Autoantibody Seromics - CDI Labs Applications

Autoantibody Discovery and Profiling

To better understand diseases and their potential treatments, it is important to study human immune responses - the precise molecular-level targets recognized by the adaptive immune system.

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Introduction to Seromics

Our immune system is adaptive and ever-changing, and it is informed by infections, foods, injuries, microbes, and our environment – made up of hundreds of billions of independent immune cells competing to seek, destroy, and remember antigen targets across our lifetime.

A lifetime of immune experiences drives the body to make countless antibody clones

This creates an antigen-specific antibody fingerprint that's more unique than the genome

Adaptive Antibodies Figure

Autoantibody Seromics Overview

Antibodies Come in a Variety of Forms Called Isotypes

Depending on the isotype, antibodies can promote cell killing, protective immune tolerance, or help educate T cells.

Antibody Types Figure

Biomarker Discovery

Autoantibodies serve as valuable biomarkers for a wide range of diseases and conditions, including autoimmune disorders, infectious diseases, cancer, and neurological disorders. As biomarkers, autoantibodies offer several advantages, including their specificity, accessibility, and potential for early detection. The identification of autoantibody biomarkers or signatures can provide researchers with highly valuable information relevant to many aspects of human disease, including disease mechanisms, diagnostic information, disease classification, prediction of therapy response and likelihood of adverse treatment reactions.

In the featured publication, HuProt™ microarrays were used to identify 501 candidate biomarkers differentially expressed in a training cohort (p>0.01). From these findings, 20 autoantibodies were selected for further analysis: CFAP36, DCD, DR1, GPBP1, HNRNPD, IKZF5, KEAP1, MED21, MIDIP1, MYBPH, NAP1L5, NAT9, NIP30, PJA2, PNMA1, RAB27A, SGPL1, TAF10, Ubiquillin 2, ZNF696. The publication concluded that novel circulating autoantibodies may have the potential to select patients with actionable lesions on lung cancer screening low-dose computed tomography scans.

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Patient Stratification

Patient stratification involves categorizing individuals with a particular disease or condition into subgroups based on the presence or absence of specific autoantibodies. This stratification approach can help tailor treatment strategies, predict disease progression, and identify individuals at higher risk of complications or disease recurrence. For example, patients with certain autoantibodies may have a higher risk of developing complications or experiencing disease flares, as is the case with systemic lupus erythematosus (SLE). By analyzing the pattern of autoantibody reactivity in cancer patients, clinicians can identify distinct subtypes associated with different clinical outcomes or treatment responses.

In the featured publication, baseline plasma autoantibodies (AAbs) were assessed in 336 DLBCL patients. In the discovery phase (n=20) HuProt™ was used to expound AAb profiles. In the verification phase (n=181), with a DLBCL-focused microarray, comparative results based on event-free survival at 24 months (EFS24) and lasso Cox regression models of progression-free survival (PFS) and overall survival (OS) were integrated to identify potential biomarkers. In conclusion this study identified a novel prognostic panel of CREB1, N4BP1, DEAF1, and UBAP2 AAbs that is independent of the International Prognostic Index (IPI) in DLBCL.

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Patient Response and Adverse Reaction Prediction

Autoantibodies can play a role in predicting patient response to treatment. Combining multiple autoantibodies into biomarker panels or signatures can improve the accuracy of stratification and predict both treatment response and immune-related adverse events (irAEs). Integrating autoantibody data with other clinical and molecular markers can enhance risk prediction models and facilitate personalized treatment approaches.

In the featured publication, HuProt™ microarray was used to identify autoantibody signatures that have the potential to predict both disease recurrence and immune-related adverse events in melanoma patients treated with checkpoint inhibitor adjuvant immunotherapy.

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Diagnostic Development

Autoantibodies are frequently detected in the serum or plasma of patients with a wide range of diseases, including autoimmune diseases such as rheumatoid arthritis, systemic lupus erythematosus (SLE), autoimmune thyroid diseases (e.g., Hashimoto's thyroiditis, Graves' disease), type 1 diabetes, and autoimmune liver diseases (e.g., autoimmune hepatitis, primary biliary cholangitis). The presence of specific autoantibodies is often a key diagnostic criterion for these diseases. Autoantibody panels consisting of several specific autoantibodies have the potential to be used for the diagnosis and classification of autoimmune diseases where diagnosis is challenging due to the heterogeneity of the clinical course, symptoms and severity of the disease.

The HuProt™ Proteome Microarray has been used to profile autoantibodies in patients with systemic lupus erythematosus (SLE). Further analysis with a larger patient cohort using SLE-focused microarrays identified 31 autoantibodies with potential as biomarkers for diagnosis and 11 for differential diagnosis of SLE.

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Vaccine Development and Safety

Measurement of vaccine-induced autoantibody responses can provide insights into the magnitude and specificity of the immune response elicited by a vaccine. Autoantibodies may also be involved in the safety evaluation of vaccines by assessing the risk of autoimmune reactions or immune-related adverse events (irAEs) associated with vaccination. Vaccines can occasionally trigger the production of autoantibodies targeting self-antigens, which on rare occasions can lead to autoimmune diseases or exacerbation of pre-existing autoimmune conditions. Incorporating autoantibodies into biomarker panels or profiles may improve the accuracy of safety assessments for vaccines. Combining autoantibody data with other immunological, clinical, and genetic markers can enhance risk prediction models and facilitate personalized vaccine recommendations.

Cancer vaccines work by stimulating the body's immune system to recognize and attack cancer cells specifically. These vaccines are designed to stimulate the immune system to trigger an anti-tumor response by utilizing tumor antigens.

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