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

精准癌症医学中肿瘤依赖性的系统识别和药理学靶向

基本信息

批准号：
9362806
负责人：
ANDREA CALIFANO
金额：
$ 116.45万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9362806
关键词：
Address Adenocarcinoma Affect Anaplastic Meningioma Area Biochemical Biological Assay Biological Markers Bladder Breast Cancer Model Cancer Patient Categories Cell Communication Cell Line Cells Chronic Obstructive Airway Disease Clinical Clonal Expansion Colon Combined Modality Therapy Complex Data Dependency Disease Resistance Drug Combinations Drug Targeting Drug resistance Engineering Epigenetic Process FDA approved Gastrointestinal Stromal Tumors Genetic Genetic Transcription Goals Human Immune system In Vitro Individual Investigation Investigational Drugs Lung Maintenance Malignant Neoplasms Methodology Modeling Molecular Profiling Mutation Network-based Oncogenes Oncoproteins Organoids Patients Pharmaceutical Preparations Pharmacodynamics Pharmacology Phase Phenotype Primary Neoplasm Property Prostate Proteins Regulation Relapse Reporter Research Personnel Role Sampling Solid Neoplasm System Testing Therapeutic Tumorigenicity Universities Validation Xenograft Model Xenograft procedure base candidate identification drug sensitivity falls follow-up immune checkpoint in vivo in vivo Model individual patient inhibitor/antagonist molecular marker neoplastic cell new therapeutic target oncology partial response patient biomarkers personalized approach phase 3 study population stratification precision oncology resistance mechanism small molecule inhibitor success targeted cancer therapy targeted treatment therapeutic target transcriptome sequencing tumor tumorigenesis tumorigenic

项目摘要

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.

项目摘要癌症治疗的成功靶标属于三个主要类别：引起肿瘤特异性的癌基因由于其在肿瘤发生（癌基因依赖性）中的直接作用，因此引起合成的蛋白质具有特定突变目的地的致死性，在肿瘤发生（非癌基因依赖性）中缺乏直接作用，并且蛋白质与肿瘤细胞与免疫系统的相互作用有关（免疫检查依赖性）。但是，鉴于我们目前对癌症是一个复杂且高度异质系统的理解，这是很难想象单个蛋白质可能代表了所有数十亿个细胞的有效目标组成一个典型的质量。确实，虽然基于遗传的靶向疗法和免疫学有很大的希望大多数患者仍然没有反应或最终将与耐药性肿瘤相关，这表明可能需要重新审视治疗靶标作为单一蛋白质的概念。为了实现这一目标，我们将利用中心调查人员开发的非常成功的框架为了识别和药物靶向肿瘤依赖性紧密调控的肿瘤检查点模块中少数主要调节剂（MR）蛋白的活性。具体而言，我们将阐明和实验验证MR蛋白和相关肿瘤检查点通过进行基于网络的稀有和无法治愈的恶性肿瘤模块使用从原发性肿瘤进行反向设计的调节模型分析肿瘤样品特征样品。然后，我们将优先考虑一组FDA批准的药物和II期后期研究药物第三阶段研究（肿瘤药物）基于其靶向必需/合成致命的能力 MRS（Oncotarget）或扭转肿瘤的完整MR签名（Oncotreat）。适当匹配的肿瘤模型的RNASEQ轮廓与可用的肿瘤药物扰动将是获得并分析以评估单个药物和药物诱导的差异肿瘤检查点活性合并，然后进行低通量研究，以阐明其活性的调节基础。模型 - 包括细胞系，肿瘤外植体的短期有机培养物（exp），器官（ORG）和患者衍生的Xenographictic（PDX） - 将根据MR蛋白保护选择。我们将验证这些发现以及优先药物和组合，药效特性，生物标志物的总体效率适合体外和体内模型中抗性的精度和灵敏度以及抗性机制。如果成功，这将代表精确癌症医学的第一种机械方法治疗靶标，相关抑制剂和人群分层生物标志物是系统地得出的从对肿瘤状态调节及其药物诱导的调节的精确理解。