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。

• 批准号: 10602920
• 负责人: CHRISTOPHER J KEMP
• 金额: $ 37.88万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-14 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10602920
• 关键词: Personalized; cancer; models; discover; develop

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.

项目概要/摘要 癌症基因组学的丰富数据为开发更有效的靶向药物提供了绝佳的机会。 疗法。然而，许多常见突变的癌症基因会抵制药物、基因治疗等靶向治疗的努力。 肿瘤的异质性混淆了药物的选择或疗效，并且通常会产生耐药性 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。利用常见突变基因突变产生的细胞脆弱性， 例如合成致死率是一种很有前途的方法，最近批准的 PARP 抑制剂就说明了这一点 奥拉帕尼治疗卵巢癌。我们开发并优化了一个合成致死发现平台，该平台需要 高通量筛选，以确定患者来源的癌细胞培养物和同基因细胞中的新靶点 系统。将功能筛查结果与患者特异性（N of 1）和基于人群的结果相结合 基因组数据用于优先考虑对最大数量的患者有用且最合适的目标 基因组和分子背景。优先目标经过详尽的确认和正交 在生理相关环境中进行验证，包括基因组特征的患者来源的细胞培养物， 类器官和患者来源的异种移植（PDX）模型。我们的平台识别出的合成致死基因是 跨物种保守，已被确认为多种人类癌症的候选药物靶点 类型并导致研究者发起临床试验，说明我们平台的转化效用。 该提案的结果将是针对多种人类的新的经过验证的靶点和治疗策略 癌症类型，包括对标准护理药物具有抗药性的癌症类型，以及对癌症生物学的更深入了解 几个主要的癌症基因。