Abstract A032: Using machine learning to tackle tumor heterogeneity

Abstract Cancer develops through an evolutionary process, which creates highly diverse primary tumors and metastases. This intra- and intertumoral heterogeneity is a challenging aspect for clinical practice. Selecting therapeutic targets that exist only in a subpopulation of tumor cells can lead to ineffective treatments and in worst cases even to more resistant tumors and relapses. Therefore, the characterization of tumor heterogeneity and the ability to identify targets that are present in all cells of a primary tumor and related metastases are essential for an effective therapy. Truncal mutations are somatic mutations that appear very early in tumor development and are therefore carried by all cells of a tumor throughout subsequent generations. Currently, multiple tumor samples from a patient are necessary to identify truncal mutations, a requirement that can be rarely met in a standard clinical setting. However, the computational classification of mutations as truncal or non-truncal from a single tumor sample could provide more effective targets for individualized therapies. To this purpose, we have analyzed 12 published whole-exome sequencing datasets of matched primary-metastasis samples from 10 different cancer entities. Overall, approximately 24,000 somatic mutations were identified. The detected variants were annotated with a range of features, for instance mutation-specific features, but also sample- or gene-level features, and labeled as “shared-clonal” (proxy for truncal mutations) or “non-shared-clonal” (proxy for non-truncal mutations) based on their clonal presence or absence in the matched samples. Approximately 30% of all mutations were labeled as “shared-clonal”. However, this proportion appears to be very heterogeneous among cancer entities and patients. Exploratory data analysis also suggested that the received treatment affects the proportion of shared-clonal variants. Overall, the data exploration showed that no single variable alone could distinguish between “shared-clonal” and “non-shared-clonal” mutations. In a next step, Random Forest models with different feature sets were therefore trained to distinguish the two mutation classes. We found that the performance of these models was strongly influenced by the cancer entity used during training and that gene-level features were consistently uninformative for the models. Overall, our findings underline that tumor heterogeneity can present in multiple, biologically distinct patterns which can be taken into account for treatment decisions. The optimized model that classifies mutations as truncal or non-truncal will enable the clinical utilization of previously inaccessible information on intra- and intertumoral heterogeneity, thereby improving the efficacy of personalized immunotherapies. Citation Format: Jennifer Neumaier, Luisa Bresadola, Jonas Ibn-Salem, Ranganath Gudimella, Pablo Riesgo Ferreiro, Barbara Schrörs, Ugur Sahin. Using machine learning to tackle tumor heterogeneity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A032.

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