A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer
Abstract Ovarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to identify a set of molecular markers for predicting platinum sensitivity. A signature predicting cisplatin sensitivity was generated using the Ge...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-96072-6 |
| _version_ | 1818420187456602112 |
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| author | Nicholas Brian Shannon Laura Ling Ying Tan Qiu Xuan Tan Joey Wee-Shan Tan Josephine Hendrikson Wai Har Ng Gillian Ng Ying Liu Xing-Yi Sarah Ong Ravichandran Nadarajah Jolene Si Min Wong Grace Hwei Ching Tan Khee Chee Soo Melissa Ching Ching Teo Claramae Shulyn Chia Chin-Ann Johnny Ong |
| author_facet | Nicholas Brian Shannon Laura Ling Ying Tan Qiu Xuan Tan Joey Wee-Shan Tan Josephine Hendrikson Wai Har Ng Gillian Ng Ying Liu Xing-Yi Sarah Ong Ravichandran Nadarajah Jolene Si Min Wong Grace Hwei Ching Tan Khee Chee Soo Melissa Ching Ching Teo Claramae Shulyn Chia Chin-Ann Johnny Ong |
| author_sort | Nicholas Brian Shannon |
| collection | DOAJ |
| description | Abstract Ovarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to identify a set of molecular markers for predicting platinum sensitivity. A signature predicting cisplatin sensitivity was generated using the Genomics of Drug Sensitivity in Cancer and The Cancer Genome Atlas databases. Four potential biomarkers (CYTH3, GALNT3, S100A14, and ERI1) were identified and optimized for immunohistochemistry (IHC). Validation was performed on a cohort of patients (n = 50) treated with surgical resection followed by adjuvant carboplatin. Predictive models were established to predict chemosensitivity. The four biomarkers were also assessed for their ability to prognosticate overall survival in three ovarian cancer microarray expression datasets from The Gene Expression Omnibus. The extreme gradient boosting (XGBoost) algorithm was selected for the final model to validate the accuracy in an independent validation dataset (n = 10). CYTH3 and S100A14, followed by nodal stage, were the features with the greatest importance. The four gene signature had comparable prognostication as clinical information for two-year survival. Assessment of tumour biology by means of gene expression can serve as an adjunct for prediction of chemosensitivity and prognostication. Potentially, the assessment of molecular markers alongside clinical information offers a chance to further optimise therapeutic decision making. |
| first_indexed | 2024-12-14T12:50:29Z |
| format | Article |
| id | doaj.art-0247321c07774b35a5929fc1ffdacadd |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T12:50:29Z |
| publishDate | 2021-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0247321c07774b35a5929fc1ffdacadd2022-12-21T23:00:40ZengNature PortfolioScientific Reports2045-23222021-08-0111111010.1038/s41598-021-96072-6A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancerNicholas Brian Shannon0Laura Ling Ying Tan1Qiu Xuan Tan2Joey Wee-Shan Tan3Josephine Hendrikson4Wai Har Ng5Gillian Ng6Ying Liu7Xing-Yi Sarah Ong8Ravichandran Nadarajah9Jolene Si Min Wong10Grace Hwei Ching Tan11Khee Chee Soo12Melissa Ching Ching Teo13Claramae Shulyn Chia14Chin-Ann Johnny Ong15Department of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Obstetrics and Gynaecology, Division of Surgery and Surgical Oncology, Singapore General HospitalDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeDepartment of Sarcoma, Peritoneal and Rare Tumours (SPRinT), Division of Surgery and Surgical Oncology, National Cancer Centre SingaporeAbstract Ovarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to identify a set of molecular markers for predicting platinum sensitivity. A signature predicting cisplatin sensitivity was generated using the Genomics of Drug Sensitivity in Cancer and The Cancer Genome Atlas databases. Four potential biomarkers (CYTH3, GALNT3, S100A14, and ERI1) were identified and optimized for immunohistochemistry (IHC). Validation was performed on a cohort of patients (n = 50) treated with surgical resection followed by adjuvant carboplatin. Predictive models were established to predict chemosensitivity. The four biomarkers were also assessed for their ability to prognosticate overall survival in three ovarian cancer microarray expression datasets from The Gene Expression Omnibus. The extreme gradient boosting (XGBoost) algorithm was selected for the final model to validate the accuracy in an independent validation dataset (n = 10). CYTH3 and S100A14, followed by nodal stage, were the features with the greatest importance. The four gene signature had comparable prognostication as clinical information for two-year survival. Assessment of tumour biology by means of gene expression can serve as an adjunct for prediction of chemosensitivity and prognostication. Potentially, the assessment of molecular markers alongside clinical information offers a chance to further optimise therapeutic decision making.https://doi.org/10.1038/s41598-021-96072-6 |
| spellingShingle | Nicholas Brian Shannon Laura Ling Ying Tan Qiu Xuan Tan Joey Wee-Shan Tan Josephine Hendrikson Wai Har Ng Gillian Ng Ying Liu Xing-Yi Sarah Ong Ravichandran Nadarajah Jolene Si Min Wong Grace Hwei Ching Tan Khee Chee Soo Melissa Ching Ching Teo Claramae Shulyn Chia Chin-Ann Johnny Ong A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer Scientific Reports |
| title | A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer |
| title_full | A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer |
| title_fullStr | A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer |
| title_full_unstemmed | A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer |
| title_short | A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer |
| title_sort | machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer |
| url | https://doi.org/10.1038/s41598-021-96072-6 |
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