The HER2-directed antibody-drug conjugate DHES0815A in advanced and/or metastatic breast cancer: preclinical characterization and phase 1 trial results.

Approved antibody-drug conjugates (ADCs) for HER2-positive breast cancer include trastuzumab emtansine and trastuzumab deruxtecan. To develop a differentiated HER2 ADC, we chose an antibody that does not compete with trastuzumab or pertuzumab for binding, conjugated to a reduced potency PBD (pyrrolobenzodiazepine) dimer payload. PBDs are potent cytotoxic agents that alkylate and cross-link DNA. In our study, the PBD dimer is modified to alkylate, but not cross-link DNA. This HER2 ADC, DHES0815A, demonstrates in vivo efficacy in models of HER2-positive and HER2-low cancers and is well-tolerated in cynomolgus monkey safety studies. Mechanisms of action include induction of DNA damage and apoptosis, activity in non-dividing cells, and bystander activity. A dose-escalation study (ClinicalTrials.gov: NCT03451162) in patients with HER2-positive metastatic breast cancer, with the primary objective of evaluating the safety and tolerability of DHES0815A and secondary objectives of characterizing the pharmacokinetics, objective response rate, duration of response, and formation of anti-DHES0815A antibodies, is reported herein. Despite early signs of anti-tumor activity, patients at higher doses develop persistent, non-resolvable dermal, ocular, and pulmonary toxicities, which led to early termination of the phase 1 trial.

Phase I Study of Safety and Pharmacokinetics of RO7297089, an Anti-BCMA/CD16a Bispecific Antibody, in Patients with Relapsed, Refractory Multiple Myeloma.

INTRODUCTION: This phase 1 trial assessed the safety, pharmacokinetics, and preliminary antitumor activity of RO7297089, an anti-BCMA/CD16a bispecific antibody. METHODS: RO7297089 was administered weekly by intravenous infusion to patients with relapsed/refractory multiple myeloma. The starting dose was 60 mg in this dose-escalation study utilizing a modified continual reassessment method with overdose control model. RESULTS: Overall, 27 patients were treated at doses between 60 and 1850 mg. The maximally administered dose was 1850 mg due to excipients in the formulation that did not allow for higher doses to be used. The maximum tolerated dose was not reached. The most common adverse events irrespective of grade and relationship to the drug were anemia, infusion-related reaction, and thrombocytopenia. Most common treatment-related grade ≥ 3 toxicities were ALT/AST increase and reduced lymphocyte count. Pharmacokinetic studies suggested non-linear pharmacokinetics and target-mediated drug disposition, with a trend of approaching linear pharmacokinetics at doses of 1080 mg and higher. Partial response was observed in two patients (7%), minimal response in two patients (7%), and stable disease in 14 patients (52%). CONCLUSIONS: RO7297089 was well tolerated at doses up to 1850 mg, and the efficacy data supported activity of RO7297089 in multiple myeloma. Combination with other agents may further enhance its potential as an innate immune cell engager in multiple myeloma. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04434469; Registered June 16, 2020; https://www. CLINICALTRIALS: gov/ct2/show/NCT04434469 .

Pre- and post-treatment blood-based genomic landscape of patients with ROS1 or NTRK fusion-positive solid tumours treated with entrectinib.

Genomic tumour profiling informs targeted treatment options. Entrectinib is a tyrosine kinase inhibitor with efficacy in NTRK fusion-positive (-fp) solid tumours and ROS1-fp non-small cell lung cancer. FoundationOne® Liquid CDx (F1L CDx), a non-invasive in vitro next-generation sequencing (NGS)-based diagnostic, detects genomic alterations in plasma circulating tumour DNA (ctDNA). We evaluated the clinical validity of F1L CDx as an aid in identifying patients with NTRK-fp or ROS1-fp tumours and assessed the genomic landscape pre- and post-entrectinib treatment. Among evaluable pre-treatment clinical samples (N = 85), positive percentage agreements between F1L CDx and clinical trial assays (CTAs) were 47.4% (NTRK fusions) and 64.5% (ROS1 fusions); positive predictive value was 100% for both. The objective response rate for CTA+ F1L CDx+ patients was 72.2% in both cohorts. The median duration of response significantly differed between F1L CDx+ and F1L CDx- samples in ROS1-fp (5.6 vs. 17.3 months) but not NTRK-fp (9.2 vs. 12.9 months) patients. Fifteen acquired resistance mutations were detected. We conclude that F1L CDx is a clinically valid complement to tissue-based testing to identify patients who may benefit from entrectinib and those with acquired resistance mutations associated with disease progression.

