The 7.9m new building containing state of the art laboratories is home to 100 scientists and doctors from biological sciences, epidemiology, cardiovascular research and biostatistics. They are working on understanding how our environment makes us susceptible to heart disease, cancer and Alzheimer's to developing treatments based on understanding these conditions on a protein and genetic level.

The University of Leeds has some of the world's most 'intense' research into treating heart attack patients with drugs to break down the fibrin - which gives structure to the blockages that cause heart attacks. The project led by Institute director Professor Peter Grant is working on new treatments to reduce the build-up of fibrin and reducing the burden of heart attacks.

Professor Peter Grant said: "LIGHT provides a wonderful interdisciplinary environment for the training of tomorrow's scientists and clinician scientists. This is particularly important in relation to the development of clinician scientists at a time when recruitment to clinical academic careers is dwindling. "

LIGHT was funded by the Science Research Infrastructure Fund (SRIF). The Medical Research Council is funding a number of projects within the Institute.

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Dr Foekens concludes: Since only 30-40% of untreated lymph-node negative patients develop tumour recurrence, our prognostic signature could provide a powerful tool to identify those patients at low risk preventing overtreatment in substantial numbers of patients. If confirmed in further studies, the recommendation of additional hormone or chemotherapy in patients with lymph-node negative breast cancer could be guided by this prognostic signature.

In an accompanying commentary Tor-Kristian Jenssen (PubGene AS, Vinderen, Norway) and Eivind Hovig (Norwegian Radium Hospital, Montebello, Norway) state that several large scale breast cancer studies have identified signature gene lists that could potentially predict clinical outcome but when comparing these there is virtually complete lack of agreement in the genes included.

Dr Jenssen comments: Faced with alternative gene signatures for very similar prediction problems, we are left with the obvious questions of which to trust and why they differ. Although Wang and colleagues present the largest study of this type of data, it may still be too small to provide a final selection of genes for signature inclusion. Thus the signature is there, but it is still necessary to read the fine print.

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