Medical Policy Manual. Topic: Gene Expression Analysis for Prostate Cancer Management. Date of Origin: January 2014

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1 Medical Policy Manual Topic: Gene Expression Analysis for Prostate Cancer Management Date of Origin: January 2014 Section: Genetic Testing Last Reviewed Date: February 2015 Policy No: 71 Effective Date: May 1, 2015 IMPORTANT REMINDER Medical Policies are developed to provide guidance for members and providers regarding coverage in accordance with contract terms. Benefit determinations are based in all cases on the applicable contract language. To the extent there may be any conflict between the Medical Policy and contract language, the contract language takes precedence. PLEASE NOTE: Contracts exclude from coverage, among other things, services or procedures that are considered investigational or cosmetic. Providers may bill members for services or procedures that are considered investigational or cosmetic. Providers are encouraged to inform members before rendering such services that the members are likely to be financially responsible for the cost of these services. DESCRIPTION Gene expression profile analysis has been proposed as a means to risk-stratify patients with low-risk prostate cancer, diagnosed by needle biopsy, to guide treatment decisions. Background Localized prostate cancers may appear very similar clinically at diagnosis. [1] However, they often exhibit diverse risk of progression that may not be captured by accepted clinical risk categories (e.g., D Amico criteria) or prognostic tools that are based on clinical findings including PSA titers, Gleason grade, or tumor stage. [2-6] In studies of conservative management, the risk of localized disease progression based on prostate cancer-specific survival rates at 10 years may range from 15% [7,8] to 20% [9] to perhaps 27% at 20-year follow-up. [10] Among elderly men (aged 70 years or more) with this type of low-risk disease, comorbidities typically supervene as a cause of death; these men die with prostate cancer present, rather than from the cancer. Other very similar-appearing low-risk tumors may progress unexpectedly rapidly, quickly disseminating and becoming incurable. The divergent behavior of localized prostate cancers creates uncertainty about whether or not to treat immediately. [11,12] A patient may choose definitive treatment upfront, such as surgery (radical prostatectomy), external-beam radiation therapy (EBRT), brachytherapy, high-intensity-focused ultrasound, systemic chemotherapy, hormonal therapy, cryosurgery, or combinations are used to treat patients with prostate cancer. [12-14] Complications associated with those treatments most commonly 1 GT71

2 reported (radical prostatectomy, EBRT) and with the greatest variability were incontinence (0-73%) and other genitourinary toxicities (irritative and obstructive symptoms); hematuria (typically 5% or less); gastrointestinal and bowel toxicity including nausea and loose stools (25-50%); proctopathy, including rectal pain and bleeding (10-39%); and erectile dysfunction including impotence (50-90%). [14] In addition, the American Urological Association (AUA) guidelines for the management of clinically localized prostate cancer suggest that patients with low- and intermediate-risk disease have the option of active surveillance, taking into account patient age, patient preferences, and health conditions related to urinary, sexual, and bowel function. [14] With this approach the patient forgoes immediate therapy and continues regular monitoring until signs or symptoms of disease progression are evident, at which point curative treatment is instituted. [15,16] Given the unpredictable behavior of early prostate cancer, additional prognostic methods to biologically stratify this disease are under investigation. These include microarray-based gene expression profiling, which refers to analysis of mrna expression levels of many genes simultaneously in a tumor specimen. [17-22] The test results are intended to be used in combination with accepted clinical criteria (Gleason score, prostate specific antigen [PSA], clinical stage) to stratify biopsy-diagnosed localized prostate cancer according to biological aggressiveness. Examples of microarray-based gene expression profiling tests to biologically stratify prostate cancers include the Prolaris (Myriad Genetics), the Oncotype DX Prostate Cancer Assay (Genomic Health), and the Decipher Prostate Cancer Classifier. Archived tumor specimens are used as the mrna source, reverse transcriptase polymerase chain reaction amplification, and the TaqMan low-density array platform (Applied Biosystems). Prolaris is used to quantify expression levels of 31 cell cycle progression (CCP) genes and 15 housekeeper genes to generate a CCP score. Oncotype