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Soft tissue sarcomas (STS) are a group of rare and heterogeneous mesenchymal malignancies. The extensive clinical and biological heterogeneity of STS complicates clinical disease management, and in the advanced setting prognosis is poor. Incomplete biological understanding of STS has long hampered efforts to drive clinical improvements for patients. At present, there is a lack of methods to stratify patients based on risk or their likelihood of treatment response. Additionally, there are limited targeted therapies available for STS patients, and current standard of care is largely a 'one size fits all' approach. Whilst the genomic, epigenomic, and transcriptomic basis of STS has been previously assessed, there is no proteomic understanding of the disease. Herein, my project conducts comprehensive proteomic profiling, by mass spectrometry, of 321 formalin-fixed paraffin-embedded primary tumour specimens from STS patients. This is the largest proteomic characterisation of STS to date and provides an overview of the baseline STS proteome. Specifically, heterogeneity in leiosyosarcoma was investigated, and 3 robust proteomic subtypes were identified. These molecular subtypes showed different functional biology and were associated with different survival outcomes, highlighting potential for risk stratification. Analysis of the immune landscape of undifferentiated pleomorphic sarcoma and dedifferentiated liposarcoma, highlighted a subpopulation of tumours with low lymphocyte infiltration and high complement activity. This revealed this complement cascade as a candidate therapeutic target. Finally, this project defined a protein-centric view of the STS proteome comprised of 'sarcoma proteome modules'. These modules transcended histological subtypes and covered a range of biological activities. Furthermore, modules were found to be associated with clinical outcome, again highlighting the potential for molecular risk stratification in STS. Overall, this project demonstrates the utility of comprehensive proteomic profiling in improving disease understanding, facilitating risk stratification, and identifying candidate therapies. In doing so, it establishes a rich resource for the STS research community.