Fraction of Genome Altered, Age, Microsatellite Instability Score, Tumor Mutational Burden, Cancer Type, Metastasis Status, and Choice of Cancer Therapy Predict Overall Survival in Multiple Machine Learning Models

Background/Objectives: The accurate prediction of overall survival (OS) in cancer patients is crucial for personalized treatment strategies. Methods: In this study, we developed machine learning models to predict OS by integrating clinical and mutational features from a cohort of 25,508 cancer patients. Key features included tumor mutational burden (TMB), microsatellite instability (MSI), fraction of genome altered (FGA), copy number alterations (CNA), age, sex, race, cancer type, and metastasis status. Results: We applied multiple Random Forest, Gradient Boosting, and Ensemble models, achieving an accuracy of 74% for overall survival status, and a C-Index of 0.76 using the Random Survival Forest model. Importantly, FGA, age, MSI score, TMB, cancer type, and metastasis status were identified as major predictors of OS across all models. We also integrated treatment data from 16,603 patients, demonstrating that therapies like platinum, carboplatin, and taxanes are associated with differences in survival predictions, with some therapeutic regimens showing minimal impact. Conclusions: Our findings highlight the potential of using machine learning to predict OS by incorporating both clinical and mutational features. These models offer a promising approach for improving patient outcomes and could be further validated in prospective studies for clinical use.