Patent Application
20120028264
The present disclosure relates to molecular diagnostic assays that provide information concerning methods to use gene expression profiles to determine prognostic information for cancer patients. Specifically, the present disclosure provides genes and microRNAs, the expression levels of which may be used to determine the likelihood that a prostate cancer patient will experience a local or distant cancer recurrence.
Prostate cancer is the most common solid malignancy in men and the second most common cause of cancer-related death in men in North America and the European Union (EU). In 2008, over 180,000 patients will be diagnosed with prostate cancer in the United States alone and nearly 30,000 will die of this disease. Age is the single most important risk factor for the development of prostate cancer, and applies across all racial groups that have been studied. With the aging of the U.S. population, it is projected that the annual incidence of prostate cancer will double by 2025 to nearly 400,000 cases per year.
Since the introduction of prostate-specific antigen (PSA) screening in the 1990's, the proportion of patients presenting with clinically evident disease has fallen dramatically such that patients categorized as “low risk” now constitute half of new diagnoses today. PSA is used as a tumor marker to determine the presence of prostate cancer as high PSA levels are associated with prostate cancer. Despite a growing proportion of localized prostate cancer patients presenting with low-risk features such as low stage (T1) disease, greater than 90% of patients in the US still undergo definitive therapy, including prostatectomy or radiation. Only about 15% of these patients would develop metastatic disease and die from their prostate cancer, even in the absence of definitive therapy. A. Bill-Axelson, et al., J Nat'l Cancer Inst. 100(16):1144-1154 (2008). Therefore, the majority of prostate cancer patients are being over-treated.
Estimates of recurrence risk and treatment decisions in prostate cancer are currently based primarily on PSA levels and/or tumor stage. Although tumor stage has been demonstrated to have significant association with outcome sufficient to be included in pathology reports, the College of American Pathologists Consensus Statement noted that variations in approach to the acquisition, interpretation, reporting, and analysis of this information exist. C. Compton, et al., Arch Pathol Lab Med 124:979-992 (2000). As a consequence, existing pathologic staging methods have been criticized as lacking reproducibility and therefore may provide imprecise estimates of individual patient risk.
This application discloses molecular assays that involve measurement of expression level(s) of one or more genes, gene subsets, microRNAs, or one or more microRNAs in combination with one or more genes or gene subsets, from a biological sample obtained from a prostate cancer patient, and analysis of the measured expression levels to provide information concerning the likelihood of cancer recurrence. For example, the likelihood of cancer recurrence could be described in terms of a score based on clinical or biochemical recurrence-free interval.
In addition, this application discloses molecular assays that involve measurement of expression level(s) of one or more genes, gene subsets, microRNAs, or one or more microRNAs in combination with one or more genes or gene subsets, from a biological sample obtained to identify a risk classification for a prostate cancer patient. For example, patients may be stratified using expression level(s) of one or more genes or microRNAs associated, positively or negatively, with cancer recurrence or death from cancer, or with a prognostic factor. In an exemplary embodiment, the prognostic factor is Gleason pattern.
The biological sample may be obtained from standard methods, including surgery, biopsy, or bodily fluids. It may comprise tumor tissue or cancer cells, and, in some cases, histologically normal tissue, e.g., histologically normal tissue adjacent the tumor tissue. In exemplary embodiments, the biological sample is positive or negative for a TMPRSS2 fusion.
In exemplary embodiments, expression level(s) of one or more genes and/or microRNAs that are associated, positively or negatively, with a particular clinical outcome in prostate cancer are used to determine prognosis and appropriate therapy. The genes disclosed herein may be used alone or arranged in functional gene subsets, such as cell adhesion/migration, immediate-early stress response, and extracellular matrix-associated. Each gene subset comprises the genes disclosed herein, as well as genes that are co-expressed with one or more of the disclosed genes. The calculation may be performed on a computer, programmed to execute the gene expression analysis. The microRNAs disclosed herein may also be used alone or in combination with any one or more of the microRNAs and/or genes disclosed.
In exemplary embodiments, the molecular assay may involve expression levels for at least two genes. The genes, or gene subsets, may be weighted according to strength of association with prognosis or tumor microenvironment. In another exemplary embodiment, the molecular assay may involve expression levels of at least one gene and at least one microRNA. The gene-microRNA combination may be selected based on the likelihood that the gene-microRNA combination functionally interact.
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.
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 cancer of the urogenital tract, such as prostate cancer.
The “pathology” of cancer includes all phenomena that compromise 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, etc.
As used herein, the term “prostate cancer” is used interchangeably and in the broadest sense refers to all stages and all forms of cancer arising from the tissue of the prostate gland.
According to the tumor, node, metastasis (TNM) staging system of the American Joint Committee on Cancer (AJCC), AJCC Cancer Staging Manual (7th Ed., 2010), the various stages of prostate cancer are defined as follows: Tumor: T1: clinically inapparent tumor not palpable or visible by imaging, T1a: tumor incidental histological finding in 5% or less of tissue resected, T1b: tumor incidental histological finding in more than 5% of tissue resected, T1c: tumor identified by needle biopsy; T2: tumor confined within prostate, T2a: tumor involves one half of one lobe or less, T2b: tumor involves more than half of one lobe, but not both lobes, T2c: tumor involves both lobes; T3: tumor extends through the prostatic capsule, T3a: extracapsular extension (unilateral or bilateral), T3b: tumor invades seminal vesicle(s); T4: tumor is fixed or invades adjacent structures other than seminal vesicles (bladder neck, external sphincter, rectum, levator muscles, or pelvic wall). Node: NO: no regional lymph node metastasis; N1: metastasis in regional lymph nodes. Metastasis: M0: no distant metastasis; M1: distant metastasis present.
The Gleason Grading system is used to help evaluate the prognosis of men with prostate cancer. Together with other parameters, it is incorporated into a strategy of prostate cancer staging, which predicts prognosis and helps guide therapy. A Gleason “score” or “grade” is given to prostate cancer based upon its microscopic appearance. Tumors with a low Gleason score typically grow slowly enough that they may not pose a significant threat to the patients in their lifetimes. These patients are monitored (“watchful waiting” or “active surveillance”) over time. Cancers with a higher Gleason score are more aggressive and have a worse prognosis, and these patients are generally treated with surgery (e.g., radical prostectomy) and, in some cases, therapy (e.g., radiation, hormone, ultrasound, chemotherapy). Gleason scores (or sums) comprise grades of the two most common tumor patterns. These patterns are referred to as Gleason patterns 1-5, with pattern 1 being the most well-differentiated. Most have a mixture of patterns. To obtain a Gleason score or grade, the dominant pattern is added to the second most prevalent pattern to obtain a number between 2 and 10. The Gleason Grades include: G1: well differentiated (slight anaplasia) (Gleason 2-4); G2: moderately differentiated (moderate anaplasia) (Gleason 5-6); G3-4: poorly differentiated/undifferentiated (marked anaplasia) (Gleason 7-10).
Stage groupings: Stage I: T1a N0 M0 G1; Stage II: (T1a N0M0G2-4) or (T1b, c, T1, T2, N0 M0 Any G); Stage III: T3 N0 M0 Any G; Stage 1V: (T4 N0 M0 Any G) or (Any T N1 M0 Any G) or (Any T Any N M1 Any G).
As used herein, the term “tumor tissue” refers to a biological sample containing one or more cancer cells, or a fraction of one or more cancer cells. Those skilled in the art will recognize that such biological sample may additionally comprise other biological components, such as histologically appearing normal cells (e.g., adjacent the tumor), depending upon the method used to obtain the tumor tissue, such as surgical resection, biopsy, or bodily fluids.
As used herein, the term “AUA risk group” refers to the 2007 updated American Urological Association (AUA) guidelines for management of clinically localized prostate cancer, which clinicians use to determine whether a patient is at low, intermediate, or high risk of biochemical (PSA) relapse after local therapy.
As used herein, the term “adjacent tissue (AT)” refers to histologically “normal” cells that are adjacent a tumor. For example, the AT expression profile may be associated with disease recurrence and survival.
As used herein “non-tumor prostate tissue” refers to histologically normal-appearing tissue adjacent a prostate tumor.
Prognostic factors are those variables related to the natural history of cancer, which influence the recurrence rates and outcome of patients once they have developed cancer. Clinical parameters that have been associated with a worse prognosis include, for example, increased tumor stage, PSA level at presentation, and Gleason grade or pattern. Prognostic factors are frequently used to categorize patients into subgroups with different baseline relapse risks.
