表皮生长因子受体-酪氨酸激酶抑制剂（EGFR-TKI）对EGFR敏感突变的晚期非小细胞肺癌（NSCLC）患者的一线治疗表现出明显的疗效差异，目前尚无综合有效的EGFR-TKI疗效预测手段。前期我们基于临床治疗样本的癌症相关基因、代谢物及自身抗体的靶向检测，发现了5个高频基因突变、27个代谢物及7个自身抗体与EGFR-TKI疗效差异相关，并基于27个代谢物构建了疗效预测模型。进一步，我们拟扩大临床样本从基因-代谢-自身抗体三个层面寻找疗效相关的新的生物标志物，并结合临床治疗资料建立并验证单水平及多水平综合疗效预测模型，同时进行三个层面疗效相关分子的互作网络分析，并在细胞水平探索这些分子的生物学功能及其调控机制。本研究建立的EGFR-TKI多组学疗效预测模型将对患者进行精准分层，有效预测患者的临床获益，并为临床及时选择最佳治疗方案提供依据和理论指导。

Epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) displays obvious efficacy divergence in the first-line therapy for non-small cell lung cancer patients with EGFR sensitive mutations.However,to date, there is no comprehensive and effective prediction means of EGFR-TKI efficacy. Our previous work identified 5 gene mutations with high mutant allele frequency, 27 metabolites and 7 autoantibodies related to the efficacy of EGFR-TKI based on the targeted detection of cancer-related genes, metabolites and autoantibodies in clinical pre-treatment samples. Simultaneously, we established efficacy predictive model based on the 27 metabolites. Further, we intend to expand clinical samples size to find new biomarkers associated to efficacy from the three levels of genes-metabolites-autoantibodies. And then combine the clinical treatment data to establish and validate the single and multi-level efficacy prediction model as well as undertake the interaction network analysis of efficacy related molecules. Last, we intend to explore the biological functions and regulatory mechanisms of these molecules at the cellular level. Our study will establish the multi-omics prediction model of EGFR-TKI efficacy, which will accurately stratify patients, effectively predict the clinical benefit of patients, and provide basis and theoretical guidance for selection of the best treatment plan in time.