Abstract 4298: Machine learning-based model for survival prediction after immunotherapy in patients with solid tumor

Abstract

Abstract Background: Immune checkpoint inhibitors (ICIs) have led to a paradigm shift in solid tumors treatment. However, not all patients respond favorably to these drugs, highlighting the need for reliable predictions to achieve more personalized care and better management. This study aimed to create and validated ML model to predict survival in solid tumors patients receiving ICIs. Methods: We obtained clinical and genomic data from cBio Cancer Genomics Portal. Data were randomly divided into a training set (80%) and a validation set (20%). light gradient boosting algorithm was trained to predict patients’ survival at different time points. Results: We identified 1660 patients with a median survival of 18 months. LGB yielded AUCs of 67.91% at 1 year, 79.89.6% at 2 years, and 79.75% at 3 years, respectively. The most important predictors that influenced the performance of the model in predicting 3-year-survival were: Age (22.89%), tumor mutational burden (18.24%), and tumor purity (13.23%). Moreover, multivariate analysis was performed and drug type was identified as an independent prognostic indicator (P< .001). So, a Subgroup analysis was done and the OS rates were: 98.57%, 75.55%, 33.84% in patients who received CTLA-4, 98.57%, 75.55%, 33.84% with PD-1/PD-L1, and 97.99%, 9.94%, 4.62% with combo treatment, at 1-, 3-, and 5 years, respectively. Conclusion: Our ML-based model that integrates both clinical and genomic data is an improved tool for survival prediction, enabling an accurate risk classification and leading to a more precise decision-making. Moreover, this study highlights the importance of age and tumor microenvironment as the main contributors in making survival prediction in patients receiving ICIs. Table. Performance in survival prediction among training and testing sets Table 1. Survival Average AUC Average Accuracy Survival Duration Positive Predictive Value (Precision) Sensitivity (Recall) 1-year 67.91% 63.18% <12 months 58% 54% >=12 months 67% 70% 2-years 79.89% 78.70% <24 months 86% 84% >=24 months 62% 65% 3-years 79.75% 86.02% <36 months 95% 89% >=36 months 45% 65% Citation Format: Salma Y. Fala, Mohamed Osman. Machine learning-based model for survival prediction after immunotherapy in patients with solid tumor. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4298.