Abstract A006: Modeling the impact of driver mutations on local tumor heterogeneity

Abstract

Abstract Tumors consist of an accumulation of somatic mutations, some of which are “passenger” mutations which do not contribute to positive selection of cancer cells and others of which are “driver” mutations which confer a growth advantage, the benefit of which may vary depending on the type of mutation, to tumor cells. As genomic sequencing to identify driver mutations is typically only done on small samples of a tumor, identification of these driver mutations may be complicated by intra-tumor spatial heterogeneity. With this in mind, we decided to create a basic model of different classes of driver mutations to investigate the impact of the timing and type of these mutations on tumor heterogeneity. Using the PhysiCell framework as a foundation, a platform developed for creating multicellular cancer models, we built a model to identify spatial trends specific to certain classes of driver mutations and distinguish them from passenger and “hitchhiker” (heritable mutations with an increased frequency due to their relation with a driver mutation) mutations. We model cells within a single, solid, non-cancer-specific tumor grown from a single cell, allowing these cells to mutate and daughter cells to potentially gain broad types of driver mutations at each division. We define three “driver” mutation classes in this model: an increase in the reproduction rate of tumor cells (defined as fitness), a decrease in the death rate of tumor cells (defined as survivability), and an increase in the motility rate of tumor cells. To investigate the distribution of common mutations, we define “common mutations” as those appearing in at least 5% of the tumor cells at end-point, when the tumor has reached 10,000 cells. To investigate spatial heterogeneity within this model, we split the tumor into 9 distinct spatial regions at the end-point of each simulation. We use these regions to assess the distribution of common, driver, and passenger mutations and quantify spatial heterogeneity. We find that in tumors with driver mutations that increase fitness or motility, we require on average fewer samples to detect all the common mutations than within tumors containing no driver mutations. On the other hand, we find that tumors containing driver mutations increasing only survivability tend to yield similar sample heterogeneity to tumors without driver mutations. These results suggest that in addition to the timing of a mutation, the type of driver mutation can have an impact on the spatial heterogeneity within a tumor which may inform biopsy strategies in clinical settings. In future work, this model could be used to observe the relationship between spatial tumor heterogeneity and the prevalence of different classes of driver mutations within different microenvironments through the implementation of hypoxic or therapeutic agent gradients. Our model could be compared with results of clinical tissue sampling data to further understanding of the impact of spatial sampling and driver mutation type on the discovery of driver mutations in different tumors and microenvironments. Citation Format: David Oh, Steph Owen, Luka Opasic, Jacob Scott. Modeling the impact of driver mutations on local tumor heterogeneity [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A006.