Development and validation of MOSPROC, an easily applicable predictive score of mortality for patients with prostate cancer.

Abstract

e13598 Background: Predicting life expectancy in prostate cancer patients is particularly difficult as different clinical factors significantly influence clinical outcome. Here we have developed MOSPROC, a simplified mortality predictive score to identify prostate cancer patients at high risk of mortality. Methods: The training set (TRS) (n=2,035) included metastatic Castration Resistant PC (mCRPC) patients from 4 randomized phase 3 trials (NCT00273338, NCT00988208, NCT00617669, and NCT00519285). The validation set included patients from NCT00554229 (mCRPC; n=256) (TS1) and NCT00626548 (M0 CRPC; n=661) (TS2) trials. Clinical trial datasets were obtained from www.projectdatasphere.org . All datasets contained more than 100 clinical variables per patient at baseline. Synthetic representations of every patient were generated and input into a deep learning framework to identify subgroup of patients based on their similarities. The resultant subgroups were correlated with overall survival (OS). Differential variables between subgroups were identified and included in a multivariable logistic regression model. Independent predictive factors found to be statistically significant were used to generate a predictive score of survival that was validated in the validation sets. Results: The deep learning framework identified two different subpopulations in the TRS: LL (n=824) and HH (n=1,204) with significantly different survival outcome. Patients in HH had a higher risk of death compared to LL (median OS 17.6 months vs. 27.3 months, respectively; HR 2.09 95%CI 1.84-2.39; p<.0001). Feature contribution analysis identified 36 differential features between both subpopulations including anthropometrics, performance status, vital signs, biochemistry, previous and current cancer therapies, and concomitant medications. From these, PSA, ALP, and AST were the best independent predictive factors that were then selected to create a simplified predictive score for survival. A score ≥34 points was considered a high score (HS) while if < 34, a low score (LS). When applied to TRS, the predictive score yielded an area under the curve of 0.91. Patients with HS presented a significantly shorter OS (median OS 18.4 vs. 23.9 months; HR 1.49 95%CI 1.31-1.71; p<.005). The predictive score was then validated in two additional trials with similar results. In both cases, patients with a HS presented a significantly shorter OS (TS1: HR 2.56, 95%CI 1.79-3.66, p<.01; TS2: HR: 3.46, 95%CI 1.89-6.33, <.001). Conclusions: By combining deep learning and classical biostatistical tools, we have developed MOSPROC, an easily applicable predictive score that, based on baseline values of PSA, ALP and AST, can identify prostate cancer patients with a higher risk of death. Further work is required to validate this approach in prospective cohorts of PC patients in additional clinical trials and/or real-world datasets.