Prediction of glioma tissue stiffness using metabolomic signatures

Gliomas are aggressive tumors in critical need of improved therapeutic options. Recent work has demonstrated that glial tissue from core (inner) and edge (infiltrating) regions possesses distinct metabolic signatures and biomechanical properties that are linked to tumor aggression and migration. In this proof-of-concept study, Young’s moduli (stiffness) of core and edge tissue are predicted using paired metabolic signal intensities. Core and edge stiffness previously measured from n = 25 patients were paired with metabolomic data previously obtained using 2D liquid chromatography-mass spectrometry/mass spectrometry. Low (≤median) and high (>median) stiffness were predicted from paired core and edge metabolomics using a machine learning (ML) workflow that included forward feature selection, model training, grid search hyperparameter tuning, and repeated k-fold cross-validation. Key core metabolites predictive of low and high stiffness in core tissue included N6-methyllysine, 2',3'-cyclic UMP, and gamma-amino-n-butyric acid. Top core metabolites in predicting edge moduli included guanosine, acetylcholine, glutamic acid, and N6-methyllysine. The top edge metabolite in predicting edge moduli was DL-p-hydroxyphenyllactic acid. Using ≤5 features, machine learning models predicted core and edge moduli using core and edge metabolites individually and in combination, achieving AUROC, maximum F1, and PRAUC values ≥0.90. This study shows that regions of differing glioma core and edge stiffnesses exhibit unique metabolic signatures. These signatures could potentially be explored to develop personalized therapeutic strategies.