Assessing the impact of genomic alterations on protein networks is fundamental in identifying the mechanisms that shape cancer heterogeneity. We use isobaric labelling to characterize the proteomic landscapes of various cancers and decipher the functional consequences of somatic genomic variants. Using robust quantification of proteins and phosphopeptides enables the de novo construction of a functional protein correlation network, which ultimately exposed the collateral effects of mutations on protein complexes.
We also leverage the quantified proteome to build predictive models of drug response in cancer. Overall, we take a deep integrative view of the functional network and the molecular structure underlying the heterogeneity of cancer.
Proteogenomics and Personal Proteomics
As part of the GENCODE consortium we use proteomics data to assist in the complete annotation of human and mouse protein coding genes. Using integrative pipelines we collate multi-omics data to validate the existence and translation of protein isoforms. Targeted experimental approaches are used to discover and validate novel protein coding genes, and develop tools and methods for the analysis of paired transcriptomic and proteomic data. Additionally, we conduct experiments across multiple individuals to investigate personal variation and the influence of genome on the proteome.
Proteome Characterisation and Quantification Method Development
We are constantly developing novel mass spectrometry and informatics techniques to identify and quantify proteins and their modifications. These include profiling the changes in protein expression in cancerous tissues, analysing protein localisation to subcellular organelles, examining protein synthesis and turn-over and determining the absolute quantification of protein species.