Vaginal swabs provide "sufficient signal" to detect endometrial cancer, study finds

A study published in the International Journal of Gynecological Cancer demonstrated that molecular signals associated with endometrial cancer can be detected using a simple vaginal swab, challenging the long-standing clinical requirement for direct uterine sampling. The research, which utilized whole-transcriptome sequencing and machine learning, established a scientific foundation for a less invasive diagnostic pathway for women presenting with symptoms such as abnormal or postmenopausal bleeding.^1,2

Endometrial cancer is the most common gynecologic malignancy in the United States and remains 1 of the few cancers with a rising mortality rate. In 2026, the American Cancer Society estimates more than 69,000 new cases and over 14,000 deaths. Currently, the standard of care for ruling out the disease in symptomatic women involves invasive tissue-based methods that are often painful and anxiety-provoking. Every year, tens of thousands of women undergo these procedures, even though fewer than 1 in 10 will ultimately receive a cancer diagnosis.^1,2

Study design and methodology

The exploratory study, titled PNK001 (NCT6460818), enrolled 236 participants between February and November 2024. Eligible participants included women aged over 18 years with positive biopsies or those aged 45 years or older with benign or pre-malignant conditions scheduled for a hysterectomy. Samples were collected at 3 study sites: Mayo Clinic in Rochester, Minnesota; Columbia University in New York, New York; and Houston Methodist in Houston, Texas.¹

Immediately before uterine surgery, researchers collected vaginal swabs, ectocervical swabs, and endocervical cytobrushes. Following the hysterectomy, uterine tissues were sampled and frozen for research use to serve as reference labels. The research team analyzed these samples for differential gene expression, cell type signatures, and expressed somatic variants. To refine the analysis, the team also integrated RNA sequencing data from The Cancer Genome Atlas and endometrial tissues from the Cooperative Human Tissue Network.

Machine learning and diagnostic performance

The investigators utilized machine learning classifiers trained on tissue samples, which achieved receiver operating characteristic area under the curve (AUC) values of 0.97 and 0.98 on an independent test set. When these classifiers were applied to the less invasive samples, significant differential gene expression and elevated somatic variant counts were identified in the vaginal swabs, ectocervical swabs, and endocervical cytobrushes of women with cancer.

Classifiers trained on expressed genes and variant counts successfully distinguished 5 benign cases from 15 malignant cases across the non-invasive sample types. In cross-validation, the AUC values for these samples ranged from 0.6 to 0.96. The findings indicated that the molecular signatures associated with the disease were present in the lower reproductive tract, even without accessing the uterus itself.

Clinical implications and future development

The study's results suggest a fundamental shift in how gynecologic cancers might be evaluated. Bonnie Anderson, co-founder, chair, and chief executive officer of PinkDx, stated: "The gynecologic cancer diagnostic journey is too invasive, too uncertain, and too burdensome for women. This peer-reviewed publication provides clear evidence that endometrial cancer signals can be detected from a vaginal swab. It represents a scientific breakthrough and a critical step toward a diagnostic pathway designed around women—not procedures."²

Giulia Kennedy, PhD, co-founder and chief scientific officer of PinkDx, noted the significance of challenging medical assumptions: "The assumption has always been that you have to sample the uterus to understand what's happening in the uterus. Our findings challenge that assumption. We show that molecular signals associated with endometrial cancer can be detected from a vaginal swab—a result with meaningful implications for how we rethink the diagnostic experience for women."

Based on the sufficient signal provided by the vaginal swabs, researchers selected this as the primary sample type for a second study, PNK002.¹ Jason Wright, MD, chair of Obstetrics and Gynecology and Obstetrician- and Gynecologist-in-Chief at Tufts Medical Center, and study co-author, emphasized the patient-centered potential of the technology: "These data suggest a future where a non-invasive swab could help better identify which women with abnormal or postmenopausal bleeding may warrant additional evaluation. Molecular insights that inform treatment decisions without invasive sampling represent an important advance toward more patient-centered care."²

References:

1. Wright JD, Pankratz DG, Liu Guoying, et al. Whole-transcriptome sequencing and machine learning detect molecular signatures of endometrial cancer in noninvasive vaginal swabs. Int J Gynecol Cancer. doi.org/10.1016/j.ijgc.2026.104546
2. PinkEx publishes groundbreaking evidence in endometrial cancer detection using a simple vaginal swab. PinkDx. Press release. Published April 6, 2026. Accessed April 6, 2026. https://www.newswise.com/articles/pinkdx-publishes-groundbreaking-evidence-in-endometrial-cancer-detection-using-a-simple-vaginal-swab