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Just over two years ago a landmark study took our knowledge of breast cancer to a new level.
An international team of scientists, led by Professor Carlos Caldas and his team at the Cancer Research UK Cambridge Institute, mapped the genetic landscape of breast cancer in unprecedented detail, redefining it as 10 distinct diseases.
But as with many of these vast genetic explorations, the study revealed as much unexplored terrain as it mapped – exposing the complexity faced when diagnosing and treating breast cancer.
Today, the same research team add an important stamp of approval to their map. Writing in the journal Genome Biology, they’ve carefully compared how well their classification system performs in spotting the genetic differences present in other, similar collections of breast cancer tumour samples.
The outcome? Their map stands up to this rigorous scrutiny, making it the most comprehensive view of the disease to date. While it will take several years for these findings to help doctors improve things for patients, in the future, it offers new opportunities for diagnosing and treating breast cancer.
Check your working
Translating vast quantities of genetic data into something that can be routinely used by doctors is a huge challenge. In the lab, research teams take years to piece together the details of what makes tumours tick, but for doctors – and most importantly patients – this information is needed in a matter of days rather than years.
In their 2012 ‘METABRIC’ (Molecular Taxonomy of Breast Cancer International Consortium) study, Professor Caldas’ team threw a whole range of analytical techniques at around 1000 tumour samples, identifying 10 subtypes of breast cancer – which they called integrative clusters or ‘IntClust subtypes’. In their new paper, they’ve checked to see if the subtypes could be spotted using just one of these techniques – measuring the activity levels of genes within the samples (known as expression levels).
This is important – doctors need a test with enough detail to accurately spot which ‘type’ of breast cancer a patient has, but one that’s simple – and cheap enough – to be reproduced around the world. In other words, they need a map showing the clear boundaries between countries, cities and towns, but not necessarily the colour of each individual front door in that area
They picked a list of 612 genes from the original study. Next they used the initial 1000-odd METABRIC samples to ‘train’ a computer programme to spot the 10 subtypes based on how active the 612 genes were.
To check the computer had ‘learnt’ correctly, they used the InClust system to analyse another collection of around 1000 tumours from the original study. Crucially, the gene activity data from the second set of samples was accurately grouped into the 10 distinct subtypes.
But what about breast cancer data beyond METABRIC?
“We wanted to really test the accuracy of the system. So we tried it out on as many collections of breast cancer samples – or ‘datasets’ – as possible,” says Dr Raza Ali, lead scientist on the new study.
“Only by challenging our system in this way can we confirm the accuracy of the 10 IntClust subtypes.”
On a study-by-study basis the team turned to the gigabytes of data available from studies around the world – encompassing over 7,500 breast tumours from more than 40 studies – and set about grouping these samples.
“By looking at the genetic data we can gather important information about what’s driving these deadly tumours, which could be used to develop new targeted treatments in the future.” – Dr Raza Ali
The same 10 subtypes emerged once again from each study, confirming their 2012 findings – the IntClust system is a ‘real’ phenomenon.
But they didn’t stop there. Next the team looked at how well their IntClust system performed against two other genetic tests for breast cancer. The first – called PAM50 – splits breast tumour samples into five groups, and the second – known as SCMGENE – identifies four groups.
Crucially, the IntClust system performed just as well as the other tests at predicting how patients responded to treatment, and how well they fared in terms of survival, across each of the external studies.
They also made some important new discoveries that could focus further research. “We found that one rare subtype of breast cancer appears very resistant to treatment,” says Ali.
“By looking at the genetic data we can gather important information about what’s driving these deadly tumours, which could be used to develop new targeted treatments in the future.”
Right now, this study won’t change how breast cancer is treated for women diagnosed in the immediate future. But it does provide an important focus for future research, opening up the possibility of a new ‘genetic Sat Nav’ to help explore this complex map of breast cancer, and bring new experimental treatments to future patients who could benefit.
By taking the rigorous route, double and triple checking their findings, Professor Caldas and his team are leading the way in how we now view not one, but 10 different diseases.
- A version of this blog post has also been published on the BioMed Central blog
Map and compass image from Flickr
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