Current views of human disease are based on simple correlation between clinical syndromes and pathological analysis dating from the late 19th century. Although, this approach to disease diagnosis, prognosis, and treatment has served the medical establishment and society well for many years, it has serious shortcomings for the modern era of the genomic medicine that stem from its reliance on reductionist principles of experimentation and analysis. Quantitative, holistic systems biology applied to human disease offers a unique approach for diagnosing established disease, defining disease predilection, and developing personalized treatment strategies that can take full advantage of modern molecular pathobiology and the comprehensive data sets that are rapidly becoming available for populations and individuals. In this way, systems pathobiology offers the promise of redefining our approach to disease and the field of medicine.
The translation of new knowledge about mechanisms that govern human pathobiology into effective preventive, diagnostic, and therapeutic strategies is a slow and cumbersome process. A major contributor to this translational delay is the use of the traditional characterization and definition of human disease, which dates to the 19th century and is largely based on Oslerian clinicopathological correlation. The Oslerian formalism for human disease links clinical presentation with pathological findings. As a result, disease is defined on the basis of the principal organ system in which symptoms and signs are manifest, and in which gross anatomic pathology and histopathology are correlated. This approach has held sway for over a century, and although there has been continual refinement of the pathological markers used for correlation, for example, biochemical measurements, immunohistochemistry, flow cytometry, and, more recently, molecular pathological analyses of expressed genes, the general principles remain the same as when the approach was first proposed. Current classification of disease pathophenotype is, then, the result of inductive generalization from clinicopathological evidence predicated on the law of reductive parsimony. This paradigm has been helpful to clinicians as it establishes syndromic patterns that limit the number of potential pathophenotypes they may need to consider. Although quite useful in an earlier era, classifying disease in this way vastly over generalizes pathophenotypes, does not usually take into consideration susceptibility states or preclinical disease manifestations, and cannot be used to individualize disease diagnosis or therapy.
Based on this history, it is hardly surprising that these conventional pathophenotypes are far too limited to be useful in the postgenomic era. A simple example illustrates this shortcoming. The classic Mendelian disorder, sickle cell disease, is caused by a single point mutation at position 6 of the β-chain of hemoglobin, which changes hemoglobin’s oxygen affinity and promotes polymerization under hypoxic conditions. Notwithstanding Mendelian predictions to the contrary, this simple biochemical phenotype and its corresponding monogenotype do not yield a single pathophenotype. Individuals with sickle cell disease can present with painful crisis, osteonecrosis, acute chest syndrome, stroke, profound anemia, or mild anemia. There are many reasons for these different clinical pathophenotypes, ranging from the presence of disease modifying genes, for example, hemoglobin F to environmental influences; for example, hypoxia. Clearly, even the simplest genetically determined disease is manifestly complex in its expression, a fundamental observation that emphasizes the importance of the genomic and environmental contexts within which disease evolves.
Although conventional reductionist pathophenotyping has guided steady progress in diagnostics and therapeutics for many years, it is fraught with shortcomings, some of which are highlighted by this example, that are particularly problematic for contemporary molecular and genomic analyses. Put another way, in using this sorely outdated approach to defining human disease, one can construct nosological silos that focus exclusively on end-stage pathological processes in a single organ largely driven by late-appearing, generic end-stage mechanisms rather than true disease-specific susceptibility determinants viewed in their holistic, systems-based complexity.
With this background, one can rationally catalog the limitations of traditional disease definition as disease is typically defined by late-appearing manifestations in a dysfunctional organ system, without regard for or knowledge of preclinical pathophenotype or susceptibility factors that precede overt abnormalities. Thus, the focus is not on the specific genetic or environmental susceptibility determinants of the disease phenotype, but, rather, on the late-appearing, intermediate pathophenotypes like generic endopathophenotypes, including inflammation, immunity, fibrosis, thrombosis, hemorrhage, cell proliferation, apoptosis, and necrosis within a given organ system. As a result, typical therapeutic strategies do not focus on truly unique, targeted disease determinants, but on these same intermediate pathophenotypes, for example, anti-inflammatory or antithrombotic therapies for acute myocardial infarction.
Conventional disease paradigms generally neglect underlying pathobiological mechanisms that may extend beyond the disease-defining organ system, and do not typically consider the molecular (deterministic) and environmental (stochastic) factors that govern disease evolution from susceptibility state to preclinical pathophenotype to overt pathophenotype.
Conventional definitions of disease are excessively inclusive of the range of pathophenotypes and are based on the pathophysiological characterizations largely of the premolecular era. These inclusive definitions of disease not only obscure subtle, but potentially important, differences among individuals with common clinical presentations, but also neglect underlying disease mechanisms that cross organ systems and may yield more appropriate and specific therapeutic targets.
Yet another dimension to this problem stems from the reductionist approach we use to identify disease mechanisms or therapeutic targets. Disease is rarely, if ever, a simple consequence of
an abnormality in a single effector gene product, but, rather, is a reflection of pathobiological processes (deterministic and stochastic) that interact in a complex network to yield pathophenotype, which may be viewed as an emergent property, that is to say, discernible only by appreciating the behavior of the network as a whole rather than of its component parts in reductionist isolation of a pathobiological system.
These shortcomings of conventional disease definition account for many limitations of major recent genomebased efforts to define disease determinants, for example, the weak effect size of linked alleles observed in genomewide association studies of complex disease and to design rational therapies, for example, the failure of >90% of drug candidates. Thus, solving this problem is not simply an exercise in nosology, but is essential for moving the entire health care enterprise forward to reduce the burden of human disease and suffering.
This highlights the clear need to reconsider and redefine the determinants of human disease. All disease is complex, even simple Mendelian disorders. Pathophenotype reflects the action of a deterministic, defective molecular network within a stochastic environmental context that modulates network function. Defined in this way, disease is the result of the output of a complex modular network of –omic and environmental nodes linked mechanistically to yield pathophenotype. With this background and rationale, we can redefine all human disease using a combination of approaches to identify systems-based pathobiological mechanisms that render one susceptible to preclinical and overt pathophenotypes. This approach challenges the existing disease paradigm directly, and is justifiable owing to the largely heuristic strategies that have been used to identify disease mechanisms and treatments to date.
