The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma

Cancer genomics and cancer mutation databases have made an available\r\nwealth of information about missense mutations found in cancer patient\r\nsamples. Contextualizing by means of annotation and predicting the\r\neffect of amino acid change help identify which ones are more likely to\r\nhave a pathogenic impact. Those can be validated by means of\r\nexperimental approaches that assess the impact of protein mutations on\r\nthe cellular functions or their tumorigenic potential. Here, we propose\r\nthe integrative bioinformatic approach Cancermuts, implemented as a\r\nPython package. Cancermuts is able to gather known missense cancer\r\nmutations from databases such as cBioPortal and COSMIC, and annotate\r\nthem with the pathogenicity score REVEL as well as information on their\r\nsource. It is also able to add annotations about the protein context\r\nthese mutations are found in, such as post-translational modification\r\nsites, structured/unstructured regions, presence of short linear motifs,\r\nand more. We applied Cancermuts to the intrinsically disordered protein\r\nAMBRA1, a key regulator of many cellular processes frequently\r\nderegulated in cancer. By these means, we classified mutations of AMBRA1\r\nin melanoma, where AMBRA1 is highly mutated and displays a\r\ntumor-suppressive role. Next, based on REVEL score, position along the\r\nsequence, and their local context, we applied cellular and molecular\r\napproaches to validate the predicted pathogenicity of a subset of\r\nmutations in an in vitro melanoma model. By doing so, we have identified\r\ntwo AMBRA1 mutations which show enhanced tumorigenic potential and are\r\nworth further investigation, highlighting the usefulness of the tool.