Breast Cancer and Systems Biology Unit, Translational Research Laboratory, Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, Barcelona 08908, Catalonia, Spain.
Carcinogenesis. 2014 Mar;35(3):578-85. doi: 10.1093/carcin/bgt403. Epub 2013 Dec 2.
Dozens of common genetic variants associated with cancer risk have been identified through genome-wide association studies (GWASs). However, these variants only explain a modest fraction of the heritability of disease. The missing heritability has been attributed to several factors, among them the existence of genetic interactions (G × G). Systematic screens for G × G in model organisms have revealed their fundamental influence in complex phenotypes. In this scenario, G × G overlap significantly with other types of gene and/or protein relationships. Here, by integrating predicted G × G from GWAS data and complex- and context-defined gene coexpression profiles, we provide evidence for G × G associated with cancer risk. G × G predicted from a breast cancer GWAS dataset identified significant overlaps [relative enrichments (REs) of 8-36%, empirical P values < 0.05 to 10(-4)] with complex (non-linear) gene coexpression in breast tumors. The use of gene or protein data not specific for breast cancer did not reveal overlaps. According to the predicted G × G, experimental assays demonstrated functional interplay between lipoma-preferred partner and transforming growth factor-β signaling in the MCF10A non-tumorigenic mammary epithelial cell model. Next, integration of pancreatic tumor gene expression profiles with pancreatic cancer G × G predicted from a GWAS corroborated the observations made for breast cancer risk (REs of 25-59%). The method presented here can potentially support the identification of genetic interactions associated with cancer risk, providing novel mechanistic hypotheses for carcinogenesis.
通过全基因组关联研究（GWAS）已经确定了数十种与癌症风险相关的常见遗传变异。然而，这些变异仅能解释疾病遗传力的一小部分。遗传力的缺失归因于多种因素，包括遗传相互作用（G×G）的存在。在模式生物中进行的 G×G 系统筛选揭示了它们对复杂表型的基本影响。在这种情况下，G×G 与其他类型的基因和/或蛋白质关系有很大的重叠。在这里，通过整合来自 GWAS 数据的预测 G×G 以及复杂和上下文定义的基因共表达谱，我们为与癌症风险相关的 G×G 提供了证据。从乳腺癌 GWAS 数据集预测的 G×G 与乳腺癌肿瘤中的复杂（非线性）基因共表达存在显著重叠[相对富集（RE）为 8%-36%，经验 P 值<0.05 至 10（-4）]。使用不是专门针对乳腺癌的基因或蛋白质数据不会显示重叠。根据预测的 G×G，实验检测证实了脂肪瘤优先伙伴和转化生长因子-β信号在 MCF10A 非致瘤乳腺上皮细胞模型中的功能相互作用。接下来，将胰腺肿瘤基因表达谱与从 GWAS 预测的胰腺癌 G×G 进行整合，证实了乳腺癌风险的观察结果（RE 为 25%-59%）。这里提出的方法可以潜在地支持与癌症风险相关的遗传相互作用的识别，为癌变提供新的机制假设。
