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

Farnoosh Abbas-Aghababazadeh; Naoko Sasamoto; Mary K Townsend; Tianyi Huang; Kathryn L Terry; Allison F Vitonis; Kevin M Elias; Elizabeth M Poole; Jonathan L Hecht; Shelley S Tworoger; Brooke L Fridley

doi:10.3389/fonc.2023.1090092

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

Front Oncol. 2023 Jan 24:13:1090092. doi: 10.3389/fonc.2023.1090092. eCollection 2023.

Authors

Affiliations

¹ Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
² University Health Network, Princess Margaret Cancer Center, Toronto, ON, Canada.
³ Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.
⁴ Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
⁵ Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States.
⁶ Sanofi Genzyme, Global Medical Affairs, Cambridge, MA, United States.
⁷ Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, United States.

Abstract

Objective: 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.

Methods: 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).

Results: 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).

Conclusions: 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.

Keywords: debulking; immunohistochemistry; ovarian cancer; prediction model; residual disease; tissue microarray.

Grants and funding

This study was funded by the National Cancer Institute grants UM1 CA186107, P01 CA87969, U01 CA176726, R01 CA054419, P50 CA105009, and the Marsha Rivkin Center for Ovarian Cancer Research Skacel Family Scholar Award. NS was supported by the Department of Defense award W81XWH-21-1-0320 and Marsha Rivkin Center for Ovarian Cancer Research Rivkin Scientific Scholars Award. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dana-Farber/Harvard Cancer Center is supported in part by an NCI Cancer Center Support Grant # NIH 5 P30 CA06516. This work has been supported in part by the Biostatistics and Bioinformatics Shared Resource and the Analytic Microscopy Core Facility at the H. Lee Moffitt Cancer Center & Research Institute, an NCI designated Comprehensive Cancer Center (P30-CA076292).