Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients.
BACKGROUND
One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures.


RESULTS
We propose a new method called Reweighted Recursive Feature Elimination. It is based on the hypothesis that a gene with a low fold-change should have an increased influence on the classifier if it is connected to differentially expressed genes. We used a modified version of Google's PageRank algorithm to alter the ranking criterion of the SVM-RFE algorithm. Evaluations of our method on an integrated breast cancer dataset comprising 788 samples showed an improvement of the area under the receiver operator characteristic curve as well as in the reproducibility and interpretability of selected genes.


BACKGROUND
The R code of the proposed algorithm is given in Supplementary Material.
Johannes
Marc
M
German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 280, 69120 Heidelberg. m.johannes@DKFZ-heidelberg.de
Brase
Jan C
JC
Fröhlich
Holger
H
Gade
Stephan
S
Gehrmann
Mathias
M
Fälth
Maria
M
Sültmann
Holger
H
Beissbarth
Tim
T
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D011379:Prognosis; D012372:ROC Curve; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000235:genetics; D012307:Risk Factors
D018719:Erb-b2 Receptor Tyrosine Kinases; C508053:ERBB2 protein, human