THE KNOWLEDGE NETWORK OF DISEASE WOULD INCORPORATE MULTIPLE PARAMETERS AND ENABLE A TAXONOMY HEAVILY ROOTED IN THE INTRINSIC BIOLOGY OF DISEASE

Physical signs and symptoms are the overt manifestations of disease observed by physicians and patients. However, symptoms are not the best descriptors of disease. Symptoms are often non-specific and rarely identify a disease unambiguously. Physical signs and symptoms are generally also difficult to measure quantitatively. Furthermore, numerous diseases—including some of the most common ones such as cancer, cardiovascular disease, and HIV infection—are asymptomatic in early stages. Indeed, in a strict sense, all diseases are presumably asymptomatic for some “latent period” following the initiation of pathological processes. As a consequence, diagnosis based on traditional “signs and symptoms” alone carries the risk of missing opportunities for prevention, or early intervention can readily misdiagnose patients altogether. Even when histological analysis is performed, typically on tissue obtained after diseases become clinically evident, obtaining optimal diagnostic results can depend on supplementing standard histology with ancillary genetic or immunohistochemical testing to identify specific mutations or marker proteins.

Biology-based indicators of disease such as genetic mutations, marker-protein molecules, and other metabolites have the potential to be precise descriptors of disease. They can be measured accurately and precisely—be it in the form of a standardized biochemical assay or a genetic sequence—thus enabling comparison across datasets obtained from independent studies. Particularly when multiple molecular indicators are used in combination with conventional clinical, histological, and laboratory findings, they offer the opportunity for a more accurate and precise description and classification of disease.

Numerous molecularly-based disease markers are already available, and the number will grow rapidly in the future. Among the most prominent parameters of disease are an individual’s:

  • Genome
  • Transcriptome
  • Proteome
  • Metabolome
  • Lipidome
  • Epigenome

As discussed in Chapter 2, it is increasingly feasible to obtain substantial information about these biological features for each individual patient. The cost of sequencing an individual’s genome is rapidly dropping, and significant advances in the ability to globally and affordably characterize proteomes, me-



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