Transcriptome evaluation of the relation between body mass index and prostate cancer outcomes.

BACKGROUND: Large epidemiological studies indicate that an increased body mass index (BMI) is associated with increased prostate cancer (PCa) mortality. Data indicate that there is no association between elevated metabolic pathway proteins and PCa mortality. There are no published studies evaluating the relation between BMI and metabolic pathways with respect to PCa outcomes with a genomics approach. METHODS: The Decipher Genomic Resource Information Database was queried for patients who had undergone prostatectomy and had BMI information available. These patients came from Thomas Jefferson University (TJU) and Johns Hopkins Medical Institution (JHMI); the latter provided 2 cohorts (I and II). A high-BMI group (≥30 kg/m2 ) and a low-BMI group (<25 kg/m2 ) were identified, and genomic data were interrogated for differentially expressed genes with an interquartile range filter and a Wilcoxon test. P values were adjusted for multiple testing with the Benjamini-Hochberg false-discovery rate method. RESULTS: A total of 477 patients with a median follow-up of 108 months had BMI information available. Two genes were found to interact with BMI in both the JHMI I cohort and the TJU cohort, but there was no statistical significance after adjustments for multiple comparisons. Aberrant metabolic gene expression was significantly correlated with distant metastases (P < .05). No relation was found between BMI and metastases or overall survival (both P values > .05). CONCLUSIONS: In a genomic analysis of prostatectomy specimens, metabolic gene expression, but not BMI, was associated with PCa metastases. Cancer 2017;123:2240-2247. © 2017 American Cancer Society.

Decipher test impacts decision making among patients considering adjuvant and salvage treatment after radical prostatectomy: Interim results from the Multicenter Prospective PRO-IMPACT study.

BACKGROUND: Patients with prostate cancer and their providers face uncertainty as they consider adjuvant radiotherapy (ART) or salvage radiotherapy (SRT) after undergoing radical prostatectomy. The authors prospectively evaluated the impact of the Decipher test, which predicts metastasis risk after radical prostatectomy, on decision making for ART and SRT. METHODS: A total of 150 patients who were considering ART and 115 who were considering SRT were enrolled. Providers submitted a management recommendation before processing the Decipher test and again at the time of receipt of the test results. Patients completed validated surveys on prostate cancer (PCa)-specific decisional effectiveness and PCa-related anxiety. RESULTS: Before the Decipher test, observation was recommended for 89% of patients considering ART and 58% of patients considering SRT. After Decipher testing, 18% (95% confidence interval [95% CI], 12%-25%) of treatment recommendations changed in the ART arm, including 31% among high-risk patients; and 32% (95% CI, 24%-42%) of management recommendations changed in the salvage arm, including 56% among high-risk patients. Decisional Conflict Scale (DCS) scores were better after viewing Decipher test results (ART arm: median DCS before Decipher, 25 and after Decipher, 19 [P<.001]; SRT arm: median DCS before Decipher, 27 and after Decipher, 23 [P<.001]). PCa-specific anxiety changed after Decipher testing; fear of PCa disease recurrence in the ART arm (P = .02) and PCa-specific anxiety in the SRT arm (P = .05) decreased significantly among low-risk patients. Decipher results reported per 5% increase in 5-year metastasis probability were associated with the decision to pursue ART (odds ratio, 1.48; 95% CI, 1.19-1.85) and SRT (odds ratio, 1.41; 95% CI, 1.09-1.81) in multivariable logistic regression analysis. CONCLUSIONS: Knowledge of Decipher test results was associated with treatment decision making and improved decisional effectiveness among men with PCa who were considering ART and SRT. Cancer 2017;123:2850-59. © 2017 American Cancer Society.

Molecular Analysis of Low Grade Prostate Cancer Using a Genomic Classifier of Metastatic Potential.

