Improved prediction of prostate cancer recurrence through systems pathology

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Abstract

We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning to develop a model based on clinicopathologic variables, histologic tumor characteristics, and cell type–specific quantification of biomarkers. The initial study was based on a cohort of 323 patients and identified that high levels of the androgen receptor, as detected by immunohistochemistry, were associated with a reduced time to PSA recurrence. The model predicted recurrence with high accuracy, as indicated by a concordance index in the validation set of 0.82, sensitivity of 96%, and specificity of 72%. We extended this approach, employing quantitative multiplex immunofluorescence, on an expanded cohort of 682 patients. The model again predicted PSA recurrence with high accuracy, concordance index being 0.77, sensitivity of 77% and specificity of 72%. The androgen receptor was selected, along with 5 clinicopathologic features (seminal vesicle invasion, biopsy Gleason score, extracapsular extension, preoperative PSA, and dominant prostatectomy Gleason grade) as well as 2 histologic features (texture of epithelial nuclei and cytoplasm in tumor only regions). This robust platform has broad applications in patient diagnosis, treatment management, and prognostication.

Authors

Carlos Cordon-Cardo, Angeliki Kotsianti, David A. Verbel, Mikhail Teverovskiy, Paola Capodieci, Stefan Hamann, Yusuf Jeffers, Mark Clayton, Faysal Elkhettabi, Faisal M. Khan, Marina Sapir, Valentina Bayer-Zubek, Yevgen Vengrenyuk, Stephen Fogarsi, Olivier Saidi, Victor E. Reuter, Howard I. Scher, Michael W. Kattan, Fernando J. Bianco Jr., Thomas M. Wheeler, Gustavo E. Ayala, Peter T. Scardino, Michael J. Donovan