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

项目总结 肿瘤的异质性对癌症生物学是至关重要的，因为治疗细胞群体的差异生存是 对抵抗负责。因此，了解亚克隆进化的速度对开发治疗方法至关重要 可以将耐药性和死亡率降至最低的策略。然而，对肿瘤内种群的量化 仍然是一个挑战。这是因为样本的数量和多样性对于准确 量化以及计算推理算法的最佳参数选择是未知的。 我们的实验室已经证明，目前的测量方法，如单样本exome-seq，并不是很好。 有能力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 进行一系列广泛的多样本、多处理比较测序分析 阴性的乳腺癌异种移植，我们已经确定了一个密切相关的亚克隆群体系统 对顺铂治疗有不同反应的肿瘤。这种亚克隆系统的一个独特特征是 它们可以在一种常见的有机化合物中进行治疗，以确定依赖于治疗的进化动力学 相关癌症亚群。我们建议将该系统与高通量测序结合使用 以及强大的高含量共聚焦成像技术在工程有机物上提供经过验证的 精确测量三阴性乳腺肿瘤异质性的定量计算方法 癌症和其他实体肿瘤。该项目将由癌症基因组学专家J·庄博士领导。 计算生物学和分子进化。PI与E.Liu医学博士和F.Menghi博士(乳房)合作 癌症遗传学)和O.Anczukow-Camarda PhD(癌症基因表达和有机体)，协调 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.凭据 应用测序和高含量共聚焦成像对患者来源的癌症的异质性进行量化 有机混合物。目标2.优化通过测序确定异质性的计算方法 和空间数据。目的3.在癌症大数据中确定肿瘤内选择的流行率。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， 这可能会显著提高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.