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How patient data is helping beat cancer

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by Cancer Research UK | In depth

21 November 2023

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Two cancer researchers looking at a cell image on screen


Cancer is the most complex health challenge we face. There are more than 200 different types of it, for a start; and even that might be putting it too simply. Look closely, and hundreds of cancer types split into millions of specific cases. 

At a genetic level, each individual cancer is as unique as the person it affects. There’s no guarantee that the drug that worked for one person’s bowel cancer, for instance, will work for the next. And yet, despite that, our research has helped double cancer survival over the last 40 years. By steadily increasing our understanding of cancer’s intricacies, we’ve been able to make more treatments work for more people. 

But there are still big questions about how to make our treatments even better, and our scientists are seeking the answers. Helpfully, we know where a lot of them might be – in our health data. People and cancers are unique, so we need to look at them as directly as possible. By bringing patient data into research, we can start to do that.  

To find out more, we spoke to some of our scientists about the ways they’re using patient data to untangle cancer’s complexities and bring tomorrow’s cures closer. 

Predicting a tumour’s future with patient data 

At the heart of the CRUK Scotland Institute is a team of brilliant scientists who specialise in using supercomputers to understand how cancer behaves. Those supercomputers are so important because of what they can do with patient data. 

“Data gives us insight into things you can’t see down a microscope,” says Professor Crispin Miller, who leads the team. “It has the potential to [lead to] truly targeted therapies. To find the right therapy for the right person at the right time, that is the goal.” 

Microscopes are still useful, but tumours are too diverse and complicated for us to fully understand them just by zooming in on specific areas. Miller’s team are using supercomputers to spot genetic and structural patterns that we can’t see any other way. Whereas microscope images show what cancers look like, researchers can use these patterns to work out why and how they develop.  

For Miller, it’s like the difference between looking at the stars and knowing how they got there.  

“Biology is exciting,” he says. “The questions of modern cancer research, like how the DNA in one cell can define an entire living person – to me that’s as exciting as understanding the first few microseconds of the Big Bang.”

You need more than a zoom function for that work, too. But when it comes to the universe of cells inside us, things are a little more practical: Miller’s team is trying to understand the past in order to change the future. The patterns they’re looking for contain the information we need to improve our treatment options. 

Crispin Miller
Crispin Miller

We can only see those patterns thanks to the generosity of the patients who have kindly allowed their doctors to share their anonymised tumour genetic data and scan images with Miller’s team. That information is also helping to train an AI system designed to predict how tumours are likely to respond to treatment. In the years to come, it could help doctors pick the best treatment for each of their patients.   

“Data is central to the research we are doing,” explains Miller. “The more tumours we study, the more we understand how they differ from each other and how we can use this information to improve outcomes for people with cancer. I am incredibly grateful to the patients who donate their data and tissue. I couldn’t do my work without it.” 

Professor Miller spoke to Hattie Brooks, a science engagement manager at Cancer Research UK. 

“Data is everything” – understanding how cancer spreads and resists treatment

Dr Irene Lobon sees data everywhere. She’s usually analysing and learning from it, too. In the kitchen, it’s how she improves her favourite recipes; in the lab, the same skills could lead to lifesaving discoveries. 

Dr Lobon is a biomedical researcher in The Francis Crick Institute’s Cancer Dynamics Laboratory, which is led by Professor Samra Turajlic. She’s part of a team studying metastatic melanoma, a type of advanced, spreading skin cancer that is particularly hard to treat and often becomes resistant to drugs. 

A computer algorithm identifying individual cells in an image of a tumour.
A computer algorithm identifying individual cells in an image of a tumour. Credit: Carl Dupont/Shutterstock.com

Lobon and her colleagues are trying to find out more about how melanoma changes over time, what happens as it spreads, and what features lead to drug resistance. To do that, they need patient data; and they have some of the most complete data of all. Their samples are donated by patients who have passed away from cancer. 

These samples are available to the team thanks to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) study, a huge collaborative project in which researchers take samples from participants during their treatment journey and after they have died. The study supports scientists working on a range of different cancers.

Professor Turajlic’s team employed sophisticated genetic sequencing techniques to study nearly 600 separate samples from 14 different PEACE study participants with metastatic melanoma.

“What we’ve observed is that there are many variables that contribute tiny bits to a cancer’s progression,” explains Lobon. “It’s like having a fossil. We can study cancers at different points in time.”  

Irene Lobon
Irene Lobon

The team shared the results of their extensive study earlier this year. It’s already helping scientists understand the complex ways that melanomas evade drugs. The researchers hope that it could also lead to the development of more effective treatments for people diagnosed with metastatic melanoma.  

This sort of analysis, piecing together a complex timeline of how cancer develops, wasn’t possible before PEACE. Without the generosity of the study’s participants, it still might not be. 

“There is no other way we could get such specific information without these samples,” says Lobon. For her, particularly when it comes to these complex questions, “data is everything”. 

