Abstract 5670: Machine learning methods to identify salient genomic predictors of clinical responses to immune checkpoint inhibitor therapy

Immune checkpoint inhibitors (ICIs) have clinical activity across many different cancer types, but only a subset of patients respond to these therapies, highlighting a need for biomarkers to enrich for clinical benefit. Several tumor genomic and putative neoantigen characteristics have individually been associated with favorable clinical responses, but the combined predictive value of these features and the degree of informativeness of each respective feature remains unknown. To address these questions, we utilized machine learning methods on available paired tumor and normal whole-exome sequencing (WES) data from clinically annotated tumor specimens treated with anti-CTLA4 (n=145, melanoma) and anti-PD1 therapies (n=94, NSCLC, melanoma, head and neck, bladder cancer, and others). WES data were processed to calculate genomic and neoantigen-based features. The features in the analyses include numbers of mutations identified within specific categories, numbers of neoantigens with various maximum predicted binding affinities and increased affinity over wild type peptides, and clinical information. A novel tumor heterogeneity score was evaluated as well. In total, 14-17 features were used. A random forest classifier was trained and tested on various subsets of the dataset using scikit-learn. As the ~30% patient ICI response rate creates an imbalanced dataset, data divisions were stratified and ADASYN was used. Model hyperparameters were tuned to maximize recall score. Recall (equivalent to diagnostic sensitivity) is the score of choice as clinical use would dictate maximizing identification of all responders. Relative feature contribution to the classifier was calculated using permutation importance. Overall performance of the trained classifiers were the following: anti-PD1 treated cohort model recall was ~0.60 with precision of ~0.55; anti-CTLA-4 treated cohort model recall was ~0.70 with precision of ~0.40. When the model was trained on anti-PD1 NSCLC patients and tested on bladder cancer patients, the model recall was ~0.95 with precision of ~0.90. Relative feature importance indicates that tumor mutational burden is the main predictive feature, with number of frameshift mutations, patient age, splice site mutations, and the novel tumor heterogeneity score contributing roughly an order of magnitude less weight for this dataset. Cancer type and various aggregate neoantigen counts did not contribute to predictions across analyses. Novel findings include an evaluation of the ability to predict ICI patient response using random forest classifiers, and a semi-quantitative evaluation of the relative contributions of features included the model. Additionally, analyses suggest the ability to transfer learning on one tumor type to identify responders in other diseases. Machine learning may provide a powerful method to enhance patient selection for ICI therapy. Citation Format: Emma G. Bigelow, Alexander Baras, Mark Yarchoan, Elizabeth M. Jaffee. Machine learning methods to identify salient genomic predictors of clinical responses to immune checkpoint inhibitor therapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5670.