Amy Brock, Ph.D. - US grants

Abstract Cancer cell populations are marked by significant intratumoral heterogeneity and this heterogeneity varies over time with disease progression and with response to therapies. To monitor heterogeneity among large cell populations, barcode sequencing is useful for quantifying relative lineage frequencies with high resolution. However, current approaches preclude the simultaneous isolation of a particular lineage of interest. In the study of cancer and the development of clinical treatments, there are many instances in which it would be useful to perform subsequent molecular characterization or cellular functional assays on purified populations of cells exclusively of one specific lineage. A further challenge is that it is often not possible to identify which lineage is of interest until a longitudinal study has been completed?for example, comparing the survival and relative fitness of many cell lineages as they proliferate over many cell generations in a tumor. Only at the conclusion of a lineage tracing study does it become clear which specific lineages had a survival advantage. In this proposal we develop a novel lineage tracing method, Barcode Assisted Ancestral Recall (BAAR), that allows for high- resolution lineage tracking and subsequent isolation of purified cell lineages for downstream analysis. Lineage tracing via barcoding is typically a destructive measurement; with the BAAR system we gain the ability to return to an earlier time point in the evolutionary trajectory and retrieve selected cells of interest. The ability to concurrently track clonal fitness dynamics and generate lineage specific genomic and transcriptomic data over longitudinal studies will give us unprecedented insight into the behaviors of heterogeneous populations of cancer cells.

SUMMARY    In ecosystem modeling, the Allee effect describes a correlation between population size (number of members)  and mean fitness (proliferation rate) of individuals. Increased proliferation with increased population size  implies cooperative interactions.  A contrasting principle in ecosystems is population ?carrying capacity? which  describes a decrease in growth rates with increasing population size, due to competition for limited resources.   While the concept of carrying capacity has been studied in tumors (which may become limited in nutrients or  oxygen), the Allee effect has been almost entirely overlooked. Allee effects are significant in small populations  and thus may be critical to understanding the earliest steps in tumor iniation.  To investigate the fundamental  contribution of the Allee effect on tumor ecosystems requires the novel experimental designs and integration of  quantitative experimental measurements with mathematical modeling. The overall goal of this project is to  dissect the contribution of population size and cell-­cell communication in a few selected, well-­suited tumor cell  lines through quantitative single-­cell resolution, real-­time monitoring of cell populations. Mathematical models  of growth kinetics will show that the Allee effect and cell-­cell communication has consequences on a single  cancer cell?s decision to initiate a growing tumor or to stay dormant.  These communication networks will be  further mapped by identification of specific paracrine factors and receptors. Disruption of the communication  network that establishes a proliferative tumor will be performed by targeting specific cell subpopulation  interactions. Perturbations will include depletion of specific cell subtypes, manipulation of overall paracrine  signaling, and blocking of specific ligand-­receptor combinations on interacting species. These strategies may  be novel tools to trigger tumor cell population collapse. Population-­level effects in a nascent tumor population  have not been quantitatively explored and may explain the phenomenon of critical thresholds and define a  mechanistic role for intratumor heterogeneity. This project focuses on a fundamental generic principle that is  broadly applicable in many contexts and thus could complement and enrich the modeling efforts of the cancer  research community.    

PROJECT SUMMARY In recent years, improvements in diagnosis and treatment have extended the lives of many patients with triple negative breast cancer, but resistance to treatment remains a major clinical and scientific challenge. While standard-of-care treatment and chemotherapy is effective in many TNBC patients, approximately 40% of patients display resistance, leading to poor overall survival. TNBC are characterized by significant intratumor heterogeneity, which further complicates treatment. Mechanisms of chemoresistance in TNBC patients remain poorly understood, in part due to a lack of available methods and models to measure intratumor heterogeneity and track changes in heterogeneous tumor compositions over time. Here we propose to use a new technology to track individual cells and clones as they respond to different chemotherapeutic agents; this more detailed information about the tumor cell population will be used to build mathematical models better predict and optimize therapeutic response. We first measure individual cell gene expression changes in response to treatment and then assemble these measurements into cell subpopulation trajectories, taking advantage of a barcoding technology developed in our lab to quantify clonally-resolved single cell transcriptomes. These Aim 1 studies will build a compendium of gene expression, cell growth and survival data that describes how each of the heterogeneous cells in major experimental models of subtypes of triple negative breast cancer responds to clinically-relevant therapeutic agents. The new ability to layer clonal identifier information on single cell gene expression data reveals the detailed trajectories of individual cells that escape therapy. It also distinguishes subpopulations with pre-existing treatment resistance from those in which a resistant state is induced. At a higher conceptual level, this proposal seeks to also address a broad practical challenge: the high-dimensional ?omics? data collected in many large-scale efforts points often points to correlations in disease progression but not been informative for building mechanistic models to aid in the predictive of tumor response. Often, other types of data are more readily available-- lower dimensional data with more frequent measurements. We therefore next ask: How can these distinct data types be integrated into a useful framework to build predictive models of tumor cell response to therapy? This seems a fitting goal for the systems biology of cancer community. We propose to tackle this challenge with our barcode tracking technology; relative fractions of sensitive and resistance phenotypes, along with separate longitudinal measurements of cell number (low dimension data), become the inputs for a mechanistic model to predict therapeutic response and resistance (Aim 2). In Aim 3, we will perform trajectory-mapping and model testing using patient-derived triple negative breast cancer cells, towards understanding the potential for translational utility. By integrating different data types into a cohesive framework, we aim to describe how sensitive and resistant subpopulations in TNBC grow, die, and transition in response to treatment.