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Decipher the T-cell receptor cancer-recognition code  

Founders' logos
Dr Michael Birnbaum

Dr Michael Birnbaum, Team Lead

Associate Professor of Biological Engineering

Massachusetts Institute of Technology




Germany, the Netherlands, Norway, UK, US


Cancer Research UK, National Cancer Institute, The Mark Foundation for Cancer Research


cancer immunology, computer science, high-throughput method development, structural biology

Solving TCR recognition and design via integrated high-throughput screening, structural, functional and computational approaches

Funded by:


By harnessing advances in high-throughput approaches and computational prediction, MATCHMAKERS will take an integrated approach to understand and predict how T cells recognise tumours, paving the way for personalised immunotherapies.

T cells are central players in the immune response, and harnessing their power in the development of immunotherapies is transforming the treatment landscape for some cancers. However, despite the clinical success of immunotherapies, their effects are not universal across cancer types and patient subsets. 

The molecular diversity of T-cell receptors (TCRs) has made it difficult to understand what tumour antigens are recognised by T cells, how T cells interact with the antigens presented by protein complexes on the surfaces of cancer cells (called major histocompatibility complexes or MHCs), and how these interactions differ in those who respond to treatment versus those who don’t.  

To drive this understanding forward, MATCHMAKERS will develop novel methods and new algorithms, and create large, integrated datasets of TCRs and peptide-MHCs (pMHCs) to improve the understanding of the interactions between MHC-bound antigens and TCRs. 

The team’s overarching goal is to predict what T cells recognise in individual tumours using simple laboratory tests and computational prediction, and develop the tools that can be used to design TCRs for personalised immunotherapies.   

Tackling the T-cell receptors challenge

MATCHMAKERS will focus on three key aims: 

  1. Generate datasets to capture the diversity of TCR-pMHC interactions

An obstacle to computational prediction of TCR-pMHC interactions is a lack of data to train computer algorithms. But recent advances in high-throughput approaches mean that it is now possible to map the interactions between TCRs and antigens at scale. 

MATCHMAKERS will use existing methods and develop new high-throughput approaches to match TCRs to the antigens they recognise, generating a dataset of thousands of TCR-pMHC pairs.  

As well as collecting these datasets from naturally occurring sources of T cell responses, such as human clinical samples and mouse models in the context of both cancer and immunity more generally, the team will generate synthetic antigens and TCRs in the lab to identify new TCR-pMHC pairs.  

  1. Understand how TCRs and pMHCs interact at the structural level

TCR-pMHC pairing is based on the 3D structure of the binding sites on both the TCR and pMHC. Using structural and biochemical analyses, MATCHMAKERS will look at the 3D structures of TCRs and their pMHC targets to understand how the two proteins physically interact. This knowledge will allow for more accurate predictions of how TCRs and MHCs work together, and will enable these interactions to be better exploited for treatment.  

  1. Develop AI tools that can be used for TCR-pMHC prediction

The team will integrate the data it collects to develop and train cutting-edge computer algorithms that can predict the antigens that are recognised by different TCRs, and design new TCRs for use in immunotherapies.

MATCHMAKERS aims to transform the understanding of how an individual’s T cells recognise their tumour and revolutionise the creation of TCRs for personalised treatment approaches. If the team succeeds, the knowledge gained will have implications beyond cancer, such as for infectious diseases, autoimmunity and allergies. 

Dr Michael Birnbaum

Dr Michael Birnbaum, Team Lead

Associate Professor of Biological Engineering

Our team has the combination of immunologists, computer scientists and engineers needed to create the technologies to make it possible to predict what a T cell recognises, which will enable the development patient-specific TCR immunotherapies.

Plain language summary

In the past decade, there has been a revolution in the treatment of some cancers using therapies that harness a person’s immune system. These therapies, called immunotherapies, use the power of T cells – a type of white blood cell that are key players in the immune response to infection and cancer. T cells have receptors on their surfaces that recognise ‘antigen’ molecules that come from pathogens or cancer cells. After a T cell attaches to a cancer antigen, the cancer cell is ‘marked’ for destruction by the immune system. 

Each of the millions of T cells in each person has a unique T-cell receptor that recognises different antigens. This diversity has limited our ability to know what exactly T cells recognise in the people who respond to treatment. If we can predict what cancer antigens a T cell ‘sees’ based on its receptor, we could find ways to create immunotherapies specific to each individual’s cancer and increase the overall effectiveness of these therapies across cancer types.  

To bring us closer to this goal, MATCHMAKERS will collect data on T-cell receptors and the different antigens they interact with. Using this data and recent advances in artificial intelligence, the team will build computer models to predict the antigens that are recognised by different T-cell receptors. These predictions will be used to design T-cell receptors that are specific to an individual’s cancer. 

If successful, MATCHMAKERS could transform the understanding of how an individual’s T cells recognise cancer cells from something that now can only happen in a few laboratories around the world, for a few people at a time, into a routine process. This will allow for better matching of patients with therapies that could lead to durable responses including remission and cure. 

Dr Michael Birnbaum
David Baker
Regina Barzilay
Dr Peter Bruno
Dr Dirk Busch
Dr Brandon DeKosky
Stephen Elledge
Dr Christopher Garcia
Professor Johanna Olweus
Professor Sergio Quezada
Dr Ton Schumacher
Dr Nik Sgourakis