PURPOSE: We determined how frequently histological Gleason 3 + 3 = 6 tumors have the molecular characteristics of disease with metastatic potential. MATERIALS AND METHODS: We analyzed prostatectomy tissue from 337 patients with Gleason 3 + 3 disease. All tissue was re-reviewed in blinded fashion by genitourinary pathologists using 2005 ISUP (International Society of Urological Pathology) Gleason grading criteria. A previously validated Decipher® metastasis signature was calculated in each case based on a locked model. To compare patient characteristics across pathological Gleason score categories we used the Fisher exact test or the ANOVA F test. The distribution of Decipher scores among different clinicopathological groups was compared with the Wilcoxon rank sum test. The association of Decipher score with adverse pathology features was examined using logistic regression models. The significance level of all statistical tests was 0.05. RESULTS: Of men with Gleason 3 + 3 = 6 disease only 269 (80%) had a low Decipher score with intermediate and high scores in 43 (13%) and 25 (7%), respectively. Decipher scores were significantly higher among pathological Gleason 3 + 3 = 6 specimens from cases with adverse pathological features such as extraprostatic extension, seminal vesicle involvement or positive margins (p <0.001). The median Decipher score in patients with margin negative pT2 disease was 0.23 (IQR 0.09-0.42) compared to 0.30 (IQR 0.17-0.42) in patients with pT3 disease or positive margins (p = 0.005). CONCLUSIONS: Using a robust and validated prognostic signature we found that a small but not insignificant proportion of histological Gleason 6 tumors harbored molecular characteristics of aggressive cancer. Molecular profiling of such tumors at diagnosis may better select patients for active surveillance at diagnosis and trigger appropriate intervention during followup.

Evaluation of a 24-gene signature for prognosis of metastatic events and prostate cancer-specific mortality.

OBJECTIVES: To determine the prognostic potential of a 24-gene signature, Sig24, for identifying patients with prostate cancer who are at risk of developing metastases or of prostate cancer-specific mortality (PCSM) after radical prostatectomy (RP). PATIENTS AND METHODS: Sig24 scores were calculated from previously collected gene expression microarray data from the Cleveland Clinic and Mayo Clinic (I and II). The performance of Sig24 was determined using time-dependent c-index analysis, Cox proportional hazards regression and Kaplan-Meier survival analysis. RESULTS: Higher Sig24 scores were significantly associated with higher pathological Gleason scores in all three cohorts. Analysis of the Mayo Clinic II cohort, which included time-to-event information, indicated that patients with high Sig24 scores also had a higher risk of developing metastasis (hazard ratio [HR] 3.78, 95% confidence interval [CI]: 1.96-7.29; P < 0.001) or of PCSM (HR 6.54, 95% CI: 2.16-19.83; P < 0.001). CONCLUSIONS: The findings of the present study show the applicability of Sig24 for the prognosis of metastasis or PCSM after RP. Future studies investigating the combination of Sig24 with available prognostic tests may provide new approaches to improve risk stratification for patients with prostate cancer.

SPINK1 Defines a Molecular Subtype of Prostate Cancer in Men with More Rapid Progression in an at Risk, Natural History Radical Prostatectomy Cohort.

PURPOSE: Prostate cancer is clinically and molecularly heterogeneous. We determined the prognosis of men with ERG-ETS fusions and SPINK1 over expression. MATERIALS AND METHODS: Men were identified with intermediate or high risk localized prostate cancer treated with radical prostatectomy and no therapy before metastasis. A case-cohort design sampled a cohort (262) enriched with metastasis from the entire cohort and a cohort (213) enriched with metastasis from patients with biochemical recurrence. We analyzed transcriptomic profiles and subtyped tumors as m-ERG+, m-ETS+, m-SPINK1+ or Triple Negative (m-ERG─/m-ETS─/m-SPINK1─), and multivariable logistic regression analyses, Kaplan-Meier and multivariable Cox models were used to evaluate subtypes as predictors of clinical outcomes. RESULTS: Overall 36%, 13%, 11% and 40% of prostate cancer was classified as m-ERG+, m-ETS+, m-SPINK1+ and Triple Negative, respectively. Univariable analysis demonstrated that m-SPINK1+ tumors were more common in African-American men (OR 5, 95% CI 1.6-16) but less commonly associated with positive surgical margins (OR 0.16, 95% CI 0.03-0.69) compared to the m-ERG+ group. Compared to the Triple Negative group, m-SPINK1+ showed similar associations with race and surgical margins in univariable and multivariable analyses across the entire cohort. Survival analyses did not show significant differences among m-ERG+, m-ETS+ and Triple Negative cases. m-SPINK1+ independently predicted prostate cancer specific mortality after metastasis (HR 2.48, 95% CI 0.96-6.4) and biochemical recurrence (HR 3, 95% CI 1.1-8). CONCLUSIONS: SPINK1 over expression is associated with prostate cancer specific mortality in at risk men with biochemical and clinical recurrence after prostatectomy. ERG-ETS alterations are not prognostic for outcome.

