Gene Expression Profiling and Protein Biomarkers for Prostate Cancer Management

• Benefit Application
• Description
• Prior Approval
• Policy
• Procedure Codes
• Selected References
• Policy History

 

Medical Policy: 02.04.57 

Original Effective Date: June 2016 

Reviewed: May 2018 

Revised: May 2018 

 

Benefit Application:

Benefit determinations are based on the applicable contract language in effect at the time the services were rendered. Exclusions, limitations or exceptions may apply. Benefits may vary based on contract, and individual member benefits must be verified. Wellmark determines medical necessity only if the benefit exists and no contract exclusions are applicable. This medical policy may not apply to FEP. Benefits are determined by the Federal Employee Program.

 

This Medical Policy document describes the status of medical technology at the time the document was developed. Since that time, new technology may have emerged or new medical literature may have been published. This Medical Policy will be reviewed regularly and be updated as scientific and medical literature becomes available.

 

Description:

Prostate cancer is the most commonly diagnosed cancer and the second leading cause of cancer deaths in American men. In 2017, it is estimated that 161,360 men will be diagnosed with prostate cancer and 26,730 will die of this disease. Gene expression profile analysis and protein biomarkers have been proposed as a means to risk-stratify patients with prostate cancer to guide treatment decisions. These tests are intended to be used either on prostate needle-biopsy tissue to guide management decisions regarding active surveillance versus therapeutic intervention, or to guide radiotherapy use after radical prostatectomy (RP). 

 

Localized prostate cancers may appear very similar clinically at diagnosis. However, they often exhibit diverse risk of progression that may not be captured by clinical risk categories or prognostic tools based on clinical findings, including PSA titers, Gleason grade, or tumor stage. 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% to 20% to perhaps 27% at 20 year follow-up. Among older men (ages ≥ 70 years) with low risk disease, comorbidities typically result in the cause of death; these men will die with prostate cancer present, rather than from the cancer itself. Other very similar low risk tumors may progress unexpectedly rapidly, quickly disseminating and becoming incurable.

 

In the United States, most prostate cancers are clinically localized at diagnosis due in part to the widespread use of PSA testing. Clinicopathologic characteristics are used to stratify patients by risk based on the extent of the primary tumor (T category), nearby lymph node involvement (N category), metastasis (M category), PSA level and Gleason score. The National Comprehensive Cancer Network (NCCN) and American Urological Association (AUA) risk categories for clinically localized prostate cancer are similar, and broadly include low, intermediate or high-risk as follows as well as subcategories within these groups:

• Low: T1-T2a and Gleason score ≤ 6 grade group 1 and PSA level ≤ 10 ng/mL;
• Intermediate: T2b-T2c or Gleason score 3+4/Gleason grade group 2 or Gleason score 4+3=7/Gleason grade group 3 or PSA level 10-20 ng/mL;
• High: T3a or Gleason score 8/Gleason grade 4 or Gleason score 9-10/Gleason grade 5 or PSA level > 20 ng/mL

 

Risk stratification is combined with patient age, life expectancy, and treatment preferences to make initial therapy decisions.

 

Monitoring after radical prostatectomy (RP), all normal prostate tissue and tumor tissue is theoretically removed during RP and the serum level of PSA should be undetectable following RP. Detectable PSA post RP indicates residual prostate tissue and presumably persistent or recurrent disease. PSA is serially measured following RP to detect early disease recurrence. The National Comprehensive Cancer Network (NCCN) recommends monitoring serum PSA every 6 to 12 months for the first 5 years and annually thereafter. Many recurrences following RP can be successfully treated. The American Urological Association (AUA) has recommended a biochemical recurrence be defined as a serum PSA of 0.2 ng/mL or higher, which is confirmed by a second determination with a PSA level of 0.2 ng/mL or higher.

 

Commercially Available Tests 

The following are commercially available gene expression profiling (GEP) tests and protein biomarkers:

• Prolaris^® (Myriad Genetics, Salt Lake City, UT) is used to directly measure tumor cell growth characteristics for stratifying the risk of disease progression in prostate cancer patients. The 46 gene expression profiling test which includes 31 cell cycle progression (CCP) genes and 15 housekeeper genes to generate a CCP score. The testing combines traditional risk factors (Gleason score, PSA, clinical stage) with a molecular assessment and the score helps identify those patients with less or more aggressive prostate cancer, thus assisting with individualized prostate cancer treatment/management decisions. 
• Oncotype Dx^® Prostate Cancer Assay (Genomic Health, Redwood City, CA) is a gene expression profiling test used to quantify expression levels of 12 cancer-related and 5 reference genes to generate a Genomic Prostate Score (GPS). In the final analysis, the cell cycle progression (CCP) score or GPS is combined in proprietary algorithms with clinical risk criteria (PSA, Gleason grade, tumor stage) to generate low risk categories (i.e. reclassification) intended to reflect biological indolence or aggressiveness of individual lesions, and thus inform management decisions.
• Decipher^® Prostate Cancer Test/ Decipher® Prostate Cancer Classifier (GenomeDx Biosciences, Vancouver, BC) is a tissue based tumor 22-biomarker gene expression profile test intended to calculate the probability of clinical metastasis within 5 years of radical prostatectomy (RP) surgery. The gene expression classifier is a continuous risk score between 0 and 1, with higher risk scores indicating a greater probability of developing metastasis.
• ProMark^™ Protein Biomarker Test (Metamark Genetics, Cambridge, MA) is an automated quantitative imaging method to measure 8 protein biomarkers (DERL1, PDSS2, pS6, YBX1, HSPA9, FUS, SMAD4, and CUL2) using immunofluorescence and automated quantitative images in intact biopsy tissue to risk stratify patients to active surveillance or therapeutic intervention. The assay results are combined using predefined coefficients for each marker from a logistic regression model to calculate a risk score and the risk score is continuous number between 0 and 1 which stratify the patient into a favorable or less favorable risk score.

Level of Evidence 

The level of evidence (LOE) will be evaluated using the Simon et. al. (2009) framework for study classification, and LOE for prognostic studies using archived specimens. The following are the elements of tumor marker studies that constitute level of evidence determinations:

 

 

 

Initial Management Decision: Active Surveillance and Therapeutic Intervention

In men newly diagnosed with clinically localized prostate cancer, the purpose of gene expression profiling (GEP) and protein biomarker testing is to inform a decision whether to undergo immediate therapy or to forgo immediate therapy and begin active surveillance.

 

The divergent behavior of localized prostate cancers creates uncertainty whether to treat immediately or follow with active surveillance. With active surveillance the patient will forgo immediate therapy and continue regular monitoring until signs or symptoms of disease progression are evident, at which point curative treatment is instituted. A patient may alternatively choose potentially curative treatment upfront. Surgery (i.e. radical prostatectomy [RP] or external beam radiotherapy [EBRT]) is most commonly used to treat patients with localized prostate cancer. Complications most commonly reported with RP or EBRT and with the greatest variability are incontinence (0%-73%) and other genitourinary toxicities (irritative and obstructive symptoms); hematuria (typically ≤ 5%); 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%). A 2014 population based retrospective cohort study (Nam et.al.) using administrative hospital data, physician billing codes, and cancer registry data estimated the 5 year cumulative incidence of admission to hospital for a treatment related complication following RP or EBRT to be 22% (95% confidence interval [CI], 21.7% to 22.7%).

 

In the Prostate Testing for Cancer and Treatment (ProtecT) trial (2016 Hamdy et. al.), active surveillance, immediate radical prostatectomy (RP) and immediate external beam radiotherapy (EBRT) for the treatment of clinically localized prostate cancer were compared in 1643 men identified through prostate specific antigen (PSA) testing. About 90% of the participants had PSA level less than 10 ng/mL; two-thirds were Gleason score 6 and 20% were Gleason score 7; all were clinical stage T1c or T2. The mean age was 62 years. At a median of 10 year follow-up, prostate cancer specific survival was high and similar across the 3 treatment groups: 98.8% (95% CI, 97.4% to 99.5%) in active surveillance; 99.0% (95% CI, 97.2% to 99.6%) in the surgery group; and 99.6% (95% CI, 98.4% to 99.9%) in the radiotherapy (RT) group. Surgery and RT were associated with a lower incidence of disease progression and metastases compared with active surveillance. Approximately 55% of men in the active surveillance group had received a radical treatment by the end of follow-up.

 

Prostate Cancer Intervention Versus Observational Trial (PIVOT) (Wilt et. al. 2012 and 2017) randomized 731 men in the United States with localized prostate newly diagnosed cancer to radical prostatectomy (RP) or observation. The patients were 40% low risk, 34% intermediate risk and 21% high risk. Results from PIVOT also concluded that RP did not prolong survival compared with observation through 12 years and 19.5 years of follow-up in the primary analyses including all risk groups. However, among men with intermediate risk tumors, surgery was associated with a 31% relative reduction in all-cause mortality compared with observation (hazard ration [HR], 0.69; 95% CI, 0.49 to 0.98; absolute risk reduction 12.6%).

 

An observational study (2012 Van den Bergh et. al.) comparing sexual function of men with low risk prostate cancer who chose active surveillance with men who received radiation therapy (RT) or radical prostatectomy (RP) found those who chose active surveillance were more often sexually active than similar men who received RP. In a 2011 report (Johanssen et. al.) of quality of life for men in the Scandinavian Prostate Cancer Group Study Number 4 (SPCG-4), after a median follow-up of more than 12 years, distress caused by treatment-related side effects was reported significantly more often by men assigned to RP than by men assigned to watchful waiting.

 

The American Urological Association (AUA), in a joint guideline with ASTRO (American Society of Radiation Oncology) and SUO (Society of Urologic Oncology) (2017), have suggested that physicians recommend active surveillance for most men with low risk localized prostate cancer patients, and may offer definitive treatment (i.e. radical prostatectomy (RP) or radiotherapy (RT)) to select low risk localized prostate cancer patients who may have a high probability of progression on active surveillance. The guidelines also suggest that the physician should recommend radical prostatectomy (RP) or radiotherapy (RT) plus androgen deprivation therapy (ADT) as standard treatment options for patients with intermediate risk localized prostate cancer. 

 

Patients 

The relevant population of interest is individuals with newly diagnosed low or intermediate risk localized prostate cancer, who have not undergone treatment for prostate cancer, and who are deciding between therapeutic intervention or surveillance.

 

Interventions 

Gene expression profiling (GEP) refers to analysis of messenger RNA (mRNA) expression levels of many genes simultaneously in a tumor specimen and protein biomarkers. Two GEP tests and 1 protein biomarker test are intended to stratify biologically prostate cancers diagnosed on prostate needle biopsy: Prolaris and OncotypeDx Prostate Cancer Assay are GEP tests that use archived tumor specimens as the mRNA source, reverse transcriptase polymerase chain reaction amplification, and the TaqMan low density array platform. A protein biomarker test, ProMark is an automated quantitative imaging method to measure protein biomarkers by immunofluorescent staining in defined areas in intact formalin-fixed paraffin-embedded (FFPE) biopsy tissue to provide independent prognostic information to aid in the stratification of patients with prostate cancer to active surveillance or therapy.

 

Comparators  

Clinicopathologic risk stratification along with age/life expectancy and patient preference are currently being used to make decisions about prostate cancer management. Clinical characteristics (e.g. stage, biopsy Gleason score, serum PSA level) and demographic characteristics (e.g. age, life expectancy) are combined to classify men according to risk. National Comprehensive Cancer Network (NCCN) and American Urological Association (AUA) have provided treatment recommendations based on risk stratification and life expectancy. The Kattan et. al. (2003) nomogram was developed, a statistical model that accurately predicts the presence of small moderately differentiated confined cancer based on clinical variables (serum PSA, clinical stage, prostate biopsy Gleason grade and ultrasound volume) and variables derived from the analysis of systematic biopsies. The authors concluded nomograms incorporating pretreatment variables (clinical stage, Gleason grade, PSA and the amount of care in a systematic biopsy specimen) can predict the probability that a man with prostate cancer has an indolent tumor. These nomograms have good discriminatory ability and calibration, and may benefit the patient and clinician when the various treatment options for prostate cancer are being considered. The Cancer of the Prostate Risk Assessment (CAPRA) (Cooperberg et. al.) is a pretreatment nomogram that predicts the risk of biochemical recurrence following radical prostatectomy (RP) developed from a cohort of RP patients. The authors concluded the UCSF-CAPRA accurately predicted outcomes after radical prostatectomy among large, diverse, cohort of men. These results validated the effectiveness of this powerful and straightforward instrument.

