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: 9920135
• 负责人: Jeffrey Hsu-Min Chuang
• 金额: $ 64.94万
• 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/9920135
• 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.

项目摘要 肿瘤异质性对于癌症生物学至关重要，因为治疗的细胞种群的差异存活率为 负责抵抗。因此，了解亚克隆进化的速率对于开发治疗至关重要 可以最大程度地减少抵抗力和死亡率的策略。但是，定量肿瘤内人群 仍然是一个挑战。这是因为准确所需的样本的数量和多样性 量化以及计算推理算法的最佳参数选择是未知的。 我们的实验室表明，当前的测量方法，例如单样本外显季节不是很好 有动力区分肿瘤内的进化过程。结果，即使是基本的进化 问题，例如肿瘤中中性和适应性进化的平衡，仍然引起争议。经过 进行一系列多样本的多样性，多样化的比较测序分析 负乳腺癌异种移植物，我们已经确定了与密切相关的亚克隆人群的系统 对顺铂治疗有不同反应的肿瘤。这种子克隆系统的独特特征是 可以在共同的器官中对其进行处理，以确定依赖治疗的进化动力学 相关的癌症亚群。我们建议将该系统与高通量测序一起利用 以及在工程器官上的强大的高含量共聚焦成像技术，以提供经过验证的 定量计算方法准确测量三阴性乳房的肿瘤异质性 癌症和其他实体瘤。该项目将由癌症基因组学专家J. Chuang PhD领导， 计算生物学和分子进化。 PI与E. Liu MD和F. Menghi PhD合作（乳房 癌症遗传学）和O. anczukow-camarda博士（癌基因表达和器官），在协调中 由P. Robson博士在杰克逊实验室领导的单细胞生物学核心。目标1。证书 使用测序和对患者衍生癌症的高内含物共焦成像对异质性进行定量 器官混合物。 AIM 2。优化确定测序异质性的计算方法 和空间数据。目标3。确定大癌症数据中肿瘤内选择的患病率。影响： 结果将为开发首次基于进化的癌症治疗方法铺平道路 这可能会大大改善转移性癌症的发病率和死亡率。