Proteomic alterations in ovarian cancer-Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation

Abstract

Introduction: High-grade serous ovarian cancer (HGSOC) is the most aggressive and prevalent subtype of ovarian Treatment outcomes are significantly influenced by residual disease status following neoadjuvant chemotherapy (NACT). Predicting residual disease before surgery can improve patient stratification and personalized treatment strategies.

Methods: This study analyzed pre-NACT proteomic data from 20 HGSOC patients treated with NACT. Patients were categorized into two groups based on surgical outcomes: no residual disease (R0, n = 14) and suboptimal residual disease (R1, n = 6). From an initial set of 97 differentially expressed proteins, 18 significant proteins were selected using the BORUTA feature selection method. Three machine learning models-Random Forest (RF), Support Vector Machine (SVM), and Bootstrap Aggregation with Classification and Regression Trees (BaggedCART)-were developed and evaluated.

Results: The Random Forest model achieved the best performance with an AUC of 0.955, accuracy of 0.830, sensitivity of 0.904, specificity of 0.763, and F1-score of 0.839. SHapley Additive exPlanations (SHAP) analysis identified five proteins (P48637, O43491, O95302, Q96CX2, and P49189) as the most influential predictors of residual disease. These proteins, including glutathione synthetase and peptidyl-prolyl cis-trans isomerase FKBP9, are associated with chemotherapy resistance mechanisms.

Discussion: The findings demonstrate the potential of integrating proteomic data with machine learning techniques for predicting surgical outcomes in HGSOC. Identified protein signatures may serve as valuable biomarkers for anticipating NACT response and informing clinical decision-making, ultimately contributing to personalized patient care.

Keywords: SHAP analysis; high-grade serous ovarian cancer (HGSOC); machine learning; neoadjuvant chemotherapy (NACT); proteomic biomarkers.

Figure 1

(A) Global SHAP annotations of the random forest model for residual disease prediction. The bee swarm plot shows how features in the model affect predictions. Each dot represents a data sample, and the positions of the dots on the x-axis represent SHAP values (positive or negative impact). The colors of the dots represent feature values (blue—low, red—high). (B) Protein importance plots based on the mean SHAP values of the random forest model for residual disease prediction. The bar graph shows the average of the absolute SHAP values of the marginal contribution of each variable to the model output. This graph presents the relative importance of the variables on the model predictions in a hierarchical structure.