Breast cancer is the most common cancer in women both in the developed and less developed world, claims the World Health Organization.
Thus, scientists have come up with a simple blood test to predict future risk of breast cancer and in the long term the scientists hope that the new method will lead to better prevention and early treatment of the disease.
Professor Rasmus Bro at University of Copenhagen said that the method was better than mammography, which could only be used when the disease had already occurred. It was amazing that breast cancer could now be predicted early. He stressed the method has been tested and validated only for a single population (cohort) and needs to be validated more widely before it can be used practically. The method has been developed in cooperation with the Danish Cancer Society.
The researchers' approach to developing the method was adopted from food science, where it is used for control of complex industrial processes. Basically, it involves handling and analyzing huge amounts of biological data in a holistic and explorative way. The researchers analyzed all compounds a blood sample contains instead of - as is often done in health and medical science - examining what a single biomarker means in relation to a specific disease.
Professor Rasmus Bro explained that when a huge amount of relevant measurements from many individuals was used to assess health risks, here breast cancer, it created very high quality information. The more measurements the analyses contained, the better the model handled complex problems.
The model does not reveal anything about the importance of the single biomarkers in relation to breast cancer, but it does reveal the importance of a set of biomarkers and their interactions.
While a mammography can detect newly developed breast cancer with a sensitivity of 75%, the new metabolic blood profile is able to predict the likelihood of a woman developing breast cancer within the next 2 to 5 years with a sensitivity of 80%.
The study was recently published in Metabolomics