Transcriptome classification reveals molecular subtypes in psoriasis
- Equal contributors
1 Centre for Bioinformatics, Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, Strand, London, WC2R 2LS, UK
2 St John’s Institute of Dermatology, Division of Genetics and Molecular Medicine, King’s College London, Tower Wing, Guy’s Hospital, Great Maze Pond, London, SE1 9RT, UK
3 Centre for Systems, Dynamics and Control, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter, EX4 4QF, UK
4 Department of Dermatology, School of Medicine, University of Michigan, Box 0932, Ann Arbor, MI 48109-0932, USA
5 Present address: Computational Genomics Unit, Institute of Agrobiotechnology, Centre for Research & Technology Hellas, Thessaloniki, Greece
6 Present address: Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, 160 College Street, Toronto, ON, M5S 3E1, Canada
Citation and License
BMC Genomics 2012, 13:472 doi:10.1186/1471-2164-13-472Published: 12 September 2012
Psoriasis is an immune-mediated disease characterised by chronically elevated pro-inflammatory cytokine levels, leading to aberrant keratinocyte proliferation and differentiation. Although certain clinical phenotypes, such as plaque psoriasis, are well defined, it is currently unclear whether there are molecular subtypes that might impact on prognosis or treatment outcomes.
We present a pipeline for patient stratification through a comprehensive analysis of gene expression in paired lesional and non-lesional psoriatic tissue samples, compared with controls, to establish differences in RNA expression patterns across all tissue types. Ensembles of decision tree predictors were employed to cluster psoriatic samples on the basis of gene expression patterns and reveal gene expression signatures that best discriminate molecular disease subtypes. This multi-stage procedure was applied to several published psoriasis studies and a comparison of gene expression patterns across datasets was performed.
Overall, classification of psoriasis gene expression patterns revealed distinct molecular sub-groups within the clinical phenotype of plaque psoriasis. Enrichment for TGFb and ErbB signaling pathways, noted in one of the two psoriasis subgroups, suggested that this group may be more amenable to therapies targeting these pathways. Our study highlights the potential biological relevance of using ensemble decision tree predictors to determine molecular disease subtypes, in what may initially appear to be a homogenous clinical group. The R code used in this paper is available upon request.