The term “prognosis” is used herein to refer to the likelihood that a cancer patient will have a cancer-attributable death or progression, including recurrence, metastatic spread, and drug resistance, of a neoplastic disease, such as prostate cancer. For example, a “good prognosis” would include long term survival without recurrence and a “bad prognosis” would include cancer recurrence.
As used herein, the term “expression level” as applied to a gene refers to the normalized level of a gene product, e.g. the normalized value determined for the RNA expression level of a gene or for the polypeptide expression level of a gene.
The term “gene product” or “expression product” are used herein to refer to the RNA (ribonucleic acid) transcription products (transcripts) of the gene, including mRNA, and the polypeptide translation products of such RNA transcripts. A gene product can be, for example, an unspliced RNA, an mRNA, a splice variant mRNA, a microRNA, a fragmented RNA, a polypeptide, a post-translationally modified polypeptide, a splice variant polypeptide, etc.
The term “RNA transcript” as used herein refers to the RNA transcription products of a gene, including, for example, mRNA, an unspliced RNA, a splice variant mRNA, a microRNA, and a fragmented RNA.
The term “microRNA” is used herein to refer to a small, non-coding, single-stranded RNA of ˜18-25 nucleotides that may regulate gene expression. For example, when associated with the RNA-induced silencing complex (RISC), the complex binds to specific mRNA targets and causes translation repression or cleavage of these mRNA sequences.
Unless indicated otherwise, each gene name used herein corresponds to the Official Symbol assigned to the gene and provided by Entrez Gene (URL: www.ncbi.nlm.nih.gov/sites/entrez) as of the filing date of this application.
The terms “correlated” and “associated” are used interchangeably herein to refer to the association between two measurements (or measured entities). The disclosure provides genes, gene subsets, microRNAs, or microRNAs in combination with genes or gene subsets, the expression levels of which are associated with tumor stage. For example, the increased expression level of a gene or microRNA may be positively correlated (positively associated) with a good or positive prognosis. Such a positive correlation may be demonstrated statistically in various ways, e.g. by a cancer recurrence hazard ratio less than one. In another example, the increased expression level of a gene or microRNA may be negatively correlated (negatively associated) with a good or positive prognosis. In that case, for example, the patient may experience a cancer recurrence.
The terms “good prognosis” or “positive prognosis” as used herein refer to a beneficial clinical outcome, such as long-term survival without recurrence. The terms “bad prognosis” or “negative prognosis” as used herein refer to a negative clinical outcome, such as cancer recurrence.
The term “risk classification” means a grouping of subjects by the level of risk (or likelihood) that the subject will experience a particular clinical outcome. A subject may be classified into a risk group or classified at a level of risk based on the methods of the present disclosure, e.g. high, medium, or low risk. A “risk group” is a group of subjects or individuals with a similar level of risk for a particular clinical outcome.
The term “long-term” survival is used herein to refer to survival for a particular time period, e.g., for at least 5 years, or for at least 10 years.
The term “recurrence” is used herein to refer to local or distant recurrence (i.e., metastasis) of cancer. For example, prostate cancer can recur locally in the tissue next to the prostate or in the seminal vesicles. The cancer may also affect the surrounding lymph nodes in the pelvis or lymph nodes outside this area. Prostate cancer can also spread to tissues next to the prostate, such as pelvic muscles, bones, or other organs. Recurrence can be determined by clinical recurrence detected by, for example, imaging study or biopsy, or biochemical recurrence detected by, for example, sustained follow-up prostate-specific antigen (PSA) levels ≧0.4 ng/mL or the initiation of salvage therapy as a result of a rising PSA level.
The term “clinical recurrence-free interval (cRFI)” is used herein as time (in months) from surgery to first clinical recurrence or death due to clinical recurrence of prostate cancer. Losses due to incomplete follow-up, other primary cancers or death prior to clinical recurrence are considered censoring events; when these occur, the only information known is that up through the censoring time, clinical recurrence has not occurred in this subject. Biochemical recurrences are ignored for the purposes of calculating cRFI.
The term “biochemical recurrence-free interval (bRFI)” is used herein to mean the time (in months) from surgery to first biochemical recurrence of prostate cancer. Clinical recurrences, losses due to incomplete follow-up, other primary cancers, or death prior to biochemical recurrence are considered censoring events.
The term “Overall Survival (OS)” is used herein to refer to the time (in months) from surgery to death from any cause. Losses due to incomplete follow-up are considered censoring events. Biochemical recurrence and clinical recurrence are ignored for the purposes of calculating OS.
The term “Prostate Cancer-Specific Survival (PCSS)” is used herein to describe the time (in years) from surgery to death from prostate cancer. Losses due to incomplete follow-up or deaths from other causes are considered censoring events. Clinical recurrence and biochemical recurrence are ignored for the purposes of calculating PCSS.
The term “upgrading” or “upstaging” as used herein refers to a change in Gleason grade from 3+3 at the time of biopsy to 3+4 or greater at the time of radical prostatectomy (RP), or Gleason grade 3+4 at the time of biopsy to 4+3 or greater at the time of RP, or seminal vessical involvement (SVI), or extracapsular involvement (ECE) at the time of RP.
In practice, the calculation of the measures listed above may vary from study to study depending on the definition of events to be considered censored.
The term “microarray” refers to an ordered arrangement of hybridizable array elements, e.g. oligonucleotide or polynucleotide probes, on a substrate.
The term “polynucleotide” generally refers to any polyribonucleotide or polydeoxribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA. Thus, for instance, polynucleotides as defined herein include, without limitation, single- and double-stranded DNA, DNA including single- and double-stranded regions, single- and double-stranded RNA, and RNA including single- and double-stranded regions, hybrid molecules comprising DNA and RNA that may be single-stranded or, more typically, double-stranded or include single- and double-stranded regions. In addition, the term “polynucleotide” as used herein refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The strands in such regions may be from the same molecule or from different molecules. The regions may include all of one or more of the molecules, but more typically involve only a region of some of the molecules. One of the molecules of a triple-helical region often is an oligonucleotide. The term “polynucleotide” specifically includes cDNAs. The term includes DNAs (including cDNAs) and RNAs that contain one or more modified bases. Thus, DNAs or RNAs with backbones modified for stability or for other reasons, are “polynucleotides” as that term is intended herein. Moreover, DNAs or RNAs comprising unusual bases, such as inosine, or modified bases, such as tritiated bases, are included within the term “polynucleotides” as defined herein. In general, the term “polynucleotide” embraces all chemically, enzymatically and/or metabolically modified forms of unmodified polynucleotides, as well as the chemical forms of DNA and RNA characteristic of viruses and cells, including simple and complex cells.
The term “oligonucleotide” refers to a relatively short polynucleotide, including, without limitation, single-stranded deoxyribonucleotides, single- or double-stranded ribonucleotides, RNArDNA hybrids and double-stranded DNAs. Oligonucleotides, such as single-stranded DNA probe oligonucleotides, are often synthesized by chemical methods, for example using automated oligonucleotide synthesizers that are commercially available. However, oligonucleotides can be made by a variety of other methods, including in vitro recombinant DNA-mediated techniques and by expression of DNAs in cells and organisms.
The term “Ct” as used herein refers to threshold cycle, the cycle number in quantitative polymerase chain reaction (qPCR) at which the fluorescence generated within a reaction well exceeds the defined threshold, i.e. the point during the reaction at which a sufficient number of amplicons have accumulated to meet the defined threshold.
The term “Cp” as used herein refers to “crossing point.” The Cp value is calculated by determining the second derivatives of entire qPCR amplification curves and their maximum value. The Cp value represents the cycle at which the increase of fluorescence is highest and where the logarithmic phase of a PCR begins.
The terms “threshold” or “thresholding” refer to a procedure used to account for non-linear relationships between gene expression measurements and clinical response as well as to further reduce variation in reported patient scores. When thresholding is applied, all measurements below or above a threshold are set to that threshold value. Non-linear relationship between gene expression and outcome could be examined using smoothers or cubic splines to model gene expression in Cox PH regression on recurrence free interval or logistic regression on recurrence status. D. Cox, Journal of the Royal Statistical Society, Series B 34:187-220 (1972). Variation in reported patient scores could be examined as a function of variability in gene expression at the limit of quantitation and/or detection for a particular gene.
