A prognostic model of drug tolerant persister-related genes in lung adenocarcinoma based on single cell and bulk RNA sequencing data

Background: Acquired resistance to targeted drugs is a major challenge in cancer. The drugtolerant state has been proposed to be an initial step towards acquisition of real drugresistance. Drug tolerant persister (DTP) cells are purported to survive during treatment and stay dormant for several years. Single cell sequencing can provide a comprehensive landscape of gene expression in DTP cells, which can facilitate investigation of heterogeneity of a drug tolerant state and identification of new anticancer targets.Methods: The genetic profiling of DTPs was explored by integrating Gene Expression Omnibus (GEO) datasets, and a prognostic signature of DTP-related genes (DTPRGs) in lung adenocarcinoma of TCGA LUAD cohort was constructed. The scores of infiltrating immune cells were calculated and activity of immune-related pathways was evaluated by single-sample gene set enrichment analysis (ssGSEA). Functional enrichment analysis of the DTPRGs between low- and high-risk groups was performed. Immune cell subtypes and immune-related pathways were analyzed.Results: An 11-gene panel (MT2A, UBE2S, CLTB, KRT7, IGFBP3, CTSH, NPC2, HMGA1, HNRNPAB, DTYMK, and IHNA) was established. DTPRGs were mainly correlated with nuclear division, chromosome segregation, and cell cycle pathways. Infiltration of immune cells was lower in the high-risk group while the inflammation-promoting and MCH-class I response pathway had higher activity in the high-risk group. A nomogram was generated with prognostic accuracy, further validated using clinical outcomes following therapy with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs).Discussion: A prognostic model of lung adenocarcinoma based on DTPRGs was constructed. Targeting DTP cells is a potential therapeutic approach to prevent a drug tolerant state.