Parallel functional annotation of cancer-associated missense mutations in histone methyltransferases

Abstract

Abstract Using exome sequencing for biomarker discovery and precision medicine requires connecting nucleotide-level variation with functional changes in encoded proteins. However, for functionally annotating the thousands of cancer-associated missense mutations, or variants of uncertain significance (VUS), purifying variant proteins for biochemical and functional analysis is cost-prohibitive and inefficient. We describe parallel functional annotation (PFA) of large numbers of VUS using small cultures and crude extracts in 96-well plates. Using members of a histone methyltransferase family, we demonstrate high-throughput structural and functional annotation of cancer-associated mutations. By combining functional annotation of paralogs, we discovered two phylogenetic and clustering parameters that improve the accuracy of sequence-based functional predictions to over 90%. Our results demonstrate the value of PFA for defining oncogenic/tumor suppressor functions of histone methyltransferases as well as enhancing the accuracy of sequence-based algorithms in predicting the effects of cancer-associated mutations.