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...
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Nature Portfolio
2021
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oai:doaj.org-article:0247321c07774b35a5929fc1ffdacadd2021-12-02T18:51:41ZA machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer10.1038/s41598-021-96072-62045-2322https://doaj.org/article/0247321c07774b35a5929fc1ffdacadd2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96072-6https://doaj.org/toc/2045-2322Abstract 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.Nicholas Brian ShannonLaura Ling Ying TanQiu Xuan TanJoey Wee-Shan TanJosephine HendriksonWai Har NgGillian NgYing LiuXing-Yi Sarah OngRavichandran NadarajahJolene Si Min WongGrace Hwei Ching TanKhee Chee SooMelissa Ching Ching TeoClaramae Shulyn ChiaChin-Ann Johnny OngNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
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DOAJ |
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DOAJ |
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EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q 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 |
| 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. |
| format |
article |
| 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 |
| title |
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_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_sort |
machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/0247321c07774b35a5929fc1ffdacadd |
| work_keys_str_mv |
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