Moiso Enrico, Provero Paolo
Department of Molecular Biotechnology and Health Sciences, University of Turin, 10126 Turin, Italy.
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Cancers (Basel). 2022 Apr 25;14(9):2145. doi: 10.3390/cancers14092145.
The alterations of metabolic pathways in cancer have been investigated for many years, beginning long before the discovery of the role of oncogenes and tumor suppressors, and the last few years have witnessed renewed interest in this topic. Large-scale molecular and clinical data on tens of thousands of samples allow us to tackle the problem from a general point of view. Here, we show that transcriptomic profiles of tumors can be exploited to define metabolic cancer subtypes, which can be systematically investigated for associations with other molecular and clinical data. We find thousands of significant associations between metabolic subtypes and molecular features such as somatic mutations, structural variants, epigenetic modifications, protein abundance and activation, and with clinical/phenotypic data, including survival probability, tumor grade, and histological types, which we make available to the community in a dedicated web resource. Our work provides a methodological framework and a rich database of statistical associations, which will contribute to the understanding of the role of metabolic alterations in cancer and to the development of precision therapeutic strategies.
癌症中代谢途径的改变已被研究多年,早在癌基因和肿瘤抑制因子的作用被发现之前就已开始,并且在过去几年中,人们对这一话题重新产生了兴趣。数万个样本的大规模分子和临床数据使我们能够从总体角度解决这个问题。在这里,我们表明肿瘤的转录组谱可用于定义代谢性癌症亚型,进而可以系统地研究这些亚型与其他分子和临床数据之间的关联。我们发现代谢亚型与诸如体细胞突变、结构变异、表观遗传修饰、蛋白质丰度和激活等分子特征以及与临床/表型数据(包括生存概率、肿瘤分级和组织学类型)之间存在数千种显著关联,我们通过一个专门的网络资源将这些数据提供给科学界。我们的工作提供了一个方法框架和一个丰富的统计关联数据库,这将有助于理解代谢改变在癌症中的作用,并有助于开发精准治疗策略。