Patients with Muscle-Invasive Bladder Cancer with Nonluminal Subtype Derive Greatest Benefit from Platinum Based Neoadjuvant Chemotherapy.

PURPOSE: Neoadjuvant chemotherapy (NAC) prior to radical cystectomy (RC) in patients with nonmetastatic muscle-invasive bladder cancer (MIBC) confers an absolute survival benefit of 5%-10%. There is evidence that molecular differences between tumors may impact response to therapy, highlighting a need for clinically validated biomarkers to predict response to NAC. MATERIALS AND METHODS: Four bladder cancer cohorts were included. Inverse probability weighting was used to make baseline characteristics (age, sex and clinical tumor stage) between NAC-treated and untreated groups more comparable. Molecular subtypes were determined using a commercial genomic subtyping classifier. Survival rates were estimated using weighted Kaplan-Meier curves. Cox proportional hazards models were used to evaluate the primary and secondary study end points of overall survival (OS) and cancer-specific survival, respectively. RESULTS: A total of 601 patients with MIBC were included, of whom 247 had been treated with NAC and RC, and 354 underwent RC without NAC. With NAC, the overall net benefit to OS and cancer-specific survival at 3 years was 7% and 5%, respectively. After controlling for clinicopathological variables, nonluminal tumors had greatest benefit from NAC, with 10% greater OS at 3 years (71% vs 61%), while luminal tumors had minimal benefit (63% vs 65%) for NAC vs non-NAC. CONCLUSIONS: In patients with MIBC, a commercially available molecular subtyping assay revealed nonluminal tumors received the greatest benefit from NAC, while patients with luminal tumors experienced a minimal survival benefit. A genomic classifier may help identify patients with MIBC who would benefit most from NAC.

Transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate-risk clear cell kidney cancer.

BACKGROUND: Risk stratification of kidney cancer patients after nephrectomy may tailor surveillance intensity and selection for adjuvant therapy. Transcriptomic approaches are effective in predicting recurrence, but whether they add value to clinicopathologic models remains unclear. METHODS: Data from patients with clear cell renal cell carcinoma (ccRCC) was downloaded from The Cancer Genome Atlas. Clinicopathologic variables were used to calculate SSIGN (stage, size, grade, and necrosis) scores. The 16 gene recurrence score (RS) signature was generated using RNA-seq data. Transcriptomic risk groups were calculated using the original thresholds. SSIGN groups were divided into low, intermediate, and high risk. Disease-free status was the primary endpoint assessed. RESULTS: SSIGN and RS were calculated for 428 patients with non-metastatic ccRCC. SSIGN low-, intermediate-, and high-risk groups demonstrated 2.7%, 15.2%, and 27.5%, 3-year recurrence risk, respectively. On multivariable analysis, the RS was associated with disease-free status (sub-distribution hazard ratio (sHR) 1.43 per 25 RS [95% CI (1.00-1.43)], p = 0.05). By risk groups, RS further risk stratified the SSIGN intermediate-risk group (sHR 2.22 [95% CI 1.10-4.50], p = 0.03). SSIGN intermediate-risk patients with low and high RS had a 3-year recurrence rate of 8.0% and 25.2%, respectively. Within this risk group, the area under the curve (AUC) at 3 years was 0.69 for SSIGN, 0.74 for RS, and 0.78 for their combination. CONCLUSIONS: Transcriptomic recurrence scores improve risk prediction even when controlling for clinicopathologic factors. Utility may be best suited for intermediate-risk patients who have heterogeneous outcomes and further refinement for clinical utility is warranted.

Genomic Classifier Performance in Intermediate-Risk Prostate Cancer: Results From NRG Oncology/RTOG 0126 Randomized Phase 3 Trial.

PURPOSE: Intermediate-risk prostate cancer is a heterogeneous disease state with diverse treatment options. The 22-gene Decipher genomic classifier (GC) retrospectively has shown to improve risk stratification in these patients. We assessed the performance of the GC in men with intermediate-risk disease enrolled in NRG Oncology/RTOG 01-26 with updated follow-up. METHODS AND MATERIALS: After National Cancer Institute approval, biopsy slides were collected from NRG Oncology/RTOG 01-26, a randomized phase 3 trial of men with intermediate-risk prostate cancer randomized to 70.2 Gy versus 79.2 Gy of radiation therapy without androgen deprivation therapy. RNA was extracted from the highest-grade tumor foci to generate the locked 22-gene GC model. The primary endpoint for this ancillary project was disease progression (composite of biochemical failure, local failure, distant metastasis, prostate cancer-specific mortality, and use of salvage therapy). Individual endpoints were also assessed. Fine-Gray or cause-specific Cox multivariable models were constructed adjusting for randomization arm and trial stratification factors. RESULTS: Two-hundred fifteen patient samples passed quality control for analysis. The median follow-up was 12.8 years (range, 2.4-17.7). On multivariable analysis, the 22-gene GC (per 0.1 unit) was independently prognostic for disease progression (subdistribution hazard ratio [sHR], 1.12; 95% confidence interval [CI], 1.00-1.26; P = .04), biochemical failure (sHR, 1.22; 95% CI, 1.10-1.37; P < .001), distant metastasis (sHR, 1.28; 95% CI, 1.06-1.55; P = .01), and prostate cancer-specific mortality (sHR, 1.45; 95% CI, 1.20-1.76; P < .001). Ten-year distant metastasis in GC low-risk patients was 4% compared with 16% for GC high-risk patients. In patients with lower GC scores, the 10-year difference in metastasis-free survival rate between arms was -7%, compared with 21% for higher GC patients (P-interaction = .04). CONCLUSIONS: This study represents the first validation of a biopsy-based gene expression classifier, assessing both its prognostic and predictive value, using data from a randomized phase 3 trial of intermediate-risk prostate cancer. Decipher improves risk stratification and can aid in treatment decision-making in men with intermediate-risk disease.

Impact of Decipher on use of post-operative radiotherapy: Individual patient analysis of two prospective registries.

Objective: To assess the association between Genomic Classifier (GC)-risk group and post-radical prostatectomy treatment in clinical practice. Methods: Two prospective observational cohorts of men with prostate cancer (PCa) who underwent RP in two referral centers and had GC testing post-prostatectomy between 2013 and 2018 were included. The primary endpoint of the study was to assess the association between GC-risk group and time to secondary therapy. Univariable (UVA) and multivariable (MVA) Cox proportional hazards models were constructed to assess the association between GC-risk group and time to receipt of secondary therapy after RP, where secondary therapy is defined as receiving either RT or ADT after RP. Results: A total of 398 patients are included in the analysis. Patients with high-GC risk were more likely to receive any secondary therapy (OR: 6.84) compared to patients with low/intermediate-GC risk. The proportion of high-GC risk patients receiving RT at 2 years post-RP was 31.5%, compared to only 6.3% among the low/intermediate-GC risk patients. Conclusion: This study demonstrates that physicians in routine practice used GC to identify high risk patients who might benefit the most from secondary treatment. As such, GC score was independent predictor of receipt of secondary treatment.