Mass spectrometry strategies to understand treatment effects and heterogeneity in soft tissue sarcomas

Abstract

Soft Tissue sarcoma are rare and heterogenous mesenchymal malignancies. Optimal disease management in STS is surgical excision as the primary treatment modality. Despite negative surgical margins, ~50% of patients relapse. Neoadjuvant therapy (NAT) is given prior to surgery for locally advanced and/metastatic disease. However, its use as a standard treatment is contentious due to conflicting results from clinical trials. Additionally, there remains a limited understanding of the underlying biology governing NAT response and resistance. There is an urgent need for improved prognostication to identify patients who would derive substantial benefits from NAT, as well as the identification of predictive biomarkers for NAT response. Proteomic analysis of Sarculator-nomogram risk group, a prognostic tool, rationalized the effectiveness of anthracycline therapy in high-risk patients. This has translated into enhanced overall survival outcomes among high-risk patients, attributed to elevated levels of MCM proteins, integral components of the MCM complex responsible for initiating DNA replication prior to the G1 phase of the cell cycle. Notably, this study marks the first integration of proteomics data with the Sarculator nomogram to develop advanced prognostic tools. Furthermore, both transcriptomics and proteomics data identified the presence of two distinct molecular subtypes within synovial sarcoma (SS), each characterized by unique biological features. The analysis has also unveiled pathways associated with NAT response, including epithelial-mesenchymal transition, MYC targets, and heightened immune signalling. Moreover, through comparative analysis of transcriptomic and proteomic datasets, distinct prognostic biomarkers associated with each omics platform have been identified, emphasizing the valuable and distinct information contained within each dataset. By integrating these two datasets, a panel of 58 biomarkers (comprising 45 proteins and 13 genes) have been identified, effectively segregating NAT-treated SS samples from treatment-naïve samples. In summary, this project offers valuable insights into the biology underlying NAT and demonstrates the potential of integrating proteomic signatures with nomograms to create advanced prognostic tools for the future.