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

项目摘要 肿瘤和生殖系样本的全面分子表征的可及性增加， 癌症患者加速了转化发现并显著影响了患者护理。这些 这些方法最终形成了精确癌症医学的基础， 关于患者肿瘤和生殖系基因组谱的数据，具体定义为诊断、预后和 预测性标记物在护理点用于指导治疗决策。虽然这些战略 已经在某些用例中取得了成功，了解体细胞和生殖细胞成分的方法 通常独立考虑癌症患者，并系统表征相互作用 在诊断和预测临床相关性的背景下， 尚未在大型患者队列中进行系统性研究。这部分是由于 能够同时考虑这些特征的计算算法，沿着缺乏耐心， 同时具有体细胞和生殖细胞特征的队列，以及相关治疗反应的临床注释， 指导这些调查。我们之前的研究已经证明，通过创新的计算 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。该提案的目的是直接 回答挑衅性问题PQ 3：生殖系变异和体细胞变异之间的遗传相互作用 突变导致肿瘤演变或对治疗反应的差异？我们的总体 一种假说认为，关键DNA内部和之间种系和体细胞特征之间复杂的相互作用 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 和新兴的免疫疗法。具体而言，在本提案中，我们将利用现有和新出现的队列 来自患者的肿瘤和生殖系全外显子组/转录组数据，沿着相关表型数据 关于对化疗和免疫疗法的反应，并开发创新的计算生物学 算法来系统地剖析这些群体，并确定生殖系和 躯体事件塑造临床可操作性。这项提案的独特之处在于，它利用了广泛而新颖的 丹娜-法伯癌症研究所/哈佛癌症中心和麻省理工学院布罗德研究所的资源， 哈佛，沿着一个国际合作者小组，来解决这里概述的假设。的 提出的具体目标是：1）通过整合来确定实体瘤中的遗传性癌症风险 计算生物学，2）评估体细胞和生殖系相互作用对DNA修复缺陷的影响 和对实体瘤中基于铂的化疗的反应，以及3）鉴定体细胞和生殖细胞 这些特征协调改变免疫微环境并影响对免疫应答的选择性反应。 实体瘤中的检查点阻断。这些研究将确定生殖系和体细胞之间的关键关系 形成肿瘤生物学的变异，对理解患者发生癌症的风险具有意义， 对化疗和免疫治疗的选择性反应。此外，该项目还将建立新的 计算算法，使广泛的综合考虑生殖系和体细胞特征， 在科学界得到更广泛的应用。最后，该项目将加速生殖细胞的临床相关性 和体细胞分子分析，以实现精确的癌症医学，并更广泛地作为一种创新的 将临床肿瘤学与计算生物学相结合的模型。

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.