Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients
Hersh K. Bhargava, Patrick Leo, Robin Elliott, Andrew Janowczyk, Jon Whitney, Sanjay Gupta, Pingfu Fu, Kosj Yamoah, Francesca Khani, Brian D. Robinson, Timothy R. Rebbeck, Michael D. Feldman, Priti Lal, Anant Madabhushi
Abstract
Abstract Purpose: Between 30%–40% of patients with prostate cancer experience disease recurrence following radical prostatectomy. Existing clinical models for recurrence risk prediction do not account for population-based variation in the tumor phenotype, despite recent evidence suggesting the presence of a unique, more aggressive prostate cancer phenotype in African American (AA) patients. We investigated the capacity of digitally measured, population-specific phenotypes of the intratumoral stroma to create improved models for prediction of recurrence following radical prostatectomy. Experimental Design: This study included 334 radical prostatectomy patients subdivided into training (VT, n = 127), validation 1 (V1, n = 62), and validation 2 (V2, n = 145). Hematoxylin and eosin–stained slides from resected prostates were digitized, and 242 quantitative descriptors of the intratumoral stroma were calculated using a computational algorithm. Machine learning and elastic net Cox regression models were constructed using VT to predict biochemical recurrence-free survival based on these features. Performance of these models was assessed using V1 and V2, both overall and in population-specific cohorts. Results: An AA-specific, automated stromal signature, AAstro, was prognostic of recurrence risk in both independent validation datasets [V1,AA: AUC = 0.87, HR = 4.71 (95% confidence interval (CI), 1.65–13.4), P = 0.003; V2,AA: AUC = 0.77, HR = 5.7 (95% CI, 1.48–21.90), P = 0.01]. AAstro outperformed clinical standard Kattan and CAPRA-S nomograms, and the underlying stromal descriptors were strongly associated with IHC measurements of specific tumor biomarker expression levels. Conclusions: Our results suggest that considering population-specific information and stromal morphology has the potential to substantially improve accuracy of prognosis and risk stratification in AA patients with prostate cancer.