic alterations that can be better assessed from RNA. RNA-Seq includes whole transcriptome sequencing as well as target specific sequencing.
The term “computer-based system,” as used herein, refers to the hardware means, software means, and data storage means used to analyze information. The minimum hardware of a patient computer-based system comprises a central processing unit (CPU), input means, output means, and data storage means. A skilled artisan can readily appreciate that many of the currently available computer-based system are suitable for use in the present invention and may be programmed to perform the specific measurement and/or calculation functions of the present invention.
To “record” data, programming or other information on a computer readable medium refers to a process for storing information, using any such methods as known in the art. Any convenient data storage structure may be chosen, based on the means used to access the stored information. A variety of data processor programs and formats can be used for storage, e.g. word processing text file, database format, etc.
A “processor” or “computing means” references any hardware and/or software combination that will perform the functions required of it. For example, any processor herein may be a programmable digital microprocessor such as available in the form of an electronic controller, mainframe, server or personal computer (desktop or portable). Where the processor is programmable, suitable programming can be communicated from a remote location to the processor, or previously saved in a computer program product (such as a portable or fixed computer readable storage medium, whether magnetic, optical or solid state device based). For example, a magnetic medium or optical disk may carry the programming, and can be read by a suitable reader communicating with each processor at its corresponding station.
A “mapped string” or “string” as used herein refers to the result of investigating the expression of a population of RNAs is evaluated to identify individual RNA species associated with disease recurrence rate, where each member of the subset of genes that is identified at a given false discovery rate as associating with recurrence risk is then graphically placed on its chromosomal locus. Mapped strings demonstrate that genes associated with good prognosis tend to distribute in long strings uninterrupted by genes associated with bad prognosis, and the reverse is true for genes associated with bad prognosis. Each identified string may then be evaluated as a unit (metagene) candidate biomarker for risk of disease recurrence.
A “transcriptome profile map” or “TPM” as used herein refers to the result of determining the average level of each transcript that associates with disease recurrence for an entire patient cohort. This is used to construct for each patient a transcriptome profile physical map where the X-axis represents the chromosomal locus for each RNA species and the Y-axis represents, for each RNA species, the difference between the average level in the entire patient cohort and the level within the particular patient tumor, standardized by dividing by half the population interquartile range (IQR) for the RNA species. These graphs reveal striking patient to patient differences, for example various sizes and numbers of spike-like features (segments) and global clouds of data points that vary considerably in their dispersion, features that can be tested as candidate biomarkers for risk of disease recurrence. A “TPM feature” refers to a measure of TPM dispersion that associates with a particular outcome, such as the rate of recurrence of disease, for example, recurrence of breast cancer. Measures of TPM dispersion may include mean absolute deviation from median and mean absolute deviation location to location.
Mapped strings may be generated by first determining the abundance of a gene expression species (RNA or protein) as measured by techniques including next generation sequencing RNA-Seq, RT-PCR, expression arrays, or proteomic methods such as Western blots and mass spectrometry.
The gene expression data may then be normalized according to the methods described herein, for example, by 3rd quartile, candidate reference genes.
Next, the level of RNA or protein expression may be correlated with a clinical outcome, such as prognosis or prediction of cancer, diabetes, inflammatory diseases, neurodegenerative diseases, or heart disease. For each RNA or protein species, a degree of association to the clinical outcome, including magnitude and direction, may be estimated. A criterion for significant association (based on, for example, false discovery rate (FDR, q-value) or statistical significance (p-value)) may then be established.
RNA or protein species that are significantly associated with the clinical outcome may be carried forward and assigned to their corresponding gene position on a human chromosomal coordinate physical map, for example, human genome browser hg19 assembly, UCSC.
From the chromosomally ordered representation of genes described above, mapped strings may be defined as an uninterrupted sequence of genes that have the same direction of association with outcome (e.g. good prognosis constituting one direction of association and bad prognosis representing the opposite direction of association). Mapped strings may be defined, however, in various ways, for example by minimum number of genes (or by introns found within a minimum number of genes) or by physical boundary. Mapped strings may also be restricted by other measures such as functional homogeneity or co-expression.
In some embodiments, mapped strings are defined by a minimum number of genes or introns found within a minimum number of genes. For example, mapped strings may be defined by at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, or at least 45 genes. In other embodiments, mapped strings are defined by physical boundary, for example, the end of a chromosome or the end of a chromosomal arm. In some embodiments, mapped strings are defined by functional homogeneity, for example groups of genes with similar function, such as proliferative genes or genes belonging to a common signaling pathway. In other embodiments, mapped strings are defined by genes that co-express with one another, for example, gene groups that have a co-expression value of R>0.4. In various embodiments, mapped strings are defined by any of the above aspects, alone or in combination.
Mapped strings can be represented as metagenes which can be used to create univariate or multivariate models that predict clinical outcomes. The quantitative contribution of individual genes in these metagenes can be set in a variety of ways. For example, the standardized normalized measure of the abundance of each RNA or protein species can be summed.
Transcriptome profile maps (TPMs) may be generated by first measuring the amounts of individual gene expression product species in a sample of patient tissue. For example, the gene expression product species could be RNA or protein species. For example, the tissue could be blood, or solid tissue, and could be healthy or diseased (such as tumor) tissue. Several different technology platforms could be used to make these measurements, e.g., RNA-Seq, DNA microarrays, mass spectrometry, or ELISA.
Next, the measured amounts of the species can be normalized to compensate for variation in tissue amount or integrity from patient to patient. Examples of normalization methods include third quartile normalization, normalization using global expression, and reference gene normalization.
The central tendency of the normalized level of each RNA or protein species for the entire investigated patient population, for example, the median or the mean level, can then be calculated. This step could also be carried out for a patient population that is different from the study population but which has similar attributes (e.g., all estrogen receptor positive early breast cancer). In one embodiment, the level of each species is standardized to produce a centered, scaled score, for example, a robust z score such as z=(level−median)/(0.5*(3rd population quartile−1st population quartile)).
Additionally, a graphic (transcriptome physical map) can then be created for each patient study tissue in the study population, wherein the X-axis represents the chromosomal locus for each RNA or protein species gene and the Y-axis represents, for that species, the patient's standardized, normalized abundance value. In certain embodiments, gene expression product species present at low abundance or low population variance in abundance (e.g., below the 50th percentile) can be excluded from transcriptome physical maps.
Next, the data density features of these transcriptome physical maps can be mined for biomarkers of patient clinical outcomes, for example sensitivity or resistance to certain drugs, or progression from early cancer to a more advanced stage, or survival. Examples of such map density features are global dispersion of transcriptome z-scores, and number of Y-axis map segments. Map segments can be defined and quantified in several ways (e.g, by using circular binary segmentation or piecewise constant fitting programs). Individual segments are also evaluated and defined by minimum z-score cutoffs and minimum number of gene product species in a row on the same side of the Y-axis zero value.
