High-Resolution Profiling of Lung Adenocarcinoma Identifies Expression Subtypes with Specific Biomarkers and Clinically Relevant Vulnerabilities.
High-Resolution Profiling of Lung Adenocarcinoma Identifies Expression Subtypes with Specific Biomarkers and Clinically Relevant Vulnerabilities.
复制标题
DOI：
10.1158/0008-5472.can-22-0432
复制
发表时间：
2022-11-02
期刊：
影响因子：
11.2
通讯作者：
中科院分区：
文献类型：
--
作者：
肺腺癌（LUAD）是最常见的癌症类型之一，有多种治疗选择。需要更好的生物标志物来预测治疗反应，以指导治疗方式的选择并改进精准医疗。在这里，我们对癌症基因组图谱 (TCGA) 中的 509 个 LUAD 病例采用共识层次聚类方法，以确定五种稳健的 LUAD 表达亚型。然后整合来自患者样本和细胞系的基因组和蛋白质组数据，以帮助定义对靶向治疗和免疫疗法反应的生物标志物。这种方法定义了具有独特蛋白质组和依赖性特征的亚型。亚型 4 (S4) 相关细胞系表现出对 CDK6 和 CDK6-细胞周期蛋白 D3 复合体基因 (CCND3) 丢失的特定脆弱性。 S3 的特点是依赖 CDK4、免疫相关表达模式和改变的 MET 信号传导。实验验证表明，S3 相关细胞系对 MET 抑制剂有反应，导致 PD-L1 表达增加。在一个独立的真实世界患者数据集中，S3 肿瘤患者中富含对免疫检查点阻断 (ICB) 的反应者。 S3 和 S4 的基因组特征被进一步确定为能够对这些亚型进行临床诊断的生物标志物。总体而言，我们的共识层次聚类方法确定了稳健的肿瘤表达亚型，并且我们随后对基因组学、蛋白质组学和 CRISPR 筛选数据的综合分析揭示了亚型特异性生物学和脆弱性。这些肺腺癌表达亚型及其生物标志物可以帮助识别可能对 CDK4/6、MET 或 PD-L1 抑制剂产生反应的患者，从而有可能改善患者的预后。
Lung adenocarcinoma (LUAD) is one of the most common cancer types and has various treatment options. Better biomarkers to predict therapeutic response are needed to guide choice of treatment modality and improve precision medicine. Here we utilized a consensus hierarchical clustering approach on 509 LUAD cases from The Cancer Genome Atlas (TCGA) to identify five robust LUAD expression subtypes. Genomic and proteomic data from patient samples and cell lines was then integrated to help define biomarkers of response to targeted therapies and immunotherapies. This approach defined subtypes with unique proteogenomic and dependency profiles. Subtype 4 (S4)-associated cell lines exhibited specific vulnerability to loss of CDK6 and CDK6-cyclin D3 complex gene (CCND3). S3 was characterized by dependency on CDK4, immune-related expression patterns, and altered MET signaling. Experimental validation showed that S3-associated cell lines responded to MET inhibitors, leading to increased expression of PD-L1. In an independent real-world patient dataset, patients with S3 tumors were enriched with responders to immune checkpoint blockade (ICB). Genomic features in S3 and S4 were further identified as biomarkers for enabling clinical diagnosis of these subtypes. Overall, our consensus hierarchical clustering approach identified robust tumor expression subtypes, and our subsequent integrative analysis of genomics, proteomics, and CRISPR screening data revealed subtype-specific biology and vulnerabilities. These lung adenocarcinoma expression subtypes and their biomarkers could help identify patients likely to respond to CDK4/6, MET, or PD-L1 inhibitors, potentially improving patient outcome.