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探索 EGFR 突变型肺腺癌中小细胞转化的机制并构建模型。

Mechanism exploration and model construction for small cell transformation in EGFR-mutant lung adenocarcinomas.

机构信息

State Key Laboratory of Molecular Oncology, Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.

Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.

出版信息

Signal Transduct Target Ther. 2024 Oct 2;9(1):261. doi: 10.1038/s41392-024-01981-3.

DOI:10.1038/s41392-024-01981-3
PMID:39353908
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11445518/
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.

摘要

小细胞肺癌 (SCLC) 转化占 EGFR-TKI 复发肺腺癌 (LUAD) 耐药的 3-14%,其分子机制和最佳治疗策略尚不清楚。我们对 EGFR-TKI 治疗后无转化的 LUAD(LUAD-NT)、原发性 SCLC(SCLC-P)和 LUAD 转化后(转化前:LUAD-BT;转化后:SCLC-AT)的预处理样本进行了转录组分析(包括批量和空间转录组学)和多重免疫荧光分析。我们的研究发现,与 LUAD-NT 相比,LUAD-BT 具有潜在的转化转录组特征。我们确定了几个在转化过程中发生转变的途径,并且这种转化可能是由肿瘤细胞内的表观遗传改变(如 HDAC10、HDAC1、DNMT3A)而不是肿瘤微环境内的改变所促进的。对于可用药途径,转化后的 SCLC 被证明对 EGF 信号的依赖性降低,但对 FGF 信号的依赖性增加,而 VEGF-VEGFR 途径仍然活跃,表明转化后有潜在的治疗方法。我们还发现转化后的 SCLC 表现出免疫耗竭状态,这与转化前 EGFR-TKI 的持续时间有关。此外,SCLC-AT 与 SCLC-P 表现出不同的分子亚型。此外,我们基于转录组和 IHC 数据构建了一个理想的 4 标志物模型来预测 SCLC 转化,该模型在训练和测试队列中的敏感性分别为 100%和 87.5%,特异性分别为 95.7%和 100%。我们深入了解了 SCLC 转化的分子机制以及 SCLC-AT 和 SCLC-P 之间的差异,这可能为未来 SCLC 转化的预防策略和后续治疗策略提供启示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445518/227e47f89e2a/41392_2024_1981_Fig7_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445518/227e47f89e2a/41392_2024_1981_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445518/13d946384563/41392_2024_1981_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445518/562d3eb05c83/41392_2024_1981_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445518/5b17ba021c14/41392_2024_1981_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445518/d6065fc5ceb6/41392_2024_1981_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445518/6c9fe813d8ba/41392_2024_1981_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445518/78999e767871/41392_2024_1981_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445518/227e47f89e2a/41392_2024_1981_Fig7_HTML.jpg

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