Abstract 135: Elucidating tumor evolution in pediatric cancers using stochastic models and mutation accumulation experiments

Abstract

Abstract Ontology-based approaches have been utilized for identifying cancer genes. However, among the thousands of mutations in pediatric cancer genes, accurate prediction of driver genes is one of the biggest challenges. A binary logistic regression model was developed to distinguish pediatric and non-pediatric cancer genes. To build the predictive model, Gene Ontology (GO) enrichment analysis was performed, followed by the PANTHER overrepresentation test, using PANTHER GO-slim, PANTHER, Reactome pathways, and three sets of specific variables, the lists of pediatric cancer genes found in the germline, relapsed, and chemotherapy-resistant cells, respectively. This statistical model was pediatric cancer-specific, confirming known driver genes across cancers. In addition, mathematical models were developed using stochastic differential equation approaches and computational simulations to extend our understanding of the evolutionary dynamics during pediatric tumorigenesis. Moreover, our stochastic models in childhood cancers were validated by mutation accumulation experiments using E. coli strains carrying a mutation in replication restart genes. Finally, this study that elucidates tumor evolution in pediatric cancers could have profound implications for cancer therapy and drug design. Citation Format: Ana Beatriz Massei, Jasmin Jovel Platero, Kyungchan Hong, Sofia Zeledon, Taha Afzal, Hyunjeong Cha, Seung-Hwan Kim. Elucidating tumor evolution in pediatric cancers using stochastic models and mutation accumulation experiments [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 135.