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

• 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
• 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

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.

项目概要 近年来，诊断和治疗的进步延长了许多三联患者的生命 阴性乳腺癌，但对治疗的抵抗仍然是一个重大的临床和科学挑战。 标准护理治疗和化疗对许多 TNBC 患者有效，大约 40% 患者表现出耐药性，导致总体生存率较差，其特点是瘤内显着。 异质性，使 TNBC 患者的化疗耐药机制进一步复杂化。 仍然知之甚少，部分原因是缺乏测量肿瘤内的可用方法和模型 异质性并跟踪异质肿瘤成分随时间的变化。 追踪单个细胞和克隆对不同化疗药物的反应的新技术； 有关肿瘤细胞群的更详细信息将用于建立更好的数学模型 我们首先测量个体细胞基因表达的变化。 对治疗的反应，然后将这些测量结果组装成细胞亚群轨迹， 利用我们实验室开发的条形码技术来量化克隆解析的单细胞 这些 Aim 1 研究将构建基因表达、细胞生长和存活的概要。 描述三重亚型的主要实验模型中的每个异质细胞如何的数据 阴性乳腺癌对临床相关治疗药物有反应。分层克隆的新能力。 单细胞基因表达数据的标识符信息揭示了单个细胞的详细轨迹 它还将那些预先存在治疗耐药的亚群与那些逃避治疗的亚群区分开来。 在更高的概念层面上，该提案还寻求解决广泛的问题。 实际挑战：在许多大规模工作中收集的高维“组学”数据往往是点 与疾病进展的相关性，但对于建立机械模型以帮助 通常，其他类型的数据更容易获得——低维数据。 因此，我们接下来要问：如何集成这些不同的数据类型。 建立一个有用的框架来建立肿瘤细胞对治疗的反应的预测模型，这似乎是一个合适的目标？ 对于癌症界的系统生物学，我们建议通过条形码跟踪来应对这一挑战。 技术；敏感表型和耐药表型的相对分数，以及单独的纵向 细胞数量的测量（低维数据），成为机械模型预测的输入 治疗反应和抵抗（目标 2）。在目标 3 中，我们将进行轨迹映射和模型测试。 使用患者来源的三阴性乳腺癌细胞，了解转化的潜力 通过将不同的数据类型集成到一个有凝聚力的框架中，我们的目标是描述如何敏感和有效。 TNBC 中的耐药亚群会因治疗而生长、死亡和转变。