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Updated: Sep 16, 2025
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
Transcriptome and protein network analyses of 3D-tissue lung cancer models reveal combinatorial targets for
Samantha A W Crouch1, Matthias Peindl2, Elena Bencúrová1
1Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.
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.
Insights
Drug resistance in non-small cell lung cancer (NSCLC) is a challenge. This study identifies Dachshund homolog 1 (DACH1) as a key marker for KRAS inhibitor resistance using RNAseq and patient data.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Drug resistance in KRAS-mutant non-small cell lung cancer (NSCLC) necessitates advanced analytical approaches.
- Understanding distinct resistance mechanisms in KRASG12C-mutant NSCLC is crucial for therapeutic development.
Purpose of the Study:
- To investigate differential resistance scenarios in KRASG12C-mutant NSCLC cell lines using RNAseq and signaling network analysis.
- To identify novel biomarkers and therapeutic targets for overcoming KRAS inhibitor resistance.
Main Methods:
- Comparative RNA sequencing analysis of H358 and HCC44 cell lines.
- Protein-protein interaction network analysis and signaling pathway analysis.
- Correlation of gene expression with patient survival data (TCGA) and quantitative PCR validation.
Main Results:
- Differentially expressed genes identified, with HCC44 cells showing more aggressive characteristics.
- Dachshund homolog 1 (DACH1) was upregulated in treated H358 cells and predicted as a resistance marker.
- DACH1 expression correlated with patient survival outcomes, validating its role in resistance.
Conclusions:
- DACH1 is identified as a potential biomarker for KRAS inhibitor resistance in NSCLC.
- In silico analysis predicts promising combination therapy candidates.
- Integrated analysis of RNAseq, signaling networks, and patient data provides a robust framework for studying drug resistance.
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