Correlating Level of RNA Transcripts or their Expression Products to a Clinical Outcome
One skilled in the art will recognize that there are many statistical methods that may be used to determine whether there is a correlation between an outcome of interest (e.g., likelihood of survival) and levels of RNA or protein. Similarly, there are many statistical methods that may be used to determine whether there is a correlation between an outcome of interest (e.g., likelihood of survival) and TPM features as described here. This relationship can be presented as a continuous recurrence score (RS), or patients may be stratified into risk groups (e.g., low, intermediate, high). For example, a Cox proportional hazards regression model may fit to a particular clinical endpoint (e.g., RFI, DFS, OS). One assumption of the Cox proportional hazards regression model is the proportional hazards assumption, i.e. the assumption that effect parameters multiply the underlying hazard. Assessments of model adequacy may be performed including, but not limited to, examination of the cumulative sum of martingale residuals. One skilled in the art would recognize that there are numerous statistical methods that may be used to fit a flexible parametric model using the hazard scale and the Weibull distribution with natural spline smoothing of the log cumulative hazards function, with effects for treatment (chemotherapy or observation) and RS allowed to be time-dependent. See, e.g., P. Royston, M. Parmer, Statistics in Medicine 21:2175-2197, 2002).
Any of the methods described may group the levels of RNA transcripts or their expression products. The grouping of the RNA transcripts or expression products may be performed at least in part based on creation of mapped strings and/or TPMs as described herein. The formation of groups, in addition, can facilitate the mathematical weighting of the contribution of various expression levels to the recurrence/likelihood score. The weighting of a gene grouping representing a physiological process or component cellular characteristic can reflect the contribution of that process or characteristic to the pathology of the cancer and clinical outcome. Accordingly, the present invention provides gene groupings of the RNA transcripts, or their expression products, identified herein for use in the methods disclosed herein.
Methods to Predict Likelihood of Long-Term Survival without Breast Cancer Recurrence
As described above, a number of coding and non-coding RNA transcripts that correlate with breast cancer prognosis were identified previously. The levels of these RNA transcripts, or their expression products, can be determined in a tumor sample obtained from an individual patient who has breast cancer and for whom treatment is being contemplated. Depending on the outcome of the assessment, treatment with chemotherapy may be indicated, or an alternative treatment regimen may be indicated.
In carrying out the method of the present invention, a tumor sample is assayed or measured for a level of an RNA transcript, or its expression product. The tumor sample can be obtained from a solid tumor, e.g., via biopsy, or from a surgical procedure carried out to remove a tumor; or from a tissue or bodily fluid that contains cancer cells. In an embodiment of the invention, the tumor sample is obtained from a patient with breast cancer, such as ER-positive breast cancer. In another embodiment, the level of an RNA transcript, or its expression product, is normalized relative to the level of one or more reference RNA transcripts, or its expression product.
Methods of Assaying Levels of RNA Transcripts or their Expression Products
Methods of expression profiling include methods based on sequencing of polynucleotides, methods based on hybridization analysis of polynucleotides, and proteomics-based methods. Representative methods for sequencing-based analysis include Massively Parallel Sequencing (see e.g., Tucker et al., The American J. Human Genetics 85:142-154, 2009) and Serial Analysis of Gene Expression (SAGE). Exemplary methods known in the art for the quantification of mRNA expression in a sample include northern blotting and in situ hybridization (Parker & Barnes, Methods in Molecular Biology 106:247-283, 1999); RNAse protection assays (Hod, Biotechniques 13:852-854, 1992); and PCR-based methods, such as reverse transcription polymerase chain reaction (RT-PCR) (Weis et al., Trends in Genetics 8:263-264, 1992). Antibodies may be employed that can recognize sequence-specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes or DNA-protein duplexes.
Nucleic Acid Sequencing-Based Methods
Nucleic acid sequencing technologies are suitable methods for expression analysis. The principle underlying these methods is that the number of times a cDNA sequence is detected in a sample is directly related to the relative RNA levels corresponding to that sequence. These methods are sometimes referred to by the term Digital Gene Expression (DGE) to reflect the discrete numeric property of the resulting data. Early methods applying this principle were Serial Analysis of Gene Expression (SAGE) and Massively Parallel Signature Sequencing (MPSS). See, e.g., S. Brenner, et al., Nature Biotechnology 18(6):630-634 (2000).
More recently, the advent of “next-generation” sequencing technologies has made DGE simpler, higher throughput, and more affordable. As a result, more laboratories are able to utilize DGE to screen the expression of more nucleic acids in more individual patient samples than previously possible. See, e.g., J. Marioni, Genome Research 18(9):1509-1517 (2008); R. Morin, Genome Research 18(4):610-621 (2008); A. Mortazavi, Nature Methods 5(7):621-628 (2008); N. Cloonan, Nature Methods 5(7):613-619 (2008). Massively parallel sequencing methods have also enabled whole genome or transcriptome sequencing, allowing the analysis of not only coding but also non-coding sequences. As reviewed in Tucker et al., The American J. Human Genetics 85:142-154 (2009), there are several commercially available massively parallel sequencing platforms, such as the Illumina Genome Analyzer (Illumina, Inc., San Diego, Calif.), Ion Torrent Sequencer (Life Technologies, Carlsbad, Calif.), Roche GS-FLX 454 Genome Sequencer (Roche Applied Science, Germany), and the Helicos® Genetic Analysis Platform (Helicos Biosciences Corp., Cambridge, Mass.). Other developing technologies may be used.
The starting material is typically total RNA isolated from a human tumor, usually from a primary tumor. Optionally, normal tissues from the same patient can be used as an internal control. RNA can be extracted from a tissue sample, e.g., from a sample that is fresh, frozen (e.g. fresh frozen), or paraffin-embedded and fixed (e.g. formalin-fixed).
General methods for RNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987), and De Andres et al., BioTechniques 18:42044 (1995). In particular, RNA isolation can be performed using a purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer's instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Other commercially available RNA isolation kits include MasterPure™ Complete DNA and RNA Purification Kit (EPICENTRE®, Madison, Wis.), and Paraffin Block RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from a tumor sample can be isolated, for example, by cesium chloride density gradient centrifugation. The isolated RNA may then be depleted of ribosomal RNA as described in U.S. Pub. No. 2011/0111409.
The sample containing the RNA is then subjected to reverse transcription to produce cDNA from the RNA template, followed by exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MMLV-RT). The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the circumstances and the goal of expression profiling. For example, extracted RNA can be reverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, Calif., USA), following the manufacturer's instructions. The derived cDNA can then be used as a template in the subsequent PCR reaction.