DX Prostate is used to quantify expression levels of 12 cancer-related and 5 reference genes to generate a Genomic Prostate Score (GPS). The Decipher test uses oligonucleotide microarray technology to analyze the expression of 22 biomarkers to predict the probability of developing metastasis within 5 years of radical prostatectomy or within 3 years of biochemical recurrence. In the final analysis, the CCP score or GPS are combined in proprietary algorithms with clinical risk criteria (PSA, Gleason grade, tumor stage) to generate new risk categories (i.e., reclassification) intended to reflect biological indolence or aggressiveness of individual lesions, and thus inform management decisions. Regulatory Status The Prolaris, Oncotype DX Prostate Cancer Assay, and Decipher are not cleared for marketing by the U.S. Food and Drug Administration (FDA). Each is available under the auspices of the Clinical Laboratory Improvement Act (CLIA). Clinical laboratories may develop and validate tests in-house (laboratory-developed tests [LDTs]) and market them as a laboratory service; LDTs must meet the general regulatory standards of the CLIA. Laboratories that offer LDTs must be licensed by CLIA for high-complexity testing. MEDICAL POLICY CRITERIA Gene expression analysis to guide management of prostate cancer is considered investigational in all situations, including but not limited to the following tests: 1. Oncotype DX Prostate Cancer Assay 2 GT71

3 2. Prolaris 3. Decipher SCIENTIFIC EVIDENCE Validation of the clinical use of any genetic test focuses on 3 main principles: 1. The analytic validity of the test, which refers to the technical accuracy of the test in detecting a mutation that is present or in excluding a mutation that is absent; 2. The clinical validity of the test, which refers to the diagnostic performance of the test (sensitivity, specificity, positive and negative predictive values) in detecting clinical disease; and 3. The clinical utility of the test, i.e., how the results of the diagnostic test will be used to change management of the patient and whether these changes in management lead to clinically important improvements in health outcomes. The focus of this review is on evidence related to the ability of test results to: Guide decisions in the clinical setting related to either treatment, management, or prevention, and Improve health outcomes as a result of those decisions. This policy was initially based on a 2013 BlueCross BlueShield Association Technology Evaluation Center (TEC) Assessment [23] which was updated in January 2015 with a literature review through September 30, Full-length publications were sought that described the analytic validity, clinical validity, and clinical utility of either Prolaris or Oncotype DX Prostate gene expression profiling. Evidence was reviewed on the use of either test to predict the aggressiveness or indolence of newly biopsy-diagnosed localized prostate cancer. Oncotype Dx Prostate Analytic Validity In the only study of validity for the Oncotype DX Prostate test, Knezevic et al., authors from Genomic Health, Inc., the developer of the test, reported analytical accuracy and reproducibility of Oncotype Dx Prostate. [24] Estimates of analytic precision and reproducibility were derived from analysis of RNA prepared from 10 microdissected prostate tumor samples obtained by needle biopsy. Individual Gleason scores were assigned using the 2005 International Society of Urological Pathology Consensus guidelines. [25] The results showed that the assay could accurately measure expression of the 12 cancer-related and 5 reference genes over a range of absolute RNA inputs ( ng); the limit of detection in a sample was 0.5 ng/ul. The analytic accuracy showed average variation of less than 9.7% across all samples at RNA inputs typical of needle biopsy specimens. The amplification efficiency for the 17 genes in the test ranged from 88% to 100%, with a median of 93% (SD=6%) for all 17 genes in the assay. Analytic precision was assessed by examining variability between replicate results obtained using the same mrna input. Reproducibility was measured by calculating both within and between mrna input variation. A low input level of 5 ng mrna was used to reflect the lowest 2.5 percentile of a tumor sample of cm3. When converted to GPS units (unit measure for reporting test results), the standard deviation for analytic precision was 1.86 GPS units (95% confidence interval [CI], 1.60 to 3 GT71

