Inferring the roots of metastases and their effects on patient survival

项目总结 转移是癌症的最后生物学阶段，是癌症相关死亡的主要原因。使用 每种癌症类型扩散到一小部分部位，我们知道转移不是一个随机的过程。然而， 即使是同类型的肿瘤，其在不同部位的转移潜能也有显著差异，从而导致 截然不同的患者存活率和潜在的次优治疗。目前，我们无法准确预测 特定患者的癌症是否会转移。只有一小部分接受中毒治疗的患者 昂贵的治疗方法也从中受益--但我们不知道如何识别这一部分。因此，我们面临着 癌症研究中尚未满足的多种科学和临床挑战只能通过确定 个体癌症转移进展的进化规律。 通过利用重建的癌症系统学，我们最近表明，一些结直肠癌患者 表现出共同的转移来源，而其他则表现出多个不同的转移来源。初步 分析表明，系统发展史和转移的根源可以用来对患者进行分层。为了测试这一点 假设，我们提出了以下三个具体目标：i)执行基于 通过八种方法建立种群遗传学模型以有力地推断传播的根源 转移克隆，II)在49例胰腺癌和17例结直肠癌队列中一致推断转移种植模式 癌症患者(528个肿瘤样本)，以确定癌症系统发生的预测能力并量化 每个患者体内转移的拓扑分布，以及iii)开发数学模型以 描述传播和殖民的不同模式和节奏的后果，从而 为观察到的转移种植模式提供一个量化框架。初步 计算显示高度非随机的模式表明原发肿瘤中的一些亚群具有 显著增加了转移能力。我的长期目标是识别和量化进化模式 通过预测转移潜能来改善患者预后，并提供急需的 向医生提供临床上可操作的信息，以制定个性化的治疗计划。 除了独立之路奖的重要科学目标外，我们还开发了一种 课程目标领域，在这些领域，我将从以前更深入的培训和指导中受益匪浅 成为一名独立的调查员。因此，我们建议在被指导期间开展一系列培训活动 在翻译生物医学环境中获得经验并增长我的跨学科技能的阶段， 特别是在新兴的大规模生物医学数据分析领域。这些活动与拟议的 研究将促进我向独立的过渡，并将为我自己的创业提供坚实的基础 在R00阶段进行实验并编写独立的R01建议书。

PROJECT SUMMARY Metastasis, the final biological stage of cancer, is responsible for the majority of cancer-related deaths. With each cancer type spreading to a small set of sites, we know that metastasis is not a random process. However, even tumors of the same type significantly differ in their potential to seed metastases at different sites leading to drastically varying patient survival and potentially sub-optimal treatment. Currently, we cannot accurately predict whether a specific patient’s cancer will become metastatic or not. Only a fraction of patients who receive toxic and expensive therapies benefit from it – but we do not know how to identify this fraction. We therefore face multiple unmet scientific and clinical challenges in cancer research that can only be overcome by determining the evolutionary rules governing metastatic progression of individual cancers. By utilizing reconstructed cancer phylogenies, we recently showed that some colorectal cancer patients exhibit common origin of metastasis while others exhibit multiple distinct origin of metastasis. Preliminary analysis indicates that phylogenies and the roots of metastasis can be utilized to stratify patients. To test this hypothesis, we propose the following three specific aims: i) perform comprehensive in-silico benchmarking based on established population genetics models across eight methods to robustly infer the roots of spreading metastatic clones, ii) uniformly infer metastatic seeding patterns on cohorts of 49 pancreatic and 17 colorectal cancer patients (528 tumor samples) to determine the predictive power of cancer phylogenies and to quantify the topological distribution of metastases within each patient, and iii) develop mathematical models to characterize the consequences of distinct modes and tempos of dissemination and colonization and thereby provide a quantitative framework to contextualize the observed metastatic seeding patterns. Preliminary calculations show highly non-random patterns suggesting that some subpopulations in the primary tumor have drastically increased metastatic capacity. My long-term goal is to identify and quantify the evolutionary patterns of cancer to improve patient prognosis by predicting metastatic potential and provide desperately needed clinically-actionable information to the physicians for a personalized treatment plan. In addition to the important scientific goals of this Pathway to Independence award, we have developed a curriculum targeting areas in which I would highly benefit from more in-depth training and mentoring before becoming an independent investigator. We therefore propose a series of training activities during the mentored phase to gain experience in a translational biomedical environment and to grow my interdisciplinary skill set, particularly in emerging areas of large-scale biomedical data analysis. These activities coupled with the proposed research will facilitate my transition to independence and will provide a strong foundation to start my own laboratory and write an independent R01 proposal during the R00 phase.