An open-label phase I dose-escalation study of the safety and pharmacokinetics of DMUC4064A in patients with platinum-resistant ovarian cancer.

OBJECTIVES: MUC16 is overexpressed in the majority of human epithelial ovarian cancers (OC). DMUC4064A is a humanized anti-MUC16 monoclonal antibody conjugated to the microtubule-disrupting agent monomethyl auristatin E. This trial assessed the safety, tolerability, pharmacokinetics, and preliminary activity of DMUC4064A in patients with platinum-resistant OC. METHODS: DMUC4064A was administered once every 3 weeks to patients in 1.0-5.6 mg/kg dose escalation cohorts, followed by cohort expansion at the recommended Phase II dose (RP2D). RESULTS: Sixty-five patients were enrolled and received a median of 5 cycles (range 1-20) of DMUC4064A. The maximum tolerated dose was not reached; 5.2 mg/kg was the RP2D based on the overall tolerability profile. The most common adverse events were fatigue, nausea, abdominal pain, constipation, blurred vision, diarrhea, and anemia. Sixteen patients (25%) experienced related grade ≥ 3 toxicities. Twenty-six patients (40%) experienced ocular toxicities. The exposure of acMMAE was dose proportional, with a half-life of ~6 days. Sixteen patients (25%) experienced confirmed objective partial response (PR or CR) starting at ≥3.2 mg/kg dose levels, while 23 (35%) patients had best responses of PR or CR. Overall, the clinical benefit rate was 42% (27 patients with a best response [confirmed and unconfirmed] of CR, or PR or SD lasting ≥6 months). Among the 54 patients with high MUC16 immunohistochemistry scores, the clinical benefit rate was 46% (25 patients). Median progression-free survival was 3.9 months overall. CONCLUSIONS: In this Phase I study, DMUC4064A demonstrated a tolerable safety profile along with encouraging efficacy in the indication of platinum-resistant OC.

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.

Clinical and Genomic Characterization of Low-Prostate-specific Antigen, High-grade Prostate Cancer.

