In Molecular oncology
High-grade serous ovarian carcinoma (HGSOC) is the most common subtype of ovarian cancer with 5-year survival rates below 40%. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is recommended for patients with advanced-stage HGSOC unsuitable for primary debulking surgery (PDS). However, about 40% of patients receiving this treatment exhibited chemoresistance of uncertain molecular mechanisms and predictability. Here, we built a high-quality ovarian-tissue-specific spectral library containing 130,735 peptides and 10,696 proteins on Orbitrap instruments. Compared to a published DIA pan-human spectral library (DPHL), this spectral library provides 10% more ovary-specific and 3% more ovary-enriched proteins. This library was then applied to analyze data-independent acquisition (DIA) data of tissue samples from an HGSOC cohort treated with NACT, leading to 10,070 quantified proteins, which is 9.73% more than that with DPHL. We further established a six-protein classifier by parallel reaction monitoring (PRM) to effectively predict the resistance to additional chemotherapy after IDS (Log-rank test, p = 0.002).The classifier was validated with 57 patients from an independent clinical center (p = 0.014). Thus, we have developed an ovary-specific spectral library for targeted proteome analysis, and propose a six-protein classifier that could potentially predicts chemoresistance in HGSOC patients after NACT-IDS treatment.
Qian Liujia, Zhu Jianqing, Xue Zhangzhi, Gong Tingting, Xiang Nan, Yue Liang, Cai Xue, Gong Wangang, Wang Junjian, Sun Rui, Jiang Wenhao, Ge Weigang, Wang He, Zheng Zhiguo, Wu Qijun, Zhu Yi, Guo Tiannan
2023-Feb-28
Data-Independent Acquisition, MS spectral library, chemotherapy resistance, machine learning, ovarian cancer, targeted proteomics