 

Outcomes

Beneficial outcomes resulting from a true test result are prolonged survival, improved quality of life and reduction in unnecessary treatment related adverse events. Harmful outcomes resulting from a false test result are recurrence, metastases or death, and unnecessary treatments. The outcomes of interest are overall survival (OS), disease specific survival (DSS), quality of life (QOL) and treatment related morbidity. 

 

Timing 

Ten year outcomes are of interest due to the prolonged natural history of localized prostate cancer.

 

Setting 

To select a management strategy for localized prostate cancer clinicians should incorporate shared decision making with their patient by considering cancer severity (risk category), patient values and preferences, life expectancy, pre-treatment general functional and genitourinary symptoms, expected post treatment functional status, and potential for salvage treatment. Effective shared decision making in prostate cancer care requires clinicians to inform patients about immediate and long term morbidity or side effects of proposed treatment of care options.

 

Prolaris 

The Prolaris gene expression profile test combines the RNA expression levels of 31 genes involved in cell cycle progression (CCP) and 15 housekeeping genes to generate a Prolaris score (CCP score). This section will review Prolaris for initial management decisions in newly diagnosed, localized prostate cancer. Prolaris for the management after radical prostatectomy will be discussed in the following section.

 

Clinical Validity 

Cuzick et. al. (2012) examined the Prolaris prognostic value for prostate cancer death in a conservatively managed needle biopsy cohort. Cell cycle expression data were read blind to all other data. Patients were identified from 6 cancer registries in Great Britain and were included if they had clinically localized prostate cancer diagnosed by needle biopsy through 1990 through 1996, were younger than 76 years at the time of diagnosis, had a baseline PSA measurement and were conservatively managed. Patients treated with radical prostatectomy or radiation therapy, within the first 6 months after diagnosis or who died or showed evidence of metastatic disease within 6 months of diagnosis were exclude. Men who had hormone therapy before the diagnostic biopsy were also excluded. The original biopsy specimens were retrieved and centrally reviewed by a panel of expert urologic pathologists to confirm the diagnosis, and where necessary to reassign Gleason scores. Of 776 patients diagnosed by needle biopsy and for which a sample was available to review histology, needle biopsies were retrieved for 527 (68%), 442 (84%) of which had adequate material to assay. From the 442 samples, 349 (79%) produced a CCP (cell cycle progression) score and had complete baseline and follow-up information, representing 45% of 776 patients initially identified. The median follow-up time was 11.8 years. A total of 90 deaths from prostate cancer occurred within the 2799 person-years of actual follow-up. In univariate analysis (n=349), the hazard ratio (HR) for death from prostate cancer was 2.02 (95% CI (1.62, 2.53), P<10^-9) for a one unit increase in CCP score. The CCP score was only weakly correlated with standard prognostic factors and in a multivariate analysis. CCP score dominated (HR for one unit increase = 1.65, 95% CI (1.31, 2.09), P = 3 x 10^-5), with Gleason score (P=5 x 10^-4) and prostate specific antigen (PSA) (P=0.017) providing significant additional contributions. The authors concluded, based on exploratory analyses presented here there is evidence that the CCP score may have a non-linear impact on the predicted probability of prostate cancer death. This could either be due to a true non-linear relationship between the CCP score and risk of death from prostate cancer, or lack of proportional hazards in that the CCP is a better predictor of earlier deaths. Although the data set is not large enough to distinguish between these two possibilities, we think that the latter is likely to be at least a partial explanation. The most obvious clinical use of the CCP score is to help identify low risk patients who can be safely managed by surveillance. In this series, we were unable to identify a clinically significant subgroup with a 10 year risk of dying from prostate cancer of less than 5%. However, the CCP score increased the ability to identify men with a less than 10% risk of dying from prostate cancer within 10 years, from 7 to 14%. In addition, for patients with Gleason score of 6, where considerable uncertainty still exists as to appropriate treatment, the predicted 10 year prostate cancer death rate with the addition of the CCP score ranged from 3.5 to 41.0% (compared with 5.1 to 20.9% using clinical parameters only). We believe this is relevant information when considering appropriate care. However, as deaths from prostate cancer are rare in this group, larger cohorts are needed to fully characterize the value of the CCP score in identifying very low risk patients, and clearer relationship may emerge when more patients have been studied.

 

Measures that would suggest improved discriminatory ability (e.g. area under the curve (AUC) or reclassification) compared with an existing nomogram were not reported in Cuzick et. al. (2012). The authors did not provide evidence that the test could correctly reclassify men initially at high risk ot lower risk to avoid overtreatment, or conversely, correctly reclassify those initially at lwo risk to high risk to avoid undertreatment.  

 

Cuzick et. al. (2015) examined 3 Great Britain cancer registries from 1990 to 2003 to identify men with prostate cancer who were conservatively managed following needle biopsy, with follow-up through December 2012. The authors stated that the samples did not overlap with Cuziek et. al. (2012). Men were included in this study if they were aged < 76 years at diagnosis and had clinically localized prostate cancer diagnosed by needle biopsy. Patients treated by radical prostatectomy or radiation therapy within 6 months of diagnosis were excluded. Additionally, those with objective evidence of metastatic disease (by bone scan, X-ray, radiograph, CT scan, MRI, bone biopsy, lymph node biopsy, pelvic lymph node dissection) or clinical indications of metastatic disease (including pathological fracture, soft tissue metastases, spinal compression or bone pain), or a PSA measurement > 100 ng ml-1 at or within 6 months of diagnosis were also excluded. Men who had hormone therapy prior ot the diagnostic biopsy were also excluded because of the influence of hormone treatment on interpreting Gleason score. Also, excluded were men who died within 6 months of diagnosis or had < 6 months of follow-up. Original histological specimens from the diagnostic procedure were requested and centrally reviewed by panel of urological pathologists to confirm diagnosis and reassign Gleason scores using a contemporary and consistent interpretation of the Gleason scoring system. The full cohort comprised of 989 men. A total of 145 (15%) samples had inadequate tumor and a further 83 (8%) failed CCP score quality assurance. For those with adequate amounts of tumor visible on the H&E (n=844), 90% produced a CCP score. One patient lacked information about extent of disease, two patients had missing baseline PSA information and a further 173 were missing clinical stage, leaving 585 with a CCP score and all clinical variables for analysis in the primary analysis cohort. In univariate analysis, the CCP score hazard ratio (HR) was 2.08 (95% CI (1.76, 2.46), P < 10^-13) for one unit change of the score. In multivariate analysis including CAPRA (Cancer of the Prostate Risk Assessment), the CCP score hazard ratio was 1.76 (95% CI (1.44, 2.14), P < 10^-6). The predefined CCR (clinical cell cycle risk) score was highly predictive, hazard ratio 2.17 (95% CI (1.83, 2.57),  χ²=89.0, P < 10^-20) and captured virtually all available prognostic information. The authors concluded, these results confirm our previous findings for the prognostic value of the CCP score measured in diagnostic needle biopsies. For conservatively managed patients, the CCP score was highly prognostic for death from prostate cancer and provided important independent information that could not be obtained from clinical data. In addition, this study provides a fully independent validation in a new data set of a predefined CCR score as a linear combination of the CCP score and clinical variables (combined in the CAPRA score), which almost completely accounted for all molecular and clinical prognostic information. Further work is needed to determine if DNA based or other markers can add useful information to our combined score.

 

In 2016, Sommariva et. al. performed a systematic review to assess the evidence on the value of the CCP (cell cycle progression) instrument in prostate cancer treatment by reviewing current publications and integrating the results via a meta-analysis supported by Myriad Genetics were reported. Published and unpublished studies of prognostic validity or clinical utility of CCP testing were eligible for inclusion.  The results show that use of the CCP score is better than existing assessments at elucidating the aggressive potential of prostate cancer in an individual. The pooled hazard ratio for biochemical recurrence (BCR) per 1-unit increase in the CCP score was 1.88 in a univariate model and 1.63 in a multivariate model. Four studies showed that CCP testing can impact the decisions of physicians regarding treatment, and potentially lead to a decrease in surgical interventions for low-risk patients. The authors concluded, this review offers a comprehensive review of existing evidence on CCP testing and provides clinicians, patients and policy makers with a strong summary measure of its prognostic validity and clinical utility. It will be important to develop economic studies to measure the impact of such technology on health care systems.

 

Clinical Utility 

Three decision impact studies assessed the potential impact of Prolaris on physicians’ treatment decisions in patients. The authors of each study Crawford et. al. 2014; Shore et. al. 2014; and Shore et. al. 2016 have suggested their finding support the “clinical utility” of the test, based on whether the results would lead to a change in treatment. Pathology results were not reported for these studies. Given the lack of established clinical validity and no reported outcomes, it is uncertain whether any treatment changes were clinically appropriate.

 

In trying to construct an indirect chain of evidence from clinical validity and clinical utility, there are several obstacles to draw conclusions. From the clinical validity section, it is not clear if the test provides incremental value over the CAPRA (Cancer of the Prostate Risk Assessment) score for decision making. In the example of reclassification given by Cuzick (2015) et. al., 11 men with a CAPRA estimated 10 year mortality risk rate of 4% were reclassified as having higher 10-year mortality estimated by CCR (clinical cell cycle risk) score with risk ranging from just greater than 4% to about 8%, and 31 men with CAPRA 10 year mortality risk rate of 5.7% were reclassified as having lower estimated risk by CCR of about 2.5% to 4%. It is not clear that these reclassifications would change treatment decisions.

 

Given that the PIVOT trial (Prostate Cancer Intervention Versus Observational Trial) supported radical prostatectomy (RP) for the intermediate risk group, showing a 30% relative and 12% absolute benefit for overall survival, in order to be suitable for clinical decision making, the test would have to identify a lower risk group of intermediate risk men with a very negative predictive value for survival with tight CIs (confidence intervals). Because it is not clear how the Cuzick (2012) or Cuzick (2015) results would apply specifically to intermediate risk men, it is not clear whether the test could be used to identify intermediate risk men who can delay radical prostatectomy (RP) or radiation therapy (RT).      

 

In 2017, Health Quality Ontario reported on a health technology assessment including a systematic review of the literature on the clinical utility, economic impact and patients’ perceptions of the value of   Prolaris CCP (cell cycle progression) test in low and intermediate risk localized prostate cancer. The authors conducted a systematic review of the clinical and economic evidence of the CCP test in low and intermediate risk, localized prostate cancer. Medical and health economic databases were searched from 2010 to June or July 2016. The critical appraisal of the clinical evidence included risk of bias and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group criteria. We also analyzed the potential budget impact of adding the CCP test into current practice, from the perspective of the Ontario Ministry of Health and Long-Term Care. Qualitative interviews were conducted with men with prostate cancer, on the factors that influenced their treatment decision making. For the review of clinical effectiveness 3,021 citations were screened, and two before and after studies met inclusion criteria. In one study, the results of the CCP test appeared to change the treatment plan (from initial to final plan) in 64.9% of cases overall (GRADE rating of the quality of evidence: Very low). In the other study, the CCP test changed the treatment received in nearly half of cases overall, compared with the initial plan (GRADE: Very low). No evidence was available on clinical outcomes of patients who treatment was informed by CCP results. For the review of cost effectiveness, 100 citations were identified and screened. No studies met the inclusion criteria. In the economic evaluation, it was estimated that publicly funding the CCP test would result in a total net budget impact of $41.3 million in the first 5 years, mostly due to the cost of the CCP test. In the model, the relatively cost savings ($7.3 million) due to treatment change (increased use of active surveillance and decreasd use of interventional treatment) was not large enough to offset the high cost of the test. Patients viewed the test as potentially helpful but, due to the complexity of treatment decision making, the reviewers were unsure the test would ultimately change treatment choices. The authors concluded, that they found no evidence to demonstrate the impact of the Prolaris CCP test on patient important clinical outcomes. The limited evidence available shows that the test appears to provide information that, when considered in addition to the clinical risk stratification, may change the treatment plan or actual treatment for some low and intermediate risk prostate cancer patients. As a result, there is insufficient data to inform the cost-effectiveness of the CCP test.