PCR-based methods use a thermostable DNA-dependent DNA polymerase, such as a Taq DNA polymerase. For example, TaqMan® PCR typically utilizes the 5′-nuclease activity of Taq or Tth polymerase to hydrolyze a hybridization probe bound to its target amplicon, but any enzyme with equivalent 5′ nuclease activity can be used. Two oligonucleotide primers are used to generate an amplicon typical of a PCR reaction product. A third oligonucleotide, or probe, can be designed to facilitate detection of a nucleotide sequence of the amplicon located between the hybridization sites of the two PCR primers. The probe can be detectably labeled, e.g., with a reporter dye, and can further be provided with both a fluorescent dye, and a quencher fluorescent dye, as in a Taqman® probe configuration. Where a Taqman® probe is used, during the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resultant probe fragments disassociate in solution, and signal from the released reporter dye is free from the quenching effect of the second fluorophore. One molecule of reporter dye is liberated for each new molecule synthesized, and detection of the unquenched reporter dye provides the basis for quantitative interpretation of the data.
TaqMan® RT-PCR can be performed using commercially available equipment, such as, for example, ABI PRISM 7700™ Sequence Detection System™ (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), or Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In a preferred embodiment, the 5′ nuclease procedure is run on a real-time quantitative PCR device such as the ABI PRISM 7700™ Sequence Detection System™. The system consists of a thermocycler, laser, charge-coupled device (CCD), camera and computer. The system amplifies samples in a 384-well format on a thermocycler. The RT-PCR may be performed in triplicate wells with an equivalent of 2 ng RNA input per 10 μL-reaction volume. During amplification, laser-induced fluorescent signal is collected in real-time through fiber optics cables for all wells, and detected at the CCD. The system includes software for running the instrument and for analyzing the data.
5′-Nuclease assay data are generally initially expressed as a threshold cycle (“Ct”). Fluorescence values are recorded during every cycle and represent the amount of product amplified to that point in the amplification reaction. The threshold cycle (Ct) is generally described as the point when the fluorescent signal is first recorded as statistically significant.
To minimize errors and the effect of sample-to-sample variation, RT-PCR is usually performed using an internal standard. The ideal internal standard gene (also referred to as a reference gene) is expressed at a constant level among cancerous and non-cancerous tissue of the same origin (i.e., a level that is not significantly different among normal and cancerous tissues), and is not significantly affected by the experimental treatment (i.e., does not exhibit a significant difference in expression level in the relevant tissue as a result of exposure to chemotherapy).
RNAs most frequently used to normalize patterns of gene expression are mRNAs for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and β-actin. Gene expression measurements can be normalized relative to the mean of one or more (e.g., 2, 3, 4, 5, or more) reference genes. Reference-normalized expression measurements can range from 0 to 15, where a one unit increase generally reflects a 2-fold increase in RNA quantity.
Real time PCR is compatible both with quantitative competitive PCR, where an internal competitor for each target sequence is used for normalization, and with quantitative comparative PCR using a normalization gene contained within the sample, or a housekeeping gene for RT-PCR. For further details see, e.g. Held et al., Genome Research 6:986-994 (1996).
PCR primers and probes can be designed based upon exon, intron, or intergenic sequences present in the RNA transcript of interest. Primer/probe design can be performed using publicly available software, such as the DNA BLAT software developed by Kent, W. J., Genome Res. 12(4):656-64 (2002), or by the BLAST software including its variations.
Where necessary or desired, repetitive sequences of the target sequence can be masked to mitigate non-specific signals. Exemplary tools to accomplish this include the Repeat Masker program available on-line through the Baylor College of Medicine, which screens DNA sequences against a library of repetitive elements and returns a query sequence in which the repetitive elements are masked. The masked sequences can then be used to design primer and probe sequences using any commercially or otherwise publicly available primer/probe design packages, such as Primer Express (Applied Biosystems); MGB assay-by-design (Applied Biosystems); Primer3 (Steve Rozen and Helen J. Skaletsky (2000) Primer3 on the WWW for general users and for biologist programmers. In: Rrawetz S, Misener S (eds) Bioinformatics Methods and Protocols: Methods in Molecular Biology. Humana Press, Totowa, N.J., pp 365-386).
Other factors that can influence PCR primer design include primer length, melting temperature (Tm), and G/C content, specificity, complementary primer sequences, and 3′-end sequence. In general, optimal PCR primers are generally 17-30 bases in length, and contain about 20-80%, such as, for example, about 50-60% G+C bases, and exhibit Tm's between 50 and 80 OC, e.g. about 50 to 70° C.
For further guidelines for PCR primer and probe design see, e.g. Dieffenbach, C W. et al, “General Concepts for PCR Primer Design” in: PCR Primer, A Laboratory Manual, Cold Spring Harbor Laboratory Press., New York, 1995, pp. 133-155; Innis and Gelfand, “Optimization of PCRs” in: PCR Protocols, A Guide to Methods and Applications, CRC Press, London, 1994, pp. 5-11; and Plasterer, T. N. Primerselect: Primer and probe design. Methods Mol. Biol. 70:520-527 (1997), the entire disclosures of which are hereby expressly incorporated by reference.
In MassARRAY-based methods, such as the exemplary method developed by Sequenom, Inc. (San Diego, Calif.) following the isolation of RNA and reverse transcription, the obtained cDNA is spiked with a synthetic DNA molecule (competitor), which matches the targeted cDNA region in all positions, except a single base, and serves as an internal standard. The cDNA/competitor mixture is PCR amplified and is subjected to a post-PCR shrimp alkaline phosphatase (SAP) enzyme treatment, which results in the dephosphorylation of the remaining nucleotides. After inactivation of the alkaline phosphatase, the PCR products from the competitor and cDNA are subjected to primer extension, which generates distinct mass signals for the competitor- and cDNA-derived PCR products. After purification, these products are dispensed on a chip array, which is pre-loaded with components needed for analysis with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis. The cDNA present in the reaction is then quantified by analyzing the ratios of the peak areas in the mass spectrum generated. For further details see e.g., Ding and Cantor, Proc. Natl. Acad. Sci. USA 100:3059-3064 (2003).
Further PCR-based techniques that can find use in the methods disclosed herein include, for example, BeadArray® technology (Illumina, San Diego, Calif.; Oliphant et al., Discovery of Markers for Disease (Supplement to Biotechniques), June 2002; Ferguson et al., Analytical Chemistry 72:5618, 2000); BeadsArray for Detection of Gene Expression® (BADGE), using the commercially available LuminexlOO LabMAP® system and multiple color-coded microspheres (Luminex Corp., Austin, Tex.) in a rapid assay for gene expression (Yang et al., Genome Res. 11:1888-1898, 2001); and high coverage expression profiling (HiCEP) analysis (Fukumura et al., Nucl. Acids. Res. 31(16) e94, 2003).
