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：
9750650
负责人：
ANDREA CALIFANO
金额：
$ 114.5万
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/9750650
关键词：
Address Adenocarcinoma Affect Anaplastic Meningioma Area Biochemical Biological Assay Biological Markers Bladder Adenocarcinoma Cancer Model Cancer Patient Categories Cell Communication Cell Line Cells Clinical Clonal Expansion Colon Combined Modality Therapy Complex Data Dependence Disease Resistance Drug Combinations Drug Targeting Drug resistance Engineering Epigenetic Process FDA approved Gastrointestinal Stromal Tumors Genetic Genetic Transcription Glioblastoma Goals Human Immune system In Vitro Individual Investigation Investigational Drugs Lung Adenocarcinoma Maintenance Malignant Neoplasms Malignant neoplasm of prostate Methodology Modeling Molecular Profiling Mutation Network-based Oncogenes Oncoproteins Organoids Pathway interactions Patients Pharmaceutical Preparations Pharmacodynamics Pharmacology Phase Phenotype Primary Neoplasm Progression-Free Survivals Property Proteins Regulation Relapse Reporter Research Personnel Role Sampling Solid Neoplasm System Testing Therapeutic 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 leukemia/lymphoma malignant breast neoplasm 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.

项目概要癌症治疗的成功靶标分为三大类：引发肿瘤特异性的癌基因重要性，因为它们在肿瘤发生中直接发挥作用（癌基因依赖性），诱导合成的蛋白质尽管在肿瘤发生中缺乏直接作用（非癌基因依赖性），但特定突变的致死率，以及与肿瘤细胞与免疫系统相互作用相关的蛋白质（免疫检查点依赖性）。然而，鉴于我们目前对癌症作为一个复杂且高度异质的系统的理解，很难想象单个蛋白质可能代表所有数十亿个细胞的有效靶标事实上，基于基因的靶向治疗和免疫肿瘤学前景广阔。大多数患者仍然没有反应或最终会因耐药肿瘤而复发，这表明可能需要重新审视作为单一蛋白质的治疗靶点的概念。为了实现这一目标，我们将利用我们中心研究人员开发的非常成功的框架用于由协调一致的机构实施的肿瘤依赖性的鉴定和药理学靶向严格调控的肿瘤检查点模块中少数主调节蛋白（MR）的活性。具体来说，我们将阐明并通过实验验证 MR 蛋白和相关的肿瘤检查点通过基于网络执行罕见和无法治愈的恶性肿瘤的模块，以个体患者为基础使用从原发肿瘤逆向工程的调控模型分析肿瘤样本特征然后，我们将优先考虑一组 FDA 批准的药物和 II 期或后期研究药物。肿瘤学（肿瘤药物）的 III 期研究基于其针对必需/合成致死的能力 MR (OncoTarget) 或逆转肿瘤的完整 MR 特征 (OncoTreat)。受到现有肿瘤药物干扰的适当匹配的肿瘤模型的 RNASeq 谱将被获得并分析以评估个体药物和药物诱导的差异肿瘤检查点活性组合，然后进行低通量研究以阐明其活动的监管基础 - 包括细胞系、来自肿瘤外植体的短期器官型培养物 (EXPL)、类器官 (ORG) 和患者衍生异种移植物（PDX）——将根据 MR 蛋白质保守性进行选择，我们将验证这些发现。以及优先药物和组合的总体疗效、药效学特性、生物标志物准确性和灵敏度，以及合适的体外和体内模型中的耐药机制。如果成功，这将代表精准癌症医学的第一个机械方法，其中系统地得出治疗靶点、相关抑制剂和群体分层生物标志物来自对肿瘤状态调节及其药物诱导调节的精确、机械的理解。