Proteogenomic insights suggest druggable pathways in endometrial carcinoma

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the most frequent gynecological cancers but has limited treatment options. We characterized an independent cohort of 138 prospectively collected UCEC tumors and 20 enriched normal adjacent tissues using 10 different omics platforms, including the newly added targeted proteomics and glycoproteomics. Analysis of this independent cohort not only confirmed findings from the published study but also provided new biological insights relevant to potential therapeutic strategies. Targeted Selected Reaction Monitoring (SRM) analysis identified two peptides that can accurately predict activity of the antigen processing and presentation machinery (APM), providing candidate biomarkers to inform selection of UCEC patients for treatment with immune checkpoint inhibitors. Multi-omics analysis demonstrated a negative correlation between MYC activity and treatment with the anit-diabetic drug metformin, which has been shown to improve overall survival of diabetic UCEC patients. As many non-diabetic patients displayed elevated MYC activity, particularly in association with decreased BMI, these results suggest a role for metformin treatment in non-diabetic UCEC patients. Proteogenomic dissection of the PI3K/AKT pathway mutations specifically associated PIK3R1 inframe indels with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors, suggesting the use of these mutations as a predictive marker for AKT inhibition. CTNNB1 hotspot mutations were concentrated near the phosphorylation sites mediating DKK induced degradation of 𝛃-catenin, which may render Wnt/FZD antagonists ineffective for UCECs with CTNNB1 hotspot mutations. Deep learning models accurately predicted UCEC subtypes and mutations from analysis of histopathology images, which may be useful for rapid diagnosis of certain subtypes as a complement of genome sequencing. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for improved precision treatment of UCEC.