Personalized medicine in psoriasis: developing a genomic classifier to predict histological response to Alefacept

BackgroundAlefacept treatment is highly effective in a select group patients with moderate-to-severe psoriasis, and is an ideal candidate to develop systems to predict who will respond to therapy. A clinical trial of 22 patients with moderate to severe psoriasis treated with alefacept was conducted in 2002-2003, as a mechanism of action study. Patients were classified as responders or non-responders to alefacept based on histological criteria. Results of the original mechanism of action study have been published. Peripheral blood was collected at the start of this clinical trial, and a prior analysis demonstrated that gene expression in PBMCs differed between responders and non-responders, however, the analysis performed could not be used to predict response.MethodsMicroarray data from PBMCs of 16 of these patients was analyzed to generate a treatment response classifier. We used a discriminant analysis method that performs sample classification from gene expression data, via "nearest shrunken centroid method". Centroids are the average gene expression for each gene in each class divided by the within-class standard deviation for that gene.ResultsA disease response classifier using 23 genes was created to accurately predict response to alefacept (12.3% error rate). While the genes in this classifier should be considered as a group, some of the individual genes are of great interest, for example, cAMP response element modulator (CREM), v-MAF avian musculoaponeurotic fibrosarcoma oncogene family (MAFF), chloride intracellular channel protein 1 (CLIC1, also called NCC27), NLR family, pyrin domain-containing 1 (NLRP1), and CCL5 (chemokine, cc motif, ligand 5, also called regulated upon activation, normally T expressed, and presumably secreted/RANTES).ConclusionsAlthough this study is small, and based on analysis of existing microarray data, we demonstrate that a treatment response classifier for alefacept can be created using gene expression of PBMCs in psoriasis. This preliminary study may provide a useful tool to predict response of psoriatic patients to alefacept.

[1]  Franck Molina,et al.  Gene expression signature in advanced colorectal cancer patients select drugs and response for the use of leucovorin, fluorouracil, and irinotecan. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[2]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[3]  James G Krueger,et al.  Novel Insight into the Agonistic Mechanism of Alefacept In Vivo: Differentially Expressed Genes May Serve as Biomarkers of Response in Psoriasis Patients1 , 2007, The Journal of Immunology.

[4]  Shinzaburo Noguchi,et al.  Prediction of docetaxel response in human breast cancer by gene expression profiling. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  G. Tsokos,et al.  Molecular Basis of Deficient IL-2 Production in T Cells from Patients with Systemic Lupus Erythematosus1 , 2001, The Journal of Immunology.

[6]  Z. Ye,et al.  NLR, the nucleotide-binding domain leucine-rich repeat containing gene family. , 2008, Current opinion in immunology.

[7]  Mayte Suárez-Fariñas,et al.  Harshlight: a "corrective make-up" program for microarray chips , 2005, BMC Bioinformatics.

[8]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[9]  Maurice P H M Jansen,et al.  Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  J. Franklin,et al.  The elements of statistical learning: data mining, inference and prediction , 2005 .

[11]  Klaus Nordhausen,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .

[12]  Reinhard Guthke,et al.  Molecular discrimination of responders and nonresponders to anti-TNFalpha therapy in rheumatoid arthritis by etanercept , 2008, Arthritis research & therapy.

[13]  S. Bixler,et al.  Specific interaction of lymphocyte function-associated antigen 3 with CD2 can inhibit T cell responses , 1993, The Journal of experimental medicine.

[14]  K. Wittkowski,et al.  Alefacept (anti-CD2) causes a selective reduction in circulating effector memory T cells (Tem) and relative preservation of central memory T cells (Tcm) in psoriasis , 2007, Journal of Translational Medicine.

[15]  C. Molina,et al.  Inducibility and negative autoregulation of CREM: An alternative promoter directs the expression of ICER, an early response repressor , 1993, Cell.

[16]  D. Kioussis,et al.  Mechanism of lymphocyte function-associated molecule 3-Ig fusion proteins inhibition of T cell responses. Structure/function analysis in vitro and in human CD2 transgenic mice. , 1994, Journal of immunology.

[17]  T. Ørntoft,et al.  Gene expression in colorectal cancer. , 2002, Cancer research.

[18]  K. Wittkowski,et al.  Alefacept reduces infiltrating T cells, activated dendritic cells, and inflammatory genes in psoriasis vulgaris. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[19]  J. Inazawa,et al.  Structural organization of the human oxytocin receptor gene. , 1994, The Journal of biological chemistry.

[20]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[21]  G. Krueger,et al.  Development and use of alefacept to treat psoriasis. , 2003, Journal of the American Academy of Dermatology.

[22]  Zhiguo Liu,et al.  Prediction of doxorubicin sensitivity in gastric cancers based on a set of novel markers. , 2008, Oncology reports.

[23]  H. Nomiyama,et al.  Chemokines in immunity. , 2001, Advances in immunology.