Integrative Somatic and Germline Computational Biology to Redefine Clinical Actionability in Solid Tumors

综合体细胞和种系计算生物学重新定义实体瘤的临床可操作性

• 批准号: 9913487
• 负责人: Eliezer M Van Allen
• 金额: $ 40.72万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9913487
• 关键词: Address; Algorithms; Alternative Splicing; Area; Automobile Driving; Award; Cancer Center; Cancer Etiology; Cancer Patient; Clinical; Clinical Oncology; Communities; Complex; Computational Biology; Computational algorithm; Computing Methodologies; DNA Repair; DNA Repair Gene; Dana-Farber Cancer Institute; Data; Decision Making; Defect; Development; Diagnostic; Dissection; Etiology; Event; Evolution; Genes; Genetic; Genetic Polymorphism; Genetic Transcription; Genome; Genomics; Germ-Line Mutation; Goals; Immune; Immune signaling; Immunotherapy; Infrastructure; Inherited; Institutes; Interdisciplinary Study; International; Investigation; Malignant Neoplasms; Malignant neoplasm of urinary bladder; Mediating; Medical; Modality; Modeling; Molecular; Molecular Profiling; Mutation; Oncology; Pathogenicity; Pathway interactions; Patient Care; Patient risk; Patients; Pattern; Platinum; Process; Prognostic Marker; Research; Resources; Risk; Risk Assessment; Role; Sampling; Shapes; Signal Pathway; Solid Neoplasm; Somatic Mutation; Splice-Site Mutation; Therapeutic; Translating; Tumor Biology; Variant; Work; advanced prostate cancer; base; cancer genomics; cancer initiation; cancer risk; cancer therapy; cancer type; checkpoint therapy; chemotherapy; clinical development; clinical predictors; clinical risk; clinically actionable; clinically relevant; cohort; diagnostic biomarker; exome; experimental study; genomic profiles; immune checkpoint blockade; innovation; loss of function; molecular marker; novel; oncology program; phenotypic data; point of care; precision oncology; predictive marker; prognostic; programs; response; transcriptome; treatment response; tumor; tumor-immune system interactions

PROJECT SUMMARY The increased accessibility of comprehensive molecular characterization of tumors and germline samples from cancer patients has accelerated translational discoveries and significantly impacted patient care. These approaches ultimately form the basis for precision cancer medicine, whereby “clinically actionable” molecular data about a patient's tumor and germline genomic profile, specifically defined as diagnostic, prognostic, and predictive markers, are used at the point of care to guide treatment decision-making. While these strategies have been successful in certain use cases, the approaches to understand somatic and germline components of cancer patients are typically considered independently, and systematic characterization of the interaction between the somatic and germline genomes in the context of diagnostic and predictive clinical relevance have not yet been systematically performed across large cohorts of patients. This is in part the result of an absence of computational algorithms that are able to consider these features simultaneously, along with a lack of patient cohorts with both somatic and germline features and clinical annotations of relevant treatment responses to guide these investigations. Our previous studies have demonstrated, through innovative computational oncology approaches, how integrated germline and somatic analysis can determine diagnostic and predictive features that have immediate clinical impact in select clinical contexts. The goal of this proposal is to directly respond to Provocative Question PQ3: Do genetic interactions between germline variations and somatic mutations contribute to differences in tumor evolution or response to therapy? Our overarching hypothesis is that complex interactions between germline and somatic features within and across key DNA repair and immune pathways mediate inherited clinical risk, and selective response to existing chemotherapies and emerging immunotherapies. Specifically, in this proposal, we will leverage existing and emerging cohorts of tumor and germline whole exome/transcriptome data from patients, along with relevant phenotypic data regarding response to chemotherapies and immunotherapies, and develop innovative computational biology algorithms to systematically dissect these cohorts and determine how interactions between germline and somatic events shape clinical actionability. This proposal is unique in that it leverages the extensive and novel resources at both the Dana-Farber Cancer Institute/Harvard Cancer Center and the Broad Institute of MIT and Harvard, along with an international team of collaborators, to address the hypotheses outlined herein. The proposed specific aims are: 1) To determine inherited cancer risk in solid tumors through integrative computational biology, 2) To evaluate the impact of somatic and germline interactions on DNA repair defects and response to platinum-based chemotherapies in solid tumors, and 3) To identify somatic and germline features that coordinate to alter the immune microenvironment and impact selective response to immune checkpoint blockade in solid tumors. These studies will define key relationships between germline and somatic variants that shape tumor biology, with implications for understanding patient risk for cancer development and selective response to chemotherapy and immunotherapy. In addition, this project will establish new computational algorithms to enable widespread integrated consideration of germline and somatic features for broader use in the scientific community. Finally, this project will accelerate the clinical relevance of germline and somatic molecular profiling to enable precision cancer medicine, and serve more broadly as an innovative model for intersecting clinical oncology with computational biology.

项目摘要 从 癌症患者已经加快了转化发现，并显着影响患者护理。这些 方法最终构成了精密癌症医学的基础，从而“临床上可起作”分子 有关患者肿瘤和种系基因组特征的数据，该数据特异性定义为诊断，预后和 预测标记在护理点使用以指导治疗决策。而这些策略 在某些用例中取得了成功，了解躯体和种系组件的方法 通常将癌症患者独立考虑，并系统地表征相互作用 在诊断和预测性临床相关性的背景下，体细胞和种系基因组之间 尚未系统地在大量患者中进行。这部分是缺席的结果 能够简单地考虑这些功能的计算算法以及缺乏患者 具有体细胞和种系特征和相关治疗反应的临床注释的队列 指导这些投资。我们以前的研究通过创新的计算证明了 肿瘤学方法，整合种系和躯体分析如何确定诊断和预测 在某些临床环境中具有立即临床影响的特征。该提议的目的是直接 回答挑衅性问题PQ3：种系变异与体细胞之间的遗传相互作用 突变导致肿瘤进化或对治疗反应的差异？我们的总体 假设是生殖线与跨关键DNA之间的种系和躯体特征之间的复杂相互作用 维修和免疫途径媒体继承了临床风险，并选择性反应现有化学疗法 和新兴的免疫疗法。特别是在此提案中，我们将利用现有和新兴人群 来自患者的肿瘤和种系全外显/转录组数据以及相关的表型数据 关注对化学疗法和免疫疗法的反应，并发展创新的计算生物学 算法系统地剖析这些队列并确定种系之间的相互作用如何 躯体事件塑造临床可行性。该提议的独特之处在于它利用了广泛的新颖性 Dana-Farber癌症研究所/哈佛大学癌症中心和麻省理工学院广泛研究所的资源 哈佛大学与国际合作者团队一起解决了这里概述的假设。这 提出的特定目的是：1）通过综合确定实体瘤的遗传性癌症风险 计算生物学，2）评估体细胞和种系相互作用对DNA修复缺陷的影响 以及对实体瘤的基于铂的化学疗法的反应，以及3）识别体细胞和种系 协调以改变免疫微环境并影响免疫反应的特征 检查点封锁在实体瘤中。这些研究将定义种系和躯体之间的关键关系 塑造肿瘤生物学的变体，对了解患者的癌症发展风险和 对化学疗法和免疫疗法的选择性反应。此外，该项目将建立新的 计算算法可以使宽度综合考虑种系和躯体特征的考虑 在科学界更广泛使用。最后，该项目将加速种系的临床相关性 和躯体分子分析以实现精确的癌症医学，并更广泛地作为创新性服务 与计算生物学相交的模型。