Forecasting individual breast cancer risk using plasma metabolomics and biocontours
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Forecasting individual breast cancer risk using plasma metabolomics and biocontours. / Bro, Rasmus; Kamstrup-Nielsen, Maja Hermann; Engelsen, Søren Balling; Savorani, Francesco; Rasmussen, Morten Arendt; Hansen, Louise; Olsen, Anja; Tjønneland, Anne; Dragsted, Lars Ove.
In: Metabolomics, Vol. 11, No. 5, 2015, p. 1376-1380.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Forecasting individual breast cancer risk using plasma metabolomics and biocontours
AU - Bro, Rasmus
AU - Kamstrup-Nielsen, Maja Hermann
AU - Engelsen, Søren Balling
AU - Savorani, Francesco
AU - Rasmussen, Morten Arendt
AU - Hansen, Louise
AU - Olsen, Anja
AU - Tjønneland, Anne
AU - Dragsted, Lars Ove
N1 - CURIS 2015 NEXS 103
PY - 2015
Y1 - 2015
N2 - 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 wedefine 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 sensitivityand 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 biocontoursopens new possibilities for early prediction of individual cancer risk and thus for efficient screening. This may provide new avenues for research into disease mechanisms.
AB - 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 wedefine 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 sensitivityand 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 biocontoursopens new possibilities for early prediction of individual cancer risk and thus for efficient screening. This may provide new avenues for research into disease mechanisms.
U2 - 10.1007/s11306-015-0793-8
DO - 10.1007/s11306-015-0793-8
M3 - Journal article
VL - 11
SP - 1376
EP - 1380
JO - Metabolomics
JF - Metabolomics
SN - 1573-3882
IS - 5
ER -
ID: 132678020