BACKGROUND: The consequences of low prostate-specific antigen (PSA) in high-grade (Gleason 8-10) prostate cancer are unknown. OBJECTIVE: To evaluate the clinical implications and genomic features of low-PSA, high-grade disease. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective study of clinical data for 494 793 patients from the National Cancer Data Base and 136 113 patients from the Surveillance, Epidemiology, and End Results program with cT1-4N0M0 prostate cancer (median follow-up 48.9 and 25.0 mo, respectively), and genomic data for 4960 patients from the Decipher Genomic Resource Information Database. Data were collected for 2004-2017. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Multivariable Fine-Gray and Cox regressions were used to analyze prostate cancer-specific mortality (PCSM) and all-cause mortality, respectively. RESULTS AND LIMITATIONS: For Gleason 8-10 disease, using PSA 4.1-10.0ng/ml (n=38 719) as referent, the distribution of PCSM by PSA was U-shaped, with an adjusted hazard ratio (AHR) of 2.70 for PSA ≤2.5ng/ml (n=3862, p<0.001) versus 1.97, 1.36, and 2.56 for PSA of 2.6-4.0 (n=4199), 10.1-20.0 (n=17 372), and >20.0ng/ml (n=16 114), respectively. By contrast, the distribution of PCSM by PSA was linear for Gleason ≤7 (using PSA 4.1-10.0ng/ml as the referent, n=359 898), with an AHR of 0.41 (p=0.13) for PSA ≤2.5ng/ml (n=37 812) versus 1.38, 2.28, and 4.61 for PSA of 2.6-4.0 (n=54 152), 10.1-20.0 (n=63 319), and >20.0ng/ml (n=35 459), respectively (pinteraction<0.001). Gleason 8-10, PSA ≤2.5ng/ml disease had a significantly higher PCSM than standard high-risk/very high-risk disease with PSA >2.5ng/ml (AHR 2.15, p=0.002; 47-mo PCSM 14% vs 4.9%). Among Gleason 8-10 patients treated with radiotherapy, androgen deprivation therapy was associated with a survival benefit for PSA >2.5ng/ml (AHR 0.87; p<0.001) but not ≤2.5ng/ml (AHR 1.36; p=0.084; pinteraction=0.021). For Gleason 8-10 tumors, PSA ≤2.5ng/ml was associated with higher expression of neuroendocrine/small-cell markers compared to >2.5ng/ml (p=0.046), with no such relationship for Gleason ≤7 disease. CONCLUSIONS: Low-PSA, high-grade prostate cancer has very high risk for PCSM, potentially responds poorly to androgen deprivation therapy, and is associated with neuroendocrine genomic features. PATIENT SUMMARY: In this study, we found that low-prostate-specific antigen, high-grade prostate cancer has a very high risk for prostate cancer death, may not respond well to androgen deprivation therapy, and is associated with neuroendocrine genomic features. These findings suggest that current nomograms and treatment paradigms may need modification.

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.

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.

Impact of Molecular Subtypes in Muscle-invasive Bladder Cancer on Predicting Response and Survival after Neoadjuvant Chemotherapy.

BACKGROUND: An early report on the molecular subtyping of muscle-invasive bladder cancer (MIBC) by gene expression suggested that response to neoadjuvant chemotherapy (NAC) varies by subtype. OBJECTIVE: To investigate the ability of molecular subtypes to predict pathological downstaging and survival after NAC. DESIGN, SETTING, AND PARTICIPANTS: Whole transcriptome profiling was performed on pre-NAC transurethral resection specimens from 343 patients with MIBC. Samples were classified according to four published molecular subtyping methods. We developed a single-sample genomic subtyping classifier (GSC) to predict consensus subtypes (claudin-low, basal, luminal-infiltrated and luminal) with highest clinical impact in the context of NAC. Overall survival (OS) according to subtype was analyzed and compared with OS in 476 non-NAC cases (published datasets). INTERVENTION: Gene expression analysis was used to assign subtypes. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Receiver-operating characteristics were used to determine the accuracy of GSC. The effect of GSC on survival was estimated by Cox proportional hazard regression models. RESULTS AND LIMITATIONS: The models generated subtype calls in expected ratios with high concordance across subtyping methods. GSC was able to predict four consensus molecular subtypes with high accuracy (73%), and clinical significance of the predicted consensus subtypes could be validated in independent NAC and non-NAC datasets. Luminal tumors had the best OS with and without NAC. Claudin-low tumors were associated with poor OS irrespective of treatment regimen. Basal tumors showed the most improvement in OS with NAC compared with surgery alone. The main limitations of our study are its retrospective design and comparison across datasets. CONCLUSIONS: Molecular subtyping may have an impact on patient benefit to NAC. If validated in additional studies, our results suggest that patients with basal tumors should be prioritized for NAC. We discovered the first single-sample classifier to subtype MIBC, which may be suitable for integration into routine clinical practice. PATIENT SUMMARY: Different molecular subtypes can be identified in muscle-invasive bladder cancer. Although cisplatin-based neoadjuvant chemotherapy improves patient outcomes, we identified that the benefit is highest in patients with basal tumors. Our newly discovered classifier can identify these molecular subtypes in a single patient and could be integrated into routine clinical practice after further validation.

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.

Utilization of a Genomic Classifier for Prediction of Metastasis Following Salvage Radiation Therapy after Radical Prostatectomy.

