Focus on colorectal and endometrial cancers
Correlative analysis of human tumours
Functional cancer models
Vaccines for cancer interception
Our research aims to understand the origins of tumours and the mechanisms through which they evade immune surveillance. We focus particularly on colorectal and endometrial cancers — two of the most common and clinically challenging tumour types — to uncover how genetic mutations and immune interactions shape disease progression.
A central theme of our work is antigen presentation, which plays a crucial role in determining whether the immune system can recognise and eliminate tumour cells. We investigate how DNA mutations characteristic of these cancers influence the repertoire of tumour-associated antigens and how T cells respond to them. By integrating multi-layered genomic and immunological data, we seek to define the key features that enable tumours to grow, adapt, and escape immune detection.
To achieve this, we employ a wide range of state-of-the-art sequencing technologies, including WGS, RNAseq, spatial transcriptomics, and TCRseq. These approaches allow us to dissect tumour–immune dynamics at single-cell and spatial resolution, providing a comprehensive view of cancer–immune co-evolution.
Our studies combine correlative analyses of human tumours with cancer models that recapitulate the mutations found in human HLA genes and their consequences for immune recognition. We use functional systems that faithfully mimic disease development and immune escape, alongside analyses of patient samples obtained through collaborations with academic and industry partners.
Ultimately, our goal is to translate these insights into novel biomarkers and therapeutic strategies, with a particular emphasis on cancer interception and therapeutic vaccination. Through this integrated approach, we aim to contribute to the development of more effective and personalised immunotherapies that improve patient outcomes.
Collaborations
Links
Our team
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David Church
Professor
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Yi Cai
DPhil student in Clinical Medicine
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Ryan Byrne
Senior Bioinfomatician
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Marcos Garcia Lacarte
Senior Bioinformatician
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Luciana Gneo
Senior Post Doc
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Laila Latifi
DPhil Student in Cancer Science
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Nikita Manoharan
Research Assistant
Single-cell AI-based detection of DNA mismatch repair deficiency - Cell Rep Med 2024
We present AIMMeR, an AI-based method for determination of mismatch repair (MMR) protein expression at a single-cell level in routine pathology samples.
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