As used herein, the term “amplicon,” refers to pieces of DNA that have been synthesized using amplification techniques, such as polymerase chain reactions (PCR) and ligase chain reactions.
“Stringency” of hybridization reactions is readily determinable by one of ordinary skill in the art, and generally is an empirical calculation dependent upon probe length, washing temperature, and salt concentration. In general, longer probes require higher temperatures for proper annealing, while shorter probes need lower temperatures. Hybridization generally depends on the ability of denatured DNA to re-anneal when complementary strands are present in an environment below their melting temperature. The higher the degree of desired homology between the probe and hybridizable sequence, the higher the relative temperature which can be used. As a result, it follows that higher relative temperatures would tend to make the reaction conditions more stringent, while lower temperatures less so. For additional details and explanation of stringency of hybridization reactions, see Ausubel et al., Current Protocols in Molecular Biology (Wiley Interscience Publishers, 1995).
“Stringent conditions” or “high stringency conditions”, as defined herein, typically: (1) employ low ionic strength and high temperature for washing, for example 0.015 M sodium chloride/0.0015 M sodium citrate/0.1% sodium dodecyl sulfate at 50° C.; (2) employ during hybridization a denaturing agent, such as formamide, for example, 50% (v/v) formamide with 0.1% bovine serum albumin/0.1% Ficoll/0.1% polyvinylpyrrolidone/50 mM sodium phosphate buffer at pH 6.5 with 750 mM sodium chloride, 75 mM sodium citrate at 42° C.; or (3) employ 50% formamide, 5×SSC (0.75 M NaCl, 0.075 M sodium citrate), 50 mM sodium phosphate (pH 6.8), 0.1% sodium pyrophosphate, 5×Denhardt's solution, sonicated salmon sperm DNA (50 μg/ml), 0.1% SDS, and 10% dextran sulfate at 42° C., with washes at 42° C. in 0.2×SSC (sodium chloride/sodium citrate) and 50% formamide, followed by a high-stringency wash consisting of 0.1×SSC containing EDTA at 55° C.
“Moderately stringent conditions” may be identified as described by Sambrook et al., Molecular Cloning: A Laboratory Manual, New York: Cold Spring Harbor Press, 1989, and include the use of washing solution and hybridization conditions (e.g., temperature, ionic strength and % SDS) less stringent that those described above. An example of moderately stringent conditions is overnight incubation at 37° C. in a solution comprising: 20% formamide, 5×SSC (150 mM NaCl, 15 mM trisodium citrate), 50 mM sodium phosphate (pH 7.6), 5×Denhardt's solution, 10% dextran sulfate, and 20 mg/ml denatured sheared salmon sperm DNA, followed by washing the filters in 1×SSC at about 37-500 C. The skilled artisan will recognize how to adjust the temperature, ionic strength, etc. as necessary to accommodate factors such as probe length and the like.
The terms “splicing” and “RNA splicing” are used interchangeably and refer to RNA processing that removes introns and joins exons to produce mature mRNA with continuous coding sequence that moves into the cytoplasm of an eukaryotic cell.
The terms “co-express” and “co-expressed”, as used herein, refer to a statistical correlation between the amounts of different transcript sequences across a population of different patients. Pairwise co-expression may be calculated by various methods known in the art, e.g., by calculating Pearson correlation coefficients or Spearman correlation coefficients. Co-expressed gene cliques may also be identified using graph theory. An analysis of co-expression may be calculated using normalized expression data. A gene is said to be co-expressed with a particular disclosed gene when the expression level of the gene exhibits a Pearson correlation coefficient greater than or equal to 0.6.
A “computer-based system” refers to a system of hardware, software, and data storage medium used to analyze information. The minimum hardware of a patient computer-based system comprises a central processing unit (CPU), and hardware for data input, data output (e.g., display), and data storage. An ordinarily skilled artisan can readily appreciate that any currently available computer-based systems and/or components thereof are suitable for use in connection with the methods of the present disclosure. The data storage medium may comprise any manufacture comprising a recording of the present information as described above, or a memory access device that can access such a manufacture.
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, a suitable processor 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.
As used herein, the terms “active surveillance” and “watchful waiting” mean closely monitoring a patient's condition without giving any treatment until symptoms appear or change. For example, in prostate cancer, watchful waiting is usually used in older men with other medical problems and early-stage disease.
As used herein, the term “surgery” applies to surgical methods undertaken for removal of cancerous tissue, including pelvic lymphadenectomy, radical prostatectomy, transurethral resection of the prostate (TURP), excision, dissection, and tumor biopsy/removal. The tumor tissue or sections used for gene expression analysis may have been obtained from any of these methods.
As used herein, the term “therapy” includes radiation, hormonal therapy, cryosurgery, chemotherapy, biologic therapy, and high-intensity focused ultrasound.
As used herein, the term “TMPRSS fusion” and “TMPRSS2 fusion” are used interchangeably and refer to a fusion of the androgen-driven TMPRSS2 gene with the ERG oncogene, which has been demonstrated to have a significant association with prostate cancer. S. Perner, et al., Urologe A. 46(7):754-760 (2007); S. A. Narod, et al., Br J Cancer 99(6):847-851 (2008). As used herein, positive TMPRSS fusion status indicates that the TMPRSS fusion is present in a tissue sample, whereas negative TMPRSS fusion status indicates that the TMPRSS fusion is not present in a tissue sample. Experts skilled in the art will recognize that there are numerous ways to determine TMPRSS fusion status, such as real-time, quantitative PCR or high-throughput sequencing. See, e.g., K. Mertz, et al., Neoplasis 9(3):200-206 (2007); C. Maher, Nature 458(7234):97-101 (2009).
Gene Expression Methods Using Genes, Gene Subsets, and microRNAs
The present disclosure provides molecular assays that involve measurement of expression level(s) of one or more genes, gene subsets, microRNAs, or one or more microRNAs in combination with one or more genes or gene subsets, from a biological sample obtained from a prostate cancer patient, and analysis of the measured expression levels to provide information concerning the likelihood of cancer recurrence.
The present disclosure further provides methods to classify a prostate tumor based on expression level(s) of one or more genes and/or microRNAs. The disclosure further provides genes and/or microRNAs that are associated, positively or negatively, with a particular prognostic outcome. In exemplary embodiments, the clinical outcomes include cRFI and bRFI. In another embodiment, patients may be classified in risk groups based on the expression level(s) of one or more genes and/or microRNAs that are associated, positively or negatively, with a prognostic factor. In an exemplary embodiment, that prognostic factor is Gleason pattern.
Various technological approaches for determination of expression levels of the disclosed genes and microRNAs are set forth in this specification, including, without limitation, RT-PCR, microarrays, high-throughput sequencing, serial analysis of gene expression (SAGE) and Digital Gene Expression (DGE), which will be discussed in detail below. In particular aspects, the expression level of each gene or microRNA may be determined in relation to various features of the expression products of the gene including exons, introns, protein epitopes and protein activity.
The expression level(s) of one or more genes and/or microRNAs may be measured in tumor tissue. For example, the tumor tissue may obtained upon surgical removal or resection of the tumor, or by tumor biopsy. The tumor tissue may be or include histologically “normal” tissue, for example histologically “normal” tissue adjacent to a tumor. The expression level of genes and/or microRNAs may also be measured in tumor cells recovered from sites distant from the tumor, for example circulating tumor cells, body fluid (e.g., urine, blood, blood fraction, etc.).
The expression product that is assayed can be, for example, RNA or a polypeptide. The expression product may be fragmented. For example, the assay may use primers that are complementary to target sequences of an expression product and could thus measure full transcripts as well as those fragmented expression products containing the target sequence. Further information is provided in Table A (inserted in specification prior to claims).
The RNA expression product may be assayed directly or by detection of a cDNA product resulting from a PCR-based amplification method, e.g., quantitative reverse transcription polymerase chain reaction (qRT-PCR). (See e.g., U.S. Pat. No. 7,587,279). Polypeptide expression product may be assayed using immunohistochemistry (IHC). Further, both RNA and polypeptide expression products may also be is assayed using microarrays.
Prostate cancer is currently diagnosed using a digital rectal exam (DRE) and Prostate-specific antigen (PSA) test. If PSA results are high, patients will generally undergo a prostate tissue biopsy. The pathologist will review the biopsy samples to check for cancer cells and determine a Gleason score. Based on the Gleason score, PSA, clinical stage, and other factors, the physician must make a decision whether to monitor the patient, or treat the patient with surgery and therapy.
