A Pharmacogenomic Approach Using Precision Models of Hepatocellular Carcinoma to Identify Novel Therapeutics

使用肝细胞癌精密模型来识别新疗法的药物基因组学方法

基本信息

批准号：
10179334
负责人：
Alexander Rialdi
金额：
$ 6.64万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-24 至 2022-06-23
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10179334
关键词：
3-Dimensional ATAC-seq Antineoplastic Agents Arvin BAY 54-9085 Benchmarking Biology Biopsy CRISPR interference Cancer Etiology Cessation of life Chemicals Clinical Complex Development Drug Screening Drug Targeting Genetic Genomics Human Immune checkpoint inhibitor Immunocompetent In Vitro Injections Lead Libraries Malignant neoplasm of liver Modeling Modification Mus Mutation Nature Nivolumab Oncogenes Oncogenic Organoids Patients Pharmaceutical Preparations Pharmacogenomics Phosphotransferases Primary carcinoma of the liver cells Reproducibility Resistance Side Signal Pathway Stratification TP53 gene Tail Therapeutic Tumor Biology Tumor Suppressor Proteins Validation Veins World Health Organization analog anticancer research base beta catenin biobank cancer cell cost drug discovery drug efficacy drug structure drug testing epigenomics improved in vivo in vivo Model innovation kinase inhibitor medullary thyroid carcinoma mouse model multidisciplinary novel novel therapeutics patient derived xenograft model patient response patient stratification personalized medicine pre-clinical scaffold standard of care targeted treatment transcriptome sequencing transcriptomics tumor tumor microenvironment

项目摘要

Project Summary: This proposal uses a multidimensional approach for target validation and drug discovery in hepatocellular carcinoma (HCC) that focuses on pharmacogenomic predictions, a proprietary chemical analog library built on approved HCC KI scaffolds, and the use of multiple precision models of HCC: 1) Genetically defined murine HCCs induced in a fully immunocompetent background. This will allow for in vivo drug testing and to assess the drug impact in the context of the tumor microenvironment and in combination with checkpoint inhibitors; 2) Three-dimensional tumor organoids derived from these same murine models, which allow scalability for drug screening. Tumor organoids are a major breakthrough for convenient omics-based analyses of tumor biology and preclinical drug discovery, and are shown to accurately recapitulate patient responses to anticancer agents; 3) Patient derived organoids and 4) Patient derived xenografts (PDX), which allow testing of drug efficacy in genetically complex primary human tumors. Our close collaborator, Arvin Dar, has successfully applied aspects of this approach to kinases involved in medullary thyroid carcinoma (Dar et al., Nature, 2012; Sonoshita et al., Nature Chemical Biology, 2018). We have also recently applied a simplified approach and confirmed increased anti-tumoral activity of a new KI -AD80- compared to the standard-of-care (sorafenib) in experimental HCC models (Yu et al, accepted). To date, no single study has combined the multidisciplinary innovation presented in this proposal for drug discovery in liver cancer. The major hypothesis of this project is that different HCC oncogenic drivers will establish unique therapeutic vulnerabilities within tumor organoid lines. By using multiple precision models and informed modifications to KIs, we will be able to suggest stratification strategies and identify better therapeutics for HCC patients. This rationale is currently unprecedented in liver cancer research.

项目摘要：该建议使用多维方法来靶向肝细胞中的靶向验证和药物发现致力于药物基因组预测的癌（HCC），这是一个建立在基于的专有化学模拟库批准的HCC Ki支架，以及使用多种精度模型的HCC：1）遗传定义的鼠 HCC在完全免疫能力的背景下引起。这将允许体内药物测试并评估药物在肿瘤微环境和结合检查点抑制剂的背景下影响； 2）从这些相同的鼠模型得出的三维肿瘤类器官，可伸缩药物筛选。肿瘤类器官是方便基于法术的肿瘤生物学分析的主要突破和临床前药物发现，并被证明可以准确概括患者对抗癌的反应代理人； 3）患者衍生的类器官和4）患者衍生的异种移植物（PDX），允许对药物进行测试遗传复杂的原发性人类肿瘤的功效。我们的亲密合作者Arvin Dar成功了这种方法应用于甲状腺甲状腺癌参与激酶（Dar等人，自然，2012年； Sonoshita等，《自然化学生物学》，2018年）。我们最近还采用了一种简化的方法，与现有标准（索拉非尼）相比，新的Ki -AD80-的抗肿瘤活性增加实验性HCC模型（Yu等人，接受）。迄今为止，尚无单一研究结合了多学科这项在肝癌中发现药物发现提出的创新。该项目的主要假设是不同的HCC致癌驱动因素将在肿瘤器官中建立独特的治疗脆弱性线。通过使用多种精确模型并对KIS进行了知情修改，我们将能够建议分层策略并为HCC患者确定更好的治疗疗法。这个理由目前是肝癌研究中的前所未有。