Chapter 21

Conclusions and Future Directions William J. Calhoun and Allan R. Brasier

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Introduction Heterogeneity of Manifestations of Asthma

Considerable variation exists in the clinical expression of asthma. As detailed in the corresponding section of this monograph, the heterogeneity of asthma is expressed clinically, by commonly observable characteristics, but these clinical phenotypes overlap and have indistinct borders. By a variety of assessment technologies and approaches (epidemiologic, physiologic, and clinical), this syndromic disease defies clear and unambiguous classification. Collectively, clinical phenotyping has not led to reliable categories of asthma that inform treatment, predict progression of lung function abnormalities, or define risk for exacerbations and death. It is for this reason that—omics approaches have been explored, and by which new stratagems for molecular phenotyping have been developed.

21.1.2

Genetics, Epigenetics, and Gene Expression Profiling

The last half of the twentieth century was marked by exponential growth in the understanding of human genetics and its application to human diseases.

W.J. Calhoun, M.D. (*) Department of Internal Medicine, University of Texas Medical Branch, 4.118 John Sealy Annex, 301 University Blvd, Galveston, TX, USA A.R. Brasier, M.D. University of Texas Medical Branch, 8.128 Medical Research Building, 301 University Blvd, Galveston, TX, USA e-mail: [email protected] A.R. Brasier (ed.), Heterogeneity in Asthma, Advances in Experimental Medicine and Biology 795, DOI 10.1007/978-1-4614-8603-9_21, © Springer Science+Business Media New York 2014

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The advances in technology that have allowed these advances to occur span several orders of magnitude in throughput. Studies of structural genetics have informed understanding of risk factors, severity, and characteristic features of asthma but have not been as helpful in understanding temporal variation in this disease. The advent of gene expression profiling (genomics), and proteomics, in which quantities of gene product are measured and analyzed, offers the potential to understand the occurrence of exacerbations, time-dependent variation in lung function and other manifestations of asthma, and the critically important interaction between host (structural genetics) and the environment (allergens, viruses, etc.). Finally, the emerging field of epigenetics is allowing us to understand how environmental exposures may modify the noncoding structure of DNA in ways that are stable, affect gene transcription, and are heritable within cellular generations. Collectively, these fields offer great promise to understand and explain the heterogeneity of disease expression in asthma.

21.1.3

Proteomics, Metabolomics, and Systems Biology

As discussed in this edition and in the supporting literature, asthma is a heterogeneous disease with a complex phenotype that resists clear and absolute classification. To decipher the pattern of symptoms and derive a molecular description of the disease requires multidisciplinary approaches, with an unbiased focus. That is, at a basic level, a combination of global analyses spanning genomics, proteomics, and metabolomics may lead to a description of the molecular events that lead to the complex phenotypes we collectively define as “asthma.” The integration of the “omics” observations into a coherent “system” would be described as the “systems biology” of asthma and likely lead to astonishing insights into its etiology. This edition illustrates the major sensors and signaling molecules constituting the airway innate immune response (IIR), its coupling to adaptive immunity, and how these molecular events may lead to acute decompensation/exacerbations. Recent advances in quantitative proteomics technology that can be used to monitor the status of the IIR. In patients with severe asthma, the mechanisms for glucocorticoid (GC) resistance have remained enigmatic. Applications of functional proteomics can be used to characterize GC signaling, estimate GC receptor function, and characterization GC resistance through posttranslational modifications and differential abundance measurements. These new exciting methodologies and approaches for measurement of dysregulation of the metabolism, inflammatory subtypes, including the innate immune pathway, and insights into normal and dysfunctional signaling of the GC receptor will be informative in translational research. As these technologies evolve, integrating metabolomics with functional proteomics will reveal novel insights into disease and response to treatments.

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New Analytical Approaches

Analysis of complex datasets involves two philosophically distinct approaches: one, the application of supervised learning to develop a predictive model of disease outcome based on some mathematical combination of measured variables and two, unbiased analysis in which no preconceived hypothesis regarding the relationships among variables is assumed. This is an ordered process where statistical methods are used to rank the variables based on the degree to which they are significantly different between the classes and to remove uninformative or correlated data. An illustration of supervised classification using multivariate cytokines has been informative to show that protein patterns in BAL can be meaningfully combined into predictive models of airway reactivity. The emerging field of visual analytics tightly integrates these methods with powerful interactive visualizations designed to enable comprehension of complex patterns using one of the most developed methods called network analysis. Network analysis enables both a visual and a quantitative analysis of the data using a unified representation and has led to the identification of a previously unrecognized innate endotype in BAL proteins. These kinds of approaches hold great promise for deconvoluting the complexities of biologic systems and their perturbations in disease.

