Around 13% of the human genome displays high sequence similarity with at least one other chromosomal position and thereby poses challenges for computational analyses such as detection of somatic events in cancer. We here extract features of sequencing data from across non-unique regions and employ a machine learning pipeline to describe a landscape of somatic substitutions in 2,658 cancers from the PCAWG cohort. We show mutations in non-unique regions are consistent with mutations in unique regions in terms of mutation load and substitution profiles, and can be validated with linked-read sequencing. This uncovers hidden mutations in ~1,700 coding sequences and thousands of regulatory elements, including known cancer genes, immunoglobulins, and highly mutated gene families. ### Competing Interest Statement The authors have declared no competing interest.
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