In this method, polynucleotide sequences of interest (including cDNAs and oligonucleotides) are arrayed on a substrate. The arrayed sequences are then contacted under conditions suitable for specific hybridization with detectably labeled cDNA generated from RNA of a sample. The source of RNA typically is total RNA isolated from a tumor sample, and optionally from normal tissue of the same patient as an internal control or cell lines. RNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples.
For example, PCR amplified inserts of cDNA clones of a gene to be assayed are applied to a substrate in a dense array. Usually at least 10,000 nucleotide sequences are applied to the substrate. For example, the microarrayed genes, immobilized on the microchip at 10,000 elements each, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After washing under stringent conditions to remove non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance.
With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pair wise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. The miniaturized scale of the hybridization affords a convenient and rapid evaluation of the expression pattern for large numbers of genes. Such methods have been shown to have the sensitivity required to detect rare transcripts, which are expressed at a few copies per cell, and to reproducibly detect at least approximately two-fold differences in the expression levels (Schena et at, Proc. Natl. Acad. Sci. USA 93(2): 106-149, 1996). Microarray analysis can be performed on commercially available equipment, following the manufacturer's protocols, such as by using the Affymetrix GenChip® technology, or Incyte's microarray technology.
Isolating RNA from Body Fluids
Methods of isolating RNA for expression analysis from blood, plasma and serum (see for example, Tsui N B et al. Clin. Chem. 48, 1647-53, 2002 and references cited therein) and from urine (see for example, Boom R et al. J Clin Microbiol. 28, 495-503, 1990 and references cited therein) have been described.
Immunohistochemistry methods are also suitable for detecting the expression levels of genes and applied to the method disclosed herein. Antibodies (e.g., monoclonal antibodies) that specifically bind a gene product of a gene of interest can be used in such methods. The antibodies can be detected by direct labeling of the antibodies themselves, for example, with radioactive labels, fluorescent labels, hapten labels such as biotin, or an enzyme such as horse radish peroxidase or alkaline phosphatase. Alternatively, unlabeled primary antibody can be used in conjunction with a labeled secondary antibody specific for the primary antibody. Immunohistochemistry protocols and kits are well known in the art and are commercially available.
The term “proteome” is defined as the totality of the proteins present in a sample (e.g. tissue, organism, or cell culture) at a certain point of time. Proteomics includes, among other things, study of the global changes of protein expression in a sample (also referred to as “expression proteomics”). Proteomics typically includes the following steps: (1) separation of individual proteins in a sample by 2-D gel electrophoresis (2-D PAGE); (2) identification of the individual proteins recovered from the gel, e.g. my mass spectrometry or N-terminal sequencing, and (3) analysis of the data using bioinformatics.
General Description of the RNA Isolation and Preparation from Fixed, Paraffin-Embedded Samples for Whole Transcriptome Sequencing
The steps of a representative protocol for profiling gene expression levels using fixed, paraffin-embedded tissues as the RNA source are provided in various published journal articles. (See, e.g., T. E. Godfrey et al., J. Molec. Diagnostics 2: 84-91, 2000; K. Specht et al., Am. J. Pathol. 158: 419-29, 2001, M. Cronin, et al., Am J Pathol 164:35-42, 2004). Modified methods can used for whole transcriptome sequencing as described in the Examples section. Briefly, a representative process starts with cutting a tissue sample section (e.g. about 10 μm thick sections of a paraffin-embedded tumor tissue sample). The RNA is then extracted, and ribosomal RNA may be deleted as described in U.S. Pub. No. 2011/0111409. cDNA sequencing libraries may be prepared that are directional and single or paired-end using commercially available kits such as the ScriptSeq™ mRNA-Seq Library Preparation Kit (Epicenter Biotechnologies, Madison, Wis.). The libraries may also be barcoded for multiplex sequencing using commercially available barcode primers such as the RNA-Seq Barcode Primers from Epicenter Biotechnologies (Madison, Wis.). PCR is then carried out to generate the second strand of cDNA to incorporate the barcodes and to amplify the libraries. After the libraries are quantified, the sequencing libraries may be sequenced as described herein.
To perform particular biological processes, genes often work together in a concerted way, i.e. they are co-expressed. Co-expressed gene networks identified for a disease process like cancer can also serve as prognostic biomarkers. Such co-expressed genes can be assayed in lieu of, or in addition to, assaying the biomarker with which they co-express.
One skilled in the art will recognize that many co-expression analysis methods now known or later developed will fall within the scope and spirit of the present invention. These methods may incorporate, for example, correlation coefficients, co-expression network analysis, clique analysis, etc., and may be based on expression data from RT-PCR, microarrays, sequencing, and other similar technologies. For example, gene expression clusters can be identified using pair-wise analysis of correlation based on Pearson or Spearman correlation coefficients. (See e.g, Pearson K. and Lee A., Biometrika 2:357, 1902; C. Spearman, Amer. J. Psychol. 15:72-101, 1904; J. Myers, A. Well, Research Design and Statistical Analysis, p. 508, 2nd Ed., 2003) In general, a correlation coefficient of equal to or greater than 0.3 is considered to be statistically significant in a sample size of at least 20. (See e.g., G. Norman, D. Streiner, Biostatistics: The Bare Essentials, 137-138, 3rd Ed. 2007)
In order to minimize expression measurement variations due to non-biological variations in samples, e.g., the amount and quality of product to be measured, the level of an RNA transcript or its expression product may be normalized relative to the mean levels obtained for one or more reference RNA transcripts or their expression products. Examples of reference RNA transcripts or expression products include housekeeping genes, such as GAPDH. Alternatively, all of the assayed RNA transcripts or expression products, or a subset thereof, may also serve as reference. On a transcript (or protein)-by-transcript (or protein) basis, measured normalized amount of a patient tumor RNA or protein may be compared to the amount found in a cancer tissue reference set. See e.g., Cronin, M. et al., Am. Soc. Investigative Pathology 164:35-42 (2004). The normalization may be carried out such that a one unit increase in normalized level of an RNA transcript or expression product generally reflects a 2-fold increase in quantity present in the sample.
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 present invention thus provides kits comprising agents, which may include primers and/or probes, for quantitating the level of the disclosed RNA transcripts or their expression products via methods such as whole transcriptome sequencing or RT-PCR for predicting prognostic outcome. Such kits may optionally contain reagents for the extraction of RNA from tumor samples, in particular, fixed paraffin-embedded tissue samples and/or reagents for whole transcriptome sequencing. In addition, the kits may optionally comprise the 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 microliter 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, 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). Mathematical algorithms used to estimate or quantify prognostic information are also potential components of kits.