 

Summary: Prolaris 

In a cohort of men conservatively managed following needle biopsy, Cuzick et. al. (2012) suggested that the CCP (cell cycle progression) score alone was more prognostic than either PSA level or Gleason score for tumor specific mortality at 10 year follow-up based on HRs (hazard ratio). Comparison with CAPRA (Cancer of the Prostate Risk Assessment) score was not provided in Cuzick et. al. (2012). Cuzick et. al. (2015) found that discriminiation improved somewhat by adding the CCP score to the CAPRA score. Ten-year mortality rates based on CCP were inconsistent within Prolaris risk categories across Cuzick (2012) and Cuzick (2015). Numeric summaries of mortality rates for the CCR (cell cycle risk) score were provided in Cuzick (2015). The men included in the Great Britain registries were not screen selected, and a large proportion of men in the validation studies were not low or intermediate risk. Validation studies were Simon category C.   

 

No direct evidence is available to support clinical utility of Prolaris for improving net outcomes of patients with localized prostate cancer. The chain of evidence is incomplete. The PROTECT (Prostate Testing for Cancer and Treatment) trial showed 99% ten year disease specific survival in all 3 treatment groups: active surveillance, radiation therapy (RT) and radical prostatectomy (RP) including predominately low-risk but also some intermediate risk men. American Urological Association (AUA) has recommended active surveillance in low-risk men. The low mortality rate estimated with tight precision makes it unlikely that a test intended to identify a subgroup of low risk men with a net benefit from immediate treatment instead of active surveillance would find such a group.

 

The PIVOT trial (Prostate Cancer Intervention Versus Observational Trial) preplanned subgroup analysis showed reduction in mortality for radical prostatectomy (RP) compared with observation for men with intermediate risk; American Urological Association (AUA) has recommended radiation therapy (RT) or radical prostatectomy (RP) for such men. For intermediate risk men, a test designed to identify men who can receive active surveillance instead of RP or RT would need to show very high predictive valude for disease specific mortality at 10 years and improvement in prediction compared with existing tools used to select such men. To forgo evidence based beneficial treatment, there would have to be a very high standard of evidence for the clinical validity of the test. Level 1 evidence as defined by Simon would be necessary.

 

OncotypeDx Prostate 

The OncotypeDx Prostate Assay includes 5 reference genes and 12 cancer genes that represent 4 molecular pathways of prostate cancer oncogenesis: androgen receptor, cellular organization, stromal response, and proliferation. The assay results are combined to produce a GPS (Genomic Prostate Score), which ranges from 0 to 100. Higher GPS scores indicated more risk.

 

Clinical Validity 

Klein et. al. (2014) reported on a clinical validation study to identify and validate a biopsy based gene expression signature that predicts clinical recurrence, prostate cancer (PCa) death and adverse pathology. Gene expression was quantified by reverse transcription-polymerase chain reaction for three studies-a discovery prostatectomy study (n=441), a biopsy study (n=167), and a prospectively designed, independent clinical validation study (n=395)-testing retrospectively collected needle biopsies from contemporary (1997-2011) patients with low to intermediate clinical risk who were candidates for active surveillance (AS). The main outcome measures defining aggressive PCa were clinical recurrence, PCa death, and adverse pathology at prostatectomy. Cox proportional hazards regression models were used to evaluate the association between gene expression and time to event end points. Results from the prostatectomy and biopsy studies were used to develop and lock a multigene-expression-based signature, called the Genomic Prostate Score (GPS); in the validation study, logistic regression was used to test the association between the GPS and pathologic stage and grade at prostatectomy. Decision-curve analysis and risk profiles were used together with clinical and pathologic characteristics to evaluate clinical utility. Of the 732 candidate genes analyzed, 288 (39%) were found to predict clinical recurrence despite heterogeneity and multi-focality, and 198 (27%) were predictive of aggressive disease after adjustment for prostate-specific antigen, Gleason score, and clinical stage. Further analysis identified 17 genes representing multiple biological pathways that were combined into the GPS algorithm. In the validation study, GPS predicted high-grade (odds ratio [OR] per 20 GPS units: 2.3; 95% confidence interval [CI], 1.5-3.7; p<0.001) and high-stage (OR per 20 GPS units: 1.9; 95% CI, 1.3-3.0; p=0.003) at surgical pathology. GPS predicted high-grade and/or high-stage disease after controlling for established clinical factors (p<0.005) such as an OR of 2.1 (95% CI, 1.4-3.2) when adjusting for Cancer of the Prostate Risk Assessment (CAPRA) score. A limitation of the validation study was the inclusion of men with low-volume intermediate-risk PCa (Gleason score 3+4), for whom some providers would not consider AS. The actual number of patients correctly or incorrectly reclassified across all 3 categories cannot be ascertained from the data provided. The results suggest that the combination of GPS plus clinical criteria can reclassify patients on an individual basis within established clinical risk categories. However, whether these findings support a conclusion that the GPS could predict the disease-specific survival 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 RP within 6 months of diagnostic biopsy. Also, the data suggests the GPS can reclassify patient 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.    

 

A retrospective cohort study by Cullen et.al. (2015) included biopsies from 431 men treated for National Comprehensive Cancer Network (NCCN) very low, low, or intermediate risk prostate cancer (PCa) between 1990 and 2011 at two U.S. military medical centers and were tested to validate the association between GPS (Genomic Prostate Score) and biochemical recurrence (BCR) and to confirm the association with AP (adverse pathology). Metastatic recurrence (MR) was also evaluated. Cox proportional hazards models were used for BCR and MR, and logistic regression was used for AP. Central pathology review was performed by one uropatholgist. AP was defined as primary Gleason pattern 4 or any pattern 5 and/or pT3 disease. Selected patients were older (61.0 years vs 59.7 years, p=0.013) and had both higher Gleason scores (p<0.001) and NCCN risk classification (29.8% vs 32.9% intermediate, p=0.035). Median follow-up was 5.2 years and biochemical recurrence (BCR) occurred in 62 (15.4%). GPS results (scale: 0-100) were obtained in 402 cases (93%); 62 men (15%) experienced BCR, 5 developed metastases, and 163 had AP.

 

 

Adverse pathology was noted in 163 (34% of men). In an analysis adjusted for baseline characteristics, the GPS was associated with BCR-free survival and adverse pathology following radical prostatectomy (RP), see the below table:

 

CI=confidence interval; HR=hazard ratio

 

The Genomic Prostate Score (GPS) improved the C statistic (concordance statistic) for adverse pathology over NCCN risk alone from 0.63 to 0.72. Comparisons with other predictors such as CAPRA or Gleason score alone were not reported. Study implications were limited by the low proportion of eligible men in the analysis and difference between excluded and included men.

 

Whalen et. al. (2016) prospectively evaluated the correlation of Genomic Prostate Score (GPS) with final pathology at radical prostatectomy (RP) in a clinical practice setting. Eligible men were 50 years of age and older with more than 10 years life expectancy, PSA levels of 20 ng/mL or less, stage cT1c-cT2c newly diagnosed, untreated prostate cancer, and who met NCCN classifications as very low risk, low risk, or low-intermediate risk. Men were enrolled from May 2013 to August 2014 at an academic medical center. Genomic Health reclassified patients’ cancers as “less favorable,” “consistent with,” or “more favorable” than what would have been predicted by their NCCN risk group. The primary outcome was adverse pathology at radical prostatectomy (RP) defined as any pT3 stage and primary Gleason grade 4 or any pattern 5. ROC (receiver operating characteristics) analyses was used to determine the optimal cutoff point of likelihood of favorable pathology for each NCCN risk group. A total of 95 patients were enrolled and 50 patients (53%) underwent radical prostatectomy. Adverse pathology was found in 21 patients (42%), grouped as very low risk 0%, low risk 32.4% and low volume intermediate risk 71.4%. Among those with low risk disease, ROC analysis determined that a likelihood of favorable pathology cutoff of 76% or greater performed the best, correctly classifying 91.2% of patients with a sensitivity of 95.7% and specificity of 81.8% and AUC (area under the curve) 0.95. For intermediate risk patients the optimal likelihood of favorable pathology cutoff was 68% or greater, with 92.3% correct, sensitivity 75%, specificity 100% and AUC 0.95.

 

Systematic Reviews 

In 2016, Brand et. al. combined the Klein (2014) and Cullen (2015) studies using a patient specific meta-analysis (MA). The patient specific meta-analysis (MA) was performed on data from these 2 studies (732 patients) using the Genomic Prostate Score (GPS; scale 0 to 100) together with the Cancer of the Prostate Risk Assessment (CAPRA) score or National Comprehensive Cancer Network (NCCN) risk groups as predictors of the likelihood of favorable pathology (LFP). Risk profile curves associating GPS with LFP by CAPRA score and NCCN risk group were generated. Decision curves and receiver operating characteristics (ROC) curves were calculated using patient specific MA risk estimates. A model utilizing GPS and CAPRA provided the most risk discrimination. The area under the receiver operating characteristic curve (AUC) improved from 0.68 to 0.73 by adding the GPS to CAPRA score. The AUC improved from 0.64 to 0.70 by adding the GPS to the NCCN risk group. The improvements were reported to be significant but the confidence intervals (CIs) were not provided.

 

Clinical Utility 

Direct Evidence 

No studies were identified that directly supported the clinical utility of OncotypeDx Prostate.

 

Indirect Evidence 

Decision impact studies have assessed the potential impact of OncotypeDx Prostate on physicians’ treatment decisions to include Albala et. al. (2016) and Eure et. al. (2017). Given the lack of established clinical validity, it is uncertain whether any treatment changes were clinically appropriate.

 

Badani et. al. (2015) prospectively evaluated the decision impact of obtaining a Genomic Prostate Score (GPS) in men with National Comprehensive Cancer Network (NCCN) defined very low, low and low – intermediate risk prostate cancer. Following test results, recommendations for active surveillance increased from 41% to 51%. Actual treatments received and accuracy of predicted outcomes were not assessed, thereby limiting implications of the study. The study was supported by Genomic Health and all authors reported financial or other relationships with the funder.

 

Summary: OncotypeDx Prostate 

The evidence from 3 studies on clinical validity (Klein et. al. 2014; Cullen et. al. 2015; Whalen et. al. 2016) for OncotypeDx Prostate has suggested the Genomic Prostate Score (GPS) can reclassify a patient’s risk of recurrence or risk of adverse pathology at radical prostatectomy (RP) based on a biopsy specimen. However, whether these findings support a conclusion that the GPS could predict disease specific survival is unclear. Moreover, generalizing this evidence to a true active surveillance population, for which most in the study would be otherwise eligible, is difficult because all patients had elective RP. Thus the findings do not reflect a clinical scenario of predicting the risk of 10 year disease specific survival in untreated patients under active surveillance. The publications also lack precision estimates for important variables such as risk estimates for recurrence. All validations studies are Simon Category C.

 

No direct evidence of clinical utility was found. The chain of evidence is incomplete. A decision curve analysis suggested the potential for the combined Genomic Prostate Score (GPS) and CAPRA (Cancer of the Prostate Risk Assessment) score data to help patients make decisions based on relative risks associated with immediate treatment or deferred treatment (i.e. active surveillance). This would reflect clinical utility of the test. However, it is difficult to attribute possible clinical utility of OncotypeDx Prostate in active surveillance because all patients regardless of clinical criteria elected radical prostatectomy (RP) within 6 months of diagnostic biopsy.

 

ProMark Protein Biomarker Test 

The ProMark Protein Biomarker test includes 8 protein biomarkers that predict prostate pathology aggressiveness and lethal outcomes: (DERL1, PDSS2, pS6, YBX1, HSPA9, FUS, SMAD4, and CUL2). The assay results are combined using predefined coefficients for each marker from a logistic regression model to calculate a risk score and the risk score is continuous number between 0 and 1 which stratify the patient into a favorable or less favorable risk score.   

 

Clinical Validity 

Blume-Jensen et. al. (2015) reported on a study of 381 biopsies matched to prostatectomy used to develop an 8-biomarker proteomic assay to predict prostate final pathology on prostatectomy specimen using risk scores. A second blinded study of 276 cases validated this assay’s ability to distinguish “favorable” versus “non-favorable” pathology independently and relative to current risk classification systems National Comprehensive Cancer Network (NCCN and D’Amico). A favorable biomarker risk score of ≤0.33, and a non-favorable risk score of >0.80 (possible range between 0 and 1) were defined on "false-negative" and "false-positive" rates of 10% and 5%, respectively. Results from the study showed that, at a risk score of less than or equal to 0.33, predictive values for favorable pathology in very low-risk and low-risk NCCN and low-risk D'Amico groups were 95%, 81.5%, and 87.2%, respectively, higher than for these current risk classification groups themselves (80.3%, 63.8%, and 70.6%, respectively). The predictive value for non-favorable pathology was 76.9% at biomarker risk scores >0.8 across all risk groups. Increased biomarker risk scores correlated with decreased frequency of favorable cases across all risk groups. The validation study met its two co-primary endpoints, separating favorable from non-favorable pathology (AUC, 0.68; P < 0.0001; OR, 20.9) and GS-6 versus non-GS-6 pathology (AUC, 0.65; P < 0.0001; OR, 12.95). The authors concluded this study supports further evaluation of this biopsy based prognostic biomarkers assay for personalized prognostication of prostate cancer and its impact on therapeutic choice. The ability to provide differential information for the individual patient relative to current risk stratification systems, in which prognostic values are more limited, makes it a potentially useful addition in practice to improve accuracy of clinical decision. 

