Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot Study
Cholangiocarcinoma (CCA) is a highly lethal disease because most patients are asymptomatic until they progress to advanced stages. Current CCA diagnosis relies on clinical imaging tests and tissue biopsy, while specific CCA biomarkers are still lacking. This study employed a translational proteomic...
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MDPI AG
2022-09-01
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| Online Access: | https://www.mdpi.com/1420-3049/27/18/5904 |
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| author | Kamolwan Watcharatanyatip Somchai Chutipongtanate Daranee Chokchaichamnankit Churat Weeraphan Kanokwan Mingkwan Virat Luevisadpibul David S. Newburg Ardythe L. Morrow Jisnuson Svasti Chantragan Srisomsap |
| author_facet | Kamolwan Watcharatanyatip Somchai Chutipongtanate Daranee Chokchaichamnankit Churat Weeraphan Kanokwan Mingkwan Virat Luevisadpibul David S. Newburg Ardythe L. Morrow Jisnuson Svasti Chantragan Srisomsap |
| author_sort | Kamolwan Watcharatanyatip |
| collection | DOAJ |
| description | Cholangiocarcinoma (CCA) is a highly lethal disease because most patients are asymptomatic until they progress to advanced stages. Current CCA diagnosis relies on clinical imaging tests and tissue biopsy, while specific CCA biomarkers are still lacking. This study employed a translational proteomic approach for the discovery, validation, and development of a multiplex CCA biomarker assay. In the discovery phase, label-free proteomic quantitation was performed on nine pooled plasma specimens derived from nine CCA patients, nine disease controls (DC), and nine normal individuals. Seven proteins (S100A9, AACT, AFM, and TAOK3 from proteomic analysis, and NGAL, PSMA3, and AMBP from previous literature) were selected as the biomarker candidates. In the validation phase, enzyme-linked immunosorbent assays (ELISAs) were applied to measure the plasma levels of the seven candidate proteins from 63 participants: 26 CCA patients, 17 DC, and 20 normal individuals. Four proteins, S100A9, AACT, NGAL, and PSMA3, were significantly increased in the CCA group. To generate the multiplex biomarker assays, nine machine learning models were trained on the plasma dynamics of all seven candidates (All-7 panel) or the four significant markers (Sig-4 panel) from 45 of the 63 participants (70%). The best-performing models were tested on the unseen values from the remaining 18 (30%) of the 63 participants. Very strong predictive performances for CCA diagnosis were obtained from the All-7 panel using a support vector machine with linear classification (AUC = 0.96; 95% CI 0.88–1.00) and the Sig-4 panel using partial least square analysis (AUC = 0.94; 95% CI 0.82–1.00). This study supports the use of the composite plasma biomarkers measured by clinically compatible ELISAs coupled with machine learning models to identify individuals at risk of CCA. The All-7 and Sig-4 assays for CCA diagnosis should be further validated in an independent prospective blinded clinical study. |
| first_indexed | 2024-03-09T23:01:53Z |
| format | Article |
| id | doaj.art-46beb733eb30439ba7c5c3d6739a42a4 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T23:01:53Z |
| publishDate | 2022-09-01 |
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| series | Molecules |
| spelling | doaj.art-46beb733eb30439ba7c5c3d6739a42a42023-11-23T18:00:55ZengMDPI AGMolecules1420-30492022-09-012718590410.3390/molecules27185904Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot StudyKamolwan Watcharatanyatip0Somchai Chutipongtanate1Daranee Chokchaichamnankit2Churat Weeraphan3Kanokwan Mingkwan4Virat Luevisadpibul5David S. Newburg6Ardythe L. Morrow7Jisnuson Svasti8Chantragan Srisomsap9Laboratory of Biochemistry, Chulabhorn Research Institute, Bangkok 10210, ThailandPediatric Translational Research Unit, Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, ThailandLaboratory of Biochemistry, Chulabhorn Research Institute, Bangkok 10210, ThailandLaboratory of Biochemistry, Chulabhorn Research Institute, Bangkok 10210, ThailandDivision of Surgery, Sapphasitthiprasong Hospital, Ubon Ratchathani 34000, ThailandDivision of Information and Technology, Ubonrak Thonburi Hospital, Ubon Ratchathani 34000, ThailandCenter for Population Health Science and Analytics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USACenter for Population Health Science and Analytics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USALaboratory of Biochemistry, Chulabhorn Research Institute, Bangkok 10210, ThailandLaboratory of Biochemistry, Chulabhorn Research Institute, Bangkok 10210, ThailandCholangiocarcinoma (CCA) is a highly lethal disease because most patients are asymptomatic until they progress to advanced stages. Current CCA diagnosis relies on clinical imaging tests and tissue biopsy, while specific CCA biomarkers are still lacking. This study employed a translational proteomic approach for the discovery, validation, and development of a multiplex CCA biomarker assay. In the discovery phase, label-free proteomic quantitation was performed on nine pooled plasma specimens derived from nine CCA patients, nine disease controls (DC), and nine normal individuals. Seven proteins (S100A9, AACT, AFM, and TAOK3 from proteomic analysis, and NGAL, PSMA3, and AMBP from previous literature) were selected as the biomarker candidates. In the validation phase, enzyme-linked immunosorbent assays (ELISAs) were applied to measure the plasma levels of the seven candidate proteins from 63 participants: 26 CCA patients, 17 DC, and 20 normal individuals. Four proteins, S100A9, AACT, NGAL, and PSMA3, were significantly increased in the CCA group. To generate the multiplex biomarker assays, nine machine learning models were trained on the plasma dynamics of all seven candidates (All-7 panel) or the four significant markers (Sig-4 panel) from 45 of the 63 participants (70%). The best-performing models were tested on the unseen values from the remaining 18 (30%) of the 63 participants. Very strong predictive performances for CCA diagnosis were obtained from the All-7 panel using a support vector machine with linear classification (AUC = 0.96; 95% CI 0.88–1.00) and the Sig-4 panel using partial least square analysis (AUC = 0.94; 95% CI 0.82–1.00). This study supports the use of the composite plasma biomarkers measured by clinically compatible ELISAs coupled with machine learning models to identify individuals at risk of CCA. The All-7 and Sig-4 assays for CCA diagnosis should be further validated in an independent prospective blinded clinical study.https://www.mdpi.com/1420-3049/27/18/5904biomarkercholangiocarcinomaimmunoassaymachine learningmultiplex assayplasma proteomics |
| spellingShingle | Kamolwan Watcharatanyatip Somchai Chutipongtanate Daranee Chokchaichamnankit Churat Weeraphan Kanokwan Mingkwan Virat Luevisadpibul David S. Newburg Ardythe L. Morrow Jisnuson Svasti Chantragan Srisomsap Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot Study Molecules biomarker cholangiocarcinoma immunoassay machine learning multiplex assay plasma proteomics |
| title | Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot Study |
| title_full | Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot Study |
| title_fullStr | Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot Study |
| title_full_unstemmed | Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot Study |
| title_short | Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot Study |
| title_sort | translational proteomic approach for cholangiocarcinoma biomarker discovery validation and multiplex assay development a pilot study |
| topic | biomarker cholangiocarcinoma immunoassay machine learning multiplex assay plasma proteomics |
| url | https://www.mdpi.com/1420-3049/27/18/5904 |
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