Personalized cancer models to discover and develop new therapeutic targets.

项目总结/摘要 关于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巨大的机会。 治疗然而，许多常见的突变的癌症基因抵抗药物靶向的努力，遗传 肿瘤的异质性混淆了药物的选择或疗效，并且对常见的 使用的疗法很普遍，几乎没有其他选择。需要新的方法来解决这些问题 挑战利用由于常见突变基因的突变而产生的细胞脆弱性， 例如如最近批准的PARP抑制剂所示， olaparib治疗卵巢癌我们已经开发并优化了一个合成致命发现平台， 在患者来源癌细胞培养物和同基因细胞中鉴定新靶点的高通量筛选 系统.将功能筛查结果与患者特异性（N/1）和基于人群的结果整合 基因组数据用于优先考虑对最大数量的患者有用的靶点， 基因组和分子背景。优先目标经过详尽的确认和正交 在生理学相关环境中的验证，包括基因组学表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物（PDX）模型。通过我们的平台鉴定的合成致死基因是 已被证实为多种人类癌症的候选药物靶标 类型，并已导致研究者发起的临床试验，说明了我们的平台的转化效用。 这一建议的结果将是新的验证目标和治疗策略，以几个人 癌症类型，包括那些对标准治疗药物有抗性的癌症类型，以及对癌症生物学的更深入了解， 几个主要的癌症基因

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.