A tight inducible model for oncogene function and drug testing
Abstract
Precise control of oncogene expression is crucial for advancing tumor biology research and therapeutic testing. To address the limitations of current systems, we developed a novel, tightly regulated, genome-integrable inducible expression platform based on an optimized cumate gene-switch system. By engineering a KRAB-fused, NLS-enhanced CymR repressor (CSwitch-Silencer), a CpG-free hybrid promoter, and a PiggyBac-based integration strategy, insulated by matrix attachment regions, we achieved robust, reversible, and dose-dependent transgene expression with basal activity below detection. With the receptor tyrosine kinase cMET as a model, we generated CRISPR–Cas9-derived cMET -deficient A549 cells and established inducible lines expressing wild-type and clinically relevant variants, including exon 14 skipping and H1094Y. The system enabled precise tuning of expression levels and faithful recapitulation of downstream signaling and phenotypic responses. Pharmacological profiling revealed variant-specific sensitivities to clinically relevant MET inhibitors, with the H1094Y mutation exhibiting increased sensitivity to the type II inhibitor cabozantinib, while exon 14 skipping conferred reduced sensitivity. These effects were consistent across biochemical and functional assays, including receptor activation and cell migration. Collectively, this platform provides a versatile and scalable approach for modeling oncogene function and drug response in a controlled genetic context. Its tight regulation, modular design, and stable genomic integration make it broadly applicable for functional characterization of cancer-associated variants and for preclinical evaluation of targeted therapies.
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BibTeX
@article{hamdorf2026tight,
title = {{A tight inducible model for oncogene function and drug testing}},
author = {Matthias Hamdorf and Luca Wallner and Sonja Sternberg and Glen Kristiansen and Verena Tischler},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76271-9},
url = {https://www.nature.com/articles/s41598-026-76271-9},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A tight inducible model for oncogene function and drug testing
AU - Matthias Hamdorf
AU - Luca Wallner
AU - Sonja Sternberg
AU - Glen Kristiansen
AU - Verena Tischler
PY - 2026
DA - 2026/10/21
JO - Scientific Reports
DO - 10.1038/s41598-026-76271-9
UR - https://www.nature.com/articles/s41598-026-76271-9
AB - Precise control of oncogene expression is crucial for advancing tumor biology research and therapeutic testing. To address the limitations of current systems, we developed a novel, tightly regulated, genome-integrable inducible expression platform based on an optimized cumate gene-switch system. By engineering a KRAB-fused, NLS-enhanced CymR repressor (CSwitch-Silencer), a CpG-free hybrid promoter, and a PiggyBac-based integration strategy, insulated by matrix attachment regions, we achieved robust, reversible, and dose-dependent transgene expression with basal activity below detection. With the receptor tyrosine kinase cMET as a model, we generated CRISPR–Cas9-derived cMET -deficient A549 cells and established inducible lines expressing wild-type and clinically relevant variants, including exon 14 skipping and H1094Y. The system enabled precise tuning of expression levels and faithful recapitulation of downstream signaling and phenotypic responses. Pharmacological profiling revealed variant-specific sensitivities to clinically relevant MET inhibitors, with the H1094Y mutation exhibiting increased sensitivity to the type II inhibitor cabozantinib, while exon 14 skipping conferred reduced sensitivity. These effects were consistent across biochemical and functional assays, including receptor activation and cell migration. Collectively, this platform provides a versatile and scalable approach for modeling oncogene function and drug response in a controlled genetic context. Its tight regulation, modular design, and stable genomic integration make it broadly applicable for functional characterization of cancer-associated variants and for preclinical evaluation of targeted therapies.
SN - 2045-2322
ER -