The Evolutionary Landscape of Pan-Cancer Drives Clinical Aggression

Abstract

Although cancer mechanisms differ from occurrence and development, some of them have similar oncogenesis, which leads to similar clinical phenotypes. Most existing genotyping studies look at \"omics\" data, but intentionally or unintentionally avoided that cancer is a time-dependent evolutionary process, biologically represented by the time evolution of tumor clones. We used the Bayesian mutation landscape approach to reconstruct the evolutionary process of cancer by acquiring somatic mutation data consisting of 21 cancer types. Four representative evolution patterns of pan-cancer have been discovered: trees, chaos, biconvex, and Cambrian, and a strong correlation between these four evolutionary patterns and clinical aggressivity. We further explained the characteristics of the corresponding biological systems in the evolution of pan cancer by analyzing the function of differentially expressed protein-protein interaction networks. Our results explained the difference in clinical aggressivity between cancer evolution patterns from the evolution of tumor clones and exposed the functional mechanism behind.