Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.

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.

• Authors: 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 Feldman, Priti Lal, Anant Madabhushi
• Journal: Clinical cancer research : an official journal of the American Association for Cancer Research (Clin Cancer Res) Vol. 26 Issue 8 Pg. 1915-1923 (04 15 2020) ISSN: 1557-3265 [Electronic] United States
• PMID: 32139401 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.)
• Copyright: ©2020 American Association for Cancer Research.

Chemical References

• Biomarkers, Tumor
• Prostate-Specific Antigen

Topics

• Black or African American (statistics & numerical data)
• Biomarkers, Tumor (analysis)
• Disease Progression
• Humans
• Image Processing, Computer-Assisted (methods)
• Machine Learning
• Male
• Middle Aged
• Neoplasm Recurrence, Local (metabolism, pathology, surgery)
• Nomograms
• Predictive Value of Tests
• Prognosis
• Prostate-Specific Antigen (blood)
• Prostatectomy (methods)
• Prostatic Neoplasms (metabolism, pathology, surgery)
• ROC Curve
• Risk Assessment (methods)
• Stromal Cells (pathology)
• Survival Rate