Toward ovarian cancer screening with protein biomarkers using dried, self-sampled cervico-vaginal fluid
Julia Hedlund Lindberg,
Anna Widgren,
Emma Ivansson,
Inger Gustavsson,
Karin Stålberg,
Ulf Gyllensten,
Karin Sundfeldt,
Jonas Bergquist,
Stefan Enroth
Affiliations
Julia Hedlund Lindberg
Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, SE-75108 Uppsala, Sweden
Anna Widgren
Analytical Chemistry, Department of Chemistry-Biomedical Center, Uppsala University, SE-75237 Uppsala, Sweden
Emma Ivansson
Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, SE-75108 Uppsala, Sweden
Inger Gustavsson
Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, SE-75108 Uppsala, Sweden
Karin Stålberg
Department of Women’s and Children’s Health, Uppsala University, SE-75185 Uppsala, Sweden
Ulf Gyllensten
Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, SE-75108 Uppsala, Sweden
Karin Sundfeldt
Department of Obstetrics and Gynaecology, Institute of Clinical Sciences, Sahlgrenska Academy at Gothenburg University, SE-41685 Gothenburg, Sweden
Jonas Bergquist
Analytical Chemistry, Department of Chemistry-Biomedical Center, Uppsala University, SE-75237 Uppsala, Sweden
Stefan Enroth
Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, SE-75108 Uppsala, Sweden; Swedish Collegium for Advanced Study, Thunbergsvägen 2, SE-752 38 Uppsala, Sweden; Corresponding author
Summary: Early detection is key for increased survival in ovarian cancer, but no general screening program exists today due to lack of biomarkers and overall cost versus benefit over traditional clinical methods. Here, we used dried cervico-vaginal fluid (CVF) as sampling matrix coupled with mass spectrometry for detection of protein biomarkers. We find that self-collected CVF on paper cards yields robust results and is suitable for high-throughput proteomics. Artificial intelligence–based methods were used to identify an 11-protein panel that separates cases from controls. In validation data, the panel achieved a sensitivity of 0.97 (95% CI 0.91–1.00) at a specificity of 0.67 (0.40–0.87). Analyses of samples collected prior to development of symptoms indicate that the panel is informative also of future risk of disease. Dried CVF is used in cervical cancer screening, and our results opens the possibility for a screening program also for ovarian cancer, based on self-collected CVF samples.
