Data-Rich Tissue Analysis for Clinical Discovery and Personalised Care
John Le Quesne is a clinician-scientist and a diagnostic histopathologist specialising in respiratory disease, who leads a research group at the University of Glasgow, based at the Cancer Research UK Scotland Institute. He explains how new technologies are increasing the amount of data produced, which will accelerate clinical discovery and personalised care.
Can you talk to us a bit about your research work?
At CRUK Scotland Institute, we tackle a lot of questions in cancer biology about tumour function and biomarker discovery. We use a variety of classical and novel digital image analysis and artificial intelligence methods to pull information out of tissue images.
You’re creating huge amounts of data through your research. What are the sorts of challenges you face in processing it all?
We generate more data than ever before. It would be amazing if people could actually agree on how to analyse these data sets. The plethora of options in taking a highly multiplex image to meaningful data is absolutely baffling for people coming in from the outside. It’s also quite difficult for us at times.
If a universal method could be discovered and applied, that would be a huge benefit.
Will this huge data generation exercise achieve clinical translation?
I think it is inevitable that these new and very data-rich ways of looking at pieces of tissue will make their way into clinical usage, or they really ought to. I find it hard to imagine a future for personalised cancer therapy, for example, which doesn't start to really make use of these technologies. But the barriers to implementation are just huge. It'll be really interesting to see how we get through that.
Can you describe what these barriers are?
They’re technological, ethical and relate to digital maturity. There’s also a challenge in getting the physical technology into a diagnostic lab and integrating it with digital pathology workflows.
Then there are EQA and regulatory issues around these much more complex assays. Finally, there’s the probable need to employ AI in image interpretation and all the challenges that faces as well.
It sounds like a huge challenge. What does this mean for the future of research?
We have come to appreciate that there is a whole career path around these new methods and technologies. It’s technical, but at the same time, it’s hands-on, getting involved in the analytical and biological side of research.
As an organisation and a speciality, we need to ensure that people from diverse backgrounds get fully exposed to all sides of what is a vital part of what we do.
There are some incredible careers and opportunities that will emerge that involve using these innovative technologies in industry, academia and other places.
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