Personalized cancer models to discover and develop new therapeutic targets.

个性化癌症模型以发现和开发新的治疗靶点。

基本信息

批准号：
9767101
负责人：
CHRISTOPHER J KEMP
金额：
$ 82.14万
依托单位：
FRED HUTCHINSON CANCER RESEARCH CENTER
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：
2017-09-14 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9767101
关键词：
Address Affect Autologous Biochemical Bioinformatics Biological Assay Biological Markers Biology Biopsy Cancer Biology Cancer Model Cell Culture Techniques Cells Clinical Clinical Oncology Clinical Trials Clustered Regularly Interspaced Short Palindromic Repeats Complex Computational Biology DNA sequencing Data Development Drug Combinations Drug Targeting Drug resistance Event Future Gene Targeting Genes Genetic Genetic Heterogeneity Genome Genomics Genotype Goals Head and Neck Squamous Cell Carcinoma Human Imagery Immunotherapeutic agent KRAS2 gene Knock-out Lethal Genes Malignant Neoplasms Malignant neoplasm of ovary Methods Modeling Molecular Mutate Mutation Neoadjuvant Therapy Oncogenes Operative Surgical Procedures Organoids Outcome Outcomes Research Patient Representative Patients Pharmaceutical Preparations Phenotype Physiological Predictive Value Research Personnel Resistance Resistance development Screening Result Small Interfering RNA Solid Neoplasm Surgical Oncology System TP53 gene Test Result Testing Therapeutic Agents Translations Tumor-Derived Validation Work Xenograft Model cancer cell cancer genomics cancer type drug candidate drug development drug discovery drug efficacy druggable target exhaustion gene function genomic aberrations genomic data high throughput screening inhibitor/antagonist innovation insight molecular subtypes mouse model mutant neoplastic cell new therapeutic target novel novel strategies novel therapeutics oncology population based pre-clinical precision medicine precision oncology preclinical development predictive modeling profiles in patients response screening small hairpin RNA small molecule inhibitor standard of care statistics success targeted agent targeted cancer therapy targeted treatment tool tumor tumor heterogeneity

项目摘要

PROJECT SUMMARY/ABSTRACT The wealth of data on the genomics of cancer provides a great opportunity to develop more effective targeted therapies. However, many commonly mutated cancer genes resist efforts to target with drugs, genetic heterogeneity of tumors confounds choice or efficacy of drugs, and development of resistance to commonly used therapies is common, leaving few alternatives. New approaches are needed to address these challenges. Exploiting cellular vulnerabilities generated as a result of mutations in commonly mutated genes, e.g. synthetic lethality, is a promising approach, as illustrated by the recent approval of the PARP inhibitor olaparib in ovarian cancer. We have developed and optimized a synthetic lethal discovery platform that entails high throughput screening to identify novel targets in patient-derived cancer cell cultures and isogenic cell systems. Integration of functional screen results with both patient specific (N of 1) and population-based genomic data is used to prioritize targets useful to the greatest number of patients and in the most appropriate genomic and molecular contexts. Prioritized targets undergo exhaustive confirmation and orthogonal validation in physiologically-relevant settings including genomically characterized patient-derived cell cultures, organoids and patient derived xenograft (PDX) models. Synthetic lethal genes identified with our platform are conserved across species, have been confirmed as candidate drug targets across multiple human cancer types and have led to an investigator initiated clinical trial, illustrating the translational utility of our platform. The outcome of this proposal will be novel validated targets and therapeutic strategies to several human cancer types including those resistant to standard of care agents and a deeper understanding of the biology of several major cancer genes.

项目摘要/摘要关于癌症基因组学的大量数据为开发更有效的目标提供了一个很好的机会疗法。但是，许多通常突变的癌症基因抵制努力用药物靶向遗传肿瘤的异质性混淆了药物的选择或功效，并且对通常的抗性发展使用的疗法很常见，几乎没有其他选择。需要新的方法来解决这些问题挑战。由于普遍突变基因突变而产生的细胞脆弱性，例如综合杀伤力是一种有前途的方法，如最近批准的PARP抑制剂所示卵巢癌中的奥拉帕里布（Olaparib）。我们已经开发并优化了一个需要的合成致命发现平台高吞吐量筛选以识别患者衍生的癌细胞培养和等生细胞中的新靶标系统。功能屏幕结果与患者特异性（1的n）和基于群体的集成结果基因组数据用于确定对最大患者和最合适的患者有用的目标的优先级。基因组和分子环境。优先的目标进行详尽的确认和正交在生理上相关的环境中的验证，包括具有基因组特征的患者衍生细胞培养物，器官和患者衍生的异种移植（PDX）模型。我们平台确定的合成致死基因是跨物种保守的已被确认为多个人类癌症的候选药物靶标类型并导致了一项研究人员启动临床试验，说明了我们平台的翻译实用程序。该提案的结果将是对几个人类的新型验证目标和治疗策略癌症类型，包括那些对护理剂的抵抗力的癌症类型以及对生物学的更深入了解几个主要的癌症基因。