Abstract
Breast cancer is a major cause of death for women. To improve treatment, current oncology research focuses on discovering and validating new biomarkers for early detection of cancer; so far with limited success. Metabolic profiling of plasma samples and auxiliary lifestyle information was combined by chemometric data fusion. It was possible to create a biocontour, which we define as a complex pattern of relevant biological and phenotypic information. While single markers or known risk factors have close to no predictive value, the developed biocontour provides a forecast which, several years before diagnosis, is on par with how well most current biomarkers can diagnose current cancer. Hence, while e.g. mammography can diagnose current cancer with a sensitivity and specificity of around 75 %, the currently developed biocontour can predict that there is an increased risk that breast cancer will develop in a subject 2–5 years after the sample is taken with sensitivity and specificity well above 80 %. The model was built on data obtained in 1993–1996 and tested on persons sampled a year later in 1997. Metabolic forecasting of cancer by biocontours opens new possibilities for early prediction of individual cancer risk and thus for efficient screening. This may provide new avenues for research into disease mechanisms.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-015-0793-8) contains supplementary material, which is available to authorized users.
Highlights
Breast cancer is the major cause of death for women in the first decade after menopause
Metabolic profiling of plasma samples and auxiliary lifestyle information was combined by chemometric data fusion
While single markers or known risk factors have close to no predictive value, the developed biocontour provides a forecast which, several years before diagnosis, is on par with how well most current biomarkers can diagnose current cancer
Summary
Breast cancer is the major cause of death for women in the first decade after menopause. The platforms form a basis for prediction modeling at the individual level, i.e. individual prediction of disease risk. This translational aspect has not been exploited to any large extent until now, primarily due to the inherent difficulties associated with the technologies. In the current analysis the cancer is not present (let alone diagnosed) at the time of the sampling but is diagnosed years later It is the prediction of this later diagnosis of cancer that is the aim of this study. Such a method of early prediction of breast cancer risk at a time before diagnosis will have further substantial ethical implications but may offer new leads for understanding cancer causation and for early detection of cancer
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