title: Transcription-based prediction of response to IFNbeta using supervised computational methods.
abstract: Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s). Recombinant human interferon beta (rIFNbeta) is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNbeta to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNbeta engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects.
authors.affiliation: Department of Neurology, School of Medicine University of California, San Francisco, USA. sebaran@cgl.ucsf.edu <sebaran@cgl.ucsf.edu>
authors.forename: Sergio E
authors.initials: SE
authors.lastname: Baranzini
authors.forename: Parvin
authors.initials: P
authors.lastname: Mousavi
authors.forename: Jordi
authors.initials: J
authors.lastname: Rio
authors.forename: Stacy J
authors.initials: SJ
authors.lastname: Caillier
authors.forename: Althea
authors.initials: A
authors.lastname: Stillman
authors.forename: Pablo
authors.initials: P
authors.lastname: Villoslada
authors.forename: Matthew M
authors.initials: MM
authors.lastname: Wyatt
authors.forename: Manuel
authors.initials: M
authors.lastname: Comabella
authors.forename: Larry D
authors.initials: LD
authors.lastname: Greller
authors.forename: Roland
authors.initials: R
authors.lastname: Somogyi
authors.forename: Xavier
authors.initials: X
authors.lastname: Montalban
authors.forename: Jorge R
authors.initials: JR
authors.lastname: Oksenberg
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D005260:Female; D005786:Gene Expression Regulation; D006801:Humans; D016899:Interferon-beta / Q000378:metabolism / Q000627:therapeutic use*; D007700:Kinetics; D007963:Leukocytes, Mononuclear / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D011994:Recombinant Proteins / Q000627:therapeutic use; D020133:Reverse Transcriptase Polymerase Chain Reaction; D012984:Software; D013997:Time Factors; D014158:Transcription, Genetic
chemical_list: D011994:Recombinant Proteins; D016899:Interferon-beta