With the ever-increasing potential of new technology and the exponential growth of the life sciences field, researchers are always running into new problems to solve. In this interview series, we get scientists’ opinions on the ‘Big Challenge’ in their field and the steps being taken to address it. From new and unique hurdles to fresh takes on common problems, we dive into the complexities of the research landscape.
In this interview, we chat to Trevor Graham (Institute of Cancer Research) about the ‘big challenge’ in cancer evolution and how this impacts drug resistance.
FLG: What is your background and role?
Trevor: I’m a Professor of Cancer Genomics and Evolution at the Institute of Cancer Research (ICR) in London, and I also lead the Centre for Evolution in Cancer.
My field is cancer evolution, so I use ideas from evolution to understand how cancers change over time and change through space. Then, using our knowledge of what causes those changes, we can exploit the mechanisms that we’ve learned to improve outcomes for patients. We try and predict who with a premalignant condition – a kind of pre-cancerous disease – is actually going to get cancer, so we can deliver care to just those who need it and send home people who have nothing to worry about. We also think about how cancers change when we treat them – because it’s those changes that occur during treatment that stop therapies working – so that we can give different treatments, or give the treatments differently, in order to anticipate and prevent those changes and increase treatment effectiveness. They’re the two big clinical goals.
FLG: What is the ‘big challenge’ in your field?
Trevor: Oh, there are lots of big challenges. The abstract problem is that, these days, we have a whole tonne of data, we have more and more data all the time. In genomics, the cost of sequencing a genome is falling incredibly rapidly. When I first started my research career as a PhD student, we were doing Sanger sequencing, and I produced a few thousand base pairs of sequencing total over my whole PhD. Whereas now, we do that in a few seconds. We sequence whole genomes routinely, for an even lower cost. So, we have just an absolute wealth of data.
The real challenge now is not generating data, but making sense of the data that we already have. There was a really interesting editorial in Nature a year or so ago, that basically said ‘we need more theory in biology, we need less data and more theory.’ I think that’s a really insightful thing to say, you need to make sense of the data, we need new ideas, and we need new ways of conceptualising those ideas and predicting how those ideas will be represented in data. That space, I think, is extremely interesting. We try and work in that space, and others do as well. I feel like that’s where some really exciting things will happen in the coming years.
From a more applied perspective, the challenges I highlighted before are really important. From a premalignant disease perspective, it’s about working out who’s going to get cancer, and equally importantly, who isn’t, so that we can apportion care appropriately. And from a cancer treatment perspective, the outcomes for advanced cancers haven’t changed very much, with a few notable exceptions, like cancers where immunotherapy works. As we find new drugs, they work, they give people extra time, but we measure the outcome difference in months. We really need radically new approaches to tackle the problem of drug resistance, which is all about cancer evolution, to be able to dramatically increase the effectiveness of treatment.
FLG: Why should people care about this?
Trevor: It’s increasingly clear that cancers do change, they change over time, and they change from when they’re premalignant disease to being malignant. And that change is obvious. The cancer suddenly becomes invasive and can spread around the body. And they change through treatment. That change is slightly less obvious, but initially that cancer will respond to treatment, and then they change so that they don’t respond to the treatment anymore. I think what’s really important, a really big challenge, is to be able to anticipate those changes. Because if we know what’s going to happen in the future, or we have a good guess at what’s going to happen in the future, we can start to change the way we’re doing things to be anticipatory about those changes. That will make our clinical care more effective.
FLG: What is being done to tackle the issue, or what should be done to tackle the issue?
Trevor: Well, I’ve highlighted two challenges for you. One is the clinical challenge about understanding how cancers change, and the other one is the basic challenge about making sense of this wealth of data. I think I’ll start with the more abstract one – making sense of the data. One thing that I see in my own lab, and I see in many of my colleagues’ labs, is that we really struggle to attract enough people from quantitative backgrounds who will come in equipped with the skills that they’ll need to be able to do this work. So, I think a really important solution for the future is to train the next generation of quantitative scientists who can make those theoretical leaps.
On the more clinical side, there are lots of challenges. One is related to the other, which is, if we have a new theory to understand the data, we’ll be able to use that theory to understand how cancers are changing. I think there’s lots of thinking and infrastructure surrounding this idea that cancers change.
The way a clinical trial is set up at the moment for a new cancer drug is to find the maximum tolerated dose and give as much as we can, because we think that will have the best outcome. But if we start to understand how the cancer is changing through treatment, then working out what the maximum we can give might still be valuable information, but there might be even more valuable information. For example, understanding how cancer changes when we give a bit less of a drug, and work out how to give different combinations. So, there might be a whole new way of thinking and all the associated infrastructure that has to surround this idea of dealing with cancer changes.
FLG: What is your advice to people breaking into the field?
Trevor: For people coming from a quantitative background, who might not know lots of cancer biology, I think ‘be bold!’ I think the reason that we need this new thinking in the field is because there isn’t enough thinking from people with those kinds of skills already, so they have lots to offer. The impact of a good idea applied in the right way can be huge. So, be bold.
From my own experience, I think in biology it’s possible to drop into an area and then start to learn outwards from that area and gather enough knowledge to be able to do something new and make a contribution. Whereas I think with maths, it’s much harder to do that because everything is built on foundational knowledge, and you have to build up. I think there’s a kind of mindset shift that has to happen if, say, a mathematician jumps into biology. They don’t need to get right back to the beginning. They can be confident and just kind of go for it. So, be bold, is my one key bit of advice.
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