
The study evaluated chemoresistance in patients with high-grade serous ovarian carcinoma (HGSOC) receiving neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS). Using global proteomics and machine learning, the researchers identified 40 proteins associated with chemoresistance and validated these findings with a targeted proteome analysis. They then developed a six-protein classifier using parallel reaction monitoring (PRM) to predict chemoresistance in HGSOC patients after NACT-IDS treatment effectively. The study also included the creation of an ovary-specific spectral library for targeted proteome analysis. The findings suggest that the six-protein classifier could be a helpful tool for predicting chemoresistance in HGSOC patients undergoing NACT-IDS treatment.
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