An Ensemble of Classifiers for Time Course Classification of Response to Treatment in Psoriatic Patients

• Activity Number: 437
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Joel Correa da Rosa* and Sandra Garcet and Mayte Suarez Farinas
• Companies: Rockefeller University and Rockefeller University and Rockefeller University
• Keywords: Time Course Expression ; Microarray ; GSVA ; Classifier

Abstract

Psoriasis is caused by the complex interaction of genetic, environmental and immunological factors. In the last 10 years there has been an explosion of drug testing in Psoriasis with more than 35 new drugs in different phases of drug development. In this study the main goal is to build a genomic classifier to select, based on earlier time points genes and pathways that can predict in psoriatic patients response-to-treatment at week 12 or later. To develop the classifier, we have transformed longitudinal skin expression profiles from patients treated with 7 different drugs into time course scores using LDA-projections. By using Gene Set Variation Analysis (GSVA) we evaluated activity for some known psoriasis pathways. A ensemble of classifiers was built based on 5 methods: PLS, LDA, GLMnet, TGDR and PAM. Results from 500 bootstrapped samples pointed out that accuracy is increasingly gained as time points are incorporated in the time-course score. Classification accuracy at baseline was not better than random for most of the cases. More than 90% of accuracy in predicting response to treatment is achieved at 1st week for the specific-treatment classifier.