Individual patient level meta-analysis of the performance of the Decipher genomic classifier in high-risk men post-prostatectomy to predict development of metastatic disease.
133 Background: The genomic classifier, Decipher, has been validated to predict risk of metastasis after radical prostatectomy (RP). However, the cohort size and event rate in the previous studies did not allow for a thorough investigation into performance within individual clinicopathologic or treatment subgroups. In this study, we present the first meta-analysis of the performance of the 22-marker genomic classifier in men with prostate cancer (PCa) post-RP. Methods: MEDLINE, EMBASE, and the Decipher genomic resource information database were searched for published reports of men with PCa treated by RP between 2010 and 2016 where the benefit of the Decipher genomic classifier test was assessed. The primary end point was the ability of Decipher to independently improve prognostication of regional or distant metastasis over routine clinicopathologic factors. Meta-analysis was performed with random-effects modeling, and extent of heterogeneity between studies was determined with the I2 test. Results: Five studies (975 total patients, and 855 with individual patient genomic and clinicopathologic data) were eligible for analysis. The median follow-up was 8 years. All patients had clinical high-risk disease, yet 60.9%, 22.6%, and 16.5% of patients were classified as low, intermediate, and high-risk, respectively by Decipher and had 10-year cumulative incidence rates of metastases of 5.5%, 15.0% and 26.7% (p < 0.001), respectively. Adjusting for standard clinicopathologic variables, on multivariable analysis Decipher remained a statistically significant predictor of metastasis (hazard ratio [HR] 1.30 per 0.1 unit, 95% confidence interval [CI] 1.14-1.47, p < 0.001), and the summary HR for metastasis of Decipher across the 5 studies was 1.52 (95% CI 1.39-1.67) per 0.1 unit. Conclusions: The genomic classifier test, Decipher, has the ability to independently improve prognostication of men post-RP, as well as within nearly all clinicopathologic and treatment subgroups. Strong consideration should be given to incorporating the use of genomic testing in clinical decision making and clinical trials to better individualize treatment.
Chromosome 8 alterations and PTEN loss in Gleason grade 3 tumor to predict the presence of unsampled grade 4 tumor: Implications for active surveillance.
93 Background: A key eligibility criterion in many active surveillance (AS) programs is that the biopsy exhibit only Gleason pattern 3 (G3) for a Gleason score of 6 or less. However, 25 to 35% of biopsy Gleason 6 is upgraded to Gleason 7 or higher in the prostatectomy (RP) specimen. Thus, there is a great need for biomarkers that, when measured on G3 tissue in a Gleason 6 biopsy, can predict the presence of unsampled higher grade tumor in the whole prostate. We evaluated PTEN loss by immunohistochemistry (IHC), and PTEN deletion, chromosome 8q (MYC) gain and 8p (LPL) loss by fluorescence in situ hybridization (FISH) for their ability to predict unsampled G4 tumor. Methods: A tissue microarray (TMA) was constructed of RP tissue from three groups of patients (n=50 per group) whose prostates exhibited only Gleason 3+3, only 3+4, or only 4+3 tumor, matched on age, year of RP, and race. In each patient, multiple cores sampled only from areas of G3 were evaluated for PTEN deletion by FISH, PTEN loss by IHC, and chromosome 8p/8q alterations by FISH. Biomarker results were compared between Gleason 6 versus 7 tumors using conditional logistic regression. Results: Patients underwent RP in 2001 to 2009, had median age 60, and median prostate-specific antigen 5.2; 63% of tumors were organ confined. In univariate analyses 8q gain (odds ratio OR=8.9, p<.0001), 8p loss (OR=6.9, p<.0001), PTEN loss by IHC (OR=5.7, p=.025), but not PTEN deletion by FISH (OR=1.5, p=.477) were significantly more common in G3 cores from Gleason 7 tumors than G3 cores from Gleason 6 tumors. In multivariable analyses, 8q gain (OR=6.2, p=.002) and 8p loss (OR=5.2, p=.0002) remained highly significant. At least one high risk biomarker (8q gain, 8p loss, PTEN loss, or PTEN deletion) was found in 35.7% of Gleason 3+3 versus 77.1% of Gleason 3+4 versus 91.3% of Gleason 4+3 tumors, p<.0001. Adjustment for confounding factors did not change the results. Conclusions: Chromosomal 8q gains (MYC), 8p loss (LPL), and PTEN loss measured in Gleason G3 TMA cores strongly differentiate whether the core comes from Gleason 6 or Gleason 7 tumor. If validated in biopsy Gleason 6 cores to predict prostatectomy Gleason 7 tumor these biomarkers could facilitate safe selection of men for active surveillance.