BACKGROUND: Despite salvage radiation therapy (SRT) for recurrent prostate cancer (PCa) after radical prostatectomy (RP), some patients still progress to metastases. Identifying these men would allow them to undergo systemic therapy including testing novel therapies to reduce metastases risk. OBJECTIVE: To test whether the genomic classifier (GC) predicts development of metastatic disease. DESIGN, SETTING, AND PARTICIPANTS: Retrospective multi-center and multi-ethnic cohort study from two academic centers and one Veterans Affairs Medical Center in the United States involving 170 men receiving SRT for recurrent PCa post-RP. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Time from SRT to development of metastatic disease tested using Cox regression, survival c-index, and decision curve analysis. Performance of GC was compared to the Cancer of the Prostate Risk Assessment Score and Briganti risk models based on these metrics. RESULTS AND LIMITATIONS: With a median 5.7 yr follow-up after SRT, 20 patients (12%) developed metastases. On multivariable analysis, for each 0.1 unit increase in GC (scaled from 0 to 1), the hazard ratio for metastasis was 1.58 (95% confidence interval 1.16-2.17; p=0.002). Adjusting for androgen deprivation therapy did not materially change the results. The c-index for GC was 0.85 (95% confidence interval 0.73-0.88) versus 0.63-0.65 for published clinico-pathologic risk models. The 5-yr cumulative incidence of metastasis post-SRT in patients with low, intermediate, and high GC scores was 2.7%, 8.4%, and 33.1%, respectively (p<0.001). CONCLUSIONS: While validation in larger, prospectively collected cohorts is required, these data suggest GC is a strong predictor of metastases among men receiving SRT for recurrent PCa post-RP, accurately identifying men who are excellent candidates for systemic therapy due to their very high-risk of metastases. PATIENT SUMMARY: Genomic classifier and two clinico-pathologic risk models were evaluated on their ability to predict metastases among men receiving salvage radiation therapy for recurrent prostate cancer. Genomic classifier was able to identify candidates for further therapies due to their very high-risk of metastases.

Decipher Genomic Classifier Measured on Prostate Biopsy Predicts Metastasis Risk.

OBJECTIVES: To evaluate the ability of the Decipher genomic classifier in predicting metastasis from analysis of prostate needle biopsy diagnostic tumor tissue specimens. MATERIALS AND METHODS: Fifty-seven patients with available biopsy specimens were identified from a cohort of 169 men treated with radical prostatectomy in a previously reported Decipher validation study at Cleveland Clinic. A Cox multivariable proportional hazards model and survival C-index were used to evaluate the performance of Decipher. RESULTS: With a median follow up of 8 years, 8 patients metastasized and 3 died of prostate cancer. The Decipher plus National Comprehensive Cancer Network (NCCN) model had an improved C-index of 0.88 (95% confidence interval [CI] 0.77-0.96) compared to NCCN alone (C-index 0.75, 95% CI 0.64-0.87). On multivariable analysis, Decipher was the only significant predictor of metastasis when adjusting for age, preoperative prostate-specific antigen and biopsy Gleason score (Decipher hazard ratio per 10% increase 1.72, 95% CI 1.07-2.81, P = .02). CONCLUSION: Biopsy Decipher predicted the risk of metastasis at 10 years post radical prostatectomy. While further validation is required on larger cohorts, preoperative knowledge of Decipher risk derived from biopsy could indicate the need for multimodality therapy and help set patient expectations of therapeutic burden.

Racial Variations in Prostate Cancer Molecular Subtypes and Androgen Receptor Signaling Reflect Anatomic Tumor Location.

