CASPAR:")?>
DNA microarrays allow the simultaneous measurement of thousands of
gene expression levels in any given patient sample. Gene expression
data have been shown to correlate with survival in several cancers,
however, analysis of the data is difficult, since typically at most a
few hundred patients are available, resulting in severely
underdetermined regression or classification models. Several approaches exist to classify patients in different risk
classes, however, relatively little has been done with respect to the
prediction of actual survival times. We introduce CASPAR, a novel
method to predict true survival times for the individual patient based
on microarray measurements. CASPAR is based on a multivariate Cox
regression model that is embedded in a Bayesian framework. A
hierarchical prior distribution on the regression parameters is
specifically designed to deal with high dimensionality (large number
of genes) and low sample size settings, that are typical for
microarray measurements. This enables CASPAR to automatically select
small, most informative subsets of genes for prediction.A Hierarchical Bayesian Approach to predict Survival Times in Cancer from Gene Expression Data
Motivation:
Results:
Validity of the method is demonstrated on two publicly available
datasets on diffuse large B-cell lymphoma (DLBCL) and on
adenocarcinoma of the lung. The method successfully identifies long
and short survivors, with high sensitivity and specificity. We compare
our method to two alternative methods from the literature,
demonstrating superiour results of our approach. In addition, we show
that CASPAR can further refine predictions made using clinical scoring
systems such as the International Prognostic Index (IPI) for DLBCL and
clinical staging for lung cancer, thus providing an additional tool
for the clinician. An analysis of the genes identified confirms
previously published results, and furthermore, new candidate genes
correlated with survival are identified.
Publication:")?> This work has been submitted for publication. Kaderali,L., Zander,T., Faigle,U., Wolf,J., Schultze,J.L. and Schrader,R.: CASPAR: A Hierarchical Bayesian Approach to predict Survival Times in Cancer from Gene Expression Data. Software:")?> The software is available for free from the authors. Publication supplement")?>