4 2.20) on the 100-unit scale. The standard deviation for reproducibility was 2.11 GPS units (95% CI, 1.83 to 2.50) on the 100-point scale. This study provides sufficient evidence to establish the analytic validity of Oncotype Dx Prostate. Clinical Validity One publication compiled results of 3 cohorts of contemporary ( ) patients in a prostatectomy study (N=441; Cleveland Clinic database, ), a biopsy study (N=167; Cleveland Clinic database, ), and an independent clinical validation study cohort (N=395; mean age, 58 years; University of California, San Francisco Urologic Oncology Data Base, ). [26] Results from the clinical validation study and prostatectomy study provide information on the potential clinical validity of this test. The cohorts had a mix of low to low-intermediate clinical risk characteristics using National Comprehensive Cancer Network (NCCN) or American Urological Association (AUA) criteria. Patients included in the validation and prostatectomy studies would be considered (a) eligible for active surveillance based on clinical and pathologic findings and (b) representative of patients in contemporary clinical practice. However, all patients elected radical prostatectomy within 6 months of their initial diagnostic biopsies. The clinical validation study was designed to evaluate the ability of Oncotype Dx Prostate to predict tumor pathology in needle biopsy specimens. It was prospectively designed, used masked review of prostatectomy pathology results, and as such met the REMARK (Reporting Recommendations for Tumor Marker Prognostic Studies) guidelines for biomarker validation. [27] In the prostatectomy study, all patients with clinical recurrence (local recurrence or distant metastasis) were selected, together with a random sample of those who did not recur, using a stratified cohort sampling method to construct a 1:3 ratio of recurrent to nonrecurrent patients. The prespecified primary end point of the validation study was the ability of the Genomic Prostate Score (GPS) to predict the likelihood of favorable pathology in the needle biopsy specimen. Favorable pathology was defined as freedom from high-grade or nonorgan-confined disease. In the prostatectomy study, the ability of the GPS to further stratify patients within AUA groupings was related to clinical recurrence-free interval in regression-to-the-mean estimated survival curves. The validation study results showed that the GPS could refine stratification of patients within specific NCCN criteria groupings, as summarized in Table 3. The proportions in Table 1 were estimated from a plot of GPS versus the percent likelihood of favorable pathology. [28] These findings suggest that a lower GPS would reclassify the likelihood of favorable pathology (i.e., less biologically aggressive disease) upward (i.e., a potentially lower risk of progression), and vice versa within each clinical stratum. For example, among patients in the cohort classified by NCCN criteria as low risk, the mean likelihood of favorable pathology in a tumor biopsy was about 76%, with 24% then having unfavorable pathology. With the GPS, the estimated likelihood of favorable tumor pathology was broadened, ranging from 55% to 86%, conversely reflecting a 45% to 14% likelihood of adverse pathology, respectively. Table 1. Reclassification of Prostate Cancer Risk Categories With Oncotype Dx Prostate NCCN Risk Level Estimated Mean Likelihood of Favorable Tumor Pathology Estimated Corresponding GPS, Range NCCN Criteria, % GPS + NCCN, % Range Very low Low Intermediate GPS: Genomic Prostate Score; NCCN: National Comprehensive Cancer Network. 4 GT71

5 In effect, the risk of adverse tumor pathology indicated by the GPS could be nearly halved (24%-14%) at 1 extreme, or nearly doubled (24%-45%) at the other, but the actual number of patients correctly or incorrectly reclassified between all 3 categories cannot be ascertained from the data provided. The results suggested that the combination of GPS plus clinical criteria could reclassify patients on an individual basis within established clinical risk categories. However, whether these findings support a conclusion that the GPS could predict the biological aggressiveness of a tumor hence its propensity to progress based solely on the level of pathology in a biopsy specimen is unclear. Moreover, extrapolation of this evidence to a true active surveillance population, for which the majority in the study would be otherwise eligible, is difficult because all patients had elective radical prostatectomy within 6 months of diagnostic biopsy. The prostatectomy study provided estimates of clinical recurrence rates stratified by AUA criteria (Epstein et al.), compared with rates after further stratification according to the GPS from the validation study. The survival curves for clinical recurrence reached a duration of nearly 18 years based on the dates individuals in the cohort were entered into the database ( ). The restratifications are summarized in Table 2. The GPS groups are defined by tertiles defined in the overall study. Table 2. Reclassification of Prostate Cancer 10-Year Clinical Recurrence Risk With Oncotype Dx Prostate Overall 