Validation of a Genomic Classifier for Predicting Post-Prostatectomy Recurrence in a Community Based Health Care Setting.

PURPOSE: We determined the value of Decipher®, a genomic classifier, to predict prostate cancer outcomes among patients after prostatectomy in a community health care setting. MATERIALS AND METHODS: We examined the experience of 224 men treated with radical prostatectomy from 1997 to 2009 at Kaiser Permanente Northwest, a large prepaid health plan in Portland, Oregon. Study subjects had aggressive prostate cancer with at least 1 of several criteria such as preoperative prostate specific antigen 20 ng/ml or greater, pathological Gleason score 8 or greater, stage pT3 disease or positive surgical margins at prostatectomy. The primary end point was clinical recurrence or metastasis after surgery evaluated using a time dependent c-index. Secondary end points were biochemical recurrence and salvage treatment failure. We compared the performance of Decipher alone to the widely used CAPRA-S (Cancer of the Prostate Risk Assessment Post-Surgical) score, and assessed the independent contributions of Decipher, CAPRA-S and their combination for the prediction of recurrence and treatment failure. RESULTS: Of the 224 patients treated 12 experienced clinical recurrence, 68 had biochemical recurrence and 34 experienced salvage treatment failure. At 10 years after prostatectomy the recurrence rate was 2.6% among patients with low Decipher scores but 13.6% among those with high Decipher scores (p=0.02). When CAPRA-S and Decipher scores were considered together, the discrimination accuracy of the ROC curve was increased by 0.11 compared to the CAPRA-S score alone (combined c-index 0.84 at 10 years after radical prostatectomy) for clinical recurrence. CONCLUSIONS: Decipher improves our ability to predict clinical recurrence in prostate cancer and adds precision to conventional pathological prognostic measures.

Clinical and genomic analysis of metastatic prostate cancer progression with a background of postoperative biochemical recurrence.

OBJECTIVE: To better characterize the genomics of patients with biochemical recurrence (BCR) who have metastatic disease progression in order to improve treatment decisions for prostate cancer. METHODS: The expression profiles of three clinical outcome groups after radical prostatectomy (RP) were compared: those with no evidence of disease (NED; n = 108); those with BCR (rise in prostate-specific antigen [PSA] level without metastasis; n = 163); and those with metastasis (n = 192). The patients were profiled using Human Exon 1.0 ST microarrays, and outcomes were supported by a median 18 years of follow-up. A metastasis signature was defined and verified in an independent RP cohort to ensure the robustness of the signature. Furthermore, bioinformatics characterization of the signature was conducted to decipher its biology. RESULTS: Minimal gene expression differences were observed between adjuvant treatment-naïve patients in the NED group and patients without metastasis in the BCR group. More than 95% of the differentially expressed genes (metastasis signature) were found in comparisons between primary tumours of metastasis patients and the two other outcome groups. The metastasis signature was validated in an independent cohort and was significantly associated with cell cycle genes, ubiquitin-mediated proteolysis, DNA repair, androgen, G-protein coupled and NOTCH signal transduction pathways. CONCLUSION: This study shows that metastasis development after BCR is associated with a distinct transcriptional programme that can be detected in the primary tumour. Patients with NED and BCR have highly similar transcriptional profiles, suggesting that measurement of PSA on its own is a poor surrogate for lethal disease. Use of genomic testing in patients undergoing RP with an initial rise in PSA level may be useful to improve secondary therapy decision-making.

RWD-derived response in multiple myeloma.

