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  • 1
    In: Translational Oncology, Elsevier BV, Vol. 3, No. 4 ( 2010-08), p. 252-263
    Type of Medium: Online Resource
    ISSN: 1936-5233
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2010
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  • 2
    In: Magnetic Resonance Materials in Physics, Biology and Medicine, Springer Science and Business Media LLC, Vol. 33, No. 2 ( 2020-04), p. 329-330
    Abstract: The original version of this article unfortunately contained a mistake in Fig. 6.
    Type of Medium: Online Resource
    ISSN: 0968-5243 , 1352-8661
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 1502491-X
    SSG: 11
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 4_Supplement ( 2020-02-15), p. P1-13-01-P1-13-01
    Abstract: Background: Adjuvant breast cancer treatment may cause metabolic perturbations, such as dyslipidaemia, potentially exacerbating risk of cardiometabolic disease as well as risk of breast cancer recurrence. Physical exercise may have beneficial metabolic effects, but it’s effect on serum lipoprotein- and metabolite profiles during adjuvant breast cancer treatment including chemotherapy is not yet well established. Methods: The women participating in this pilot study of Energy Balance and Breast Cancer Aspects (EBBA)-II, were aged 38-69 years and diagnosed with stage I-II breast cancer. 60 breast cancer patients were randomized after surgery to a control group (n = 29, usual care) or an intervention group (n = 31, intervention), stratified by menopausal status. The patients in the intervention group received a detailed exercise program and met for supervised training sessions in groups of 10-12 women for 60 minutes twice a week during a 12 month period, and were in addition asked to perform at least 60 minutes of exercise at home (a total of 180 minutes of exercise weekly). Fasting serum samples were collected pre-surgery and after six months, and analysed by nuclear magnetic resonance (NMR)-spectroscopy and mass spectrometry. 170 metabolites and 109 lipoprotein subclass variables were quantified and analysed using orthogonalized partial least squares discriminant analysis. Statistical significance was assessed by permutation testing. Single variables were tested with Mann Whitney U-tests or multiple linear regression (NCT02240836). Results: The breast cancer patients (n = 60) had at pre-surgery the following means: Age at diagnosis of 55.4 years (38-69 years), low density lipoprotein (LDL)-cholesterol 145.4 mg/dl (3.76 mmol/L), high density lipoprotein (HDL)-cholesterol 70.4 mg/dl (1.82 mmol/L), and triglycerides 101.9 mg/dl (1.15 mmol/L), and 58.3 % of the patients underwent chemotherapy (paclitaxel/docetaxel/5-FU/epirubicin/cyclophosphamide based adjuvant chemotherapy). Physical exercise ameliorated chemotherapy-induced increases in very low density lipoprotein (VLDL)- and intermediate density lipoprotein (IDL)-associated lipids, and reduced triglyceride enrichment in LDL and HDL compared with chemotherapy controls (p = 0.003). Physical exercise also significantly increased apoA1 (4.6 % increase vs 11.3 % decrease, q = 0.02) and apoA2 (5.2 % increase vs 13.0 % decrease, q = 0.01) compared with chemotherapy control patients. The NMR-measured lipid signal at 1.55-1.60 ppm increased after six months in chemotherapy recipients, but this was attenuated among chemotherapy recipients in the intervention group. No statistically significant effect of physical exercise on serum levels of small-molecular metabolites was detected. Conclusion: Our findings suggest that physical exercise may prevent atherogenic alterations in lipoprotein profile induced by chemotherapy. The results indicate increased HDL particle number- and function, as well as increased triglyceride clearance in the intervention group. Thus, atherogenic alterations in lipoprotein profile may play a role in evaluating breast cancer treatment, and could potentially be biomarkers of importance for breast cancer prognosis and co-morbidity. Citation Format: Torfinn Støve Madssen, Vidar Gordon Flote, Inger Thune, Gro Falkener Bertheussen, Anders Husøy, Steinar Lundgren, Hanne Frydenberg, Erik Wist, Ellen Schlichting, Jon Lømo, Anne McTiernan, Tone Frost Bathen, Guro Fanneløb Giskeødegård. Lipoprotein and metabolite responses to physical exercise during adjuvant breast cancer treatment [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-13-01.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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  • 4
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 35, No. 15_suppl ( 2017-05-20), p. e23043-e23043
    Abstract: e23043 Serum metabolism during breast cancer treatment Background: Breast cancer treatment may include surgery, systemic therapy and radiation, often involving side-effects. Many patients experience weight gain during treatment, which is associated with decreased survival rates 1 . The purpose of this study was to describe serum metabolic alterations in breast cancer patients undergoing treatment, and relate these alterations to weight gain during treatment. Methods: This pilot study includes 60 breast cancer patients, aged 35-75 years, with histologically verified stage I/II disease. All patients underwent tumor surgery, and were treated according to national guidelines. Samples were collected before and 6 months after surgery, and analyzed by MR spectroscopy (MRS) and mass spectrometry (MS). 170 metabolites and 105 lipoprotein subfractions were quantified by combined MRS and MS analyses. Results: Multilevel PLS-DA showed significant alterations in serum metabolite profiles post-treatment, both in patients receiving (n = 35) and not receiving (n = 25) chemotherapy (classification accuracy: 86.7% and 77.0%, resp., p 〈 0.001). Lipoprotein profiles were also significantly altered in both groups (p 〈 0.001). Chemotherapy recipients had decreased levels of citrate, ornithine, and methionine after treatment, while non-recipients had increased levels of glutamate, alanine, proline and two biogenic amines, and decreased levels of acylcarnitines. 17/52 patients (32.7%) gained weight (≥ 1.5 kg) during treatment. Weight gain was predicted from pre-treatment samples with accuracy 67.0% (p = 0.020). Weight gain patients had lower levels of three acylcarnitines and 20 phosphocholines, and higher levels of lysine and isoleucine, suggesting aberrant lipid and amino acid metabolism. Weight gain was also reflected in the post-treatment samples (accuracy 66.8%, p = 0.015), with weight gain patients having higher levels of five acylcarnitines, and lower levels of glycine, isoleucine and valine. Conclusions: This study indicates that treatment induces changes in serum metabolite levels. Patients gaining weight had significantly different metabolite profiles than those not gaining weight both before and after treatment. 1. Chan et al, Ann Oncol 25: 1901-14, 2014.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2017
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  • 5
    In: Magnetic Resonance Materials in Physics, Biology and Medicine, Springer Science and Business Media LLC, Vol. 33, No. 2 ( 2020-04), p. 317-328
    Abstract: To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images. Materials and methods Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE–MRI images (manual DCE) and using GMM with corresponding PET images (GMM–PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement. Results No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM–PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM–PET for untreated and treated lesions. The mean Dice score for GMM–PET was 0.770 and 0.649 for untreated and treated lesions, respectively. Discussion Using PET/MRI, tumor area and mean ADC value estimated with a GMM–PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.
