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

Nature Reviews Nephrology volume 16, pages 657–668 (2020)Cite this article

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Abstract

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.

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 identified through approaches that involve the integration of various datasets has been demonstrated in clinical trials.

  • Combining targeted therapies with predictive biomarkers puts nephrology in a position to test the concept of precision medicine, enabling trials to identify the right trial for the right patient at the right time.

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Fig. 1: Integration of varied data types can help to address clinically relevant questions.
Fig. 2: Interrogation of public databases to provide insights into disease pathways.

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Acknowledgements

Support for the work described in this Review is provided by the George M. O’Brien Michigan Kidney Translational Core Center, funded by NIH/National Institute of Diabetes and Digestive and Kidney Diseases grant 2P30-DK-081943. Nephroseq is supported as part of the applied systems biology core by the University of Michigan George M. O’Brien Michigan Kidney Translational Core Center.

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  1. Division of Nephrology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USA

    Sean Eddy, Laura H. Mariani & Matthias Kretzler

  2. Department of Computational Medicine and Bioinformatics, Michigan Medicine, Ann Arbor, MI, USA

    Matthias Kretzler

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Contributions

All authors contributed to discussing the article’s content, writing and reviewing/editing of the manuscript before submission.

Corresponding author

Correspondence to Matthias Kretzler.

Ethics declarations

Competing interests

M.K. holds a US patent, PCT/EP2014/073413 “Biomarkers and methods for progression prediction for chronic kidney disease”. S.E. and L.H.M. declare no competing interests.

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Related links

Attack Diabetic Kidney Disease: www.beat-dkd.eu

Connectivity Map: clue.io

Lincs Consortium: lincsproject.org

Rhapsody: www.imi-rhapsody.eu

The Kidney Precision Medicine Project: www.kpmp.org

Glossary

In cis

Located on the same strand of DNA as the gene or variant in question and in the case of eQTLs, located in relatively close proximity to the transcriptional start site of a gene.

Co-expression networks

Sets of genes that display similar trends in their direction of regulation across patients and are thus presumed to be co-regulated and therefore functionally related.

Weighted gene co-expression network analysis

A computational method of assessing global co-expression of genes across a dataset resulting in distinct sets of co-expression networks and modules.

Quantitative pathway score

An aggregate of individual pathway components in a single quantitative measure.

MultiPLIER

A bioinformatics approach to inferring pathway or cell-type levels leveraging bulk tissue profiles across multiple datasets.

Pathway-level information extractor

A bioinformatics method of inferring pathway or cell-type levels from a bulk tissue profile.

Latent variables

Variables that are not directly measured but are inferred from a set of observations in a dataset.

Federated databases

Database management systems in which data can be securely stored while allowing users and applications to access the data through plugins.

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Eddy, S., Mariani, L.H. & Kretzler, M. Integrated multi-omics approaches to improve classification of chronic kidney disease. Nat Rev Nephrol 16, 657–668 (2020). https://doi.org/10.1038/s41581-020-0286-5

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