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：
9910997
负责人：
Alexander Rialdi
金额：
$ 6.49万
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/9910997
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 Xenograft procedure 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 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.

项目概要：该提案采用多维方法进行肝细胞靶标验证和药物发现癌症 (HCC)，专注于药物基因组预测，这是一个基于批准的 HCC KI 支架，以及多种 HCC 精密模型的使用：1) 基因定义的小鼠在完全免疫功能正常的背景下诱导肝癌。这将允许进行体内药物测试并评估药物在肿瘤微环境中以及与检查点抑制剂相结合的影响； 2）源自这些相同小鼠模型的三维肿瘤类器官，可实现药物的可扩展性筛选。肿瘤类器官是方便的基于组学的肿瘤生物学分析的重大突破和临床前药物发现，并被证明可以准确地概括患者对抗癌的反应代理； 3) 患者来源的类器官和 4) 患者来源的异种移植物 (PDX)，可用于测试药物对遗传复杂的原发性人类肿瘤的功效。我们的密切合作者 Arvin Dar 已成功该方法的应用方面涉及甲状腺髓样癌中涉及的激酶（Dar 等人，Nature，2012； Sonoshita 等人，《自然化学生物学》，2018）。我们最近还应用了一种简化的方法证实与标准治疗（索拉非尼）相比，新的 KI -AD80- 的抗肿瘤活性有所增加实验性 HCC 模型（Yu 等人，已接受）。迄今为止，还没有一项研究将多学科结合起来该提案中提出了肝癌药物发现的创新。该项目的主要假设是不同的 HCC 致癌驱动因素将在肿瘤类器官中建立独特的治疗漏洞线。通过使用多种精确模型和对 KI 进行明智的修改，我们将能够提出建议分层策略并为 HCC 患者确定更好的治疗方法。目前这个理由肝癌研究史无前例。