 

Clinical Utility 

No published studies on the clinical utility of ProMark Protein Biomarker test were identified.

 

Summary: ProMark Protein Biomarker Test 

The data is insufficient to establish the clinical validity or clinical utility of the ProMark Protein Biomarker test.

 

Management Decision After Radical Prostatectomy (RP) 

Clinical Context and Test Purpose   

The purpose of gene expression profiling and protein biomarkers tests in patients who have prostate cancer and who have undergone radical prostatectomy is to inform management decisions.

 

For example, the optimal timing of radiation therapy (RT) after radical prostatectomy (RP) is debated. Adjuvant radiation therapy (RT) may maximize cancer control outcomes; however, early salvage radiation therapy (RT) (at first evidence of rising serym PSA level) can minimize overtreatment and still lead to acceptable oncologic outcomes. Adjuvant RT in men with pT3 or margin positive cancer has been compared with observation in randomized controlled trials (RCTs); such comparisons have shown that adjuvant RT improves the biochemical and local control rates among patients with adverse pathology at RP. Although the observation arms in these trials included men who received adjuvant therapy, the trials did not directly compare early salvage RT with immediate adjuvant RT because they included varying or unspecified thresholds for the initiation of salvage therapy RT.

 

Several observational analyses have shown conflicting conclusions whether adjuvant RT is favored over salvage RT. RCTs comparing adjuvant with early stage RT are underway. American Urological Association (AUA) has recommended that adjuvant RT should be offered to patients with adverse pathologic findings at RP, and salvage RT should be offered to patients with PSA or local recurrence after RP.

 

Patients   

The relevant population of interest is individuals who have undergone radical prostatectomy (RP) for prostate cancer, and who are deciding on subsequent management such as adjuvant radiation therapy (RT) versus no adjuvant RT.

 

Interventions 

Prolaris is also intended to classify individuals who have undergone radical prostatectomy (RP).

 

Decipher is a tissue based tumor 22-biomarker gene expression profiling (GEP) test intended to classify high risk individuals who have undergone RP.

 

Comparators 

Clinicopathologic risk stratification is currently being used to make decisions about prostate cancer management following radical prostatectomy (RP). Clinical characteristics (e.g. stage, biopsy Gleason grade, serum PSA level, surgical margin, disease involvement) and demographic characteristics (e.g. age, life expectancy) are combined to classify men according to risk. The National Comprehensive Cancer Network (NCCN) and the American Urological Association (AUA) provide risk stratification guidelines. The Stephenson nomogram and the Cancer of the Prostate Risk Assessment-Surgical (CAPRA-S) nomogram can be used to predict outcomes after RP.

 

Outcomes 

Beneficial outcomes resulting from a true test are prolonged survival, improved quality of life and reduction in unnecessary treatment related adverse effects. Harmful outcomes resulting from false test result are recurrence, metastases or death, and unnecessary treatments. Outcomes of interest are overall survival (10 year survival), disease specific survival (10 year prostate cancer free survival; 10 year prostate cancer death rate; 10 year recurrence rate), quality of life and treatment related morbidity (adverse events of radiotherapy or radical prostatectomy).

 

Timing 

Ten year outcomes are of interest due to the prolonged nature of prostate cancer.

 

Setting 

Decisions about management of prostate cancer following radical prostatectomy (RP) are typically made by patients and urologists in the secondary or tertiary care setting.

 

Prolaris 

This section reviews Prolaris for management after radical prostatectomy (RP).

 

Clinical Validity 

Cuzick et. al. (2011) examined the potential use of the Prolaris CCP (cell cycle progression) test combined with a clinical score following radical prostatectomy (RP), using a retrospective cohort of archived samples from a tumor registry. The study also included a cohort of men with localized prostate cancer detected from specimens obtained during transurethral resection of the prostate (TURP), which is not a population of interest here, and so it is not described. Men conservatively managed after RP between 1985 and 1995 were identified from a tumor registry (n=366 with CCP scores, Scott and White Clinic, in Texas). The primary end point was time to BCR (biochemical recurrence), and the secondary end point was prostate cancer death. Myriad Genetics assessed CCP scores blindly. The median age of patients was 68 years and median follow-up 9.4 years. Cancers were clinically staged as T3 in 34%; following RP, 64 was judged pathologic stage T3. After prostatectomy, the CCP score was useful for predicting biochemical recurrence in the univariate analysis (hazard ratio for a 1-unit change [doubling] in CCP 1·89; 95% CI 1·54-2·31; p=5·6×10^-9)) and the best multivariate analysis (1·77, 1·40-2·22; p=4·3×10^-6). In the best predictive model (final multivariate analysis), the CCP score and prostate-specific antigen (PSA) concentration were the most important variables and were more significant than any other clinical variable. Analyses of prostate cancer deaths in the RP cohort was problematic, owing to only 12 (3%) deaths. The AUC (area under the curve) for BCR within 5 years of the RP cohort as 0.825 for the clinical score and 0.842 for the combined score including the CCP score. Although the CCP score increased the AUC by 2%, whether that improvement is clinically useful is unclear because of the lack of reclassification data and analysis of net benefit.

 

Cooperberg et. al. (2013) evaluated the cell cycle progression (CCP) score, in predicting radical prostatectomy (RP) outcomes. RNA was quantified from paraffin embedded RP specimens. The CCP score was calculated as average expression of 31 CCP genes, normalized to 15 housekeeper genes. Recurrence was defined as two prostate specific antigen (PSA) levels ≥ 0.2 ng/mL or any salvage treatment. Associations between CCP score and recurrence were examined, with adjustment for clinical and pathologic variables using Cox proportional hazards regression and partial likelihood ratio tests. The CCP score was assessed for independent prognostic utility beyond a standard postoperative risk assessment, Cancer of the Prostate Risk Assessment post-Surgical (CAPRA-S) score, and a score combining CAPRA-S and CCP was validated. The validation cohort was obtained from patients identified from the UCSF Urologic Oncology Database. Tissue sufficient to obtain a CCP score was available for 413 men (69% of the 600 eligible samples). Both UCSF and Myriad Genetics performed statistical analyses. In the validation cohort, 95% had Gleason scores of 7 or lower, 16% of samples had positive margins, 4% had seminal vesicle invasion, and 23% had extracapsular extension. Eighty-two (19.9%) of 413 men experienced recurrence. The hazard ratio (HR) for each unit increase in CCP score (range, -1.62 to 2.16) was 2.1 (95% CI, 1.6 to 2.9); with adjustment for CAPRA-S, the HR was 1.7 (95% CI, 1.3 to 2.4). The score was able to substratify patients with low clinical risk as defined by CAPRA-S ≤ 2 (HR, 2.3; 95% CI, 1.4 to 3.7). Combining the CCP and CAPRA-S improved the concordance index for both the overall cohort and low-risk subset; the combined CAPRA-S + CCP score consistently predicted outcomes across the range of clinical risk. This combined score outperformed both individual scores on decision curve analysis. The authors concluded the CCP score was validated to have significant prognostic accuracy after controlling for all available clinical and pathologic data. The score may improve accuracy of risk stratification for men with clinically localized prostate cancer, including those with low risk disease. Future studies will include explicit heterogeneity studies to compare CCP score findings between biopsy and prostatectomy tissues and from different samples taken from the same tumor. Although heterogeneity may have been a potential source of misclassification in our study, it would tend, if anything to bias the results toward null. The real world effectiveness of this assay depends on its applicability in the community setting. Also, postoperative risk prediction models may perform differently in academic and community based settings, so future validation in nonacademic cohorts will also be important.

 

Bisoff et. al. (2014) evaluated the prognostic usefulness of the cell cycle progression (CCP) score derived from biopsy specimens in men treated with radical prostatectomy (RP). The study included 3 cohorts: the Martini Clinic (283), Durham Veterans Affairs Medical Center (176) and Intermountain Healthcare (123). The score was derived from simulated biopsy (Martini Clinic) or diagnostic biopsy (Durham Veterans Affairs Medical Center and Intermountain Healthcare) and evaluated for an association with biochemical recurrence and metastatic disease. The combined analysis included 582 patients. Univariate analysis (HR per score unit 1.60, 95% CI 1.35-1.90, p=2.4×10^-7) and multivariate analysis (HR per score unit 1.47, 95% CI 1.23-1.76, p=4.7×10^-5). Metastatic events (n=12) were too few to draw conclusions.

 

Kock et. al. (2016) evaluated whether the CCP (cell cycle progression) score could discriminate between systemic disease and local recurrence in patients with biochemical recurrence (BCR) after radical prostatectomy (RP). All 60 patients given RP as primary therapy at an academic medical center between 1995 and 2010 for whom samples were available and who had a BCR and either developed metastatic disease or received salvage EBRT with at least 2 years of follow-up were eligible for retrospective analysis. Data from 5 patients were excluded for failing to meet clinical eligibility requirements (no clarification provided) or because data were incomplete; sample blocks from 3 patients contained insufficient tumor for assay and data from 6 patients were excluded due to lack of “passing” CCP score. Forty-seven patients were included in the analysis. Outcomes were classified into 3 categories: (1) metastatic disease (n=22); (2) nonresponse to salvage EBRT (n=14); and (3) durable response to salvage EBRT (n=11). Analyses were performed with a binary outcome (categories 1 and 2 combined). For each 1-unit change in the CCP score, the univariate odds ratio (OR) for metastatic disease or nonresponse was 3.72 (95% CI, 1.29 to 10.7). Multivariate analysis was performed; however, due to the very small number of participants in the durable response group, CIs (confidence intervals) were very wide. The authors concluded this study is limited by its retrospective nature and small patient cohort size. Nonetheless, this is the largest population of prostate cancer patients with metastatic disease evaluated for the role of CCP score thus far in this setting. Larger studies are necessary to validate these results and prove that the score adds prognostic information after adjusting for clinical variables.

  

Clinical Utility 

Direct Evidence 

No studies were identified that directly supported the clinical utility of Prolaris for management after radical prostatectomy (RP).

 

Indirect Evidence 

In a decision curve analysis, Cooperberg et. al. (2013) found the CAPRA-S (Cancer of the Prostate Risk Assessment post-Surgical) score superior to CCP (cell cycle progression) alone (as well as treat-none or treat-all strategies) in men after prostatectomy. A combined CCP predictor appeared only slightly better than CAPRA-S alone for thresholds of approximately 30% or more. For example, at a threshold of 30% (i.e. meaning a man would value the harm-to-benefit of treatment such as RT as 3:7), the CCP score would detect about 2 or more men per 100 likely to experience biochemical recurrence (BCR) if the false-positive rate was fixed. However, the lack of CIs (confidence interval) for the decision-curve analysis, together with the small difference, is consistent with an uncertain net benefit obtained by adding CCP to the CAPRA-S score. Also, it is not clear whether the group of patients identified as high risk of experiencing BCR would have a net benefit from adjuvant therapy instead of early salvage radiation therapy (RT).   

 

Summary: Prolaris 

Four identified studies examined the clinical validity of Prolaris in men after radical prostatectomy (RP) using a biochemical recurrence (BCR) or systemic disease end point. Cuzieck et. al. (2011) found that the CCP (cell cycle progression) score offered little improvement in the AUC (area under the curve) (2%) over clinicopathologic predictors and did not examine reclassification. Cooperberg et. al. (2013) found the AUC for BCR improved from 0.73 CAPRA-S alone to 0.77 by adding CCP score. Bishoff et. al. (2014) and Kock et. al. (2016) did not report any classification or discrimination measures. Koch was performed in patients who had a BCR following RP. All validation studies were Simon category C or D.

 

No direct evidence is available to support the clinical utility of Prolaris for improving net health outcomes of patients with localized prostate cancer following radical prostatectomy (RP). The chain of evidence is also incomplete. Decision curve analysis did not provided convincing evidence of meaningful improvement in net benefit by incorporating the CCP (cell cycle progression) score. Proloaris CCP score may have an association with biochemical recurrence (BCR), but disease-specific survival outcomes were not reported. A larger number of disease-specific survival events and precision estimates for discrimination measures are needed. Simon category C studies are not sufficient to determine with confidence the prognosis of CCP score.

