Periodic Reporting for period 2 - DECOD-Ag (Revolutionary Platform to Decipher Immunogenicity of Tumour Neoantigens- the Ultimate Targets for Future Immunotherapies to Eradicate Cancer)
Reporting period: 2022-06-01 to 2023-09-30
Immunotherapy has revolutionised cancer treatment, providing survival benefits in patients with hard-to-treat tumours. Tragically, these benefits are unevenly distributed. Clinical efficacy varies dramatically between (and within) cancer types, and severe side-effects persist. To address this, new treatment strategies seek the rational design of personalised therapies to achieve a full eradication of most cancers. Optimal and highly personalised treatments can be achieved by inducing immune responses against the ultimate tumour-specific targets: immunogenic neoantigens (iNeoAg). Neoantigens are expressed mutant proteins caused by mutations in the tumour genome, of which only ~2% can induce an immune response (iNeoAg). Adoptive cell therapies and cancer vaccines targeting iNeoAgs promise exceptional specificity and limited toxicity against any mutation-carrying cancer, including skin, breast, and lung cancer, amongst many others. However, the selection of iNeoAgs from the thousands of tumour neoantigens is a major challenge.
To address this challenge, the DECOD-Ag consortium will realise an unbiased high-throughput transformative immunogenicity profiling platform that, for the first time, uniquely identifies iNeoAg.
To deliver DECOD-Ag radical vision, this project will combine several interdisciplinary and technological approaches across immunoinformatics, analytical biochemistry, high-throughput profiling, and oncogenomics.
Our goal is to significantly advance our fundamental understanding of anti-tumour immunity and with this revolutionise the emerging field of personalised immunotherapy, leading to the development of more potent, safer and durable cancer therapies with huge impact on society.
DECOD-Ag aims to achieve the following objectives:
1) To develop an in vitro system using a large, mutated library of sequences and antigen-presenting cells to test immunogenicity
2) To develop a high-throughput platform that can identify immunogenic mutations
3) To generate a predictive model of neoantigens immunogenicity by utilising machine learning algorithms
4) Deliver a clinical validation platform, an integrated platform combining multiple cutting-edge immune monitoring technologies to validate the prediction model on clinical data
To address this challenge, the DECOD-Ag consortium will realise an unbiased high-throughput transformative immunogenicity profiling platform that, for the first time, uniquely identifies iNeoAg.
To deliver DECOD-Ag radical vision, this project will combine several interdisciplinary and technological approaches across immunoinformatics, analytical biochemistry, high-throughput profiling, and oncogenomics.
Our goal is to significantly advance our fundamental understanding of anti-tumour immunity and with this revolutionise the emerging field of personalised immunotherapy, leading to the development of more potent, safer and durable cancer therapies with huge impact on society.
DECOD-Ag aims to achieve the following objectives:
1) To develop an in vitro system using a large, mutated library of sequences and antigen-presenting cells to test immunogenicity
2) To develop a high-throughput platform that can identify immunogenic mutations
3) To generate a predictive model of neoantigens immunogenicity by utilising machine learning algorithms
4) Deliver a clinical validation platform, an integrated platform combining multiple cutting-edge immune monitoring technologies to validate the prediction model on clinical data
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
- TCR expansions verified by TCR-seq.
3. Developed a novel pMHC:TCR identification assay (details are confidential - patent application in preparation)
- Preparation and optimisation of cell, antigen library and sequencing components
- First release of a bespoke bioinformatics pipeline to recover signal from assay data.
- Established limit-of-detection for first prototype (as low as 0.4% Ag-expression)
4. Developed a novel neoantigen screening assay (details are confidential - patent application in preparation)
- 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.
5. Developed an AI-based CD8+ immunogenicity predictor with state-of-the-art performance (patent pending)
- 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 performed; results presented at CICON23.
- 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
- TCR expansions verified by TCR-seq.
3. Developed a novel pMHC:TCR identification assay (details are confidential - patent application in preparation)
- Preparation and optimisation of cell, antigen library and sequencing components
- First release of a bespoke bioinformatics pipeline to recover signal from assay data.
- Established limit-of-detection for first prototype (as low as 0.4% Ag-expression)
4. Developed a novel neoantigen screening assay (details are confidential - patent application in preparation)
- 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.
5. Developed an AI-based CD8+ immunogenicity predictor with state-of-the-art performance (patent pending)
- 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 performed; results presented at CICON23.
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.
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.