Transcriptome and protein network analyses of 3D-tissue lung cancer models reveal combinatorial targets for KRASG12C-mutation

  • Lung Cancer. 2025 Aug:206:108624. doi: 10.1016/j.lungcan.2025.108624.
Samantha A W Crouch  1 ,  Matthias Peindl  2 ,  Elena Bencúrová  1 ,  Aylin Caliskan  1 ,  Thomas Dandekar  3 ,  Gudrun Dandekar  2 ,  Tim Breitenbach  1
Affiliations
  • 1. Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.
  • 2. Department for Functional Materials in Medicine and Dentistry, University Hospital of Würzburg, Röntgenring 11, 97070 Würzburg, Germany.
  • 3. Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany. Electronic address: [email protected].
PMID: 40644857 DOI: 10.1016/j.lungcan.2025.108624
Abstract

Detailed analysis of RNAseq datasets combined with signaling networksin physiologically relevant models are fundamental steps in the fight against drug resistance. For this purpose, we compare RNAseq data of different responsive KRASG12C-mutant cell lines, H358 and HCC44, to illustrate distinct resistance scenarios against a KRAS-inhibitor. We apply the following steps to investigate this: (I) Analysis of variance shows several differentially expressed marker genes for Non-Small Cell Lung Cancer (NSCLC) and confirms HCC44 cells to be more aggressive. (II) Protein network analysis of key players of resistance reveals upregulation of Dachshund homolog 1 (DACH1) only in treated H358 cells. (III) A systematic analysis of NSCLC with a semi-quantitative signaling network predicts several protein targets counter-acting the KRASG12C-mutation, including DACH1. (IV) We show correlations between the expression level of these genes and resulting survival outcomes using TCGA data from patients. (V) We validate key signature genes by quantitative PCR in H358 cells. Our study identifies DACH1 as marker for resistance from RNAseq data of a 3D tissue NSCLC model validated with patient survival data. We predictmost promising candidates for combination therapies in silico.

Keywords
3D tissue model; Gene regulatory network; Mathematical optimization; Mechanistic model; Non-small cell lung cancer; RNAseq analysis pipeline.