Topoisomerase II alpha amplification may predict benefit from adjuvant anthracyclines in HER2 positive early breast cancer |
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Authors: | Edurne Arriola Socorro Maria Rodriguez-Pinilla Maryou B K Lambros Robin L Jones Michelle James Kay Savage Ian E Smith Mitch Dowsett Jorge S Reis-Filho |
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Institution: | (1) Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), 7489 Trondheim, Norway;(2) Department of Surgery, St. Olavs University Hospital, 7006 Trondheim, Norway;(3) Department of Laboratory Medicine, Children’s and Women’s Health, Norwegian University of Science and Technology (NTNU), 7489 Trondheim, Norway;(4) MRi_Consulting, Kingston, Ontario, Canada;(5) St. Olavs University Hospital, Cancer Clinic, 7006 Trondheim, Norway;(6) Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7489 Trondheim, Norway |
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Abstract: | The purpose of the study was to evaluate the use of metabolic phenotype, described by high-resolution magic angle spinning
magnetic resonance spectroscopy (HR MAS MRS), as a tool for prediction of histological grade, hormone status, and axillary
lymphatic spread in breast cancer patients. Biopsies from breast cancer (n = 91) and adjacent non-involved tissue (n = 48) were excised from patients (n = 77) during surgery. HR MAS MR spectra of intact samples were acquired. Multivariate models relating spectral data to histological
grade, lymphatic spread, and hormone status were designed. The multivariate methods applied were variable reduction by principal
component analysis (PCA) or partial least-squares regression-uninformative variable elimination (PLS-UVE), and modelling by
PLS, probabilistic neural network (PNN), or cascade correlation neural network. In the end, model verification by prediction
of blind samples (n = 12) was performed. Validation of PNN training resulted in sensitivity and specificity ranging from 83 to 100% for all predictions.
Verification of models by blind sample testing showed that hormone status was well predicted by both PNN and PLS (11 of 12
correct), lymphatic spread was best predicted by PLS (8 of 12), whereas PLS-UVE PNN was the best approach for predicting grade
(9 of 12 correct). MR-determined metabolic phenotype may have a future role as a supplement for clinical decision-making-concerning
adjuvant treatment and the adaptation to more individualised treatment protocols. |
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Keywords: | Breast cancer HR MAS MRS Metabolomics MR spectroscopy Predictive factors |
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