1. Mechanism exploration and model construction for small cell transformation in EGFR-mutant lung adenocarcinomas.
- Author
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Li Y, Xie T, Wang S, Yang L, Hao X, Wang Y, Hu X, Wang L, Li J, Ying J, and Xing P
- Subjects
- Humans, Small Cell Lung Carcinoma genetics, Small Cell Lung Carcinoma pathology, Small Cell Lung Carcinoma drug therapy, Mutation, Gene Expression Regulation, Neoplastic drug effects, Gene Expression Regulation, Neoplastic genetics, Female, Male, Protein Kinase Inhibitors pharmacology, ErbB Receptors genetics, ErbB Receptors metabolism, Adenocarcinoma of Lung genetics, Adenocarcinoma of Lung pathology, Adenocarcinoma of Lung drug therapy, Lung Neoplasms genetics, Lung Neoplasms pathology, Lung Neoplasms drug therapy, Cell Transformation, Neoplastic genetics
- Abstract
Small-cell lung cancer (SCLC) transformation accounts for 3-14% of resistance in EGFR-TKI relapsed lung adenocarcinomas (LUADs), with unknown molecular mechanisms and optimal treatment strategies. We performed transcriptomic analyses (including bulk and spatial transcriptomics) and multiplex immunofluorescence on pre-treated samples from LUADs without transformation after EGFR-TKI treatment (LUAD-NT), primary SCLCs (SCLC-P) and LUADs with transformation after EGFR-TKI treatment (before transformation: LUAD-BT; after transformation: SCLC-AT). Our study found that LUAD-BT exhibited potential transcriptomic characteristics for transformation compared with LUAD-NT. We identified several pathways that shifted during transformation, and the transformation might be promoted by epigenetic alterations (such as HDAC10, HDAC1, DNMT3A) within the tumor cells instead of within the tumor microenvironment. For druggable pathways, transformed-SCLC were proved to be less dependent on EGF signaling but more relied on FGF signaling, while VEGF-VEGFR pathway remained active, indicating potential treatments after transformation. We also found transformed-SCLC showed an immuno-exhausted status which was associated with the duration of EGFR-TKI before transformation. Besides, SCLC-AT exhibited distinct molecular subtypes from SCLC-P. Moreover, we constructed an ideal 4-marker model based on transcriptomic and IHC data to predict SCLC transformation, which obtained a sensitivity of 100% and 87.5%, a specificity of 95.7% and 100% in the training and test cohorts, respectively. We provided insights into the molecular mechanisms of SCLC transformation and the differences between SCLC-AT and SCLC-P, which might shed light on prevention strategies and subsequent therapeutic strategies for SCLC transformation in the future., (© 2024. The Author(s).)
- Published
- 2024
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