对肺腺癌中标志性分子通路的转录分析产生了具有潜在治疗意义的临床相关分类。
Transcriptional analysis of landmark molecular pathways in lung adenocarcinoma results in a clinically relevant classification with potential therapeutic implications.
发表日期:2023 Nov 09
作者:
Sara Hijazo-Pechero, Ania Alay, David Cordero, Raúl Marín, Noelia Vilariño, Ramón Palmero, Jesús Brenes, Aina Montalban-Casafont, Ernest Nadal, Xavier Solé
来源:
Molecular Oncology
摘要:
肺腺癌(LUAD)是一种分子异质性疾病。除了基因组改变之外,癌症转录谱还有助于调整癌症治疗并估计每个患者的结果。使用基因集变异分析 (GSVA) 方法推断 4,573 名 LUAD 患者的 50 条分子通路的转录活性水平。根据所研究途径的组合行为,定义并独立验证了 7 种 LUAD 亚型:AD(腺癌亚型)1-7。 AD1、AD4 和 AD5 亚型与更好的总体生存率相关。 AD1 和 AD4 亚型富含表皮生长因子受体 (EGFR) 突变,而 AD2 和 AD6 则显示出更高的肿瘤蛋白 p53 (TP53) 改变频率。根据 LUAD 细胞系的数据,AD2 和 AD6 亚型与较高的基因组不稳定性、增殖相关通路表达和对化疗的特异性敏感性相关。除了 CD274 (PD-L1) 基因表达和肿瘤突变负荷 (TMB) 之外,LUAD 亚型还能够预测免疫治疗反应。 AD2 和 AD4 亚型分别与免疫治疗的潜在耐药性和反应相关。因此,转录组数据分析可以改善基因组学和单一生物标志物(即 PD-L1、TMB)之外的患者分层,并可能为更个性化的治疗途径奠定基础,特别是在驾驶员阴性 LUAD 中。本文受版权保护。版权所有。
Lung adenocarcinoma (LUAD) is a molecularly heterogeneous disease. In addition to genomic alterations, cancer transcriptional profiling can be helpful to tailor cancer treatment and to estimate each patient's outcome. Transcriptional activity levels of 50 molecular pathways were inferred in 4,573 LUAD patients using Gene Set Variation Analysis (GSVA) method. Seven LUAD subtypes were defined and independently validated based on the combined behavior of the studied pathways: AD (adenocarcinoma subtype)1-7. AD1, AD4 and AD5 subtypes were associated with better overall survival. AD1 and AD4 subtypes were enriched in epidermal growth factor receptor (EGFR) mutations, whereas AD2 and AD6 showed higher tumor protein p53 (TP53) alteration frequencies. AD2 and AD6 subtypes correlated with higher genome instability, proliferation-related pathways expression and specific sensitivity to chemotherapy, based on data from LUAD cell lines. LUAD subtypes were able to predict immunotherapy response in addition to CD274 (PD-L1) gene expression and tumor mutational burden (TMB). AD2 and AD4 subtypes were associated with potential resistance and response to immunotherapy, respectively. Thus, analysis of transcriptomic data could improve patient stratification beyond genomics and single biomarkers (i.e., PD-L1, TMB) and may lay the foundation for more personalized treatment avenues, especially in driver-negative LUAD.This article is protected by copyright. All rights reserved.