Systematic Identification and Pharmacological Targeting of Tumor Dependencies for Precision Cancer Medicine

项目总结 癌症治疗的成功靶点分为三大类：引发肿瘤特异性的癌基因 由于它们在肿瘤发生中的直接作用(癌基因依赖性)，导致合成的蛋白质具有重要意义 特定突变的致命性，尽管在肿瘤发生中缺乏直接作用(非癌基因依赖)，以及 与肿瘤细胞与免疫系统相互作用相关的蛋白质(免疫检查点依赖)。 然而，鉴于我们目前对癌症作为一个复杂和高度异质系统的理解，它是 很难想象一种单独的蛋白质可能代表着所有数十亿个细胞的有效靶点 组成一个典型的团块。事实上，尽管基于基因的靶向治疗和免疫肿瘤学有着巨大的希望 大多数患者对耐药肿瘤仍然没有反应或最终会复发，这表明 将治疗靶点作为单一蛋白质的概念可能需要重新考虑。 为了实现这一目标，我们将利用我们中心调查人员开发的非常成功的框架 用于识别和药理学靶向的肿瘤依赖性实施的协调 在严格调控的肿瘤检查点模块中，少数主调节蛋白(MR)的活性。 具体地说，我们将阐明和实验验证mr蛋白和相关的肿瘤检查点。 罕见和不可治愈的恶性肿瘤模块，以患者个人为基础，通过基于网络的 利用原发肿瘤反向工程调控模型分析肿瘤样本信号 样本。然后，我们将在第二阶段或第二阶段优先考虑FDA批准的一组药物和后期研究药物 肿瘤学(肿瘤学药物)的第三阶段研究，基于其靶向基本/合成致命的能力 MRS(肿瘤靶向)或逆转肿瘤的完整MR信号(OncoTreat)。 被可用的肿瘤药物干扰的适当匹配的肿瘤模型的RNAseq图谱将是 获取并分析以评估个别药物和药物诱导的不同肿瘤检查点活性 结合，然后进行低通量研究，以阐明其活动的调节基础。机型- 包括细胞系、肿瘤外植体的短期器官分型培养(EXPL)、器官类化合物(ORG)和患者 衍生异种移植物(PDX)-将根据MR蛋白保守性进行选择。我们将验证这些发现 以及优先考虑的药物和组合的总体疗效、药效学特性、生物标志物 在合适的体外和体内模型中，耐药的准确性和敏感性以及耐药机制。 如果成功，这将是精准癌症医学的第一个机械化方法，其中 治疗靶点、相关抑制物和群体分层生物标志物被系统地派生。 来自对肿瘤状态调节及其药物诱导调节的精确的、机械的理解。

PROJECT SUMMARY Successful targets for cancer therapy fall in three main categories: oncogenes that elicit tumor-specific essentiality because of their direct role in tumorigenesis (oncogene dependency), proteins that elicit synthetic lethality with specific mutations despite lack of a direct role in tumorigenesis (non-oncogene dependency), and proteins related to the interaction of tumor cells with the immune system (immune-checkpoint dependency). However, given our current understanding of cancer as a complex and highly heterogeneous system, it is difficult to imagine that an individual protein may represent an effective target for all the billions of cells that make up a typical mass. Indeed, while genetic-based targeted therapy and immunoncology hold great promise a majority of patients still does not respond or will eventually relapse with drug resistant tumors, suggesting that the concept of therapeutic targets as single proteins may need to be revisited. To accomplish this goal, we will leverage a highly successful framework developed by our center investigators for the identification and pharmacological targeting of tumor dependencies implemented by the concerted activity of a handful of Master Regulator (MR) proteins within tightly regulated tumor checkpoint modules. Specifically, we will elucidate and experimentally validate MR proteins and associated Tumor Checkpoint modules of rare and incurable malignancies, on an individual patient basis, by performing network-based analysis of tumor samples signatures using regulatory models reverse engineered from primary tumor samples. We will then prioritize a set of FDA approved drugs and late stage investigational drugs in phase II or phase III studies in oncology (oncology drugs) based on their ability to either target essential/synthetic-lethal MRs (OncoTarget) or to reverse the full MR signature of a tumor (OncoTreat). RNASeq profiles of appropriately matched tumor models perturbed with available oncology drugs will be obtained and analyzed to assess the differential tumor checkpoint activity induced by individual drugs and drug combinations, followed by low-throughput studies to elucidate the regulatory basis of their activity. Models – including cell lines, short-term organotypic culture from tumor explants (EXPL), organoids (ORG), and patient derived xenografts (PDX) – will be selected based on MR protein conservation. We will validate these findings as well as overall efficacy of prioritized drugs and combinations, pharmacodynamic properties, biomarker accuracy and sensitivity, and mechanisms of resistance in suitable in vitro and in vivo models. If successful, this would represent the first mechanistic approach for precision cancer medicine, where therapeutic targets, associated inhibitors, and population stratification biomarkers are systematically derived from precise, mechanistic understanding of tumor state regulation and of its drug-induced modulation.