小细胞肺癌 (SCLC) 转化占 EGFR-TKI 复发性肺腺癌 (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 治疗后未转化的 LUAD (LUAD-NT)、原发性 SCLC (SCLC-P) 和 EGFR-TKI 治疗后转化的 LUAD 的预处理样品进行了转录组分析（包括体积转录组学和空间转录组学）和多重免疫荧光分析（改造前：LUAD-BT；改造后：SCLC-AT）。我们的研究发现，与 LUAD-NT 相比，LUAD-BT 表现出潜在的转化转录组特征。我们确定了转化过程中发生变化的几条途径，并且肿瘤细胞内而不是肿瘤微环境内的表观遗传改变（例如 HDAC10、HDAC1、DNMT3A）可能会促进转化。对于药物途径，转化的 SCLC 被证明较少依赖于 EGF 信号传导，但更依赖于 FGF 信号传导，而 VEGF-VEGFR 途径仍然活跃，表明转化后的潜在治疗方法。我们还发现转化的 SCLC 显示出免疫衰竭状态，这与转化前 EGFR-TKI 的持续时间相关。此外，SCLC-AT 表现出与 SCLC-P 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 标记模型来预测 SCLC 转化，该模型在训练和测试队列中分别获得了 100% 和 87.5% 的敏感性、95.7% 和 100% 的特异性。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。© 2024。作者。

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).