Towards Precision Oncology : Advancing Multiomic Biomarker Discovery with Mass Spectrometry Proteomics

Over the past couple of decades, considerable advances in automation and high-throughput omics technologies have contributed to the generation of unprecedented amounts of molecular data, challenging the traditional “one size fits all” approach in favour of personalised medicine, where the individual needs of patients are addressed.However, the speed at which such molecular data is being acquired does not translate in a proportional number of clinical implementations. One way in which the molecular data can contribute to individualised treatment is by the discovery of novel and more robust biomarkers.Proteins are interesting biomarker candidates, as they mediate most processes in living organisms, and are by far the most utilised molecules in clinical use. For these reasons, global analysis of proteins could contribute to the development of better biomarkers.By combining mass spectrometry-based proteomics with automation solutions, workflows that can potentially improve biomarker discovery are discussed and showcased in this thesis. Such workflows are exemplified in (i) the multiplexed enrichment of blood plasma, allowing higher throughput and a unique platform for automation of affinity enrichment, (ii) the acquisition of matching multiomics data from a large number of breast cancer patient samples, allowing the optimisation of data acquisition strategies and utilisation of such data for functional analyses of the intrinsic subtypes, (iii) and the multiomics data analysis of patient samples, contributing to the most comprehensive molecular profile of metastatic processes in oestrogen receptor-positive breast cancer utilising transcriptomics, proteomics, phosphoproteomics and immune infiltration data acquired from the same tumour samples.In summary, the work presented in this thesis highlights strategies incorporating mass spectrometry-based proteomics and automation, allowing a greater number of samples to be analysed, more data to be extracted from samples and making better use of these data, which would hopefully improve biomarkers discovery, contributing to the field of personalised medicine.