BioProject

The purpose of our study was to define robust glioma subtypes by applying rigorous preprocessing and validation steps to 1,952 microarray samples aggregated from public data repositories for 16 prior studies. More...

The purpose of our study was to define robust glioma subtypes by applying rigorous preprocessing and validation steps to 1,952 microarray samples aggregated from public data repositories for 16 prior studies. We evaluated each sample for quality-control issues, normalized high-quality samples using the Single-Channel Array Normalization (SCAN) algorithm (PMID: 22959562), corrected for probe-composition biases and inter-platform variability, and adjusted for intra- and inter-study batch effects. The deposited data in GEO include the 1,841 microarray samples that passed quality control tests, and underwent normalization and batch effect adjustment. Where available, we retrieved treatment, histological and clinical data, such as tumor grade, histopathology, age-at-diagnosis, and survival time after diagnosis for these samples. Using a training/testing validation design, we identified six transcriptional subtypes in the training set, and evaluated clinically observable characteristics in the test set. Three of our clusters contained a heterogeneous mix of histopathological subtypes and tumor grades. We evaluated age, survival, and treatment patterns across our test samples and observed highly significant differences among the clusters. We also observed the potential to use gene expression patterns to further understanding of the biological mechanisms that drive gliomagenesis for each subtype. Our findings provide clinical and biological insights that may not be apparent with alternative approaches or smaller data sets, and our approach serves as an example for gene-expression meta-analyses that can be applied to other complex diseases. Less...