 

Decipher Prostate Cancer Test/Decipher Prostate Cancer Classifier 

The Decipher Prostate Cancer Test/Decipher Prostate Cancer Classifier uses the expression of the biomarkers (22 RNA biomarkers) associated with the aggressive prostate cancer, and calculates the probability of clinical metastasis within 5 years of radical prostatectomy (RP) surgery. This gene expression profiling (GEP) test is a continuous risk score between 0 and 1, with higher risk scores indicating a greater probability of developing metastasis.

 

Clinical Validity 

The clinical validity of the Decipher Prostate Cancer Test/Decipher Prostate Cancer Classifier has been reported in 11 studies to predict metastasis, mortality, or biochemical recurrence (BCR) after radical prostatectomy (RP) in patients with postoperative high-risk features like pathologic state T2 with positive margins, pathologic stage T3 disease, or rising PSA level, see the below tables:

 

BCR: biochemical recurrence; CapSURE: Cancer of the Prostate Strategic Urologic Research Endeavor

^aAppears to be subgroup with BCR from Karnes et. al. (2013)

 

AUC: area under the curve; BCR: biochemical recurrence; CI: confidence interval; GC: genomic classifier; RT: radiotherapy

 

All studies were conducted retrospectively from registry data or clinical records. The development study (training) had a nested case control design. The 5 and 10 year results of 1 study were published separately. Four were case-cohort studies, and 5 used retrospective cohorts. Because of the apparent overlap in samples, the number of unique patients in the studies is difficult to ascertain. Seven studies were supported by GenomeDx, which offers the Decipher test; all studies identified multiple authors as company employees. Studies were considered according to whether after radical prostatectomy (RP) men were observed or treated with radiation therapy (RT) (adjuvant or salvage), resulting in the following groupings: 1) observation or RT; 2) observation only and 3) RT only.

 

Four studies including the test (validation) sample from the development study, examined men observed following radical prostatectomy (RP) and undergoing adjuvant or salvage radiation therapy (RT). Median follow-up periods ranged from 6.4 to 16.9 years. The distributions of Gleason scores in the studies varied from 17.8% to 49.3% for those with Gleason scores of 8 or higher and for 0.4% to 15.1% for those with Gleason scores of 6 or lower. Extracapsular extension of the tumor ranged from 42.7% and 72.3% of men across studies.

 

Validation Studies: Observation and RT Samples 

Cooperberg et. al. (2015) and Karnes et. al. (2017) evaluated the prognostic accuracy of the test for prostate cancer mortality; the others, for the development of metastasis. Karnes et. al. (2013) reported a 2.4% five year cumulative incidence of metastasis in 338 men with genomic classifier (GC) scores of less than 0.4, but 22.5% in the 77 men with scores 0.6 or more. In men wo had developed biochemical recurrence (BCR), Ross et. al. (2014) found the GC score was associated with a 5 year cumulative incidence of metastasis in 10% of men with scores of 0.4 or lower versus 54.0% in those with higher scores. The GC and AUCs (area under the curve) for predicting metastasis ranged from 0.75 to 0.82. The AUC for the comparators ranged from 0.66 to 0.75.  Among the 69 men developing mestatasis in Karnes et. al. (2013), of the 29 Gleason scores of 7 or lower, 10 were correctly reclassified to the highs GC risk (score > 0.6), but of the 40 men with Gleason scores of 8 or higher, 10 were incorrectly reclassified to the lowest GC risk group (score < 0.4). For prostate cancer mortality, compared with CAPRA-S (Cancer of the Prostate Risk Assessment-Surgical score), Cooperberg et. al. (2015) found that the GC improved reclassification somewhat of the 19 men with CAPRA-S scores of 5 or lower, 12 were correctly reclassified to the highest GC risk, and 1 was incorrectly reclassified with a CAPRA-S score greater than 6 to low risk; all men had CAPRA-S scores of 3 or more. Similarly, Karnes et. al. (2017) found that adding the GC to CAPRA-S improved the AUC from 0.73 to 0.77 with highly overlapping CIs (confidence intervals). 

 

Validation Studies: Observation-Only Samples 

Three validation studies reported in 4 publications, Klein et. al. (2015), Klein et. al. (2016), Ross et. al. (2016) and Glass et. al. (2016) excluded men receiving any adjuvant therapy following radical prostatectomy (RP) over median follow-up periods ranging from 7.8 to 9 years. The Klein and Ross studies assessed the prognostic accuracy for metastasis through 5 or 10 years. Glass assessed the prognostic accuracy for clinical recurrence, defined as local, regional or distant recurrence or metastasis confirmed by clinical or radiologic evidence. Ross reported on the basis of previously defined CAPRA-S (Cancer of the Prostate Risk Assessment-Surgical score) risk categories, 6, 58 and 36% of men were classified as low (0–2), intermediate (3–5) and high risk (6-12), respectively and the cumulative incidence of metastasis at 10 years post RP was 11.3, 3.3 and 21.4%, respectively. In contrast, Decipher score classified 57, 27 and 16% as low (< 0.45), intermediate (0.45–0.60) and high risk (> 0.60), respectively. Cumulative incidence of metastasis at 10 years post RP was 6.8, 10.3 and 21.9% for these risk groups. The AUCs (area under the curve) are shown in the above table. Glass reported a 2.6% clinical recurrence rate by 10 years among patients with low Decipher scores but 13.6% among those with high Decipher scores (p=0.02). Ross et. al reported 10 year cumulative incidence of metastasis stratified by GC (genomic classifier) and CAPRA-S. The GC appeared to discriminate within intermediate CAPRA-S categories.

 

Validation Studies: RT Only samples  

Three analyses of overlapping retrospectively assembled cohorts of men undergoing either adjuvant or salvage RT (radiation therapy). One study examined the prognostic ability of the genomic classifier (GC) for BCR (biochemical recurrence), while the others examined its prognostic ability for metastasis. Median follow-up in Den et. al. (2014) and Den et. al (2015) exceeded 10 years; the medial follow-up in Freedland et. al. (2016) was 7.4 years. Just over three-quarters of the men in the studies had positive surgical margins. Den et. al. (2014) found that the GCs AUC (area under the curve) for BCR was 0.75 compared with 0.70 for the Stephenson nomogram. The AUCs for the clinical outcomes are in the above table. In Den (2015), 7 (21.2%) of men with high GC scores (> 0.6) developed metastasis compared with 12 (15.2%) men with CAPRA-S (Cancer of the Prostate Risk Assessment-Surgical) scores exceeding 5. However, overall 19 (10.1%) men had developed metastasis. Among the 160 men who did not, the GC reclassified 27 of 67 men with high CAPRA-S scores into a low risk group, but given the small number of men developing metastasis the reclassifications were somewhat uncertain.
The authors also explored whether the classifier might identify men likely to benefit from adjuvant RT over salvage therapy, suggesting that adjuvant therapy might be preferred in men with a GC score greater than 0.4. However, that result was based on only 14 men with GC scores of 0.4 and 3 men with lower values. In Freedland (2016), 20 men developed metastasis. In reclassification analysis, 31 (39%) of patients in the upper 2 tertiles of risk by Briganti were classified as low risk by the GC, and one of them developed metastases during follow-up. Sevently-three (49%) of patients who were categorized as intermediate or high risk using CAPRA-S were classified as GC low risk; three developed metastasis during follow-up.

 

Systematic Review  

Spratt et. al. (2017) performed the first meta-analysis of the performance of the genomic classifier test, Decipher, in men with prostate cancer post-prostatectomy. Decipher genomic resource information database were searched for published reports between 2011 and 2016 of men treated by prostatectomy that assessed the benefit of the Decipher test. Multivariable Cox proportional hazards models fit to individual patient data were performed; meta-analyses were conducted by pooling the study-specific hazard ratios (HRs) using random-effects modeling. Extent of heterogeneity between studies was determined with the I² test. Results Five studies (975 total patients, and 855 patients with individual patient-level data) were eligible for analysis, with a median follow-up of 8 years. Of the total cohort, 60.9%, 22.6%, and 16.5% of patients were classified by Decipher as low, intermediate, and high risk, respectively. The 10-year cumulative incidence metastases rates were 5.5%, 15.0%, and 26.7% ( P < .001), respectively, for the three risk classifications. Pooling the study-specific Decipher HRs across the five studies resulted in an HR of 1.52 (95% CI, 1.39 to 1.67; I² = 0%) per 0.1 unit. In multivariable analysis of individual patient data, adjusting for clinicopathologic variables, Decipher remained a statistically significant predictor of metastasis (HR, 1.30; 95% CI, 1.14 to 1.47; P < .001) per 0.1 unit. The C-index for 10-year distant metastasis of the clinical model alone was 0.76; this increased to 0.81 with inclusion of Decipher. Conclusion The genomic classifier test, Decipher, can independently improve prognostication of patients post-prostatectomy, as well as within nearly all clinicopathologic, demographic, and treatment subgroups. Future study of how to best incorporate genomic testing in clinical decision-making and subsequent treatment recommendations is warranted.

 

Clinical Utility 

Direct Evidence 

No studies reporting direct evidence of clinical utility were identified.

 

Indirect Evidence: Decision Curves 

Eight studies have included decision curves comparing the net benefit of different strategies using metastases or survival as the outcome (see below table). In observational and RT samples from Karnes et al (2013) and Ross et al (2014), over a 15% to 25% range of thresholds for decision making (i.e, suspected probability of developing metastases) would be expected to identify correctly as few as no men or as many as 4 per 100 likely to experience metastases. This range of thresholds makes several assumptions: it assumes those making the decisions are relying on the genomic classifier (GC) result for adjuvant RT decisions, compared with treating based on the best comparator test and it assumes no increase in false positives. No CIs (confidence intervals) were provided for the net benefit estimates and uncertainty cannot be evaluated. In the 2 observation-only samples, although the GC improved the net benefit over a “treat none” strategy over 15% to 25% thresholds, it appeared to offer little over the comparator test (e.g, about 1 additional patient would be likely to experience metastases without an increase in false positives). In Ross, the net benefit for CAPRA-S (Cancer of the Prostate Risk Assessment-Surgical) score exceeded that of the GC, with the net benefit of the GC plus CAPRA-S score being slightly better than the CAPRA-S score alone. Finally, among men undergoing RT, decision curves suggested that the test would identify 3 or 4 men developing metastases per 100 tested at a fixed false-positive rate. Lobo et al (2015) reported an individualized decision analysis comparing the GC with “usual care” using data from the cohorts in Karnes et al (2013) and Den et al (2014). The usual care probabilities of receiving each treatment were derived from the published literature. A 6% threshold for the GC score was used for GC-based treatment. Using the cohort from Karnes (2013), the estimated 10-year probability of metastasis or death was 0.32 (95% CI, 0.32 to 0.33) for usual care compared with 0.31 (95% CI, 0.30 to 0.32) for GC-based treatment. In the cohort from Den (2014), the estimated 10-year probability of metastasis or death was 0.28 (95% CI, 0.27 to 0.29) for usual care compared with 0.26 (95% CI, 0.25 to 0.27) for GC-based treatment.

 

^aFor 25% threshold

 

Indirect Evidence: Changes in Management 

Several studies have compared physician’s treatment recommendations before and after receiving genomic classifier (GC) results. Because the studies did not include information on outcomes and clinical validity has not been established, it is not known whether these treatment decisions represent a clinical improvement in management.

 

Indirect Evidence: Analysis of the Association Between the Genomic Classifier (GC) and Treatment Effects 

Ross et al (2016) reported on results of a retrospective, comparative study of radiation therapy (RT) after radical prostatectomy (RP) for 422 men with pT3 disease or positive margins. The men were from 4 cohorts previously described (Karnes et al, 2013; Den et al, 2014; Ross et al, 2016; Freedland et al, 2016). The 4 treatment groups were adjuvant RT (n=111), minimal residual disease salvage RT (n=70), salvage RT (n=83), and no RT (n=157). The primary end point was metastasis. Thirty-seven men developed metastasis, and the median follow-up was 8 years. Both CAPRA-S (Cancer of the Prostate Risk Assessment-Surgical score) (HR=1.39; 95% CI, 1.18 to 1.62) and Decipher (HR=1.28; 95% CI, 1.08 to 1.52) were independently associated with metastasis in multivariable analysis. There was no evidence that treatment effect was dependent on genomic risk (interaction p=0.16 for CAPRA-S, p=0.39 for Decipher). Men with low CAPRA-S or low Decipher scores had a low risk of metastatic events regardless of treatment selection and men with high CAPRA-S or Decipher scores benefitted from adjuvant RT compared with the other treatments.

