黑色素瘤特异性突变热点在远端非编码区、启动子相互作用区域，暗示新的潜在驱动基因
Melanoma-specific mutation hotspots in distal, non-coding, promoter-interacting regions implicate novel candidate driver genes
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影响因子:6.8
分区:医学2区 Top / 肿瘤学2区
发表日期:2024 Nov
作者:
Michael Pudjihartono, Nicholas Pudjihartono, Justin M O'Sullivan, William Schierding
DOI:
10.1038/s41416-024-02870-w
摘要
为了开发靶向治疗，关键在于识别黑色素瘤中的全谱遗传驱动因素，包括非编码区域的突变。然而，近期关于非编码区域的研究主要集中在基因邻近元素如启动子和非编码RNA，远端调控元件的研究尚不充分。我们利用黑色素瘤细胞的Hi-C染色质接触数据，绘制了全基因组范围内的远端、非编码、启动子相互作用调控元素。结合全基因组测序和基因表达数据，建立多元线性回归模型，识别影响启动子活性的远端突变热点。我们发现8个反复突变的热点，这些热点新颖、黑色素瘤特异，位于启动子相互作用的远端调控元素中，改变转录因子结合基序，影响基因表达（如HSPB7、CLDN1、ADCY9和FDXR），这些基因在多种癌症中被认为是肿瘤抑制基因或癌基因。我们的研究表明，除了已知的TERT启动子外，黑色素瘤中存在其他非编码驱动突变，为非编码突变破坏复杂调控网络、促进黑色素瘤发生提供了新见解。此外，该研究提出了一种整合多层次生物数据、识别癌症特异性非编码驱动的框架。
Abstract
To develop targeted treatments, it is crucial to identify the full spectrum of genetic drivers in melanoma, including those in non-coding regions. However, recent efforts to explore non-coding regions have primarily focused on gene-adjacent elements such as promoters and non-coding RNAs, leaving intergenic distal regulatory elements largely unexplored.We used Hi-C chromatin contact data from melanoma cells to map distal, non-coding, promoter-interacting regulatory elements genome-wide in melanoma. Using this "promoter-interaction network", alongside whole-genome sequence and gene expression data from the Pan Cancer Analysis of Whole Genomes, we developed multivariate linear regression models to identify distal somatic mutation hotspots that affect promoter activity.We identified eight recurrently mutated hotspots that are novel, melanoma-specific, located in promoter-interacting distal regulatory elements, alter transcription factor binding motifs, and affect the expression of genes (e.g., HSPB7, CLDN1, ADCY9 and FDXR) previously implicated as tumour suppressors/oncogenes in various cancers.Our study suggests additional non-coding drivers beyond the well-characterised TERT promoter in melanoma, offering new insights into the disruption of complex regulatory networks by non-coding mutations that may contribute to melanoma development. Furthermore, our study provides a framework for integrating multiple levels of biological data to uncover cancer-specific non-coding drivers.