Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan.
Mol Cancer. 2024 Jun 11;23(1):126. doi: 10.1186/s12943-024-02035-6.
In an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD.
We performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases.
We demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations.
Our findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.
在对肺腺癌（LUAD）的广泛基因组分析中，已经发现驱动突变是分子治疗的潜在靶点。然而，仍有一些情况下无法确定靶基因。每个病例中经常会发现数百个超级增强子和结构变异。尽管如此，大多数癌症研究还是分别对这些数据集进行分析，没有合并和比较数据，也没有 LUAD 中整合分析的例子。
我们对 174 例缺乏临床可操作遗传改变的 LUAD 病例进行了超级增强子和结构变异的综合分析。为此，我们对具有驱动基因突变的样本和没有驱动基因突变的样本进行了 WGS 和 H3K27Ac ChIP-seq 分析，从而全面研究了超级增强子在 LUAD 病例中的潜在作用。
我们证明，位于这些重叠区域的大多数基因与已知和以前未知的驱动基因以及超级增强子伴随的基因组结构异常引起的异常表达有关。Hi-C 和长读测序数据进一步证实了这一观点。当我们使用 CRISPR-Cas9 诱导模拟具有异常 ERBB2 基因表达的病例的结构异常时，我们观察到 ERBB2 表达的升高。这些异常与手术后复发风险增加有关，无论是否存在驱动突变。
我们的研究结果表明，与结构多态性相关的异常基因表达可以通过促进驱动突变和预后因素的识别，显著影响个性化癌症治疗，有助于更全面地了解 LUAD 的发病机制。
Zotero 插件
