The joint WCM-NYGC Center for Functional and Clinical Interpretation of Tumor Profiles

威尔康奈尔医学-纽约基因组中心(WCM-NYGC)功能和临床中心 肿瘤轮廓的解释是根据RFA-CA-20-053提交的。继续我们的 过去五年参与基因组数据分析网络(GDAN)并利用 由我们小组开发的新算法和方法，该中心将对 编码和非编码变体，以解开特定类别突变的功能并评估其 临床潜力。正如RFA中规定的那样，我们选择专注于两个核心能力：(1) DNA突变(编码区和非编码区、体细胞和/或生殖系)和(2)复制 数 / 纯度 分析 ，专注于复杂的结构变量。我们的团队开发了新的算法 和分析编码区和非编码区DNA突变的管道 复杂的结构变异、肿瘤进化和连锁阅读测序。我们已经开发了三个 明确的目标。在目标1中，我们将对编码和非编码进行系统的临床和功能注释 编码突变。这包括(1)编码变体的临床注释，(2)优先顺序和功能 非编码变体的注释(3)整合转录分析，如细胞类型 不纯肿瘤样本的去卷积为体细胞变异提供间质背景(4)相关性 具有临床表型的变异，包括对治疗的反应。在目标2中，我们将进行临床分析 全基因组体细胞变化模式的相关特征。我们将利用我们最先进的技术 复杂结构变异表征和突变地形连接的分析工具(1) 突变过程和(2)起源细胞足迹对癌症结局和药物反应的影响。我们会 此外(3)采用我们尖端的基因组图可视化工具来构建交互式数据门户 浏览不纯样品中复杂的结构变化模式。在目标3中，我们将识别和 使用多样本分析来表征驱动肿瘤进化的变异。我们将应用我们的状态- 最先进的计算工具研究多个肿瘤样本的结构变异进化(1) 在匹配的原发和复发/转移样本中确定耐药和复发的驱动因素 以及(2)评估原发肿瘤和匹配的肿瘤器官之间的基因组差异。

The Weill Cornell Medicine-New York Genome Center (WCM-NYGC) Center for Functional and Clinical Interpretation of Tumor Profiles is submitted in response to RFA-CA-20-053. Continuing our involvement in the Genome Data Analysis Network (GDAN) over the past five years and leveraging novel algorithms and methods developed by our group, the Center will perform integrative analyses of coding and non-coding variants to unravel the function of specific classes of mutations and assess their clinical potential. As specified in the RFA, we have chosen to focus on two Core Competencies: (1) DNA Mutations (in coding and non-coding regions, somatic and/or germline) and (2) Copy Number / Purity Analysis ,with a focus on complex structural variants.Our team has developed novel algorithms and pipelines for the analysis of DNA mutations in coding and non-coding regions, characterization of complex structural variants, tumor evolution and linked-read sequencing. We have developed three Specific Aims. In Aim 1, we will perform systematic clinical and functional annotation of coding and non- coding mutations. This includes (1) clinical annotation of coding variants, (2) prioritization and functional annotation of non-coding variants (3) integration of transcriptomic analyses, such as cell type deconvolution of impure tumor samples to provide stromal context to somatic variants (4) correlation of variants with clinical phenotypes, including response to therapy. In Aim 2, we will analyze clinically relevant signatures of genome-wide somatic alteration patterns. We will utilize our state-of-the-art analytic tools for complex structural variant characterization and mutational topography to link (1) mutational processes and (2) cell-of-origin footprints to cancer outcome and drug response. We will also (3) adapt our cutting-edge genome graph visualization tools to build interactive data portals for browsing complex structural variation patterns in impure samples. In Aim 3, we will dentify and characterize variants that drive tumor evolution using multi-samples analysis. We will apply our state- of-the-art computational tools to study structural variant evolution across multiple tumor samples to (1) identify drivers of drug resistance and relapse in matched primary and recurrence/metastasis samples and (2) assess genomic divergence between primary tumors and matched tumor organoids.