title: Developing a Lupus Nephritis urinary biomarker panel in children for use in a clinical trial
Creators: name: Smith, EMD
Publisher: name: University of Liverpool Repository
Year: 2018

description: Background: Juvenile-onset systemic lupus erythematosus (JSLE) is a rare, severe multisystem autoimmune disease affecting the kidney (Lupus Nephritis, LN) in up to 80% of children. Numerous individual urinary biomarkers have previously been investigated. No individual biomarker has achieved ‘excellence’ in differentiating active from inactive LN. Aims: (1) To assess performance of traditional clinical biomarkers for LN monitoring and prediction of LN outcomes. (2) To select biomarkers warranting assessment in a ‘LN biomarker panel’; to cross-sectionally assess if combining novel/traditional biomarkers improves active LN identification. (3) To validate the optimal UK LN biomarkers within independent, ethnically distinct JSLE cohorts. (4) To longitudinally assess if urine biomarkers predict LN flare/remission. (5) To streamline methods for LN urine biomarker panel quantification. Methods: Clinical data and urine samples from UK, United States (US) and South African (SA) JSLE patients were utilised within cross-sectional and longitudinal studies assessing combinations of novel urine/traditional clinical biomarkers. A custom LN biomarker panel multiplex assay was developed/validated in collaboration with Merck Millipore. Results: 37% of UK patients displayed active LN as an initial presenting feature, with a further 17% developing LN after a median of 2.04 years [IQR 0.8-3.7]. ACR score (&gt;5) and C3 levels (&lt;0.9g/L) at baseline were significant predictors of subsequent LN development. 39% of patients recovered from proteinuria following an LN flare during the study period, within a median of 17 months (IQR 4-49) with the remaining 61% continuing to have proteinuria after a median of 22 months (IQR 12-41). Younger patients (&lt;14 yrs), those with a lower eGFR (&lt;80mls/min) and haematological involvement at LN onset, displayed a longer time to proteinuria recovery. ESR, C3, WCC, neutrophils, lymphocytes and IgG contributed significantly to an optimal non-renal traditional biomarker model for active LN identification (AUC 0.72). Novel urine biomarkers were selected for assessment by detailed literature review. Cross-sectional fitting a binary logistic regression models with data from 61 UK patients, the optimal biomarker combination included AGP+CP+LPGDS+TF (AUC 0.920). Inclusion of traditional biomarkers within the model did not improve the AUC further. The novel biomarker panel displayed equivalent ability for identifying active LN in 30 US and 23 SA patients (AUC of 0.991 and 1.00 respectively). Within the longitudinal study, including 244 observations from 80 UK/US/SA patients, a Markov Multi-State model identified AGP to be predictive flare, and CP of remission (model AIC =135). By entering individual AGP/CP patient values into the model, 3, 6, 9 and 12 month probabilities of disease state transition are provided. The multiplex assay demonstrated acceptable cross reactivity between multiplexed antibodies, satisfactory spike recovery, intra/inter-assay precision. Linearity of dilution was unacceptable, therefore rigorous range finding in 106 UK/US/SA samples identified the optimal dilutions required for each biomarker. Combining multiplex biomarker values for AGP+CP+LPGDS+TF within a binary logistic regression model, equivalent ability for identification of active LN was seen (multiplex AUC = 0.998, ELISA AUC = 0.952). Conclusions: This thesis has demonstrated and validated an ‘excellent’ urinary biomarker panel for active LN identification in three ethnically distinct JSLE cohorts. Different constituents of the biomarker panel are best at predicting LN flare/remission. A custom urine biomarker panel multiplex assay has been developed, demonstrating improved ability for active LN identification over existing ELISAs. Future clinical studies prospectively measuring the urine biomarker panel by multiplex are warranted, facilitating refinement of the Markov Multi-State Model and assessing if biomarker-led monitoring can actually improve renal outcomes for children with LN.