Fusion Gene Detection in Prostate Cancer Samples Enhances the Prediction of Prostate Cancer Clinical Outcomes from Radical Prostatectomy through Machine Learning in a Multi-Institutional Analysis

Prostate cancer remains one of the most fatal malignancies in men in the United States. Predicting the course of prostate cancer is challenging given that only a fraction of prostate cancer patients expe-rience cancer recurrence after radical prostatectomy or radiation therapy. This study examined the expressions of 14 fusion genes in 607 prostate cancer samples from the University of Pittsburgh, Stanford University , the University of Wisconsin-Madison. The profiling of 14 fusion genes was integrated with Gleason score of the primary prostate cancer , serum prostate-specific antigen level to develop machine-learning models to predict the recurrence of prostate cancer after radical prosta-tectomy. Machine-learning algorithms were developed by analysis of the data from the University of Pittsburgh cohort as a training set using the leave-one-out cross-validation method. These algorithms were then applied to the data set from the combined Stanford/Wisconsin cohort (testing set). The results showed that the addition of fusion gene profiling consistently improved the prediction accuracy rate of prostate cancer recurrence by Gleason score, serum prostate-specific antigen level, or a com-bination of both. These improvements occurred in both the training and testing cohorts and were corroborated by multiple models. (Am J Pathol 2023, 193: 392-403; https://doi.org/10.1016/ j.ajpath.2022.12.013)