The methods of this invention are suited for the preparation of reports summarizing the predictions resulting from the methods of the present invention. A “report” as described herein, is an electronic or tangible document that includes elements that provide information of interest relating to a likelihood assessment and its results. A subject report includes at least a likelihood assessment, e.g., an indication as to the likelihood that a cancer patient will exhibit long-term survival without breast cancer recurrence. A subject report can be completely or partially electronically generated, e.g., presented on an electronic display (e.g., computer monitor). A 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 include a normalized level of one or more RNA transcripts of interest, and 6) other features.
The present invention therefore provides methods of creating reports and the reports resulting therefrom. The report may include a summary of the levels of the RNA transcripts, or the expression products of such RNA transcripts, in the cells obtained from the patient's tumor sample. The report may include a prediction that the patient has an increased likelihood of long-term survival without breast cancer recurrence or the report may include a prediction that the subject has a decreased likelihood of long-term survival without breast cancer recurrence. The report may include a recommendation for a treatment modality such as surgery alone or surgery in combination with chemotherapy. The report may be presented in electronic format or on paper.
Thus, in some embodiments, the methods of the present invention further include generating a report that includes information regarding the patient's likelihood of long-term survival without breast cancer recurrence. For example, the methods of the present invention can further include a step of generating or outputting a report providing the results of a patient response likelihood assessment, which 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 likelihood that a patient will exhibit long-term survival without breast cancer recurrence, is provided to a user. An assessment as to the likelihood that a cancer patient will exhibit long-term survival without breast cancer recurrence, is referred to as a “likelihood assessment.” A person or entity who prepares a report (“report generator”) may also perform the likelihood assessment. 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 RNA transcript or its expression product; d) measuring a level of a reference RNA transcript or its expression product; and e) determining a normalized level of an RNA transcript or its expression product. Alternatively, an entity other than the report generator can perform one or more sample gathering, sample processing, and data generation.
The term “user” or “client” refers 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 for use in the likelihood assessment. In some cases, the person or entity 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.” 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., an oncologist, surgeon, pathologist), 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).
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.
The methods and systems described herein can be implemented in numerous ways. In one embodiment of the invention, the methods involve use of a communications infrastructure, for example, the internet. Several embodiments of the invention are discussed below. The present invention may also 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 (e.g., at a service provider's facility).
In an embodiment of the invention, 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 result and/or generate a report in the reviewer's computing environment. The score can be a numerical score (representative of a numerical value) or a non-numerical score representative of a numerical value or range of numerical values (e.g., “A”: representative of a 90-95% likelihood of a positive response; “High”: representative of a greater than 50% chance of a positive response (or some other selected threshold of likelihood); “Low”: representative of a less than 50% chance of a positive response (or some other selected threshold of likelihood), and the like.
As a computer system, the system generally includes a processor unit. The processor unit operates to receive information, which can include test data (e.g., level of an RNA transcript or its expression product; level of a reference RNA transcript or its expression product; normalized level of an RNA transcript or its expression product) and may also include other data such as patient data. 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, 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 the invention, all or a portion of the input data and/or 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 that may exist in a confidential database as the healthcare facility.
The present 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 results of a likelihood assessment 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 includes a program that 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 out a likelihood assessment (e.g., primers, probes, arrays, or such other kit components).
Having described the invention, the same will be more readily understood through reference to the following Examples, which are provided by way of illustration, and are not intended to limit the invention in any way. All citations through the disclosure are hereby expressly incorporated by reference.
Unless otherwise noted, the materials and methods described in Examples 1 and 2 are also described in PCT/US2012/063313, filed Nov. 2, 2012, now WO 2013/070521, which is incorporated by reference in its entirety. Furthermore, accession numbers of genes and intronic coordinates for genes and introns listed in the present disclosure are identified in PCT/US2012/063313, filed Nov. 2, 2012, now WO 2013/070521.
One hundred and thirty-six primary breast cancer FFPE tumor specimens with clinical outcomes were provided by Providence St. Joseph Medical Center (Burbank, Calif.), with institutional review board approval. The time to first recurrence of breast cancer or death due to breast cancer (including death due to unknown cause) was determined from these records. Patients who were still alive without breast cancer recurrence or who died due to known other causes were considered censored at the time of last follow-up or death. These tumor specimens were used for biomarker discovery in the development of the Oncotype DX® assay. See e.g., U.S. Pat. No. 7,081,340; S. Paik et al., The New England Journal of Medicine 351, 2817, 2004). For the present study, 136 specimens had adequate RNA remaining. Among the 136 patients, 26 experienced breast cancer recurrence or death due to breast cancer.
Total RNA was prepared from three 10-μm-thick sections of FFPE tumor tissue as previously described using the MasterPure™ Purification Kit (Epicentre® Biotechnologies, Madison, Wis.). M. Cronin et al.; Am. J. Pathol. 164, 35-42, 2004. One hundred nanograms of the isolated RNA were depleted of ribosomal RNA as described. See U.S. Pub. No. 2011/0111409. Sequencing libraries for whole transcriptome analysis were prepared using ScriptSeq™ mRNA-Seq Library Preparation Kits (Epicentre® Biotechnologies, Madison, Wis.). During the cDNA synthesis step, additional incubation for 90 minutes at 37° C. was implemented in the reverse transcription step to increase library yield. After 3′-terminal tagging, the di-tagged cDNA was purified using MinElute® PCR Purification Kits (Qiagen, Valencia, Calif.). Two 6 base index sequences were used to prepare barcoded libraries for duplex sequencing (RNA-Seq Barcode Primers; Epicentre® Biotechnologies, Madison, Wis.). PCR was carried out through 16 cycles to generate the second strand of cDNA, incorporate barcodes, and amplify libraries. The amplified libraries were size-selected by a solid phase reversible immobilization, paramagnetic bead-based process (Agencourt® AMPure® XP System; Beckman Coulter Genomics, Danvers, Mass.). Libraries were quantified by PicoGreen® assay (Life Technologies, Carlsbad, Calif.) and visualized with an Agilent Bioanalyzer using a DNA 1000 kit (Agilent Technologies, Waldbronn, Germany).
TruSeq™ SR Cluster Kits v2 (Illumina Inc.; San Diego, Calif.) were used for cluster generation in an Illumina cBOT™ instrument following the manufacturer's protocol. Two indexed libraries were loaded into each lane of flow cells. Sequencing was performed on an Illumina HiSeq®2000 instrument (Illumina, Inc.) by the manufacturer's protocol. Multiplexed single-read runs were carried out with a total of 57 cycles per run (including 7 cycles for the index sequences).
Each sequencing lane was duplexed with two patient sample libraries using a 6 base barcode to differentiate between them. The mean read ratio+/−SD between the two samples in each lane was 1.05±0.38 and the mean+/−SD percentage of un-discerned barcodes was 2.08%+1.63%. Using principal components analysis and other exploratory data analysis methods, no systematic differences were found among samples associated with flow cell or barcode.
