IntClust is a classification of breast cancer comprising ten subtypes based on molecular drivers identified through the integration of genomic and transcriptomic data from around 1,000 breast tumours and validated in a further 1,000. The new research findings, published in the journal Genome Biology, indicate that IntClust subtypes are reproducible, show clinical validity and best capture variation in genomic drivers. IntClust is likely to become increasingly relevant as more targeted biological therapies become available.
As we learn more about breast cancer, we are seeing that it is not one single disease - the mutations in the genes that cause different cancers are not alike, and this is why tumours respond differently to treatment and grow at different rates.
Spotting the trends in tumour genetics and creating a system to diagnose tumour types is a primary objective of cancer scientists. To this end, researchers at Cancer Research UK and the University of Cambridge have been developing the IntClust system, which uses genomic technology to create a classification system with enough detail to more accurately pinpoint which type of breast cancer a patient has, and therefore what treatment would be most appropriate.
Genome-Driven Integrated Classification of Breast Cancer Validated in Over 7,500 Samples
To test the system, the scientists looked at the 997 tumour samples they had used to develop the system, and 7,544 samples from public databases, along with the genomic and clinical data including data from The Cancer Genome Atlas (TCGA). They classified these using their IntClust system, and the two main systems in use today - PAM50, which groups cancers into five types, and SCMGENE, which classifies cancer into four.
All ten subtypes were observable in most studies at comparable frequencies. The IntClust subtypes are significantly associated with relapse-free survival and recapitulate patterns of survival observed previously. In studies of neo-adjuvant chemotherapy, IntClust reveals distinct patterns of chemosensitivity.
Finally, patterns of expression of genomic drivers reported by TCGA are better explained by IntClust as compared to the PAM50 classifier. The system identified a previously unnoticed subgroup of tumours in just 3.1% of women with very poor survival rates, which appeared to be resistant to treatment. Identifying the genomic signatures for this group could flag up these high risk cancers early, and having the genomic data for these could aid in the investigation of new avenues for treatments for this type of cancer.
At present, using this system to classify tumours would be costly for most clinicians, and interpreting the results requires training that many clinical settings don't have access to. But the detail and accuracy of this system could be of great use to breast cancer researchers, who will be able to investigate the reasons that certain groups of cancer respond better to certain treatments, in order to find clinical markers, or to identify new targets for breast cancer treatments.
The study team led by Carlos Caldas concluded, in their article published on 28 August 2014, that IntClust subtypes are reproducible in a large meta-analysis, show clinical validity and best capture variation in genomic drivers. IntClust is a driver-based breast cancer classification and is likely to become increasingly relevant as more targeted biological therapies become available.
Raza Ali, the first author of the article, from Cancer Research UK Cambridge Institute, says: "Our findings highlight the potential of this approach in the era of targeted therapies, and lay the foundation for the generation of a clinical test to assign tumors to IntClust subtypes."