HLA Informatics Group
HLA Informatics Group Leader
Professor Steven GE Marsh
What is HLA?
The HLA family of genes encode proteins that form part of our immune system and are critical in allowing it to distinguish between ‘self’ (our own cells) and ‘non-self’ (i.e., pathogens, which cause disease). They are the most diverse genes in our genome and the principle genetic factor for matching stem cell transplant patients with potential donors.
Improving our understanding of HLA gene diversity within the population and determining how mismatches in HLA genes affect transplant outcomes are both key to ensuring we can select the best possible match for every patient.
What do our researchers look at?
Our HLA Informatics Group consist of two teams that combine laboratory-based research with computational analysis to determine how our HLA genes, along with other genetics factors influence transplant success.
This relatively new field utilises computer science, mathematics and biology to address complex biological questions. This is achieved through statistics, building mathematical models of biological processes, and using computer science to store and analyse DNA sequences.
At Anthony Nolan, the Bioinformatics Team:
- Maintains an internationally recognised database of all known HLA gene variants. This resource is the world’s leading authority on cataloguing, naming and publishing data on these genes.
- Creates mathematical models to better understand the make-up of our donor register and measure the amount of HLA genetic diversity present within different populations.
- Develops computing and software solutions for analysing complex data created by new and improved methods of sequencing HLA genes.
This area of research focuses on how our genes are able to influence our immune system. This knowledge can then be applied to determine who is the best possible donor for a patient undergoing an unrelated donor haematopoietic stem cell transplant.
At Anthony Nolan, the Immunogenetics Team:
- Determines how matching HLA genes effects transplant outcome and how sequencing genes at a higher resolution improves our understanding of the process. They also investigate where and when HLA mismatching can be tolerated.
- Identifies other genetic and clinical factors that influence transplant success.
- Designs and develops specialised DNA sequencing assays to characterise new complex immunogenetic factors, including non-HLA genes of interest.
What impact will this have for stem cell transplant patients?
This research continues to change practice at Anthony Nolan as well as clinical practice, both nationally and internationally. Our findings on what makes an optimal match have already led to improvements in how we perform HLA tissue typing, refined our donor recruitment criteria and enhanced our donor selection strategies.
By improving our understanding of the genetic variation found within key genes, we can determine the genetic factors that make a match successful. We can then use this information when assessing the diversity of the Anthony Nolan register to better understand how to meet the needs of all our patients.
Summaries of the group’s current research projects are available here.
- Dr James Robinson, BSc, MSc, PhD, Head of Bioinformatics
- Dr Michaela Agapiou, BSc, PhD, Post-Doctoral Bioinformatics Research Scientist
- Dominic Barker, BSc, MSc, Bioinformatics Research Scientist
- Gabriel Benitez, BSc, MSc, Bioinformatics Research Scientist
- Richard Natarajan, MEng, Bioinformatics Developer
- Michael Cooper, BSc, Research Scientist and Database Curator
- Sebastian Hopper, BSc, MSc, Research Scientist and Database Curator
- Dr Richard Szydlo, PhD, Medical Statistician (part-time)
Bioinformatics Team Leader
Dr James Robinson is a bioinformatics scientist with over 25 years of experience in developing bioinformatics solutions within the transplant field. He has a BSc in Genetics from the University of Nottingham, an MSc in Biological Computation from the University of York and has recently completed his PhD at University College London.
As part of his MSc, he obtained a placement in the Bioinformatics Department of the Wellcome Sanger Institute. This furthered his interest in applying computer science to biological problems, particularly the analysis, storage and classification of DNA sequences. From here he moved to Cancer Research UK and started working on a reference database for the sequences of genes most important to a successful transplant. This work was done in collaboration with the EMBL-European Bioinformatics Institute with whom he still collaborates today. He is also an honorary Lecturer at the Cancer Institute of University College London.
- Dr Neema P Mayor BSc, PhD, Head of Immunogenetics Research
- Dr Thomas R Turner BSc, PhD, Senior Post-Doctoral Research Scientist
- Charlotte A Cambridge BSc, Senior Research Assistant
- Albert French MSci, Research Assistant
- Mia Holloway MBiochem, Research Assistant
- Jonathan AM Lucas MSci, PhD Student
- Shelley Hewerdine BSc, Research Data Manager
- Elizabeth Minshall, BSc Industrial Placement Student
Immunogenetics Team Leader
Dr Neema Mayor is an immunogeneticist with over 20 years’ experience in the field of HLA typing and matching for unrelated donor haematopoietic stem cell transplantation. She has a BSc in Human Biology and obtained her PhD in Haematology from University College London.