Real-world data (RWD) are important for understanding the treatment course and response patterns of patients with multiple myeloma. This exploratory pilot study establishes a way to reliably assess response from incomplete laboratory measurements captured in RWD. A rule-based algorithm, adapted from International Myeloma Working Group response criteria, was used to derive response using RWD. This derived response (dR) algorithm was assessed using data from the phase III BELLINI trial, comparing the number of responders and non-responders assigned by independent review committee (IRC) versus the dR algorithm. To simulate a real-world scenario with missing data, a sensitivity analysis was conducted whereby available laboratory measurements in the dataset were artificially reduced. Associations between dR and overall survival were evaluated at 1) individual level and 2) treatment level in a real-world patient cohort obtained from a nationwide electronic health record-derived de-identified database. The algorithm's assignment of responders was highly concordant with that of the IRC (Cohen's Kappa 0.83) using the BELLINI data. The dR replicated the differences in overall response rate between the intervention and placebo arms reported in the trial (odds ratio 2.1 vs. 2.3 for IRC vs. dR assessment, respectively). Simulation of missing data in the sensitivity analysis (-50% of available laboratory measurements and -75% of urine monoclonal protein measurements) resulted in a minor reduction in the algorithm's accuracy (Cohen's Kappa 0.75). In the RWD cohort, dR was significantly associated with overall survival at all landmark times (hazard ratios 0.80-0.81, p<0.001) at the individual level, while the overall association was R2 = 0.67 (p<0.001) at the treatment level. This exploratory pilot study demonstrates the feasibility of deriving accurate response from RWD. With further confirmation in independent cohorts, the dR has the potential to be used as an endpoint in real-world studies and as a comparator in single-arm clinical trials.

Activity of tafasitamab in combination with rituximab in subtypes of aggressive lymphoma.

Background: Despite recent advances in the treatment of aggressive lymphomas, a significant fraction of patients still succumbs to their disease. Thus, novel therapies are urgently needed. As the anti-CD20 antibody rituximab and the CD19-targeting antibody tafasitamab share distinct modes of actions, we investigated if dual-targeting of aggressive lymphoma B-cells by combining rituximab and tafasitamab might increase cytotoxic effects. Methods: Antibody single and combination efficacy was determined investigating different modes of action including direct cytotoxicity, antibody-dependent cell-mediated cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) in in vitro and in vivo models of aggressive B-cell lymphoma comprising diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL). Results: Three different sensitivity profiles to antibody monotherapy or combination treatment were observed in in vitro models: while 1/11 cell lines was primarily sensitive to tafasitamab and 2/11 to rituximab, the combination resulted in enhanced cell death in 8/11 cell lines in at least one mode of action. Treatment with either antibody or the combination resulted in decreased expression of the oncogenic transcription factor MYC and inhibition of AKT signaling, which mirrored the cell line-specific sensitivities to direct cytotoxicity. At last, the combination resulted in a synergistic survival benefit in a PBMC-humanized Ramos NOD/SCID mouse model. Conclusion: This study demonstrates that the combination of tafasitamab and rituximab improves efficacy compared to single-agent treatments in models of aggressive B-cell lymphoma in vitro and in vivo.

Atezolizumab alone or in combination did not demonstrate a favorable risk-benefit profile in myelodysplastic syndrome.

We present primary results from the phase 1b GO29754 study evaluating the safety and tolerability of atezolizumab, a programmed death-ligand 1 inhibitor, alone and in combination with azacitidine, a hypomethylating agent (HMA), in patients with relapsed/refractory (R/R) or HMA-naïve myelodysplastic syndrome (MDS). Patients with R/R MDS received atezolizumab for 12 months (cohort A) or atezolizumab plus azacitidine for 6 cycles followed by atezolizumab as maintenance for 8 cycles (cohort B). Patients with HMA-naïve MDS received atezolizumab plus azacitidine until loss of clinical benefit (cohort C). Safety, activity, and exploratory end points were investigated. Forty-six patients were enrolled and received treatment (cohort A, n = 11; cohort B, n = 14; cohort C, n = 21). All patients experienced ≥1 adverse event (AE) on study, and all patients discontinued atezolizumab. In cohort A, 7 patients (63.6%) died, and no patients responded. In cohort B, 8 patients (57.1%) discontinued azacitidine, 11 (78.6%) died, and 2 (14.3%) responded. In cohort C, all 21 patients discontinued azacitidine, 13 died (61.9%), and 13 (61.9%) responded. The study was terminated by the sponsor before completion of recruitment because of the unexpected high early death rate in cohort C (6 [46.2%] of 13 deaths were due to AEs and occurred within the first 4 treatment cycles.). The high death rate and poor efficacy observed in this study do not support a favorable risk-benefit profile for atezolizumab as a single agent or in combination with azacitidine in R/R or HMA-naïve MDS. This trial was registered at www.clinicaltrials.gov as #NCT02508870.

