Inferring the roots of metastases and their effects on patient survival

推断转移的根源及其对患者生存的影响

• 批准号: 10197844
• 负责人: Johannes G Reiter
• 金额: $ 24.5万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-08-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10197844
• 关键词: Address; Area; Award; Benchmarking; Biological; Birth; Cancer Etiology; Cancer Patient; Cells; Cessation of life; Clinic; Clinical; Code; Collaborations; Colon Carcinoma; Colorectal; Colorectal Cancer; Communities; Computer software; Computing Methodologies; Conflict (Psychology); Coupled; Data; Data Analyses; Development; Diagnosis; Disease; Distant; Educational Curriculum; Ensure; Environment; Evolution; Exhibits; Face; Foundations; Funding; Future; Genes; Genetic Models; Goals; Individual; Laboratories; Lymphatic; Malignant Neoplasms; Malignant neoplasm of pancreas; Mentors; Metastatic/Recurrent; Methods; Modeling; Neoplasm Metastasis; Outcome; Pancreas; Pathway interactions; Patients; Pattern; Phase; Phenotype; Phylogenetic Analysis; Phylogeny; Physicians; Plant Roots; Population Genetics; Primary Neoplasm; Probability; Process; Prognosis; Program Development; Recurrence; Research; Research Personnel; Resources; Sampling; Scientific Advances and Accomplishments; Scientist; Seeds; Series; Site; Statistical Methods; Testing; Training; Training Activity; Translations; Uncertainty; Work; Writing; anticancer research; base; big biomedical data; biomedical data science; cancer cell; cancer type; career development; clinical decision support; clinical decision-making; clinically actionable; cohort; colon cancer patients; design; driver mutation; experience; improved; in silico; individual patient; individualized medicine; insight; mathematical model; migration; next generation; optimal treatments; patient stratification; personalized medicine; precision medicine; prognostic value; programs; reconstruction; skills; tool; treatment planning; tumor; tumor progression

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.

项目摘要 转移是癌症的最后生物学阶段，是大多数癌症相关死亡的原因。与 每一种癌症类型都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，在这方面， 即使是同一类型的肿瘤在不同部位的转移潜能也有显著差异， 极大地改变了患者的存活率和潜在的次优治疗。目前，我们无法准确预测 特定病人的癌症是否会转移只有一小部分接受毒性药物治疗的患者 昂贵的治疗方法也从中受益--但我们不知道如何确定这一部分。因此，我们面临 癌症研究中存在着许多尚未解决的科学和临床挑战，只有通过确定 控制个别癌症转移进展的进化规则。 通过利用重建的癌症基因，我们最近发现，一些结直肠癌患者， 表现出共同的转移起源，而其他的表现出多个不同的转移起源。初步 分析表明，肿瘤的起源和转移的根源可用于对患者进行分层。为了验证这一 假设，我们提出了以下三个具体目标：i）执行全面的计算机基准测试， 基于八种方法建立的群体遗传学模型，以有力地推断传播的根源。 转移性克隆，ii）在49个胰腺和17个结直肠队列中推断一致的转移性播种模式 癌症患者（528个肿瘤样本），以确定癌症发生的预测能力，并量化 每个患者内转移瘤的拓扑分布，以及iii）开发数学模型， 描述传播和殖民化的不同模式和节奏的后果， 提供了一个定量的框架，使观察到的转移性播种模式的背景化。初步 计算显示出高度非随机的模式，表明原发性肿瘤中的一些亚群 转移能力急剧增加。我的长期目标是确定和量化 通过预测转移潜力来改善患者预后， 临床上可操作的信息提供给医生用于个性化的治疗计划。 除了这个独立之路奖的重要科学目标外，我们还开发了一个 课程针对的领域，我将高度受益于更深入的培训和指导之前， 成为独立调查员。因此，我们建议在接受辅导期间举办一系列培训活动， 阶段，以获得在翻译生物医学环境中的经验，并发展我的跨学科技能， 特别是在大规模生物医学数据分析的新兴领域。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R 00阶段，您可以在实验室中编写一份独立的R 01提案。

项目成果

A mathematical model of ctDNA shedding predicts tumor detection size.

• DOI: 10.1126/sciadv.abc4308
• 发表时间: 2020-12
• 期刊: Science advances
• 影响因子: 13.6
• 作者: Avanzini S;Kurtz DM;Chabon JJ;Moding EJ;Hori SS;Gambhir SS;Alizadeh AA;Diehn M;Reiter JG
• 通讯作者: Reiter JG