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PUBLIC RELEASE DATE:
27-Aug-2014

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Contact: Shane Canning
shane.canning@biomedcentral.com
44-203-192-2429
BioMed Central
@biomedcentral

Better classification to improve treatments for breast cancer

Breast cancer can be classified into ten different subtypes, and scientists have developed a tool to identify which is which. The research, published in the journal Genome Biology, could improve treatments and targeting of treatments for the disease.

Cancer arises due to genetic changes which cause normal cells to develop into tumors. 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 tumors respond differently to treatment and grow at different rates. Currently, there are two key markers that clinicians use to predict response to treatments.

Spotting the trends in tumor genetics and creating a system to diagnose tumor 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.

To test the system, the scientists looked at the 997 tumor 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. 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.

They found that IntClust was at least as good at predicting patients' prognosis and response to treatment as the existing system. But the system identified a previously unnoticed subgroup of tumors 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 tumors 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.

Raza Ali, lead author from Cancer Research UK Cambridge Institute, says: "We have developed an expression-based method for classification of breast tumours into the IntClust subtypes. 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."

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Media Contacts

Shane Canning
Media Officer
BioMed Central
T: +44 (0)20 3192 2243
E: shane.canning@biomedcentral.com

Notes to Editor

1. Research

Genome-driven integrated classification of breast cancer validated in over 7,500 samples

Hamid R Ali, Oscar M Rueda, Suet-Feung Chin, Christina Curtis, Mark J Dunning, Samuel AJR Aparicio and Carlos Caldas

Genome Biology 2014, 15:431

For a copy of the article during embargo period please contact Shane Canning (shane.canning@biomedcentral.com)

After embargo, article available at journal website here: http://genomebiology.com/2014/15/8/431/abstract

Please name the journal in any story you write. If you are writing for the web, please link to the article. All articles are available free of charge, according to BioMed Central's open access policy.

2. Genome Biology serves the biological research community as an international forum for the dissemination, discussion and critical review of information about all areas of biology informed by genomic research. Key objectives are to provide a guide to the rapidly developing resources and technology in genomics and its impact on biological research, to publish large datasets and extensive results that are not readily accommodated in traditional journals, and to help establish new standards and nomenclature for post-genomic biology.

3. BioMed Central is an STM (Science, Technology and Medicine) publisher which has pioneered the open access publishing model. All peer-reviewed research articles published by BioMed Central are made immediately and freely accessible online, and are licensed to allow redistribution and reuse. BioMed Central is part of Springer Science+Business Media, a leading global publisher in the STM sector.



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