In a run-in phase of the study, duplicate libraries were prepared for 8 samples selected at random from the study set of 136. RefSeq RNA coverage for these libraries ranged between 3.1M and 6.7M uniquely mapped reads. Log count Pearson correlations among duplicate libraries ranged between 0.947 and 0.985. Single libraries were prepared for the remaining 128 samples and distributed in duplex mode among the lanes of 8 flow-cells. Sequencing in 3 lanes failed. Two libraries had low yield, resulting in low coverage. Three lanes were flagged by various Illumina process monitoring indices: low Q30 (coverage=2.8M and 4.2M), high cluster density (coverage=1.6M and 1.8M), or inadequate imaging (coverage=3.3M and 3.1M). For the remaining lanes, sample coverage ranged between 2.5M and 7.3M reads. New libraries for the samples that had low yield were prepared and sequenced. Libraries in the failed and flagged lanes, as well as some of the low coverage samples, were re-sequenced. Replicate correlations among all sequenced samples were very high, 0.985 for the samples with the high cluster density in the original run, and over 0.990 for all others. For the analysis data set, data for one of each of the duplicate libraries from the run-in experiment were kept. For the samples for which new libraries were prepared and for the samples in the failed and flagged lanes, the reads from the subsequent run were used. For the samples with low coverage for which the library was reprocessed, reads from the two runs were pooled. For the rest of the samples, the reads from the single lane were used. Results differed little when other data analysis procedures were used, for example, using only the second run when libraries were reprocessed.
With the exception noted below, all primary analysis of sequence data was performed in CASAVA 1.7, the standard data processing package from Illumina. De-multiplexing of sample indices was set with 1 mismatch tolerance to separate the two samples within each lane. Raw FASTQ sequences were trimmed from both ends before mapping to the human genome (UCSC release, version 19), to address 3′ end adapter contamination and random RT primer artifacts, and 5′ end terminal-tagging oligonucleotide artifacts. The libraries as prepared contain strand-of-origin (directional) sequence information. Annotated RNA counts (defined by refFlat.txt from UCSC) were calculated by CASAVA 1.7 both with and without consideration of strand-of-origin information. Although retained in the mapping process, CASAVA does not provide directional counts by default. These counts were obtained by splitting the mapped (export.txt) file into two parts, one with sense strand counts, the other with antisense strand counts, and processing them independently. Raw FASTQ sequence was mapped with Bowtie (B. Langmead et al., Genome Biology 10, R25, (2009) in parallel with CASAVA to count ribosomal RNA transcripts.
Data were analyzed in 3 categories: first, RefSeq RNAs, about 80% of which are exon sequences, consolidated for each gene; second, intronic RNA sequences, consolidated for each gene; third, intergenic sequences. RNAs with maximum counts less than 5 among the 136 patients were excluded from analysis. Of 21,283 total RefSeq transcripts counted by CASAVA, 821 had a maximum count less than 5, leaving 20,462 RefSeq transcripts for analysis. Similar to a recently published procedure described by Bullard et al. (BMC Bioinformatics 11, 94, 2010), log 2 raw RNA counts (setting the log 2 for a 0 count to 0) were normalized by subtracting the 3rd quartile of the log 2 RefSeq RNA counts and adding the cohort mean 3rd quartile (“Q3 normalization”). For analysis of RefSeq and intergenic RNAs normalization, RefSeq RNA data were used. For analysis of intronic RNAs normalization, intronic RNA data were used.
Standardized hazard ratios for breast cancer recurrence for each RNA, that is, the proportional change in the hazard with a 1-standard deviation increase in the normalized level of the RNA, were calculated using univariate Cox proportional hazard regression analyses (Cox, Journal of the Royal Statistical Society: Series B (Methodological) 34, 187, 1972). The robust standard error estimate of Lin and Wei (Journal of the American Statistical Society, 84, 1074, 1989) was used to accommodate possible departures from the assumptions of Cox regression, including nonlinearity of the relationship of gene expression with log hazard and nonproportional hazards. False discovery rates (FDR, q-values) were assessed using the method of Storey (Journal of the Royal Statistical Society, Series B 64, 479, 2002) with a “tuning parameter” of λ=0.5. Analyses were conducted to identify true discovery degree of association (TDRDA) sets of RNAs with absolute standardized hazard ratio greater than a specified lower bound while controlling the FDR at 10% (Crager, Statistics in Medicine 29, 33-45, 2010). Taking individual RNAs identified at this FDR, the analysis finds the maximum lower bound for which the RNA is included in a TDRDA set. Also computed was an estimate of each RNA's actual standardized hazard ratio corrected for regression to the mean.
Expression of 192 transcripts in the same tumor RNAs was measured using previously described RT-PCR methods (Cronin et al., Am. J. Pathol. 164, 35-42, 2004; Cronin et al., Clinical Chemistry 50, 1464, 2004). Standardized hazard ratios associating the expression of each gene (normalized by subtracting each gene's crossing threshold (CT) from the cohort median CT) with cancer recurrence were computed using the same methods used for evaluation of the RNA-Seq data.
Intergenic regions were identified by a novel program that evaluates genomic regions that vary widely in length and on a population basis. This program was developed to evaluate intergenic regions having wide variations in length, and to use data from a population of subjects rather than an individual subject. The uniquely mapped reads from all 136 patients were analyzed to identify clusters of reads that might arise from intergenic transcripts. Genomic regions containing less than 2 mapped reads of genomic sequence were not counted to eliminate potential noise from mis-mapping or genomic DNA contamination. The remaining reads were clustered into individual read “islands” based on the overlap of their mapped coordinates to the hg 19 reference human genome, which resulted in 12,750,071 islands in all 136 patient samples. Any islands within 30 base pairs (bp) of each other were grouped together as regions of interest (ROI) producing a total of 6,633,258 ROIs. The number of ROIs were further reduced by the following criteria: 1) The average number of reads mapped to the ROI was >5 across all 136 patients, 2) the length of the ROI was at least 100 bp, and 3) the read depth (average read number divided by the length of the ROI) was >0.075. Applying these criteria reduced the number of ROIs to 23,024. ROIs were classified as intergenic regions if they did not overlap with the transcripts (including non-coding ones) annotated in the refFlat.txt file obtained from UCSC, thereby eliminating overlap with known exons and introns of protein-coding genes and well annotated non-protein coding transcripts. A total of 2,101 intergenic regions were identified by this computational procedure.
Patient clinical characteristics are shown in Table 1. One-hundred and ten patients (81%) had no involved nodes. There was a mixture of chemotherapy and hormonal therapy usage. Estrogen receptor (ER) status was not included in patient records. Therefore, normalized ESR1 mRNA levels obtained in the present RNA-Seq study were used to identify 111 tumors as estrogen-receptor positive and 25 as estrogen-receptor negative. Use of RT-PCR rather than RNA-Seq for this purpose yielded similar but not identical results, identifying as ER+ two more patients, for a total of 113. Archive ages of FFPE tumor blocks ranged from 5 to 12.4 years (median 8.5 years).