At present, clinical decision-making in early stage prostate cancer is governed by certain histopathologic and clinical factors. These include: (1) tumor factors, such as clinical stage (e.g. T1, T2), PSA level at presentation, and Gleason grade, that are very strong prognostic factors in determining outcome; and (2) host factors, such as age at diagnosis and co-morbidity. Because of these factors, the most clinically useful means of stratifying patients with localized disease according to prognosis has been through multifactorial staging, using the clinical stage, the serum PSA level, and tumor grade (Gleason grade) together. In the 2007 updated American Urological Association (AUA) guidelines for management of clinically localized prostate cancer, these parameters have been grouped to determine whether a patient is at low, intermediate, or high risk of biochemical (PSA) relapse after local therapy. I. Thompson, et al., Guideline for the management of clinically localized prostate cancer, J Urol. 177(6):2106-31 (2007).
Although such classifications have proven to be helpful in distinguishing patients with localized disease who may need adjuvant therapy after surgery/radiation, they have less ability to discriminate between indolent cancers, which do not need to be treated with local therapy, and aggressive tumors, which require local therapy. In fact, these algorithms are of increasingly limited use for deciding between conservative management and definitive therapy because the bulk of prostate cancers diagnosed in the PSA screening era now present with clinical stage T1c and PSA ≦10 ng/mL.
Patients with T1 prostate cancer have disease that is not clinically apparent but is discovered either at transurethral resection of the prostate (TURP, T1a, T1b) or at biopsy performed because of an elevated PSA (>4 ng/mL, T1c). Approximately 80% of the cases presenting in 2007 are clinical T1 at diagnosis. In a Scandinavian trial, OS at 10 years was 85% for patients with early stage prostate cancer (T1/T2) and Gleason score ≦7, after radical prostatectomy.
Patients with T2 prostate cancer have disease that is clinically evident and is organ confined; patients with T3 tumors have disease that has penetrated the prostatic capsule and/or has invaded the seminal vesicles. It is known from surgical series that clinical staging underestimates pathological stage, so that about 20% of patients who are clinically T2 will be pT3 after prostatectomy. Most of patients with T2 or T3 prostate cancer are treated with local therapy, either prostatectomy or radiation. The data from the Scandinavian trial suggest that for T2 patients with Gleason grade ≦7, the effect of prostatectomy on survival is at most 5% at 10 years; the majority of patients do not benefit from surgical treatment at the time of diagnosis. For T2 patients with Gleason >7 or for T3 patients, the treatment effect of prostatectomy is assumed to be significant but has not been determined in randomized trials. It is known that these patients have a significant risk (10-30%) of recurrence at 10 years after local treatment, however, there are no prospective randomized trials that define the optimal local treatment (radical prostatectomy, radiation) at diagnosis, which patients are likely to benefit from neo-adjuvant/adjuvant androgen deprivation therapy, and whether treatment (androgen deprivation, chemotherapy) at the time of biochemical failure (elevated PSA) has any clinical benefit.
Accurately determining Gleason scores from needle biopsies presents several technical challenges. First, interpreting histology that is “borderline” between Gleason pattern is highly subjective, even for urologic pathologists. Second, incomplete biopsy sampling is yet another reason why the “predicted” Gleason score on biopsy does not always correlate with the actual “observed” Gleason score of the prostate cancer in the gland itself. Hence, the accuracy of Gleason scoring is dependent upon not only the expertise of the pathologist reading the slides, but also on the completeness and adequacy of the prostate biopsy sampling strategy. T. Stamey, Urology 45:2-12 (1995). The gene/microRNA expression assay and associated information provided by the practice of the methods disclosed herein provide a molecular assay method to facilitate optimal treatment decision-making in early stage prostate cancer. An exemplary embodiment provides genes and microRNAs, the expression levels of which are associated (positively or negatively) with prostate cancer recurrence. For example, such a clinical tool would enable physicians to identify T2/T3 patients who are likely to recur following definitive therapy and need adjuvant treatment.
In addition, the methods disclosed herein may allow physicians to classify tumors, at a molecular level, based on expression level(s) of one or more genes and/or microRNAs that are significantly associated with prognostic factors, such as Gleason pattern and TMPRSS fusion status. These methods would not be impacted by the technical difficulties of intra-patient variability, histologically determining Gleason pattern in biopsy samples, or inclusion of histologically normal appearing tissue adjacent to tumor tissue. Multi-analyte gene/microRNA expression tests can be used to measure the expression level of one or more genes and/or microRNAs involved in each of several relevant physiologic processes or component cellular characteristics. The methods disclosed herein may group the genes and/or microRNAs. The grouping of genes and microRNAs may be performed at least in part based on knowledge of the contribution of those genes and/or microRNAs according to physiologic functions or component cellular characteristics, such as in the groups discussed above. Furthermore, one or more microRNAs may be combined with one or moregenes. The gene-microRNA combination may be selected based on the likelihood that the gene-microRNA combination functionally interact. The formation of groups (or gene subsets), in addition, can facilitate the mathematical weighting of the contribution of various expression levels to cancer recurrence. The weighting of a gene/microRNA group 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.
Optionally, the methods disclosed may be used to classify patients by risk, for example risk of recurrence. Patients can be partitioned into subgroups (e.g., tertiles or quartiles) and the values chosen will define subgroups of patients with respectively greater or lesser risk.
The utility of a disclosed gene marker in predicting prognosis may not be unique to that marker. An alternative marker having an expression pattern that is parallel to that of a disclosed gene may be substituted for, or used in addition to, that co-expressed gene or microRNA. Due to the co-expression of such genes or microRNAs, substitution of expression level values should have little impact on the overall utility of the test. The closely similar expression patterns of two genes or microRNAs may result from involvement of both genes or microRNAs in the same process and/or being under common regulatory control in prostate tumor cells. The present disclosure thus contemplates the use of such co-expressed genes, gene subsets, or microRNAs as substitutes for, or in addition to, genes of the present disclosure.
The methods and compositions of the present disclosure 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. Exemplary 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).
Methods of gene expression profiling include methods based on hybridization analysis of polynucleotides, methods based on sequencing of polynucleotides, and proteomics-based methods. Exemplary methods known in the art for the quantification of RNA 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 PCT (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. Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression (SAGE), and gene expression analysis by massively parallel signature sequencing (MPSS).
Reverse Transcriptase PCR (RT-PCR)
Typically, mRNA or microRNA is isolated from a test sample. 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. Such normal tissue can be histologically-appearing normal tissue adjacent a tumor. mRNA or microRNA 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 mRNA and microRNA 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 Andrés 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 tumor can be isolated, for example, by cesium chloride density gradient centrifugation.
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 avilo 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 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, high-throughput platforms such as the 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 procedure is run on a LightCycler® 480 (Roche Diagnostics) real-time PCR system, which is a microwell plate-based cycler platform.
5′-Nuclease assay data are commonly 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. Alternatively, data may be expressed as a crossing point (“Cp”). The Cp value is calculated by determining the second derivatives of entire qPCR amplification curves and their maximum value. The Cp value represents the cycle at which the increase of fluorescence is highest and where the logarithmic phase of a PCR begins.
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 quite 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), and expressed at a quite constant level among the same tissue taken from different patients. For example, reference genes useful in the methods disclosed herein should not exhibit significantly different expression levels in cancerous prostate as compared to normal prostate tissue. RNAs frequently used to normalize patterns of gene expression are mRNAs for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and (3-actin. Exemplary reference genes used for normalization comprise one or more of the following genes: AAMP, ARF1, ATP5E, CLTC, GPS1, and PGK1. 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 2 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 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).
The steps of a representative protocol for use in the methods of the present disclosure use fixed, paraffin-embedded tissues as the RNA source. For example, mRNA isolation, purification, primer extension and amplification can be performed according to methods available in the art. (see, e.g., Godfrey et al. J. Molec. Diagnostics 2: 84-91 (2000); Specht et al., Am. J. Pathol. 158: 419-29 (2001)). Briefly, a representative process starts with cutting about 10 μm thick sections of paraffin-embedded tumor tissue samples. The RNA is then extracted, and protein and DNA depleted from the RNA-containing sample. After analysis of the RNA concentration, RNA is reverse transcribed using gene specific primers followed by RT-PCR to provide for cDNA amplification products.
