Cargando…
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 (...
Autores principales: | , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399723/ https://www.ncbi.nlm.nih.gov/pubmed/36855266 http://dx.doi.org/10.1002/1878-0261.13410 |
_version_ | 1785084305910792192 |
---|---|
author | 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 |
author_facet | 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 |
author_sort | Qian, Liujia |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-10399723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103997232023-08-04 Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral library 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 Mol Oncol Research Articles 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. John Wiley and Sons Inc. 2023-03-19 /pmc/articles/PMC10399723/ /pubmed/36855266 http://dx.doi.org/10.1002/1878-0261.13410 Text en © 2023 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles 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 Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral library |
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 | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399723/ https://www.ncbi.nlm.nih.gov/pubmed/36855266 http://dx.doi.org/10.1002/1878-0261.13410 |
work_keys_str_mv | AT qianliujia resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT zhujianqing resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT xuezhangzhi resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT gongtingting resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT xiangnan resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT yueliang resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT caixue resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT gongwangang resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT wangjunjian resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT sunrui resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT jiangwenhao resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT geweigang resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT wanghe resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT zhengzhiguo resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT wuqijun resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT zhuyi resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary AT guotiannan resistancepredictioninhighgradeserousovariancarcinomawithneoadjuvantchemotherapyusingdataindependentacquisitionproteomicsandanovaryspecificspectrallibrary |