Low Tristetraprolin Expression Is Associated with Lethal Prostate Cancer.

BACKGROUND: Inflammation is linked to prostate cancer progression and is mediated by NF-κB. Tristetraprolin is a key node of NF-κB activation and we investigated its biological and prognostic role in lethal prostate cancer. METHODS: In vitro assays assessed the function of tristetraprolin and the association between low mRNA tristetraprolin levels and lethal prostate cancer (metastatic disease or death) was assessed across independent prostatectomy cohorts: (i) nested case-control studies from Health Professionals Follow-up Study and Physicians' Health Study, and (ii) prostatectomy samples from Cleveland Clinic, Mayo Clinic, Johns Hopkins and Memorial Sloan Kettering Cancer Center. Tristetraprolin expression levels in prostatectomy samples from patients with localized disease and biopsies of metastatic castration-resistant prostate cancer (mCRPC) were assessed in a Cornell University cohort. RESULTS: In vitro tristetraprolin expression was inversely associated with NF-κB-controlled genes, proliferation, and enzalutamide sensitivity. Men with localized prostate cancer and lower quartile of tumor tristetraprolin expression had a significant, nearly two-fold higher risk of lethal prostate cancer after adjusting for known clinical and histologic prognostic features (age, RP Gleason score, T-stage). Tristetraprolin expression was also significantly lower in mCRPC compared with localized prostate cancer. CONCLUSIONS: Lower levels of tristetraprolin in human prostate cancer prostatectomy tissue are associated with more aggressive prostate cancer and may serve as an actionable prognostic and predictive biomarker. IMPACT: There is a clear need for improved biomarkers to identify patients with localized prostate cancer in need of treatment intensification, such as adjuvant testosterone suppression, or treatment de-intensification, such as active surveillance. Tristetraprolin levels may serve as informative biomarkers in localized prostate cancer.

Gene Expression Correlates of Site-specific Metastasis Among Men With Lymph Node Positive Prostate Cancer Treated With Radical Prostatectomy: A Case Series.

Predictors of site-specific metastasis after radical prostatectomy (RP) are unknown despite prognostic differences between metastatic sites. We performed RNA expression analysis for 19 genes known to be correlated with aggressive prostate cancer in primary tumors of 63 men pN+ at RP (N = 35 developing metastases after RP vs N = 28 without metastases after RP). Of the men developing metastases, 22 (62.9%) had bone metastases, 8 (22.9%) had nonregional nodal metastases, and 5(14.3%) had visceral metastases. Patients with nodal metastases had higher androgen receptor expression relative to other metastatic sites and nonmetastatic controls (P = .001). This may explain the favorable prognosis of nodal metastases as it may be more androgen dependent.

Validation of a Genomic Risk Classifier to Predict Prostate Cancer-specific Mortality in Men with Adverse Pathologic Features.

BACKGROUND: Risk of prostate cancer-specific mortality (PCSM) is highly variable for men with adverse pathologic features at radical prostatectomy (RP); a majority will die of other causes. Accurately stratifying PCSM risk can improve therapy decisions. OBJECTIVE: Validate the 22 gene Decipher genomic classifier (GC) to predict PCSM in men with adverse pathologic features after RP. DESIGN, SETTING, AND PARTICIPANTS: Men with adverse pathologic features: pT3, pN1, positive margins, or Gleason score >7 who underwent RP in 1987-2010 at Johns Hopkins, Cleveland Clinic, Mayo Clinic, and Durham Veteran's Affairs Hospital. We also analyzed subgroups at high risk (prostate-specific antigen >20 ng/ml, RP Gleason score 8-10, or stage >pT3b), or very high risk of PCSM (biochemical recurrence in<2 yr [BCR2], or men who developed metastasis after RP [MET]). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Logistic regression evaluated the association of GC with PCSM within 10 yr of RP (PCSM10), adjusted for the Cancer of the Prostate Risk Assessment Postsurgical Score (CAPRA-S). GC performance was evaluated with area under the receiver operating characteristic curve (AUC) and decision curves. RESULTS AND LIMITATIONS: Five hundred and sixty-one men (112 with PCSM10), median follow-up 13.0 yr (patients without PCSM10). For high GC score (> 0.6) versus low-intermediate (≤ 0.6), the odds ratio for PCSM10 adjusted for CAPRA-S was 3.91 (95% confidence interval: 2.43-6.29), with AUC=0.77, an increase of 0.04 compared with CAPRA-S. Subgroup odds ratios were 3.96, 3.06, and 1.95 for high risk, BCR2, or MET, respectively (all p<0.05), with AUCs 0.64-0.72. GC stratified cumulative PCSM10 incidence from 2.8% to 30%. Combined use of case-control and cohort data is a potential limitation. CONCLUSIONS: In a large cohort with the longest follow-up to date, Decipher GC demonstrated clinically important prediction of PCSM at 10 yr, independent of CAPRA-S, in men with adverse pathologic features, BCR2, or MET after RP. PATIENT SUMMARY: Decipher genomic classifier may improve treatment decision-making for men with adverse or high risk pathology after radical prostatectomy.