Molecular and clinical characterization of 1,577 primary prostate cancer tumors to reveal novel clinical and biological insights into its subtypes.
9 Background: Prostate cancer molecular subtypes based on ETS gene fusions and SPINK1 were originally identified through outlier gene expression profiling analysis. Such molecular subtypes may have utility in disease stratification and clonality assessment, complementing available purely prognostic tests. Hence, we determined the analytical validity of molecular subtyping in a large sample of PCa treated with radical prostatectomy. Methods: We analyzed Affymetrix Human Exon 1.0ST GeneChip expression profiles for 1,577 patients from 8 radical prostatectomy (RP) cohorts. Multi-feature random forest classifiers and outlier analysis were used to define microarray-based molecular subtypes. Results: A random forest (RF) classifier was trained and validated to predict ERG fusion status using a subset with known ERG rearrangement status defined by FISH, achieving >95% sensitivity and specificity in the validation subset. Less frequent rearrangements involving other ETS genes or SPINK1 over-expression were predicted based on gene expression outlier analysis. Across cohorts, 45%, 9% 8% and 38% of PCa were classified as ERG+, ETS+, SPINK+, and Triple Negative, respectively. Global gene expression analysis shows that the four subtypes could be collapsed into three entities (ERG+, ETS+ and SPINK+/Triple Negative) based on expression patterns and clinical characteristics similarity. A series of multivariable analyses further revealed, ERG+ to be associated with lower pre PSA and Gleason scores but more likely to have EPE and occur in patients with European American ancestry compared to the ETS+, SPINK+/Triple Negative tumors (p<0.001). In contrast, patients with ETS+ were more likely to have SVI compared to both ERG+ and SPINK/Triple Negative (p=0.01), while SPINK+/Triple Negative had higher Gleason scores and were more likely to occur in African Americans (p<0.001). Conclusions: The Decipher platform can accurately determine ERG rearrangement status and PCa molecular subtypes. Inclusion of molecular subtyping, such as m-ERG status, may enable additional precision medicine opportunities in prognostic tests
Efficacy of early and delayed radiation in a prostatectomy cohort adjusted for genomic and clinical risk.
12 Background: In 3 published randomized trials, adjuvant radiation therapy (ART) for prostate cancer (PCa) resulted in improved progression free survival. However, the impact on metastases and overall survival is unclear. To date, there have been no published prospective trials examining the impact of salvage radiation therapy (SRT) in this disease state. Hence, we conducted a retrospective, nonrandomized comparative study of ART, SRT, or no radiation following radical prostatectomy (RP) for men with pT3 disease or positive margins (adverse pathologic features, APF). Methods: 422 PCa men treated at 4 institutions with RP and having APF were analyzed with a primary end point of clinical metastasis. ART (n = 111), early SRT (n = 70) and delayed SRT (n = 83) were defined by PSA levels of < 0.2, 0.2 to 0.5, and ≥ 0.5 ng/mL, respectively, prior to RT initiation. Remaining 157 men who did not receive additional therapy prior to metastatic onset formed the no RT arm. Clinical-genomic risk was assessed by CAPRA-S and Decipher. Cox multivariable (MVA) model was used to evaluate the impact of treatment on outcome. Results: During study follow-up, 37 men developed metastasis with a median follow-up of 8 years. Both CAPRA-S and Decipher had independent predictive value on MVA for metastatic outcome (both p < 0.05). On MVA adjusting for clinical-genomic risk, delayed SRT and no RT had an HR of 4.31 (95%CI, 1.20-15.47) and 5.42 (95% CI, 1.59-18.44) for metastasis compared to ART. No significance difference was observed between early SRT and ART (p = 0.28). Men with low to intermediate CAPRA-S and low Decipher have a low rate of metastatic events regardless of treatment selection. In contrast, men with high CAPRA-S and Decipher benefit from ART, however the cumulative incidence of metastasis remains high. Conclusions: The decision as to the timing and need for additional local therapy following RP is nuanced and requires providers and men to balance risks of morbidity with improved oncologic outcomes. This analysis provides the most robust and accurate quantification of risk for these men. Post-RP treatment can be safely avoided for men who are low risk by clinical-genomic risk, whereas those at high risk should favor enrollment in clinical trials.
Development and validation of an ADT resistance signature to predict adjuvant hormone treatment failure.
