Genetic Determinants of Tumor Growth and Drug Sensitivity in EGFR Mutant Lung Cancer

项目摘要 发现EGFR突变驱动肺癌生长并赋予酪氨酸激酶敏感性 抑制剂（TKI）已经改变了肺癌的治疗。然而，对TKI的反应是可变的， 最终产生了阻力。因此，EGFR驱动的肺癌在美国每年仍导致约20，000人死亡。 这些肿瘤在不同的肿瘤抑制基因（TSGs）中经常发生改变，但是这些基因中的哪一个？ 改变共同驱动肿瘤生长，它们如何影响癌细胞状态，以及它们是否是关键 对治疗反应的决定因素在很大程度上仍是未知的。当前揭示关系的方法 EGFR和TSGs之间的相互作用主要依赖于相关的人类基因组研究和基于细胞系的模型。 然而，基因组研究在揭示遗传相互作用方面的统计学力量往往不足， 提供有关TSG功能的信息。相反，细胞系研究不能概括体内环境 并且存在的有限细胞系仅代表EGFR突变肿瘤的一个子集。为了克服这些限制 为了更好地了解体内肺癌生长和药物反应的基因组驱动因素，我们最近 将一种新的致癌EGFR驱动的肺癌的本地小鼠模型与CRISPR/Cas9- 介导的体细胞基因组编辑和高通量肿瘤条形码测序。使用这种多路复用的体内 模型中，我们发现剪接因子RBM 10是一种特征不明确的肿瘤生长抑制因子， 出乎意料地发现“肿瘤抑制因子”Lkb 1的失活与致癌EGFR是合成致死的。 在这里，我们将研究基因型如何控制致癌EGFR驱动的肺癌的生物学。具体地说， 我们将建立Rbm 10失活的细胞和分子后果，并阐明其机制。 这是EGFR和Lkb 1之间的合成致死性的基础。此外，了解肿瘤基因型 影响治疗反应可能揭示基因型特异性治疗弱点。因此，我们将利用 我们的多重体内基因编辑平台，以确定额外的TSGs对EGFR突变型肺的影响， 癌症生长并揭示对治疗反应的基因组驱动因素。这项工作将增加我们的基本 了解EGFR突变型肺癌生长的基因组决定因素，并揭示新的 治疗靶向的促肿瘤发生途径。这些发现最终可以为精确治疗提供信息 用于致癌EGFR驱动的肺癌患者。

PROJECT SUMMARY The discovery of mutations in EGFR that drive lung cancer growth and confer sensitivity to tyrosine kinase inhibitors (TKIs) has transformed the treatment of lung cancer. However, responses to TKIs are variable and resistance ultimately develops. Thus, EGFR-driven lung cancers still cause ~20,000 deaths annually in the US. These tumors have frequent alterations in diverse tumor suppressor genes (TSGs), however which of these alterations co-operate to drive tumor growth, how they impact cancer cell state, and whether they are key determinants of responses to therapy remains largely unknown. Current methods to uncover relationships between EGFR and TSGs largely rely on correlative human genomic studies and cell line-based models. However, genomic studies are often statistically underpowered to uncover genetic interactions and do not provide information on TSG function. Conversely, cell line studies do not recapitulate the in vivo environment and the limited cell lines that exist represent only a subset of EGFR mutant tumors. To overcome these limitations and better understand the genomic drivers of lung cancer growth and drug responses in vivo, we recently integrated a novel autochthonous mouse model of oncogenic EGFR-driven lung cancer with CRISPR/Cas9- mediated somatic genome editing and high-throughput tumor barcode sequencing. Using this multiplexed in vivo model, we uncovered the splicing factor RBM10 as a poorly characterized suppressor of tumor growth and unexpectedly found that inactivation of the “tumor suppressor” Lkb1 is synthetic lethal with oncogenic EGFR. Here, we will investigate how genotype controls the biology of oncogenic EGFR-driven lung cancer. Specifically, we will establish the cellular and molecular consequences of Rbm10 inactivation and elucidate the mechanism that underlies the synthetic lethality between EGFR and Lkb1. Moreover, understanding how tumor genotype influences treatment responses could reveal genotype-specific therapeutic vulnerabilities. Thus, we will leverage our multiplexed in vivo gene editing platform to determine the impact of additional TSGs on EGFR mutant lung cancer growth and uncover genomic drivers of responses to therapy. This work will increase our fundamental understanding of the genomic determinants of EGFR mutant lung cancer growth and reveal novel and therapeutically targetable pro-tumorigenic pathways. These findings could ultimately inform precision treatments for patients with oncogenic EGFR-driven lung cancer.