Project 2: Functional Genetic Networks for Systems-Guided Precision Medicine

CCMI v2.0 项目2：用于系统引导的精准医学的功能遗传网络 项目负责人：Prashant Mali和Stephanie Fraley;共同研究者：Alan Ashworth，Jennifer Grandis， 西尔维奥·古特金德，特雷·艾德克，还有劳拉·货车不转向. 总结 精准医学旨在根据患者肿瘤的遗传和分子背景定制治疗。的 这种疗法的发展面临着许多障碍，其中许多来自我们对遗传学的无知。 肿瘤发生的潜在网络和可能的干预机制。为了阐明遗传逻辑 项目2将使用CRISPR/Cas9基因扰动方法， 组合的、功能的和机械的屏幕的集合。屏幕将集中在PI 3 K途径， p53肿瘤抑制因子和头颈部浸润性乳腺癌（BRCA）中突变的蛋白系统 鳞状细胞癌（HNSCC）和肺鳞状细胞癌（LUSC）， 每年在全世界造成超过一百万人死亡。这种专注将使我们能够审问一个 细胞系、实验和微环境条件的广泛收集。首先，我们将建立在重要的 初步数据，以建立地图的合成致死和上位性遗传相互作用集中在频繁 上述途径和癌症亚型中的突变基因和治疗靶点（Aim 1）。二是 将CRISPR/Cas9筛选与一组可扩展的功能测定相结合，用于大规模测量癌症 细胞增殖以外的表型（Aim 2）。第三，我们将试点一项新技术STAG-CRISPR， CRISPR/Cas9筛选活细胞中的真实的时间分子事件，提供更深层次的阵列 迄今为止，系统遗传学一直在研究表型（Aim 3）。最后，为了方便临床 翻译的基因，基因表型，基因机制和基因药物相互作用，我们将 应用我们广泛的BRCA、HNSCC和LUSC患者源性异种移植物（PDX）模型库， 在体内有吸引力的线索。我们还将使用来自BRCA的患者数据验证所识别的交互网络 I-SPY 2试验和UCSD的HNSCC患者（目标4）。总之，我们的综合方法建立了 基因、药物和多种表型终点之间广泛验证的相互作用网络， 推进精准肿瘤学的实践。

CCMI v2.0 Project 2: Functional Genetic Networks for Systems-Guided Precision Medicine Project Leads: Prashant Mali and Stephanie Fraley; Co-Investigators: Alan Ashworth, Jennifer Grandis, Silvio Gutkind, Trey Ideker, and Laura van ’t Veer. SUMMARY Precision medicine aims to tailor therapies to the genetic and molecular background of a patient’s tumor. The development of such therapies faces numerous obstacles, many deriving from our ignorance of the genetic networks underlying tumorigenesis and the mechanisms of possible interventions. To clarify the genetic logic that governs therapeutic efficacy, Project 2 will use CRISPR/Cas9 genetic perturbation methodologies in an ensemble of combinatorial, functional, and mechanistic screens. Screens will focus on the PI3K pathway, the p53 tumor suppressor, and the protein systems mutated in invasive breast cancer (BRCA), head and neck squamous cell carcinoma (HNSCC), and lung squamous cell carcinoma (LUSC), pathways and diseases that together result in well over one million deaths each year worldwide. This focus will enable us to interrogate a broad collection of cell lines, experiments, and microenvironmental conditions. First, we will build on significant preliminary data to establish maps of synthetic lethal and epistatic genetic interactions centered on frequently mutated genes and therapeutic targets in the above pathways and cancer subtypes (Aim 1). Second, we will couple CRISPR/Cas9 screening to a panel of scalable functional assays for large-scale measurement of cancer phenotypes beyond cell proliferation (Aim 2). Third, we will pilot a new technology, STAG-CRISPR, to link CRISPR/Cas9 screening to real time molecular events in living cells, providing access to an even deeper array of phenotypes that have been recalcitrant to systems genetics thus far (Aim 3). Finally, to facilitate clinical translation of the identified gene-gene, gene-phenotype, gene-mechanism and gene-drug interactions, we will apply our extensive library of BRCA, HNSCC, and LUSC patient-derived xenograft (PDX) models to test compelling leads in vivo. We will also validate the identified interaction networks with patient data from the BRCA I-SPY 2 trial and from HNSCC patients at UCSD (Aim 4). Taken together, our integrated approach establishes a network of extensively validated interactions among genes, drugs and multiple phenotypic endpoints to advance the practice of precision oncology.