Efforts towards personalized treatment are complicated by several factors, including the high degree of intratumor heterogeneity. This means even within an individual patient, different tumor cells respond differently to treatment. Some cells are effectively killed by a therapy while other cells escape. Subpopulations of surviving cells may use different strategies to escape killing by the treatment. Thus, the tumor population includes cells with many different gene and protein states. These also lead to functional variation, in growth rate, metabolism, and response to other drugs. To improve therapeutic strategies in cancer, new tools are required to measure the contributions of heterogeneo...

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Efforts towards personalized treatment are complicated by several factors, including the high degree of intratumor heterogeneity. This means even within an individual patient, different tumor cells respond differently to treatment. Some cells are effectively killed by a therapy while other cells escape. Subpopulations of surviving cells may use different strategies to escape killing by the treatment. Thus, the tumor population includes cells with many different gene and protein states. These also lead to functional variation, in growth rate, metabolism, and response to other drugs. To improve therapeutic strategies in cancer, new tools are required to measure the contributions of heterogeneous cell populations. Our lab developed the ClonMapper system to address this challenge and to help reduce delays in identifying the most effective treatment option for an individual patient. In this approach, each clone in a population is tagged with a unique, heritable nucleic acid sequence that acts as a barcode identifier. The barcode is copied along with the cell's genome each time the cell divides and is passed on to all descendants of that cell. Clonal abundance can be measured by DNA sequencing. Changes in clonal dynamics after perturbations, such as treatment with a pharmacological agent, reveal variation in how different clones within the tumor population respond to the therapy. In this study, we apply the system to an important clinical challenge in the treatment of estrogen receptor positive breast cancer. The majority of patients benefit from endocrine therapy and from a new class of targeted treatments called CDK inhibitors. However, many patients also develop resistance to CSK inhibitors over time. Here we will integrate this new type of clone-level data in a systems biology approach, using bioinformatic and mathematical modeling frameworks to better understand and predict tumor heterogeneity and clonal evolution during cellular adaptation to CDK inhibition.

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