Comprehensive Genomic Analysis of Normal and Cancer Cells Elucidates the Elevated Mutational Burden in Cancer

Abstract Self-renewing normal tissues generate several somatic mutations at each division. Previous studies have reported that cancer cells have more mutations than their normal counterparts. It is not obvious why dramatic differences in mutational burdens between normal tissues and cancers should exist. To fully understand human tumorigenesis, the increase in mutational burden across cancers will have to be understood. In this study, we provided a systematic comparison of mutational burdens in normal and cancer cells from 5 different organs, revealing a 4-fold increase in mutational burden in cancerous versus noncancerous cells. Three proposed hypotheses could account for the increased mutational burdens in cancer: the classical hypothesis, in which driver gene mutations explain the higher mutational burden; the catastrophic hypothesis, in which extreme mutational events lead to large-scale genomic alterations; and the tail hypothesis, in which differences in baseline mutation rates among individuals account for the differences. Testing through orthogonal observations showed that the observed medians and distributions of mutational burdens in cancers could be explained by the hypotheses to various degrees of significance, and only the tail hypothesis could easily explain the increase in median mutational burdens in the normal tissues of patients with cancer compared with the normal tissues of noncancer patients. Overall, this study characterizes an increased mutational burden across multiple types of cancer compared with normal tissue and provides insights into the contributing factors. A tenable hypothesis proposed in this study involving fundamental differences in baseline mutation rates among individuals could have implications for cancer prevention strategies. Significance: Comprehensive analysis of mutation burden distributions across normal and cancerous tissues reveals consistently higher burden in cancers, with a proposed explanation that may improve risk prediction and cancer prevention.