Design of Intron-Based PCR Primers and Probes
PCR primers and probes can be designed based upon exon or intron sequences present in the mRNA transcript of the gene 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 intron 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. See S. Rrawetz, S. Misener, Bioinformatics Methods and Protocols: Methods in Molecular Biology, pp. 365-386 (Humana Press).
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° C., e.g. about 50 to 70° C.
For further guidelines for PCR primer and probe design see, e.g. Dieffenbach, CW. 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.
Table A provides further information concerning the primer, probe, and amplicon sequences associated with the Examples disclosed herein.
MassARRAY® System
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 inactivarion 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-derives 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).
Other PCR-Based Methods
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).
Microarrays
Expression levels of a gene or microArray of interest can also be assessed using the microarray technique. 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 test sample. As in the RT-PCR method, 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 RNA 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. ScL USA 93(2):106-149 (1996)). Microarray analysis can be performed by commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix GenChip® technology, or Incyte's microarray technology.
Serial Analysis of Gene Expression (SAGE)
Serial analysis of gene expression (SAGE) is a method that allows the simultaneous and quantitative analysis of a large number of gene transcripts, without the need of providing an individual hybridization probe for each transcript. First, a short sequence tag (about 10-14 bp) is generated that contains sufficient information to uniquely identify a transcript, provided that the tag is obtained from a unique position within each transcript. Then, many transcripts are linked together to form long serial molecules, that can be sequenced, revealing the identity of the multiple tags simultaneously. The expression pattern of any population of transcripts can be quantitatively evaluated by determining the abundance of individual tags, and identifying the gene corresponding to each tag. For more details see, e.g. Velculescu et al., Science 270:484-487 (1995); and Velculescu et al., Cell 88:243-51 (1997).
Gene Expression Analysis by Nucleic Acid Sequencing
Nucleic acid sequencing technologies are suitable methods for analysis of gene expression. 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 expression of the RNA 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 genes 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).
Isolating RNA from Body Fluids
Methods of isolating RNA for expression analysis from blood, plasma and serum (see, e.g., K. Enders, et al., Clin Chem 48, 1647-53 (2002) (and references cited therein) and from urine (see, e.g., R. Boom, et al., J Clin Microbiol. 28, 495-503 (1990) and references cited therein) have been described.
Immunohistochemistry
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.
Proteomics
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 mRNA/microRNA Isolation, Purification and Amplification
The steps of a representative protocol for profiling gene expression using fixed, paraffin-embedded tissues as the RNA source, including mRNA or microRNA isolation, purification, primer extension and amplification 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)). 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 protein and DNA are removed. After analysis of the RNA concentration, RNA repair is performed if desired. The sample can then be subjected to analysis, e.g., by reverse transcribed using gene specific promoters followed by RT-PCR.
Statistical Analysis of Expression Levels in Identification of Genes and microRNAs
One skilled in the art will recognize that there are many statistical methods that may be used to determine whether there is a significant relationship between a parameter of interest (e.g., recurrence) and expression levels of a marker gene/microRNA as described here. In an exemplary embodiment, the present invention provides a stratified cohort sampling design (a form of case-control sampling) using tissue and data from prostate cancer patients. Selection of specimens was stratified by T stage (T1, T2), year cohort (<1993, ≧1993), and prostatectomy Gleason Score (low/intermediate, high). All patients with clinical recurrence were selected and a sample of patients who did not experience a clinical recurrence was selected. For each patient, up to two enriched tumor specimens and one normal-appearing tissue sample was assayed.
All hypothesis tests were reported using two-sided p-values. To investigate if there is a significant relationship of outcomes (clinical recurrence-free interval (cRFI), biochemical recurrence-free interval (bRFI), prostate cancer-specific survival (PCSS), and overall survival (OS)) with individual genes and/or microRNAs, demographic or clinical covariates Cox Proportional Hazards (PH) models using maximum weighted pseudo partial-likelihood estimators were used and p-values from Wald tests of the null hypothesis that the hazard ratio (HR) is one are reported. To investigate if there is a significant relationship between individual genes and/or microRNAs and Gleason pattern of a particular sample, ordinal logistic regression models using maximum weighted likelihood methods were used and p-values from Wald tests of the null hypothesis that the odds ratio (OR) is one are reported.
The present disclosure provides a method to determine tumor stage based on the expression of staging genes, or genes that co-express with particular staging genes. To perform particular biological processes, genes often work together in a concerted way, i.e. they are co-expressed. Co-expressed gene groups identified for a disease process like cancer can serve as biomarkers for tumor status and disease progression. Such co-expressed genes can be assayed in lieu of, or in addition to, assaying of the staging gene with which they are co-expressed.
In an exemplary embodiment, the joint correlation of gene expression levels among prostate cancer specimens under study may be assessed. For this purpose, the correlation structures among genes and specimens may be examined through hierarchical cluster methods. This information may be used to confirm that genes that are known to be highly correlated in prostate cancer specimens cluster together as expected. Only genes exhibiting a nominally significant (unadjusted p<0.05) relationship with cRFI in the univariate Cox PH regression analysis will be included in these analyses.
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).)
The expression data used in the methods disclosed herein can be normalized. Normalization refers to a process to correct for (normalize away), for example, differences in the amount of RNA assayed and variability in the quality of the RNA used, to remove unwanted sources of systematic variation in Ct or Cp measurements, and the like. With respect to RT-PCR experiments involving archived fixed paraffin embedded tissue samples, sources of systematic variation are known to include the degree of RNA degradation relative to the age of the patient sample and the type of fixative used to store the sample. Other sources of systematic variation are attributable to laboratory processing conditions.
Assays can provide for normalization by incorporating the expression of certain normalizing genes, which do not significantly differ in expression levels under the relevant conditions. Exemplary normalization genes disclosed herein include housekeeping genes. (See, e.g., E. Eisenberg, et al., Trends in Genetics 19(7):362-365 (2003).) Normalization can be based on the mean or median signal (Ct or Cp) of all of the assayed genes or a large subset thereof (global normalization approach). In general, the normalizing genes, also referred to as reference genes should be genes that are known not to exhibit significantly different expression in prostate cancer as compared to non-cancerous prostate tissue, and are not significantly affected by various sample and process conditions, thus provide for normalizing away extraneous effects.
In exemplary embodiments, one or more of the following genes are used as references by which the mRNA or microRNA expression data is normalized: AAMP, ARF1, ATP5E, CLTC, GPS1, and PGK1. In another exemplary embodiment, one or more of the following microRNAs are used as references by which the expression data of microRNAs are normalized: hsa-miR-106a; hsa-miR-146b-5p; hsa-miR-191; hsa-miR-19b; and hsa-miR-92a. The calibrated weighted average CT or Cp measurements for each of the prognostic and predictive genes or microRNAs may be normalized relative to the mean of five or more reference genes or microRNAs.
Those skilled in the art will recognize that normalization may be achieved in numerous ways, and the techniques described above are intended only to be exemplary, not exhaustive.
The expression data used in the methods disclosed herein can be standardized. Standardization refers to a process to effectively put all the genes or microRNAs on a comparable scale. This is performed because some genes or microRNAs will exhibit more variation (a broader range of expression) than others. Standardization is performed by dividing each expression value by its standard deviation across all samples for that gene or microRNA. Hazard ratios are then interpreted as the relative risk of recurrence per 1 standard deviation increase in expression.
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 disclosure thus provides kits comprising agents, which may include gene (or microRNA)-specific or gene (or microRNA)-selective probes and/or primers, for quantifying the expression of the disclosed genes or microRNAs for predicting prognostic outcome or response to treatment. 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 RNA amplification. 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 materials or reagents (typically in concentrated form) utilized in the methods, including, for example, chromatographic columns, 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 or predictive information are also properly potential components of kits.
The methods of this invention, when practiced for commercial diagnostic purposes, generally produce a report or summary of information obtained from the herein-described methods. For example, a report may include information concerning expression levels of one or more genes and/or microRNAs, classification of the tumor or the patient's risk of recurrence, the patient's likely prognosis or risk classification, clinical and pathologic factors, and/or other information. The methods and reports of this invention can further include storing the report in a database. The method can create a record in a database for the subject and populate the record with data. The report may be a paper report, an auditory report, or an electronic record. The report may be displayed and/or stored on a computing device (e.g., handheld device, desktop computer, smart device, website, etc.). It is contemplated that the report is provided to a physician and/or the patient. The receiving of the report can further include establishing a network connection to a server computer that includes the data and report and requesting the data and report from the server computer.
