NeoScreen MHC/Peptide Binding Assays

NeoScreen from Immunitrack is a leading platform technology that facilitates accurate and high-throughput studies of MHC I and II interactions with epitopes of interest. The platform contributes to increasing the safety and efficacy of new biologics by yielding reliable data that can be used for early assessment of immunogenic epitopes.

MHC/Epitope Stability is Critical for Immunogenicity Assessments

The contribution of cellular responses, and in particular those from CD8+ T cells, is increasingly recognised as a critical consideration when designing new anti-infective vaccines e.g. against flaviviruses^{1, 2} and new cancer vaccines.³ The majority of T cell epitopes used in vaccine development are identified using in silico prediction platforms. The most widely used prediction platform for such epitopes is the Immune Epitope Database and Analysis Resource (IEBD). ^4-8 IEDB identifies epitopes using an algorithm that incorporates multiple aspects of MHC class-specific affinity, which are based on binding motifs as well as experimental affinity measurements.

Although widely used, there are major limitations to affinity-based strategies. For any epitope to be immunogenic, it must be able to bind a compatible MHC molecule and remain bound for long enough to be presented to and recognised by T cells to elicit an immune response. In other words, stable MHC/epitope interactions are required for immunogenicity.

Since non stable epitopes continuously bind and detach from MHC molecules in vivo, experimental and/or in silico affinity measurements alone may identify false positives, resulting in unreliable predictions about epitope immunogenicity. Identifying such false positives early during vaccine development enables optimisation to include immunogenic epitopes, ultimately leading to vaccines with greater efficacy.⁹

NeoScreen Yields Accurate Data About MHC/Epitope Stability

For MHC I, it is well documented that the stability of the MHC/epitope complex is a better indicator of immunogenicity than affinity.¹⁰ Data generated in-house by Immunitrack and through numerous partnerships with researchers and drug developers suggests that this is also the case for MHC II. However, MHC/epitope stability is a parameter that is often overlooked when determining epitope immunogenicity.¹¹

At Immunitrack, we address MHC/epitope interactions in a unique way by conducting affinity and stability assessments of MHC/epitope complexes through our NeoScreen platform. NeoScreen encompasses 2 types of assay: ultra sensitive affinity-based assays (that can measure Kds in the low nanomolar range) combined with unique stability assays, allowing researchers to accurately identify the epitopes that bind most stably to MHC I and II molecules from large pools of potential epitopes.

Use NeoScreen to Rank Epitopes According to Their Immunogenicity

By combining affinity and stability assays, we are able to eliminate up to 95 % of false positives and provide reliable data that can be used to rank epitopes according to their immunogenicity. Although NeoScreen is an in vitro platform, it has demonstrated remarkable accuracy in finding validated T cell epitopes as shown in Figure 1 below.

Areas of Application for NeoScreen

• Immuno-oncology: Neo-epitope selection for personalised cancer vaccines, and screening for novel neo-epitopes
• Vaccine development: selection of immunogenic epitopes from virus and bacteria
• De-immunisation of biologics: reduce the propensity for anti-drug antibodies by identifying and removing CD4 T cell epitopes in biologics
• TCR Off-Target: de-risking candidate TCR and TCR-like therapies.
• Biomarker development and precision medicine: customised peptide-MHC complexes to monitor specific T cell populations in patients or mice. Identify and exploit neo-epitopes as biomarkers to predict responders from checkpoint inhibitor cancer therapies.

What You Get from NeoScreen

• By combining affinity and stability assessments of MHC/epitope interactions, the NeoScreen platform reduces the number of inaccuracies and false positives that may occur in studies that are based on affinity measurements alone.
• Immunitrack’s outstanding expertise in MHC II manufacture provides the unique possibility for immunogenicity screening using MHC II. Up to 80% of relevant neo-epitopes from cancer may be presented by MHC II.

NeoScreen combines more than a decade of experience in MHC/epitope binding assays with a high level of automation, allowing Immunitrack to rapidly and accurately screen libraries with thousands of (neo-)epitopes, from viruses and bacteria, as well as biologics, vaccines and cancers. Depending on the desired immune response our clients and partners can then pick the ‘best’ epitope to take forward for therapeutic development.

