Keywords

Glioma, Bioinformatics, Disease subtyping, Meta-analysis, Clustering analysis

Abstract

Background Gliomas traditionally have been sub-classified
based on histopathological observations. However, this ap-
proach is subject to inter-observer variability, and histopatho-
logical features may not reflect the biological mechanisms that
drive tumor growth. High-throughput transcriptional profiling
has shown promise in objectively and reproducibly identify-
ing glioma subtypes. Most prior studies have typically used
only modest sample sizes and have sometimes overlooked
important data-processing steps to ensure sample quality and
to evaluate the robustness of quantitative findings. The pur-
pose of our study was to define robust glioma subtypes by
applying rigorous preprocessing and validation steps to 1,952
microarray samples aggregated from 16 prior studies. This
data set is the most comprehensive collection of glioma mi-
croarray samples compiled to date.
Methods and results We evaluated each sample for quality-
control issues, corrected for probe-composition biases, and
adjusted for intra- and inter-study batch effects. Using a
training/testing validation design that simulates a “bench-to-
bedside process,” we identified six transcriptional subtypes
that contained a heterogeneous mix of histopathological sub-
types and tumor grades. Similar to prior studies, age, survival
and treatment patterns differed significantly across the tran-
scriptional subtypes. However, due to our large sample size,
we also observed that within a given histopathological sub-
type, our transcriptional subtypes provided additional prog-
nostic value. Lastly, we used a pathway-based approach to
elucidate the biological mechanisms associated with each
subtype.
Conclusions Our findings provide clinical and biological in-
sights that may not be apparent with alternative approaches or
smaller data sets, and our approach serves as an example for
meta-analyses that can be applied to other complex diseases.

Original Publication Citation

Lee S, Piccolo SR, Allen-Brady K. “Robust meta-analysis shows that glioma transcriptional subtyping complements traditional approaches.” Cellular Oncology, 2014 Oct; 37(5):317-29