Predictors of residual disease after debulking surgery in advanced stage ovarian cancer

• Published in: Frontiers in oncology Vol. 13; p. 1090092
• Main Authors: Abbas-Aghababazadeh, Farnoosh, Sasamoto, Naoko, Townsend, Mary K., Huang, Tianyi, Terry, Kathryn L., Vitonis, Allison F., Elias, Kevin M., Poole, Elizabeth M., Hecht, Jonathan L., Tworoger, Shelley S., Fridley, Brooke L.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 24.01.2023
• Subjects: debulking; immunohistochemistry; Oncology; ovarian cancer; prediction model; residual disease; tissue microarray; debulking; prediction model; immunohistochemistry; ovarian cancer; tissue microarray; residual disease
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2023.1090092

Optimal debulking with no macroscopic residual disease strongly predicts ovarian cancer survival. The ability to predict likelihood of optimal debulking, which may be partially dependent on tumor biology, could inform clinical decision-making regarding use of neoadjuvant chemotherapy. Thus, we developed a prediction model including epidemiological factors and tumor markers of residual disease after primary debulking surgery. Univariate analyses examined associations of 11 pre-diagnosis epidemiologic factors (n=593) and 24 tumor markers (n=204) with debulking status among incident, high-stage, epithelial ovarian cancer cases from the Nurses' Health Studies and New England Case Control study. We used Bayesian model averaging (BMA) to develop prediction models of optimal debulking with 5x5-fold cross-validation and calculated the area under the curve (AUC). Current aspirin use was associated with lower odds of optimal debulking compared to never use (OR=0.52, 95%CI=0.31-0.86) and two tissue markers, ADRB2 (OR=2.21, 95%CI=1.23-4.41) and FAP (OR=1.91, 95%CI=1.24-3.05) were associated with increased odds of optimal debulking. The BMA selected aspirin, parity, and menopausal status as the epidemiologic/clinical predictors with the posterior effect probability ≥20%. While the prediction model with epidemiologic/clinical predictors had low performance (average AUC=0.49), the model adding tissue biomarkers showed improved, but weak, performance (average AUC=0.62). Addition of ovarian tumor tissue markers to our multivariable prediction models based on epidemiologic/clinical data slightly improved the model performance, suggesting debulking status may be in part driven by tumor characteristics. Larger studies are warranted to identify those at high risk of poor surgical outcomes informing personalized treatment.