Revolutionary Platform to Decipher Immunogenicity of Tumour Neoantigens- the Ultimate Targets for Future Immunotherapies to Eradicate Cancer

Informations projet

DECOD-Ag

N° de convention de subvention: 964998

DOI

10.3030/964998

Date de signature de la CE 8 Mars 2021

Date de début 1 Juin 2021

Date de fin 31 Janvier 2027

Financé au titre de

• EXCELLENT SCIENCE - Future and Emerging Technologies (FET)

Coût total

€ 2 999 993,75

Contribution de l’UE

€ 2 999 993,75

Coordonné par

UNIVERSITY COLLEGE LONDON
United Kingdom

Periodic Reporting for period 3 - DECOD-Ag (Revolutionary Platform to Decipher Immunogenicity of Tumour Neoantigens- the Ultimate Targets for Future Immunotherapies to Eradicate Cancer)

Période du rapport: 2023-10-01 au 2025-01-31

Résumé du contexte et des objectifs généraux du projet

Immunotherapy has transformed cancer treatment, offering survival benefits in hard-to-treat tumours. However, these benefits remain uneven. Clinical efficacy varies across cancer types, and serious side effects persist. To overcome this, new strategies aim to design personalised therapies that eradicate cancer by targeting the most specific tumour markers: immunogenic neoantigens (iNeoAgs).

Neoantigens are tumour-specific mutated proteins, but only ~2% trigger an immune response (iNeoAgs). Adoptive cell therapies and vaccines targeting iNeoAgs offer exceptional specificity and minimal toxicity across cancers like skin, breast, and lung. However, identifying iNeoAgs among thousands of tumour neoantigens remains a major hurdle. The DECOD-Ag consortium will develop the first unbiased, high-throughput platform to uniquely identify iNeoAgs. This transformative project will integrate immunoinformatics, analytical biochemistry, high-throughput profiling, and oncogenomics to advance our understanding of anti-tumour immunity and revolutionise personalised immunotherapy, enabling safer, effective, and durable cancer treatments.
DECOD-Ag objectives:
1-Develop an in vitro system using a large mutated sequence library and antigen-presenting cells to test immunogenicity
2 - Create a high-throughput platform to identify immunogenic mutations
3 - Build a predictive model of neoantigen immunogenicity using machine learning
4- Validate the model using clinical data

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

1. Established high content mutant libraries of increasing complexity as a vast source of candidate neoantigens.
- Rational design of a targeted library.
- Design of a tagging system to track transcripts derived from synthetic DNA.
- Synthesis of target libraries (WT & mutated).
- Optimisation of oligo pool cloning and vector assembly.
- Streamlining of workflow to increase throughput by an order of magnitude, to enable cloning of ultra-high diversity libraries.
- Optimisation of vector for neoantigen expression and adapted for compatibility with scRNA-seq protocol.
- Optimisation of Random Mutagenesis protocol (maximizing single amino-acid mutation count)
- Positive control libraries established to support assay optimization.
- Library characteristics verified by NGS.
- MOI optimization, informed by in silico simulation.
- Optimisation of vector to improve immunogenicity underway.
- Neoantigen presentation confirmed by immunopeptidomics.

2. Established TCR libraries for screening
- Expansion and storage of naïve-T-cells from healthy donors
- In vitro stimulation of healthy donor derived T-cells using engineered aAPCs expressing large mutant Libraries
- Generation of aAPCs expressing multiple HLAs for later incorporation of large mutant libraires

3. Developed a novel pMHC:TCR identification assay
- Preparation and optimisation of cell, antigen library and sequencing components
- First release of a bespoke bioinformatics pipeline to recover signal from assay data.
- evaluated multiple approaches to increase limit of detectoin
- Established limit-of-detection not sufficient for screening our naïve T cell libraries, however we believe it can be used to screen tumour infiltrating lymphocyte samples as they contain a higher fraction of reactive T cells (median of 4% in our experience with melanoma and NSCLC)

4. Developed a novel neoantigen screening assay (details are confidential - patent application filed CleavER)
- Developed and optimised assay for complex library screening, including iterative vector design for neoantigen reactivity system and development of reporter cell line. Proof-of-Concept experiments complete, demonstrating that assay functions in control settings and is antigen-specific in the presence of antigen-specific T-cells.
- Assay functions in the context of a high content mutant library.
- CleavER validation using DNA barcode-labelled MHC multimers (from the Hadrup lab), showing that expanded naïve like cells with the library contained neoantigen-specific T cells against the CleavER identified hits
- Further increased the sensitivity of CleavER
- Established HANSolo as complementary tool to increase the generation of neoantigen hits.

5. Developed an AI-based CD8+ immunogenicity predictor with state-of-the-art performance (patent filed- ImmungenX)
- Established engineering infrastructure to expedite model development.
- Baseline model established using public data.
- Innovative model training regime included.
- Additional predictive features and architectures explored.
- Learning curve analysis to estimate data requirements
- Rigorous benchmarking for our predictor (ImmugenX) performed; results presented at CICON23
- ImmungenX results published in peer review journal (Hugh et al. PLoS Comput Biol. 2024 Nov 11;20(11)) highlighting ImmugenX as: a modular protein language model developed for immunogenicity prediction from peptide-MHC pairs. The model has comparable or improved performance compared to currently used models and encompasses three specific contexts: binding affinity, stability, elution. The pipeline is shareable and can be used for discovery/validation analyses.  

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

DECOD-Ag ambition is to deliver a profiling platform that, combined with a highly advanced in silico approach, can identify iNeoAgs and predict their immunogenicity in silico. The DECOD-Ag project aims to provide Proof-of-Concept for this vision by developing the platform enabling technologies piece by piece. After the project, DECOD-Ag platform and its approach could be widely applied as a robust and reliable system to identify new immunotherapeutic targets to treat most cancers. The realisation of this project will set a precedent in the field of cancer immunotherapy, equipping cancer immunologists and oncologists with novel capacity to improve therapeutic outcomes through the next generation of cancer immunotherapies, including adoptive T cell therapy or vaccines. As such, this project displays a radical long-term vision of science-, practice- and technology-enabled future in cancer medicine.

DECOD-Ag has the potential to lead to significant improvements in target identification for personalised immunotherapies, with great impact on:
-development of cancer immunotherapies and cancer vaccines: DECOD-Ag will set a precedent in the field of cancer immunotherapy, equipping researchers, drug and cancer vaccine developers to identify iNeoAg with therapeutic potential more efficiently, potentially reducing the chance of failure of new immunotherapies in clinical trials.
-cancer patients: DECOD-Ag will enable the development of biomarkers for stratification, more efficacious and more patient-tailored immunotherapies, with long-lasting responses and higher response rates
-society: the economic burden associated with cancer is enormous. As the treatment of cancer patients undergoes drastic changes over the next decade, from traditional one-size-fits-all regimes (e.g. chemotherapy) to targeted therapy, the long-term societal impact of DECOD-Ag could drastically alleviate the burden of difficult-to-treat cancers on patients, their families, and society as a whole.

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