The values from the assays described above, such as expression data, can be calculated and stored manually. Alternatively, the above-described steps can be completely or partially performed by a computer program product. The present invention thus provides a computer program product including a computer readable storage medium having a computer program stored on it. The program can, when read by a computer, execute relevant calculations based on values obtained from analysis of one or more biological sample from an individual (e.g., gene expression levels, normalization, standardization, thresholding, and conversion of values from assays to a score and/or text or graphical depiction of tumor stage and related information). The computer program product has stored therein a computer program for performing the calculation.
The present disclosure provides systems for executing the program described above, which system generally includes: a) a central computing environment; b) an input device, operatively connected to the computing environment, to receive patient data, wherein the patient data can include, for example, expression level or other value obtained from an assay using a biological sample from the patient, or microarray data, as described in detail above; c) an output device, connected to the computing environment, to provide information to a user (e.g., medical personnel); and d) an algorithm executed by the central computing environment (e.g., a processor), where the algorithm is executed based on the data received by the input device, and wherein the algorithm calculates an expression score, thresholding, or other functions described herein. The methods provided by the present invention may also be automated in whole or in part.
All aspects of the present invention may also be practiced such that a limited number of additional genes and/or microRNAs that are co-expressed or functionally related with the disclosed genes, for example as evidenced by statistically meaningful Pearson and/or Spearman correlation coefficients, are included in a test in addition to and/or in place of disclosed genes.
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.
Clinical tools based on prostate needle core biopsies are needed to guide treatment planning at diagnosis for men with localized prostate cancer. Limiting tissue in needle core biopsy specimens poses significant challenges to the development of molecular diagnostic tests. This study examined RNA extraction yields and gene expression profiles using an RT-PCR assay to characterize RNA from manually micro-dissected fixed paraffin embedded (FPE) prostate cancer needle biopsy cores. It also investigated the association of RNA yields and gene expression profiles with Gleason score in these specimens.
Patients and Samples
This study determined the feasibility of gene expression profile analysis in prostate cancer needle core biopsies by evaluating the quantity and quality of RNA extracted from fixed paraffin-embedded (FPE) prostate cancer needle core biopsy specimens. Forty-eight (48) formalin-fixed blocks from prostate needle core biopsy specimens were used for this study. Classification of specimens was based on interpretation of the Gleason score (2005 Int'l Society of Urological Pathology Consensus Conference) and percentage tumor (<33%, 33-66%, >66%) involvement as assessed by pathologists.
Assay Methods
Fourteen (14) serial 5 μm unstained sections from each FPE tissue block were included in the study. The first and last sections for each case were H&E stained and histologically reviewed to confirm the presence of tumor and for tumor enrichment by manual micro-dissection.
RNA from enriched tumor samples was extracted using a manual RNA extraction process. RNA was quantitated using the RiboGreen® assay and tested for the presence of genomic DNA contamination. Samples with sufficient RNA yield and free of genomic DNA tested for gene expression levels of a 24-gene panel of reference and cancer-related genes using quantitative RT-PCR. The expression was normalized to the average of 6 reference genes (AAMP, ARF1, ATP5E, CLTC, EEF1A1, and GPX1).
Statistical Methods
Descriptive statistics and graphical displays were used to summarize standard pathology metrics and gene expression, with stratification for Gleason Score category and percentage tumor involvement category. Ordinal logistic regression was used to evaluate the relationship between gene expression and Gleason Score category.
Results
The RNA yield per unit surface area ranged from 16 to 2406 ng/mm2. Higher RNA yield was observed in samples with higher percent tumor involvement (p=0.02) and higher Gleason score (p=0.01). RNA yield was sufficient (>200 ng) in 71% of cases to permit 96-well RT-PCR, with 87% of cases having >100 ng RNA yield. The study confirmed that gene expression from prostate biopsies, as measured by qRT-PCR, was comparable to FPET samples used in commercial molecular assays for breast cancer. In addition, it was observed that greater biopsy RNA yields are found with higher Gleason score and higher percent tumor involvement. Nine genes were identified as significantly associated with Gleason score (p<0.05) and there was a large dynamic range observed for many test genes.
Patients and Samples
Approximately 2600 patients with clinical stage T1/T2 prostate cancer treated with radical prostatectomy (RP) at the Cleveland Clinic between 1987 and 2004 were identified. Patients were excluded from the study design if they received neo-adjuvant and/or adjuvant therapy, if pre-surgical PSA levels were missing, or if no tumor block was available from initial diagnosis. 127 patients with clinical recurrence and 374 patients without clinical recurrence after radical prostatectomy were randomly selected using a cohort sampling design. The specimens were stratified by T stage (T1, T2), year cohort (<1993, ≧1993), and prostatectomy Gleason score (low/intermediate, high). Of the 501 sampled patients, 51 were excluded for insufficient tumor; 7 were excluded due to clinical ineligibility; 2 were excluded due to poor quality of gene expression data; and 10 were excluded because primary Gleason pattern was unavailable. Thus, this gene expression study included tissue and data from 111 patients with clinical recurrence and 330 patients without clinical recurrence after radical prostatectomies performed between 1987 and 2004 for treatment of early stage (T1, T2) prostate cancer.
Two fixed paraffin embedded (FPE) tissue specimens were obtained from prostate tumor specimens in each patient. The sampling method (sampling method A or B) depended on whether the highest Gleason pattern is also the primary Gleason pattern. For each specimen selected, the invasive cancer cells were at least 5.0 mm in dimension, except in the instances of pattern 5, where 2.2 mm was accepted. Specimens were spatially distinct where possible.
Histologically normal appearing tissue (NAT) adjacent to the tumor specimen (also referred to in these Examples as “non-tumor tissue”) was also evaluated. Adjacent tissue was collected 3 mm from the tumor to 3 mm from the edge of the FPET block. NAT was preferentially sampled adjacent to the primary Gleason pattern. In cases where there was insufficient NAT adjacent to the primary Gleason pattern, then NAT was sampled adjacent to the secondary or highest Gleason pattern (A2 or B1) per the method set forth in Table 2. Six (6) 10 μm sections with beginning H&E at 5 μm and ending unstained slide at 5 μm were prepared from each fixed paraffin-embedded tumor (FPET) block included in the study. All cases were histologically reviewed and manually micro-dissected to yield two enriched tumor samples and, where possible, one normal tissue sample adjacent to the tumor specimen.
Assay Method
In this study, RT-PCR analysis was used to determine RNA expression levels for 738 genes and chromosomal rearrangements (e.g., TMPRSS2-ERG fusion or other ETS family genes) in prostate cancer tissue and surrounding NAT in patients with early-stage prostate cancer treated with radical prostatectomy.
The samples were quantified using the RiboGreen assay and a subset tested for presence of genomic DNA contamination. Samples were taken into reverse transcription (RT) and quantitative polymerase chain reaction (qPCR). All analyses were conducted on reference-normalized gene expression levels using the average of the of replicate well crossing point (CP) values for the 6 reference genes (AAMP, ARF1, ATP5E, CLTC, GPS1, PGK1).
Statistical Analysis and Results
Primary statistical analyses involved 111 patients with clinical recurrence and 330 patients without clinical recurrence after radical prostatectomy for early-stage prostate cancer stratified by T-stage (T1, T2), year cohort (<1993, ≧1993), and prostatectomy Gleason score (low/intermediate, high). Gleason score categories are defined as follows: low (Gleason score ≦6), intermediate (Gleason score=7), and high (Gleason score ≧8). A patient was included in a specified analysis if at least one sample for that patient was evaluable. Unless otherwise stated, all hypothesis tests were reported using two-sided p-values. The method of Storey was applied to the resulting set of p-values to control the false discovery rate (FDR) at 20%. J. Storey, R. Tibshirani, Estimating the Positive False Discovery Rate Under Dependence, with Applications to DNA Microarrays, Dept. of Statistics, Stanford Univ. (2001).
Analysis of gene expression and recurrence-free interval was based on univariate Cox Proportional Hazards (PH) models using maximum weighted pseudo-partial-likelihood estimators for each evaluable gene in the gene list (727 test genes and 5 reference genes). P-values were generated using Wald tests of the null hypothesis that the hazard ratio (HR) is one. Both unadjusted p-values and the q-value (smallest FDR at which the hypothesis test in question is rejected) were reported. Un-adjusted p-values <0.05 were considered statistically significant. Since two tumor specimens were selected for each patient, this analysis was performed using the 2 specimens from each patient as follows: (1) analysis using the primary Gleason pattern specimen from each patient (Specimens A1 and B2 as described in Table 2); (2) analysis using the highest Gleason pattern specimen from each patient (Specimens A1 and B1 as described in Table 2).