10-Year Risk, % (AUA Risk Level) 10-Year Risk, % (GPS Low Group) 10-Year Risk, % (GPS Intermediate Group) 10-Year Risk, % (GPS High Group) 3.4 Low (Intermediate) (high) AUA: American Urological Association; GPS: Genomic Prostate Score. In the NCCN intermediate group, for example, the 10-year recurrence rate among radical prostatectomy patients was 9.6%. When the GPS was used in the analysis, the 10-year recurrence rate fell to as low as 2.0% (71% reduction) among patients in the low GPS group and 5.1% (47% reduction) in the intermediate GPS group, but rose to 14.3% (49% increase) in the high GPS group. These data suggest the GPS can reclassify a patient s risk of recurrence based on a specimen obtained at biopsy. However, the findings do not necessarily reflect a clinical scenario of predicting disease progression in untreated patients under active surveillance. In summary, the evidence from the Klein paper [28] on clinical validity for Oncotype Dx Prostate suggests the GPS can reclassify a patient s risk of recurrence based on a specimen obtained at biopsy. However, whether these findings support a conclusion that the GPS could predict the biological aggressiveness of a tumor hence its propensity to progress based solely on the level of pathology in a biopsy specimen is unclear. Clinical Utility Klein et al. also reported a decision-curve analysis that they have proposed reflects the clinical utility of Oncotype Dx Prostate. [28] The analysis investigated the predictive impact of the GPS in combination with the Cancer of the Prostate Risk Assessment (CAPRA) validated tool [29] versus the CAPRA score alone on the net benefit for the outcomes of patients with high-grade disease (Gleason >4+3), high-stage disease, and combined high-grade and high-stage disease. They reported that, over a range of threshold probabilities for implementing treatment, incorporation of the GPS would be expected to lead to fewer 5 GT71

6 treatments of patients who have favorable pathology at prostatectomy without increasing the number of patients with adverse pathology left untreated. Thus, an individual patient could use the findings to assess his balance of benefits and harms (net benefit) when weighing the choice to proceed immediately to curative radical prostatectomy with its attendant adverse sequelae, or deciding to enter an active surveillance program. The latter would have an immediate benefit realized by forgoing radical prostatectomy, but perhaps would be associated with greater downstream risks of disease progression and subsequent therapies. Klein s decision-curve analyses suggest a potential ability of the combined GPS and CAPRA data to help patients make decisions based on relative risks associated with immediate treatment or deferred treatment (i.e., active surveillance). This would reflect the clinical utility of the test. However, it is difficult to ascribe possible clinical utility of Oncotype Dx Prostate in active surveillance because all patients regardless of clinical criteria elected radical prostatectomy within 6 months of diagnostic biopsy. Moreover, the validity of using different degrees of tumor pathology as markers to extrapolate the risk of progression of a tumor in vivo is unclear. Polaris Analytic Validity No direct evidence on the analytic validity of Prolaris was identified. Information was available on the performance of the TaqMan array platform (Applied Biosystems) used in Prolaris and Oncotype DX Prostate from the MicroArray Quality Control (MAQC) project. [30] In the MAQC project, initiated and led by FDA scientists, expression data on 4 titration pools from 2 distinct reference RNA samples were generated at multiple test sites on 7 microarray-based and 3 alternative technology platforms including TaqMan. According to the investigators, the results provided a framework to assess the potential of array technologies as a tool to provide reliable gene expression data for clinical and regulatory purposes. The results showed very similar performance across platforms, with a median coefficient of variation of 5% to 15% for the quantitative signal and 80% to 95% concordance for the qualitative detection call between sample replicates. Thus, the analytic validity of gene expression analysis for prostate cancer management using Prolaris is indirectly suggested by results from the MAQC project, but remains to be specifically established. Clinical Validity Peer-reviewed evidence on the clinical validity of Prolaris comprises a retrospective cohort (n=349) culled from 6 cancer registries in Great Britain. The investigators assert the CCP score alone was more prognostic than either PSA titer or Gleason score for tumor-specific mortality at 10-year follow-up. Although the patients may be similar to those of a modern U.S. cohort, comparability is unclear from the