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.

项目总结 近年来，诊断和治疗的进步延长了许多三叉戟患者的生命。 乳腺癌是阴性的，但对治疗的抗药性仍然是一个重大的临床和科学挑战。而当 标准护理治疗和化疗对许多TNBC患者有效，约40% 患者表现出抵抗力，导致总体存活率较低。TNBC的特点是显著的瘤内肿瘤 异质性，这使治疗进一步复杂化。TNBC患者化疗耐药机制的研究 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，并跟踪不同的肿瘤成分随时间的变化。在这里，我们建议使用 追踪单个细胞和克隆细胞对不同化疗药物反应的新技术；这 关于肿瘤细胞种群的更详细的信息将被用来建立更好的数学模型 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 对治疗的反应，然后将这些测量结果组合成细胞亚群轨迹， 我们实验室开发的条形码技术用于量化克隆分辨的单个细胞的优势 抄本。这些目标1的研究将建立一个关于基因表达、细胞生长和存活的纲要。 数据说明了每个异质细胞如何在主要的实验模型中分成三种亚型 阴性乳腺癌对临床相关的治疗药物有反应。克隆蛋鸡的新能力 单细胞基因表达数据的识别符信息揭示了单个细胞的详细轨迹 那种逃避疗法。它还区分了具有预先存在的治疗抗药性的亚群和那些 其中诱导了一种抵抗状态。在更高的概念层面上，这项提议还寻求解决广泛的 实践挑战：在多个大规模努力点收集的高维‘组学’数据往往点 疾病进展的相关性，但对于建立机制模型以帮助 对肿瘤反应的预测。通常，其他类型的数据更容易获得--低维数据 有更频繁的测量。因此，我们接下来要问：如何集成这些不同的数据类型 变成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 研究癌症社区的系统生物学。我们建议通过我们的条形码跟踪来应对这一挑战 技术.敏感和抗性表型的相对比例，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 使用患者来源的三重阴性乳腺癌细胞，以了解翻译的可能性 实用程序。通过将不同的数据类型集成到一个紧密结合的框架中，我们的目标是描述如何敏感和 TNBC中的抗性亚群生长、死亡和对治疗的反应过渡。