21.1.5

Neural and Behavioral Contributions to Asthma Heterogeneity

The questions of how psychological and cultural forces impact the experience of asthma, and thereby contribute to the heterogeneity of its expression are critically important to a full understanding of this disorder. These chapters complement the broad, multidisciplinary research that currently is employed in investigations that provide insight into asthma and can be considered part of the larger conceptualizing of systems biology, using two different perspectives: how the central nervous system, and its cognitive processes influence the expression of asthma via neurochemical mechanisms, and how cultural and sociologic factors influence the expression of disease. Although the latter seems at first to be unrelated to biological heterogeneity, in fact the true prevalence of asthma in underestimated in various cultures and asthma goes untreated and the burden of the disease is underestimated. Cultural perspectives thus have the potential to integrate relevant, interlinked phenomena of biological, ecological, social, and economic factors that are relevant to a multidisciplinary understanding.

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Challenges and Opportunities in Translational Research

Simply put, translational research seeks to improve human health. To accomplish meaningful health impact of a new drug, devices, or practice modification, a broad range of expertise must be effectively engaged. Scholars who have considered the translational spectrum have broken the process into at least two domains, the first concerned with proving an intervention that modifies a biological process has efficacy in humans, a process known as “bench-to-bedside” translation (T1 phase), and the second proving that intervention applied to real world populations has measurable benefit, informing evidence-based practice and health policy, a process referred to as “bedside-to-curbside” translation (T2-4 phase, Khoury et al. 2007; Woolf 2008). The complexity and regulatory issues surrounding this broad concept of translational research continuum poses major challenges to its efficient conduct (Sung et al. 2003). As a result of increasing emphasis on bringing basic biomedical discoveries into improved human health, the NIH Roadmap initiative created is the Clinical and Translational Sciences Award (CTSA), whose primary goal is to stimulate the speed and effectiveness of translational research (Zerhouni 2007). Currently, 61 CTSAs and the NIH Hatfield Clinical Research Center have been funded across academic health centers in the USA that are providing strategies and promoting institutional transformation to overcome the inherent bottlenecks in the translational research continuum. One important strategy taken by CTSA has been to place increasing emphasis on team science approaches. The justification for team science has begun to be studied by the emerging field of the “Science of Team Science” (SciTS, Borner et al. 2010; Falk-Krzesinski et al. 2010). In science and engineering research, the approach of teams has dramatically accelerated over the last quarter century, making multiuniversity collaborations the fastest growing authorship structure (Jones et al. 2008). Research and intellectual property developed by highly functioning multidisciplinary research teams has greater impact in peer recognition through citations and patent uses than research products from siloed investigators (Wuchty et al. 2007). This transition has been accelerated by the recognition that increasingly specialized scientific fields must develop collaborations to enhance creativity and accelerate the pace of discovery to address major societal health problems (Disis and Slattery 2010). A local implementation taken by the UTMB CTSA is the development of multidisciplinary translational teams (MTTs, Calhoun et al. 2013). We consider an MTT to be a hybrid academic–business construct embracing academic missions of generating new knowledge, educating trainees, and building capacity, yet focused on development of product-like translational goals. These goals would be to develop or apply a device, therapeutic, or intervention to improve human health (Calhoun et al. 2013). Of relevance here, the content of this book has been developed by the severe asthma MTT in collaboration with other leading investigators. The severe asthma