single publication that is available. Furthermore, the study is limited by the use of archived biopsy specimens, with attendant issues of reproducibility and test reliability. One full-length, peer-reviewed article reported a validation study of Prolaris to determine its prognostic value for prostate cancer death in a conservatively managed needle biopsy cohort. [31] Cuzick et al. reported the results of a validation study of Prolaris to determine its prognostic value for prostate cancer death in a conservatively managed needle biopsy cohort. The authors did not state whether this study adhered to the PRoBE (prospective-specimen-collection, retrospective-blinded evaluation) criteria suggested by Pepe and colleagues for an adequate biomarker validation study. [32] They noted that the 6 GT71

7 cell cycle expression data were read blind to all other data, which conformed to the criteria; however, patients were identified retrospectively from tumor registries and there were no case-control subjects, which does not conform to the criteria. Patients with clinically localized prostate cancer diagnosed by needle biopsy between 1990 through 1996 were identified in 6 registries. Additional inclusion criteria included age younger than 76 years at diagnosis, available baseline prostate-specific antigen (PSA) measurement, and conservative management. [33] Exclusion criteria included radical prostatectomy, death, evidence of metastatic disease within 6 months of diagnosis, or hormone therapy prior to diagnostic biopsy. A cell cycle progression (CCP) score consisting of expression levels of 31 predefined cell cycle progression genes and 15 housekeeper genes was generated using TaqMan low-density arrays. The values of each of the 31 CCP genes were normalized by subtraction of the average of up to 15 nonfailed housekeeper genes for that replicate. Of 776 patients diagnosed by needle biopsy, 349 (79%) produced a CCP score and had complete baseline and follow-up information. The median potential follow-up time was 11.8 years during which a total of 90 deaths from prostate cancer occurred within 2799 person-years of actual follow-up. The main assessment of the study was a univariate analysis of the association between death from prostate cancer and the CCP score. A further predefined assessment of the added prognostic information after adjustment for the baseline variables was also undertaken. The primary end point was time to death from prostate cancer. A number of covariates were evaluated: centrally reviewed Gleason primary grade and score; baseline PSA value; clinical stage; extent of disease (percent of positive cores); age at diagnosis; Ki-67 immunohistochemistry; and initial treatment. The results are shown in Table 3. Table 3. Univariate and Multivariate Analysis for Death From Prostate Cancer in the Cuzick 2012 Validation Study Variable N Univariate Multivariate Hazard Ratio (95% CI) Hazard Ratio (95% CI) 1-unit increase in CCP score (1.62 to 2.53) 1.65 (1.31 to 2.09) Gleason score < (0.25 to 0.86) 0.61 (0.32 to 1.16) Referent Referent > (1.72 to 4.23) 1.90 (1.18 to 3.07) log (1+PSA)/(ng/mL) (1.31 to 2.20) 1.37 (1.05 to 1.79) Proportion of positive cores <50% (0.22 to 1.12) 50 to <100% 106 Referent 100% (1.01 to 2.73) Age at diagnosis (y) (0.96 to 1.04) Clinical stage T (0.32 to 1.75) T2 106 Referent T (0.90 to 3.38) Hormone use No 200 Referent Yes (1.30 to 2.98) CI: confidence interval The median CCP score was 1.03 (IQ range, ). The primary univariate analysis suggested that a 1-unit increase in CCP score was associated with a 2-fold increase in the risk of dying from prostate cancer. In preplanned multivariate analyses, extent of disease, age, clinical stage, and use of hormones 7 GT71

8 had no statistically significant effect on risk; only the Gleason score and PSA remained in the final model. Further exploratory multivariate modeling to produce a combined score, including CCP, Gleason score, and PSA level, suggested a strong, predominant nonlinear influence of the CCP score in predicting the risk of death from prostate cancer (p=0.008). Cuzick and colleagues suggested this combined score provided additional discriminatory information to help identify low-risk patients who could be safely managed by active surveillance. For example, among patients with a Gleason score of 6, for whom uncertainty existed as to the appropriate management approach, the predicted 10-year prostate cancer death rate ranged from 5.1% to 20.9% based on Gleason score and PSA; the range when assessed against the combined CCP, Gleason, and PSA score was 3.5% to 41%. However, the authors cautioned that because death rates were rare in this group, larger cohorts would be required to fully assess the value of the CCP combined score. Table 4 shows Kaplan-Meier analyses of 10-year risk of prostate cancer