The MSK Genomic Data Analysis Center for Tumor Evolution

MSK 肿瘤进化基因组数据分析中心

• 批准号: 10671087
• 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
• 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/10671087
• 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; 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; cBioPortal; cancer cell; cancer genome; cancer genomics; cancer type; cell free DNA; cell killing; cell type; clinical development; clinical sequencing; data visualization; 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; relapse prevention; 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

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基因组数据分析中心旨在实施工具，最佳实践和 用于研究癌症基因组和转录组测序数据的癌症进化的分析工作流程。 在过去的15年中，许多癌症类型的患者种群的调查测序阐明了新颖的新型 机械造成疾病发病机理的驱动突变。癌症基因组图集 （TCGA）和个别实验室的努力扩大了对受生物过程影响的理解 体细胞突变并揭示了实现临床影响的新的治疗靶标。但是，大多数 工作是基于原发性肿瘤和患者的单个活检的大量DNA测序。很好 了解癌症是一个进化过程，在此过程中，患者的克隆扩张会产生 随着时间的流逝，转移性部位的细胞群体的异质性和表型多样性（有或没有） 治疗干预）。确实，经常根据突变发现而开发的相同靶向疗法 选择抗性克隆，使耐用的疗法无法触及。我们将开发分析方法，工具和 软件基础设施通过进化的镜头研究癌症的进展，从分析转移了重点 原发性肿瘤进行临床轨迹的动态分析。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全克隆曲目，并研究如何和方式的决定因素 为什么肿瘤进化。我们在实验室中使用的工具在三个关键领域中建立了良好的工具：i）变体解释 从转移和治疗后样品发现治疗性抗性突变（AIM 1）； ii）多 跨解剖空间的样本分析和/或时间序列数据来自串行活检或无细胞DNA到跟踪和 型号克隆动力学（AIM 2）； iii）克隆分解和克隆特异性的单细胞方法 患者内的表型（AIM 3）。我们的团队在发展的目标方面处于良好状态 通过MSK Impact，开发支持TCGA和临床测序的领先软件基础架构 通过无细胞测序对患者进行纵向监测的临床批准测定法（MSK- 访问）以及通过研究批量和单细胞分辨率的克隆进化。我们将实施并改进 支持每个目标的工具，包括癌症热点，Oncokb和cbioportal for AIM 1，Pyclone 以及用于AIM 2和Clonealign和Cellassign的Fitclone，用于AIM 3，量身定制和定制软件以支持 研究人类癌的动态和进化性质。这些工具包括软件 通过变异等位基因解释，多样本分析和单一的基础设施集中于癌症演化 细胞研究。我们的基础设施将使研究人员能够自动化疾病的进化解释 动力学以更好地了解转移性进展和治疗性抗性的临床终点。