Kidney Week

Abstract: FR-PO1126

Identifying Functional Subclasses of Nephrotic Syndrome by Consensus Non-Negative Matrix Factorization Clustering of Glomerular Transcriptome

Session Information

• Glomerular Diseases: Clinical, Outcomes, Trials - II
  October 26, 2018 | Location: Exhibit Hall, San Diego Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Glomerular Diseases

• 1203 Glomerular Diseases: Clinical, Outcomes, and Trials

Authors

• Hamidi, Habib, University of Michigan, Ann Arbor, Michigan, United States

Habib Hamidi,

• Eddy, Sean, University of Michigan, Ann Arbor, Michigan, United States

Sean Eddy,

• Kretzler, Matthias, University of Michigan, Ann Arbor, Michigan, United States

Matthias Kretzler,

• Sedor, John R., Cleveland Clinic, Cleveland, Ohio, United States

John R. Sedor,

• Holzman, Lawrence B., University of Pennsylvania, Philadelphia, Pennsylvania, United States

Lawrence B. Holzman,

• O'Toole, John F., Cleveland Clinic, Cleveland, Ohio, United States

John F. O'Toole,

• Mariani, Laura H., University of Michigan, Ann Arbor, Michigan, United States

Laura H. Mariani,

Background

Defining nephrotic syndrome in mechanistic terms is a prerequisite to develop targeted therapies. Consensus non-negative matrix factorization (NMF) is a clustering approach used with tumor specimen transcriptomics to identify functionally relevant subtypes.

Methods

NMF clustering was applied to glomerular mRNA expression from nephrotic syndrome patient biopsies (NEPTUNE cohort). Individual gene expression levels from the glomerular compartment were normalized to mean expression to maximize individual patient differences over shared disease & tissue expression. Cox proportional hazards models were fit for complete proteinuria remission (CR, UPCR <0.3 mg/mg) and ESRD/40% eGFR decline. Significance analysis of microarray identified cluster specific differentially expressed genes that were used in pathway enrichment analysis to determine functional relevance.

Results

NMF separated 138 patients into 4 clusters which did not differ by diagnosis (MCD, FSGS, MN and IgAN, p 0.25), age (p 0.72), sex (p 0.25) or UPCR (p 0.28). Cluster 1 had lower mean eGFR (63 mL/min vs 81, 77 and 85; p 0.04) and greater black race (54% vs 10%, 24%, 19%, p <0.01). In unadjusted models, cluster 1 had least CR (HR 0.51, 95%CI 0.3–1.0, p-value 0.06) and greatest loss of eGFR (HR 5.4 95%CI 2.5–12.0, p-value <0.01). The eGFR association persisted after adjustment for baseline eGFR and race. Pathway enrichment of cluster-specific genes demonstrated unique processes: cluster 1-targetable signaling pathways (e.g. integrin, EGF-R, TLR), cluster 2-pentose phosphate metabolism, cluster 3-metabolic pathways (e.g.Vit D, pyruvate, galactose), cluster 4-cadherin signaling.

Conclusion

NMF clustering of glomerular kidney tissue mRNA expression levels revealed 4 clusters which were distinct from conventional classification. Pathway enrichment of gene signatures for each cluster revealed distinct molecular processes which may help to inform future treatment strategies.

Funding

• NIDDK Support