Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients
- 15 April 2020
- journal article
- research article
- Published by American Association for Cancer Research (AACR) in Clinical Cancer Research
- Vol. 26 (8), 1915-1923
- https://doi.org/10.1158/1078-0432.ccr-19-2659
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.Keywords
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Funding Information
- NCI
- NIH (1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1, 1U01 CA239055-01)
- National Center for Research Resources (1 C06 RR12463-01)
- Department of Veterans Affairs Biomedical Laboratory Research and Development Service (IBX004121A)
- Department of Defense Prostate Cancer Idea Development (W81XWH-15-1-0558)
- Department of Defense Lung Cancer Investigator-Initiated Translational Research (W81XWH-18-1-0440)
- Department of Defense Peer Reviewed Cancer Research Program (W81XWH-16-1-0329)
- Department of Defense Prostate Cancer Disparity (W81XWH-19-1-0720)
- Hartwell Foundation T32
- Case Western Reserve University Nephrology Training (5T32DK007470)
- National Science Foundation Graduate Research Fellowship Program (CON501692)
- Wallace H. Coulter Foundation
- Department of Biomedical Engineering and the Clinical and Translational Science
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