Performance of a Prostate Cancer Genomic Classifier in Predicting Metastasis in Men with Prostate-specific Antigen Persistence Postprostatectomy.

BACKGROUND: Prostate cancer patients who have a detectable prostate-specific antigen (PSA) postprostatectomy may harbor pre-existing metastatic disease. To our knowledge, none of the commercially available genomic biomarkers have been investigated in such men. OBJECTIVE: To evaluate if a 22-gene genomic classifier can independently predict development of metastasis in men with PSA persistence postoperatively. DESIGN, SETTING, AND PARTICIPANTS: A multi-institutional study of 477 men who underwent radical prostatectomy (RP) between 1990 and 2015 from three academic centers. Patients were categorized as detectable PSA (n=150) or undetectable (n=327) based on post-RP PSA nadir ≥0.1 ng/ml. OUTCOME MEASUREMENTS AND STATISITICAL ANALYSIS: Cumulative incidence curves for metastasis were constructed using Fine-Gray competing risks analysis. Penalized Cox univariable and multivariable (MVA) proportional hazards models were performed to evaluate the association of the genomic classifier with metastasis. RESULTS AND LIMITATIONS: The median follow-up for censored patients was 57 mo. The median time from RP to first postoperative PSA was 1.4 mo. Detectable PSA patients were more likely to have higher adverse pathologic features compared with undetectable PSA patients. On MVA, only genomic high-risk (hazard ratio [HR]: 5.95, 95% confidence interval [CI]: 2.02-19.41, p=0.001), detectable PSA (HR: 4.26, 95% CI: 1.16-21.8, p=0.03), and lymph node invasion (HR: 12.2, 95% CI: 2.46-70.7, p=0.003) remained prognostic factors for metastasis. Among detectable PSA patients, the 5-yr metastasis rate was 0.90% for genomic low/intermediate and 18% for genomic high risk (p<0.001). Genomic high risk remained independently prognostic on MVA (HR: 5.61, 95% CI: 1.48-22.7, p=0.01) among detectable PSA patients. C-index for Cancer of the Prostate Risk Assessment Postsurgical score, Gandaglia nomogram, and the genomic classifier plus either Cancer of the Prostate Risk Assessment Postsurgical score or Gandaglia were 0.69, 0.68, and 0.82 or 0.81, respectively. Sample size was a limitation. CONCLUSIONS: Despite patients with a detectable PSA harboring significantly higher rates of aggressive clinicopathologic features, Decipher independently predicts for metastasis. Prospective validation of these findings is warranted and will be collected as part of the ongoing randomized trial NRG GU-002. PATIENT SUMMARY: Decipher independently predicted metastasis for patients with detectable prostate-specific antigen after prostatectomy.

Development and Validation of a Prostate Cancer Genomic Signature that Predicts Early ADT Treatment Response Following Radical Prostatectomy.

