Breast cancer is not just one uniform disease; the specific genes and proteins that are present in the tumor cells (the molecular signature) show significant variation from person to person. These differences affect the way in which individual breast tumors respond to chemotherapy and other cancer treatments.
Breast cancers can already be grouped into several different classes based on molecular signature. Physicians depend on this information when deciding how to treat each new breast cancer patient. For example:
- Tumours will only respond to hormone treatment if they contain the estrogen receptor or progesterone receptor proteins that bind to female hormones and pass their messages on to the cell.
- Only tumours that contain the human epidermal growth factor receptor 2 (HER2) protein will respond to chemotherapy using herceptin.
Directing treatments only to those patients with the best chance of responding to them avoids putting people through unnecessary and debilitating treatments, and ensures that the resources available for breast cancer treatment are used in the most efficient and beneficial way.
Aims and Relevance
METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) is a Canada-UK project that aims to classify breast tumours into further subcategories, based on molecular signatures that will help determine the optimal course of treatment.
Our efforts to date have reclassified breast cancer into ten completely new categories based on the genetic fingerprint of a tumour. Many of these genes could offer much-needed insight into breast cancer biology, allowing doctors to predict whether a tumour will respond to a particular treatment. Whether the tumour is likely to spread to other parts of the body or if it is likely to return following treatment.
The study, published in the international journal Nature, is the largest global study of breast cancer tissue ever performed and the culmination of decades of research into the disease. In the future, this information could be used by doctors to better tailor treatment to the individual patient.
Study milestones include:
- Classified breast cancer into 10 subtypes grouped by common genetic features, which correlate with survival. This new classification could change the way drugs are tailored to treat women with breast cancer.
- Discovered several completely new genes that had never before been linked to breast cancer. These genes that drive the disease are all targets for new drugs that may be developed. This information will be available to scientists worldwide to boost drug discovery and development.
- Revealed the relationship between these genes and known cell signaling pathways – networks that control cell growth and division. This could pinpoint how these gene faults cause cancer, by disrupting important cell processes.
Recent related papers from the Aparicio Laboratory
- Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y, Gräf S, Ha G, Haffari G, Bashashati A, Russell R, McKinney S, METABRIC Group, Langerød A, Green A, Provenzano E, Wishart G, Pinder S, Watson P, Markowetz F, Murphy L, Ellis I, Purushotham A, Børresen-Dale AL, Brenton JD, Tavaré S, Caldas C, Aparicio S. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012, doi:10.1038/nature10983.
- Holland D, Burleigh A, Git A, Goldgraben MA, Perez-Mancera PA, Chin SF, Hurtado A, Bruna A, Ali R, Greenwood W, Dunning MJ, Samarajiwa S, Menon S, Rueda OM, Lynch AG, McKinney S, Ellis IO, Eaves CJ, Carroll JS, Curtis C, Aparicio S, Caldas C. ZNF703 is a common Luminal B breast cancer oncogene that differentially regulates luminal and basal progenitors in human mammary epithelium. EMBO Mol Med 2011 3:1–14
- Papatheodorou I, Crichton C, Morris L, Maccallum P; METABRIC Group, Davies J, Brenton JD, Caldas C. A metadata approach for clinical data management in translational genomics studies in breast cancer. BMC Med Genomics 2009: 2:66.