RMISTCR - Rapidly mining the immune system for rare therapeutic T-Cell Receptors to treat solid tumour cancers
Lead Participant:
EXOGENE LIMITED
Abstract
**Need**
According to Cancer Research UK, ~367,000 new cancer cases are diagnosed every year in the UK. Cancer kills 165,000 people each year in the UK. There is the urgent need for "new curative treatments for solid tumour cancer... cheaply and effectively" (NHS-Long Term Plan).
**Challenge**
T-cell-receptor-based cell therapies are promising curative treatments for otherwise untreatable advanced solid tumours. Consisting of immune cells known as T-cells, they are derived from patients, then genetically reprogrammed with a cancer-targeting T-cell receptor (TCR), before being multiplied, and reintroduced into patients to destroy cancer cells.
Unfortunately finding these curative TCRs is a needle in a haystack problem and a major challenge using existing technologies in the lab such as mammalian display, which has an average discovery time of ~6 years and costs \\\>£10million/biopsy. There is an urgent need for computational methods to automate and streamline the process.
**Innovation**
We intend to remove this roadblock to identifying rare, cancer-targeting TCRs through the use of advanced AI (neural networks and deep learning algorithm architecture) and a novel high-throughput library-on-library wet lab screening technique (lentiviral display).
This will enable our platform to rapidly screen billions of TCRs from patients in order to computationally identify rare TCRs that can target and destroy cancer cells. The AI learns from the billions of interactions between TCRs and cancer cells that we analyse at our labs using lentiviral display.
**Impact**
Unlike currently available display assays, which focus on the analysis of one TCR at the time, our innovative wet-lab+AI approach enables rapid TCR screening to run concurrently, facilitating the comprehensive screening of hundreds of thousands TCR in ~4 weeks, (~£3.5k/biopsy), dramatically increasing the chances of identifying rare cancer specific TCRs.
This is a fundamental stepping stone to developing life-saving cell therapies for a wide range of untreatable solid tumours and helping millions of patients worldwide in their fight against cancer.
The technology will speed-up UK drug discovery, accelerate the development of new life-saving cancer treatments for previously untreatable solid tumours, and ultimately reduce treatment costs to the NHS.
The Biomedical Catalyst project will validate our approach, strengthen our AI- training data and enable us to demonstrate clinical validity using real blood samples. Whilst from a commercial perspective, the results of the project outputs will unlock a partnership with Immunocore, UK world-leading TCR therapy developer.
According to Cancer Research UK, ~367,000 new cancer cases are diagnosed every year in the UK. Cancer kills 165,000 people each year in the UK. There is the urgent need for "new curative treatments for solid tumour cancer... cheaply and effectively" (NHS-Long Term Plan).
**Challenge**
T-cell-receptor-based cell therapies are promising curative treatments for otherwise untreatable advanced solid tumours. Consisting of immune cells known as T-cells, they are derived from patients, then genetically reprogrammed with a cancer-targeting T-cell receptor (TCR), before being multiplied, and reintroduced into patients to destroy cancer cells.
Unfortunately finding these curative TCRs is a needle in a haystack problem and a major challenge using existing technologies in the lab such as mammalian display, which has an average discovery time of ~6 years and costs \\\>£10million/biopsy. There is an urgent need for computational methods to automate and streamline the process.
**Innovation**
We intend to remove this roadblock to identifying rare, cancer-targeting TCRs through the use of advanced AI (neural networks and deep learning algorithm architecture) and a novel high-throughput library-on-library wet lab screening technique (lentiviral display).
This will enable our platform to rapidly screen billions of TCRs from patients in order to computationally identify rare TCRs that can target and destroy cancer cells. The AI learns from the billions of interactions between TCRs and cancer cells that we analyse at our labs using lentiviral display.
**Impact**
Unlike currently available display assays, which focus on the analysis of one TCR at the time, our innovative wet-lab+AI approach enables rapid TCR screening to run concurrently, facilitating the comprehensive screening of hundreds of thousands TCR in ~4 weeks, (~£3.5k/biopsy), dramatically increasing the chances of identifying rare cancer specific TCRs.
This is a fundamental stepping stone to developing life-saving cell therapies for a wide range of untreatable solid tumours and helping millions of patients worldwide in their fight against cancer.
The technology will speed-up UK drug discovery, accelerate the development of new life-saving cancer treatments for previously untreatable solid tumours, and ultimately reduce treatment costs to the NHS.
The Biomedical Catalyst project will validate our approach, strengthen our AI- training data and enable us to demonstrate clinical validity using real blood samples. Whilst from a commercial perspective, the results of the project outputs will unlock a partnership with Immunocore, UK world-leading TCR therapy developer.
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
| EXOGENE LIMITED | £670,308 | £ 469,216 |
People |
ORCID iD |
| James Ravenscroft (Project Manager) |