Authors: Greg Schwing; Marc Moisi, MD; Chindo Hicks, PhD (Detroit, MI)


“Cox-nnet” is an Artificial Neural Network that performs Cox (Proportional Hazards) Regression. The neural network learns through training, optimizing, and testing to find hidden connections between variables to estimate hazard.


The combined mRNA Expression z-Scores from Affymetrix arrays, Agilent arrays, and RNASeq of Mackay et al. 2018 were retrieved from pedcbioportal(dot)org. The inclusion criteria were reporting of the following clinical covariates – Diagnosis, WHO Grade, Tumor location, Gender, Age, Censored Survival, and Histone subtype. The Samples meeting inclusion criteria (n=162) were analyzed with Cox-nnet, using the expression z-scores of 8,540 genes and clinical covariates as inputs.


The Kaplan-Meier curves generated by partitioning the test set above and below estimated median log hazard ratio (MLHR = 0.04) were statistically significant by log-rank test (Chisq= 7.4 on 1 degrees of freedom, p= 0.006). The five most important clinical covariates (weight given in parentheses) were WHO Grade (0.078), Histone 3.3 K27M mutation (0.025), Wild-Type Histone (0.0156), Hemispheric (0.009), and Midline (0.008). The five most important genes were those encoding for Perforin-1 (0.674), Neuropeptide-Y (0.498), DRC11 Antibody (0.280), Serine Protease 3 (0.280), and Zinc-Alpha-2-Glycoprotein-1 (0.222). The three most important pathways were Hepatic Fibrosis (2.021); Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis (1.848); and Axonal Guidance Signaling (1.617). The pHGG-specific pathways were Glioblastoma Multiforme Signaling (0.785), Glioma Invasiveness Signaling (0.449), and Embryonic Stem Cell Pluripotency (0.630).


The predictive model was strong, as indicated by the significant log-rank test. Of the top five genes, four were either immunologic or CNS-related. Only one, Zinc-Alpha-2-Glycoprotein-1, was tumorigenic. The pathways followed a similar trend. This partially explains the difficulty in treating pHGG. Most of the important features are either immunologic, a field in the early stages of drug development, or related to the underlying CNS physiology, which could be devastating to disrupt.