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Systems Approaches to Understanding Subpopulation Heterogeneity in Therapeutic Resistance

理解治疗耐药性亚群异质性的系统方法

基本信息

批准号：
10307901
负责人：
Amy Brock
金额：
$ 4.29万
依托单位：
UNIVERSITY OF TEXAS AT AUSTIN
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-08 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10307901
关键词：
Address Bar Codes Behavior Big Data Biological Specimen Banks Breast Cancer Cell Cancer Biology Cell Count Cell Line Cell Survival Cells Chemoresistance Clinical Communities Data Developmental Therapeutics Program Diagnosis Dimensions Disease Progression Doxorubicin Drug resistance Experimental Models Fluorouracil Future Gene Expression Genomics Goals Growth Heterogeneity Human Individual Link Malignant Neoplasms Maps Measurement Measures Methods Modeling Molecular Paclitaxel Patients Phenotype Play Population Prediction of Response to Therapy Regimen Resistance Role Sampling System Systems Biology Technology Testing Texas Therapeutic Therapeutic Agents Time Treatment Failure Treatment Protocols Universities Variant austin cancer therapy cell dimension cell growth chemotherapeutic agent chemotherapy clinically relevant cohesion cost epigenetic variation experimental study high dimensionality individualized medicine mathematical model medical schools model development multidimensional data neoplastic cell new technology novel parent grant predictive modeling response single-cell RNA sequencing standard of care therapy resistant transcriptome transcriptomics treatment response triple-negative invasive breast carcinoma tumor tumor heterogeneity

项目摘要

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.

项目总结