Analysis of gene expression and Gleason pattern (3, 4, 5) was based on univariate ordinal logistic regression models using weighted maximum likelihood estimators for each gene in the gene list (727 test genes and 5 reference genes). P-values were generated using a Wald test of the null hypothesis that the odds ratio (OR) is one. Both unadjusted p-values and the q-value (smallest FDR at which the hypothesis test in question is rejected) were reported. Un-adjusted p-values <0.05 were considered statistically significant. Since two tumor specimens were selected for each patient, this analysis was performed using the 2 specimens from each patient as follows: (1) analysis using the primary Gleason pattern specimen from each patient (Specimens A1 and B2 as described in Table 2); (2) analysis using the highest Gleason pattern specimen from each patient (Specimens A1 and B1 as described in Table 2).
It was determined whether there is a significant relationship between cRFI and selected demographic, clinical, and pathology variables, including age, race, clinical tumor stage, pathologic tumor stage, location of selected tumor specimens within the prostate (peripheral versus transitional zone), PSA at the time of surgery, overall Gleason score from the radical prostatectomy, year of surgery, and specimen Gleason pattern. Separately for each demographic or clinical variable, the relationship between the clinical covariate and cRFI was modeled using univariate Cox PH regression using weighted pseudo partial-likelihood estimators and a p-value was generated using Wald's test of the null hypothesis that the hazard ratio (HR) is one. Covariates with unadjusted p-values <0.2 may have been included in the covariate-adjusted analyses.
It was determined whether there was a significant relationship between each of the individual cancer-related genes and cRFI after controlling for important demographic and clinical covariates. Separately for each gene, the relationship between gene expression and cRFI was modeled using multivariate Cox PH regression using weighted pseudo partial-likelihood estimators including important demographic and clinical variables as covariates. The independent contribution of gene expression to the prediction of cRFI was tested by generating a p-value from a Wald test using a model that included clinical covariates for each nodule (specimens as defined in Table 2). Un-adjusted p-values <0.05 were considered statistically significant.
Tables 3A and 3B provide genes significantly associated (p<0.05), positively or negatively, with Gleason pattern in the primary and/or highest Gleason pattern. Increased expression of genes in Table 3A is positively associated with higher Gleason score, while increased expression of genes in Table 3B are negatively associated with higher Gleason score.
To identify genes associated with recurrence (cRFI, bRFI) in the primary and the highest Gleason pattern, each of 727 genes were analyzed in univariate models using specimens A1 and B2 (see Table 2, above). Tables 4A and 4B provide genes that were associated, positively or negatively, with cRFI and/or bRFI in the primary and/or highest Gleason pattern. Increased expression of genes in Table 4A is negatively associated with good prognosis, while increased expression of genes in Table 4B is positively associated with good prognosis.
Tables 5A and 5B provide genes that were significantly associated (p<0.05), positively or negatively, with recurrence (cRFI, bRFI) after adjusting for AUA risk group in the primary and/or highest Gleason pattern. Increased expression of genes in Table 5A is negatively associated with good prognosis, while increased expression of genes in Table 5B is positively associated with good prognosis.
Tables 6A and 6B provide genes that were significantly associated (p<0.05), positively or negatively, with recurrence (cRFI, bRFI) after adjusting for Gleason pattern in the primary and/or highest Gleason pattern. Increased expression of genes in Table 6A is negatively associated with good prognosis, while increased expression of gene in Table 6B is positively associated with good prognosis.
Tables 7A and 7B provide genes significantly associated (p<0.05), positively or negatively, with clinical recurrence (cRFI) in negative TMPRSS fusion specimens in the primary or highest Gleason pattern specimen. Increased expression of genes in Table 7A is negatively associated with good prognosis, while increased expression of genes in Table 7B is positively associated with good prognosis.
Tables 8A and 8B provide genes that were significantly associated (p<0.05), positively or negatively, with clinical recurrence (cRFI) in positive TMPRSS fusion specimens in the primary or highest Gleason pattern specimen. Increased expression of genes in Table 8A is negatively associated with good prognosis, while increased expression of genes in Table 8B is positively associated with good prognosis.
Tables 9A and 9B provide genes significantly associated (p<0.05), positively or negatively, with TMPRSS fusion status in the primary Gleason pattern. Increased expression of genes in Table 9A are positively associated with TMPRSS fusion positivity, while increased expression of genes in Table 10A are negatively associated with TMPRSS fusion positivity.
A molecular field effect was investigated, and determined that the expression levels of histologically normal-appearing cells adjacent to the tumor exhibited a molecular signature of prostate cancer. Tables 10A and 10B provide genes significantly associated (p<0.05), positively or negatively, with cRFI or bRFI in non-tumor samples. Table 10A is negatively associated with good prognosis, while increased expression of genes in Table 10B is positively associated with good prognosis.
Table 11 provides genes that are significantly associated (p<0.05) with cRFI or bRFI after adjustment for Gleason pattern or highest Gleason pattern.
Tables 12A and 12B provide genes that are significantly associated (p<0.05) with prostate cancer specific survival (PCSS) in the primary Gleason pattern. Increased expression of genes in Table 12A is negatively associated with good prognosis, while increased expression of genes in Table 12B is positively associated with good prognosis.
Analysis of gene expression and upgrading/upstaging was based on univariate ordinal logistic regression models using weighted maximum likelihood estimators for each gene in the gene list (727 test genes and 5 reference genes). P-values were generated using a Wald test of the null hypothesis that the odds ratio (OR) is one. Both unadjusted p-values and the q-value (smallest FDR at which the hypothesis test in question is rejected) were reported. Un-adjusted p-values <0.05 were considered statistically significant. Since two tumor specimens were selected for each patient, this analysis was performed using the 2 specimens from each patient as follows: (1) analysis using the primary Gleason pattern specimen from each patient (Specimens A1 and B2 as described in Table 2); and (2) analysis using the highest Gleason pattern specimen from each patient (Specimens A1 and B1 as described in Table 2). 200 genes were found to be significantly associated (p<0.05) with upgrading/upstaging in the primary Gleason pattern sample (PGP) and 203 genes were found to be significantly associated (p<0.05) with upgrading/upstaging in the highest Gleason pattern sample (HGP).
Tables 13A and 13B provide genes significantly associated (p<0.05), positively or negatively, with upgrading/upstaging in the primary and/or highest Gleason pattern. Increased expression of genes in Table 13A is positively associated with higher risk of upgrading/upstaging (poor prognosis), while increased expression of genes in Table 13B is negatively associated with risk of upgrading/upstaging (good prognosis).
MicroRNAs function by binding to portions of messenger RNA (mRNA) and changing how frequently the mRNA is translated into protein. They can also influence the turnover of mRNA and thus how long the mRNA remains intact in the cell. Since microRNAs function primarily as an adjunct to mRNA, this study evaluated the joint prognostic value of microRNA expression and gene (mRNA) expression. Since the expression of certain microRNAs may be a surrogate for expression of genes that are not in the assessed panel, we also evaluated the prognostic value of microRNA expression by itself.
Patients and Samples
Samples from the 127 patients with clinical recurrence and 374 patients without clinical recurrence after radical prostatectomy described in Example 2 were used in this study. The final analysis set comprised 416 samples from patients in which both gene expression and microRNA expression were successfully assayed. Of these, 106 patients exhibited clinical recurrence and 310 did not have clinical recurrence. Tissue samples were taken from each prostate sample representing (1) the primary Gleason pattern in the sample, and (2) the highest Gleason pattern in the sample. In addition, a sample of histologically normal-appearing tissue adjacent to the tumor (NAT) was taken. The number of patients in the analysis set for each tissue type and the number of them who experienced clinical recurrence or death due to prostate cancer are shown in Table 14.
Assay Method
Expression of 76 test microRNAs and 5 reference microRNAs were determined from RNA extracted from fixed paraffin-embedded (FPE) tissue. MicroRNA expression in all three tissue type was quantified by reverse transcriptase polymerase chain reaction (RT-PCR) using the crossing point (Cp) obtained from the Taqman® MicroRNA Assay kit (Applied Biosystems, Inc., Carlsbad, Calif.).
