癌症基因组包含几个驱动突变。然而，在某些情况下，尚未识别出已知的驱动程序；这些剩余领域的需求未得到满足，导致癌症治疗进展有限。全基因组测序（WGS）可以识别与疾病相关的非编码改变。因此，利用 WGS 和其他组学数据（例如 ChIP 测序 (ChIP-seq)）来探索非编码区域来辨别与肿瘤发生相关的新改变和机制，近年来一直很有吸引力。综合多组学分析，包括 WGS、ChIP-对来自肺腺癌 (LUAD) 中具有非临床可操作基因改变 (non-CAGA) 的患者样本进行了 seq、DNA 甲基化和 RNA 测序 (RNA-seq)。进行二级聚类分析是为了加强 RNA-seq 确定的与患者生存相关的相关性。 随后进行差异基因表达分析，以确定潜在的药物靶标。通过分析 RNA-seq 数据，发现并证实了非 CAGAs LUAD 中 H3K27ac 标记的差异，其中 mastermind 样转录共激活因子 2 (MAML2) 受到抑制。表达与 MAML2 表达相关的下调基因与患者预后相关。 WGS 分析揭示了与 MAML2 区域 H3K27ac 标记相关的体细胞突变，并且在肿瘤样本中观察到 MAML2 中高水平的 DNA 甲基化。二级聚类分析实现了患者分层，随后的分析确定了潜在的治疗靶基因和治疗方案。我们通过将多组学数据与临床信息相结合的新方法，克服了识别与肿瘤发生相关的编码区的改变或驱动突变的持续挑战揭示非 CAGA LUAD 的分子机制，对患者进行分层以改善患者预后，并确定潜在的治疗靶点。这种方法可能适用于需求未得到满足的其他癌症的研究。© 2024。作者。

The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days.Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets.Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options.We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.© 2024. The Author(s).