Neema joined Anthony Nolan in 2001 as part of the Patient/Donor project, an on-going study that aims to identify how genetic and clinical factors of the patient and donor impact on haematopoietic stem cell transplant outcome. She is now Head of Immunogenetics Research within the HLA informatics group, managing a team of lab-based scientists and students looking at ways to improve the outcome of haematopoietic stem cell transplants to treat blood cancer and blood disorders. She is also an Honorary Lecturer at the Cancer Institute at University College London, an Associate Editor for the journal Human Immunology, and is on the Editorial Board of HLA and the International Journal for Immunogenetics.
Current research projects
The Patient/Donor Project
This on-going study of over 2,500 patient and donor paired samples, coordinated by Anthony Nolan, aims to identify how genetically similar, or different, patients and their unrelated donors are and correlate this with the transplant outcome. These studies include looking at which HLA genes we should be matching for and to what resolution, as well as what other genetic factors should be included during the matching process to improve the transplant outcome for all patients.
To date, we have demonstrated the importance of the following:
- Using 12/12 ultra-high resolution HLA matched donors where possible.
- Including the HLA-DPB1 gene into our matching algorithms.
- The beneficial impact of younger donor age on patient outcome.
- Combining CMV status matching with HLA matching to improve transplant outcomes.
- Identifying which HLA mismatches to avoid.
- How KIR genes affect transplant outcome for patients with AML.
We are currently typing our project cohort for their full-length HLA-DRB1, -DQB1 and -DPB1 genes, aiming to show for the first time, the impact of matching at this resolution on patient outcome. We are also expanding our panel of HLA gene typing to include, among others, HLA-E.
This is a national, collaborative study with all UK centres that perform unrelated-donor transplantation being part of the project. The results of the Patient/Donor Project studies are translated into practice at Anthony Nolan and influences clinical practice worldwide.
Presence of donor-encoded centromeric KIR B content increases the risk of infectious mortality in recipients of myeloablative, T-cell deplete, HLA-matched HCT to treat AML.
Bultitude WP, Schellekens J, Szydlo RM, Anthias C, Cooley SA, Miller JS, Weisdorf DJ, Shaw BE, Roberts CH, Garcia-Sepulveda CA, Lee J, Pearce RM, Wilson MC, Potter M, Byrne JL, Russell NH, MacKinnon S, Bloor AJ, Patel A, McQuaker IG, … Marsh, SGE
Bone Marrow Transplantation (2020) 55(10), 1975–1984.
Recipients Receiving Better HLA Matched Haematopoietic Cell Grafts, Uncovered By A Novel HLA Typing Methodology, Have Superior Survival: A Retrospective Study
Mayor NP, Hayhurst JD, Turner TR, Szydlo RM, Shaw BE, Bultitude WP, Sayno JR, Tavarozzi F, Latham K, Anthias C, Robinson J, Braund H, Danby R, Perry J, Wilson MC, Bloor AJ, McQuaker IG, MacKinnon S, Marks DI, Pagliuca A, Potter MN, Potter VT, Russell NH, Thomson KJ, Madrigal JA, Marsh SGE
Biol Blood Marrow Transplant. 2019 Mar;25(3):443-450
Recipient/donor HLA and CMV matching in recipients of T-cell-depleted unrelated donor haematopoietic cell transplants.
Shaw BE, Mayor NP, Szydlo RM, Bultitude WP, Anthias C, Kirkland K, Perry J, Clark A, Mackinnon S, Marks DI, Pagliuca A, Potter MN, Russell NH, Thomson K, Madrigal JA, Marsh SGE.
Bone Marrow Transplantation (2017) May;52(5):717-725
The Feasibility Project
The Feasibility Project builds on the work of the Patient/Donor Project by using the data from the retrospective studies to determine the probability of finding the best matched donor, and how to improve our chances of finding this donor. Following on from our most recent clinical publication, we are currently working on determining the probabilities of finding a donor when we consider HLA-DPB1 matching and allow for permissive mismatching, as well as CMV status matching. This project will help determine the best approach for how we select our donors for each patient, based on their immunogenetic profile.