Immunitrack have used NeoScreen to provide analysis for some of the world’s leading research groups working in the field of immuno-oncology, and the assays have been validated in several NIH-funded projects and peer-reviewed articles, accounting for more than 100,000 Kd measurements in the Immune Epitope Database (IEDB).

How Does NeoScreen Compare to Alternative Prediction Approaches?

Want to Know More?

To hear more about NeoScreen and find out how this might help your research and development, contact us by email at info@immunitrack.com or use our online contact form ▸

Quotations and Ordering:

You can request a quotation or place an order by writing to us at info@immunitrack.com or by using our online contact form ▸

In order to provide you a quote we ask you to provide the following information:

• The number of affinity/stability measurements you require for each MHC allele.
• A list of the alleles you wish to test.
• Are you providing the peptides to be analysed, and if yes in which format?

Publications involving NeoScreen Technology

Smit, M.J, et al. First-in-human use of a modular capsid virus-like vaccine platform: an open-label, non-randomised, phase 1 clinical trial of the SARS-CoV-2 vaccine ABNCoV2. The Lancet Microbe. 2023.

Khan et al. Characterization of HLA‐A* 33: 03 epitopes via immunoprecipitation and LC‐MS/MS. Proteomics. 2022: 2100171.

Bing, S.J. et al. Differential T cell immune responses to deamidated adeno-associated virus vector. Methods & Clinical Development. 2022; 24: 255-267.

Ouspenskaia, T. et al. Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer. Nature Biotechnology. 2022; 40(2): 209-217.

Caushi, J. et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature. 2021 596: 126-132.

Giacomo, O. et al. Phenotype specificity and avidity of antitumor CD8+ T cells in melanoma. Nature. 2021 596: 119-125.

Prachar M. et al. Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools. Scientific Reports, 2020, 10: 20465.

Prachar, M. et al. COVID-19 Vaccine Candidates: Prediction and Validation of 174 SARS-CoV-2 Epitopes. Biorxiv. 2020

Sarkizova, S. et al. A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat Biotechnol. 2020 38:199-209 .

Zhang, J. et al. The combination of neoantigen quality and T lymphocyte infiltrates identifies glioblastomas with the longest survival.
Commun Biol. 2019 23(2):135.

Smith, K.N. et al. Persistent mutant oncogene specific T cells in two patients benefitting from anti-PD-1. J Immunother Cancer. 2019 7(1):40.

Le, D.T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science. 2017 357(6349):409-413.

References Cited

1. Grifoni, A. et al. Prior Dengue Virus Exposure Shapes T Cell Immunity to Zika Virus in Humans. J Virol 2017,91(24):pii: e01469-17
2. Turtle, L. et al. Human T cell responses to Japanese encephalitis virus in health and disease. J Exp Med 2016,213(7):1331-52.
3. Sahin, U. & Tureci, O. Personalized vaccines for cancer immunotherapy. Science 2018,359(6382):1355-1360.
4. Kim, Y. et al. Immune epitope database analysis resource. Nucleic Acids Res 2012,40 (Web Server issue), W525-30.
5. Nielsen, M. et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 2003,12(5):1007-17.
6. Lundegaard, C. et al. Major histocompatibility complex class I binding predictions as a tool in epitope discovery. Immunology 2010,130(3):309-18.
7. Peters, B. et al. The immune epitope database and analysis resource: from vision to blueprint. PLoS Biol 2005,3(3):e91.
8. Sidney, J. et al. Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res 2008,4(2).
9. Koşaloğlu-Yalçın, Z. et al. Predicting T cell recognition of MHC class I restricted neoepitopes. Oncoimmunology 2018,7(11):e1492508.
10. Harndahl, M. et al. Peptide-MHC class I stability is a better predictor than peptide affinity of CTL immunogenicity. Eur J Immunol 2012,42(6):1405-16.
11. Blaha, D. T. et al. High-Throughput Stability Screening of Neoantigen/HLA Complexes Improves Immunogenicity Predictions. Cancer Immunol Res 2019,7(1):50-61.
12. Zhao W. & Xinwei S. Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes. PLoS Comp. Biol. 2018, 4(10):e1000173.