背景:癌症基因组包含几个驱动突变。然而,在某些情况下,尚未确定已知的驾驶员;这些剩余的未满足需求的领域,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,使用WGS和ChIP测序(ChIP-seq)等其他组学数据探索非编码区,以辨别与肿瘤发生相关的新改变和机制,目前一直很有吸引力.
方法:综合多组学分析,包括WGS,ChIP-seq,DNA甲基化,和RNA测序(RNA-seq),对肺腺癌(LUAD)中具有非临床可操作遗传改变(非CAGA)的患者的样本进行了分析。进行了二级聚类分析,以加强与患者生存相关的相关性,如通过RNA-seq鉴定的。进行随后的差异基因表达分析以鉴定潜在的可成药靶标。
结果:通过分析RNA-seq数据发现并证实了非CAGAsLUAD中H3K27ac标记的差异,其中策划者样转录共激活因子2(MAML2)被抑制。表达与MAML2表达相关的下调基因与患者预后相关。WGS分析显示,在肿瘤样品中观察到与MAML2区域中的H3K27ac标记相关的体细胞突变和MAML2中的高水平DNA甲基化。第二级聚类分析使患者能够分层,随后的分析确定了潜在的治疗目标基因和治疗选择。
结论:我们克服了识别与肿瘤发生相关的编码区改变或驱动突变的持续挑战,通过一种新的方法,将多组学数据与临床信息相结合,以揭示非CAGAsLUAD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具有未满足的需求的其他癌症的研究。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.