  We utilized abundant transcriptomic data for the primary classes of brain
cancers to study the feasibility of separating all of these diseases
simultaneously based on molecular data alone. These signatures were based on a
new method reported herein that resulted in a brain cancer marker panel of 44
unique genes. Many of these genes have established relevance to the brain
cancers examined, with others having known roles in cancer biology. Analyses on
large-scale data from multiple sources must deal with significant challenges
associated with heterogeneity between different published studies, for it was
observed that the variation among individual studies often had a larger effect
on the transcriptome than did phenotype differences, as is typical. We found
that learning signatures across multiple datasets greatly enhanced
reproducibility and accuracy in predictive performance on truly independent
validation sets, even when keeping the size of the training set the same. This
was most likely due to the meta-signature encompassing more of the
heterogeneity across different sources and conditions, while amplifying signal
from the repeated global characteristics of the phenotype. When molecular
signatures of brain cancers were constructed from all currently available
microarray data, 90 percent phenotype prediction accuracy, or the accuracy of
identifying a particular brain cancer from the background of all phenotypes,
was found. Looking forward, we discuss our approach in the context of the
eventual development of organ-specific molecular signatures from peripheral
fluids such as the blood.
