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

• Published in: BMC dermatology Vol. 10; no. 1; p. 1
• Main Authors: Suárez-Fariñas, Mayte, Shah, Kejal R, Haider, Asifa S, Krueger, James G, Lowes, Michelle A
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 12-02-2010; BioMed Central
• Subjects: Adult; Aged; Alefacept; Dermatologic Agents - therapeutic use; Drug Resistance - genetics; Drug therapy; Female; Gene expression; Genome, Human; Genomics; Health aspects; Humans; Male; Middle Aged; Oligonucleotide Array Sequence Analysis; Precision Medicine - methods; Predictive Value of Tests; Psoriasis; Psoriasis - drug therapy; Psoriasis - genetics; Psoriasis - pathology; Recombinant Fusion Proteins - therapeutic use; Research article; Severity of Illness Index; United States
• ISSN: 1471-5945; 1471-5945
• DOI: 10.1186/1471-5945-10-1

Alefacept 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.
Microarray 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.
A 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).
Although 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.