Class prediction models of thrombocytosis using genetic biomarkers

Abstract

AbstractCriteria for distinguishing among etiologies of thrombocytosis are limited in their capacity to delineate clonal (essential thrombocythemia [ET]) from nonclonal (reactive thrombocytosis [RT]) etiologies. We studied platelet transcript profiles of 126 subjects (48 controls, 38 RT, 40 ET [24 contained the JAK2V617F mutation]) to identify transcript subsets that segregated phenotypes. Cross-platform consistency was validated using quantitative real-time polymerase chain reaction (RT-PCR). Class prediction algorithms were developed to assign phenotypic class between the thrombocytosis cohorts, and by JAK2 genotype. Sex differences were rare in normal and ET cohorts (< 1% of genes) but were male-skewed for approximately 3% of RT genes. An 11-biomarker gene subset using the microarray data discriminated among the 3 cohorts with 86.3% accuracy, with 93.6% accuracy in 2-way class prediction (ET vs RT). Subsequent quantitative RT-PCR analysis established that these biomarkers were 87.1% accurate in prospective classification of a new cohort. A 4-biomarker gene subset predicted JAK2 wild-type ET in more than 85% patient samples using either microarray or RT-PCR profiling, with lower predictive capacity in JAK2V617F mutant ET patients. These results establish that distinct genetic biomarker subsets can predict thrombocytosis class using routine phlebotomy.