The genome was divided into 1 Mb non-overlapping windows. CDS regions, immunoglobulin loci, and poorly mappable regions were masked from the genomic windows. Windows smaller than 250 kb after masking were removed. We calculated the mean signal of each epigenetic feature (in Fig. 1b) and the mutation rate of each tumor in each window. The Pearson correlations between the epigenetic features and mutation rates of the each tumor were calculated. To identify the contributions of epigenetic features to the variance in the mutation rate of individual tumors, we performed PCA on the correlation matrix between the mutation rates of individual tumors and epigenetic features using the “prcomp function in R. The contribution of each feature to a principal component is calculated as the feature’s loading (rotation) divided by the sum of loadings of all features for that principal component.