UNLABELLED: Prostate cancer (PCa) subtypes based on ETS gene expression have been described. Recent studies suggest there are racial differences in tumor location, with PCa located anteriorly more often among African-American (AA) compared to Caucasian-American (CA) men. In this retrospective analysis of a multi-institutional cohort treated by radical prostatectomy (179 CA, 121 AA), we evaluated associations among molecular subtype, race, anatomic tumor location, and androgen receptor (AR) signaling. Subtype (m-ERG(+), m-ETS(+), m-SPINK1(+), or triple-negative) was determined using distribution-based outlier analysis. AR signaling was investigated using gene expression profiling of canonical AR targets. m-ERG(+) was more common in CA than AA men (47% vs 22%, p<0.001). AA men were more likely to be m-SPINK1(+) (13% vs 7%; p=0.069) and triple-negative (50% vs 37%; p=0.043). Racial differences in molecular subtypes did not persist when tumors were analyzed by location, suggesting a biologically important relationship between tumor location and subtype. Accordingly, anterior tumor location was associated with higher Decipher scores and lower global AR signaling. PATIENT SUMMARY: This study demonstrates associations among patient race, prostate cancer molecular subtypes, and tumor location. Location-specific differences in androgen regulation may further underlie these relationships.

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.

Tissue-based Genomics Augments Post-prostatectomy Risk Stratification in a Natural History Cohort of Intermediate- and High-Risk Men.

BACKGROUND: Radical prostatectomy (RP) is a primary treatment option for men with intermediate- and high-risk prostate cancer. Although many are effectively cured with local therapy alone, these men are by definition at higher risk of adverse pathologic features and clinical disease recurrence. It has been shown that the Decipher test predicts metastatic progression in cohorts that received adjuvant and salvage therapy following RP. OBJECTIVE: To evaluate the Decipher genomic classifier in a natural history cohort of men at risk who received no additional treatment until the time of metastatic progression. DESIGN, SETTING, AND PARTICIPANTS: Retrospective case-cohort design for 356 men who underwent RP between 1992 and 2010 at intermediate or high risk and received no additional treatment until the time of metastasis. Participants met the following criteria: (1) Cancer of the Prostate Risk Assessment postsurgical (CAPRA-S) score ≥3; (2) pathologic Gleason score ≥7; and (3) post-RP prostate-specific antigen nadir <0.2 ng/ml. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary endpoint was defined as regional or distant metastases. Time-dependent receiver operating characteristic (ROC) curves, extension of decision curve analysis to survival data, and univariable and multivariable Cox proportional-hazards models were used to measure the discrimination, net benefit, and prognostic potential of genomic and pathologic risk factors. Cumulative incidence curves were constructed using Fine-Gray competing-risks analysis with appropriate weighting of the controls to account for the case-cohort study design. RESULTS AND LIMITATIONS: Ninety six patients had unavailable tumor blocks or failed microarray quality control. Decipher scores were then obtained for 260 patients, of whom 99 experienced metastasis. Decipher correlated with increased cumulative incidence of biochemical recurrence, metastasis, and prostate cancer-specific mortality (p<0.01). The cumulative incidence of metastasis was 12% and 47% for patients with low and high Decipher scores, respectively, at 10 yr after RP. Decipher was independently prognostic of metastasis in multivariable analysis (hazard ratio 1.26 per 10% increase; p<0.01). Decipher had a c-index of 0.76 and increased the c-index of Eggener and CAPRA-S risk models from 0.76 and 0.77 to 0.86 and 0.87, respectively, at 10 yr after RP. Although the cohort was large, the single-center retrospective design is an important limitation. CONCLUSIONS: In a patient population that received no adjuvant or salvage therapy after prostatectomy until metastatic progression, higher Decipher scores correlated with clinical events, and inclusion of Decipher scores improved the prognostic performance of validated clinicopathologic risk models. These results confirm the utility already reported for Decipher. PATIENT SUMMARY: The Decipher test improves identification of patients most at risk of metastatic progression and death from prostate cancer after radical prostatectomy.

Novel Biomarker Signature That May Predict Aggressive Disease in African American Men With Prostate Cancer.

