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 转移是癌症的最终生物学阶段，是大多数与癌症相关的死亡的原因。和 每种癌症类型扩散到一小部分地点，我们知道转移不是一个随机过程。然而， 甚至相同类型的肿瘤在不同地点的种子转移的潜力显着不同，导致 患者的生存率差异很大，可能是最佳治疗。目前，我们无法准确预测 特定患者的癌症是否会转移。只有一小部分接受有毒的患者 而且昂贵的疗法受益于此 - 但我们不知道如何识别这一部分。因此，我们面对面 癌症研究中的多个未满足的科学和临床挑战，只能通过确定才能克服 控制各个癌症转移进展的进化规则。 通过使用重建的癌症系统发育，我们最近表明一些有色癌症患者 表现出转移的共同起源，而其他人则表现出多种不同的转移来源。初步的 分析表明，系统发育和转移的根可用于对患者进行分层。测试这个 假设，我们提出以下三个特定目的：i）基于基于基准的全面基准制定 关于跨越八种方法的既定种群遗传学模型 转移性克隆，ii）均匀推断出49个胰腺和17结直肠肠的转移性播种模式 癌症患者（528个肿瘤样本），以确定癌症系统发育的预测能力并量化 每个患者内转移的拓扑分布，以及iii）开发数学模型 表征不同模式的后果和传播和殖民的节奏，从而 提供一个定量框架，以将观察到的转移播种模式进行环境化。初步的 计算显示高度非随机模式，表明原发性肿瘤中的某些亚群 转移能力急剧增加。我的长期目标是识别和量化进化模式 通过预测转移潜力并提供迫切需要的癌症来改善患者提示 临床上的信息向医生提供个性化治疗计划。 除了这一独立途径奖的重要科学目标外，我们还开发了一个 课程定位领域，我将在更深入的培训和心理中受益匪浅 成为独立研究者。因此，我们在此问题期间提出了一系列培训活动 在翻译的生物医学环境中获得经验并提高我的跨学科技能的阶段， 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，并将为我自己的稳健基础提供自己的基础 实验室并在R00阶段编写独立的R01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