1.
Sieri, Sabina, et al.
(author)
Dietary Fat Intake and Development of Specific Breast Cancer Subtypes.
2014
In: Journal of the National Cancer Institute. - : Oxford University Press (OUP). - 1460-2105 .- 0027-8874. ; 106:5, s. 068-068
Journal article (peer-reviewed) abstract
We prospectively evaluated fat intake as predictor of developing breast cancer (BC) subtypes defined by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor 2 receptor (HER2), in a large (n = 337327) heterogeneous cohort of women, with 10062 BC case patients after 11.5 years, estimating BC hazard ratios (HRs) by Cox proportional hazard modeling. High total and saturated fat were associated with greater risk of ER(+)PR(+) disease (HR = 1.20, 95% confidence interval [CI] = 1.00 to 1.45; HR = 1.28, 95% CI = 1.09 to 1.52; highest vs lowest quintiles) but not ER(-)PR(-) disease. High saturated fat was statistically significantly associated with greater risk of HER2(-) disease. High saturated fat intake particularly increases risk of receptor-positive disease, suggesting saturated fat involvement in the etiology of this BC subtype.
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4.
Dossus, Laure, et al.
(author)
Tumor necrosis factor (TNF)-α, soluble TNF receptors and endometrial cancer risk : the EPIC study
2011
In: International Journal of Cancer. - Geneve : International union against cancer. - 0020-7136 .- 1097-0215. ; 129:8, s. 2032-2037
Journal article (peer-reviewed) abstract
Chronic inflammation has been hypothesized to play a role in endometrial cancer development. Tumor necrosis factor-α (TNF-α), one of the major pro-inflammatory cytokines, has also been implicated in endometrial physiology. We conducted a case-control study nested within the European prospective investigation into cancer and nutrition (EPIC) to examine the association of TNF-α and its two soluble receptors (sTNFR1 and sTNFR2) with endometrial cancer risk. Two-hundred-seventy cases and 518 matched controls were analyzed using conditional logistic regression. All statistical tests were two-sided. We observed an increased risk of endometrial cancer among women in the highest versus lowest quartile of TNF-α (odds ratio [OR]: 1.73, 95% CI: 1.09-2.73, Ptrend = 0.01), sTNFR1 (OR: 1.68, 95% CI: 0.99-2.86, Ptrend = 0.07) and sTNFR2 (OR: 1.53, 95%CI: 0.92-2.55, Ptrend = 0.03) after adjustment for body-mass-index, parity, age at menopause and previous postmenopausal hormone therapy use. Further adjustments for estrogens and C-peptide had minor effect on risk estimates. Our data show that elevated prediagnostic concentrations of TNF-α and its soluble receptors are related to a higher risk of endometrial cancer, particularly strong in women diagnosed within 2 years of blood donation. This is the first study of its kind and therefore deserves replication in further prospective studies.
5.
Hoggart, Clive, et al.
(author)
A Risk Model for Lung Cancer Incidence
2012
In: Cancer Prevention Research. - Philadelphia : American Association for Cancer Research. - 1940-6207 .- 1940-6215. ; 5:6, s. 834-846
Journal article (peer-reviewed) abstract
Risk models for lung cancer incidence would be useful for prioritising individuals for screening and participation in clinical trials of chemoprevention. We present a risk model for lung cancer built using prospective cohort data from a general population which predicts individual incidence in a given time period.We build separate risk models for current and former smokers utilising 169,035 ever smokers from the multicentre European Prospective Investigation into Cancer and Nutrition (EPIC) and considered a model for never smokers. The data set was split into independent training and test sets. Lung cancer incidence was modelled using survival analysis, stratifying by age started smoking, and for former smokers, also smoking duration. Other risk factors considered were smoking intensity, ten occupational/environmental exposures previously implicated with lung cancer, and SNPs at two loci identified by genome-wide association studies of lung cancer. Individual risk in the test set was measured by the predicted probability of lung cancer incidence in the year preceding last follow-up time, predictive accuracy was measured by the area under the receiver operator characteristic curve (AUC).Utilising smoking information alone gave good predictive accuracy: the AUC and 95% confidence interval in ever smokers was 0.843 (0.810, 0.875), the Bach model applied to the same data gave an AUC of 0.775 (0.737, 0.813). Other risk factors had negligible effect on the AUC, including never smokers for whom prediction was poor.Our model is generalisable and straightforward to implement. Its accuracy can be attributed to its modelling of lifetime exposure to smoking.
