The MSK Genomic Data Analysis Center for Tumor Evolution

MSK 肿瘤进化基因组数据分析中心

基本信息

批准号：
10469512
负责人：
Nikolaus Schultz
金额：
$ 41.63万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
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：
2021-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10469512
关键词：
Aftercare Alleles Anatomy Area Biological Assay Biological Process Biopsy Cancer Center Cell Communication Cells ClinVar Clinical Clonal Evolution Clonal Expansion Computer software Copy Number Polymorphism Custom DNA DNA Mutational Analysis DNA Sequence Alteration DNA sequencing Data Data Analyses Data Sources Diagnosis Disease Disease Progression Engineering Epigenetic Process Evolution Gene Cluster Gene Expression Profile Genes Genome Data Analysis Center Genomics Growth Heterogeneity Human Characteristics Immune Individual Infrastructure Investigation Laboratories Lead Literature Machine Learning Malignant Neoplasms Malignant neoplasm of ovary Maps Measures Methods Modeling Modification Molecular Monitor Mutation Neoplasm Metastasis Oncogenic Pathogenesis Pathway interactions Patient Monitoring Patients Pharmacotherapy Phenotype Phylogenetic Analysis Ploidies Population Positioning Attribute Prevalence Primary Neoplasm Process RNA Recurrence Relapse Research Personnel Resistance Resolution Sample Size Sampling Scientist Series Signal Pathway Site Somatic Mutation Specificity Statistical Methods Statistical Models Surveys The Cancer Genome Atlas Therapeutic Intervention Time Treatment Failure Trees Variant Visualization Work analytical method base cBioPortal cancer cell cancer genome cancer genomics cancer prevention cancer type cell free DNA cell killing cell type clinical development clinical sequencing data visualization disorder later incidence prevention driver mutation exome fitness genetic variant genome analysis genome sequencing improved innovation lens longitudinal analysis multimodality neoplastic cell new therapeutic target novel patient population precision oncology programs resistance mutation single cell analysis single-cell RNA sequencing software infrastructure targeted treatment therapy development therapy resistant time use tool transcriptome transcriptome sequencing treatment response tumor tumor growth tumor heterogeneity tumor progression whole genome

项目摘要

Abstract The MSK Genomic Data Analysis Center for Tumor Evolution seeks to implement tools, best practices and analytical workflows for studying cancer evolution from cancer genome and transcriptome sequencing data. Over the last 15 years, survey sequencing of patient populations of many cancer types has elucidated novel driver mutations which are mechanistically responsible for disease pathogenesis. The Cancer Genome Atlas (TCGA) and individual laboratory efforts have broadened the understanding of biological processes impacted by somatic mutation and revealed new therapeutic targets that have achieved clinical impact. However, most of this work has been based on bulk DNA sequencing from primary tumors and single biopsies from patients. It is well understood that cancer is an evolutionary process during which clonal expansions within patients generates heterogeneity and phenotypic diversity of cell populations across metastatic sites over time (with or without therapeutic intervention). Indeed, the same targeted therapies developed based on mutation discoveries often select for resistant clones, keeping durable cures out of reach. We will develop analytical methods, tools and software infrastructure to study cancer progression through the lens of evolution, shifting emphasis from analysis of primary tumors to dynamic analyses over clinical trajectories. We expect our program will advance the ability to study clinical trajectories of patients in a more comprehensive approach, including temporal, spatial and single cell analysis to better represent the full clonal repertoires of tumors and to study the determinants of how and why tumors evolve. We use tools, well established in our laboratories, in three key areas: i) variant interpretation from metastatic and post-treatment samples for discovery of therapeutic resistance mutations (Aim 1); ii) multi- sample analysis across anatomic space, and/or time series data from serial biopsy or cell free DNA to track and model clonal dynamics (Aim 2); iii) single cell approaches for clonal decomposition and clone-specific phenotyping within patients (Aim 3). Our team is well positioned to carry out our objectives having developed leading software infrastructures supporting TCGA and clinical sequencing through MSK-IMPACT, development of clinically approved assays for longitudinal monitoring of patients through cell free DNA sequencing (MSK- ACCESS) and through study of clonal evolution at bulk and single cell resolution. We will implement and improve tools to support each of these aims, including Cancer Hotspots, OncoKB, and cBioPortal for Aim 1, PyClone and fitClone for Aim 2 and CloneAlign and CellAssign for Aim 3, tailoring and customizing software to support investigations into the dynamic and evolutionary nature of human cancers. These tools comprise a software infrastructure focused on cancer evolution through variant allele interpretation, multi-sample analysis and single cell investigation. Our infrastructure will enable researchers to automate evolutionary interpretation of disease dynamics to better understand the clinical end points of metastatic progression and therapeutic resistance.

抽象的 MSK 肿瘤进化基因组数据分析中心致力于实施工具、最佳实践和用于从癌症基因组和转录组测序数据研究癌症进化的分析工作流程。在过去 15 年中，对多种癌症类型的患者群体进行的调查测序已经阐明了新的发现驱动突变在机械上负责疾病的发病机制。癌症基因组图谱（TCGA）和各个实验室的努力拓宽了对受生物过程影响的理解体细胞突变并揭示了已取得临床影响的新治疗靶点。然而，这大部分工作基于原发性肿瘤的大量 DNA 测序和患者的单次活检。很好了解癌症是一个进化过程，在此过程中患者体内的克隆扩张产生随着时间的推移，转移部位细胞群的异质性和表型多样性（有或没有治疗干预）。事实上，基于突变发现而开发的相同靶向疗法经常选择耐药克隆，使持久治疗遥不可及。我们将开发分析方法、工具和通过进化的视角研究癌症进展的软件基础设施，将重点从分析转移原发肿瘤的临床轨迹动态分析。我们希望我们的计划能够提高能力以更全面的方法研究患者的临床轨迹，包括时间、空间和单一细胞分析，以更好地代表肿瘤的完整克隆库，并研究如何和如何的决定因素为什么肿瘤会进化。我们在三个关键领域使用实验室成熟的工具：i) 变异解释从转移和治疗后样本中发现治疗耐药突变（目标 1）； ii) 多跨解剖空间的样本分析，和/或来自连续活检或无细胞 DNA 的时间序列数据，以跟踪和克隆动力学模型（目标 2）； iii) 用于克隆分解和克隆特异性的单细胞方法患者体内的表型分析（目标 3）。我们的团队有能力实现我们已制定的目标通过 MSK-IMPACT 开发支持 TCGA 和临床测序的领先软件基础设施通过无细胞 DNA 测序（MSK- ACCESS）并通过批量和单细胞分辨率的克隆进化研究。我们将落实并改进支持每个目标的工具，包括 Cancer Hotspots、OncoKB 和 cBioPortal for Aim 1、PyClone 用于 Aim 2 的 fitClone 以及用于 Aim 3 的 CloneAlign 和 CellAssign，剪裁和定制软件以支持研究人类癌症的动态和进化本质。这些工具包括一个软件通过变异等位基因解释、多样本分析和单样本分析，重点关注癌症进化的基础设施细胞调查。我们的基础设施将使研究人员能够自动解释疾病的进化动力学，以更好地了解转移进展和治疗耐药的临床终点。