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

项目摘要 肿瘤异质性对于癌症生物学是必不可少的，因为治疗的细胞群的差异存活是 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，肿瘤内群体的定量 仍然是一个挑战。这是因为准确测量所需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。 我们的实验室已经表明，目前的测量方法，如单样本exome-seq，并不好- 能够区分肿瘤内的进化过程。因此，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，仍然存在激烈的争论。通过 进行了一系列广泛的多样本，多治疗比较测序分析的三重， 阴性乳腺癌异种移植物，我们已经确定了一个系统的密切相关的亚克隆群体内 对顺铂治疗反应不同的肿瘤。这种亚克隆系统的一个独特特征是 它们可以在一个共同的类器官中被处理，以确定它们的处理依赖性进化动力学。 相关癌症亚群。我们建议利用这个系统与高通量测序一起 以及强大的高内涵共聚焦成像技术，为工程类器官提供经验证的 准确测量三阴性乳腺癌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 MD和F. Menghi博士（乳房 cancer genetics）和O. Anczukow-Camarda博士（癌症基因表达和类器官），协调 由杰克逊实验室的P. Robson博士领导的单细胞生物学核心。目标1.凭证 使用测序和高内容共聚焦成像对患者来源的癌症进行异质性量化 类器官混合物目标二。优化计算方法以确定测序的异质性 空间数据。目标3。确定肿瘤内选择在癌症大数据中的流行率。影响： 这些结果将为开发第一种基于进化的癌症治疗方法铺平道路， 其可显著改善转移性癌症的发病率和死亡率。

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.