bioRxivCancer BiologyPreprint
Multiomics Characterization of Potential Therapeutic Vulnerabilities in Low-grade Serous Ovarian Carcinoma
R. Shrestha, M. L. Fernandez, A. Dawson, J. Hoenisch, S. Volik, Y.-Y. Lin, S. Anderson, H. Kim, A. Haegert, S. Colborne, B. McConeghy, R. H. Bell, S. Brahmbhatt, G. E. DiMattia, S. Le Bihan, G. B. Morin, C. C. Collins, M. S. Carey
Abstract
BackgroundLow-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. MethodsFourteen 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. ResultsKRAS mutations were exclusively found in MEKi-sensitive and NRAS mutations mostly in MEKi-resistant 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. ConclusionsOur 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 in LGSOC, establishing effective drug prediction programs and identifying novel therapeutic targets in LGSOC.
- organism: human
- assay: whole-exome sequencing
- assay: bulk RNA-seq
- assay: proteomics
- research focus: drug response / drug discovery
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
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