Mucinous Ovarian Cancer (MOC) is a rare subtype that is highly distinctive from other ovarian carcinomas. Treatments that work well for other ovarian cancer subtypes are no more likely to work for MOC than for any other tissue type. Indeed, MOC is intrinsically resistant to standard ovarian cancer chemotherapy (platinum/taxane). With the outcome for advanced stage patients being dire, any new effective therapy will have a massive and immediate impact.
We have leveraged our unique multi-platform genomics data on MOC to discover protein-protein interaction networks that offer a therapeutic opportunity. By incorporating protein structural elements into the analysis and computationally assessing the key protein-protein interactions for their druggability, we can conduct an effective therapeutic screening program which will readily translate to patients.
The interactions we discover may also be applicable to tumour types with related key drivers, such as low-grade serous ovarian carcinoma and pancreatic cancer. In addition, our approach can also be applied to established ovarian cancer genomic data sets, such as those for the more common high-grade serous ovarian carcinoma.