A Novel Method to Identify Autoantibodies against Putative Target Proteins in Serum from beta-Thalassemia Major: A Pilot Study

The role of pathogens in diabetes pathogenesis and the potential of immunoproteomics as a diagnostic and prognostic tool

Diabetes mellitus (DM) is a group of metabolic diseases marked by hyperglycemia, which increases the risk of systemic infections. DM patients are at greater risk of hospitalization and mortality from bacterial, viral, and fungal infections. Poor glycemic control can result in skin, blood, bone, urinary, gastrointestinal, and respiratory tract infections and recurrent infections. Therefore, the evidence that infections play a critical role in DM progression and the hazard ratio for a person with DM dying from any infection is higher. Early diagnosis and better glycemic control can help prevent infections and improve treatment outcomes. Perhaps, half (49.7%) of the people living with DM are undiagnosed, resulting in a higher frequency of infections induced by the hyperglycemic milieu that favors immune dysfunction. Novel diagnostic and therapeutic markers for glycemic control and infection prevention are desirable. High-throughput blood-based immunoassays that screen infections and hyperglycemia are required to guide timely interventions and efficiently monitor treatment responses. The present review aims to collect information on the most common infections associated with DM, their origin, pathogenesis, and the potential of immunoproteomics assays in the early diagnosis of the infections. While infections are common in DM, their role in glycemic control and disease pathogenesis is poorly described. Nevertheless, more research is required to identify novel diagnostic and prognostic markers to understand DM pathogenesis and management of infections. Precise monitoring of diabetic infections by immunoproteomics may provide novel insights into disease pathogenesis and healthy prognosis.

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.

Identification of circulating biomarkers for differentiating patients with papillary thyroid cancers from benign thyroid tumors

BACKGROUND

This study aimed to identify the potential circulating biomarkers of protein, mRNAs, and long non-coding RNAs (lncRNAs) to differentiate the papillary thyroid cancers from benign thyroid tumors.

METHODS

The study population of 100 patients was classified into identification (10 patients with papillary thyroid cancers and 10 patients with benign thyroid tumors) and validation groups (45 patients with papillary thyroid cancers and 35 patients with benign thyroid tumors). The Sengenics Immunome Protein Array-combined data mining approach using the Open Targets Platform was used to identify the putative protein biomarkers, and their expression validated using the enzyme-linked immunosorbent assay. Next-generation sequencing by Illumina HiSeq was used for the detection of dysregulated mRNAs and lncRNAs. The website Timer v2.0 helped identify the putative mRNA biomarkers, which were significantly over-expressed in papillary thyroid cancers than in adjacent normal thyroid tissue. The mRNA and lncRNA biomarker expression was validated by a real-time polymerase chain reaction.

RESULTS

Although putative protein and mRNA biomarkers have been identified, their serum expression could not be confirmed in the validation cohorts. In addition, seven lncRNAs (TCONS_00516490, TCONS_00336559, TCONS_00311568, TCONS_00321917, TCONS_00336522, TCONS_00282483, and TCONS_00494326) were identified and validated as significantly downregulated in patients with papillary thyroid cancers compared to those with benign thyroid tumors. These seven lncRNAs showed moderate accuracy based on the area under the curve (AUC = 0.736) of receiver operating characteristic in predicting the occurrence of papillary thyroid cancers.

CONCLUSIONS

We identified seven downregulated circulating lncRNAs with the potential for predicting the occurrence of papillary thyroid cancers.

Identification of novel autoantibodies in ascites of relapsed paclitaxel-resistant gastric cancer with peritoneal metastasis using immunome protein microarrays and proteomics

BACKGROUND

Gastric cancer (GC) patients with peritoneal metastasis usually have extremely poor prognosis. Intraperitoneal infusion of paclitaxel (PTX) provides an effective treatment, but relapse and PTX-resistance are unavoidable disadvantages, and it is difficult to monitor the occurrence of PTX-resistance.

OBJECTIVE

The aim of this study was to explore novel autoantibodies in the ascites of individuals with relapsed PTX-resistant GC with peritoneal metastasis.

METHODS

Ascites samples were collected before PTX infusion and after the relapse in 3 GC patients. To determine the expression of significantly changed proteins, we performed autoantibody profiling with immunome protein microarrays and tandem mass tag (TMT) quantitative proteomics, and then, the overlapping proteins were selected.

RESULTS

Thirty-eight autoantibodies that were differentially expressed between the ascites in the untreated group and relapsed PTX-resistant group were identified. For confirmation of the results, TMT quantitative proteomics was performed, and 842 dysregulated proteins were identified. Four proteins, TPM3, EFHD2, KRT19 and vimentin, overlapped between these two assays.

CONCLUSIONS

Our results first revealed that TPM3, EFHD2, KRT19 and vimentin were novel autoantibodies in the ascites of relapsed PTX-resistant GC patients. These autoantibodies may be used as potential biomarkers to monitor the occurrence of PTX-resistance.

Identification of Novel Native Autoantigens in Rheumatoid Arthritis

The majority of patients diagnosed with rheumatoid arthritis (RA) have developed autoantibodies against neoepitopes in proteins that have undergone post-translational modification, e.g., citrullination or carbamylation. There is growing evidence of their molecular relevance and their potential utility to improve diagnosis, patient stratification, and prognosis for precision medicine. Autoantibodies reacting to native proteins may also have a role in RA pathogenesis, however, their reactivity patterns remain much less studied. We hypothesized that a high-density protein array technology could shed light onto the normal and disease-related autoantibodies produced in healthy and RA patient subgroups. In an exploratory study, we investigated the global reactivity of autoantibodies in plasma pools from 15 anti-cyclic citrullinated peptide (CCP)-positive and 10 anti-CCP-negative RA patients and 10 healthy donors against more than 1600 native and unmodified human proteins using a high-density protein array. A total of 102 proteins recognized by IgG autoantibodies were identified, hereof 86 were recognized by antibodies from CCP-positive RA patients and 76 from anti-CCP-negative RA patients, but not by antibodies from healthy donors. Twenty-four of the identified autoantigens have previously been identified in synovial fluid. Multiple human proteins in their native conformation are recognized by autoantibodies from anti-CCP-positive as well as anti-CCP-negative RA patients.