Statistical Analysis
Using univariate proportional hazards regression (Cox D R, Journal of the Royal Statistical Society, Series B 34:187-220, 1972), applying the sampling weights from the cohort sampling design, and using variance estimation based on the Lin and Wei method (Lin and Wei, Journal of the American Statistical Association 84:1074-1078, 1989), microRNA expression, normalized by the average expression for the 5 reference microRNAs hsa-miR-106a, hsa-miR-146b-5p, hsa-miR-191, hsa-miR-19b, and hsa-miR-92a, and reference-normalized gene expression of the 733 genes (including the reference genes) discussed above, were assessed for association with clinical recurrence and death due to prostate cancer. Standardized hazard ratios (the proportional change in the hazard associated with a change of one standard deviation in the covariate value) were calculated.
This analysis included the following classes of predictors:
1. MicroRNAs alone
2. MicroRNA-gene pairs Tier 1
3. MicroRNA-gene pairs Tier 2
4. MicroRNA-gene pairs Tier 3
5. All other microRNA-gene pairs Tier 4
The four tiers were pre-determined based on the likelihood (Tier 1 representing the highest likelihood) that the gene-microRNA pair functionally interacted or that the microRNA was related to prostate cancer based on a review of the literature and existing microarray data sets.
False discovery rates (FDR) (Benjamini and Hochberg, Journal of the Royal Statistical Society, Series B 57:289-300, 1995) were assessed using Efron's separate class methodology (Efron, Annals of Applied Statistics 2:197-223., 2008). The false discovery rate is the expected proportion of the rejected null hypotheses that are rejected incorrectly (and thus are false discoveries). Efron's methodology allows separate FDR assessment (q-values) (Storey, Journal of the Royal Statistical Society, Series B 64:479-498, 2002) within each class while utilizing the data from all the classes to improve the accuracy of the calculation. In this analysis, the q-value for a microRNA or microRNA-gene pair can be interpreted as the empirical Bayes probability that the microRNA or microRNA-gene pair identified as being associated with clinical outcome is in fact a false discovery given the data. The separate class approach was applied to a true discovery rate degree of association (TDRDA) analysis (Crager, Statistics in Medicine 29:33-45, 2010) to determine sets of microRNAs or microRNA-gene pairs that have standardized hazard ratio for clinical recurrence or prostate cancer-specific death of at least a specified amount while controlling the FDR at 10%. For each microRNA or microRNA-gene pair, a maximum lower bound (MLB) standardized hazard ratio was computed, showing the highest lower bound for which the microRNA or microRNA-gene pair was included in a TDRDA set with 10% FDR. Also calculated was an estimate of the true standardized hazard ratio corrected for regression to the mean (RM) that occurs in subsequent studies when the best predictors are selected from a long list (Crager, 2010 above). The RM-corrected estimate of the standardized hazard ratio is a reasonable estimate of what could be expected if the selected microRNA or microRNA-gene pair were studied in a separate, subsequent study.
These analyses were repeated adjusting for clinical and pathology covariates available at the time of patient biopsy: biopsy Gleason score, baseline PSA level, and clinical T-stage (T1-T2A vs. T2B or T2C) to assess whether the microRNAs or microRNA-gene pairs have predictive value independent of these clinical and pathology covariates.
The analysis identified 21 microRNAs assayed from primary Gleason pattern tumor tissue that were associated with clinical recurrence of prostate cancer after radical prostatectomy, allowing a false discovery rate of 10% (Table 15). Results were similar for microRNAs assessed from highest Gleason pattern tumor tissue (Table 16), suggesting that the association of microRNA expression with clinical recurrence does not change markedly depending on the location within a tumor tissue sample. No microRNA assayed from normal adjacent tissue was associated with the risk of clinical recurrence at a false discovery rate of 10%. The sequences of the microRNAs listed in Tables 15-21 are shown in Table B.
aThe q-value is the empirical Bayes probability that the microRNA's association with clinical recurrence is a false discovery, given the data.
bDirection of association indicates where higher microRNA expression is associated with higher (+) or lower (−) risk of clinical recurrence.
cRM: regression to the mean.
aThe q-value is the empirical Bayes probability that the microRNA's association with death due to prostate cancer is a false discovery, given the data.
bDirection of association indicates where higher microRNA expression is associated with higher (+) or lower (−) risk of clinical recurrence.
cRM: regression to the mean.
Table 17 shows microRNAs assayed from primary Gleason pattern tissue that were identified as being associated with the risk of prostate-cancer-specific death, with a false discovery rate of 10%. Table 18 shows the corresponding analysis for microRNAs assayed from highest Gleason pattern tissue. No microRNA assayed from normal adjacent tissue was associated with the risk of prostate-cancer-specific death at a false discovery rate of 10%.
aThe q-value is the empirical Bayes probability that the microRNA's association with death due to prostate cancer endpoint is a false discovery, given the data.
bDirection of association indicates where higher microRNA expression is associated with higher (+) or lower (−) risk of death due to prostate cancer.
cRM: regression to the mean.
aThe q-value is the empirical Bayes probability that the microRNA's association with clinical endpoint is a false discovery, given the data.
bDirection of association indicates where higher microRNA expression is associated with higher (+) or lower (−) risk of death due to prostate cancer.
cRM: regression to the mean.
Table 19 and Table 20 shows the microRNAs that can be identified as being associated with the risk of clinical recurrence while adjusting for the clinical and pathology covariates of biopsy Gleason score, baseline PSA level, and clinical T-stage. The distributions of these covariates are shown in
Two microRNAs assayed from primary Gleason pattern tumor tissue were found that had predictive value for death due to prostate cancer independent of Gleason score, baseline PSA, and clinical T-stage (Table 21).
aThe q-value is the empirical Bayes probability that the microRNA's association with clinical recurrence is a false discovery, given the data.
bDirection of association indicates where higher microRNA expression is associated with higher (+) or lower (−) risk of clinical recurrence.
cRM: regression to the mean.
aThe q-value is the empirical Bayes probability that the microRNA's association with clinical clinical recurrence is a false discovery, given the data.
bDirection of association indicates where higher microRNA expression is associated with higher (+) or lower (−) risk of clinical recurrence.
c RM: regression to the mean.
aThe q-value is the empirical Bayes probability that the microRNA's association with clinical recurrence is a false discovery, given the data.
bDirection of association indicates where higher microRNA expression is associated with higher (+) or lower (−) risk of clinical recurrence.
cRM: regression to the mean.
Accordingly, the normalized expression levels of hsa-miR-93; hsa-miR-106b; hsa-miR-21; hsa-miR-449a; hsa-miR-182; hsa-miR-27a; hsa-miR-103; hsa-miR-141; hsa-miR-92a; hsa-miR-22; hsa-miR-29b; hsa-miR-210; hsa-miR-331; hsa-miR-191; hsa-miR-425; and hsa-miR-200c are positively associated with an increased risk of recurrence; and hsa-miR-30e-5p; hsa-miR-133a; hsa-miR-30a; hsa-miR-222; hsa-miR-1; hsa-miR-145; hsa-miR-486-5p; hsa-miR-19b; hsa-miR-205; hsa-miR-31; hsa-miR-155; hsa-miR-206; hsa-miR-99a; and hsa-miR-146b-5p are negatively associated with an increased risk of recurrence.
Furthermore, the normalized expression levels of hsa-miR-106b; hsa-miR-21; hsa-miR-93; hsa-miR-331; hsa-miR-150; hsa-miR-27b; and hsa-miR-10a are positively associated with an increased risk of prostate cancer specific death; and the normalized expression levels of hsa-miR-30e-5p; hsa-miR-30a; hsa-miR-133a; hsa-miR-222; hsa-miR-1; hsa-miR-26a; and hsa-miR-152 are negatively associated with an increased risk of prostate cancer specific death.
Table 22 shows the number of microRNA-gene pairs that were grouped in each tier (Tiers 1-4) and the number and percentage of those that were predictive of clinical recurrence at a false discovery rate of 10%.
This application claims the benefit of priority to U.S. Provisional Application Nos. 61/368,217, filed Jul. 27, 2010; 61/414,310, filed Nov. 16, 2010; and 61/485,536, filed May 12, 2011, all of which are hereby incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 61368217 | Jul 2010 | US | |
| 61414310 | Nov 2010 | US | |
| 61485536 | May 2011 | US |