RNA-Seq results were successfully generated for all 136 patients, with an average of 43 million median reads per patient (86 million median reads per Illumina Hiseq 2000 flow cell lane). Sixty-nine percent of these uniquely mapped to the human genome: 19.2% to exons, 64.9% to introns, and 15.9% to intergenic regions. Ribosomal RNA accounted for less than 0.3% of the total reads. On average, 17,248 RefSeq transcripts were detected per patient, 66% with greater than 10 counts, and 47% with greater than 100 counts.
In the present set of experiments, gene expression was normalized using the 3rd quartile (J. Bullard et al., BMC Bioinformatics, 11:94, 2010, expressed on the log 2 scale, and standardized to have standard deviation 1 across the study population. When combining genes into meta-gene strings with common direction of association, the standardized gene expressions were summed over the genes in the string to compute the meta-gene expression. Standardized log hazard ratios (that is, the change in the log hazard with a 1-standard deviation change in the covariate) were corrected for regression to the mean (M. Crager, Stat. Med. 29(1): 33-45, 2010) using the distribution of standardized log hazard ratios for all the individual genes in the study. This is expected to be a conservative adjustment (that is, over-correcting for regression to the mean), because in general the prognostic value of multiple genes models is great than the prognostic power of single gene models.
FIG. 1A of PCT/US2012/063313, filed Nov. 2, 2012, now WO 2013/070521 displays results from the historical RT-PCR 192 candidate gene screen of the Providence 136 patient cohort, relating increasing mRNA expression to recurrence risk hazard ratios and statistical significance. As shown, fourteen of the sixteen cancer-related genes in the Oncotype DX® panel were assayed, and most were identified with Hazard Ratios greater than 1.2 or less than 0.8 and P values<0.05.
The effect sizes and statistical significance of Oncotype DX® genes were similar when screening was carried out by whole transcriptome RNA-Seq rather than RT-PCR (compare FIGS. 1A and 1B of PCT/US2012/063313, filed Nov. 2, 2012, now WO 2013/070521)). This is shown in detail on a gene by gene basis in box plots (see FIG. 2 of PCT/US2012/063313, filed Nov. 2, 2012, now WO 2013/070521). A scatter plot of log hazard ratios demonstrates overall concordance between the 192 gene RT-PCR results with the RNA-Seq analyses (Lin et al., Journal of the Royal Statistical Society, Series B 84, 1074 (1989)) (Lin concordance correlation: 0.810; Pearson correlation coefficient: 0.813; see FIG. 3 of PCT/US2012/063313, filed Nov. 2, 2012, now WO 2013/070521). Significantly, RNA-Seq further associates many RefSeq RNAs with disease recurrence: a total of 1307 at FDR<10% (see PCT/US2012/063313, filed Nov. 2, 2012, now WO 2013/070521), hereafter referred to as “identified RefSeq RNAs.” In contrast, the 192 gene RT-PCR study identified 32 RNAs at FDR<10%, and consumed five-fold more input RNA. Together, these results indicate that RNA-Seq can provide a practical, sensitive and precise platform for genome-wide biomarker discovery in FFPE tissue.
A total of 1307 tumor mRNA exonic species associate with breast cancer distant disease recurrence at a false discovery rate of 10% in the recently published RNA-Seq study of the 136 patient Providence cohort, roughly equal numbers associated with increased risk of recurrence (standardized hazard ratio (SHR)>1) and decreased risk of recurrence (SHR<1) (Sinicropi et al. PLoS One e7: 40092, 2012).
In the Providence RNA-Seq dataset 111 of the 136 patients were designated as ER+, and in these cases 363 genes were identified as statistically associated with recurrence risk (Sinicropi et al. PLoS One 7: e40092, 2012). As shown in
We next asked whether mapped string distribution patterns occur in an independent breast cancer dataset, using results from the NKI DNA-microarray gene expression study of 319 breast cancer patients (Van't Veer L J, et al. Nature 415: 530-536, 2002). Normalized expression values for 13120 genes are evaluable in this dataset. Using the same statistical approach as applied to the normalized Providence gene expression data (Sinicropi et al PLoS One e7: 40092, 2012), we associated 2032 genes with disease-free survival in the NKI dataset. These results are roughly in accord with those from other analyses of the NKI dataset. This list of discovered genes contains 42 of those in the NKI 70 gene panel and 11 in the Genomic Health, Inc. 21 gene panel. Mapping these 2032 genes on their chromosomal coordinates reveals many long strings of identified genes
Mapped strings create metagenes that can be explored for biomarker activity. To do this we defined a string metagene as a mapped string of 5 or more genes, the upper limit of which is bounded by either the end of the string (where HR>1 flips to HR<1 or vice versa) or the end of a chromosome arm. This yields a total of 59 strings in the Providence dataset (all patients). The 59 mapped strings identified in the Providence dataset are shown in Table 2A. Not surprisingly, the unadjusted p-values for the strings are low, ranging from 5×10-8 to 2×10−13 for the top 15 strings (Table 2A). More interestingly, the effect sizes (absolute standardized log hazard ratios corrected for regression to the mean, RMC-ASLHR) are notably high: the 15 top metagene RMC-ALSHR values ranged from 1.57 to 1.74. Mapped strings longer than 5 genes tended to have larger RMC-ALSHR values than any of the individual genes in the string, in analyses of either all patients or ER+ patients (
Because ultimately the clinical significance of a biomarker depends on its sustained performance across independent patient populations, we evaluated the prognostic performance of the 59 mapped strings from the Providence dataset in the NKI patient breast cancer dataset. It was not possible to do this precisely because a number of the genes that belong to 59 Providence strings were not identified in the NKI dataset, so the strings that we evaluated in the latter dataset were diminished by 208 genes, out of a starting total of 636. Despite this technical problem (7 of the strings evaluated in NKI contained 3 or fewer genes), 19 strings were statistically significantly associated with the DFS endpoint in the NKI cohort (Bonferroni-adjusted p<0.05), with RMC-ALSHR ranging from 1.20 to 1.34; see Tables 4 and 5 for additional data). Only one of these 19 strings that associated with DFS had a string length less than 5 in the NKI data set. In contrast, of the 19 strings with weakest statistical association with DFS, 12 contained fewer than 5 genes in the NKI dataset. The results with the DFS clinical endpoint were substantially weaker than those obtained with the OS clinical endpoint and slightly stronger than those obtained with the MFS clinical endpoint.