Purpose: Currently, no genomic signature exists to distinguish men most likely to progress on adjuvant androgen deprivation therapy (ADT) after radical prostatectomy for high-risk prostate cancer. Here we develop and validate a gene expression signature to predict response to postoperative ADT.Experimental Design: A training set consisting of 284 radical prostatectomy patients was established after 1:1 propensity score matching metastasis between adjuvant-ADT (a-ADT)-treated and no ADT-treated groups. An ADT Response Signature (ADT-RS) was identified from neuroendocrine and AR signaling-related genes. Two independent cohorts were used to form three separate data sets for validation (set I, n = 232; set II, n = 435; set III, n = 612). The primary endpoint of the analysis was postoperative metastasis.Results: Increases in ADT-RS score were associated with a reduction in risk of metastasis only in a-ADT patients. On multivariable analysis, ADT-RS by ADT treatment interaction term remained associated with metastasis in both validation sets (set I: HR = 0.18, Pinteraction = 0.009; set II: HR = 0.25, Pinteraction = 0.019). In a matched validation set III, patients with Low ADT-RS scores had similar 10-year metastasis rates in the a-ADT and no-ADT groups (30.1% vs. 31.0%, P = 0.989). Among High ADT-RS patients, 10-year metastasis rates were significantly lower for a-ADT versus no-ADT patients (9.4% vs. 29.2%, P = 0.021). The marginal ADT-RS by ADT interaction remained significant in the matched dataset (Pinteraction = 0.035).Conclusions: Patients with High ADT-RS benefited from a-ADT. In combination with prognostic risk factors, use of ADT-RS may thus allow for identification of ADT-responsive tumors that may benefit most from early androgen blockade after radical prostatectomy. We discovered a gene signature that when present in primary prostate tumors may be useful to predict patients who may respond to early ADT after surgery. Clin Cancer Res; 24(16); 3908-16. ©2018 AACR.

Development and Validation of a Novel Integrated Clinical-Genomic Risk Group Classification for Localized Prostate Cancer.

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients.

Pan-Cancer Analysis of Genomic Sequencing Among the Elderly.

PURPOSE: We hypothesized that elderly patients might have age-specific genetic abnormalities yet be underrepresented in currently available sequencing repositories, which could limit the effect of sequencing efforts for this population. METHODS AND MATERIALS: Leveraging The Cancer Genome Atlas (TCGA) data portal, 9 tumor types were analyzed. The frequency distribution of cancer by age was determined and compared with Surveillance, Epidemiology, and End Results data. Using the estimated median somatic mutational frequency of each tumor type, the samples needed beyond TCGA to detect a 10% mutational frequency were calculated. Microarray data from a separate prospective cohort were obtained from primary prostatectomy samples to determine whether elderly-specific transcriptomic alterations could be identified. RESULTS: Of the 5236 TCGA samples, 73% were from patients aged <70 years. Comparing the distribution of TCGA samples by age to the Surveillance, Epidemiology, and End Results data, patients <70 years were well represented across most tumor types, but patients aged 80 to 99 years were underrepresented in all cancers (median TCGA underrepresentation of 167%). All cancers (except for colorectal) contained enough samples to detect a 10% mutational frequency in patients aged <60 years. In contrast, no cancer type had enough samples for which a 10% mutational frequency could be detected in patients aged ≥80 years. To further interrogate whether elderly patients with cancer were likely to harbor age-specific molecular abnormalities, we accessed transcriptomic data from a separate, larger database of >2000 prostate cancer samples. That analysis revealed significant differences in the expression of 10 genes in patients aged ≥70 years compared with those <70 years, of which 7 are involved in androgen signaling and/or DNA repair. CONCLUSIONS: Elderly patients have been underrepresented in genomic sequencing studies. Our data suggest the presence of elderly-specific molecular alterations. Further dedicated efforts to understand the biology of cancer among the elderly will be important moving forward.

Genomic Classifier Augments the Role of Pathological Features in Identifying Optimal Candidates for Adjuvant Radiation Therapy in Patients With Prostate Cancer: Development and Internal Validation of a Multivariable Prognostic Model.

Purpose Despite documented oncologic benefit, use of postoperative adjuvant radiotherapy (aRT) in patients with prostate cancer is still limited in the United States. We aimed to develop and internally validate a risk-stratification tool incorporating the Decipher score, along with routinely available clinicopathologic features, to identify patients who would benefit the most from aRT. Patient and Methods Our cohort included 512 patients with prostate cancer treated with radical prostatectomy at one of four US academic centers between 1990 and 2010. All patients had ≥ pT3a disease, positive surgical margins, and/or pathologic lymph node invasion. Multivariable Cox regression analysis tested the relationship between available predictors (including Decipher score) and clinical recurrence (CR), which were then used to develop a novel risk-stratification tool. Our study adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for development of prognostic models. Results Overall, 21.9% of patients received aRT. Median follow-up in censored patients was 8.3 years. The 10-year CR rate was 4.9% vs. 17.4% in patients treated with aRT versus initial observation ( P < .001). Pathologic T3b/T4 stage, Gleason score 8-10, lymph node invasion, and Decipher score > 0.6 were independent predictors of CR (all P < .01). The cumulative number of risk factors was 0, 1, 2, and 3 to 4 in 46.5%, 28.9%, 17.2%, and 7.4% of patients, respectively. aRT was associated with decreased CR rate in patients with two or more risk factors (10-year CR rate 10.1% in aRT v 42.1% in initial observation; P = .012), but not in those with fewer than two risk factors ( P = .18). Conclusion Using the new model to indicate aRT might reduce overtreatment, decrease unnecessary adverse effects, and reduce risk of CR in the subset of patients (approximately 25% of all patients with aggressive pathologic disease in our cohort) who benefit from this therapy.