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MTT is composed of physicians, cellular biologists, proteomics, psychiatrists, bioethicists, biomedical informaticians, and involves health profession trainees as a vehicle for learning about team roles and processes. As a result, the physicians leading the severe asthma MTT has published work in molecular classification, endotyping and network analysis, for example, that would not have been produced without the team construct (Bhavnani et al. 2011; Brasier et al. 2008, 2010; Pillai et al. 2012). As an example, translating discovery science in asthma, and using this information to inform classification of distinct molecular phenotypes could be a first step towards an ultimate goal of personalized medicine. In asthma, the expression of “disease” is a common phenotype (wheezing and bronchoconstriction) that may be the result of distinct pathological processes. This translational model is conceptually based on several concepts (1) subtypes of disease are difficult to identify with conventional clinical criteria and (2) application of multidimensional profiling— mRNA, proteins, and metabolites can improve risk assessment and treatment over clinical assessment alone. In this book, we have provided a number of findings that indicate that these translational approaches to medicine are in fact applicable to diagnosis and management of asthma. The application of molecular profiling to inform personalized medicine shifts emphasis of health care to early interventions using the most efficacious therapies. For example, accurate identification of the small subgroup of patients with severe asthma, a subgroup that represents the highest rate of morbidity may lead to selection of optimal dosing and alternative therapies (Moore et al. 2007; Wenzel and Busse 2007). We contend that molecular identification of the severe asthmatic subgroup will reduce trial and error prescription of steroids, reducing side effects and morbidity from trial and error therapy. The application of evidence-based decisions for selection of therapy will mean that drugs are safer—less adverse reactions and reduce cost of health care. From a clinical investigation perspective, molecular profiling will also improve clinical trial design and reduce time and cost for drug approval. Challenges in translational research remain. The pathway through T2 to T4 (1,2) is arduous, expensive, and usually time consuming. Even a compelling and validated finding in T1 may not have the performance characteristics necessary for diagnostic or predictive utility in T2 and T3 populations. Hence, development pathways may stall for reasons unforeseen at the beginning of a promising project. As an approach advances through the translational timeline, increasing numbers of human subjects are generally required, with proportionately increasing fiscal costs. Whether these costs are borne by federal, industrial, or academic resources, the constraints on research resources will have a constraining influence on T2–T4 translation. Finally, the uptake of new approaches and technologies necessary for T4 translation by the broader array of professional organizations, federal advisory boards, guideline committees, and other stakeholders is subject to considerations well beyond the scope of scientific merit alone.

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Promises and Pitfalls of Personalized Medicine

Personalized medicine is an articulated vision in modern medicine. It is a medical model that customizes medical decisions, practices, and/or products being tailored to unique molecular features of a specific patient, derived from T1–T3 translational research. Many academic health care organizations include such concepts in their statement of purpose, or their marketing tag lines, or other institutional documents. The concept of delivering the correct molecule, at the correct dose, with the correct timing in order to maximize therapeutic efficacy and to reduce the occurrence or severity of adverse effects is attractive to physicians, patients, and ultimately to third-party payers as well. This highly customized approach is distinct from traditional practice of medicine where clinical diagnosis and management relies on expression of clinical signs and symptoms, which are imprecise and overlapping. It is true however that personalization of therapy has always been part of medical practice, whether incorporated as clinical judgment, or as empiric use of therapeutic agents for individual patients and monitoring clinical responses, or by considering patient preferences in the selection of medical or surgical therapies. How personalization occurs has been driven by technology, from group response approaches, to clinical phenotyping, to simple single parameter biochemical tests of blood, urine, or other biologic fluids. The concept of personalized medicine, however, gained most traction from the application of genetics to patients with disease. Polymorphisms in genes (both single nucleotide polymorphisms, SNPs, and other more substantive coding variations) are associated with therapeutic responses to warfarin, short-acting beta2 agonists, leukotriene inhibitors, corticosteroids, cancer therapeutics, and several others; other polymorphisms confer risk of developing diseases: asthma, atopy, bronchial hyperresponsiveness, etc. Although in some cases the clinical effect size is moderate, in most of the polymorphisms so far described, the clinical effect size is small, suggesting that other factors must also be important in the clinical expression of disease. As developed in this book, a number of other biochemical (gene expression profiling/genomics, epigenetics, proteomics, and metabolomics) and data analysis approaches also have potential to discriminate mechanistically similar groups of patients, and by so doing, reduce therapeutic variability. Asthma is a disease of temporal variability, marked by times of minimal symptoms and signs, interrupted by periods of exacerbations. This temporal variability makes the conceptual linkage between structural (unchanging) genetics and temporal variability in asthma difficult. In contrast, genomics, epigenetics, proteomics, and metabolomics involve the study of regulated expression of genes (including splice variants), their protein expression, and the consequences of protein expression and activity on biologic molecules. Accordingly, these platforms may provide better understanding of the temporal variability in the expression of clinical asthma. Finally, because of the relatively small effect size for most genetic variations (a limitation that may also apply to other -omic technologies), it is likely that combinations of analytes will be more predictive as a panel than any one of the markers in isolation. Advanced

