Abstract 5853: Regulatory network inference of lung adenocarcinoma drug-tolerant persister cells
Authors: Chabrier Lisa , Leonce Camille , Schneider Raphael , Degletagne Cyril , Saintigny Pierre , Crombach Anton , Ortiz-Cuaran Sandra 
Published: March 2024

Despite the effectiveness of the latest generation of cancer drugs, partial tumor response and subsequent disease relapse is commonly observed in patients. Recent evidence suggests that treatment resistance emerges from drug-tolerant persister cells (DTPC) that survive through non-genetic tumor cell adaptation. Although DTPC are mostly slow-cycling, a rare subset can re-enter the cell cycle under constitutive presence of treatment. Understanding how DTPC dynamically remodel their molecular portraits at the single-cell level may provide high-resolution insights into the molecular circuits associated with adaptive survival to therapy.Here, we use single-cell RNA sequencing (scRNA-seq) to identify potential mechanisms that promote survival of DTPC and decipher their underlying gene regulatory networks (GRNs), in a model of EGFR-mutant lung adenocarcinoma treated with osimertinib (EGFR inhibitor).

Gene expression analysis revealed fewer cell states in DTP compared to treatment-naïve cells (TN). We confirmed the acquisition of DTPC hallmarks (e.g., high epithelial-mesenchymal transition, slow-cycling phenotype, deficient DNA repair). We also identified subpopulations of cells that display intermediate transcriptional states: a pre-existing DTP-like state in a bulk of TN cells, and DTPC with enriched cell cycle signatures, evoking a phenotype of cycling DTPC. To characterize the GRN that govern TN cells, DTPC and the intermediate cell states, we applied the network inference tool PYSCENIC, which associates transcription factors (TFs) to their candidate target genes (TGs) on the basis of the scRNA-seq data and enrichment of TF binding motifs in the genomic regions around TGs. The resulting (GRN) consisted of 5 TF modules clustered on the basis of correlated TF activity.This analysis revealed that, compared to TN cells, DTPC and the intermediate cell states display a marked decreased expression of TFs and TGs involved in mRNA splicing, and more specifically in the initiation of spliceosome assembly through E-complex and A-complex formation. Interestingly, the splicing-related TFs all appear to have affinity for binding to a GCCxC consensus motif or close variant in genes (i.e., SNRPB and SNRPE) that code for key effectors of these initiation complexes. This pilot study highlights that scRNA-seq analyses coupled with inference of regulatory networks might enable the identification of molecular mechanisms that potentially trigger cell state transitions during drug-tolerance. The role of altered mRNA splicing in persistence to EGFR-targeted therapies will be explored experimentally in patient-derived cell lines and 3D organoid cultures.

Citation Format: Lisa Chabrier, Camille Leonce, Raphael Schneider, Cyril Degletagne, Pierre Saintigny, Anton Crombach, Sandra Ortiz-Cuaran. Regulatory network inference of lung adenocarcinoma drug-tolerant persister cells [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(6_Suppl):Abstract nr 5853.