PurposeNeoadjuvant chemotherapy (NAC) prior to radical cystectomy (RC) in patients with non-metastatic 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 MethodsFour 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 (KM) curves. Cox proportional hazards (PH) models were used to evaluate the primary and secondary study endpoints of overall survival (OS) and cancer-specific survival (CSS), respectively. ResultsA total of 601 patients with MIBC were included, where 247 had been treated with NAC and RC and 354 underwent RC without NAC. With NAC, the overall net benefit to OS and CSS at three years was 7% and 5%, respectively. After controlling for clinicopathologic variables, non-luminal 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, respectively. ConclusionsIn patients with MIBC, a commercially available molecular subtyping assay revealed non-luminal 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.

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.