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.

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.

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.

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.

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 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.

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.

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.

Low PCA3 expression is a marker of poor differentiation in localized prostate tumors: exploratory analysis from 12,076 patients.

BACKGROUND: Prostate cancer antigen 3 (PCA3) is a prostate cancer diagnostic biomarker that has been clinically validated. The limitations of the diagnostic role of PCA3 in initial biopsy and the prognostic role are not well established. Here, we elucidate the limitations of tissue PCA3 to predict high grade tumors in initial biopsy. RESULTS: PCA3 has a bimodal distribution in both biopsy and radical prostatectomy (RP) tissues, where low PCA3 expression was significantly associated with high grade disease (p<0.001). PCA3 had a poor performance of predicting high grade disease in initial biopsy (GS≥8) with 55% sensitivity and high false negative rates; 42% of high Gleason (≥8) samples had low PCA3. In RP, low PCA3 is associated with adverse pathological features, clinical recurrence outcome and greater probability of metastatic progression (p<0.001). MATERIALS AND METHODS: A total of 1,694 expression profiles from biopsy and 10,382 from RP patients with high risk tumors were obtained from the Decipher Genomic Resource Information Database (GRIDTM)prostate cancer database. The primary clinical endpoint was distant metastasis-free survival for RP and high Gleason grade for biopsy. Logistic regression analyses and Cox proportional hazards models were used to evaluate the association of PCA3 with clinical variables and risk of metastasis. CONCLUSIONS: There is high prevalence of high grade tumors with low PCA3 expression in the biopsy setting. Therefore, urologists should be warned that using PCA3 as stand-alone test may lead to high rate of under-diagnosis of high grade disease in initial biopsy setting.

Ability of a Genomic Classifier to Predict Metastasis and Prostate Cancer-specific Mortality after Radiation or Surgery based on Needle Biopsy Specimens.

BACKGROUND: Decipher is a validated genomic classifier developed to determine the biological potential for metastasis after radical prostatectomy (RP). OBJECTIVE: To evaluate the ability of biopsy Decipher to predict metastasis and Prostate cancer-specific mortality (PCSM) in primarily intermediate- to high-risk patients treated with RP or radiation therapy (RT). DESIGN, SETTING, AND PARTICIPANTS: Two hundred and thirty-five patients treated with either RP (n=105) or RT±androgen deprivation therapy (n=130) with available genomic expression profiles generated from diagnostic biopsy specimens from seven tertiary referral centers. The highest-grade core was sampled and Decipher was calculated based on a locked random forest model. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Metastasis and PCSM were the primary and secondary outcomes of the study, respectively. Cox analysis and c-index were used to evaluate the performance of Decipher. RESULTS AND LIMITATIONS: With a median follow-up of 6 yr among censored patients, 34 patients developed metastases and 11 died of prostate cancer. On multivariable analysis, biopsy Decipher remained a significant predictor of metastasis (hazard ratio: 1.37 per 10% increase in score, 95% confidence interval [CI]: 1.06-1.78, p=0.018) after adjusting for clinical variables. For predicting metastasis 5-yr post-biopsy, Cancer of the Prostate Risk Assessment score had a c-index of 0.60 (95% CI: 0.50-0.69), while Cancer of the Prostate Risk Assessment plus biopsy Decipher had a c-index of 0.71 (95% CI: 0.60-0.82). National Comprehensive Cancer Network risk group had a c-index of 0.66 (95% CI: 0.53-0.77), while National Comprehensive Cancer Network plus biopsy Decipher had a c-index of 0.74 (95% CI: 0.66-0.82). Biopsy Decipher was a significant predictor of PCSM (hazard ratio: 1.57 per 10% increase in score, 95% CI: 1.03-2.48, p=0.037), with a 5-yr PCSM rate of 0%, 0%, and 9.4% for Decipher low, intermediate, and high, respectively. CONCLUSIONS: Biopsy Decipher predicted metastasis and PCSM from diagnostic biopsy specimens of primarily intermediate- and high-risk men treated with first-line RT or RP. PATIENT SUMMARY: Biopsy Decipher predicted metastasis and prostate cancer-specific mortality risk from diagnostic biopsy specimens.

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.

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.

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.

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.

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.

Purpose To perform the first meta-analysis of the performance of the genomic classifier test, Decipher, in men with prostate cancer postprostatectomy. Methods MEDLINE, EMBASE, and the 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 postprostatectomy, 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.

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.

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.

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.

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.