Abstract 5490: Scales and dynamics of intratumor heterogeneity

Abstract

Abstract Current dogma stipulates that more heterogenous tumor cell populations results from compounding genetic and epigenetic changes and instability, ultimately driving unfavorable outcomes for these patients. Nonetheless, some cancers, including many pediatric cancers and some leukemias, have limited genomic diversity. As a result, we have a limited number of biomarkers available to stratify patients into high versus low-risk groups by their mutational cellular diversity or heterogeneity. For these cases, we have developed a method to quantify intratumor heterogeneity, for example at the transcriptomic level, using a generalized diversity index (GDI) from ecology. This index allows control of a scaling parameter called the order of diversity, q, to tailor the scale or resolution of the cellular diversity to a given context. However, the question remains, what role does the order of diversity play in the context of relative tumor age or disease stage? We show that the order of diversity parameter allows to either emphasize clonal richness (at low values of q), while high values shift the analysis toward the abundance of potential drivers of the tumor evolution. To gain deeper quantitative insight, we use evolutionary dynamics approaches that model the dynamics of heterogeneous cell populations over time, to understand how a patient's tumor diversity could emerge, persist, and change over time. Applying the replicator-mutator dynamics framework, we explore the effects and magnitude of frequency-dependent (positive or negative) selection on GDI. These analyses suggest that GDI often peaks at intermediate time points and, when run over a sufficiently long timescale, they reduce as the tumor population presumable becomes clonally dominant or otherwise stable before a major transition (e.g. metastasis). We explore GDI changes over time further in vitro in sequential single-cell RNA sequencing samples of fused breast cancer cells, where at earlier passages after fusion, the GDI peaks, and at later passages after fusion the GDI returns to levels similar to that of the parental breast cancer cell lines. Overall, GDI, as a means to quantify intratumor heterogeneity at select time points, can be a powerful tool to stratify high-risk patients. Our analyses show how computational analyses and mathematical modeling tools can be coupled with clinical and experimental data to understand eco-evolutionary dynamics of a patient's tumor. Citation Format: Meghan C. Ferrall-Fairbanks, Gregory J. Kimmel, Philipp M. Altrock. Scales and dynamics of intratumor heterogeneity [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 5490.