ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005

email@asn-online.org

202-640-4660

The Latest on X

Kidney Week

Please note that you are viewing an archived section from 2019 and some content may be unavailable. To unlock all content for 2019, please visit the archives.

Abstract: FR-PO847

Self-Clustering of Tissue Gene Expression to Classify Patients with Lupus Nephritis

Session Information

Category: Glomerular Diseases

  • 1202 Glomerular Diseases: Immunology and Inflammation

Authors

  • Almaani, Salem, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States
  • Yu, Lianbo, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States
  • Song, Huijuan, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States
  • Parikh, Samir V., The Ohio State University Wexner Medical Center, Columbus, Ohio, United States
  • Ayoub, Isabelle, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States
  • Mejia-Vilet, Juan M., Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Mexico, Mexico
  • Malvar, Ana, Hospital Fernandez, Buenos Aires, Argentina
  • Rovin, Brad H., The Ohio State University Wexner Medical Center, Columbus, Ohio, United States
Background

Histologic classification of kidney biopsy in lupus nephritis (LN), while used for treatment decisions, is not sufficiently robust to account LN's molecular heterogeneity that affects treatment response and outcomes. We tested whether unsupervised clustering of LN biopsies based on tissue gene expression was feasible to classify LN. We postulated that such a classification of LN would reflect disease pathobiology and would be more relevant to managing LN with drugs targeting specific pathogenic pathways.

Methods

Transcript levels of >500 genes involved in autoimmunity were measured using NanoString in microdissected glomeruli from 57 LN patient biopsies, and then used for unsupervised hierarchical clustering. For each gene, mRNA abundance was compared between each cluster group (CG) and the mean abundance of the other groups to determine genes that were differentially expressed. Differentially-expressed genes from each CG were used for pathway analysis. Demographic, clinical and histopathologic data were also compared between CGs using ANOVA and Fisher’s exact test as appropriate.

Results

Clustering resulted in 4 CGs. There were no significant differences in baseline creatinine, proteinuria, NIH activity or chronicity indices, or ISN/RPS class between CGs. Canonical pathway and upstream regulator analysis differentiated CGs (Table).

Conclusion

Transcript expression in the glomerular compartment of LN kidney biopsies identifies 4 subsets of patients. Inflammatory pathways expression appears to be highest in CG2 followed by CG4, and relatively suppressed in CG1 & 3. We suggest it may be feasible to tailor treatment to patients based on their CG classification of injury pathways that are differentially expressed.