Abstract 7411: CAMStool: Cancer-associated somatic mutation selection tool for cancer risk prediction in whole-genome data

Abstract Predicting cancer risk plays a useful role in presenting guidelines to prevent and manage cancers of individuals. Genomic variants have recently been widely used as a genetic factor, and the utilization of the genetic factor consisting of somatic mutations has shown remarkable predictive performance in many cancer prediction studies. Typically, the accuracy of such prediction studies is significantly affected by the composition of somatic mutations used as the genetic factor; however, in practice, choosing the optimal set of mutations with high prediction accuracy is a challenging task in cancer prediction research. From this perspective, we present a new analysis tool for selecting an optimal set of somatic mutations associated with cancers of interest, named CAMStool. The CAMStool is based on the statistical values, which are the effect size and its p-value of each somatic mutation for the cancers of interest. It heuristically searches for the only somatic mutations that directly increase the prediction accuracy of cancer, through statistical and machine-learning models. Then, our tool presents the finally selected somatic mutations as the final optimal mutation set, and additionally presents an optimal cancer risk prediction model through various machine/deep learning modeling using the optimal set as a genetic factor. For the evaluation, the CAMStool was applied to our real whole-genome dataset which are sequenced from normal-tumor tissues of 459 individuals with thyroid cancer. The CAMStool selected 36 and 55 optimal somatic mutations associated with the benign thyroid and non-invasive follicular thyroid cancer, and presented risk prediction models with sensitivity of 88.04% and 90.32%, respectively. Although the CAMStools has some limitations, it is expected to contribute to various cancer prediction studies that require selecting optimal mutation sets in the future. The CAMStools is written in R and will be released soon on our github (https://github.com/JaeYoonKim72). Citation Format: Jae-Yoon Kim, Seung-Jin Park, Seong-Hwan Park, Seon-Kyu Kim. CAMStool: Cancer-associated somatic mutation selection tool for cancer risk prediction in whole-genome data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7411.