Transcriptomic Biomarkers for Individual Risk Assessment in New-Onset Heart Failure

Abstract

Prediction of prognosis remains a major unmet need in new-onset heart failure (HF). Although several clinical tests are in use, none accurately distinguish between patients with poor versus excellent survival. We hypothesized that a transcriptomic signature, generated from a single endomyocardial biopsy, could serve as a novel prognostic biomarker in HF. Endomyocardial biopsy samples and clinical data were collected from all patients presenting with new-onset HF from 1997 to 2006. Among a total of 350 endomyocardial biopsy samples, 180 were identified as idiopathic dilated cardiomyopathy. Patients with phenotypic extremes in survival were selected: good prognosis (event-free survival for at least 5 years; n=25) and poor prognosis (events [death, requirement for left ventricular assist device, or cardiac transplant] within the first 2 years of presentation with HF symptoms; n=18). We used human U133 Plus 2.0 microarrays (Affymetrix) and analyzed the data with significance analysis of microarrays and prediction analysis of microarrays. We identified 46 overexpressed genes in patients with good versus poor prognosis, of which 45 genes were selected by prediction analysis of microarrays for prediction of prognosis in a train set (n=29) with subsequent validation in test sets (n=14 each). The biomarker performed with 74% sensitivity (95% CI 69% to 79%) and 90% specificity (95% CI 87% to 93%) after 50 random partitions. These findings suggest the potential of transcriptomic biomarkers to predict prognosis in patients with new-onset HF from a single endomyocardial biopsy sample. In addition, our findings offer potential novel therapeutic targets for HF and cardiomyopathy.