Leveraging Acquired EGFR-TKI-Resistant Models to Identify MUC16 as a Therapeutic Vulnerability in Lung Adenocarcinoma

<b>Background/Objectives</b>: Acquired resistance to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) remains a major challenge in the treatment of EGFR-mutant lung adenocarcinoma (LUAD). This study aimed to develop and characterize representative models of acquired EGFR-TKI resistance and to identify potential therapeutic targets mediating this process. <b>Methods</b>: Resistant models of PC9 and LUAD-PDCs were generated using a standardized dose-escalation protocol. The resulting models were characterized by drug response assays, morphology, and transcriptomic sequencing. Candidate target genes were validated across all resistant models using siRNA knockdown followed by re-sensitization assays. Clinical relevance was further examined through analysis of publicly available datasets. <b>Results</b>: These generated models displayed stable resistant phenotypes and unique transcriptomic alterations. Cross-model analysis revealed <i>MUC16</i> as a consistently upregulated gene associated with resistance. Functional validation demonstrated that <i>MUC16</i> depletion re-sensitized all resistant models to EGFR-TKIs. Furthermore, analysis of clinical data linked high <i>MUC16</i> expression to poorer patient outcomes. <b>Conclusions</b>: This study establishes stable in vitro models for investigating acquired resistance in EGFR-mutant LUAD and identifies <i>MUC16</i> as a functionally validated and clinically relevant mediator of EGFR-TKI resistance, providing a potential therapeutic target for overcoming drug resistance.

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Publication Details

Journal
Pharmaceuticals
Published
2025-12-25
DOI
https://doi.org/10.3390/ph19010047
Primary Topic
Lung Cancer Treatments and Mutations
Type
article
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article

Leveraging Acquired EGFR-TKI-Resistant Models to Identify MUC16 as a Therapeutic Vulnerability in Lung Adenocarcinoma

Zi Wang, Kai Xiao, Jingyao Chen, Yamei Huang et al.
Pharmaceuticals
Lung Cancer Treatments and Mutations
article

Leveraging Acquired EGFR-TKI-Resistant Models to Identify MUC16 as a Therapeutic Vulnerability in Lung Adenocarcinoma

Zi Wang, Kai Xiao, Jingyao Chen, Yamei Huang, Manyu Zhao, Chenyu Wu, Yuhang Kong, Yinhua Tan, Zhichang Liu, Chunxiu Xiao, Qiang Wu
article en

Abstract

<b>Background/Objectives</b>: Acquired resistance to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) remains a major challenge in the treatment of EGFR-mutant lung adenocarcinoma (LUAD). This study aimed to develop and characterize representative models of acquired EGFR-TKI resistance and to identify potential therapeutic targets mediating this process. <b>Methods</b>: Resistant models of PC9 and LUAD-PDCs were generated using a standardized dose-escalation protocol. The resulting models were characterized by drug response assays, morphology, and transcriptomic sequencing. Candidate target genes were validated across all resistant models using siRNA knockdown followed by re-sensitization assays. Clinical relevance was further examined through analysis of publicly available datasets. <b>Results</b>: These generated models displayed stable resistant phenotypes and unique transcriptomic alterations. Cross-model analysis revealed <i>MUC16</i> as a consistently upregulated gene associated with resistance. Functional validation demonstrated that <i>MUC16</i> depletion re-sensitized all resistant models to EGFR-TKIs. Furthermore, analysis of clinical data linked high <i>MUC16</i> expression to poorer patient outcomes. <b>Conclusions</b>: This study establishes stable in vitro models for investigating acquired resistance in EGFR-mutant LUAD and identifies <i>MUC16</i> as a functionally validated and clinically relevant mediator of EGFR-TKI resistance, providing a potential therapeutic target for overcoming drug resistance.

PharmaceuticalsVol. 19(1)
Chengdu Medical College (CN), Sichuan University (CN), West China Hospital of Sichuan University (CN)
No poverty
Openalex Percentile: Top 28%
Lung Cancer Treatments and Mutations
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