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Updated: May 26, 2025
Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids
Published on: November 22, 2021
Comprehensive transcriptome, miRNA and kinome profiling identifies new treatment options for personalized lung cancer
Shen Zhong1, Yvonne Börgeling2, Patrick Zardo3
1Centre for Pharmacology and Toxicology, Hannover Medical School, Hannover, Germany.
Background:
Basic research identified oncogenic driver mutations in lung cancer (LC). However, <10% of patients carry driver mutations. Thus, most patients are not recommended for first-line kinase inhibitor (KI)-based therapies. Through enabling technologies and bioinformatics, we gained deep insight into patient-specific signalling networks which permitted novel KI-based treatment options in LC.
Methods:
We performed molecular pathology, transcriptomics and miRNA profiling across 95 well-characterized LC patients. We confirmed results based on cross-linked immunoprecipitation-sequencing data, and used N = 524 adeno- and 497 squamous cell carcinomas as validation sets. We employed the PamGene platform to identify aberrant kinases, validated the results by evaluating independent siRNA and CRISPR-mediated mRNA knockdown studies in human LC cell lines.
Results:
Transcriptomics revealed 439, 1240, 383 and 320 significantly upregulated genes, respectively, for adeno-, squamous, neuroendocrine and metastatic cases, and there are 1092, 1477, 609 and 1267 downregulated DEGs. Based on gene enrichment analysis and experimentally validated miRNA-gene interactions, we constructed regulatory networks specific for adeno-, squamous, neuroendocrine and metastatic LC. Molecular profiling discovered 137 significantly upregulated kinases (range 2-26-fold) of which 65 and 72, respectively, are tyrosine and serine-threonine kinases while 6 kinases carry driver mutations. Meanwhile, there are 21 kinases commonly upregulated irrespective of the histological type of LC. Bioinformatics decoded networks in which kinases function as master regulators. Typically, the networks consisted of 14, 9, 16 and 19 highly regulated kinases in adeno-, squamous, neuroendocrine and metastatic LC. Inhibition of kinases which function as master regulators disrupted the signalling networks, and their gene knock-down studies confirmed inhibition of cell proliferation in a panel of human LC cell lines. Additionally, the proposed molecular profiling enables KI-based therapies in patients with acquired drug resistance.
Conclusions:
Our study broadens the perspective of KI-based therapies in LC, and we propose a framework to overcome acquired drug resistance.
Insights
This study reveals patient-specific kinase networks in lung cancer (LC), enabling new kinase inhibitor (KI) therapies for most patients and overcoming drug resistance.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Oncogenic driver mutations are found in less than 10% of lung cancer (LC) patients, limiting first-line kinase inhibitor (KI) therapies.
- Patient-specific signaling networks offer novel therapeutic strategies for LC.
- Enabling technologies and bioinformatics provide deep insights into these networks.
Purpose of the Study:
- To identify patient-specific kinase signaling networks in lung cancer.
- To explore novel kinase inhibitor (KI)-based treatment options for LC.
- To develop a framework for overcoming acquired drug resistance in LC.
Main Methods:
- Molecular pathology, transcriptomics, and miRNA profiling were performed on 95 LC patients.
- Kinase activity was assessed using the PamGene platform and validated with siRNA/CRISPR knockdown studies.
- Bioinformatics and gene enrichment analysis were used to construct regulatory networks.
Main Results:
- Transcriptomics identified numerous upregulated and downregulated genes across different LC subtypes.
- 137 kinases were significantly upregulated, with 21 commonly elevated across histological types.
- Inhibition of master regulator kinases disrupted signaling networks and inhibited cell proliferation.
- Molecular profiling identified potential KI-based therapies for acquired drug resistance.
Conclusions:
- This study expands the scope of KI-based therapies for lung cancer.
- A framework is proposed to address and overcome acquired drug resistance in LC patients.
- Patient-specific molecular profiling is key to developing personalized treatment strategies.
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