Comprehensive Determination of Prostate Tumor ETS Gene Status in Clinical Samples Using the CLIA Decipher Assay.

ETS family gene fusions are common in prostate cancer and molecularly define a tumor subset. ERG is the most commonly rearranged, leading to its overexpression, followed by ETV1, ETV4, and ETV5, and these alterations are generally mutually exclusive. We validated the Decipher prostate cancer assay to detect ETS alterations in a Clinical Laboratory Improvement Amendments-accredited laboratory. Benchmarking against ERG immunohistochemistry and ETV1/4/5 RNA in situ hybridization, we examined the accuracy, precision, and reproducibility of gene expression ETS models using formalin-fixed, paraffin-embedded samples. The m-ERG model achieved an area under curve of 95%, with 93% sensitivity and 98% specificity to predict ERG immunohistochemistry status. The m-ETV1, -ETV4, and -ETV5 models achieved areas under curve of 98%, 88%, and 99%, respectively. The models had 100% robustness for ETS status, and scores were highly correlated across sample replicates. Models predicted 41.5% of a prospective radical prostatectomy cohort (n = 4036) to be ERG+, 6.3% ETV1+, 1% ETV4+, and 0.4% ETV5+. Of prostate tumor biopsy samples (n = 509), 41.2% were ERG+, 8.6% ETV1+, 0.4% ETV4+, and none ETV5+. Higher Decipher risk status tumors were more likely to be ETS+ (ERG or ETV1/4/5) in the radical prostatectomy and the biopsy cohorts (P < 0.05). These results support the utility of microarray-based ETS status prediction models for molecular classification of prostate tumors.

Analytic, Preanalytic, and Clinical Validation of p53 IHC for Detection of TP53 Missense Mutation in Prostate Cancer.

Purpose:TP53 missense mutations may help to identify prostate cancer with lethal potential. Here, we preanalytically, analytically, and clinically validated a robust IHC assay to detect subclonal and focal TP53 missense mutations in prostate cancer.Experimental Design: The p53 IHC assay was performed in a CLIA-accredited laboratory on the Ventana Benchmark immunostaining system. p53 protein nuclear accumulation was defined as any p53 nuclear labeling in >10% of tumor cells. Fifty-four formalin-fixed paraffin embedded (FFPE) cell lines from the NCI-60 panel and 103 FFPE prostate cancer tissues (88 primary adenocarcinomas, 15 metastases) with known TP53 mutation status were studied. DU145 and VCaP xenografts were subjected to varying fixation conditions to investigate the effects of preanalytic variables. Clinical validation was performed in two partially overlapping radical prostatectomy cohorts.Results: p53 nuclear accumulation by IHC was 100% sensitive for detection of TP53 missense mutations in the NCI-60 panel (25/25 missense mutations correctly identified). Lack of p53 nuclear accumulation was 86% (25/29) specific for absence of TP53 missense mutation. In FFPE prostate tumors, the positive predictive value of p53 nuclear accumulation for underlying missense mutation was 84% (38/45), whereas the negative predictive value was 97% (56/58). In a cohort of men who experienced biochemical recurrence after RP, the multivariable HR for metastasis among cases with p53 nuclear accumulation compared with those without was 2.55 (95% confidence interval, 1.1-5.91).Conclusions: IHC is widely available method to assess for the presence of deleterious and heterogeneous TP53 missense mutations in clinical prostate cancer specimens. Clin Cancer Res; 23(16); 4693-703. ©2017 AACR.