Background: Low-grade serous ovarian carcinoma (LGSOC) is a rare tumor subtype with high case fatality rates. As such, there is a pressing need to develop more effective treatments using newly available preclinical models for therapeutic discovery and drug evaluation. Here, we use a multiomics approach to interrogate a collection of LGSOC patient-derived cell lines to elucidate novel biomarkers and therapeutic vulnerabilities.Methods: Fourteen LGSOC cell lines were interrogated using whole exome sequencing, RNA sequencing, and mass spectrometry-based proteomics. Somatic mutation, copy-number aberrations, gene and protein expression were analyzed and integrated using different computational approaches.LGSOC cell line data was compared to publicly available LGSOC tumor data (AACR GENIE cohort), and also used for predictive biomarker identification of MEK inhibitor (MEKi) efficacy. Protein interaction databases were evaluated to identify novel therapeutic targets.
Results: KRAS mutations were exclusively found in MEKi-sensitive and NRAS mutations mostly in MEKiresistant cell lines. Analysis of COSMIC mutational signatures revealed distinct patterns of nucleotide substitution mutations in MEKi-sensitive and MEKi-resistant cell lines. Deletions of CDKN2A/B and MTAP genes (chromosome 9p21) were much more frequent in cell lines than tumor samples and possibly represent key driver events in the absence of KRAS/NRAS/BRAF mutations. For in-vitro MEKi efficacy prediction, proteomic data provided better discrimination than gene expression data. Condensin, MCM, and RFC protein complexes were identified as potential treatment targets in MEKi-resistant cell lines.Conclusions: Our LGSOC cell lines are representative models of the most common molecular aberrations found in LGSOC tumors. This study highlights the importance of using proteomic data in multiomics assessment of drug prediction and identification of potential therapeutic targets. CDKN2A/B and MTAP deficiency offer an opportunity to find synthetically lethal candidates for novel treatments.Multiomics approaches are crucial to improving our understanding of the molecular aberrations inLGSOC, establishing effective drug prediction programs and identifying novel therapeutic targets inLGSOC.
BackgroundLow-grade serous ovarian carcinoma (LGSOC) is a rare subtype of epithelial ovarian cancer with a poor prognosis. Women with LGSOC are usually diagnosed with advanced-stage disease and only 10-20% are alive 10 years after diagnosis [1,2]. Research on LGSOC is challenging due to its low prevalence, uncertain etiology, and the limited availability of research models. However, in the last 5-10 years investigators have elucidated many key genomic aberrations, leading to major advancements in the molecular characterization and classification of LGSOC [3,4]. In contrast to other ovarian cancer subtypes, LGSOC are genetically characterized by high frequency of oncogenic mutations in KRAS, NRAS, and BRAF (20-40%, 7-26%, 5-33%, respectively) [5-7], a very low prevalence of TP53 mutatio...