The MSK Genomic Data Analysis Center for Tumor Evolution

摘要 MSK肿瘤进化基因组数据分析中心寻求实施工具，最佳实践和 从癌症基因组和转录组测序数据研究癌症演变的分析工作流程。 在过去的15年中，对许多癌症类型的患者群体的调查测序已经阐明了新的癌症基因。 驱动突变是疾病发病机制的机制。癌症基因组图谱 （TCGA）和个人实验室的努力扩大了对生物过程的理解， 体细胞突变，并揭示了新的治疗目标，已取得临床影响。然而，大多数 工作是基于来自原发肿瘤和来自患者的单次活检的批量DNA测序。公 我知道癌症是一个进化过程，在此过程中，患者体内的克隆扩增产生了 随着时间的推移，转移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）。事实上，基于突变发现开发的相同靶向疗法通常 选择抗性克隆，保持持久的治疗遥不可及。我们将开发分析方法、工具， 通过进化的透镜研究癌症进展的软件基础设施， 对原发性肿瘤的临床轨迹进行动态分析。我们希望我们的项目能提高 以更全面的方法研究患者的临床轨迹，包括时间，空间和单个 细胞分析，以更好地代表肿瘤的完整克隆库，并研究如何和 为什么肿瘤会进化我们在三个关键领域使用在实验室中建立的工具：i）变体解释 从转移性和治疗后样品中发现治疗性耐药突变（目的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 模型克隆动力学（目标2）; iii）克隆分解和克隆特异性的单细胞方法 患者内的表型分析（目的3）。我们的团队有能力实现我们的目标， 通过MSK-IMPACT开发支持TCGA和临床测序的领先软件基础设施 临床上批准的通过无细胞DNA测序（MSK-1）对患者进行纵向监测的测定方法 ACCESS），并通过在批量和单细胞分辨率下的克隆进化研究。落实和完善 支持这些目标的工具，包括Cancer Hotspots、OncoKB和cBioPortal for Aim 1、PyClone 和fitClone为目标2和克隆对齐和CellAssign为目标3，剪裁和定制软件，以支持 研究人类癌症的动态和进化性质。这些工具包括一个软件 基础设施通过变异等位基因解释、多样本分析和单样本分析， 手机调查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 动态，以更好地了解转移进展和治疗耐药性的临床终点。

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.