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.
METHODS: Here, we used high-throughput, custom cancer antigen microarrays to identify a clinically relevant autoantibody biomarker combination able to differentially detect PDAC. Specifically, we quantified the serological AAb profiles of 94 PDAC, chronic pancreatitis (CP), other pancreatic- (PC) and prostate cancers (PRC), non-ulcer dyspepsia patients (DYS), and healthy controls (HC).
RESULTS: Combinatorial ROC curve analysis on the training cohort data from the cancer antigen microarrays identified the most effective biomarker combination as CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Additionally, differential expression analysis on the samples run on the iOme™ array identified 4 biomarkers (ALX1-GPA33-LIP1-SUB1) upregulated in PDAC against diseased and healthy controls. Identified AAbs were validated in silico using public immunohistochemistry datasets and experimentally using a custom PDAC protein microarray comprising the 11 optimal AAb biomarker panel. The clinical utility of the biomarker panel was tested in an independent cohort comprising 223 PDAC, PC, PRC, colorectal cancer (CRC), and HC samples. Combinatorial ROC curve analysis on the validation data identified the most effective biomarker combination to be CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Subsequently, the specificity of the 11-biomarker panel was validated against other cancers (PDAC vs PC: AUC = 70·3%; PDAC vs CRC: AUC = 84·3%; PDAC vs PRC: AUC = 80·2%) and healthy controls (PDAC vs HC: AUC = 80·9%), confirming that this novel AAb biomarker panel is able to selectively detect PDAC amongst other confounding diseases.
CONCLUSION: This AAb panel may therefore have the potential to form the basis of a novel diagnostic test for PDAC.
METHODS: ELISA-based assay was developed to detect IgG and IgM antibodies against SARS-CoV-2. The assay was examined in serum and urine samples of two different cohort of patients affected by COVID-19 disease with different severity and compared to age and sex matched control group. Neutralizing antibody activity was evaluated using an RBD-ACE2 binding inhibition assay. Additionally, a Sengenics protein microarray platform was employed to assess epitope-specific antibody responses.
RESULTS: The in-house ELISA assay reliably detected antibodies in both 163 serum and 64 urine samples compared to 50 serum samples from healthy control, with strong correlations observed between antibody levels in the two biofluids. Neutralizing antibody levels correlated positively with disease severity, highlighting their clinical relevance. The performance of the in-house assays was comparable to commercial kits, and the Sengenics microarray provided detailed insights into antibody profiles, identifying dominant epitopes within the Nucleocapsid core domain and RBD.
CONCLUSIONS: The developed in-house assay demonstrated robust performance and versatility, offering a cost-effective and scalable alternative to commercial kits. Their ability to detect antibodies in both serum and urine highlighted their potential as non-invasive diagnostic tools. These findings contribute to advancing sero-diagnostic capabilities, improving understanding of immune responses to SARS-CoV-2, and supporting global efforts to monitor and manage COVID-19 effectively.
METHODS: We enrolled participants who were vaccinated through the SISONKE South African clinical trial of the Ad26.CoV2.S vaccine in healthcare workers (HCWs). PLWH in this group had well-controlled HIV infection. We also enrolled unvaccinated participants previously infected with SARS-CoV-2. Neutralization capacity was assessed by a live virus neutralization assay of the Delta variant.
RESULTS: Most Ad26.CoV2.S vaccinated HCWs were previously infected with SARS-CoV-2. In this group, Delta variant neutralization was 9-fold higher compared with the infected-only group and 26-fold higher relative to the vaccinated-only group. No decrease in Delta variant neutralization was observed in PLWH relative to HIV-negative participants. In contrast, SARS-CoV-2-infected, unvaccinated PLWH showed 7-fold lower neutralization and a higher frequency of nonresponders, with the highest frequency of nonresponders in people with HIV viremia. Vaccinated-only participants showed low neutralization capacity.
CONCLUSIONS: The neutralization response of the Delta variant following Ad26.CoV2.S vaccination in PLWH with well-controlled HIV was not inferior to HIV-negative participants, irrespective of past SARS-CoV-2 infection. In SARS-CoV-2-infected and nonvaccinated participants, HIV infection reduced the neutralization response to SARS-CoV-2, with the strongest reduction in HIV viremic individuals.