Cell Communication in Antiestrogen Resistance
Abstract
Rationale, Objective, Aims: More women die from the estrogen receptor positive (ER+) breast cancer subtype than from any other. The proportion of early ER+ recurrences (=5 years since diagnosis) approaches that for all triple-negative breast cancers alone. Late recurrences (>5 years after diagnosis), the result of dormancy, are most common in ER+ disease and can arise decades after the initial diagnosis. Since recurrent breast cancers have escaped the effects of endocrine therapies, and are lethal, we will study endocrine resistance (Tamoxifen; Fulvestrant). Our primary objective is to identify what drives breast cancer growth and determine how to stop it. We will learn about why some breast cancers are aggressive and others are indolent, and why/how some breast cancers lay dormant for years and then re-emerge. We will apply computational (e.g., artificial intelligence) and mathematical modeling (e.g., ordinary differential equations), using principles from ecology, to study how resistant (R) and sensitive (S) cells cooperate to alter the response of S+R populations to treatment. Our data show that R cells "teach" S cells to become resistant; ~75% of S cells survive treatment when the population contains only 10% of R cells. When S+R cells are mixed, S cells change the genes and proteins that they express and resemble R cells. Knowing how (and what) signals are communicated by R cells to S cells will fundamentally change our understanding of the biology of resistance. This knowledge will be used to identify new interventions, or optimizations of existing regimens, to improve outcomes for patients, i.e., reduce toxicity and improve responses to endocrine therapy. We will prioritize the use of Food and Drug Administration-approved drugs or drugs already in clinical trials to accelerate the design/implementation of new clinical trials to achieve these goals. We will take a logical and technically feasible approach to discover the basic principles of cell communication and how this communication affects drug responsiveness. We will begin by using a simple system, where we know the properties of both S and R cells growing in isolation, so that we can interpret the changes that occur when these cells are growing together, as they would in a patient s tumor. We will use a novel dual-barcoding (molecular; fluorescence) to label S and R cells for cell-mixing studies in vitro and in vivo, and use high throughput single cell and population-based omics, new computational modeling tools, and ecology-based cell population modeling approaches to study the effects of treatment. To ensure that the studies have clinical relevance, we will determine how the molecular changes we find are represented in gene expression data from tumors in patients (existing in-house and public datasets - not a new clinical trial). We will build mathematical models to study the adaptive cell population remodeling that arises as a consequence of R-S communication in response to drug. These models will allow us to study drug scheduling (dose, application intervals, rest intervals) that optimizes anticancer activity while reducing total drug dosing (toxicity). We will model this phenotype in the context of the mutualism-parasitism continuum (mutualism, commensalism, neutralism, and parasitism). Ideally, we will discover conditions that lead to S and R co-extinction, which would lead to the effective eradication of the entire cancer cell population. This is a Breakthrough Award Level 2 application since the concept is supported by preliminary data but in the early stages of idea development. Ultimate Applicability of the Research: We will discover the basic principles of how (and what) breast cancer cells communicate to drive population endocrine responsiveness. We will discover the key features of S cells that change to make them behave like R cells, providing a clear path to understanding the molecular basis of conferred resistance. To
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 29, 2018
- Source ID
- W81XWH1810723
Entities
People
- Yue Wang
Organizations
- United States Army
- Virginia Tech