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Data-driven analysis of a validated risk score for ovarian cancer identifies clinically distinct patterns during follow-up and treatment

Selected publication · Communications Medicine, 2022

Enroth S., Ivansson E., Lindberg J., Lycke M., Bergman J., Reneland A., Stålberg K., Sundfeldt K., Gyllensten U.

Disease areaApplication areaSample typeProducts
Oncology
Patient Stratification
Plasma

Editor's note

This follow-up investigation to the landmark ovarian cancer study from Ulf Gyllensten’s group at Uppsala University used their custom-developed 11-protein PEA panel to measure plasma levels of their previously identified biomarkers in 1,120 clinical samples. In addition to confirming the accuracy of the protein signature in the expanded cohort, data-driven analysis also demonstrated potential utility in monitoring disease development and predicting 5-year survival. They concluded that “the risk score pattern after diagnosis follows the common clinical responses during treatment and relapse/progression and may be useful in monitoring clinical developments during follow-up”.

Abstract

Background

Ovarian cancer is the eighth most common cancer among women and due to late detection prognosis is poor with an overall 5-year survival of 30–50%. Novel biomarkers are needed to reduce diagnostic surgery and enable detection of early-stage cancer by population screening. We have previously developed a risk score based on an 11-biomarker plasma protein assay to distinguish benign tumors (cysts) from malignant ovarian cancer in women with adnexal ovarian mass.

Methods

Protein concentrations of 11 proteins were characterized in plasma from 1120 clinical samples with a custom version of the proximity extension assay. The performance of the assay was evaluated in terms of prediction accuracy based on receiver operating characteristics (ROC) and multiple hypothesis adjusted Fisher’s Exact tests on achieved sensitivity and specificity.

Results

The assay’s performance is validated in two independent clinical cohorts with a sensitivity of 0.83/0.91 and specificity of 0.88/0.92. We also show that the risk score follows the clinical development and is reduced upon treatment, and increased with relapse and cancer progression. Data-driven modeling of the risk score patterns during a 2-year follow-up after diagnosis identifies four separate risk score trajectories linked to clinical development and survival. A Cox proportional hazard regression analysis of 5-year survival shows that at time of diagnosis the risk score is the second-strongest predictive variable for survival after tumor stage, whereas MUCIN-16 (CA-125) alone is not significantly predictive.

Conclusion

The robust performance of the biomarker assay across clinical cohorts and the correlation with clinical development indicates its usefulness both in the diagnostic work-up of women with adnexal ovarian mass and for predicting their clinical course.

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