Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer
Abstract Ovarian cancer (OC) is the most lethal gynecologic cancer due primarily to its asymptomatic nature and late stage at diagnosis. The development of non-invasive markers is an urgent priority. We report the accurate detection of epithelial OC using Artificial Intelligence (AI) and genome-wide...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-23149-1 |
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| author | Ray O. Bahado-Singh Amin Ibrahim Zaid Al-Wahab Buket Aydas Uppala Radhakrishna Ali Yilmaz Sangeetha Vishweswaraiah |
| author_facet | Ray O. Bahado-Singh Amin Ibrahim Zaid Al-Wahab Buket Aydas Uppala Radhakrishna Ali Yilmaz Sangeetha Vishweswaraiah |
| author_sort | Ray O. Bahado-Singh |
| collection | DOAJ |
| description | Abstract Ovarian cancer (OC) is the most lethal gynecologic cancer due primarily to its asymptomatic nature and late stage at diagnosis. The development of non-invasive markers is an urgent priority. We report the accurate detection of epithelial OC using Artificial Intelligence (AI) and genome-wide epigenetic analysis of circulating cell free tumor DNA (cfTDNA). In a prospective study, we performed genome-wide DNA methylation profiling of cytosine (CpG) markers. Both conventional logistic regression and six AI platforms were used for OC detection. Further, we performed Gene Set Enrichment Analysis (GSEA) analysis to elucidate the molecular pathogenesis of OC. A total of 179,238 CpGs were significantly differentially methylated (FDR p-value < 0.05) genome-wide in OC. High OC diagnostic accuracies were achieved. Conventional logistic regression achieved an area under the ROC curve (AUC) [95% CI] 0.99 [± 0.1] with 95% sensitivity and 100% specificity. Multiple AI platforms each achieved high diagnostic accuracies (AUC = 0.99–1.00). For example, for Deep Learning (DL)/AI AUC = 1.00, sensitivity = 100% and 88% specificity. In terms of OC pathogenesis: GSEA analysis identified ‘Adipogenesis’ and ‘retinoblastoma gene in cancer’ as the top perturbed molecular pathway in OC. This finding of epigenomic dysregulation of molecular pathways that have been previously linked to cancer adds biological plausibility to our results. |
| first_indexed | 2024-04-11T23:05:39Z |
| format | Article |
| id | doaj.art-bb696cacbf2445b293e29499c0c4fa77 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T23:05:39Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-bb696cacbf2445b293e29499c0c4fa772022-12-22T03:58:02ZengNature PortfolioScientific Reports2045-23222022-11-011211910.1038/s41598-022-23149-1Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancerRay O. Bahado-Singh0Amin Ibrahim1Zaid Al-Wahab2Buket Aydas3Uppala Radhakrishna4Ali Yilmaz5Sangeetha Vishweswaraiah6Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of MedicineDepartment of Obstetrics and Gynecology, Beaumont Research InstituteDepartment of Obstetrics and Gynecology, Oakland University-William Beaumont School of MedicineDepartment of Care Management Analytics, Blue Cross Blue Shield of MichiganDepartment of Obstetrics and Gynecology, Oakland University-William Beaumont School of MedicineDepartment of Obstetrics and Gynecology, Oakland University-William Beaumont School of MedicineDepartment of Obstetrics and Gynecology, Beaumont Research InstituteAbstract Ovarian cancer (OC) is the most lethal gynecologic cancer due primarily to its asymptomatic nature and late stage at diagnosis. The development of non-invasive markers is an urgent priority. We report the accurate detection of epithelial OC using Artificial Intelligence (AI) and genome-wide epigenetic analysis of circulating cell free tumor DNA (cfTDNA). In a prospective study, we performed genome-wide DNA methylation profiling of cytosine (CpG) markers. Both conventional logistic regression and six AI platforms were used for OC detection. Further, we performed Gene Set Enrichment Analysis (GSEA) analysis to elucidate the molecular pathogenesis of OC. A total of 179,238 CpGs were significantly differentially methylated (FDR p-value < 0.05) genome-wide in OC. High OC diagnostic accuracies were achieved. Conventional logistic regression achieved an area under the ROC curve (AUC) [95% CI] 0.99 [± 0.1] with 95% sensitivity and 100% specificity. Multiple AI platforms each achieved high diagnostic accuracies (AUC = 0.99–1.00). For example, for Deep Learning (DL)/AI AUC = 1.00, sensitivity = 100% and 88% specificity. In terms of OC pathogenesis: GSEA analysis identified ‘Adipogenesis’ and ‘retinoblastoma gene in cancer’ as the top perturbed molecular pathway in OC. This finding of epigenomic dysregulation of molecular pathways that have been previously linked to cancer adds biological plausibility to our results.https://doi.org/10.1038/s41598-022-23149-1 |
| spellingShingle | Ray O. Bahado-Singh Amin Ibrahim Zaid Al-Wahab Buket Aydas Uppala Radhakrishna Ali Yilmaz Sangeetha Vishweswaraiah Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer Scientific Reports |
| title | Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer |
| title_full | Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer |
| title_fullStr | Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer |
| title_full_unstemmed | Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer |
| title_short | Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer |
| title_sort | precision gynecologic oncology circulating cell free dna epigenomic analysis artificial intelligence and the accurate detection of ovarian cancer |
| url | https://doi.org/10.1038/s41598-022-23149-1 |
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