Investigator
Peking Union Medical College Hospital
The triage role of cytological DNA methylation in women with non-16/18, specifically genotyping high-risk HPV infection
Abstract Objectives To evaluate cytological DNA methylation testing methods for risk stratification in women with non-16/18 HPV, focusing on high-risk HPV (hrHPV) genotyping. Methods This study compared the triage performance of liquid-based cytology (LBC) testing, hrHPV genotyping, and PAX1/JAM3 gene methylation (CISCER) testing. The absolute risks of cervical intraepithelial neoplasia grade 2 or worse (CIN2+), grade 3 or worse (CIN3+), and colposcopy referral rates were calculated. Results The CISCER test showed a CIN3+ risk of 39.1% for positive and 0.9% for negative results. In comparison, LBC ≥ ASCUS and HPV33/35 genotyping had CIN3+ risks of 9.8% and 19.3%, respectively, for positive result. The colposcopy referral rates were 17.4% for CISCER+, 61.9% for LBC ≥ ASCUS, and 8.9% for HPV33/35+ genotyping. The CIN3+ risks were 40.0% and 50.0% when CISCER+ was combined with LBC ≥ ASCUS and HPV33/35+, respectively. The CIN3+ risks were 0.0% and 1.0% when CISCER- was combined with LBC with no intraepithelial lesions or malignancy (NILM) and non-HPV33/35, respectively. Our analysis of CIN2+ patients yielded similar results. Conclusions DNA methylation testing outperformed LBC in triaging women with non-16/18 hrHPV infections, significantly reducing unnecessary colposcopy referrals, particularly when combined with HPV33/35 genotyping.
Fragmentomics features of ovarian cancer
AbstractOvarian cancer (OC) is a major cause of cancer mortality in women worldwide. Due to the occult onset of OC, its nonspecific clinical symptoms in the early phase, and a lack of effective early diagnostic tools, most OC patients are diagnosed at an advanced stage. In this study, shallow whole‐genome sequencing was utilized to characterize fragmentomics features of circulating tumor DNA (ctDNA) in OC patients. By applying a machine learning model, multiclass fragmentomics data achieved a mean area under the curve (AUC) of 0.97 (95% CI 0.962–0.976) for diagnosing OC. OC scores derived from this model strongly correlated with the disease stage. Further comparative analysis of OC scores illustrated that the fragmentomics‐based technology provided additional clinical benefits over the traditional serum biomarkers cancer antigen 125 (CA125) and the Risk of Ovarian Malignancy Algorithm (ROMA) index. In conclusion, fragmentomics features in ctDNA are potential biomarkers for the accurate diagnosis of OC.
