Improved Prognostic Survival Models Using High Dimensional Gene Expression Data

Music City Center 

Recent genetic, epigenetic, and transcriptomic analyses have stratified Medulloblastoma (MB) into four subgroups of Wingless Type (WNT), Sonic Hedgehog (SHH), and Group 3 and Group 4, with discrete patient profiles and prognoses. Using a dataset of over ten thousand gene expression profiles, this study explores which genes can improve prognostic accuracy for survival, while accounting for molecular stratification and known clinical covariates. This approach involves a Benjamini-Hochberg screening of all genes, adjustment for molecular stratification and clinical covariates, followed by several high-dimensional models to predict survival. A case study of 483 pediatric MB patients demonstrated improved prognostic performance, as assessed by the C-index, Brier Scores, and time dependent AUCs, when gene expression data were included. Simulation studies validated the method's performance, successfully excluding non-informative genes in null scenarios and reliably identifying influential genes in alternative scenarios. This approach provides a robust framework for enhancing survival prediction and uncovering biologically significant markers.

High-dimensional genomic data

Medulloblastoma molecular stratification

prognostic models

variable selection

survival analysis 

Section on Statistics in Genomics and Genetics