High-throughput drug screening identifies novel therapeutics for Low Grade Serous Ovarian Carcinoma.
Kathleen I PishasKarla J CowleyMarta Llaurado-FernandezHannah KimJennii LuuRobert VaryNikola A BowdenIan G CampbellMark S CareyKaylene J SimpsonDane CheasleyPublished in: Scientific data (2024)
Low grade serous carcinoma (LGSOC) is a rare epithelial ovarian cancer with unique molecular characteristics compared to the more common tubo-ovarian high-grade serous ovarian carcinoma. Pivotal clinical trials guiding the management of epithelial ovarian cancer lack sufficient cases of LGSOC for meaningful subgroup analysis, hence overall findings cannot be extrapolated to rarer chemo-resistant subtypes such as LGSOC. Furthermore, there is a need for more effective therapies for the treatment of relapsed disease, as treatment options are limited. To address this, we conducted the largest quantitative high-throughput drug screening effort (n = 3436 compounds) in 12 patient-derived LGSOC cell lines and one normal ovary cell line to identify unexplored therapeutic avenues. Using a combination of high-throughput robotics, high-content imaging and novel data analysis pipelines, our data set identified 60 high and 19 moderate confidence hits which induced cancer cell specific cytotoxicity at the lowest compound dose assessed (0.1 µM). We also revealed a series of known (mTOR/PI3K/AKT) and novel (EGFR and MDM2-p53) drug classes in which LGSOC cell lines showed demonstrable susceptibility to.
Keyphrases
- high grade
- low grade
- high throughput
- data analysis
- pi k akt
- single cell
- clinical trial
- signaling pathway
- cell proliferation
- drug induced
- high resolution
- small cell lung cancer
- acute myeloid leukemia
- acute lymphoblastic leukemia
- adverse drug
- cell cycle arrest
- small molecule
- photodynamic therapy
- epidermal growth factor receptor
- randomized controlled trial
- tyrosine kinase
- hodgkin lymphoma
- emergency department
- big data
- cancer therapy
- phase iii
- mass spectrometry
- radiation therapy
- deep learning
- endothelial cells
- dna methylation
- cell death
- stress induced