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.

项目摘要 近年来，诊断和治疗方面的改进延长了许多三重患者的生命。 虽然目前的乳腺癌患者中有100%为阴性，但对治疗的耐药性仍然是一个重大的临床和科学挑战。而 标准治疗和化疗在许多TNBC患者中是有效的，大约40%的患者 患者表现出耐药性，导致总体存活率差。TNBC的特征在于显著的肿瘤内 异质性，这进一步使治疗复杂化。TNBC患者的化疗耐药机制 仍然知之甚少，部分原因是缺乏可用的方法和模型来测量肿瘤内 异质性和跟踪异质性肿瘤组成随时间的变化。这里我们建议使用一个 新技术跟踪单个细胞和克隆，因为它们对不同的化疗药物有反应;这 关于肿瘤细胞群的更详细的信息将用于更好地建立数学模型， 预测和优化治疗反应。我们首先测量单个细胞基因表达的变化， 然后将这些测量结果组装成细胞亚群轨迹， 我们实验室开发的用于定量克隆分辨单细胞的条形码技术的优势 转录组这些目标1的研究将建立一个基因表达，细胞生长和生存的纲要 数据描述了三重亚型的主要实验模型中的每个异质细胞如何 阴性乳腺癌对临床相关治疗剂有反应。新的能力层克隆 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 逃避治疗它还区分了预先存在治疗耐药性的亚群和 其中诱导抗性状态。在更高的概念层面上，这项建议还试图解决一个广泛的问题， 实际挑战：在许多大规模工作中收集的高维“组学”数据往往指向 疾病进展中的相关性，但没有为建立机制模型提供信息， 预测肿瘤反应。通常，其他类型的数据更容易获得--低维数据 更频繁的测量。因此，我们接下来要问：这些不同的数据类型如何集成 转化为一个有用的框架来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 癌症系统生物学领域的专家。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分数，沿着单独的纵向 细胞数的测量值（低维数据）成为机械模型的输入，以预测 治疗反应和耐药性（目标2）。在目标3中，我们将执行故障映射和模型测试 使用患者来源的三阴性乳腺癌细胞，以了解转译的潜力， 效用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 TNBC中的抗性亚群响应于治疗而生长、死亡和转变。