Master regulators used as breast cancer metastasis classifier.
Computational identification of prognostic biomarkers capable of withstanding follow-up validation efforts is still an open challenge in cancer research. For instance, several gene expression profiles analysis methods have been developed to identify gene signatures that can classify cancer sub-phenotypes associated with poor prognosis. However, signatures originating from independent studies show only minimal overlap and perform poorly when classifying datasets other than the ones they were generated from. In this paper, we propose a computational systems biology approach that can infer robust prognostic markers by identifying upstream Master Regulators, causally related to the presentation of the phenotype of interest. Such a strategy effectively extends and complements other existing methods and may help further elucidate the molecular mechanisms of the observed pathophysiological phenotype. Results show that inferred regulators substantially outperform canonical gene signatures both on the original dataset and across distinct datasets.
Lim
Wei Keat
WK
Center for Computational Biology and Bioinformatics, Department of Biomedical Informatics, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA.
Lyashenko
Eugenia
E
Califano
Andrea
A
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000145:classification / Q000235:genetics; D001699:Biometry; D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics* / Q000556:secondary*; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D005809:Genes, Regulator; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D010641:Phenotype; D011379:Prognosis; D050436:Regulatory Elements, Transcriptional; D049490:Systems Biology
D014408:Biomarkers, Tumor