A current major challenge in the management of asthma is to accurately identify subtypes that differ in disease pathogenesis and response to therapy. Severe asthma is differentiated from mild-moderate disease by age of onset, duration of disease, degree of airflow impairment, cellular inflammation, presence of sinusitis and history pneumonia (2
). However, these clinical features as well as noninvasive measurements of airway inflammation (sputum eosinophils and exhaled NO) have not resulted in a method for unambiguous separation of clinical phenotype, response to therapy or disease course (2
). This experience indicates that a single discriminator that accurately separates different asthma types is not yet available. Recognizing that multiple discriminators may be necessary, we have conducted this study for two purposes. First, we sought to determine whether we could use informatics analysis to subgroup subjects based on patterns of BAL cytokine expression. This approach has the potential to identify distinct groups of asthmatics based on commonalities in inflammatory disease mechanisms. Second, we sought to determine whether clinically objective phenotypes could be predicted based on patterns of cytokine expression. Similar approaches have used mRNA expression patterns to separate different groups of responsive and unresponsive cancers. Using groupings based on mRNA expression profiles, patients can be identified using that are otherwise indistinguishable using conventional clinical staging criteria (16
). However, whether this method for molecular phenotyping is generally applicable to other diseases, including asthma, has not been addressed. Although differences in mRNA expression patterns have been identified in asthmatics, not all mRNAs are translated. Therefore, our focus was on proteins (cytokines), which represent proximal mediators of the inflammatory component of asthma.
Our unsupervised hierarchical clustering analysis indicates that at least four phenotypically distinct subgroups of asthma can be identified. Recognizing that a potential confounding variable could be due to different treatment regimens, we have sought to determine whether these groupings are significantly affected as a result of chronic glucocorticoid use. Reassuringly, similar groupings are identified in subjects not taking glucocorticoids (). We were particularly interested in the phenotype of G1, a group enriched in patients that meet the ATS consensus definition for severe asthma. This group is characterized by increased BMI, reduced FEV1, reduced FVC, and enhanced sensitivity to methacholine. Importantly our findings indicate that no single cytokine value can be used to separate patients into these groups, but rather, an expression pattern consisting of a minimum of 10 distinct cytokines must be considered.
The biological roles of the chemokines that influence the G1 classification deserve some comment. Relative to the other groups, BAL samples from the subjects in the G1 group are characterized by reduced levels of IL-1 Receptor antagonist (IL-1Ra), MIP-1α, MIG (and others, see ) which are known to play various important roles in coordinating cellular trafficking and inflammation in the airways [see Ref (22
)]. Reduction of IL-1Ra may be important because this protein competes for IL-1α/β receptor binding blocking IL-1α/β mediated inflammation. In subjects in G1, a reduction in IL-1Ra may result in enhanced IL-1 signaling, a signaling event that could result in neutrophilic inflammation, features characteristic of severe asthma (9
). Similarly MIP-1α (CCL3) is a CC chemokine expressed by macrophages and airway epithelial cells to induce chemotaxis of CD8 T-lymphocytes and eosinophils and whose expression is enhanced in the asthmatic lung (23
). Again, here, reduction in MIP-1α/CCL3 may result in neutrophil-predominant inflammation. Although MIP-1 is acutely inhibited by glucocorticoids, the level of MIP-1 in BAL from patients on glucocorticoid therapy is not statistically different from those not taking glucocorticoids (Table I, Supplemental Data
). The IFN-γ inducible MIG(CXCL9) significantly reduces airway hyperresponsiveness and eosinophil accumulation in animal models of allergen challenge (24
). MIG diminishes IL-4 and enhances IL-12 levels, directing activated T cells toward a TH
1 phenotype. The reduction in these cytokines in the G1 group relative to the less severe asthmatics therefore, are biologically plausible with our understanding of the pathogenesis of severe asthma.
Although most of the molecular classifiers that have been produced have been based mRNA expression patterns, we suggest that protein expression profiles may be more useful markers of disease than gene expression patterns. This may be particularly true when the proteins themselves play important roles in the underlying disease process, such as cytokines in asthma, and therefore represent bone fide biomarkers.
Previous work examining expression of selected cytokines have associations with asthma severity, however these associations are not strong and have not been widely replicated. For example, IL-8 was shown to be enhanced in severe asthmatics during an acute exacerbation and correlates with the number of neutrophils (25
). Our study was conducted on stable asthmatics, and the processes involved in chronic inflammatory state may be different from those producing acute exacerbations. Other studies have shown increased IL-2 and –4 levels in severe asthmatics(26
). Although we were unable to demonstrate a difference in BAL concentrations for IL-2 or -4 between severe and nonsevere asthmatics in our data set (Supplemental Data Fig. 4
), these cytokines do contribute to a model predicting methacholine HR.
Our attempt to classify methacholine HR has yielded two important findings. First, methacholine HR can be accurately separated from LR based on cytokine profiles in BAL, and second, that the group of methacholine HR consists of at least three phenotypically distinct classes. HR Class A is a group characterized by low levels of IL-2, -4 and -5 and, as a group, contains those subjects with the lowest PC20 methacholine responses in our study. It will be interesting to extend this analysis to larger independent data sets to determine whether the subjects in the HR Classes have distinct clinical outcomes.
The feature reduction analysis has shown that at least 5 cytokines can be used to separate HR from LR in our dataset. It is also important to note that there are important differences in the groups of cytokines that identify G1 with those that identify HR. Specifically, IL-5, secreted into the BAL in response to allergen challenge, and a molecule important in eosinophil recruitment and survival, was identified as an important classification variable in HR, but not for G1. This observation may explain why single cytokine values alone may not be useful discriminators in asthma. Further exploration of these data are underway.
Our studies provide a first proof of concept that informative patterns of cytokines can be detected and interpreted in BAL from patients with asthma and may contribute to more objective classification of disease type. We interpret these findings to suggest that subjects with apparently similar clinical characteristics are in fact, composed of heterogeneous subtypes that can be further distinguished based on BAL cytokine profiles. However, before these findings can be applied to patient classification or management, a number of critical questions remain: what are the reproducibility of serial measurement of BAL cytokines? How stable are the individual phenotypes? Do these distinct subtypes will differ in clinical outcome or response to therapeutic interventions? Importantly, these findings and models will need to be tested on an independent study population. For these reasons, the findings of this study are not ready for translation into the clinic. Nevertheless, our findings indicate that important new diagnostic and prognostic information is available in airway fluids and indicate that future research in biomarker identification will be informative.
Definition of asthmatic phenotypes will aid in clinical investigation on the etiology and intervention for asthma.
This study is the first to identify distinct phenotypes of asthma based on BAL cytokine patterns; when validated it will advance clinical investigation by accurately identifying different subtypes of asthma.