The Bioinformatics Team maintain a number of internationally recognised databases of gene specific variation. These databases provide extensive information on the variation of the key genes involved in transplantation. They are published in collaboration with the EMBL-European Bioinformatics Institute, and internationally recognised as the gold-standard for providing data on the genes of interest:
The IPD-IMGT/HLA Database provides sequences of the human major histocompatibility complex (MHC) and includes all official sequences for the World Health Organisation Nomenclature Committee for Factors of the HLA System. The IPD-IMGT/HLA Database currently contains over 28,000 allele sequences. In addition to the DNA sequences, it holds detailed information about the material the sequence was derived from and how the data was validated.
The IPD-KIR Databases provides a centralised repository for sequences of Killer cell Immunoglobulin-like Receptors (KIRs). KIRs are members of the immunoglobulin superfamily (IgSF), formerly called Killer Cell Inhibitory Receptors. They are composed of two or three Ig-domains, a transmembrane region and cytoplasmic tail, which can in turn be short (activatory) or long (inhibitory). The Leukocyte Receptor Complex (LRC) which encodes KIR genes has been shown to be polymorphic, polygenic and complex like the MHC.
The Bioinformatics Team also work with other groups interested in providing similar databases for other polymorphic gene systems.
Barker DJ, Maccari G, Georgiou X, Cooper MA, Flicek P, Robinson J, Marsh, SGE
Nucleic Acids Research (2023), 51(D1), D1053–D1060.
Distinguishing functional polymorphism from random variation in the sequences of >10,000 HLA-A, -B and -C alleles.
Robinson J, Guethlein LA, Cereb N, Yang SY, Norman PJ, Marsh SGE, Parham P
PLoS Genetics (2017) 13(6), e1006862.
Anthony Nolan maintains a large register of potential donors, each of whom have had their HLA genes sequenced. This data is an ideal source of information for studying HLA diversity in different UK populations, including those of minority ethnic backgrounds.
We are undertaking several projects to characterise the HLA genetic diversity of the UK and other populations around the world. This will identify regions with higher diversity within the UK and allow us to improve and refine our recruitment strategies. It will give us a better understanding of the needs of our patients and contribute to creating a more diverse register that will increase the chances of every patient finding a suitable donor.
This work requires the team to develop novel software to complete the complex mathematical and statistical analysis needed to compare the HLA tissue types of nearly one million individuals. This has also meant looking at new computing methods and hardware to provide a suitable platform for analysing these large data sets.
The HLA diversity of the Anthony Nolan register.
Leen G, Stein JE, Robinson J, Maldonado Torres H, Marsh SGE
HLA (2021) 97(1), 15–29.
New DNA Sequencing Technology
To study complex immunogenetic markers, we need to use DNA sequencing techniques that allow us to sequence more of the gene than has ever been possible previously, and to generate highly accurate data.
With support from The Wolfson Foundation, Anthony Nolan has been able to invest in a new third-generation sequencing platform, the Pacific Biosciences Sequel System, that will allow us to generate sequences of a quality that has not been achieved previously. The Immunogenetics Team are working to develop our typing assays on this new platform, to increase the efficiency that is now possible and to expand the portfolio of genetic markers that we can type for.
As our sequencing technologies improve, so does our need to store, analyse and report more complex data. The Bioinformatics Team are working alongside the Immunogenetics Team to develop bespoke automated workflows, and software for the processing of this data, generated by different sequencing platforms.
The implementation of this work enabled Anthony Nolan to be one of the first registries in the world to provide clinical HLA typing using the Pacific Biosciences RS II sequencing technology. Work is also currently underway to implement workflows for the Pacific Biosciences Sequel system using Google Cloud to provide the computing infrastructure.
Single molecule real-time DNA sequencing of the full HLA-E gene for 212 reference cell lines.
Lucas JAM, Hayhurst JD, Turner TR, Gymer AW, Leen G, Robinson J, Marsh SGE, Mayor NP
HLA (2020) Jun;95(6):561-572
Single molecule real-time DNA sequencing of HLA genes at ultra-high resolution from 126 International HLA and Immunogenetics Workshop cell lines.
Turner TR, Hayhurst JD, Hayward DR, Bultitude WP, Barker DJ, Robinson J, Madrigal JA, Mayor NP, Marsh SGE
HLA (2018) 91(2), 88–101.
HLA Typing for the Next Generation.
Mayor NP, Robinson J, McWhinnie AJ, Ranade S, Eng K, Midwinter W, Bultitude WP, Chin CS, Bowman B, Marks P, Braund H, Madrigal JA, Latham K, Marsh SGE
PloS One (2015) 10(5), e0127153.