106 Background: Androgen deprivation therapy (ADT) is one of the main treatment options for locally advanced and metastatic prostate cancer. Neuroendocrine prostate cancer (NEPC) is inherently less sensitive or even resistant to ADT. NEPC can be observed de novo (e.g., small cell prostate cancer) but more commonly arises after exposure to ADT. We hypothesized that a gene expression signature of NEPC when measured in primary tumor specimens (RP) of prostatic adenocarcinoma may be useful for predicting patients with innate resistance to ADT. Methods: Expression profiles of 1023 PCa patients treated with RP were obtained from the Decipher GRID database. These were split into training (n=529) and validation (n=494) sets and stratified by the receipt of adjuvant ADT (n=243) or no adjuvant ADT (n=780). A literature review of ADT resistance and neuroendocrine genes identified 1,557 genes as candidates. This set was further filtered, using logistic regression to select a 52-gene ADT resistance signature (ARS). ARS was trained using a generalized linear model with lasso regularization. Survival c-index and Kaplan Meier was used to compare survival differences between treated and untreated patients with high and low ARS scores (defined by median split). Results: In validation cohorts, the ARS was predictive of metastasis in cohorts receiving adjuvant ADT (10-year metastasis free survival c-index of 0.69 (95% CI 0.59-0.78) as compared to 0.45 (95% CI 0.29-0.61) in patients not treated with ADT). Similarly in a separate cohort of untreated patients that received no ADT until after metastatic onset, ARS was not prognostic (c-index 0.53). Among ADT treated patients, those with low ARS scores had a 10 year MFS of 87%, versus 70% in those with high ARS scores (p<0.001). In the subset of men who received ADT after metastatic onset and who developed castrate-resistant prostate cancer (CRPC, n = 41), median time to treatment failure was 1 year in patients with high ARS compared to 2 years for those with low ARS scores (p=0.07). Conclusions: A 52-gene ADT resistance signature was developed which showed significant differences in metastasis-free survival among adjuvant hormone treated but not untreated patients.
Molecular Analysis of Low Grade Prostate Cancer Using a Genomic Classifier of Metastatic Potential
Abstract A1-64: Molecular and clinical characterization of 1,577 primary prostate cancer tumors reveals novel clinical and biological insights into its subtypes
Abstract Background: Prostate cancer molecular subtypes based on ETS gene fusions and SPINK1 were originally identified through distinct gene expression profiles. Such molecular subtypes may have utility in disease stratification and clonality assessment, complementing available purely prognostic tests. Hence, we determined the analytical validity of molecular subtyping and explored clinical associations using global gene expression profiles in a large cohort of PCa. Methods: We analyzed 1,577 patient Affymetrix Human Exon 1.0ST GeneChip expression profiles from 8 radical prostatectomy (RP) cohorts; 5 of them generated as part of the Decipher® platform for the Decipher® discovery or validation. Multi-feature random forest classifiers and outlier analysis were used to define microarray-based molecular subtypes and characterize clinical associations. Results: A random forest (RF) classifier (m-ERG) was trained and validated to predict ERG fusion status using separate subsets of a single-institution RP cohort (total n=407) with known ERG rearrangement status defined by FISH, achieving >95% sensitivity and specificity in the validation subset. The model was then applied to 7 independent RP cohorts to predict ERG rearrangement status. Less frequent rearrangements involving other ETS genes (ETV1, ETV4, ETV5, FLI1) or SPINK1 over-expression were predicted based on gene expression outlier analysis. Across cohorts, 45%, 9% 8% and 38% of PCa were classified as ERG+, ERG—ETS+, ERG—SPINK+, and Triple Negative (ERG—/ETS—/SPINK1—), respectively. Global gene expression analysis shows that the four subtypes could be collapsed into three entities (ERG+, ERG—ETS+ and SPINK+/Triple Negative) based on expression patterns and clinical characteristics similarity. Based on multivariable analysis, ERG+ is significantly associated with lower pre-PSA (p<0.001), lower Gleason score (p<0.001), the presence of extraprostatic extension (p<0.001) and European Americans (p<0.001), but not associated with outcome. ERG—ETS+ is significantly associated with SVI (p=0.01) and ERG—SPINK+ is enriched in African Americans (p<0.001). Conclusions: The Decipher® platform can accurately determine ERG rearrangement status and PCa molecular subtypes. Inclusion of molecular subtyping, such as m-ERG status, may enable additional precision medicine opportunities in prognostic tests and provides insights into the development of novel therapeutic approaches. Citation Format: Mohammed Alshalalfa, Scott A. Tomlins, Nicholas Erho, Kasra Yousefi, Shuang Zhao, Robert B. Den, Adam P. Dicker, Bruce Trock, Angelo DeMarzo, Edward M. Schaeffer, Ashley Ross, Eric A. Klein, Cristina Magi-Galluzzi, Jeffery R. Karnes, Robert B Jenkins, Elai Davicioni, Felix Feng. Molecular and clinical characterization of 1,577 primary prostate cancer tumors reveals novel clinical and biological insights into its subtypes. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-64.