Rac guanine nucleotide exchange factors in lung cancer

摘要 KRAS和EGFR突变是人肺腺癌中检测到的最普遍的遗传变异，在恶性转化和疾病进展中发挥着重要作用。小GTCRac 1是Rho家族的成员，是KRAS和EGFR致癌通路的关键信号效应子。Rac 1广泛参与癌细胞运动和侵袭所需的富含肌动蛋白的细胞结构的形成，以及致癌和转移基因表达网络的激活。Rac 1的激活（即GTP负载）由Rac-GEF介导，Rac-GEF是一个与肿瘤发生和侵袭性密切相关的鸟嘌呤核苷酸交换因子大家族。引人注目的是，关于Rac-GEFs对肺癌进展的贡献的信息有限。此外，它们与特定肺癌致癌突变的关系仍然未知。我们对负责驱动KRAS突变NSCLC细胞系中促运动表型的Rac-GEF进行了系统和无偏倚的筛选。该分析明确地鉴定了三种Rac-GEF（ARHGEF 39、FARP 1和TIAM 2）作为NSCLC细胞中皱褶形成和运动性的介体。令我们惊讶的是，充分研究的GEF，如TIAM 1，TRIO，VAV亚型和P-REX亚型，在我们的模型中表达不佳或不表达。因此，我们假设这些Rac-GEF是肺癌进展的主要参与者。在目标1中，我们将使用CRISPR/Cas9方法产生RacGEF缺陷的KRAS突变细胞系，并确定它们对小鼠模型中的侵袭、ECM蛋白酶产生和转移的贡献。此外，将在Kras G12 D/WT; p53 flox/flox小鼠（KP小鼠）中使用基于慢病毒CRISPR的基因编辑方法确定所选Rac-GEF对自体产生的转移性肺癌发展的需求。在目标2中，目标是鉴定和表征Rac-GEF作为NSCLC中的EGFR效应物。为了明确阐明它们在突变EGFR肺癌进展表型中的允许作用，将在EGFR L 858 R驱动的p53缺陷型肺腺癌小鼠模型中进行体内基于慢病毒CRISPR的Rac-GEF基因编辑。从机制上讲，我们的目标是通过对近端EGFR衔接子和效应子进行全面的信号传导分析，解开Rac-GEF激活的基础。在目标3中，我们将首先阐明由突变型KRAS和突变型EGFR驱动的Rac-GEF依赖性基因转录组和网络特征。最后，我们将确定Rac-GEF在从恶性胸腔积液（肺转移性播散的部位）分离的单个肿瘤细胞中的表达，以及在来自突变型KRAS和突变型EGFR肺腺癌患者的外周血的单个和成簇循环肿瘤细胞（CTC）中的表达。新型Rac-GEF的鉴定提供了前所未有的信息来预测肺癌患者的转移性疾病结果，并增加了鉴定转移生物标志物的可能性，最终有助于在临床环境中改善患者预后和决策。

ABSTRACT KRAS and EGFR mutations are the most prevalent genetic alterations detected in human lung adenocarcinomas, and play essential roles in malignant transformation and disease progression. The small GTPase Rac1, a member of the Rho family, is a key signaling effector of KRAS and EGFR oncogenic pathways. Rac1 has been widely implicated in the formation of actin-rich protrusive structures required for cancer cell motility and invasion, as well as in the activation of oncogenic and metastatic gene expression networks. Activation of Rac1 (i.e. GTP loading) is mediated by Rac-GEFs, a large family of Guanine nucleotide Exchange Factors largely associated with tumorigenesis and invasiveness. Strikingly, there is limited information on the contribution of Rac-GEFs to lung cancer progression. Moreover, their relationship to specific lung cancer oncogenic mutations remains unknown. We carried out a systematic and unbiased screening for Rac-GEFs responsible for driving pro-motile phenotypes in KRAS mutant NSCLC cell lines. This analysis unambiguously identified three Rac-GEFs (ARHGEF39, FARP1 and TIAM2) as mediators of ruffle formation and motility in NSCLC cells. To our surprise, well-studied GEFs, such as TIAM1, TRIO, VAV isoforms and P-REX isoforms, were either poorly expressed or dispensable in our model. We therefore hypothesize that these Rac-GEFs are major players in lung cancer progression. In Aim 1, we will generate KRAS mutant cell lines deficient in RacGEFs using a CRISPR/Cas9 approach, and determine their contribution to invasion, ECM protease production and metastasis in mouse models. In addition, the requirement of selected Rac-GEFs to the development of autochthonously-arising metastatic lung cancer will be determined using a lentiviral CRISPR-based gene editing approach in Kras G12D/WT; p53 flox/flox mice (KP mice). In Aim 2 the goal is to identify and characterize Rac-GEFs as EGFR effectors in NSCLC. To unequivocally elucidate their permissive roles in mutant EGFR lung cancer progression phenotypes, in vivo lentiviral CRISPR-based Rac-GEF gene editing in an EGFR L858R-driven, p53 deficient lung adenocarcinoma mouse model will be performed. Mechanistically, we aim to disentangle the basis of Rac-GEF activation by pursuing a comprehensive signaling analysis of proximal EGFR adaptors and effectors. In Aim 3, we will first elucidate Rac-GEF-dependent gene transcriptomes and network signatures driven by mutant KRAS and mutant EGFR. Finally, we will determine Rac-GEF expression in single tumor cells isolated from malignant pleural effusions (a site of lung metastatic dissemination), as well as in single and clustered circulating tumor cells (CTCs) from peripheral blood of mutant KRAS and mutant EGFR lung adenocarcinoma patients. The identification of novel Rac-GEFs provides unprecedented information to predict metastatic disease outcome in lung cancer patients and increase the likelihood of identifying metastasis biomarkers, ultimately aiding in refining patient prognosis and decision-making in a clinical setting.