PURPOSE: We studied the ethnicity-specific expression of prostate cancer (PC) -associated biomarkers to evaluate whether genetic/biologic factors affect ethnic disparities in PC pathogenesis and disease progression. PATIENTS AND METHODS: A total of 154 African American (AA) and 243 European American (EA) patients from four medical centers were matched according to the Cancer of the Prostate Risk Assessment postsurgical score within each institution. The distribution of mRNA expression levels of 20 validated biomarkers reported to be associated with PC initiation and progression was compared with ethnicity using false discovery rate, adjusted Wilcoxon-Mann-Whitney, and logistic regression models. A conditional logistic regression model was used to evaluate the interaction between ethnicity and biomarkers for predicting clinicopathologic outcomes. RESULTS: Of the 20 biomarkers examined, six showed statistically significant differential expression in AA compared with EA men in one or more statistical models. These include ERG (P < .001), AMACR (P < .001), SPINK1 (P = .001), NKX3-1 (P = .03), GOLM1 (P = .03), and androgen receptor (P = .04). Dysregulation of AMACR (P = .036), ERG (P = .036), FOXP1 (P = .041), and GSTP1 (P = .049) as well as loss-of-function mutations for tumor suppressors NKX3-1 (P = .025) and RB1 (P = .037) predicted risk of pathologic T3 disease in an ethnicity-dependent manner. Dysregulation of GOLM1 (P = .037), SRD5A2 (P = .023), and MKi67 (P = .023) predicted clinical outcomes, including 3-year biochemical recurrence and metastasis at 5 years. A greater proportion of AA men than EA men had triple-negative (ERG-negative/ETS-negative/SPINK1-negative) disease (51% v 35%; P = .002). CONCLUSION: We have identified a subset of PC biomarkers that predict the risk of clinicopathologic outcomes in an ethnicity-dependent manner. These biomarkers may explain in part the biologic contribution to ethnic disparity in PC outcomes between EA and AA men.

Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy.

PURPOSE: The optimal timing of postoperative radiotherapy (RT) after radical prostatectomy (RP) is unclear. We hypothesized that a genomic classifier (GC) would provide prognostic and predictive insight into the development of clinical metastases in men receiving post-RP RT and inform decision making. PATIENTS AND METHODS: GC scores were calculated from 188 patients with pT3 or margin-positive prostate cancer, who received post-RP RT at Thomas Jefferson University and Mayo Clinic between 1990 and 2009. The primary end point was clinical metastasis. Prognostic accuracy of the models was tested using the concordance index for censored data and decision curve analysis. Cox regression analysis tested the relationship between GC and metastasis. RESULTS: The cumulative incidence of metastasis at 5 years after RT was 0%, 9%, and 29% for low, average, and high GC scores, respectively (P = .002). In multivariable analysis, GC and pre-RP prostate-specific antigen were independent predictors of metastasis (both P < .01). Within the low GC score (< 0.4), there were no differences in the cumulative incidence of metastasis comparing patients who received adjuvant or salvage RT (P = .79). However, for patients with higher GC scores (≥ 0.4), cumulative incidence of metastasis at 5 years was 6% for patients treated with adjuvant RT compared with 23% for patients treated with salvage RT (P < .01). CONCLUSION: In patients treated with post-RP RT, GC is prognostic for the development of clinical metastasis beyond routine clinical and pathologic features. Although preliminary, patients with low GC scores are best treated with salvage RT, whereas those with high GC scores benefit from adjuvant therapy. These findings provide the first rational selection of timing for post-RP RT.

A genomic classifier improves prediction of metastatic disease within 5 years after surgery in node-negative high-risk prostate cancer patients managed by radical prostatectomy without adjuvant therapy.

