Quantitative Computational Methods to Accurately Measure Tumor Heterogeneity in Solid Tumors to Inform Development of Evolution-based Treatment Strategies

准确测量实体瘤中肿瘤异质性的定量计算方法，为基于进化的治疗策略的开发提供信息

• 批准号: 10172870
• 负责人: Jeffrey Hsu-Min Chuang
• 金额: $ 37.08万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-05 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10172870
• 关键词: Accounting; Aftercare; Algorithms; Breast Cancer Genetics; Cancer Biology; Cancer Model; Cell Line; Cells; Cellular biology; Characteristics; Cisplatin; Computational Biology; Computer Analysis; Computing Methodologies; Credentialing; DNA; Data; Data Set; Development; Disseminated Malignant Neoplasm; Doctor of Philosophy; Engineering; Environment; Equilibrium; Evolution; Fluorescence; Gene Expression; Genomics; Growth; Heterogeneity; High-Throughput Nucleotide Sequencing; Image; Imaging technology; Label; Malignant Neoplasms; Measurement; Measures; Methods; Modeling; Molecular Evolution; Morbidity - disease rate; Organoids; Patients; Population; Population Heterogeneity; Positioning Attribute; Prevalence; Procedures; Process; RNA; Resistance; Sampling; Series; Solid Neoplasm; System; The Cancer Genome Atlas; The Jackson Laboratory; Time; Uncertainty; Xenograft procedure; base; cancer gene expression; cancer genomics; cancer therapy; comparative; comparative treatment; confocal imaging; exome sequencing; fluorescence imaging; genetic evolution; imaging system; improved; mortality; novel; pressure; response; three dimensional cell culture; transcriptome sequencing; treatment response; treatment strategy; triple-negative invasive breast carcinoma; tumor; tumor heterogeneity; whole genome

PROJECT SUMMARY Tumor heterogeneity is essential to cancer biology, as the differential survival of treated cell populations is responsible for resistance. Understanding rates of subclonal evolution is therefore vital to developing treatment strategies that can minimize resistance and mortality. However, quantification of intratumoral populations remains a challenge. This is because the number and diversity of samples necessary for accurate quantification, as well as the optimal parameter choices for computational inference algorithms, are unknown. Our lab has shown that current measurement approaches such as single-sample exome-seq are not well- powered to distinguish evolutionary processes within tumors. As a result, even fundamental evolutionary questions, such as the balance of neutral and adaptive evolution in tumors, remain hotly contested. By performing an extensive series of multi-sample, multi-treatment comparative sequencing analyses of triple- negative breast cancer xenografts, we have identified a system of closely-related subclonal populations within a tumor that respond differentially to cisplatin treatment. A unique characteristic of this system of subclones is that they can be treated in a common organoid to determine treatment-dependent evolutionary dynamics of related cancer subpopulations. We propose to leverage this system together with high-throughput sequencing and a powerful high-content confocal imaging technology on engineered organoids to provide verified quantitative computational methods to accurately measure tumor heterogeneity for triple-negative breast cancer and other solid tumors. The project will be led by J. Chuang PhD, an expert in cancer genomics, computational biology, and molecular evolution. The PI collaborates with E. Liu MD and F. Menghi PhD (breast cancer genetics) and O. Anczukow-Camarda PhD (cancer gene expression and organoids), in coordination with the Single Cell Biology core led by P. Robson PhD at The Jackson Laboratory. Aim 1. Credential the quantification of heterogeneity using sequencing and high-content confocal imaging on patient-derived cancer organoid mixtures. Aim 2. Optimize computational approaches for determining heterogeneity from sequencing and spatial data. Aim 3. Determine the prevalence of intratumoral selection in big cancer data. Impact: Results would pave the way for the development of the first evolution-based approaches to cancer treatment, which may dramatically improve morbidity and mortality in metastatic cancers.

项目概要 肿瘤异质性对于癌症生物学至关重要，因为经过处理的细胞群的存活率存在差异 负责抵抗。因此，了解亚克隆进化的速率对于开发治疗方法至关重要 可以最大限度地减少耐药性和死亡率的策略。然而，肿瘤内群体的量化 仍然是一个挑战。这是因为准确的样本数量和多样性所必需的 量化以及计算推理算法的最佳参数选择都是未知的。 我们的实验室已经表明，当前的测量方法（例如单样本外显子组测序）效果不佳- 能够区分肿瘤内的进化过程。结果，即使是基本的进化 诸如肿瘤中性进化和适应性进化的平衡等问题仍然存在激烈争议。经过 对三重样本进行一系列广泛的多样本、多处理比较测序分析 阴性乳腺癌异种移植物，我们已经确定了一个密切相关的亚克隆群体系统 对顺铂治疗反应不同的肿瘤。该亚克隆系统的一个独特特征是 他们可以在一个共同的类器官中进行治疗，以确定治疗依赖性的进化动力学 相关癌症亚群。我们建议将该系统与高通量测序结合使用 以及针对工程类器官的强大高内涵共聚焦成像技术，以提供经过验证的 精确测量三阴性乳腺肿瘤异质性的定量计算方法 癌症和其他实体瘤。该项目将由癌症基因组学专家 J. Chuang 博士领导， 计算生物学和分子进化。 PI 与 E. Liu 医学博士和 F. Minghi 博士（乳腺癌）合作 癌症遗传学）和 O. Anczukow-Camarda 博士（癌症基因表达和类器官）合作 由杰克逊实验室 P. Robson 博士领导的单细胞生物学核心。目标 1. 证明 使用测序和高内涵共聚焦成像对源自患者的癌症进行异质性量化 类器官混合物。目标 2. 优化用于确定测序异质性的计算方法 和空间数据。目标 3. 确定癌症大数据中瘤内选择的普遍性。影响： 结果将为开发第一个基于进化的癌症治疗方法铺平道路， 这可能会显着提高转移性​​癌症的发病率和死亡率。