Project description:Quantitative high-throughput 2D drug screening (n=3436 compounds) was conducted across 12 patient-derived LGSOC cell lines representing MAPK-mutant and no-specific-molecular-profile (NSMP) subtypes, and a immortalised normal ovarian line (IOSE-523) as toxicity control. Hits were prioritised for synergy testing (29 combinations) with 6 anchor drugs and validated in 2D and 3D spheroid models. Mechanistic studies to elucidate mechanisms of drug sensitivity and resistance to drug classes was conducted via MAC-Seq transcriptomics (multiplexed analysis of cells, high-throughput RNA seq).
Project description:Low-grade serous ovarian carcinoma (LGSOC) is a rare, indolent ovarian cancer subtype with limited effective therapies. Approximately 40% of cases lack canonical MAPK/ERK or PI3K/AKT/mTOR pathway alterations and are classified as having no specific molecular profile (NSMP). These patients have poor responses to chemotherapy, MEK inhibitors, and hormonal therapies, highlighting the need for alternative strategies. This study aimed to identify novel therapeutic targets in NSMP LGSOC. A high-throughput drug screen of over 3,500 compounds (including FDA-approved, clinically tested, and investigational agents) was conducted across 12 LGSOC and one control ovarian epithelial cell line. EGFR inhibitors emerged as selective hits in NSMP cell lines and were further tested in two NSMP and two MAPK-mutant lines in combination with standard-of-care chemotherapy agents, carboplatin and paclitaxel. EGFR expression was assessed using RNA sequencing, DNA methylation profiling, and immunohistochemistry in primary tumours, followed by survival analysis based on expression levels.
Project description: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.