Discriminating activating, deactivating and resistance variants in protein kinases

We present a data-driven approach to predict the functional consequence of genetic changes in protein kinases. We first created a large curated dataset of 375 activating/gain-of-function, 1028 deactivating/loss, 98 resistance and 1004 neutral protein variants in 441 human kinases by scouring the literature and various databases. For any variant, we defined a vector of 7 types of sequence, evolutionary and structural features. We used these vectors to train machine learning predictors of kinase variant classes that obtain excellent performance (Mean AUC = 0.941), which we then applied to uncharacterized variants found in somatic cancer samples, hereditary diseases and genomes from healthy individuals. Encouragingly we predicted a greater tendency of activating variants in cancers, deactivating in hereditary diseases and few of both in healthy individuals. Using this method on clinical data can identify potential functional variants. In cancer samples we experimentally assessed the impact of several such mutations, including potential activating variants p.Ser97Asn in PIM1, where phosphorylation analysis suggests an increase in activity, and p.Ala84Thr in MAP2K3, where gene expression and mitochondrial staining shows a reduction in mitochondrial function when contrasting mutant to wild type, the opposite having been observed previously during deletion experiments. We provide an online application to study any variant in the kinase domain that provides prediction scores in addition to a detailed list of what is known across all kinases near the position of interest. Besides supporting the interpretation of genomic variants of unknown significance, knowledge of kinase activation can lead to immediate therapeutic suggestions, we thus believe our approach will be a key component in the repertoire of tools for personalised medicine.