    Type of Medium: Online Resource
    ISSN: 0968-5243 , 1352-8661
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 1502491-X
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  • 6
    Online Resource
    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2017
    In:  Journal of Clinical Oncology Vol. 35, No. 15_suppl ( 2017-05-20), p. e23095-e23095
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 35, No. 15_suppl ( 2017-05-20), p. e23095-e23095
    Abstract: e23095 Background: Breast tumors are highly heterogeneous due to subpopulations of cancer cells that differ in genetic and phenotypic characteristics. Tumor heterogeneity has been associated with treatment resistance and relapse. Therefore, it can be questioned how representative one biopsy is for the whole tumor. Tumor phenotypic heterogeneity cannot be solely attributed to genetic differences, as epigenetics and interaction with the tumor microenvironment also contribute. In this study we have examined intra-tumor heterogeneity by measuring metabolite expression in breast cancer tissue compared with fibroadenomas. Methods: Fresh frozen tissue slices from surgically removed breast tumors were used. Five cores from different areas of the slices were drilled out from 10 tumors; 6 invasive ductal carcinomas grade 2-3, and 4 fibroadenomas. Histological examination of HES-stained sections from each core was done, and metabolic profiling was performed by magnetic resonance spectroscopy (MRS). The relative concentrations of 23 metabolites were quantified. Metabolic heterogeneity was assessed by coefficient of variation (CoV) and PLSDA classification was used for prediction of tumor origin. Results: Cancer tissue showed significantly higher heterogeneity than fibroadenomas for 16/23 metabolites (mean CoV range: 0.15-0.94 for cancer samples, 0.09-0.37 for fibroadenomas, p 〈 0.05). However, 23/50 samples did not contain tumor tissue on histological examination. After exclusion of tumor-free samples, the heterogeneity of 3 metabolites (glycine, glycerophosphocholine (GPC) and phosphocholine (PCho) remained significantly different between cancer and fibroadenomas (mean CoV range: 0.12-0.65 for cancer, 0.07-0.42 for fibroadenomas, p 〈 0.05). GPC and PCho are involved in building of cell membranes and may reflect cell-turnover. Multivariate classification could correctly predict which patient a sample belonged to with 78% accuracy. Conclusions: Metabolic heterogeneity could partly be explained by differences in tumor cell and stromal content, and the origin of an unknown sample could be successfully predicted, showing that metabolic intratumor heterogeneity is smaller than the heterogeneity between patients.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2017
    detail.hit.zdb_id: 2005181-5
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  • 7
    In: Acta Radiologica, SAGE Publications, Vol. 61, No. 7 ( 2020-07), p. 875-884
    Abstract: The prognosis for women with locally advanced breast cancer (LABC) is poor and there is a need for better treatment stratification. Gray-level co-occurrence matrix (GLCM) texture analysis of magnetic resonance (MR) images has been shown to predict pathological response and could become useful in stratifying patients to more targeted treatments. Purpose To evaluate the ability of GLCM textural features obtained before neoadjuvant chemotherapy to predict overall survival (OS) seven years after diagnosis of patients with LABC. Material and Methods This retrospective study includes data from 55 patients with LABC. GLCM textural features were extracted from segmented tumors in pre-treatment dynamic contrast-enhanced 3-T MR images. Prediction of OS by GLCM textural features was assessed and compared to predictions using traditional clinical variables. Results Linear mixed-effect models showed significant differences in five GLCM features (f 1 , f 2 , f 5 , f 10 , f 11 ) between survivors and non-survivors. Using discriminant analysis for prediction of survival, GLCM features from 2 min post-contrast images achieved a classification accuracy of 73% ( P  〈  0.001), whereas traditional prognostic factors resulted in a classification accuracy of 67% ( P = 0.005). Using a combination of both yielded the highest classification accuracy (78%, P  〈  0.001). Median values for features f 1 , f 2 , f 10 , and f 11 provided significantly different survival curves in Kaplan–Meier analysis. Conclusion This study shows a clear association between textural features from post-contrast images obtained before neoadjuvant chemotherapy and OS seven years after diagnosis. Further studies in larger cohorts should be undertaken to investigate how this prognostic information can be used to benefit treatment stratification.
    Type of Medium: Online Resource
    ISSN: 0284-1851 , 1600-0455
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    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
    detail.hit.zdb_id: 2024579-8
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