Disease heterogeneity presents a formidable challenge for clinical medicine. We are unlikely to develop successful, targeted treatments unless diagnostic schemes begin to reflect the biologic complexity underlying superficially similar disease phenotypes. To address heterogeneity, there is an increasing recognition that realistic disease models need to be built upon a foundation of quantitative molecular information1. Such a “systems medicine” approach should ultimately allow patient classification into biologically more homogeneous groups, with similar prognosis and treatment response.
Oncology has been among the first specialty to embrace molecular profiling to improve clinical decision-making. Today, a wealth of expression profiling information exists for tumors and early efforts are being made to classify patients into likely treatment responders for specific chemotherapeutics2. The investigation of complex multisystem pathologies such as cardiovascular disease has been slower to incorporate molecular profiling, in part because of the involvement of multiple tissues, many of which are not readily accessible. Moreover, cardiovascular disease, unlike cancer, is not clonal in origin, making experimental analyses more challenging. It is clear, however, that broadly-defined diseases such as the cardiomyopathies are the product of diverse genetic and environmental agents3; one can expect that quantitative molecular profiling will shed light on distinct, pathologic activities underlying these conditions, thus allowing more meaningful classification schemes beyond those based solely on anatomic or hemodynamic considerations.
The manuscript by Barth et al in the current issue of Circulation Cardiovascular Genetics represents such a broadened search for molecular correlates of cardiovascular disease. The authors focus on the problem of heart failure with mechanical dyssynchrony (DHF) and its treatment by cardiac resynchronizaton therapy (CRT). Clinically, CRT has been a remarkable success, with significant gains in quality of life and mortality4. Multiple studies have investigated the physiologic consequences of dyssynchrony and the improvements resulting from CRT; however, the underlying molecular processes largely remain unclear. The current study is unique in its use of DNA microarrays to ask if heterogeneity resulting from DHF and the physiologic benefits from CRT are broadly reflected at a molecular level.
DNA microarrays allow scientists to simultaneously survey the expression level of thousands of mRNAs under a variety of experimental conditions. A key strength of microarray technology is that it is inherently free of inspection or ascertainment biases–a “transcriptome-wide” approach does not require preconceived notions of which biological processes are important. Unbiased approaches, including genome-wide association and metabolomics studies, have the potential to lead us to entirely unexpected disease mechanisms. However, large scale (‘omic) data presents analysis challenges of its own – requiring an understanding of measurement error, detection limits, and problems that arise when a plethora (sometimes thousands) of hypotheses are tested. Any of these issues can lead to erroneous conclusions and compromise the generalizability of the results. Fortunately, bioinformatics research has focused on these issues for more than a decade, and many solutions have become available.
Armed with ‘omic data sets and bioinformatics tools, Barth et al tackle the hypothesis that CRT reduces the regional heterogeneity in gene expression induced by DHF. The experimental design (Figure 1) features three groups:
- DHF: heart failure with dyssynchrony, produced by experimental left bundle branch block (LBBB) followed by rapid, sustained right atrial pacing
- CRT: cardiac resynchronization therapy, produced by synchronized bi-ventricular (bi-V) pacing of the DHF model (at the same rate as atrial pacing in DHF)
- NF: normal controls
Tissue samples from the anterior and lateral segments of the left ventricle of each dog were subjected to microarray profiling, and comparisons made within and across groups. Bioinformatics methods were used to analyze the microarray data including pathway enrichment analysis and hierarchical clustering. Each of these will be discussed below.