肺癌病理表型转化的预测模型及分子机制研究

Lung cancer is the leading cause of cancer related death whether worldwide or in China with an increasing trend. Most of patients with lung cancer were advanced stage in initial diagnosis. Molecular targeted therapy such as EGFR-TKIs (Epithelial growth factor receptor tyrosine kinase inhibitors) is one of the main strategies. However, pathological phenotype transformation is one of the important reasons for EGFR-TKI resistance, the frequency of which might be underestimated because a secondary biopsy is not easy to obtain in clinical practice. In our previous study, we had explored the usefulness of GW-CNVs (Genome-wide copy number variations) in differentiating pathological phenotype of lung cancer. Further, we will establish noninvasive predicting model for lung cancer phenotype in free DNA of peripheral blood, through a method combined with Hidden Markov Model(HMM) model and hierarchical cluster analysis. Moreover, we will study the molecular mechanisms of phenotypic transformation on the basis of GW-CNVs and transcriptiomics, especially focusing on the transformation of lung adenocarcinoma with EGFR mutations to small cell carcinoma after EGFR-TKI resistance. The aims of the study are constructing noninvasive method monitoring phenotypic transformation, and providing potential targets theoretical basis to overcome the clinical phenotype transformation.

肺癌的发病率和死亡率在各恶性肿瘤中均居于前列，并呈逐年上升趋势。大部分肺癌诊断时已处于晚期，以表皮生长因子受体酪氨酸激酶抑制剂（Epithelial growth factor receptor tyrosine kinase inhibitors, EGFR-TKIs）为代表的分子靶向治疗是主要的治疗手段之一。病理表型转化是EGFR-TKI耐药的重要原因之一。由于耐药后二次活检不易获得且不作为临床常规，很可能低估这一耐药机制所占的比例。因此，我们拟在前期基因组拷贝数变异（Genome-wide copy number variations, GW-CNVs）研究的基础上，利用隐马尔可夫模型联合谱系聚类法，建立基于外周血游离DNA的肺癌病理表型无创预测模型，以期实现肺癌病理表型转化的无创性适时监测。并借助GW-CNVs及转录组学研究肺癌表型转化的分子机制及与EGFR-TKI耐药的相关性。

本研究对非小细胞肺癌小细胞转化的组织及体液标本进行二代测序，发现了转化前后的差异基因，初步明确肺腺癌转化为小细胞癌的分子发生基础及导致表型转化的关键基因组群。同时对比了体液及组织标本的一致性，建立基于外周血游离DNA 的肺癌病理表型无创预测模型，为肺癌小细胞转化无创模型的建立提供了理论基础。同时建立小细胞转化的细胞模型，为后续进一步研究创造条件。本研究还对穿刺小标本移植瘤模型的构建及一致性进行了方法学的研究，为后续此类研究建立了成熟的方法。本研究提高对肺癌病理表型转化导致治疗失败的认知度，并为实现更精准的肺癌“个体化”治疗提供研究基础。