"Google Earth" for cancer

How scientists are literally zooming in on cancer
02 May 2017

Interview with 

Dr Josephine Bunch, The National Physical Laboratory


USA at night


The charity Cancer Research UK recently announced the winners of four of its new Grand Challenge awards, which are multi-million-pound grants aimed at tackling some of the biggest questions in cancer today. One of the lucky recipients is Dr Josephine Bunch from the National Physical Laboratory in London, who’s leading an international consortium aiming to create a kind of ‘Google Earth’ for cancer, mapping the multitude of molecules that make up a tumour and eventually enabling researchers to literally zoom right into the heart of cancer. Kat Arney spoke to her.

Josephine - The analogy of a Google Earth view of cancer came originally from Cancer Research UK and one of their early blogs describing the problem. We’ve followed this through really and it works very well as a description of how you might overlay information and so if we liken this to the true Google Earth satellite imaging, this is a series of photographs taken at a range of magnifications that allows us, in the end, to zoom down into a particular location. We’re going to do exactly the same thing using our mass spectrometry imaging data and we also hope to go as far as being able to annotate these maps and make that information available for other people.

Kat - So people could, effectively, browse and go, “oh, I wonder what’s in here?”, and dig around in the same way you can on Google Earth?

Josephine - Yeah, that’s exactly right. At this stage it’s a little bit too early for us to know how much of that information will actually make browsable in that way. We don’t necessarily want to flood a database with lots of things which may not be useful but also, we don’t want to decide, at this stage, what could be useful. So one of the challenges for this is how do we store and make useful that kind of volume of data, and it’s not an easy problem.

Kat - Let’s zoom in a little bit into the kind of techniques that you’re using. How does this actually work?

Josephine - All of our imaging techniques are based on mass spectrometry. Mass spectrometers measure the molecular weight of something so really this is just a way of identifying the molecules in a sample. We move to a piece of tissue and we entice as many molecules out of that location as tissue as we can into our mass spectrometer where we’re able to identify them and with a move to the next location on tissue and do it again. So what we’re doing is building up an image piece by piece of as many molecules as we can extract from tissue at every location.

Kat - How do you weigh a molecule?

Josephine - We weight a molecule by creating an ion. So we start off with an uncharged molecule and we place upon it a charge. We do this by either giving it a proton usually, or maybe by losing an electron. Once it’s charged, we can then manipulate it and sort it according to its mass using a whole variety of different methods. This enables us to build up an understanding of which molecules were present at a different location.

If you imagine this is a little bit like when you go to the supermarket with an enormous amount of mixed change in your pockets and you throw it into a machine that helps sort it into its individual coins, the different types that you have, and we’re really doing that in our mass analyser. We’re creating charged ions, with all the molecules that are present, and then we’re sorting them very fast according to the mass to charge ratio to produce a mass spectrum, which is a kind of graph that gives us information about the relative amounts of different ions of different mass.

Kat - So you’re really mapping, in very fine detail, exactly where the molecules are in any sample of tissue, of cancer, of normal tissue?

Josephine - That’s exactly right and, importantly, we’re not deciding what we’re looking for first. The techniques that we’re using  may not currently compete with the kind of image information spatially that you can derive from more standard optical techniques. So basic microscopy is really good at giving us a really well resolved image of the distribution of something. But it’s typically allowing us to look down a microscope at a stained tissue and that stain is showing us where particular molecules of interest are, but we have to have stained them. So, in our approach, we don’t know what we’re looking for. That’s the whole point, if we knew what we’re looking for we probably know an awful lot more about tumours and tumour biology.

So we’re taking a different approach and a much more unbiased, or unsupervised approach which is we don’t know which molecules we want to look for. So we’re going to use a variety of different mass spectrometers, useful for measuring all different kinds of molecules, many, many at once, and that will help us then to produce images of more things than we would be able to using a microscope.

Kat -  I guess it’s moving from saying here’s a town, show me where the pubs and the bookshops are to here’s a town - what kind of establishments are here?

Josephine - Yeah, that’s exactly right. Afterwards, when we have that information, we can put back together a map that shows just where the pubs are, or a map that shows where the pubs are and the car parks are, or a map that just shows all of the buildings and we can start to work out patterns amongst them.

Kat - Ultimately, once you’ve built that people can zoom in and you can see all the different molecules at all these different levels, what then do you do with that information? How is this going to help us beat cancer.

Josephine - I think there’s a few different things that we hope this information will enable. Firstly, we hope that it will provide unprecedented information at the molecular level about tumour biology. So experts in this area will hopefully enormous wealths of information in our data and be able to better understand pathways or things that might be awrie in cancerous samples compared to more normal tissues. If we can understand some of these differences, then we might start to determine signatures, or even the presence of individual molecules, which could alert us to the presence of a tumour earlier than we currently can.

Kat - So that would be for earlier diagnosis?

Josephine - Exactly. This could be for earlier diagnostics and, hopefully, more accurate diagnostics as well. Then another layer of the ambition or the hope is that by better understanding of these changes at molecular level and better understanding of tumour biology, we can help develop more appropriate, more targeted medicines. Because I think part of the challenge of this project is it’s recognising the heterogeneity of tumours. Tumours are very different, different people’s tumours are very different and I think the more we can understand that, then the better and more personalised therapies that we can develop. If we can pair that with better and more personalised diagnostics, then we we might be able to treat this disease more effectively.

Kat - It does seem to me that it’s really only in recent years that technology, techniques that computing has accelerated to a point where we can do this. It must be incredibly exciting to think we can actually do this now?

Josephine - I know it’s fantastically exciting. It’s such an exciting time for this project and I think we’ve got to a stage where the capabilities of these instruments paired with, as you say, the advances in computational science have meant that we can really start mining this amount of information. I think that’s not to say that there aren't challenges. We still want to produce even more detailed images and we still wish we had better sensitivity and could measure even fewer molecules in a sample. And we’re still going to come up with real challenges as we try and mine this enormous amount of data.

To put it in perspective, one of our images might be 100 gigabytes, and we’re going to collect several of those a day across several sites for five years. So we’re talking about an enormous amount of new information.


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