Inference of tumor growth dynamics using genomic data

ABCTRACT

ABCTRACT Heterogeneity and evolvability are hallmarks of cancer. By the time of detection, a typical tumor comprises of billions of malignant cells that belong to multiple distinct subclonal cell populations, which trace their evolutionary lineage back to a single tumor initiating cell. Subclones arise at different time-points during tumor progression, and their population sizes grow (or in some cases shrink) with time. Quantitative assessment of subclonal growth rates of tumors can indicate the mode of disease progression, predict the risk of emergence of resistance, and can rationally guide clinical management of the patients in the Precision Medicine setting. It remains unclear whether the genetically distinct subclones in heterogeneous tumors tend to have major differences in fitness and growth rates in vivo, or most subclones grow comparably, as predicted by the neutral evolution model. This is due to a number of technical challenges. Patho-genomic profiling of biopsies and resected tumors provide limited and incomplete snapshots of cancer progression; much of the tumor evolution and clonal growth dynamics therein remain unobserved. Pathological assessment can indicate overall proliferative characteristics of a tumor but cannot attribute them to individual subclones and oncogenic driver mutations therein. Genomic approaches for delineating clonal architectures in tumors, or genetic and non- genetic heterogeneity also do not provide direct, quantitative estimates of subclonal growth rates. Incorrect measurements of intra-tumor subclonal properties have led to biased inference about tumor evolution and fueled controversies on multiple occasions - highlighting the immediate need for development of reliable resource in this area. To address this unmet need, this proposal aims to develop a novel framework to estimate subclonal growth rates in human tumors using emerging genomic approaches, and then validate them experimentally before applying the framework to estimate the selective advantage conferred by oncogenic drivers during tumor progression in individual patients. The resources developed in this proposal will enable us to revisit the ongoing debate about the neutral evolution and selection in cancer, and also help refine clinically relevant predictive models of tumor progression to generate testable hypotheses.