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LONDON: At Cancer Research UK Cambridge Institute, Professor Carlos Caldas leads a team searching for the genes that malfunction as breast cancer develops. The scientists analyze the genetic makeup of thousands of tumor samples, searching for genetic signatures of the disease.
Their aim is to improve the way that breast cancer is detected, monitored and treated.
Professor Caldas is focused on identifying the gene faults and variations that are important in predicting the success or failure of cancer treatments. Armed with such knowledge, doctors could be in a much better position when it comes to deciding which treatment will be most effective for each patient.
Understanding more about these genes could also help scientists develop new breast cancer treatments that target the specific faults driving the disease.
The Cancer Research UK Cambridge Institute (CRUK CI) focuses on tackling questions relating to cancer diagnosis, treatment and prevention, supported by world-class core scientific facilities.
The Institute is located on the Cambridge Biomedical Campus close to Addenbrooke’s Hospital, the University of Cambridge teaching hospital, and many collaborating institutes, including the MRC Laboratory for Molecular Biology, the MRC Cancer Unit and the Cambridge Institute for Medical Research.
It was back in 2012 that scientists in Cambridge, led by Professor Caldas, made a groundbreaking discovery about the complex nature of breast cancer. They classified the disease into ten types based on the genetic fingerprints of women’s tumors, following up with a more detailed study in 2014. In the future this knowledge will allow doctors to match each person to the treatment most likely to successfully tackle their cancer.
The 2012 ‘METABRIC’ (Molecular Taxonomy of Breast Cancer International Consortium) study, entailed subjecting some 1000 tumor samples to a variety of analytical techniques. The research team identified ten subtypes of breast cancer which they called integrative clusters or ‘IntClust subtypes’. They followed up by checking 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).
Doctors need a test with enough detail to accurately spot which ‘type’ of breast cancer a patient has – a test which needs to be simple and cheap enough to be reproduced around the world. The team picked a list of 612 genes from the original study. Next they used the initial 1000-odd METABRIC samples to ‘train’ a computer program 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 analyze another collection of around 1000 tumors from the original study. Crucially, the gene activity data from the second set of samples was accurately grouped into the 10 distinct subtypes.
“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 tumors from more than 40 studies, and set about grouping these samples.
The same 10 subtypes emerged once again from each study, confirming their 2012 findings – the IntClust system is a ‘real’ phenomenon.
The team then looked at how well their IntClust system performed against two other genetic tests for breast cancer. The first – called PAM50 – splits breast tumor samples into five groups, and the second – known as SCMGENE – identifies four groups.
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 tumors, which could be used to develop new targeted treatments in the future.”
Their work provides 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.