death stratified by CCP score groupings. Cuzick et al. reported no significance tests for the estimates. Nor did they explain the apparent substantial difference in mortality rates among patients in the 0 CCP 2 grouping (range, %) and those in the 2 < CCP 3 and > 3 groupings (range, %). The difference may simply reflect clinical criteria, for example, proportions of lower compared with higher Gleason grade cancers, respectively. Table 4. Kaplan-Meier Estimates of Prostate Cancer Death at 10 Years According to CCP Score Groupings in the Cuzick 2012 Validation Study Clinical Utility CCP Score Group N 10-Year Death Rate (%) CCP < CCP < CCP < CCP > No studies were identified to directly support the clinical utility of Prolaris. However, 2 retrospective survey studies that assessed the potential impact of Prolaris on physicians treatment decisions. [34,35] The authors of each study have suggested their findings support the clinical utility of the test, based on whether the results would lead to a change in treatment. Although this information may be useful in assessing the potential test uptake by urologists, it does not demonstrate clinical utility in clinical settings. Decipher Clinical Validity In 2014 Klein et al. evaluated whether use of the Decipher genomic classifier (GC) test improved accuracy in predicting metastasis within 5 years following radical prostatectomy (rapid metastasis [RM]). [36] Participants included 169 patients who underwent radical prostatectomy between 1987 and 2008, of which 15 were RM and 154 were non-rm controls. Metastasis developed between 1.7 and 3.3 years (median 2.3 years). Test performance was evaluated both individually and in 8 GT71

9 combination with clinical risk factors. After adjusting for clinical factors, Decipher was a significant predictor of RM (OR 1.48; p=0.018). Compared to the Stephenson model, the CAPRA-S, and previously reported biomarkers, Decipher had the highest concordance index (c-index), with the highest c-index achieved with integration of Decipher into the Stephenson nomogram. Cooperberg et al. compared the CAPRA-S and the Decipher genomic classifier to predict prostate cancer-specific mortality (CSM). From 1010 post-prostatectomy patients at high risk of cancer recurrence, 225 patients were randomly selected, of which CAPRA-S and Decipher could be determined for 185. [37] Scores were evaluated individually and in combination. Of these 185 patients, 28 experienced CSM. The CAPRA-S score stratified 82 patients as high risk. The Decipher classified 33 of these patients as high risk, 17 of which had CSM events, and reclassified the remaining 49 as low to intermediate risk, 3 of whom had CSM events. The Decipher test and CAPRA-S were both significant independent predictors of CSM. In patients with high scores for both Decipher and CAPRA-S, CSM at 10 years was 45%. The authors noted that this study was limited by its retrospective design, but concluded that, if their findings are validated prospectively, the integration of a genomic-clinical classifier may better identify post RP patients for secondary therapies and/or clinical trial participation. Karnes et al. prospectively created a randomly selected subcohort from the same initial 1010 postprostatectomy patients in the Cooperberg et al. study summarized above. Patients with metastasis at diagnosis or with any prior treatment for prostate cancer were excluded. [38] A randomly selected subcohort was created, with genomic data was available for 219 patients. Following radical prostatectomy the rates of biochemical recurrence (BCR) at 3 years was 35% and metastasis at 5 years was 6%. Median genomic classifier scores were consistently higher in patients with metastases at last follow-up (mean 6.7 years). Median genomic classifier scores also increased with higher Gleason scores. The authors concluded that the higher net benefit of genomic-based classifiers suggested increased specificity (i.e., lower false positives) compared with clinical-only risk models. Because patients with intermediate risk tumors may progress to advanced disease, the authors recommended further study of genomic classifiers in randomized datasets to determine whether genomic classifier scores from diagnostic biopsy specimens can predict metastasis as well as postoperative specimens. A possible limitation of this study was that nearly 15% of patients were node-positive and 45% received adjuvant therapy. Whether the genomic classifier predicted benefit from local (i.e., radiation) or systemic (e.g., hormone) therapies could not be determined because patients were not randomized to these treatments. Clinical Utility In a retrospective review of 110 patients with pt3 disease (71%) or positive surgical margins (PSMs) (63%) following radical prostatectomy. [39] US board certified urologists (n=107) were invited to provide adjuvant treatment recommendations for 10 cases randomly drawn from the pool of case histories. These recommendations were based on clinical variable only; GC test results were not provided. After these recommendations were completed, the urologists were asked to make recommendations with GC test results provided for the same patients, with case histories randomly re-ordered to minimize recall bias. Of the 107 urologists invited to participate, 51 took part in the study, providing 530 recommendations without GC test results. The overall change in recommendations when GC test results were added to clinical information was 31%). Of the 303 patients recommended for observation based on clinical information only, 38 (13%; 95% CI 9-17%) were recommended for radiation therapy and 9 (3%; 95% CI 1-6%) for radiation plus hormone therapies when GC results were made available. Of 193 patients recommended for radiation therapy initially, when GC results were available 77 (40%; 95% CI: 33-9 GT71

10 47%) were changed to observation. Therapy intensity was also highly correlated with higher risk according to the GC test. This study had a number of limitations which the authors noted as typical for early-stage evaluations of biomarker technologies. These limitations included the recommendations being based on case histories from chart reviews, the 51 participating urologists may not be representative of all urologists treating prostate cancer, patient health status and pathological information was not available to the urologists, and the effect on health outcomes of any changes in treatment were not studied. Clinical Practice Guidelines National Comprehensive Cancer Network The NCCN guidelines Version for prostate cancer did not include gene expression profile analysis in their recommendations, noting that the clinical utility has not been established in prospective RCTs or comparative effectiveness studies. [40] Summary Current evidence is insufficient to reach conclusions on the ability of gene expression profile testing to be used in clinical practice to improve clinical health outcomes (clinical utility) in patients with prostate cancer. Additionally, there are no evidence-based clinical practice guidelines which recommend the use of these tests for the management of prostate cancer. Therefore, gene expression analysis for prostate cancer management is considered investigational. [41] REFERENCES 1. Bangma, CH, Roemeling, S, Schroder, FH. Overdiagnosis and overtreatment of early detected prostate cancer. World journal of urology Mar;25(1):3-9. PMID: Johansson, JE, Andren, O, Andersson, SO, et al. Natural history of early, localized prostate cancer. JAMA. 2004;291: PMID: Ploussard, G, Epstein, JI, Montironi, R, et al. The contemporary concept of significant versus insignificant prostate cancer. Eur Urol. 2011;60: PMID: Harnden, P, Naylor, B, Shelley, MD, Clements, H, Coles, B, Mason, MD. The clinical management of patients with a small volume of prostatic cancer on biopsy: what are the risks of progression? A systematic review and meta-analysis. Cancer Mar 1;112(5): PMID: Brimo, F, Montironi, R, Egevad, L, et al. Contemporary grading for prostate cancer: implications for patient care. Eur Urol May;63(5): PMID: Eylert, MF, Persad, R. Management of prostate cancer. Br J Hosp Med (Lond) Feb;73(2):95-9. PMID: Eastham, JA, Kattan, MW, Fearn, P, et al. Local progression among men with conservatively treated localized prostate cancer: results from the Transatlantic Prostate Group. Eur Urol. 2008;53: PMID: Bill-Axelson, A, Holmberg, L, Ruutu, M, et al. Radical prostatectomy versus watchful waiting in early prostate cancer. N Engl J Med. 2005;352: PMID: Thompson, IM, Jr., Goodman, PJ, Tangen, CM, et al. Long-term survival of participants in the prostate cancer prevention trial. N Engl J Med Aug 15;369(7): PMID: GT71

11 10. Albertsen, PC, Hanley, JA, Fine, J. 20-year outcomes following conservative management of clinically localized prostate cancer. JAMA. 2005;293: PMID: Borley, N, Feneley, MR. Prostate cancer: diagnosis and staging. Asian J Androl. 2009;11: PMID: Freedland, SJ. Screening, risk assessment, and the approach to therapy in patients with prostate cancer. Cancer Mar 15;117(6): PMID: Liguori, PA, Peters, KL, Bowers, JM. Combination of negative pressure wound therapy and acoustic pressure wound therapy for treatment of infected surgical wounds: a case series. Ostomy Wound Manage May;54(5):50-3. PMID: Samies, J, Gehling, M. Acoustic pressure wound therapy for management of mixed partial- and full-thickness burns in a rural wound center. Ostomy Wound Manage Mar;54(3):56-9. PMID: Whitson, JM, Carroll, PR. Active surveillance for early-stage prostate cancer: defining the triggers for intervention. J Clin Oncol. 