Introduction/Background High-grade serous carcinoma (HGSC) is the most common and deadly subtype of ovarian cancer. Although most patients will initially respond to first-line treatment with a combination of surgery and platinum-based chemotherapy, up to a quarter will be resistant to treatment. We aimed to identify a new strategy to improve HGSC patient management at the time of cancer diagnosis (HGSC-1LTR).

Methodology Ready-available formalin-fixed paraffin-embedded HGSC tissues obtained at the time of diagnosis were selected for proteomic analysis. Clinical data, treatment approach and outcomes were collected for all patients. Chemoresistant (TFIp < 6 m) and chemosensitive (TFIp > 6 m) groups were evaluated using discovery proteomics (discovery cohort, n=21). Protein candidates were verified in an independent cohort using targeted proteomics (verification cohort, n=88). Predictive analysis combined with a cross-validation was used to select those proteins able to correctly classify patients into chemoresistant and chemosensitive groups. The classification performance of the protein and clinical data combinations were assessed through the generation of receiver operating characteristic (ROC) curves.

Results Using the HGSC-1LTR strategy we have identified a molecular signature (TKT, LAMC1 and FUCO) that combined with ready available clinical data (patients’ age, menopausal status, serum CA125 levels, and treatment approach) is able to predict patient response to first-line treatment with an AUC: 0.82 (95% CI 0.72 – 0.92).

Conclusion We have established a new strategy that combines molecular and clinical parameters to predict the response to first-line treatment in HGSC patients (HGSC-1LTR). This strategy can allow optimization of therapeutic decision making and individualize HGSC patients’ care.