David Sidransky

CervicalMethDx: A Precision DNA Methylation Test to Identify Risk of High-Grade Intraepithelial Lesions in Cervical Cancer Screening Algorithms

Abstract Cervical cancer is one of the most common cancers in women. Despite progress in prevention and success in early detection through cytologic screening and human papillomavirus (HPV) detection, there remains a challenge in triaging women appropriately to colposcopy and biopsy. We sought to validate the CervicalMethDx test, a precision DNA methylation classifier for cervical cancer detection, as a reflex test in women with HPV-positive samples. A blinded retrospective study was performed on well-characterized samples in PreservCyt media from a large referral clinical laboratory in the United States. DNA methylation was assessed in three gene promoters (ZNF516, FKBP6, and INTS1) and a control gene (β-actin) by quantitative real-time methylation-specific PCR (qMSP) analysis, using machine learning algorithms. We compared DNA methylation levels in HPV-positive patients presenting with lesions in the Pap test and cervical intraepithelial neoplasia grade 2 (CIN2) or CIN3 histologic diagnosis with DNA methylation levels in HPV-positive patients with lesions in the Pap test but no intraepithelial lesion or malignancy. The CervicalMethDx test correctly classified 95% of the CIN2 samples (n = 210), with 91% sensitivity, 100% specificity, and an area under the ROC curve (AUC) of 0.96, and 94% of CIN3 samples (n = 141), with 90% sensitivity, 100% specificity, and an AUC of 0.96. Moreover, the CervicalMethDx test correctly classified 94% of combined CIN2/CIN3 samples (n = 351), with 93% sensitivity, 97% specificity, and an AUC of 0.96. CervicalMethDx demonstrated strong discriminatory power for identifying CIN2/CIN3 risk and may complement current triage strategies for colposcopy referral. Prospective, population-based studies, including those in low-resource settings, are needed for further evaluation. Prevention Relevance: The CervicalMethDx test integrates DNA methylation analysis and machine learning to improve early detection of high-grade cervical lesions (high-grade squamous intraepithelial lesions), offering a noninvasive, cost-effective screening tool. Enhanced risk stratification and overtreatment reduction expand equitable access to precision prevention programs. Further validation will clarify CervicalMethDx’s alignment with global cervical cancer prevention strategies.

Ovarian tumor cells gain competitive advantage by actively reducing the cellular fitness of microenvironment cells

Cell competition and fitness comparison between cancer and tumor microenvironment (TME) cells determine oncogenic fate. Our previous study established a role for human Flower isoforms as fitness fingerprints, where the expression of Flower Win isoforms in tumor cells leads to growth advantage over TME cells expressing Lose isoforms. Here we demonstrate that the expression of Flower Lose and reduced microenvironment fitness is not a pre-existing condition but, rather, a cancer-induced phenomenon. Cancer cells actively reduce TME fitness by the exosome-mediated release of a cancer-specific long non-coding RNA, Tu-Stroma, which controls the splicing of the Flower gene in the TME cells and expression of Flower Lose isoform, which leads to reduced fitness status. This mechanism controls cancer growth, metastasis and host survival in ovarian cancer. Targeting Flower protein with humanized monoclonal antibody (mAb) in mice significantly reduces cancer growth and metastasis and improves survival. Pre-treatment with Flower mAb protects intraperitoneal organs from developing lesions despite the presence of aggressive tumor cells.