Dr Lobon spoke to Nisha Duggan, a science engagement manager at Cancer Research UK. 

How patient data is uncovering the origins of oesophageal cancer 

Oesophageal cancer is one of the biggest challenges in cancer research today. Only 1 in 10 people diagnosed with it survive for 10 years or more. Our best hope for improving things for people with oesophageal cancer is learning more about the biology behind it. Researchers and clinicians can’t do that alone. 

That’s why the OCCAMS (Oesophageal Cancer Clinical and Molecular Stratification) project, which Professor Rebecca Fitzgerald at the University of Cambridge has been leading for more than a decade, is so important. It brings people together to answer some of the biggest questions about oesophageal cancer. 

“Right now, we can’t predict how well someone will do just by how they look when we first see them in the clinic,” Fitzgerald explains. “Some people do well, and sadly some people do badly, and we need to know why.” 

As part of OCCAMS, doctors and nurses collect blood and tumour samples from people with oesophageal cancer. Scientists then use these samples to decode each cancer’s genetic sequence – producing a complete map of its DNA.  

From there, research teams match clinical data about how each person’s cancer progresses with the lab data on its genetic sequence to see how DNA changes drive oesophageal cancer.  

“This really is a team effort,” says Fitzgerald. “By sharing information, we can see the patterns and the trends, and that’s what allows the breakthroughs to happen.” 

You could say that patients are part of that team. There wouldn’t be any breakthroughs without them. Whether the researchers are investigating what makes cancers develop differently, finding out why some patients respond well to treatment while others don’t or trying to identify risk factors that could predict if a cancer is likely to return, every person’s data provides a new piece of the puzzle.  

“And we need a lot of data,” adds Fitzgerald. “Oesophageal cancer is really complicated. We can’t find the needle in the haystack by just using data from a few people. The current problem my team is working on – working out whether all oesophageal cancers start from the precursor condition Barrett’s oesophagus – is using data from 4,000 patients. That’s why we are so grateful to all the patients who allow us to do this work.” 

Rebecca Fitzgerald
Rebecca Fitzgerald

Those patients’ contributions are helping make things better for everyone with oesophageal cancer. Already, Fitzgerald’s team have been able to describe for the first time some of the DNA mutations that they think cause the disease, and there are likely to be many more findings to come. 

“We have seen some improvements over the 20 years I have been working on this,” says Fitzgerald, “but we’ve got a long way to go. Solving this problem is what gets me out of bed in the morning. And I want to get to the end of my career and see that it is really different.  

“I can’t do what I do without patient data – and I’m not done yet.”

Professor Fitzgerald spoke to Kathryn Thompson, a strategic science engagement specialist at Cancer Research UK.

Personalising radiotherapy – how patient data is helping reduce the side effects of treatment

Radiotherapy is the gold standard of treatment for many types of cancers. More than 130,000 patients benefit from it every year in the UK.  

Today, most people receive image-guided radiotherapy, which uses imaging such as x-rays and MRIs to target the beam of radiation to the tumour site as accurately as possible.  

“Many people think about imaging at the point of diagnosis to find a tumour, or to follow up on treatment to see if it’s working,” says Dr Rajesh Jena, who focuses on improving radiotherapy for people with tumours of the brain and spine. “But imaging can be so much more: it can be used to personalise and even predict response to treatment.”  

A woman receiving radiotherapy to her brain
By using scan images to guide radiotherapy, doctors can minimise its side effects. Credit: Mark_Kostich/Shutterstock.com

Image-guided radiotherapy is just the start of that. So far, it has made it possible for doctors to design treatment around their patients’ bodies, lowering the risk of radiation beams affecting healthy tissues surrounding tumours, but there’s room to make it even better. 

“The wealth of information an image holds comes with an incredible potential,” says Jena. Thanks to patients sharing their data, researchers like him can study radiotherapy images after treatment is finished. That means they can see the ways even more targeted radiation beams affect healthy tissues and design future treatment plans to stop them doing so. 

Jena’s team has also used scans shared by patients to develop an AI tool capable of reading them. Osairis, as it’s called, is the first cloud-based AI technology to be developed and deployed within the NHS. It helps doctors with the time-consuming work of finding and marking the borders between tumours and healthy tissue on radiotherapy images, so people can start treatment sooner.   

Osairis is designed to continually learn from the data it’s given, so Jena’s team is currently providing it with more images. At the same time, they’re working with mathematicians, physicists and biologists to make sure they get as much information as they can from every scan.  

“We’re so grateful to have access to patient imaging data because without it we wouldn’t be able to develop these technologies, which can make a real difference to people with cancer,” says Jena. “We’re very careful with how we use patient data, we take care to ensure that all data is anonymised and is treated with respect.”

Dr Jena spoke to Rupal Mistry, a senior science engagement manager at Cancer Research UK.

For more information on how Cancer Research UK uses patient data for research and analysis, you can visit our Patient data use hub.

    Comments

  • Dawn Asplen
    4 December 2023

    Think this is amazing