Integrated multi-omics approaches to improve classification of chronic kidney disease

• Published in: Nature reviews. Nephrology 2020-11, Vol.16 (11), p.657-668
• Main Authors: Eddy, Sean, Mariani, Laura H., Kretzler, Matthias
• Format: Article
• Language: English
• Subjects: 692/4017; 692/4022/1585; Biopsy; Care and treatment; Classification; Development and progression; Genomics - methods; Genotype; Humans; Kidney diseases; Medicine; Medicine & Public Health; Metabolomics - methods; Nephrology; Phenotype; Precision Medicine; Prognosis; Proteomics - methods; Renal Insufficiency, Chronic - classification; Renal Insufficiency, Chronic - genetics; Renal Insufficiency, Chronic - therapy; Review Article
• ISSN: 1759-5061; 1759-507X
• DOI: 10.1038/s41581-020-0286-5

Chronic kidney diseases (CKDs) are currently classified according to their clinical features, associated comorbidities and pattern of injury on biopsy. Even within a given classification, considerable variation exists in disease presentation, progression and response to therapy, highlighting heterogeneity in the underlying biological mechanisms. As a result, patients and clinicians experience uncertainty when considering optimal treatment approaches and risk projection. Technological advances now enable large-scale datasets, including DNA and RNA sequence data, proteomics and metabolomics data, to be captured from individuals and groups of patients along the genotype–phenotype continuum of CKD. The ability to combine these high-dimensional datasets, in which the number of variables exceeds the number of clinical outcome observations, using computational approaches such as machine learning, provides an opportunity to re-classify patients into molecularly defined subgroups that better reflect underlying disease mechanisms. Patients with CKD are uniquely poised to benefit from these integrative, multi-omics approaches since the kidney biopsy, blood and urine samples used to generate these different types of molecular data are frequently obtained during routine clinical care. The ultimate goal of developing an integrated molecular classification is to improve diagnostic classification, risk stratification and assignment of molecular, disease-specific therapies to improve the care of patients with CKD. Classification of kidney diseases according to their molecular mechanisms has potential to improve patient outcomes through the identification of targeted therapeutic approaches. This Review provides an overview of the ways in which omics and other data types can be integrated to enhance our understanding of the mechanisms underlying kidney function and failure. Key points Classifying kidney diseases according to their molecular mechanisms has the potential to improve patient outcomes through the identification and development of more targeted therapeutic approaches. Omics data derived from kidney biopsy tissue not only have the potential to enable identification of disease mechanisms but might also enable the identification of prognostic and predictive biomarkers; this approach might be extended to enable the use of non-invasive surrogate urinary markers that reflect molecular pathways activated in the kidney. The success of targeting molecular pathways identifi