Anthony Nolan is launching its new research strategy, to help us provide the biggest impact possible for patients. Our researchers are at the heart of this strategy, driving our life-saving work in exciting new directions. Here we’re diving into the work of Dr Michaela Agapiou in the Bioinformatics team, and finding out how the science of genetic data can transform the world of stem cell transplants.
Introduction to Bioinformatics!
Bioinformatics is the development and use of tools that allow us to process, analyse and understand biological data. As scientists get better at sequencing genes in more detail and faster than ever before, these mountains of data need to be stored, interpreted, and analysed… This is where bioinformaticians come in. I’ve previously called these scientists ‘biological data wizards,’ and Michaela tells me that she’s heard the term ‘DNA sleuths’ being thrown around. I hope these terms give you an idea of the importance of the role!
At Anthony Nolan, bioinformatics is especially important, because of the way we use the HLA genes to match patients and donors. For a patient waiting for a stem cell transplant, we must find a donor who is appropriately genetically matched – and this is done by looking at their unique collection of HLA genes, also known as a ‘tissue type.’ The HLA genes are fundamental in how the immune system works.
However, the HLA genes are the most varied genes ever discovered in humans, and you could have millions and millions of potential combinations of these genes. So matching a patient and a donor for a transplant is not as simple as, for example, finding someone with your same blood type.
Researchers are also constantly looking at ways that we can find the best matches for patients, as emerging science is showing that particular HLA genes may not need to be perfectly matched – early results suggest that in some cases, very specific mismatches could even be beneficial!
This is why advancing our understanding of genetic data is so important for the work that Anthony Nolan does, as it could introduce major changes in how we find the best possible matches for patients.
Interview with Dr Michaela Agapiou
In your own words, what are you researching?
I look at the Anthony Nolan register as a whole, alongside other important registers across the world, and study the different combinations of HLA genes that are used for matching. I can use this data to model how we might change our donor recruitment strategies in ways that could help our register provide the highest quality matches for the UK patient population.
Did you always know you wanted to be a scientist? Was there anything in particular that sparked your interest in science in the first place?
I enjoyed science and maths from a really young age – the first thing I remember wanting to be is a mathematician when I was in primary school. Then, when I was 14, I had an incredible biology teacher who would share science news articles with us if we were interested. I remember being particularly amazed by an article about using zebrafish to understand human genetics – that was probably my first exposure to modern biology research.
How did this interest in science lead you into research?
I studied biochemistry at university and my favourite modules were about ‘omics’, which is an umbrella term that covers several strands of biology research that involve large datasets in some way (e.g. genomics, proteomics, metabolomics), and to do this research bioinformatics is essential. Bioinformatics is an interdisciplinary field that uses computer science and engineering to handle biological data.
Over the last decade I learnt more and more coding alongside my biochemistry research, and finally after my PhD I made the switch to being a full time bioinformatician when I joined Anthony Nolan.
It’s a great area to be in because you can really hop between lots of different types of biology while having the essential skills. For example, in my PhD I was studying a protein important for sperm cell development in fruit flies.
Had you heard of Anthony Nolan before joining the bioinformatics team?
Yes! I joined the register in 2016 after the Match for Lara campaign. Lara was someone with similar mixed heritage to myself, and that was how I first learned about HLA and population genetics for stem cell transplantation, which are big aspects of my research now.
What’s the thing you enjoy most about working at Anthony Nolan?
I really enjoy working with all my peers, and feeling part of a larger organisation that’s driven by the same purpose. It can be unusual for research scientists to feel this sense of joint effort in a larger organisation. I love hearing updates from other parts of the organisation, from how we recruit people to the register to supporting donors and patients in their respective journeys.
We also get to hear about a wide range of different research from our other research teams, and it’s pretty special for biologists to get regular updates from people doing social or qualitative research, such as our patient reported outcomes research, which is directly connected to our own biological research.
How might your research influence the way we perform stem cell transplants?
Together with the Immunogenetics team, the Bioinformatics team is interested in modelling different ways of matching patients with donors. If we or others discover that the matching and mismatching of genes or particular features of genes are predicted to have an effect on patient outcomes then we can test the impact of these with our own datasets. We are also interested in how these models might go on to change the way donors on the register are found for patients. Ultimately we want our research to help patients to be matched with the donor that gives them the best predicted outcome.
What are the biggest barriers facing your research right now?
One of the focus areas of the new research strategy is improving and expanding our collaborations, and we're currently running a pilot study with another research team which is recruiting donors based on the analysis of our combined data. However, this study currently involves a relatively small number of people due to financial limitations, so I'd love to see these studies expanding to recruit more people to create a more robust study. More funding would also allow us to bring other collaborators into this or future studies.
Another challenge that plenty of data scientists struggle with right now is that our data is becoming increasingly larger and complex – and this is especially true in bioinformatics with the speed that sequencing technologies have developed. This is great for researchers, but it does mean we are regularly rebuilding software to adapt to the volume and complexity of our data. We’d love to be able to have more time and resources to make these adaptations as quickly as possible.
What’s the part of your work that you enjoy telling people about the most?
The extent of HLA diversity – there are an immense number of different versions of each of the HLA genes used in matching, and we are sequencing new ones all the time as well as finding people with known variants but in a combination we haven’t seen before. One of the first things people might learn about the HLA genes are that they are very diverse, and still after nearly two years of working with them I am sometimes shocked at the extent of this diversity when I am generating different models. The amount of diversity in these genes varies among different populations across the world but even the populations with the ‘least’ diversity of HLA types still have enormous genetic diversity. The average person living in the UK is about 40% likely to have a unique HLA type that we haven’t previously seen on our register of over 900,000 individuals.
What’s a biological fact that you find particularly mind-blowing?
I think it's related to scale again! If DNA from a single cell was unravelled it would reach about two metres in length, and if we multiply this by how many cells we have and how often these cells are regenerated it means that in an average human lifetime we make enough DNA to stretch up to two light years in distance. This is a very rough calculation, and I probably don’t fully understand the scale of it, but I think it's phenomenal to think about the mechanics going on inside our cells to keep that replication going amongst many other processes. Side note: I really love the biological art of David Goodsell, who produces beautiful paintings of some of the chaotic happenings inside cells.
Finally, where might we find you when you’re not in the office?