Abstract
Analysis of patient-specific nucleotide variants is a cornerstone of personalised medicine. Although only 2% of the genomic sequence is protein-coding, mutations occurring in these regions have the potential to influence protein structure and may have severe impact on disease aetiology. Of special importance are variants that affect modifiable amino acid residues, as protein modifications involved in signal transduction networks cannot be analysed by genomics. Proteogenomics enables analysis of proteomes in context of patient- or tissue-specific non-synonymous nucleotide variants. Here we developed an individualised proteogenomics workflow and applied it to study resistance to BRAF inhibitor vemurafenib in malignant melanoma cell line A375. This approach resulted in high identification and quantification of non-synonymous nucleotide variants, transcripts and (phospho)proteins. We integrated multi-omic datasets to reconstruct the perturbed signalling networks associated with BRAFi resistance, prioritise key actionable nodes and predict drug therapies with potential to disrupt BRAFi resistance mechanism in A375 cells. Notably, we showed that AURKA inhibition is effective and specific against BRAFi resistant A375 cells. Furthermore, we investigated nucleotide variants that interfere with protein modification status and potentially influence signal transduction networks. Mass spectrometry (MS) measurements confirmed variant-driven modification changes in approximately 50 proteins; among them was the transcription factor RUNX1 displaying a variant on a known phosphorylation site S(Ph)276L. We confirmed the loss of phosphorylation site by MS and demonstrated the impact of this variant on RUNX1 interactome. Our study paves the way for large-scale application of proteogenomics in melanoma.
Competing Interest Statement
The authors have declared no competing interest.