 

Summary: Decipher Prostate Cancer Test/Decipher Prostate Cancer Classifier 

The analytic validity of the Decipher test has been reported in a published study. Clinical validity has been evaluated in overlapping validation samples (including the development test set). The validation studies consisted of observational data obtained from registries with archived samples. All validation studies are Simon category C or D. Simon category C studies are insufficient to determine with confidence the prognosis of high-risk individuals. Ten-year disease-specific survival outcomes from a Simon category A, or multiple, independent Simon category B studies are needed. Although each study evaluated different outcomes (i.e., metastasis, prostate cancer‒specific mortality, biochemical recurrence [BCR]) in samples with different populations, all studies reported some incremental improvement in discrimination. CIs (confidence intervals) of AUC (area under the curve) frequently overlapped between Decipher and comparators. Results did not consistently demonstrate meaningful improvement in reclassification, possibly most importantly to higher risk categories. The performance over clinicopathologic predictors did not appear consistently and meaningfully improved. It is not clear whether the group of patients identified as high risk would have a net benefit from adjuvant instead of early salvage RT.

 

Summary of Evidence 

Initial Management Decision: Active Surveillance Versus Therapeutic Intervention 

For individuals who have low-risk clinically localized untreated prostate cancer who receive Prolaris, OncotypeDx Prostate, or ProMark protein biomarker test, the evidence includes studies of clinical validity using archived samples from patients in mixed risk categories. Relevant outcomes include overall survival, disease-specific survival, test accuracy and validity, quality of life, and treatment-related morbidity. The PROTECT trial showed 99% ten-year disease-specific survival in mostly low-risk patients receiving active surveillance. The low mortality rate estimated with tight precision makes it unlikely that a test intended to identify a subgroup of low-risk men with a net benefit from immediate treatment instead of active surveillance would find such a group.

 

For individuals who have intermediate-risk clinically localized untreated prostate cancer who receive Prolaris, the evidence includes retrospective cohort studies of clinical validity using archived samples in patients of mixed risk categories and a decision-curve analysis providing indirect evidence of clinical utility. Relevant outcomes include overall survival, disease-specific survival, test accuracy and validity, quality of life, and treatment-related morbidity. Evidence of improved clinical validity or prognostic accuracy for prostate cancer death using Prolaris Cell Cycle Progression score in patients managed conservatively after needle biopsy has shown some improvement in areas under the receiver operating characteristic curve over clinicopathologic risk stratification tools. All validation studies are Simon category C. There is limited indirect evidence for potential clinical utility. The evidence is insufficient to determine the effects of the technology on net health outcomes.

 

For individuals who have low- or intermediate-risk clinically localized untreated prostate cancer who receive OncotypeDx Prostate, the evidence includes case-cohort and retrospective cohort studies of clinical validity using archived samples in patients of mixed risk categories, and a decision-curve analysis examining indirect evidence of clinical utility. Relevant outcomes include overall survival, disease-specific survival, test accuracy and validity, quality of life, and treatment-related morbidity. Evidence for clinical validity and potential clinical utility of OncotypeDx Prostate in patients with clinically localized prostate cancer derives from a study predicting adverse pathology after radical prostatectomy (RP). The validity of using tumor pathology as a surrogate for risk of progression and cancer-specific death is unclear. It is also unclear whether results from an RP population can be generalized to an active surveillance population. All validation studies are Simon category C or D. The evidence is insufficient to determine the effects of the technology on net health outcomes.

 

For individuals who have low- or intermediate-risk clinically localized untreated prostate cancer who receive the ProMark protein biomarker test, the evidence includes a retrospective cohort study of clinical validity using archived samples, and no studies of clinical utility. Relevant outcomes include overall survival, disease-specific survival, test accuracy and validity, quality of life, and treatment-related morbidity. Current evidence does not support improved outcomes with ProMark given that only a single clinical validity study was available. The evidence is insufficient to determine the effects of the technology on net health outcomes.

 

Management Decisions After Radical Prostatectomy (RP) 

For individuals who have localized prostate cancer who are treated with radical prostatectomy (RP) who receive Prolaris, the evidence includes retrospective cohort studies of clinical validity using archived samples. Relevant outcomes include overall survival, disease-specific survival, test accuracy and validity, quality of life, and treatment-related morbidity. Evidence of improved clinical validity or prognostic accuracy for prostate cancer death using the Prolaris Cell Cycle Progression score in patients after prostatectomy has shown some improvement in areas under the receiver operating characteristic (ROC) curve over clinicopathologic risk stratification tools. All validation studies are Simon category C or D. The evidence is insufficient to determine the effects of the technology on net health outcomes.

 

For individuals who have localized prostate cancer who are treated with radical prostatectomy (RP) and who receive the Decipher prostate cancer test/Decipher prostate cancer classifier, the evidence includes prospective and retrospective studies of clinical validity using overlapping archived samples, decision-curve analyses examining indirect evidence of clinical utility, and prospective decision-impact studies without pathology or clinical outcomes. Relevant outcomes include overall survival, disease-specific survival, test accuracy and validity, quality of life, and treatment-related morbidity. The clinical validity of the Decipher genomic classifier (GC) has been evaluated in samples of patients with high-risk prostate cancer undergoing different interventions following RP. Studies reported some incremental improvement in discrimination. However, it is unclear whether there is consistent improved reclassification, particularly to higher risk categories, or whether the test could be used to predict which men will benefit from radiotherapy (RT). All validation studies are Simon category C or D. The evidence is insufficient to determine the effects of the technology on net health outcomes.

 

Practice Guidelines and Position Statements

National Comprehensive Cancer Network (NCCN) Prostate Cancer Version 2.2018

Molecular Testing

Several tissue based molecular assays have been developed in an effort to improve decision making in newly diagnosed men considering active surveillance and in treated men considering adjuvant therapy or treatment for recurrence. Uncertainty about the risk of disease progression can be reduced if such molecular assays can provided accurate and reproducible prognostic or predictive information beyond NCCN risk group assignment and currently available life expectancy tables and namograms. Retrospective case cohort studies have shown that these assays provide prognostic information independent of NCCN risk groups, which include likelihood of biochemical recurrence after radical management, likelihood of biochemical recurrence after radical prostatectomy or radiotherapy, and likelihood of developing metastasis after operation of salvage radiotherapy. No randomized controlled trials have studied the utility of these tests.  Several of these assays are available, and 3 received positive reviews by the Molecular Diagnostic Services Program (MoIDX) and are likely to be covered by CMS (Centers for Medicare & Medicaid Services). Several other tests are under development, and the use of these assays is likely to increase in the coming years.

 

Although full assessment of their clinical utility requires prospective randomized clinical trials, which are unlikely to be done, the panel believes that men with clinically localized disease may consider the use of tumor-based molecular assays at this time. Future comparative effectiveness research may allow these tests and others like them to gain additional evidence regarding their utility for better risk stratification of men with prostate cancer.     

 

 

American Urological Association (AUA)

In 2017, American Urological Association (AUA)/American Society of Radiation Oncology (ASTRO) and Society Urologic Oncology (SUO) issued a guideline for clinically localized prostate cancer, which includes the following recommendations:

• Very Low/Low Risk Disease
  • Clinicians should recommend active surveillance as the best available care option for very low-risk localized prostate cancer patients (Strong Recommendation; Evidence Level: Grade A)
  • Clinicians should recommend active surveillance as the preferable care option for most low-risk localized prostate cancer patients (Moderate Recommendation; Evidence Level: Grade B)
  • Clinicians may offer definitive treatment (i.e. radical prostatectomy or radiotherapy) to select low-risk localized prostate cancer patients who may have a high probability of progression on active surveillance (Conditional Recommendation; Evidence Level: Grade B)
  • Among most low-risk localized prostate cancer patients, tissue based genomic biomarkers have not shown a clear role in the selection of candidates for active surveillance (Expert Opinion)
• Intermediate Risk Disease
  • Clinicians should recommend radical prostatectomy or radiotherapy plus androgen deprivation therapy (ADT) as standard treatment options for patients with intermediate risk localized prostate cancer (Strong Recommendation; Evidence Level: Grade A) 
  • Active surveillance may be offered to select patients with favorable intermediate-risk localized prostate cancer; however, patients should be informed that this comes with a higher risk of developing metastases compared to definitive treatment (Conditional Recommendation; Evidence Level: Grade C)
• Active Surveillance
  • Localized prostate cancer patients who elect active surveillance should have accurate disease staging including systemic biopsy with ultrasound or MRI guided imaging (Clinical Principle)
  • Localized prostate cancer patients undergoing active surveillance should have routine surveillance PSA testing and digital rectal exams (Strong Recommendation; Evidence Level: Grade B)
  • Localized prostate cancer patients undergoing surveillance should be encouraged to have a confirmatory biopsy within the initial two years and surveillance biopsies thereafter (Clinical Principle)
  • Clinicians may consider multiparametric prostate MRI as a component of active surveillance for localized prostate cancer patients (Expert Opinion)
  • Tissue based genomic biomarkers have not shown a clear role in active surveillance for localized prostate cancer and are not necessary for following up. (Expert Opinion)
  •  Clinicians should offer definitive treatment to localized prostate cancer patients undergoing active surveillance who develop adverse reclassification (Moderate Recommendation; Evidence Level: Grade B)
• Post-Treatment Follow-Up
  • Clinicians should monitor localized prostate cancer patients post therapy with PSA, even though not all PSA recurrences are associated with metastatic disease and prostate cancer specific death. (Clinical Principle) 

Regulatory Status

Clinical laboratories may develop and validate tests in-house and market them as a laboratory service; laboratory-developed tests (LDTs) must meet the general regulatory standards of the Clinical Laboratory Improvement Act (CLIA).  Prolaris®, Oncotype DX® Prostate Cancer Assay and Decipher® gene expression profiling, and the Promark™ protein biomarker test are available under the auspices of CLIA. Laboratories that offer LDTs must be licensed by CLIA for high-complexity testing. To date, the U.S. Food and Drug Administration has chosen not to require any regulatory review of this test.

 

In November 2015, the FDA’s Office of Public Health Strategy and Analysis published a document on public health evidence for FDA oversight of LDTs. FDA argued that many tests need more FDA oversight that the regulatory requirements of CLIA. CLIA standards relate to laboratory operations, but do not address inaccuracies or unreliability of specific tests. Prolaris is among the 20 case studies in the document cited as needing FDA oversight. The document asserted that patients are potentially receiving inappropriate prostate cancer care because there is no evidence that results from the test meaningfully improve clinical outcomes.

 

Prior Approval:

 

Not applicable

 

Policy:

See Related Medical Policies

• 02.04.56 Genetic and Protein Biomarkers for the Diagnosis and Cancer Risk Assessment of Prostate Cancer
• 02.04.25 Prostate Specific Antigen Screening for Prostate Cancer

 

Use of gene expression profiling (GEP) analysis and protein biomarker testing to guide management of prostate cancer is considered investigational in all situations. This includes but is not limited to the following:

 

Gene Expression Profiling (GEP) Analysis

• Prolaris
• OncotypeDx Prostate Cancer Assay
• Decipher Prostate Cancer Test/ Decipher® Prostate Cancer Classifier

Protein Biomarker testing

• Promark

Based on review of the peer reviewed medical literature there is insufficient evidence demonstrating that gene expression profiling analysis and protein biomarker testing have a role in clinical decision making or have a beneficial effect on net health outcomes in the management of prostate cancer.  Further studies are needed to determine the clinical utility and clinical validity of these tests.  All validation studies are Simon category C or D.  Therefore, the use of gene expression profiling (GEP) analysis and protein biomarker testing to guide management of prostate cancer is considered investigational in all situations.

 

Procedure Codes and Billing Guidelines:

To report provider services, use appropriate CPT* codes, Alpha Numeric (HCPCS level 2) codes, Revenue codes and / or diagnosis codes.

