Mechanisms And Exploration Of New Treatment Strategies For EGFR-TKI Targeted Drug Resistence In Non-small Cell Lung Cancer (NSCLC)

Objective: According to the presence or absence of a particular gene mutation,current first-line therapy of advanced non-small cell lung cancer can be divided into targeted therapy for patients with specific gene mutations and platinum-based combination chemotherapy for patients without any gene mutations.However,both treatments are difficult to achieve satisfactory therapeutic effect.Targeted therapy represented by tyrosine kinase inhibitors(TKI)which selectively target epidermal growth factor receptor(EGFR)can effectively inhibit tumor growth,but drug resistance will inevitably occur after 9-12 months.Similarly,the improvement of survival outcomes is very limited for patients without gene mutations treated by chemotherapies.Therefore,this study aims to find drugs that can reverse the drug resistance of EGFR-mutated EGFR-TKI resistant lung cancer patients or improve the survival prognosis of EGFR wild-type patients from the currently known drugs,so as to achieve high efficiency and low cost to provide treatment regimen for tumors with complex gene mutation situations.Methods: 1.In vitro model construction and cellular experiments:(1)MTT assays were used to detect cell viability after drugs;(2)Colony formation assays were used to detect long-term sensitivity of drugs(drug resistance);(3)Flow cytometry with BODIPY~TM581/591 C11 staining assays were used to detect the degree of lipid peroxidation;(4)RT-qPCR assays were used to detect changes in the mRNA expression levels;(5)Western blot assays were used to detect changes in the expression level of proteins;(6)siRNA transfection inhibits the translation level of specific genes;(7)Transwell assays were used to detect the inhibitory effect of drugs on tumor cell migration;2.Subcutaneous xenograft tumor models of nude mice were used detect the combination anti-tumor effect of two drugs in vivo;3.Bioinformatics analysis:(1)Connectivity Map pharmacology online database predicts drugs that may reverse drug resistance or taget certain patients;(2)Compusyn software calculates the drug combination index(CI);(3)DAVID database predicts biological functional pathways involves in the reversal of drug resistance;(4)GSEA version 3.0 software analyzes biological functional pathway enrichment analysis in patient samples;(5)data download of patients: chip-seq data was searched and download from Gene Expression Omnibus(GEO)public database and include EGFR mutation information and survival information of non-small cell lung cancer chip data,the whole transcriptome sequencing data of lung adenocarcinoma patients in The Cancer Genome Atlas project(TCGA)was downloaded from UCSC Xena public database;(5)R packages: "ggplot" package was used for visualization,"WGCNA" package was used for weighted gene co-expression network analysis(WGCNA),"glmnet" package was used for LASSO regression analysis and screening prognosis module,"survival" package was used to construct a multi-factor proportional hazard regression model and established a risk scoring formula,and compared the prognosis of patients by Kaplan-Meier survival analysis."survival ROC" was used to draw ROC curves;the "limma" package was used to analyze differentially expressed genes(DEGs);4.Statistical method: R language software and Graph Pad Prism 7software was used for statistical analysis.All experiments were repeated at least three times.When P<0.05,the difference was statistically significant.Results: 1.The establishment and identification of drug-resistant cell lines PC9/R and HCC827/R was identified to be used as models for stable icotinib-resistant cell lines in vitro.2.Whole transcriptome sequencing combined with online pharmacology analysis,and in vitro cellular assays verified that azithromycin combined with icotinib effectively inhibited the proliferation of drug-resistant cells,and proved that azithromycin and icotinib had a synergistic inhibitory effect on drug-resistant cells;3.Ferroptosis was proved to be the main way reversing the resistance of icotinib combined with azithromycin by treatments of various cell death inhibitors and the detection of lipid peroxidation level;4.The detection of mRNA and protein levels proved that ALOX15 highly expressed in drug-resistant cells after the combined treatment of the two drugs;5.SiALOX15 could partially offset the synergistic inhibitory effect of the two drugs and reduce the production of lipid ROS caused by the combination of the two drugs in drug-resistant cells after siRNA transfection;6.Subcutaneous xenograft tumor models of nude mice was shown that azithromycin combined with icotinib significantly inhibited the growth of icotinib-resistant tumor cells in vivo and increased the expression of ALOX15 to promote ferroptosis;7.GSE31852 data set of 62 EGFR wild-type patient was used to construct a weighted gene co-expression network and prognostic predictive signature model,while GSE31210 data set and TCGA data set were used for external data verification.The results showed The PFS of the patients in the high risk-score(RS)group was significantly shorter.The predictive ability at each time point was significantly improved,especially in the prediction of EGFR/ALK/KRAS wild type.8.GSEA analysis of the gene expression profile of high-scoring population showed that pathways related to tumor metastasis ability was enriched;9.The HDAC inhibitors trichostatin-a(TSA)and vorinostat(SAHA)was predicted to be potential targeted therapies for EGFR wild-type high-scoring patients,inhibiting the proliferation and migration ability of EGFR-WT lung adenocarcinoma cells.Conclusion: 1.Azithromycin can effectively increase the sensitivity of EGFR-TKI-acquired-resistant cells to icotinib.2.Azithromycin can reverse icotinib resistance by promoting the expression of ALOX15 to induce ferroptosis.3.The combination therapy of azithromycin and icotinib in vivo will not produce obvious side effects and effectively inhibit the growth of icotinib-resistant tumor cells.4.Two-gene risk score signature can stably predict the survival of EGFR-WT lung adenocarcinoma patients,especially in patients without known gene mutations.5.Individual use of HDACis,such as TSA or SAHA,may be a potentially effective clinical drug for EGFR-WT patients with high risk scores.These drugs effectively affect the proliferation and migration ability of EGFR-WT lung adenocarcinoma cells individually at a low concentration in a shorter time.