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: 10524210
• 负责人: Amy Brock
• 金额: $ 7.94万
• 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/10524210
• 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; predictive modeling; response; single-cell RNA sequencing; standard of care; therapy resistant; transcriptome; transcriptomics; translational potential; 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.

项目摘要 近年来，诊断和治疗的改善延长了许多三重患者的寿命 乳腺癌阴性，但对治疗的抵抗力仍然是主要的临床和科学挑战。尽管 护理标准治疗和化学疗法在许多TNBC患者中都是有效的，约占40％ 患者表现出抵抗力，导致总体生存率差。 TNBC的特征是显着的肿瘤 异质性，这进一步使治疗复杂化。 TNBC患者化学抗性的机制 保持不当理解，部分原因是缺乏可用的方法和模型来测量肿瘤内 随着时间的推移，异质性和轨道变化的异质性肿瘤组成。在这里，我们建议使用 在对不同的化学治疗剂响应时跟踪单个细胞和克隆的新技术；这 有关肿瘤细胞种群的更多详细信息将用于更好地构建数学模型 预测并优化治疗反应。我们首先测量单个细胞基因表达的变化 对治疗的反应，然后将这些测量汇总到细胞亚群轨迹中，采用 我们实验室开发的条形码技术的优势，用于量化克隆可分离的单细胞 转录组。这些目标1研究将建立基因表达，细胞生长和生存的纲要 描述了三重亚型的主要实验模型中的每个异质细胞的数据 负乳腺癌对临床上与临床相关的治疗剂的反应。层层克隆的新能力 有关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 逃生疗法。它还区分了先前存在的治疗耐药性的亚群和 诱导抗性状态。在较高的概念层面上，该提议也试图解决广泛的 实用挑战：在许多大规模努力点中收集的高维度“ OMIC”数据通常 疾病进展的相关性，但对建立机械模型的帮助没有信息 预测肿瘤反应。通常，其他类型的数据更容易获得 - 较低的尺寸数据 进行更频繁的测量。因此，我们接下来问：如何集成这些不同的数据类型 成为一个有用的框架，以建立肿瘤细胞对治疗反应的预测模型？这似乎是一个合适的目标 用于癌症社区的系统生物学。我们建议通过条形码跟踪来应对这一挑战 技术;敏感和抗性表型的相对部分以及独立的纵向 细胞数的测量（低维数据），成为机械模型的输入以预测 治疗反应和抗性（AIM 2）。在AIM 3中，我们将执行轨迹映射和模型测试 使用患者来源的三重阴性乳腺癌细胞，以了解转化的潜力 公用事业。通过将不同的数据类型集成到一个内聚框架中，我们旨在描述敏感和 TNBC中的抗性亚群会因治疗而生长，死亡和过渡。