Fig. 5: Development and predictability of integrated genomic and functional biomarker.

A Schematic outlining the machine learning algorithm developed to compare the Integrated Molecular and Functional (IMF) model with the Global Molecular (GM) model through elastic net regression subject to cross validation (methods section). B Training Cohort (n = 26 gliomaspheres): Heatmaps display experimental (IR(5 Gy) or TMZ(50 µM) + BCL-Xli(0.5 µM) and predicted values for either IMF or GM models, for each gliomasphere. R2 and RSME, used to estimate error of the model calculated for each model (methods section). C Independent Verification Cohort (n = 12 gliomaspheres): Heatmaps display experimental (IR or TMZ + BCL-Xli) and predicted values for either IMF or GM models, for each gliomasphere. R2 and RSME, used to estimate error of the model calculated for each model (methods section). D Grouped analysis of all GAVA positive vs GAVA negative gliomaspheres, for both the training and verification cohorts. IR (5 Gy) or TMZ (50 µM) + BCL-Xli(0.5 µM) cell death plotted as violon plots (two-tailed, unpaired t test). E GAVA stratification applied to both training and verification gliomaspheres (n = 38) (Fischer’s exact test).