P276 - ECE_3213 - Biomarkers for progression allowing individualized management of patients with non-functioning pituitary tumors

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Abstract Introduction Post surgical tumor progression is observed in more than 50% of non-functioning pituitary tumors (NFPTs). There is no reliable method to predict individual tumor behaviour after surgery. Therefore, life-long surveillance with magnetic resonance imaging (MRI) is needed in all patients to monitor tumor growth. Biomarkers are needed to identify patients at risk of progression who may benefit from early re-operation, radiotherapy and/or medical treatment. In parallel, a deeper understanding of the biological mechanisms driving tumor progression is crucial for the development of novel therapeutic approaches. Aims In this project, we aim to investigate genome-wide DNA methylation and protein expression profiles in NFPTs to understand the interplay between the two molecular events and to potentially reveal driver events or pathways associated with tumor progression. Unravelling the underlying biology has potential for biomarker discovery and the development of novel therapeutic interventions. Methods We conducted an integrated analysis of DNA methylation and protein expression across 734 065 CpG sites and 457 differentially expressed proteins in NFPTs using hypernetwork models. We used our local retrospective cohort that included progressive (n = 25), and indolent (n = 15) NFPTs. The biomarkers identified in this study will be validated in another cohort that includes 88 NFPT cases. Results Of the 457 proteins, 101 proteins in progressive and 171 proteins in indolent NFPTs formed highly connected protein clusters that displayed coordinated epigenetic regulation. Some of the top hypernetwork proteins were encoded by genes MCM6, SAFB, HDGFL2, PSMD6 and LAMB2. Several of these proteins were primarily involved in metabolic pathways and previously reported in other tumors with role in cell proliferation and migration. All these proteins were significantly upregulated in progressive NFPTs. Area under receiver curve analysis revealed that several of these proteins could predict the progressive tumor behaviour with over 80% accuracy. Discussion Hypernetwork analysis identified proteins involved in higher-order interactions between DNA methylation and protein expression that were not ranked high in conventional differential protein expression analysis. These proteins could potentially be functionally involved in driver events in tumor biology and warrant further investigation as candidate biomarkers for identifying individuals at risk of tumor progression after surgery.

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