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进化的分析工作流程。 在过去的15年里，对许多癌症类型的患者群体的调查排序阐明了新的 驱动突变，这是疾病发病的机械原因。癌症基因组图谱 (TCGA)和个别实验室的努力拓宽了对受影响的生物过程的理解 体细胞突变，揭示了新的治疗靶点，已取得临床效果。然而，这其中的大部分 这项工作是基于原发肿瘤的大量DNA测序和患者的单一活组织检查。这很好 理解癌症是一个进化过程，在这个过程中，患者体内的克隆性扩张产生了 不同转移部位(有无转移)细胞群体的异质性和表型多样性 治疗干预)。事实上，基于突变发现开发的相同靶向疗法通常 选择抵抗克隆体，让持久的治疗变得遥不可及。我们将开发分析方法、工具和 从进化的角度研究癌症进展的软件基础设施，将重点从分析转移到 原发肿瘤的临床轨迹的动态分析。我们预计我们的计划将提高 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，以更好地表示肿瘤的完整克隆谱系，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我们实验室中建立良好的工具：i)变体口译 从转移和治疗后样本中发现治疗耐药突变(目标1)；二)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)；三)克隆分解和克隆专一性的单细胞方法 患者内的表型(目标3)。我们的团队已经做好了充分的准备来实现我们制定的目标 通过MSK支持TCGA和临床测序的领先软件基础架构-Impact、开发 临床批准的通过无细胞DNA测序对患者进行纵向监测的分析方法(MSK- Access)，并通过在整体和单细胞分辨率下克隆进化的研究。抓好落实和完善。 支持这些目标的工具，包括癌症热点、OncoKB和用于AIM 1的cBioPortal1、PyClone 以及用于AIM 2的FitClone和用于AIM 3的CloneAlign和CellAssign，定制和定制软件以支持 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研究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对疾病进行进化解释 更好地了解转移进展和治疗耐药的临床终点的动力学。

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.