• 81541 Oncology (prostate), mRNA gene expression profiling by real-time RT-PCR of 46 genes (31 content and 15 housekeeping), utilizing formalin-fixed paraffin-embedded tissue, algorithm reported as a disease-specific mortality risk (Prolaris®)
• 81479 Unlisted molecular pathology procedures (when specified as Decipher® Prostate Cancer Test/ Decipher® Prostate Cancer Classifier; or Promark™) 
• 81599 Unlisted multianalyte assay with algorithmic analysis (when specified as Decipher® Prostate Cancer Test/ Decipher® Prostate Cancer Classifier; or Promark™)
• 84999 Unlisted chemistry procedure (when specified as Decipher® Prostate Cancer Test/ Decipher® Prostate Cancer Classifier; or Promark™)
• 0047U Oncology (prostate), mRNA, gene expression profiling by real-time RT-PCR of 17 genes (12 content and 5 housekeeping), utilizing formalin-fixed paraffin-embedded tissue, algorithm reported as a risk score (OncotypeDX Prostate Cancer Assay)

 

Selected References:

• Dall'Era MA, Cooperberg MR, Chan JM, et al. Active surveillance for early-stage prostate cancer: review of the current literature. Cancer. Apr 15 2008;112(8):1650-1659. PMID 18306379
• Brawley OW. Prostate cancer epidemiology in the United States. World J Urol. Apr 2012;30(2):195-200. PMID 22476558
• Bangma CH, Roemeling S, Schroder FH. Overdiagnosis and overtreatment of early detected prostate cancer. World J Urol. Mar 2007;25(1):3-9. PMID 17364211
• Johansson JE, Andren O, Andersson SO, et al. Natural history of early, localized prostate cancer. JAMA. Jun 9 2004;291(22):2713-2719. PMID 15187052
• Ploussard G, Epstein JI, Montironi R, et al. The contemporary concept of significant versus insignificant prostate cancer. Eur Urol. Aug 2011;60(2):291-303. PMID 21601982
• Harnden P, Naylor B, Shelley MD, et al. 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 2008;112(5):971-981. PMID 18186496
• Brimo F, Montironi R, Egevad L, et al. Contemporary grading for prostate cancer: implications for patient care. Eur Urol. May 2013;63(5):892-901. PMID 23092544
• 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. Feb 2008;53(2):347-354. PMID 17544572
• Bill-Axelson A, Holmberg L, Ruutu M, et al. Radical prostatectomy versus watchful waiting in early prostate cancer. N Engl J Med. May 12 2005;352(19):1977-1984. PMID 15888698
• 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 2013;369(7):603-610. PMID 23944298
• Albertsen PC, Hanley JA, Fine J. 20-year outcomes following conservative management of clinically localized prostate cancer. JAMA. May 4 2005;293(17):2095-2101. PMID 15870412
• Freedland SJ. Screening, risk assessment, and the approach to therapy in patients with prostate cancer. Cancer. Mar 15 2011;117(6):1123-1135. PMID 20960523
• Ip S, Dahabreh IJ, Chung M, et al. An evidence review of active surveillance in men with localized prostate cancer. Evid Rep Technol Assess (Full Rep). Dec 2011;AHRQ Publication No. 12-E003-EF, Rockville, MD: Agency for Research and Quality.(204):1-341. PMID 23126653
• Thompson I, JB T, Aus G ea. American Urological Association guideline for management of clinically localized prostate cancer 2007 update.
• Whitson JM, Carroll PR. Active surveillance for early-stage prostate cancer: defining the triggers for intervention.J Clin Oncol. Jun 10 2010;28(17):2807-2809. PMID 20439633
• Albertsen PC. Treatment of localized prostate cancer: when is active surveillance appropriate? Nat Rev Clin Oncol. Jul 2010;7(7):394-400. PMID 20440282
• 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. Apr 9 2013;110(15):6121-6126. PMID 23533275
• Spans L, Clinckemalie L, Helsen C, et al. The genomic landscape of prostate cancer. Int J Mol Sci. 2013;14(6):10822-10851. PMID 23708091
• Schoenborn JR, Nelson P, Fang M. Genomic profiling defines subtypes of prostate cancer with the potential for therapeutic stratification. Clin Cancer Res. Aug 1 2013;19(15):4058-4066. PMID 23704282
• Huang J, Wang JK, Sun Y. Molecular pathology of prostate cancer revealed by next-generation sequencing: opportunities for genome-based personalized therapy. Curr Opin Urol. May 2013;23(3):189-193. PMID 23385974
• Yu YP, Song C, Tseng G, et al. Genome abnormalities precede prostate cancer and predict clinical relapse. Am J Pathol. Jun 2012;180(6):2240-2248. PMID 22569189
• 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 2012;181(5):1585-1594. PMID 23083832
• 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. Apr 2015;67(4):778-786. PMID 25466945
• Den RB, Yousefi K, Trabulsi EJ, et al. Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy. J Clin Oncol. Mar 10 2015;33(8):944-951. PMID 25667284
• Blue Cross and Blue Shield Association Technology Evaluation Center (TEC). Gene Expression Profiling for High-Risk Prostate Cancer Management Post-Radical Prostatectomy TEC Assessments 2015.
• Warf MB, Reid JE, Brown KL, et al. Analytical validation of a cell cycle progression signature used as a prognostic marker in prostate cancer. J Mol Biomark Diagn. 2015;6(4).
• Cuzick J, Stone S, Fisher G, et al. Validation of an RNA cell cycle progression score for predicting death from prostate cancer in a conservatively managed needle biopsy cohort. Br J Cancer. Jul 28 2015;113(3):382-389. PMID 26103570
• 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. Mar 13 2012;106(6):1095-1099. PMID 22361632
• Pepe MS, Feng Z, Janes H, et al. Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. J Natl Cancer Inst. Oct 15 2008;100(20):1432-1438. PMID 18840817
• Cuzick J, Swanson GP, Fisher G, et al. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol. Mar 2011;12(3):245-255. PMID 21310658
• 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. J Clin Oncol. Apr 10 2013;31(11):1428-1434. PMID 23460710
• Bishoff JT, Freedland SJ, Gerber L, et al. Prognostic utility of the cell cycle progression score generated from biopsy in men treated with prostatectomy. J Urol. Aug 2014;192(2):409-414. PMID 24508632
• Crawford ED, Scholz MC, Kar AJ, et al. Cell cycle progression score and treatment decisions in prostate cancer: results from an ongoing registry. Curr Med Res Opin. Jun 2014;30(6):1025-1031. PMID 24576172
• Shore N, Concepcion R, Saltzstein D, et al. Clinical utility of a biopsy-based cell cycle gene expression assay in localized prostate cancer. Curr Med Res Opin. Apr 2014;30(4):547-553. PMID 24320750
• 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 24103217
• 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 2014;66(3):550-560. PMID 24836057
• Cullen J, Rosner IL, Brand TC, et al. A Biopsy-based 17-gene Genomic Prostate Score Predicts Recurrence After Radical Prostatectomy and Adverse Surgical Pathology in a Racially Diverse Population of Men with Clinically Low- and Intermediate-risk Prostate Cancer. Eur Urol. Jul 2015;68(1):123-131. PMID 25465337
• Badani KK, Kemeter MJ, Febbo PG. The Impact of a Biopsy Based 17-Gene Genomic Prostate Score on Treatment Recommendations in Men with Newly Diagnosed Clinically Prostate Cancer Who are Candidates For Active Surveillance. Urology Practice. 2015;2:181-189.
• Shipitsin M, Small C, Giladi E, et al. Automated quantitative multiplex immunofluorescence in situ imaging identifies phospho-S6 and phospho-PRAS40 as predictive protein biomarkers for prostate cancer lethality. Proteome Sci. 2014;12:40. PMID 25075204
• Blume-Jensen P, Berman DM, Rimm DL, et al. Development and clinical validation of an in situ biopsy-based multimarker assay for risk stratification in prostate cancer. Clin Cancer Res. Jun 1 2015;21(11):2591-2600. PMID 25733599
• Abdueva D, Wing M, Schaub B, et al. Quantitative expression profiling in formalin-fixed paraffin-embedded samples by affymetrix microarrays. J Mol Diagn. Jul 2010;12(4):409-417. PMID 20522636
• Den RB, Feng FY, Showalter TN, et al. Genomic prostate cancer classifier predicts biochemical failure and  metastases in patients after postoperative radiation therapy. Int J Radiat Oncol Biol Phys. Aug 1 2014;89(5):1038-1046. PMID 25035207
• Cooperberg MR, Davicioni E, Crisan A, et al. Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. Eur Urol. Feb 2015;67(2):326-333. PMID 24998118
• Ross AE, Feng FY, Ghadessi M, et al. A genomic classifier predicting metastatic disease progression in men with biochemical recurrence after prostatectomy. Prostate Cancer Prostatic Dis. Mar 2014;17(1):64-69. PMID 24145624
• 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. J Urol. Dec 2013;190(6):2047-2053. PMID 23770138
• Erho N, Crisan A, Vergara IA, et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS One. 2013;8(6):e66855. PMID 23826159
• Ross AE, Johnson MH, Yousefi K, et al. Tissue-based Genomics Augments Post-prostatectomy Risk Stratification in a Natural History Cohort of Intermediate- and High-Risk Men. Eur Urol. Jun 6 2015. PMID 26058959
• Michalopoulos SN, Kella N, Payne R, et al. Influence of a genomic classifier on post-operative treatment decisions in high-risk prostate cancer patients: results from the PRO-ACT study. Curr Med Res Opin. Aug 2014;30(8):1547-1556. PMID 24803160
• Thompson IM, Valicenti RK, Albertsen P, et al. Adjuvant and salvage radiotherapy after prostatectomy: AUA/ASTRO Guideline. J Urol. Aug 2013;190(2):441-449. PMID 23707439
• Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Natl Cancer Inst. Nov 4 2009;101(21):1446-1452. PMID 19815849
• Cooperberg M, Sinko J, Cowan J. et. al. Validation of Cell-Cycle Progression Gene Panel to Improve Risk Stratification in a Contemporary Prostatectomy Cohort. Journal of Clinical Oncology Volume 31 Number 11 April 10 2013
• Shipitsin M, Small C, Choudhury S. et. al. Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error. British Journal of Cancer 2014 111. 1201-1212
• National Comprehensive Cancer Network (NCCN) Version 2.2016 Prostate Cancer
• Prolaris Prostate Cancer Testing (Myriad Genetics).
• Oncotype DX Prostate Cancer Assay (Genomic Health).
• ProMark Protein Biomarker Metamark Genetics). 
• Decipher (GenomeDX Biosciences).
• Sanda M, Chen R, Crispino T, et. al. Clinically Localized Prostate Cancer AUA/ASTRO/SUO Guideline. April 2017
• American Cancer Society. Key Statistics for Prostate Cancer. January 2017.
• ECRI. Technology Trend News. Half of Laboratory-Developed Tests on FDA’s Hit List are for Cancer. Published December 2015.
• UpToDate. Molecular Prognostic Tests for Prostate Cancer. Ashely Ross M.D., PhD, Anthony V D’Amico M.D., PhD, Stephen Freeland M.D. Topic last updated April 20, 2017.
• Hamdy FC, Donovan JL, Lane JA, et. al. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. N Engl J Med 2016 Oct 13;375(15):1415-1424. PMID 27626136
• Sommariva S, Tarricone R, Lazzeri M, et. al. Prognostic value of the cell cycle progression score in patients with prostate cancer: a systematic review and meta-analysis. Eur Urol 2016 Jan 69(1):107-15. PMID 25481455
• Shore ND, Kella N, Moran B, et. al. Impact of the cell cycle progression test on physician and patient treatment selection for localized prostate cancer. J Urol 2016 Mar 195(3):612-8. PMID 26403586
• Brand TC, Zhang N, Crager MR, et. al. Patient-specific meta-analysis of 2 clinical validation studies to predict pathologic outcomes in prostate cancer using the 17 gene genomic prostate score. PMID 26723180
• Koch MO, Cho JS, Kaimakliotis HZ, et. al. Use of the cell cycle progression (CCP) score for predicting systemic disease and response to radiation of biochemical recurrence. Cancer Biomark 2016 Jun 7;17(1):83-8. PMID 27314296
• 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. PMID 25466945
• Freeland SJ, Choeumg V, Howard L, et. al. Utilization of a genomic classifier for prediction of metastasis following salvage radiation therapy after radical prostatectomy. Eur Urol 2016 Oct;70(4):588-596. PMID 26806658
• Glass AG, Leo MC, Haddad Z, et.al. Validation of a genomic classifier for predicting post-prostatectomy recurrence in a community based health care setting. J Urol 2016 Jun;195(6):1748-53. PMID 26626216
