Diagnosing infectious diseases in 2028: Let the good times roll
Aug. 27, 2018
You may be overwhelmed by the “omics” — genomics, epigenomics, transcriptomics, proteomics, metabolomics, with good reason. “Big Data” are changing the diagnostic landscape in infectious diseases and oncology at breakneck speed. Warning: By the time this is posted, it may be obsolete!
According to Giddon et al: “Gene expression signatures derived from whole blood have been reported for several diseases including bacterial and viral infections as well as pathogen-specific diseases including malaria, typhoid fever, dengue virus infection, HIV infection, human respiratory syncytial virus infection, tularaemia, and tuberculosis. Recently, new parsimonious approaches as well as multi-cohort meta-analyses have identified gene expression signatures comprising minimal numbers of genes, paving the way for easier translation into cost-effective clinical diagnostic tests.” (1)
One of the most disconcerting clinical scenarios is trying to accurately diagnose Kawasaki disease (KD) from other infectious disorders. (Even though the etiology of KD is unknown, infectious agents — viral or bacterial — may be influential.) Rendering the diagnosis is crucial — should IVIG (aspirin, prednisone, or infliximab) be administered to prevent the development of coronary artery aneurysms? The diagnosis of KD rests on clinical criteria:
*Bilateral Conjunctivitis (80–90%)
*Changes to oropharyngeal mucous membranes, including injected and/or fissured lips, strawberry tongue and enanthema (80–90%)
*Palmar and/or plantar erythema and/or periungual desquamation (in convalescent phase) (80%)
*Polymorphous exanthema, primarily truncal, not vesicular (>90%)
*Cervical lymphadenopathy (at least one lymph node >1.5cm) (50%)
*Crucially, up to 36% of patients do not fulfill the diagnostic criteria for KD and the diagnosis can easily be missed. Unfortunately, this group of patients with “incomplete (or “atypical”) KD” exhibit a particularly high risk for the development of coronary artery aneurysms. (2) Being able to diagnose KD accurately would be game-changing.
Wright et al examined if a whole-blood gene expression signature could distinguish children with KD in the first week of illness from other febrile conditions. They performed a case-control study of a group that included a training and test set and a validation group of children with KD or comparator febrile illness. The group consisted of 606 children (404 children in the discovery cohort and 202 in the validation cohort) with infectious and inflammatory conditions (78 KD, 84 other inflammatory diseases, and 242 bacterial or viral infections in the discovery cohort) and 55 healthy controls. Whole-blood gene expression was evaluated using microarrays, and minimal transcript sets distinguishing KD were identified using a novel variable selection method (parallel regularized regression model search). A 13-transcript signature identified in the discovery training set distinguished KD from other infectious and inflammatory conditions. In the validation set, the signature distinguished KD from febrile controls, with a sensitivity of 85.9% and specificity of 89.1%. The diagnostic accuracy increased with certainty of clinical diagnosis. The authors concluded that a test incorporating the 13-transcript disease risk score may enable earlier diagnosis and treatment of KD thereby reducing inappropriate treatment in those with other diagnoses. (3)
The possibilities are limitless. For example, a four-transcript signature (RISK4), derived from samples in a South African and Gambian training set, predicted progression of tuberculosis up to two years before onset of disease in blinded test set samples from South Africa. The signatures reported represented significant and translational improvements over currently used biomarkers for predicting risk of TB, such as interferon gamma release assays or the tuberculin skin testing. (4)
Researchers are under siege by biomedical “Big Data,” needing to contend with its canonical 3 Vs — Volume (scale), Variety (heterogeneity), and Velocity (speed at which the data can be accessed and interrogated). Capturing, storing and integrating this mass of information will challenge even the most advanced information technology solutions within well-resourced research organizations. (5)
These challenges will be overcome by advanced computation and analysis. You need not be Jules Verne to predict that precise point of care diagnosis of infectious disease will be standard-of-care within the next decade. Precision in diagnosis translates to precision in therapy. The impact will be profound.
Point to remember: Diagnosing infectious diseases is being revolutionized by gene expression signatures.
1. Giddon HD, et al. Genome-wide host RNA signatures of infectious diseases: Discovery and clinical translation. Immunology 2018; 153: 171-8.
2. Hedrich CM. Kawasaki disease. Front Pediatr 2018; 6: 198.
3. Wright VJ. Diagnosis of Kawasaki disease using minimal whole-blood gene expression signature. JAMA Pediatr 2018 Aug 6 [Epub ahead of print].
4. Suliman S, et al. Four-gene pan-African blood signature predicts progression to tuberculosis. Am J Respir Crit Care Med 2018 Apr 6 [Epub ahead of print].
5. Chaussabel D, et al. Assessment of immune status using blood transcriptomics and potential implications for global health. Semin Immunol 2015; 27: 58-66.
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