Abstract: TH-OR97
Predicting Calcineurin Inhibitor Response in Glomerular Diseases
Session Information
- Podocytopathies and Membranous Nephropathy: Clinical Advances
October 24, 2024 | Location: Room 1, Convention Center
Abstract Time: 05:30 PM - 05:40 PM
Category: Glomerular Diseases
- 1402 Glomerular Diseases: Clinical, Outcomes, and Therapeutics
Authors
- Eddy, Sean, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- Helmuth, Margaret, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- McCown, Phillip J., University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- Nair, Viji, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- Boys, Charlotte May, UniversitatsKlinikum Heidelberg, Heidelberg, Baden-Württemberg, Germany
- Hartman, John R., University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- Larkina, Maria, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- Ju, Wenjun, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- Saez-Rodriguez, Julio, European Bioinformatics Institute, Cambridge, United Kingdom
- Mariani, Laura H., University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
- Kretzler, Matthias, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
Background
Glucocorticoids and calcineurin inhibitors (CNI) are part of KDIGO guidelines for treatment of steroid resistant glomerular diseases. Despite widespread use, predicting whether a patient will respond to therapy remains elusive. Given toxicities associated with prolonged CNI use, identifying patients who are both likely and unlikely to respond to CNI presents an opportunity to improve patient care. We hypothesized that patients’ intrarenal molecular profile would be predictive of future CNI response, and that this profile could be linked to non-invasive markers that predict remission.
Methods
Kidney biopsy cores were obtained from participants in the Nephrotic Syndrome Study Network (n=362) and RNAseq profiles generated as previously described. Clinical phenotypes including, steroid and CNI exposure were recorded prior to biopsy and throughout study follow up. Gene co-expression network analysis was performed to identify co-expressed gene modules associated with future complete remission (CR) during CNI exposure. Functional enrichment of module genes was performed using cell type enrichment from a dataset of 1,407,781 single cell and single nuc (sn-)RNAseq profiles. MOFAcell was used to determine cell states in patients. Modules and cell states were correlated (Pearson) with 7,000 SomaScan profiles to identify candidate blood and urine markers of CNI response, which were validated by ELISA. AUC was used to determine the ability of markers to predict future CNI response.
Results
In glomeruli and tubulointerstitium (TI), we identified 42 and 24 modules, respectively. In steroid naïve patients at time of Bx, four TI modules were associated with CR during future CNI exposure (p<0.05). Functional enrichment analysis of modules indicated immune biology and angiogenesis networks associated with lack of response, consistent with cell level expression of module genes from sn-RNAseq. Plasma (KIM-1, MMP7) and urine (TNFR-2) markers were correlated with module expression. Biomarker profiles closed to CNI exposure improved CR prediction (AUC=0.85) over a base model (eGFR, UPCR, age, sex, and race) alone (AUC=0.77).
Conclusion
Co-expression modules helped identify blood and urine markers predictive of CR during future CNI exposure. Validation of these markers in independent cohorts is ongoing.
Funding
- NIDDK Support