Some driver gene mutations, including epidermal growth factor receptor (EGFR), have been reported to be involved in expression regulation of the immunosuppressive checkpoint protein programmed cell death ligand 1 (PD-L1), but the underlying mechanism remains obscure. We investigated the potential role and precise mechanism of EGFR mutants in PD-L1 expression regulation in non-small-cell lung cancer (NSCLC) cells. Examination of pivotal EGFR signaling effectors in 8 NSCLC cell lines indicated apparent associations between PD-L1 overexpression and phosphorylation of AKT and ERK, especially with increased protein levels of phospho-IκBα (p-IκBα) and hypoxia-inducible factor-1α (HIF-1α). Flow cytometry results showed stronger membrane co-expression of EGFR and PD-L1 in NSCLC cells with EGFR mutants compared with cells carrying WT EGFR. Additionally, ectopic expression or depletion of EGFR mutants and treatment with EGFR pathway inhibitors targeting MEK/ERK, PI3K/AKT, mTOR/S6, IκBα, and HIF-1α indicated strong accordance among protein levels of PD-L1, p-IκBα, and HIF-1α in NSCLC cells. Further treatment with pathway inhibitors significantly inhibited xenograft tumor growth and p-IκBα, HIF-1α, and PD-L1 expression of NSCLC cells carrying EGFR mutant in nude mice. Moreover, immunohistochemical analysis revealed obviously increased protein levels of p-IκBα, HIF-1α, and PD-L1 in NSCLC tissues with EGFR mutants compared with tissues carrying WT EGFR. Non-small-cell lung cancer tissues with either p-IκBα or HIF-1α positive staining were more likely to possess elevated PD-L1 expression compared with tissues scored negative for both p-IκBα and HIF-1α. Our findings showed important roles of phosphorylation activation of AKT and ERK and potential interplay and cooperation between NF-κB and HIF-1α in PD-L1 expression regulation by EGFR mutants in NSCLC.
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http://dx.doi.org/10.1111/cas.13989 | DOI Listing |
Zhonghua Yi Xue Za Zhi
January 2025
Department of Medical Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing210000, China.
To investigate the impact of SMARCA4 mutations on the outcomes of patients with advanced lung adenocarcinoma with epidermal growth factor receptor (EGFR) mutations. In the Memorial Sloan Kettering Cancer Center (MSK) MetTropism study, 960 patients with advanced EGFR-mutated lung adenocarcinoma were screened and included in the MSK cohort, composing of 313 males and 647 females, with a median [(, )] age of 64 (56, 72) years. A retrospective analysis was conducted on the data of 178 patients with advanced EGFR-mutated lung adenocarcinoma who received EGFR tyrosine kinase inhibitors (TKIs) treatment in the Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, from January 2018 to December 2022.
View Article and Find Full Text PDFCancer Lett
January 2025
Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer(IBMC),Chinese Academy of Sciences, Hangzhou, Zhejiang, China. Electronic address:
Cancers (Basel)
January 2025
Department of Medical Oncology, Faculty of Medicine, İstinye University, İstanbul 34010, Turkey.
Background: Although higher-generation TKIs are associated with improved progression-free survival in advanced NSCLC patients with EGFR mutations, the optimal selection of TKI treatment remains uncertain. To address this gap, we developed a web application powered by a reinforcement learning (RL) algorithm to assist in guiding initial TKI treatment decisions.
Methods: Clinical and mutational data from advanced NSCLC patients were retrospectively collected from 14 medical centers.
Ann Oncol
January 2025
Department of Medical Oncology, National Cancer Centre Singapore, Duke-NUS Oncology Academic Clinical Programme, Singapore.
Bioengineering (Basel)
December 2024
Department of Pathology, University of Yamanashi, Yamanashi 409-3898, Japan.
The latest World Health Organization (WHO) classification of central nervous system tumors (WHO2021/5th) has incorporated molecular information into the diagnosis of each brain tumor type including diffuse glioma. Therefore, an artificial intelligence (AI) framework for learning histological patterns and predicting important genetic events would be useful for future studies and applications. Using the concept of multiple-instance learning, we developed an AI framework named GLioma Image-level and Slide-level gene Predictor (GLISP) to predict nine genetic abnormalities in hematoxylin and eosin sections: , , mutations, promoter mutations, homozygous deletion (CHD), amplification (amp), 7 gain/10 loss (7+/10-), 1p/19q co-deletion, and promoter methylation.
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