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

WCM-NYGC 肿瘤特征功能和临床解读联合中心

基本信息

批准号：
10302065
负责人：
Olivier Elemento
金额：
$ 42.63万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-16 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10302065
关键词：
Address Algorithms CLIA certified Cells Clinical Code Competence Complex Computational Biology DNA Mutational Analysis DNA Sequence Alteration Data Development Doctor of Philosophy Drug resistance Evolution Genome Genome Data Analysis Network Genomics Graph Institutes Joints Link Malignant Neoplasms Medical Oncologist Medicine Methods Mutation Neoplasm Metastasis New York Organoids Outcome Pattern Pharmaceutical Preparations Phase Play Preparation Primary Neoplasm Process Publishing Recurrence Research Personnel Sampling Specific qualifier value Structure Testing Untranslated RNA Variant Visualization software algorithmic methodologies analysis pipeline analytical tool cancer genomics cell type clinical phenotype clinically relevant computer infrastructure computerized tools data management data portal drug relapse exome sequencing experience genome sequencing genome-wide innovation novel precision medicine response single cell sequencing tool transcriptome sequencing transcriptomics tumor tumor heterogeneity whole genome

项目摘要

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.

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概况的解释是针对 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) 并利用我们小组开发的新颖算法和方法，中心将进行综合分析编码和非编码变体来揭示特定类别突变的功能并评估其临床潜力。根据 RFA 中的规定，我们选择关注两项核心能力：(1) DNA 突变（编码区和非编码区、体细胞和/或种系）和 (2) 拷贝数字 / 纯度分析，重点关注复杂的结构变体。我们的团队开发了新颖的算法用于分析编码区和非编码区 DNA 突变、表征的管道复杂的结构变异、肿瘤进化和链接读测序。我们开发了三具体目标。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) 优先级和功能非编码变异的注释 (3) 转录组分析的整合，例如细胞类型对不纯肿瘤样本进行反卷积，为体细胞变异提供基质背景 (4) 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改变模式的相关特征。我们将利用我们最先进的技术用于复杂结构变异表征和突变拓扑链接的分析工具 (1) 突变过程和（2）细胞源足迹对癌症结果和药物反应的影响。我们将还 (3) 采用我们最先进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异模式。在目标 3 中，我们将确定并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，用于研究跨多个肿瘤样本的结构变异进化（1）确定匹配的原发和复发/转移样本中耐药性和复发的驱动因素 (2)评估原发性肿瘤和匹配的肿瘤类器官之间的基因组差异。