Target Identification and Prioritization: Ovarian Cancer

High-grade serous ovarian cancer (HGSC) is the most aggressive subtype of serous ovarian carcinoma. Exhibiting little to no clinical presentation until the disease has progressed,¹ HGSC has a 10-year survival rate of just 15% at advanced stages.²

At diagnosis, HGSCs exhibit favourable response to platinum-based drugs, but poor selectivity and growing chemotherapeutic resistance is a concern, highlighting the need for targeted therapies to improve patient outcomes. Pivotal to this process is the identification of genetic mutations or resultant abnormal proteins.³

Human-centric AI can be used to accelerate target identification and prioritization, extracting evidence for the most relevant target-disease relationships. Importantly, our AI provides a view of all scientific evidence, ensuring 100% traceability of original sources for users to apply their own judgement. Here, we demonstrate how Causaly can be used to explore potential therapeutic targets for HGSC.

By machine-reading the entire volume of biomedical literature, Causaly can rapidly identify targets for a given disease. Here, 6,500+ targets for ovarian cancer were identified, over 700 of which were reported for the HGSC subtype.

Using Causaly’s advanced filtering capabilities, targets can be refined by different criteria, including by study type, primary data, publication type, journal impact factor, target-disease relationship, and novelty (Figure 1).

The aggressive nature and late-stage clinical presentation of HGSC demands the novel, targeted therapeutics to improve disease management and patient outcomes. The discovery of relevant therapeutic targets for HGSC is the first step in this process. Causaly can accelerate this process by rapidly identifying targets from biomedical literature, enabling researchers to uncover hidden connections and explore novel avenues.