Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral library
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 (...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-08-01
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| Series: | Molecular Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/1878-0261.13410 |
| _version_ | 1827878116154933248 |
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| author | Liujia Qian Jianqing Zhu Zhangzhi Xue Tingting Gong Nan Xiang Liang Yue Xue Cai Wangang Gong Junjian Wang Rui Sun Wenhao Jiang Weigang Ge He Wang Zhiguo Zheng Qijun Wu Yi Zhu Tiannan Guo |
| author_facet | Liujia Qian Jianqing Zhu Zhangzhi Xue Tingting Gong Nan Xiang Liang Yue Xue Cai Wangang Gong Junjian Wang Rui Sun Wenhao Jiang Weigang Ge He Wang Zhiguo Zheng Qijun Wu Yi Zhu Tiannan Guo |
| author_sort | Liujia Qian |
| collection | DOAJ |
| description | 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 ovary‐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 predict chemoresistance in HGSOC patients after NACT‐IDS treatment. |
| first_indexed | 2024-03-12T17:46:43Z |
| format | Article |
| id | doaj.art-8619fcefd55247989b5c3a84bdf9c3e1 |
| institution | Directory Open Access Journal |
| issn | 1574-7891 1878-0261 |
| language | English |
| last_indexed | 2024-03-12T17:46:43Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Molecular Oncology |
| spelling | doaj.art-8619fcefd55247989b5c3a84bdf9c3e12023-08-03T16:44:14ZengWileyMolecular Oncology1574-78911878-02612023-08-011781567158010.1002/1878-0261.13410Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral libraryLiujia Qian0Jianqing Zhu1Zhangzhi Xue2Tingting Gong3Nan Xiang4Liang Yue5Xue Cai6Wangang Gong7Junjian Wang8Rui Sun9Wenhao Jiang10Weigang Ge11He Wang12Zhiguo Zheng13Qijun Wu14Yi Zhu15Tiannan Guo16School of Medicine Zhejiang University Hangzhou ChinaThe Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Hangzhou ChinaKey Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences Westlake University Hangzhou ChinaDepartment of Obstetrics and Gynecology Shengjing Hospital of China Medical University Shenyang ChinaKey Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences Westlake University Hangzhou ChinaKey Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences Westlake University Hangzhou ChinaKey Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences Westlake University Hangzhou ChinaThe Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Hangzhou ChinaThe Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Hangzhou ChinaKey Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences Westlake University Hangzhou ChinaKey Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences Westlake University Hangzhou ChinaWestlake Omics (Hangzhou) Biotechnology Co., Ltd. ChinaKey Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences Westlake University Hangzhou ChinaThe Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Hangzhou ChinaDepartment of Clinical Epidemiology, Department of Obstetrics and Gynecology Shengjing Hospital of China Medical University Shenyang ChinaKey Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences Westlake University Hangzhou ChinaSchool of Medicine Zhejiang University Hangzhou ChinaHigh‐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 ovary‐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 predict chemoresistance in HGSOC patients after NACT‐IDS treatment.https://doi.org/10.1002/1878-0261.13410chemotherapy resistancedata‐independent acquisitionmachine learningMS spectral libraryovarian cancertargeted proteomics |
| spellingShingle | Liujia Qian Jianqing Zhu Zhangzhi Xue Tingting Gong Nan Xiang Liang Yue Xue Cai Wangang Gong Junjian Wang Rui Sun Wenhao Jiang Weigang Ge He Wang Zhiguo Zheng Qijun Wu Yi Zhu Tiannan Guo Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral library Molecular Oncology chemotherapy resistance data‐independent acquisition machine learning MS spectral library ovarian cancer targeted proteomics |
| title | Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral library |
| title_full | Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral library |
| title_fullStr | Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral library |
| title_full_unstemmed | Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral library |
| title_short | Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral library |
| title_sort | resistance prediction in high grade serous ovarian carcinoma with neoadjuvant chemotherapy using data independent acquisition proteomics and an ovary specific spectral library |
| topic | chemotherapy resistance data‐independent acquisition machine learning MS spectral library ovarian cancer targeted proteomics |
| url | https://doi.org/10.1002/1878-0261.13410 |
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