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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要肿瘤和种系样本的综合分子表征的可及性提高癌症患者加速了转化发现并显着影响了患者护理。方法最终构成了精准癌症医学的基础，其中“临床上可行的”分子有关患者肿瘤和种系基因组图谱的数据，具体定义为诊断、预后和这些策略在护理时使用预测标记来指导治疗决策。在某些用例中取得了成功，理解体细胞和种系成分的方法癌症患者的癌症通常被独立考虑，并且系统地表征相互作用在诊断和预测临床相关性的背景下，体细胞和种系基因组之间的关系尚未在大量患者中系统地进行，这部分是由于缺席的结果。能够同时考虑这些特征的计算算法，以及缺乏耐心具有体细胞和种系特征的队列以及相关治疗反应的临床注释我们之前的研究已经证明，通过创新的计算来指导这些调查。肿瘤学方法，综合种系和体细胞分析如何确定诊断和预测在选定的临床环境中具有直接临床影响的功能该提案的目标是直接。回答挑衅性问题 PQ3：种系变异和体细胞之间的遗传相互作用吗？突变导致肿瘤进化或治疗反应的差异？假设是关键 DNA 内部和之间的种系和体细胞特征之间存在复杂的相互作用修复和免疫途径介导遗传性临床风险以及对现有化疗的选择性反应具体来说，在本提案中，我们将利用现有和新兴的队列。来自患者的肿瘤和种系全外显子组/转录组数据以及相关表型数据关于化疗和免疫疗法的反应，并开发创新的计算生物学系统地剖析这些群体并确定种系和种系之间如何相互作用的算法该提案的独特之处在于它利用了广泛且新颖的内容。丹纳法伯癌症研究所/哈佛大学癌症中心和麻省理工学院博德研究所的资源哈佛大学与国际合作者团队一起解决本文概述的假设。提出的具体目标是： 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实体瘤对铂类化疗的反应，以及 3) 鉴定体细胞和种系协调改变免疫微环境并影响免疫选择性反应的特征这些研究将确定生殖系和体细胞之间的关键关系。塑造肿瘤生物学的变异，对于了解患者癌症发展的风险和此外，该项目还将建立新的对化疗和免疫疗法的选择性反应。计算算法能够广泛综合考虑种系和体细胞特征最后，该项目将加速生殖系的临床相关性。和体细胞分子分析，以实现精准癌症医学，并作为更广泛的创新临床肿瘤学与计算生物学交叉的模型。