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 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.

Precision oncology: Artificial intelligence, circulating cell‐free DNA, and the minimally invasive detection of pancreatic cancer—A pilot study

“…All cytosine CpGs were annotated with genomic and island regions followed by their enrichment was estimated using Fisher's exact test. Analysis details are as described in prior publications 7,8 …”

“…In the biological sciences, AI can now be coupled with genomics for highly accurate disease detection and to elucidate the mechanisms of complex disorders including cancer. 7,8 In the era of multi-omics studies, the capability of AI adds significantly to the analysis and interpretation of omics big data. In the present study, we sought to combine DNA methylation analysis of circulating cfDNA with AI analysis to identify minimally invasive biomarkers and to investigate the epigenomic pathogenesis of PC.…”

“…An exciting application of AI is the ability to identify previously unrecognized, defining features in a dataset and accurately classify or distinguish groups. In the biological sciences, AI can now be coupled with genomics for highly accurate disease detection and to elucidate the mechanisms of complex disorders including cancer 7,8 . In the era of multi‐omics studies, the capability of AI adds significantly to the analysis and interpretation of omics big data.…”

“…5 Circulating cell-free DNA (cfDNA) refers to DNA that is present in the bloodstream and that exists outside of the cells. 6 Recent publications have suggested the value of epigenomic analysis for the elucidation of cancer pathogenesis and for accurate minimally invasive detection 7,8 using cfDNA. Methylation markers identified using cfDNA have the potential to function as a standalone predictor of progression-free survival, 9 and treatment response 10 in PC.…”

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

“…cfTDNA can be released along with the occurrence of cell necrosis and apoptosis, and the release of cancer cells. The levels of circulating cell-free DNA (cfDNA) in patients are high, and mostly originate from tumor cells ( 106 ). Therefore, Bahado-Singh et al ( 106 ) performed genome-wide DNA methylation analysis of cytosine markers and used AI to identify the most predictive epigenetic markers in the genome.…”