Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.
Oncode Institute, Utrecht, The Netherlands.
BMC Med Genomics. 2024 Jul 15;17(1):186. doi: 10.1186/s12920-024-01941-4.
The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes.
We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin.
We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.
癌症的遗传背景仍然复杂且难以整合。许多已知的基因内体细胞突变会导致并驱动癌症,而癌症的全基因组关联研究(GWAS)则揭示了许多与癌症相关的种系风险因素。然而,已知的体细胞驱动基因与 GWAS 位点的定位候选基因之间的重叠却出人意料地小。我们假设,来自多个独立癌症 GWAS 位点的基因应该表现出组织特异性的共调控模式,这些模式会集中在癌症特异性的驱动基因上。
我们通过估计基因之间的共表达,研究了最近关于乳腺癌、前列腺癌、结直肠癌和皮肤癌的大型 GWAS,随后优先考虑了与癌症 GWAS 中易感性位点内映射的基因具有显著共表达的基因。我们观察到,通过从相关组织的非癌症样本中衍生的共表达网络进行优先级排序,这些优先考虑的基因在癌症驱动基因中得到了强烈富集。当使用源自相关组织的非癌症样本的共表达网络时,已知癌症驱动基因的富集最为显著。
我们展示了如何通过组织特异性共表达网络将癌症 GWAS 中识别的风险位点内的基因与已知的癌症驱动基因联系起来。这为为什么看似不相关的一组基因,无论是种系风险因素还是体细胞突变,最终都可能导致同一种疾病提供了一个重要的解释。