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Abstract The issue of drug resistance in cancer is very similar to the field of infectious disease, either existing before treatment (intrinsic) or generated during therapy (acquired), highly affected by cell endogenous factors and/or the surrounding environment. Acquired resistance can be occurred several months after targeted therapy via alternations of drug targets due to the genomic mutations of the targets, epigenetic transcriptional or translational changes, for example the C797S mutation impairs AZ9291 binding to the EGFR kinase domain. To overcome this, novel well-designed chemical compounds targeting new mutations would be urgently needed, as well as the potential new oncogenes/susceptible genes driving the resistance. Considering about the heterogeneity of primary tumor tissues, LIDE has established one functional cost-effective in vitro and in vivo screening method, named reverse engineering strategy for targets identification by using patients-derived conditional reprogramed cancer cell lines (CRs) and IO-FIVE (Immuno-Oncology drugs Five In Vivo Efficacy test). Basically, pro-siRNA library or CRISPR/sgRNA library is carried out in CRs for candidates hunting, alternatively drug-response evaluation among multiple clinical biopsies in mice cancer trials, after filtered with bioinformatic scanning from large groups of patients, and finally it is validated in vivo by CRISPR, shRNA silence or targeted compounds. In addition to these known factors (EGFR C797S, Her2/Met amplification, Ras and MAPK activation), we have discovered that epigenetic factors for DNA modification, protein translation kinases, mitochondrial/peroxisomal transporters and cell membrane lectins may contribute to AZ92921 resistance in vivo. And one previously unknown acyltransferase member has been identified as key driver gene for pan-cancer development by IO-FIVE, and patients with high expression level of this enzyme have had poor survival outcomes, accompany with uncontrolled tumor growth and immunotherapy resistance, which is becoming a promising therapeutic target. Citation Format: Bin Xie, Taimei Zhang, Le Li, Hongkui Chen, Yang Liu, Lintao Bi, Danyi Wen. Reverse engineering strategy for new targets identification abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 544.
Xie et al. (Fri,) studied this question.