Quantifying cancer heterogeneity to predict and improve targeted therapy outcomes

Abstract

Intratumor heterogeneity underlies the failure of targeted cancer therapy. The origins of this phenotypic variability derive from the coincident contributions of genetic, non-genetic, and stochastic factors. However, few studies quantify multiple sources of heterogeneity across scales. Additionally, many metrics of heterogeneity are static and are by nature difficult to relate to fundamentally dynamic processes such as cancer drug response. Ideally, quantifying heterogeneity should give insight into the dynamics of cancer cell population response to perturbations. The fundamental purpose of this study is to define a quantitative link between heterogeneity and outcomes for cancer cell populations. This work utilizes a combined experimental and computational systems biology approach to quantify the heterogeneous therapeutic response to targeted cancer response. Herein, I present high-throughput imaging methodologies to study heterogeneity, a novel framework to quantify and interpret heterogeneity across scales, and an experimental-computational strategy to predict the dynamics of drug response based on the initial heterogeneity. While cancer heterogeneity presents a formidable challenge, these findings enable it to be interpreted and addressed.