Additional mapped strings were mainly generated by enhancing the 59 mapped strings from the Providence dataset (described above) with neighboring exonic species from the 1307 tumor mRNA exonic species found to associate in the same direction with breast cancer distant disease recurrence at a false discovery rate of 10%. They were also enhanced by adding genes from other chromosomes that strongly co-expressed, for example, the proliferation genes or a subset of mapped strings that coexpressed with G3BP2. Those 1307 tumor mRNA exonic species are described above and in Sinicropi et al. PLoS One e7: 40092, 2012. Table 2B shows data from all patients in the above-described Providence patient cohort; Table 2C shows data from ER-positive in the above-referenced Providence patient cohort. These data include the exonic species present in the enhanced mapped strings, the p-value, and the RM-corrected absolute standardized hazard ratio (95% confidence interval). Notes concerning various strings in Tables 2B and 2C are identified with a superscript letter and described below the given table.
Intron expression within strings was also assessed for their relationship with recurrence of breast cancer. This analysis is performed exactly as the mapped string analysis carried out for gene exons (see Example 3, above), except performed on intronic sequences that had been associated on a univariate intron by intron basis with breast cancer recurrence risk (see Sinicropi D. et al. PLoS One e7: 40092, 2012).
Table 5A shows 79 mapped strings based on identified introns from the Providence dataset, and Table 5B shows the accession no. and exemplary location of introns in strings based on introns identified in the Providence dataset.
Mapped strings based on whole gene (exons and introns) analysis were assessed for their relationship with recurrence of breast cancer. This analysis is performed exactly as the mapped string analysis carried out for gene exons (see Example 3, above), except performed on complete gene (exonic plus intronic) sequences that had been associated with breast cancer recurrence risk on a univariate gene by gene basis.
Table 6A shows 75 mapped strings from whole gene (exon+intron) analysis of the Providence dataset. Table 6B shows the gene accession numbers for the whole genes shown in Table 6A.
Transcriptome Profile Maps
We are unable to explore the relationship between mapped strings and mapped DNA copy number alterations (CNAs) within the Providence or NKI datasets because tumor DNA is unavailable in both cases. Therefore, we sought an RNA surrogate for CNA information, reasoning that amplification of DNA in a chromosomal region would often result in RNA over-expression in that region.
Chromosomal DNA amplifications or deletions are straightforward to visualize against the diploid copy number background of the normal human genome. However, gene expression is widely variable from gene to gene. To develop a baseline against which RNA over- or under-expression could be visualized, at each gene locus we determined the average normalized expression of that gene. Then, for each patient tumor, at the locus of each coding gene we determined the difference in expressed RNA for that patient and the patient cohort average (measured by Z-scores), generating graphics that we call transcriptome profile maps (TPMs). Incorporating data from low abundance RNAs generates noise that can obscure TPM features. Therefore, for each patient/tumor we created two maps, for RNAs above or below the 50th percentile in abundance (normalized counts).
Examples of TPMs for 5 different Providence patient tumors, generated using the published RNA-Seq data (Sinicropi et al. PLoS One e7: 40092, 2012), are shown in
TPM features can be mined for candidate prognostic biomarkers. We asked whether any of three measures of TPM dispersion associate with the rate or breast cancer recurrence in the Providence dataset. Two of these, mean absolute deviation from median, and mean absolute deviation location to location associated with increased risk, at 1.62 (p=0.007) and 1.45 (p=0.018) SHRs, respectively (Table 7).
To determine whether the number of Z score spike features in TPMs associate with recurrence rate, we applied two leading segmentation algorithms: circular binary segmentation (CBS; Venkatraman ES and Olshen AB, Bioinformatics 23:657-63, 2007) and piecewise constant fitting (PCF; Nilsen G et al., BMC Genomics 13:591, 2012). For CBS we prescribed 5 as the lowest permitted number of genes that define a segment while the penalty parameter governing number of elicited segments for PCF was determined by cross-validation. Results from the two approaches exhibit good agreement (correlation 0.74) as do downstream analyses relating numbers of segments (cf the genomic instability index employed in Bilal et al., PLoS Comput Biol 9:e1003047, 2013). We present results based on PCF. Examples of TPM segment plots, derived from Providence RNA-Seq data, are shown in
Next, we developed TPMs from NKI cohort breast cancer data. Because mRNA quantification in this case is based on DNA microarray technology, we modified the protocol for producing TPMs accordingly. We did not attempt to filter data based on detected mRNA abundance. We did filter data based on variance of expression of mRNAs. TPM graphs generated with mRNAs having greatest variability inexpression (top 50th percentile) tended to have more clearly delineated features (for example, see
As observed in the Providence dataset, TPM segment counts strongly relate to all three measures of disease recurrence in this dataset (Table 8): for example, with respect to disease-free survival SHR=1.4 with Wald p-value=6×10−5. (PCF: SHR=1.5, p=2×10−6). In contrast to the results in the Providence dataset, measures of Z-score global dispersion do not associate with outcome in the NKI dataset (Table 7).
All references cited throughout the disclosure, including the examples, are hereby expressly incorporated by reference for their entire disclosure.
While the present invention has been described with reference to what is considered to be specific embodiments, it is to be understood that the invention is not so limited. To the contrary, the invention is intended to cover various modifications and equivalents included within the spirit and scope of the appended claims.
aAs a standard for comparison, we treated the ODX breast proliferation genes as a string and computed the HR and p-value. CCNB1 was not significant and was omitted. As shown, the ODX proliferation string is much weaker than most of the enhanced string HR and p-values.
bWe also treated other highly co-expressed proliferation genes as a string (R ≥ 0.8) and computed the enhanced string HR and p-value. This enhanced string's HR and p-value was also rather unremarkable relative to other enhanced strings.
cTwo small strings of G3BP2 co-expressed genes (R ≥ 0.6) from chromosomes 4 and 7 were also treated as an enhanced string.
dChromosome 19 contains 3 clusters of ZNF gene strings and multiple pairs and singleton ZNF genes. These were combined to give a highly significant HR and p-value as an enhanced string.
aThe chromosome 16 q arm contains two large strings that are separated by the gene hydin. Despite having an opposite direction of association with the two strings, including the gene hydin, increases the prognostic strength of the enhanced string (see note b below for comparison).
bThis enhanced string is identical to the one above except that it does not contain hydin.
cThis string contains the entire set of genes that are prognostic on the chromosome 7 p arm in ER positive patients.
This application is a continuation of U.S. application Ser. No. 15/033,055, filed Apr. 28, 2016, which is a national phase entry pursuant to 35 U.S.C. § 371 of International Application No. PCT/US2014/062715, filed Oct. 28, 2014, which claims the benefit of priority of U.S. Provisional Application No. 61/897,059, filed Oct. 29, 2013, each of which is hereby incorporated by reference in its entirety for any purpose.
| Number | Date | Country | |
|---|---|---|---|
| 61897059 | Oct 2013 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 15033055 | Apr 2016 | US |
| Child | 16585408 | US |
