Kristie-Ann Dickson

An organotypic model of high-grade serous ovarian cancer to test the anti-metastatic potential of ROR2 targeted Polyion complex nanoparticles

A 3D co-cultured organotypic cancer model to evaluate the anti-metastatic ability of siRNA loaded BSA decorated PICs nanoparticles has been developed.

PARP Inhibitors Display Differential Efficacy in Models of BRCA Mutant High-Grade Serous Ovarian Cancer

Several poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitors are now in clinical use for tumours with defects in BReast CAncer genes BRCA1 or BRCA2 that result in deficient homologous recombination repair (HRR). Use of olaparib, niraparib or rucaparib for the treatment of high-grade serous ovarian cancer, including in the maintenance setting, has extended both progression free and overall survival for women with this malignancy. While different PARP inhibitors (PARPis) are mechanistically similar, differences are apparent in their chemical structures, toxicity profiles, PARP trapping abilities and polypharmacological landscapes. We have treated ovarian cancer cell line models of known BRCA status, including the paired cell lines PEO1 and PEO4, and UWB1.289 and UWB1.289+BRCA1, with five PARPis (olaparib, niraparib, rucaparib, talazoparib and veliparib) and observed differences between PARPis in both cell viability and cell survival. A cell line model of acquired resistance to veliparib showed increased resistance to the other four PARPis tested, suggesting that acquired resistance to one PARPi may not be able to be rescued by another. Lastly, as a proof of principle, HRR proficient ovarian cancer cells were sensitised to PARPis by depletion of BRCA1. In the future, guidelines will need to emerge to assist clinicians in matching specific PARPis to specific patients and tumours.

Revealing genetic drivers of ovarian cancer and chemoresistance: insights from whole-genome CRISPR-knockout library screens

Abstract Understanding genetic dependencies in cancer is key to identifying novel actionable drug targets to advance precision medicine. Whole-genome CRISPR-knockout library screening methods have facilitated this goal. Pooled libraries of single guide RNAs (sgRNAs) targeting over 90% of the annotated protein coding genome are used to induce gene knockouts in pre-clinical cancer models. Novel genes of interest are identified by evaluating sgRNA dropout or enrichment following selection pressure application. This method is particularly beneficial for researching cancers where effective treatment strategies are limited. One example of a commonly chemoresistant cancer, particularly at relapse, is the low survival malignancy epithelial ovarian cancer (EOC), made up of multiple histotypes with distinct molecular profiles. CRISPR-knockout library screens in pre-clinical EOC models have demonstrated the ability to predict biomarkers of treatment response, identify targets synergistic with standard-of-care chemotherapy, and determine novel actionable targets which are synthetic lethal with cancer-associated mutations. Robust experimental design of CRISPR-knockout library screens, including the selection of strong pre-clinical cell line models, allows for meaningful conclusions to be made. We discuss essential design criteria for the use of CRISPR-knockout library screens to discover genetic dependencies in cancer and draw attention to discoveries with translational potential for EOC.