ASN's Mission

ASN leads the fight to prevent, treat, and cure kidney diseases throughout the world by educating health professionals and scientists, advancing research and innovation, communicating new knowledge, and advocating for the highest quality care for patients.

learn more

Contact ASN

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


The Latest on Twitter

Kidney Week

Abstract: FR-PO887

A Fingerprint of Response to Treatment in Lupus Nephritis: Identification of a Panel of Eight Proteins from Baseline Renal Biopsies That Predict Response

Session Information

Category: Glomerular Diseases

  • 1203 Glomerular Diseases: Clinical, Outcomes, and Trials


  • Wilson, Hannah R., Imperial College London, London, United Kingdom
  • Geary, Bethany, University of Manchester, Manchester, United Kingdom
  • Gilmore, Alyssa C., Imperial College London, London, United Kingdom
  • Gutierrez Rivera, Reyna, University of Manchester, Manchester, United Kingdom
  • Cook, H. Terence, Imperial College London, London, United Kingdom
  • Whetton, Anthony, University of Manchester, Manchester, United Kingdom
  • Lightstone, Liz, Imperial College London, London, United Kingdom

Lupus nephritis (LN) carries significant morbidity & mortality risk. Only ~50% of patients respond satisfactorily to current standard of care. Likelihood of response & long term prognosis are unclear at the outset with no reliable predictors identified. We set out to establish if proteomic analysis of baseline kidney biopsies by relative quantification SWATH mass spectrometry (MS) could reveal biomarkers associated with subsequent response to treatment or mechanisms of disease in non-responders.


32 FFPE renal biopsy tissue blocks were identified for analysis: LN class IV: 8 complete responders (CR) & 8 non responders (NR); class V: 5 CR & 4 NR; Controls: 7 thin basement membrane disease. Protein was extracted & trypsin digested using pressure-cycling technology. SWATH-MS analysis was performed using a 6600 TripleTOF mass spectrometer coupled to a Dionex Ultimate 3000 HPLC. Data analysis was performed using openSWATH plus, pyProphet & MSproteomicstools. The false discovery rate was 1%. Proteomic data was submitted for machine learning (1000 iterations) using the RandomForest R package (version 4.6-14) in which the dataset was split randomly 70:30 into a training & testing set.


Of the 5139 proteins identified, 57 were significantly up-regulated in CR compared to NR and included Th17 cell differentiation, metabolic & RNA degradation pathways. Downregulation in CR compared to NR was noted in 106 proteins which included HIF-1 & MAPK signalling & melanogenesis. Further analysis revealed that a panel of 8 proteins separated CR from NR including moesin, a key protein in immunity, & eukaryotic translation elongation factor 1 epsilon-1, a negative regulator of cell proliferation, each downregulated in CR compared to NR. A validation study in a separate set of biopsies is underway.


Whilst the protein yields from FFPE blocks are extremely small (4-8ug protein), we demonstrate these can be successfully analysed by SWATH MS. Importantly, this approach allows us to use baseline biopsies to identify CR from NR using a panel of just 8 proteins and provides novel insights into the intra-cellular mechanisms governing response to treatment in LN, biomarkers of response to therapy as well as potential targets for new therapeutics.


  • Private Foundation Support