Multiomics Characterization of Low-Grade Serous Ovarian Carcinoma Identifies Potential Biomarkers of MEK Inhibitor...

Low-grade serous ovarian carcinoma (LGSOC) is a rare tumor subtype with high case fatality rates in patients with metastatic disease. There is a pressing need to develop effective treatments using newly available preclinical models for therapeutic discovery and drug evaluation. Here, we use multiomics integration of whole-exome sequencing, RNA sequencing, and mass spectrometry-based proteomics on 14 LGSOC cell lines to elucidate novel biomarkers and therapeutic vulnerabilities. Comparison of LGSOC cell line data with LGSOC tumor data enabled predictive biomarker identification of MEK inhibitor (MEKi) efficacy, with KRAS mutations found exclusively in MEKi-sensitive cell lines and NRAS mutations found mostly in MEKi-resistant cell lines. Distinct patterns of Catalogue of Somatic Mutations in Cancer mutational signatures were identified in MEKi-sensitive and MEKi-resistant cell lines. Deletions of CDKN2A/B and MTAP genes were more frequent in cell lines than tumor samples and possibly represent key driver events in the absence of KRAS/NRAS/BRAF mutations. These LGSOC cell lines were representative models of the molecular aberrations found in LGSOC tumors. For prediction of in vitro MEKi efficacy, proteomic data provided better discrimination than gene expression data. Condensin, minichromosome maintenance, and replication factor C protein complexes were identified as potential treatment targets in MEKi-resistant cell lines. This study suggests that CDKN2A/B or MTAP deficiency may be exploited using synthetically lethal treatment strategies, highlighting the importance of using proteomic data as a tool for molecular drug prediction. Multiomics approaches are crucial to improving our understanding of the molecular underpinnings of LGSOC and applying this information to develop new therapies. SIGNIFICANCE: These findings highlight the utility of global multiomics to characterize LGSOC cell lines as research models, to determine biomarkers of MEKi resistance, and to identify potential novel therapeutic targets.