• Klein EA, Haddad Z, Yousefi K, et. al. Decipher genomic classifier measured on prostate biopsy predicts metastasis risk. Urology 2016 Apr 90:148-52. PMID 26809071
• Lobo JM, Dicker AP, Buerki C, et.al. Evaluating the clinical impact of a genomic classifier in prostate cancer using individualized decision analysis. PLoS One 2015 Apr 2;10(3):e0116866. PMID 25837660
• Nguyen PL, Shin H, Yousefi K, et. al. Impact of a genomic classifier of metastatic risk on post-prostatectomy treatment recommendations by radiation oncologists and urologists. Urology 2015 Jul;86(1):35-40. PMID 26142578
• Badani KK, Thompson DJ, Brown G, et. al. Effect of genomic classifer test on clinical practice decision for patients with high risk prostate cancer after surgery. BJU Int 2015 Mar;115(3):419-29. PMID 24784420
• Ross AE, Den RB, Yousefi K, et. al. Efficacy of post-operative radiation in prostatectomy cohort adjusted for clinical and genomic risk. Prostate Cancer Prostatic Dis 2016 Sep;19(3):277-82. PMID 27136742
• Yamoah K, Johnson MH, Choeurng V, et. al. Novel biomarker signature that may predict aggressive disease in African American men with prostate cancer. J Clin Oncol 2015 Sep 1;33(25):2789-96. PMID 26195723
• Whalen M, Hackert V, Rothberg M, et. al. Prospective correlation between likelihood of favorable pathology on the 17 gene genomic prostate score and actual pathological outcomes at radical prostatectomy. Urology Practice September 2016 Volume 3, Issue 5, Pages 379-386
• Dall’Era M, Maddala T, Polychronopoulos L, et. al. Utility of the Oncotype DX Prostate Cancer Assay in clinical practice for treatment selection in men newly diagnosed with prostate cancer: A retrospective chart review analysis. Urology Practice November 2015 Volume 2,Issue 6,Pages 343-348
• Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA Cancer J Clin. Jan 2017;67(1):7-30. PMID 28055103       
• Brimo F, Montironi R, Egevad L, et al. Contemporary grading for prostate cancer: implications for patient care. Eur Urol. May 2013;63(5):892-901. PMID 23092544
• Eylert MF, Persad R. Management of prostate cancer. Br J Hosp Med (Lond). Feb 2012;73(2):95-99. PMID 22504752      
• 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 2013;369(7):603-610. PMID 23944298
• Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Natl Cancer Inst. Nov 4 2009;101(21):1446-1452. PMID 19815849
• Borley N, Feneley MR. Prostate cancer: diagnosis and staging. Asian J Androl. Jan 2009;11(1):74-80. PMID 19050692
• Nam RK, Cheung P, Herschorn S, et al. Incidence of complications other than urinary incontinence or erectile dysfunction after radical prostatectomy or radiotherapy for prostate cancer: a population-based cohort study. Lancet Oncol. Feb 2014;15(2):223-231. PMID 24440474 
• Wilt TJ, Brawer MK, Jones KM, et al. Radical prostatectomy versus observation for localized prostate cancer. N Engl J Med. Jul 19 2012;367(3):203-213. PMID 22808955
• Wilt TJ, Jones KM, Barry MJ, et al. Follow-up of prostatectomy versus observation for early prostate cancer. N Engl J Med. Jul 13 2017;377(2):132-142. PMID 28700844
• van den Bergh RC, Korfage IJ, Roobol MJ, et al. Sexual function with localized prostate cancer: active surveillance vs radical therapy. BJU Int. Oct 2012;110(7):1032-1039. PMID 22260273
• Johansson E, Steineck G, Holmberg L, et al. Long-term quality-of-life outcomes after radical prostatectomy or watchful waiting: the Scandinavian Prostate Cancer Group-4 randomised trial. Lancet Oncol. Sep 2011;12(9):891-899. PMID 21821474
• Kattan MW, Eastham JA, Wheeler TM, et al. Counseling men with prostate cancer: a nomogram for predicting the presence of small, moderately differentiated, confined tumors. J Urol. Nov 2003;170(5):1792-1797. PMID 14532778
• Cooperberg MR, Freedland SJ, Pasta DJ, et al. Multiinstitutional validation of the UCSF cancer of the prostate risk assessment for prediction of recurrence after radical prostatectomy. Cancer. Nov 15 2006;107(10):2384-2391. PMID 17039503
• Chen RC, Chang P, Vetter RJ, et al. Recommended patient-reported core set of symptoms to measure in prostate cancer treatment trials. J Natl Cancer Inst. Jul 2014;106(7). PMID 25006192
• Sommariva S, Tarricone R, Lazzeri M, et al. Prognostic value of the Cell Cycle Progression Score in patients with prostate cancer: a systematic review and meta-analysis. Eur Urol. Jan 2016;69(1):107-115. PMID 25481455
• Cuzick J, Swanson GP, Fisher G, et al. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol. Mar 2011;12(3):245-255. PMID 21310658
• Shore N, Concepcion R, Saltzstein D, et al. Clinical utility of a biopsy-based cell cycle gene expression assay in localized prostate cancer. Curr Med Res Opin. Apr 2014;30(4):547-553. PMID 24320750
• Albala D, Kemeter MJ, Febbo PG, et al. Health Economic Impact and Prospective Clinical Utility of Oncotype DX(R) Genomic Prostate Score. Rev Urol. Nov 2016;18(3):123-132. PMID 27833462
• Eure G, Germany R, Given R, et al. Use of a 17-Gene Prognostic Assay in Contemporary Urologic Practice: Results of an Interim Analysis in an Observational Cohort. Urology. Sep 2017;107:67-75. PMID 28454985
• Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. Nov-Dec 2006;26(6):565-574. PMID 17099194
• Den RB, Yousefi K, Trabulsi EJ, et al. Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy. J Clin Oncol. Mar 10 2015;33(8):944-951. PMID 25667284
• Thompson IM, Jr., Tangen CM, Paradelo J, et al. Adjuvant radiotherapy for pathologically advanced prostate cancer: a randomized clinical trial. Jama. Nov 15 2006;296(19):2329-2335. PMID 17105795
• Bolla M, van Poppel H, Tombal B, et al. Postoperative radiotherapy after radical prostatectomy for high-risk prostate cancer: long-term results of a randomised controlled trial (EORTC trial 22911). Lancet. Dec 08 2012;380(9858):2018-2027. PMID 23084481
• Wiegel T, Bottke D, Steiner U, et al. Phase III postoperative adjuvant radiotherapy after radical prostatectomy compared with radical prostatectomy alone in pT3 prostate cancer with postoperative undetectable prostate-specific antigen: ARO 96-02/AUO AP 09/95. J Clin Oncol. Jun 20 2009;27(18):2924-2930. PMID 19433689
• Stephenson AJ, Scardino PT, Kattan MW, et al. Predicting the outcome of salvage radiation therapy for recurrent prostate cancer after radical prostatectomy. J Clin Oncol. May 20 2007;25(15):2035-2041. PMID 17513807
• Stephenson AJ, Scardino PT, Eastman JA, et. al. Postoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Clin Oncol 2005 October 1; 23(28):7005-7012  
• Cooperberg MR, Hilton JF, Carroll PR. The CAPRA-S score: A straightforward tool for improved prediction of outcomes after radical prostatectomy. Cancer. Nov 15 2011;117(22):5039-5046. PMID 21647869
• Freedland SJ, Gerber L, Reid J, et al. Prognostic utility of cell cycle progression score in men with prostate cancer after primary external beam radiation therapy. Int J Radiat Oncol Biol Phys. Aug 1 2013;86(5):848-853. PMID 23755923
• Karnes RJ, Choeurng V, Ross AE, et al. Validation of a Genomic Risk Classifier to Predict Prostate Cancer-specific Mortality in Men with Adverse Pathologic Features. Eur Urol. Apr 08 2017. PMID 28400167
• Ross AE, Den RB, Yousefi K, et al. Efficacy of post-operative radiation in a prostatectomy cohort adjusted for clinical and genomic risk. Prostate Cancer Prostatic Dis. Sep 2016;19(3):277-282. PMID 27136742
• Spratt DE, Yousefi K, Deheshi S, et al. Individual patient-level meta-analysis of the performance of the decipher genomic classifier in high-risk men after prostatectomy to predict development of metastatic disease. J Clin Oncol. Jun 20 2017;35(18):1991-1998. PMID 28358655
• Badani K, Thompson DJ, Buerki C, et al. Impact of a genomic classifier of metastatic risk on postoperative treatment recommendations for prostate cancer patients: a report from the DECIDE study group. Oncotarget. Apr 2013;4(4):600-609. PMID 23592338
• Gore JL, du Plessis M, Santiago-Jimenez M, et al. Decipher test impacts decision making among patients considering adjuvant and salvage treatment after radical prostatectomy: Interim results from the Multicenter Prospective PRO-IMPACT study. Cancer. Aug 01 2017;123(15):2850-2859. PMID 28422278
• Prensner JR, Zhao S, Erho N, et al. RNA biomarkers associated with metastatic progression in prostate cancer: a multi-institutional high-throughput analysis of SChLAP1. Lancet Oncol. Dec 2014;15(13):1469-1480. PMID 25456366
• Tomlins SA, Alshalalfa M, Davicioni E, et al. Characterization of 1577 primary prostate cancers reveals novel biological and clinicopathologic insights into molecular subtypes. Eur Urol. Oct 2015;68(4):555-567. PMID 25964175
• Barocas D. Alverez J, Resnick M, et. al. Association between radiation therapy, surgery or observation for localized prostate cancer and patient reported outcomes after 3 years. JAMA 2017;317(1):1126-1140
• Chen R, Ramsanker B, Meyer AM, et. al. Association between choice of radical prostatectomy, external beam radiotherapy, brachytherapy, or active surveillance and patient reported quality of life among men with localized prostate cancer. JAM 2017;317(11):1141-1150
• Cooperberg M, Pasta D, Elkin E, et. al. The University of California, San Francisco Cancer of the Prostate Risk Assessment Score: A straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy. The Journal of Urology Vol. 173 1938-1942 June 2005
• Donavan JL, Hamdy FC, Lane JA, et. al. Patient reported outcomes after monitoring, surgery, or radiotherapy for prostate cancer. N Engl J Med October 2016 375;15
• Draisma F, Etzioni R, Tsodikov A, et. al. Lead time and over diagnosis in prostate specific antigen screening: importance of methods and context. Journal of the National Cancer Institute Vol. 101 Issue 6 March 18, 2009
• Garzotto M. Is low-risk prostate cancer more indolent in younger patients? Journal of Clinical Oncology Volume 35 Number 17 June 2017
• Jeldres C, Cullen J, Hurwitz L, et.al. Prospective quality of life outcomes for low-risk prostate cancer: active surveillance versus radical prostatectomy. Cancer July 15; 121(14):2465-2473
• Leapman M, Cowan J, Nguyen H, et. al. Active surveillance in younger men with prostate cancer. Journal of Clinical Oncology Volume 35 Number 17 June 2017   
• Tosoian J, Mamawala M, Epstein J, et. al. Intermediate and longer-term outcomes from a prospective active surveillance program for favorable risk prostate cancer. Journal of Clinical Oncology Volume 33 2015
• MoIDX: OncotypeDx Genomic Prostate Score for Men with Favorable Intermediate Risk Prostate Cancer. Local Coverage Determination (L37262)
• MoIDX: ProMark Risk Score. Local Coverage Determination (L36665)
• MoIDX: Decipher Prostate Cancer Classifier Assay. Local Coverage Determination (L35868)  
• MoIDX: Prolaris Prostate Cancer Genomic Assay. Local Coverage Determination (L35869)
• MoIDX: Prolaris Prostate Cancer Genomic Assay for Men with Favorable Intermediate Risk Disease (L37043)

 

Policy History:

• May 2018 - Annual Review, Policy Revised
• May 2017 - Annual Review, Policy Revised
• June 2016 - New medical policy

Wellmark medical policies address the complex issue of technology assessment of new and emerging treatments, devices, drugs, etc.   They are developed to assist in administering plan benefits and constitute neither offers of coverage nor medical advice. Wellmark medical policies contain only a partial, general description of plan or program benefits and do not constitute a contract. Wellmark does not provide health care services and, therefore, cannot guarantee any results or outcomes. Participating providers are independent contractors in private practice and are neither employees nor agents of Wellmark or its affiliates. Treating providers are solely responsible for medical advice and treatment of members. Our medical policies may be updated and therefore are subject to change without notice.

 

*CPT® is a registered trademark of the American Medical Association.