BACKGROUND: Surgery is a standard first-line therapy for men with intermediate- or high-risk prostate cancer. Clinical factors such as tumor grade, stage, and prostate-specific antigen (PSA) are currently used to identify those who are at risk of recurrence and who may benefit from adjuvant therapy, but novel biomarkers that improve risk stratification and that distinguish local from systemic recurrence are needed. OBJECTIVE: To determine whether adding the Decipher genomic classifier, a validated metastasis risk-prediction model, to standard risk-stratification tools (CAPRA-S and Stephenson nomogram) improves accuracy in predicting metastatic disease within 5 yr after surgery (rapid metastasis [RM]) in an independent cohort of men with adverse pathologic features after radical prostatectomy (RP). DESIGN, SETTING, AND PARTICIPANTS: The study population consisted of 169 patients selected from 2641 men who underwent RP at the Cleveland Clinic between 1987 and 2008 who met the following criteria: (1) preoperative PSA>20 ng/ml, stage pT3 or margin positive, or Gleason score≥8; (2) pathologic node negative; (3) undetectable post-RP PSA; (4) no neoadjuvant or adjuvant therapy; and (5) minimum of 5-yr follow-up for controls. The final study cohort consisted of 15 RM patients and 154 patients as non-RM controls. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The performance of Decipher was evaluated individually and in combination with clinical risk factors using concordance index (c-index), decision curve analysis, and logistic regression for prediction of RM. RESULTS AND LIMITATIONS: RM patients developed metastasis at a median of 2.3 yr (interquartile range: 1.7-3.3). In multivariable analysis, Decipher was a significant predictor of RM (odds ratio: 1.48; p=0.018) after adjusting for clinical risk factors. Decipher had the highest c-index, 0.77, compared with the Stephenson model (c-index: 0.75) and CAPRA-S (c-index: 0.72) as well as with a panel of previously reported prostate cancer biomarkers unrelated to Decipher. Integration of Decipher into the Stephenson nomogram increased the c-index from 0.75 (95% confidence interval [CI], 0.65-0.85) to 0.79 (95% CI, 0.68-0.89). CONCLUSIONS: Decipher was independently validated as a genomic metastasis signature for predicting metastatic disease within 5 yr after surgery in a cohort of high-risk men treated with RP and managed conservatively without any adjuvant therapy. Integration of Decipher into clinical nomograms increased prediction of RM. Decipher may allow identification of men most at risk for metastatic progression who should be considered for multimodal therapy or inclusion in clinical trials. PATIENT SUMMARY: Use of Decipher in addition to standard clinical information more accurately identified men who developed metastatic disease within 5 yr after surgery. The results suggest that Decipher allows improved identification of the men who should consider secondary therapy from among the majority that may be managed conservatively after surgery.

Phthalate and bisphenol A exposure among pregnant women in Canada--results from the MIREC study.

Bisphenol A (BPA) and phthalates are endocrine disruptors possibly linked to adverse reproductive and neurodevelopmental outcomes. These chemicals have commonly been measured in urine in population surveys; however, such data are limited for large populations of pregnant women, especially for the critical first trimester of pregnancy. The aim of the study was to measure BPA and phthalate metabolites in first trimester urine samples collected in a large national-scale pregnancy cohort study and to identify major predictors of exposure. Approximately 2000 women were recruited in the first trimester of pregnancy from ten sites across Canada. A questionnaire was administered to obtain demographic and socio-economic data on participants and a spot urine sample was collected and analyzed for total BPA (GC-MS/MS) and 11 phthalate metabolites (LC-MS/MS). The geometric mean (GM) maternal urinary concentration of total BPA, uncorrected for specific gravity, was 0.80 (95% CI 0.76-0.85) µg/L. Almost 88% of the women had detectable urinary concentrations of BPA. An analysis of urinary concentrations of BPA by maternal characteristics with specific gravity as a covariate in the linear model showed that the geometric mean concentrations: (1) decreased with increasing maternal age, (2) were higher in current smokers or women who quit during pregnancy compared to never smokers, and (3) tended to be higher in women who provided a fasting urine sample and who were born in Canada, and had lower incomes and education. Several of the phthalate metabolites analyzed were not prevalent in this population (MCHP, MMP, MiNP, MOP), with percentages detectable at less than 15%. The phthalate metabolites with the highest measured concentrations were MEP (GM: 32.02 µg/L) and MnBP (GM: 11.59 µg/L). MBzP urinary concentrations decreased with maternal age but did not differ by time of urine collection; whereas the DEHP metabolites tended to be higher in older women and when the urine was collected later in the day. This study provides the first biomonitoring results for the largest population of pregnant women sampled in the first trimester of pregnancy. The results indicate that exposure among this population of pregnant women to these chemicals is comparable to or even lower than that observed in a Canadian national population-based survey.