Modeling genetic heterogeneity of drug response and resistance in cancer

Abstract

Heterogeneity in tumors is recognized as a key contributor to drug resistance and spread of advanced disease, but deep characterization of genetic variation within tumors has only recently been quantifiable with the advancement of next generation sequencing and single cell technologies. These data have been essential in developing molecular models of how tumors develop, evolve, and respond to environmental changes, such as therapeutic intervention. A deeper understanding of tumor evolution has subsequently opened up new research efforts to develop mathematical models that account for evolutionary dynamics with the goal of predicting drug response and resistance in cancer. Here, we describe recent advances and limitations of how models of tumor evolution can impact treatment strategies for cancer patients.

Figure 1.. Two models of tumor evolution under selective treatment pressure.

A precision oncology approach where a tumor biopsy (red dashed ovals) is taken and a molecular diagnostic report is generated to select treatments based on the limited view to the overall disease (large red circle). (A) The selective sweep model allows for genetic alterations (*) to arise, which present the cells with a growth advantage and these subpopulations sweep through the population. Under treatment, genetic alterations can arise due to the drug directly, or through selection for a pre-existing subclone. (B) In the Big Bang model, driving alterations are present when the tumor arises with resistant populations being present at low frequencies until the drug treatment selects for that pre-existing population.

Figure 2.. Overview into population dynamics driven modeling.

Modeling several subpopulations and their dynamics allows one to simulate effects arising from genotypic variation across different treatment scenerios, including different drug targets, dosage and scheduling. (A) In this example, there are 3 cell populations that vary in their response to two drugs (D_X and D_Y). The clinical aim is to supress the growth of population P_C, while maintaining P_A and P_B. (B) Dynamic models can be used to simulate the impact on the different cell populations in response to potential intervention strategies. (C) Based on measurements of tumor heterogeneity and subpopulation composition from a patient’s tumor, the most effective treatment strategy can be identified. In this example, an adaptive treatment strategy, where the drugs are adjusted based on how the subpopulations change over time would accomplish the objective to supress P_C and maintain P_A and P_B.