| 1. |
- Fortner, Renee T., et al.
(author)
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Correlates of circulating ovarian cancer early detection markers and their contribution to discrimination of early detection models : results from the EPIC cohort
- 2017
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In: Journal of Ovarian Research. - : Springer Science and Business Media LLC. - 1757-2215. ; 10
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Journal article (peer-reviewed)abstract
- Background: Ovarian cancer early detection markers CA125, CA15.3, HE4, and CA72.4 vary between healthy women, limiting their utility for screening.Methods: We evaluated cross-sectional relationships between lifestyle and reproductive factors and these markers among controls (n = 1910) from a nested case-control study in the European Prospective Investigation into Cancer and Nutrition (EPIC). Improvements in discrimination of prediction models adjusting for correlates of the markers were evaluated among postmenopausal women in the nested case-control study (n = 590 cases). Generalized linear models were used to calculate geometric means of CA125, CA15.3, and HE4. CA72.4 above vs. below limit of detection was evaluated using logistic regression. Early detection prediction was modeled using conditional logistic regression.Results: CA125 concentrations were lower, and CA15.3 higher, in post- vs. premenopausal women (p ≤ 0.02). Among postmenopausal women, CA125 was higher among women with higher parity and older age at menopause (ptrend ≤ 0.02), but lower among women reporting oophorectomy, hysterectomy, ever use of estrogen-only hormone therapy, or current smoking (p < 0.01). CA15.3 concentrations were higher among heavier women and in former smokers (p ≤ 0.03). HE4 was higher with older age at blood collection and in current smokers, and inversely associated with OC use duration, parity, and older age at menopause (≤ 0.02). No associations were observed with CA72.4. Adjusting for correlates of the markers in prediction models did not improve the discrimination.Conclusions: This study provides insights into sources of variation in ovarian cancer early detection markers in healthy women and informs about the utility of individualizing marker cutpoints based on epidemiologic factors.
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| 2. |
- Campa, Daniele, et al.
(author)
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Genetic variability of the forkhead box O3 and prostate cancer risk in the European Prospective Investigation on Cancer
- 2011
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In: Oncology Reports. - Athen : National Hellenic Research Foundation. - 1021-335X .- 1791-2431. ; 26:4, s. 979-986
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Journal article (peer-reviewed)abstract
- Forkhead box O3 (FOXO3) has a wide range of functions: it promotes tumor suppression, cell cycle arrest, repair of damaged DNA, detoxification of reactive oxygen species, apoptosis and plays a pivotal role in promoting longevity. FOXO3 is a key downstream target of the PI3K-Akt pathway in response to cellular stimulation by growth factors or insulin and has been proposed as a bridge between ageing and tumor suppression. Three SNPs in the FOXO3 gene (rs3800231, rs9400239 and rs479744) that have been shown to be strongly and consistently associated with longevity, were examined in relation to PC risk in a case control study of 1571 incident PC cases and 1840 controls nested within the European Prospective Investigation into Cancer and Nutrition (EPIC). There was no statistically significant association between the SNPs and PC risk regardless of the model of inheritance (dominant, codominant and recessive). The associations were not modified by disease aggressiveness, circulating levels of steroid sex hormones, or IGFs or BMI. We conclude that polymorphisms in the FOXO3 gene that are associated with longevity are not major risk factors for PC risk, in this population of Caucasian men.
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| 3. |
- Fortner, Renee T., et al.
(author)
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Endometrial cancer risk prediction including serum-based biomarkers : results from the EPIC cohort
- 2017
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In: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 140:6, s. 1317-1323
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Journal article (peer-reviewed)abstract
- Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimina-tion. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigat-ed for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selec-tion process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were select-ed into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including eti-ologic markers on independent pathways and genetic markers may further improve discrimination.
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| 4. |
- Guida, Florence, et al.
(author)
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Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins
- 2018
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In: JAMA Oncology. - : American Medical Association (AMA). - 2374-2437 .- 2374-2445. ; 4:10
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Journal article (peer-reviewed)abstract
- Importance There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases.Objective To investigate whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria.Design, Setting, and Participants Prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS).Main Outcomes and Measures Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under the receiver-operating characteristics curve [AUC], sensitivity, and specificity).Results In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P = .003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model.Conclusions and Relevance This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.
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| 5. |
- Hoeft, Birgit, et al.
(author)
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Polymorphisms in fatty acid metabolism-related genes are associated with colorectal cancer risk
- 2010
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In: Carcinogenesis. - : Oxford University Press (OUP). - 0143-3334 .- 1460-2180. ; 31:3, s. 466-472
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Journal article (peer-reviewed)abstract
- Colorectal cancer (CRC) is the third most common malignant tumor and the fourth leading cause of cancer death worldwide. The crucial role of fatty acids for a number of important biological processes suggests a more in-depth analysis of inter-individual differences in fatty acid metabolizing genes as contributing factor to colon carcinogenesis. We examined the association between genetic variability in 43 fatty acid metabolism-related genes and colorectal risk in 1225 CRC cases and 2032 controls participating in the European Prospective Investigation into Cancer and Nutrition study. Three hundred and ninety two single-nucleotide polymorphisms were selected using pairwise tagging with an r(2) cutoff of 0.8 and a minor allele frequency of > 5%. Conditional logistic regression models were used to estimate odds ratios and corresponding 95% confidence intervals. Haplotype analysis was performed using a generalized linear model framework. On the genotype level, hydroxyprostaglandin dehydrogenase 15-(NAD) (HPGD), phospholipase A2 group VI (PLA2G6) and transient receptor potential vanilloid 3 were associated with higher risk for CRC, whereas prostaglandin E receptor 2 (PTGER2) was associated with lower CRC risk. A significant inverse association (P < 0.006) was found for PTGER2 GGG haplotype, whereas HPGD AGGAG and PLA2G3 CT haplotypes were significantly (P < 0.001 and P = 0.003, respectively) associated with higher risk of CRC. Based on these data, we present for the first time the association of HPGD variants with CRC risk. Our results support the key role of prostanoid signaling in colon carcinogenesis and suggest a relevance of genetic variation in fatty acid metabolism-related genes and CRC risk.
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| 6. |
- Huesing, Anika, et al.
(author)
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Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status
- 2012
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In: Journal of Medical Genetics. - : BMJ. - 0022-2593 .- 1468-6244. ; 49:9, s. 601-608
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Journal article (peer-reviewed)abstract
- Objective There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status. Material and methods Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROC(a)). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction. Results We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROC(a) going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status. Discussion and conclusions Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.
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