Investigator

Adrianna L. Bartolomucci

Research Specialist · Johns Hopkins University, School of Medicine

ALBAdrianna L. Barto…
Papers(1)
Early Detection of Ov…
Collaborators(10)
Akshaya V. AnnapragadaBeatriz CarvalhoCarolyn HrubanChristina TherkildsenChristine A.R. LokClaus Lindbjerg Ander…Daan van den BroekDaniel C. BruhmDennis J. SlamonDimitrios Mathios
Institutions(6)
Johns Hopkins Univers…The Netherlands Cance…Hvidovre HospitalNetherlands Cancer In…Aarhus UniversityUniversity Of Califor…

Papers

Early Detection of Ovarian Cancer Using Cell-Free DNA Fragmentomes and Protein Biomarkers

Abstract Ovarian cancer is a leading cause of death for women worldwide, in part due to ineffective screening methods. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome and protein biomarker [cancer antigen 125 (CA-125) and human epididymis protein 4 (HE4)] analyses to evaluate 591 women with ovarian cancer, with benign adnexal masses, or without ovarian lesions. Using a machine learning model with the combined features, we detected ovarian cancer with specificity >99% and sensitivities of 72%, 69%, 87%, and 100% for stages I to IV, respectively. At the same specificity, CA-125 alone detected 34%, 62%, 63%, and 100%, and HE4 alone detected 28%, 27%, 67%, and 100% of ovarian cancers for stages I to IV, respectively. Our approach differentiated benign masses from ovarian cancers with high accuracy (AUC = 0.88, 95% confidence interval, 0.83–0.92). These results were validated in an independent population. These findings show that integrated cfDNA fragmentome and protein analyses detect ovarian cancers with high performance, enabling a new accessible approach for noninvasive ovarian cancer screening and diagnostic evaluation. Significance: There is an unmet need for effective ovarian cancer screening and diagnostic approaches that enable earlier-stage cancer detection and increased overall survival. We have developed a high-performing accessible approach that evaluates cfDNA fragmentomes and protein biomarkers to detect ovarian cancer.

14Works
1Papers
34Collaborators
Biomarkers, TumorBrain NeoplasmsOvarian NeoplasmsEarly Detection of Cancer

Positions

2022–

Research Specialist

Johns Hopkins University · School of Medicine