104 Background: High-grade glioma (HGG) is an aggressive heterogeneous primary CNS neoplasm with high recurrence rate and poor survival. Multiple ongoing clinical trials are leveraging targeted molecular and immunologic therapeutics (e.g., pembrolizumab, Chimeric Antigen Receptor [CAR] T-cell therapy) in effort to improve survival. Explainable predictive models have shown value in identifying biomarkers predictive of treatment response as well as informing prognosis. In this study, we developed an explainable machine learning model leveraging clinical, molecular and radiomic (imaging) features to predict overall survival in patients suffering from HGG treated with CAR T-cell therapy. Methods: In this IRB-approved phase 1 clinical trial, 60 patients (39 males, median age = 49) suffering from HGG underwent surgical resection and CAR T-cell therapy1. All patients underwent baseline MRI scans prior to both surgical resection and CAR T-cell administration in the resection cavity. Using contrast-enhanced T1-weighted MRIs, we segmented the enhancing tumor (ET) and generated radiomic features. For predictive modeling, we incorporated the following features: Age, gender, race, ethnicity, histology, tumor grade (WHO), IL-13 receptor alpha 2 (IL-13Rα2) expression (H score), unifocal or multifocal lesions, tumor location (lobe), shape-based radiomics (tumor volume, surface area, and sphericity). We utilized gradient-boosted tree models to classify whether survival is above or below 180 days with two-loop nested cross-validation. For the inner validation loop, we optimized the model with hyper-parameter tuning. For the outer validation loop, we tested the optimal model on the hold-out data and the predictions were used as survival scores (0 – 1). Larger scores imply better predicted survival. For prediction explanations, we adopted the Shapley additive explanation (SHAP) framework. Results: The outer validation loop Area Under the Receiver Operating Characteristic Curve and Area under the Precision-Recall Curve were 0.76 and 0.81, respectively. Among the top five most important features calculated from SHAP; patients with larger tumor surface area, tumor volume and age have reduced survival scores while patients with larger IL-13Rα2 and tumor sphericity have increased survival scores. We stratified the patients into two distinct prognostic sub-groups (30 patients each group) using the survival scores obtained from the outer loop, with a log-rank test p < 0.01. Conclusions: In patients with HGG treated with CAR T-cell therapy, we found that tumor surface area/volume and age are inversely related to survival while increased IL-13Rα2 expression and tumor sphericity were positive predictor of survival. Our model can potentially be used to optimize clinical trial enrollment through more precise patient screening and treatment planning.
Facing CAR T Cell Challenges on the Deadliest Paediatric Brain Tumours