Quantification of collagen content and stromal cellularity within reactive stroma is predictive of prostate cancer biochemical recurrence and specific death.

Semiquantitative reactive stromal grading has been shown to be a predictor of biochemical recurrence and prostate cancer (PCa) specific death. It has been extensively validated. In this study we tested novel technologies to introduce quantitative measures of host response, in particular collagen content and stromal cellularity. We use 3 large retrospective cohorts, the Baylor College of Medicine cohort, the Brady cohort and the Pound cohort. Slides were stained and digitized using image deconvolution and analyzed using image segmentation and image analyses. PicroSirius red stain histochemical stains were used for collagen quantification. Area of cancer and stroma were measured independently, without regard to quality of stroma. Cellularity, in each compartment, was measured using image deconvolution, image segmentation and image analysis. Two biomarkers were tested in 3 independent cohorts with two endpoints, biochemical recurrence and prostate cancer specific death. Stromal cellularity (qCollCell) and stromal collagen area (qCollArea) are independently predictive biochemical recurrence in the Hopkins Brady cohort, particularly in Gleason 6-7 patients. Multivariate analysis demonstrated that increased stroma cellularity (qCollCell) was a significant predictor of PCa specific death, when compared to an established model of PCa, in the Baylor cohort. Stromal collagen (qCollArea) independently predicts PCa-specific death in the Hopkins Pound cohort. The introduction of a computerized quantitative test of the host response increases the probability that this test will be reproducible in other cohorts. The ability to improve prediction of prostate cancer specific death might lie in the study of the host and its response.

Development and validation of a quantitative reactive stroma biomarker (qRS) for prostate cancer prognosis.

To develop and validate a new tissue-based biomarker that improves prediction of outcomes in localized prostate cancer by quantifying the host response to tumor. We use digital image analysis and machine learning to develop a biomarker of the prostate stroma called quantitative reactive stroma (qRS). qRS is a measure of percentage tumor area with a distinct, reactive stromal architecture. Kaplan Meier analysis was used to determine survival in a large retrospective cohort of radical prostatectomy samples. qRS was validated in two additional, distinct cohorts that include international cases and tissue from both radical prostatectomy and biopsy specimens. In the developmental cohort (Baylor College of Medicine, n = 482), patients whose tumor had qRS > 34% had increased risk of prostate cancer-specific death (HR 2.94; p = 0.039). This result was replicated in two validation cohorts, where patients with qRS > 34% had increased risk of prostate cancer-specific death (MEDVAMC; n = 332; HR 2.64; p = 0.02) and also biochemical recurrence (Canary; n = 988; HR 1.51; p = 0.001). By multivariate analysis, these associations were shown to hold independent predictive value when compared to currently used clinicopathologic factors including Gleason score and PSA. qRS is a new, validated biomarker that predicts prostate cancer death and biochemical recurrence across three distinct cohorts. It measures host-response rather than tumor-based characteristics, and provides information not represented by standard prognostic measurements.

Programmed Death-1 or Programmed Death Ligand-1 Blockade in Patients with Platinum-resistant Metastatic Urothelial Cancer: A Systematic Review and Meta-analysis.

CONTEXT: Several anti-programmed death-1 (anti-PD-1) and anti-programmed death ligand-1 (anti-PD-L1) antibodies have been approved by regulatory authorities for treatment of platinum-resistant metastatic urothelial cancer (mUC). The impact of these therapies on survival, and comparability of PD-1 versus PD-L1 blockade are unknown. OBJECTIVE: To determine the restricted mean survival time (RMST) of patients with platinum-resistant mUC treated with PD-1/PD-L1 inhibitors and to compare RMSTs in patients treated with PD-1 versus PD-L1 inhibitors. EVIDENCE ACQUISITION: We searched for phase 1, 2, and 3 clinical trials that assessed PD-1 or PD-L1 inhibition for patients with platinum-resistant mUC. Literature review and study selection, data abstraction, and risk of bias assessment were performed by two reviewers. Survival data were reconstructed using an algorithm that derives individual time-to-event data from published Kaplan-Meier curves. The RMST with 95% confidence interval (CIs) was calculated. EVIDENCE SYNTHESIS: From 836 references, six clinical trials were included. Survival data were reconstructed for 1315 and 736 patients treated with PD-1/PD-L1 inhibitors and chemotherapy, respectively. The RMSTs with PD-1/PD-L1 blockade up to 12 and 18mo of follow-up were 7.8mo (95% CI 7.6, 8.1) and 10mo (95% CI 9.7, 10.5), respectively. A network meta-analysis of two randomized trials revealed no significant difference in the RMST up to 18mo with PD-1 versus PD-L1 blockade (1.0mo; 95% CI -0.5, 2.3mo). Using reconstructed survival data from all six trials, the RMSTs with PD-1 versus PD-L1 blockade up to 12 and 18mo follow-up were 7.8mo (95% CI 7.7, 8.2) versus 7.8mo (95% CI 7.5, 8.2) and 10.1mo (95% CI 9.6, 10.7) versus 10mo (95% CI 9.5, 10.6), respectively. CONCLUSIONS: Our RMST estimates may be used as benchmarks to contextualize survival outcomes and inform future trial design with PD-1/PD-L1 inhibitors. PD-1 versus PD-L1 blockade in patients with mUC yields comparable survival outcomes. PATIENT SUMMARY: In this study, we found that outcomes for patients with metastatic bladder cancer treated with programmed death-1 and programmed death ligand-1 inhibitors, who received prior platinum-based chemotherapy, were similar.