Age, Disease Severity and Ethnicity Influence Humoral Responses in a Multi-Ethnic COVID-19 Cohort

The COVID-19 pandemic has affected all individuals across the globe in some way. Despite large numbers of reported seroprevalence studies, there remains a limited understanding of how the magnitude and epitope utilization of the humoral immune response to SARS-CoV-2 viral anti-gens varies within populations following natural infection. Here, we designed a quantitative, multi-epitope protein microarray comprising various nucleocapsid protein structural motifs, including two structural domains and three intrinsically disordered regions. Quantitative data from the microarray provided complete differentiation between cases and pre-pandemic controls (100% sensitivity and specificity) in a case-control cohort (n = 100). We then assessed the influence of disease severity, age, and ethnicity on the strength and breadth of the humoral response in a multi-ethnic cohort (n = 138). As expected, patients with severe disease showed significantly higher antibody titers and interestingly also had significantly broader epitope coverage. A significant increase in antibody titer and epitope coverage was observed with increasing age, in both mild and severe disease, which is promising for vaccine efficacy in older individuals. Additionally, we observed significant differences in the breadth and strength of the humoral immune response in relation to ethnicity, which may reflect differences in genetic and lifestyle factors. Furthermore, our data enabled localization of the immuno-dominant epitope to the C-terminal structural domain of the viral nucleocapsid protein in two independent cohorts. Overall, we have designed, validated, and tested an advanced serological assay that enables accurate quantitation of the humoral response post natural infection and that has revealed unexpected differences in the magnitude and epitope utilization within a population

A highly predictive autoantibody-based biomarker panel for prognosis in early-stage NSCLC with potential therapeutic implications

BACKGROUND: Lung cancer is the leading cause of cancer-related death worldwide. Surgical resection remains the definitive curative treatment for early-stage disease offering an overall 5-year survival rate of 62%. Despite careful case selection, a significant proportion of early-stage cancers relapse aggressively within the first year post-operatively. Identification of these patients is key to accurate prognostication and understanding the biology that drives early relapse might open up potential novel adjuvant therapies. METHODS: We performed an unsupervised interrogation of >1600 serum-based autoantibody biomarkers using an iterative machine-learning algorithm. RESULTS: We identified a 13 biomarker signature that was highly predictive for survivorship in post-operative early-stage lung cancer; this outperforms currently used autoantibody biomarkers in solid cancers. Our results demonstrate significantly poor survivorship in high expressers of this biomarker signature with an overall 5-year survival rate of 7.6%. CONCLUSIONS: We anticipate that the data will lead to the development of an off-the-shelf prognostic panel and further that the oncogenic relevance of the proteins recognised in the panel may be a starting point for a new adjuvant therapy

Novel Autoantibodies in Idiopathic Small Fiber Neuropathy.

OBJECTIVE Small fiber neuropathy (SFN) is clinically and etiologically heterogeneous. Although autoimmunity has been postulated to be pathophysiologically important in SFN, few autoantibodies have been described. We aimed to identify autoantibodies associated with idiopathic SFN (iSFN) by a novel high-throughput protein microarray platform that captures autoantibodies expressed in the native conformational state. METHODS Sera from 58 SFN patients and 20 age- and gender-matched healthy controls (HCs) were screened against >1,600 immune-related antigens. Fluorescent unit readout and postassay imaging were performed, followed by composite data normalization and protein fold change (pFC) analysis. Analysis of an independent validation cohort of 33 SFN patients against the same 20 HCs was conducted to identify reproducible proteins in both cohorts. RESULTS Nine autoantibodies were screened with statistical significance and pFC criteria in both cohorts, with at least 50% change in serum levels. Three proteins showed consistently high fold changes in main and validation cohorts: MX1 (FC = 2.99 and 3.07, respectively, p = 0.003, q = 0.076), DBNL (FC = 2.11 and 2.16, respectively, p = 0.009, q < 0.003), and KRT8 (FC = 1.65 and 1.70, respectively, p = 0.043, q < 0.003). Further subgroup analysis into iSFN and SFN by secondary causes (secondary SFN) in the main cohort showed that MX1 is higher in iSFN compared to secondary SFN (FC = 1.61 vs 0.106, p = 0.009). INTERPRETATION Novel autoantibodies MX1, DBNL, and KRT8 are found in iSFN. MX1 may allow diagnostic subtyping of iSFN patients. ANN NEUROL 2021

Anti-TROVE2 Antibody Determined by Immune-Related Array May Serve as a Predictive Marker for Adalimumab Immunogenicity and Effectiveness in RA

Anti-drug antibody (ADAb) development is associated with secondary therapeutic failure in biologic-treated rheumatoid arthritis (RA) patients. With a treat-to-target goal, we aimed to identify biomarkers for predicting ADAb development and therapeutic response in adalimumab-treated patients. Three independent cohorts were enrolled. In Cohort-1, 24 plasma samples (6 ADAb-positive and 6 ADAb-negative patients at baseline and week 24 of adalimumab therapy, respectively) were assayed with immune-related microarray containing 1,636 correctly folded functional proteins. Next, we executed statistically powered autoantibody profiling analysis of 50 samples in Cohort-2 (24 ADAb-positive and 26 ADAb-negative patients). Subsequently, immunofluorescence assay was performed on 48 samples in Cohort-3 to correlate with ADAb titers and drug levels. The biomarkers were identified for predicting ADAb development and therapeutic response using the immune-related microarray and machine learning approach. ADAb-positive patients had lower drug levels at week 24 (median=0.024 μg/ml) compared with ADAb-negative patients (median=6.38 μg/ml, p<0.001). ROC analysis based on the ADAb status revealed the top 20 autoantibodies with AUC≥0.7 in differentiating both groups in Cohort-1. Analysis of Cohort-2 dataset identified a panel of 8 biomarkers (TROVE2, SSB, NDE1, ZHX2, SH3GL1, CARD9, PTPN20, and KLHL12) with 80.6% specificity, 77.4% sensitivity, and 79.0% accuracy in discriminating poor from EULAR responders. Immunofluorescence assay validated that anti-TROVE2 antibody could highly predict ADAb development and poor EULAR response (AUC 0.79 and 0.89, respectively). Multivariate regression analysis proved anti-TROVE2 antibody to be an independent predictor for developing ADAb. Immune-related protein microarray and replication analysis identified anti-TROVE2 antibody as a useful biomarker for predicting ADAb development and therapeutic response in adalimumab-treated patients