  Mutual exclusivity is a widely recognized property of many cancer drivers.
Knowledge about these relationships can provide important insights into cancer
drivers, cancer-driving pathways, and cancer subtypes. It can also be used to
predict new functional interactions between cancer driving genes and uncover
novel cancer drivers. Currently, most of mutual exclusivity analyses are
preformed focusing on a limited set of genes in part due to the computational
cost required to rigorously compute p-values. To reduce the computing cost and
perform less restricted mutual exclusivity analysis, we developed an efficient
method to estimate p-values while controlling the mutation rates of individual
patients and genes similar to the permutation test. A comprehensive mutual
exclusivity analysis allowed us to uncover mutually exclusive pairs, some of
which may have relatively low mutation rates. These pairs often included likely
cancer drivers that have been missed in previous analyses. More importantly,
our results demonstrated that mutual exclusivity can also provide information
that goes beyond the interactions between cancer drivers and can, for example,
elucidate different mutagenic processes in different cancer groups. In
particular, including frequently mutated, long genes such as TTN in our
analysis allowed us to observe interesting patterns of APOBEC activity in
breast cancer and identify a set of related driver genes that are highly
predictive of patient survival. In addition, we utilized our mutual exclusivity
analysis in support of a previously proposed model where APOBEC activity is the
underlying process that causes TP53 mutations in a subset of breast cancer
cases.