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analytic techniques, as describe herein, have the potential to advance the field significantly. Personalization has potential application in many areas related to asthma (1) in the clinic to predict therapeutic response, adverse events, and the course of disease; (2) in clinical research, stratifying subjects by predictive factors that reduce variability of response can improve the signal-to-noise ratio of a study, leading to a smaller, or more cost-effective study, or a more definitive result; and (3) in the basic or translational research enterprise, phenotyping strategies that isolate specific pathways of disease allow for definitive understanding of new mechanisms of disease pathogenesis. This field has been recently reviewed (Bhakta and Woodruff 2011). Personalization of therapy does confer some downside as well. From a drug development standpoint, each aspect of personalization of asthma therapy will likely result in a smaller potential market; for example, if a particular approach identifies a mechanistically homogeneous group of patients that is 10 % of those patients with asthma, then therapeutic approaches developed for that group will be potentially marketable only to those 10 %. This consideration makes personalization in the drug-development arena a two-edged sword: more effectiveness or less toxicity, in a smaller market. In addition, predictive biomarker development must precede Phase II and III drug development, which may impact on the usable patent life. Ultimately, personalized medicine may increase the costs of bringing medications to patients. There are conceptual concerns with the concept of personalized medicine as well. It is possible that the number of different measurements required for a new biomarker to be determinative is so high as to be impractical or prohibitively expensive. Other unmeasured factors, such as diet, supplements, socioeconomic status, living conditions, and a myriad of other disease modifiers, could individually or collectively have a greater effect on the expression of asthma than the determinants in a predictive biomarker. Finally, particularly in asthma, chance encounters with viruses and other infectious causes of exacerbation are largely unpredictable, and encounters with allergens, although predictable may well be unavoidable. These known triggers of asthma exacerbations could reduce the utility of predictive phenotyping. For personalized medicine to become a reality, a number of obstacles must be overcome (Hamburg and Collins 2010). The success of personalized medicine will rely on using accurate diagnostic tests in the clinic, which may be laboratory-based, multivariate tests whose interpretation relies on complex machine-learning algorithms. For this to occur, development of coherent regulatory policy and a pathway for personalized medical tests approval will need to be done by regulatory agencies including the Federal Drug Administration (FDA)’s Critical Path Initiative (Hamburg and Collins 2010). Practicing physicians will need to adopt and use clinical information support systems. The adoption of electronic medical record provided by the health information technology act will be an important driver for acceptance of information systems in clinical practice. Another obstacle is payer resistance for tests that do not show cost–benefit, such as CMS medicare rules that do not reimburse for screening tests in absence of signs or symptoms. This policy limits the

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application of personalized medicine, because the molecular profiles used as diagnostics extend clinical assessment and are not specifically indicated by them. Another challenge is that big pharmaceutical industries are reluctant to develop drugs for small patient populations, which reduces their potential market. Finally, the development of predictive biomarkers for personalized medicine in asthma is in its infancy. Biomarker development requires a large capital investment, and many years, to navigate the processes of identification, validation, measurement of performance characteristics in relevant populations, and ultimately incorporation of such tests into clinical care. Predictive phenotyping as a basis for implementing personalized medicine has great potential, employing novel biochemical and statistical analyses for the purpose of improving care of patients with the complex syndromic disease called asthma. Despite these and other significant challenges, the promise of personalized medicine is powerful and compelling.

21.4

Future Directions in Molecular Phenotyping in Asthma

A key obstacle in this field is a lack of “gold standard” phenotypes against which a novel biomarker, genetic profile, or molecular phenotype can be assessed. The overlap in phenotypes within the US SARP experience (Moore et al. 2007) is not a manifestation of lack of rigor in the evaluation of these patients, but rather a reflection of the considerable variation in clinical presentation of asthma. A novel conceptual framework to bring clarity to this field would be to establish an empiric molecular phenotype as the “gold standard” and interpret clinical subgroups of asthma (mild, moderate, and severe persistent disease) in that context (Bhavnani et al. 2011). The experience of Woodruff et al. is instructive in that regard, as a cytokine expression pattern reliably predicted responsiveness to corticosteroids (Woodruff et al. 2009). Although none currently exist, a predictive biomarker for the important clinical outcomes of exacerbations, and accelerated decline in lung function would have important implications for drug development, improved management strategies, and prognosis. Both of these outcomes are best assessed over the long term, which adds both complexity and expense to their development. Proteins, which mediate the interaction between a host (patient) and his or her environment, are attractive candidates for molecular phenotyping, because their level and activity are both important considerations that can be measured. This situation contrasts with structural genetics, which are largely invariant over life; hence, a compelling case for genetics as a proximate cause of the temporal variation of expression of asthma is difficult to make. Accordingly, measurement of factors, which vary in relationship to the clinical expression of disease, including gene expression profiling, epigenetics, and proteomics, appears to hold the most promise for developing truly predictive biomarkers that will usher in the era of personalized medicine.

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Conclusions and future directions.

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