cfDNA Fragmentome, Protein Analyses Detect Ovarian Cancer

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Medically reviewed by Carmen Pope, BPharm. Last updated on Oct 4, 2024.

By Elana Gotkine HealthDay Reporter

FRIDAY, Oct. 4, 2024 -- Integrated cell-free DNA (cfDNA) fragmentome and protein analyses detect ovarian cancers with high performance, according to a study published online Sept. 30 in Cancer Discovery.

Jamie E. Medina, Ph.D., from the Sidney Kimmel Comprehensive Cancer Center at the Johns Hopkins University School of Medicine in Baltimore, and colleagues used whole-genome cfDNA fragmentome and protein biomarker (CA-125 and HE4) analyses to assess 591 women with ovarian cancer, with benign adnexal masses, or without ovarian lesions.

The researchers detected ovarian cancer with specificity >99 percent and sensitivity of 72, 69, 87, and 100 percent for stages I to IV using a machine learning model with the combined features. CA-125 alone detected 34, 62, 63, and 100 percent of ovarian cancers for stage I to IV at the same specificity. Benign masses were differentiated from ovarian cancers with high accuracy (area under the receiver operating characteristic curve, 0.88). Validation of these results was performed in an independent population.

"The use of both cfDNA and protein measurements may initially appear to be complex, but both types of analytes can be assessed from the same sample of blood, and optimized methods suggest that this combined approach would be cost-efficient and accessible," the authors write. "Overall, this study provides a new accessible approach for early detection of ovarian cancer that may overcome current challenges for ovarian cancer screening and reduce the morbidity and mortality of this disease."

Several authors disclosed ties to the biopharmaceutical industry.

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Disclaimer: Statistical data in medical articles provide general trends and do not pertain to individuals. Individual factors can vary greatly. Always seek personalized medical advice for individual healthcare decisions.

Source: HealthDay

Posted : 2024-10-05 06:00

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