2010;28: PMID: Albertsen, PC. Treatment of localized prostate cancer: when is active surveillance appropriate? Nat Rev Clin Oncol. 2010;7: PMID: Wu, CL, Schroeder, BE, Ma, XJ, et al. Development and validation of a 32-gene prognostic index for prostate cancer progression. Proc Natl Acad Sci U S A. 2013;110: PMID: Spans, L, Clinckemalie, L, Helsen, C, et al. The genomic landscape of prostate cancer. Int J Mol Sci. 2013;14: PMID: Schoenborn, JR, Nelson, P, Fang, M. Genomic profiling defines subtypes of prostate cancer with the potential for therapeutic stratification. Clin Cancer Res. 2013;19: PMID: Huang, J, Wang, JK, Sun, Y. Molecular pathology of prostate cancer revealed by next-generation sequencing: opportunities for genome-based personalized therapy. Current opinion in urology May;23(3): PMID: Yu, YP, Song, C, Tseng, G, et al. Genome abnormalities precede prostate cancer and predict clinical relapse. Am J Pathol. 2012;180: PMID: Agell, L, Hernandez, S, Nonell, L, et al. A 12-gene expression signature is associated with aggressive histological in prostate cancer: SEC14L1 and TCEB1 genes are potential markers of progression. Am J Pathol Nov;181(5): PMID: TEC Assessment "Microarray-based Gene Expression Analysis for Prostate Cancer Management." BlueCross BlueShield Association Technology Evaluation Center, Vol. 28, Tab Knezevic, D, Goddard, AD, Natraj, N, et al. Analytical validation of the Oncotype DX prostate cancer assay - a clinical RT-PCR assay optimized for prostate needle biopsies. BMC Genomics. 2013;14:690. PMID: Epstein, JI, Allsbrook, WC, Jr., Amin, MB, Egevad, LL. The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. United States, p Cooperberg, MR, Simko, JP, Cowan, JE, et al. Validation of a cell-cycle progression gene panel to improve risk stratification in a contemporary prostatectomy cohort. United States, p McShane, LM, Altman, DG, Sauerbrei, W, Taube, SE, Gion, M, Clark, GM. Reporting recommendations for tumor marker prognostic studies. United States, p Klein, EA, Cooperberg, MR, Magi-Galluzzi, C, et al. A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur Urol Sep;66(3): PMID: GT71

12 29. Cooperberg, MR, Broering, JM, Carroll, PR. Risk assessment for prostate cancer metastasis and mortality at the time of diagnosis. United States, p Shi, L, Reid, LH, Jones, WD, et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol. 2006;24: PMID: Cuzick, J, Berney, DM, Fisher, G, et al. Prognostic value of a cell cycle progression signature for prostate cancer death in a conservatively managed needle biopsy cohort. Br J Cancer. 2012;106: PMID: Pepe, MS, Feng, Z, Janes, H, Bossuyt, PM, Potter, JD. Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. J Natl Cancer Inst. 2008;100: PMID: Montironi, R, Mazzuccheli, R, Scarpelli, M, Lopez-Beltran, A, Fellegara, G, Algaba, F. Gleason grading of prostate cancer in needle biopsies or radical prostatectomy specimens: contemporary approach, current clinical significance and sources of pathology discrepancies. BJU Int. 2005;95: PMID: Crawford, ED, Scholz, MC, Kar, AJ, et al. Cell cycle progression score and treatment decisions in prostate cancer: results from an ongoing registry. Current medical research and opinion Jun;30(6): PMID: Shore, N, Concepcion, R, Saltzstein, D, et al. Clinical utility of a biopsy-based cell cycle gene expression assay in localized prostate cancer. Current medical research and opinion Apr;30(4): PMID: Klein, EA, Yousefi, K, Haddad, Z, et al. A Genomic Classifier Improves Prediction of Metastatic Disease Within 5 Years After Surgery in Node-negative High-risk Prostate Cancer Patients Managed by Radical Prostatectomy Without Adjuvant Therapy. Eur Urol Nov 12. PMID: Cooperberg, MR, Davicioni, E, Crisan, A, Jenkins, RB, Ghadessi, M, Karnes, RJ. Combined Value of Validated Clinical and Genomic Risk Stratification Tools for Predicting Prostate Cancer Mortality in a High-risk Prostatectomy Cohort. Eur Urol Jul 2. PMID: Karnes, RJ, Bergstralh, EJ, Davicioni, E, et al. Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population. The Journal of urology Dec;190(6): PMID: Badani, KK, Thompson, DJ, Brown, G, et al. Effect of a genomic classifier test on clinical practice decisions for patients with high-risk prostate cancer after surgery. BJU Int Mar;115(3): PMID: National Comprehensive Cancer Network (NCCN). Clinical Practice Guidelines in OncologyTM. Multiple Myeloma. v [cited 02/13/2015 ]; Available from: BlueCross BlueShield Association Medical Policy Reference Manual "Gene Expression Analysis for Prostate Cancer Management." Policy No CROSS REFERENCES Gene-Based Tests for Screening, Detection and/or Management of Prostate Cancer, Genetic Testing, Policy No. 17 DecisionDX Gene Expression Assays, Genetic Testing, Policy No GT71

13 CODES NUMBER DESCRIPTION CPT Unlisted multianalyte assay with algorithmic analysis HCPCS None 13 GT71