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- Barrdahl, Myrto, et al.
(författare)
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A comprehensive analysis of polymorphic variants in steroid hormone and insulin-like growth factor-1 metabolism and risk of in situ breast cancer : Results from the Breast and Prostate Cancer Cohort Consortium
- 2018
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Ingår i: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 142:6, s. 1182-1188
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Tidskriftsartikel (refereegranskat)abstract
- We assessed the association between 1,414 single nucleotide polymorphisms (SNPs) in genes involved in synthesis and metabolism of steroid hormones and insulin-like growth factor 1, and risk of breast cancer in situ (BCIS), with the aim of determining whether any of these were disease specific. This was carried out using 1,062 BCIS cases and 10,126 controls as well as 6,113 invasive breast cancer cases from the Breast and Prostate Cancer Cohort Consortium (BPC3). Three SNPs showed at least one nominally significant association in homozygous minor versus homozygous major models. ACVR2A-rs2382112 (ORhom=3.05, 95%CI=1.72-5.44, Phom=1.47 × 10-4), MAST2-rs12124649 (ORhom=1.73, 95% CI =1.18-2.54, Phom=5.24 × 10-3), and INSR-rs10500204 (ORhom=1.96, 95% CI=1.44-2.67, Phom=1.68 × 10-5) were associated with increased risk of BCIS; however, only the latter association was significant after correcting for multiple testing. Furthermore, INSR-rs10500204 was more strongly associated with the risk of BCIS than invasive disease in case-only analyses using the homozygous minor versus homozygous major model (ORhom=1.78, 95% CI=1.30-2.44, Phom=3.23 × 10-4). The SNP INSR-rs10500204 is located in an intron of the INSR gene and is likely to affect binding of the promyelocytic leukemia (PML) protein. The PML gene is known as a tumor suppressor and growth regulator in cancer. However, it is not clear on what pathway the A-allele of rs10500204 could operate to influence the binding of the protein. Hence, functional studies are warranted to investigate this further.
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| 2. |
- Kaaks, Rudolf, et al.
(författare)
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Tumor-associated autoantibodies as early detection markers for ovarian cancer? : A prospective evaluation
- 2018
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Ingår i: International Journal of Cancer. - : Wiley-Blackwell. - 0020-7136 .- 1097-0215. ; 143:3, s. 515-526
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Tidskriftsartikel (refereegranskat)abstract
- Immuno-proteomic screening has identified several tumor-associated autoantibodies (AAb) that may have diagnostic capacity for invasive epithelial ovarian cancer, with AAbs to P53 proteins and cancer-testis antigens (CTAGs) as prominent examples. However, the early detection potential of these AAbs has been insufficiently explored in prospective studies. We performed ELISA measurements of AAbs to CTAG1A, CTAG2, P53 and NUDT11 proteins, for 194 patients with ovarian cancer and 705 matched controls from the European EPIC cohort, using serum samples collected up to 36 months prior to diagnosis under usual care. CA125 was measured using electrochemo-luminiscence. Diagnostic discrimination statistics were calculated by strata of lead-time between blood collection and diagnosis. With lead times 6 months, ovarian cancer detection sensitivity at 0.98 specificity (SE98) varied from 0.19 [95% CI 0.08-0.40] for CTAG1A, CTAG2 and NUDT1 to 0.23 [0.10-0.44] for P53 (0.33 [0.11-0.68] for high-grade serous tumors). However, at longer lead-times, the ability of these AAb markers to distinguish future ovarian cancer cases from controls declined rapidly; at lead times >1 year, SE98 estimates were close to zero (all invasive cases, range: 0.01-0.11). Compared to CA125 alone, combined logistic regression scores of AAbs and CA125 did not improve detection sensitivity at equal level of specificity. The added value of these selected AAbs as markers for ovarian cancer beyond CA125 for early detection is therefore limited. What's new? Could autoantibodies against tumor antigens provide an early warning system for ovarian cancer? These authors tested how well certain antibodies detected ovarian cancer. They selected four candidate antibodies, to p53, CTAG1A, CTAG2 and NUDT11 proteins, which appear in elevated levels in cancer patients. None of them performed well as a herald of burgeoning cancer. They did not perform any better than the best currently available biomarker, CA125, and as lead times increased past 6 months prediagnosis, the effectiveness diminished. Surprisingly, elevated antibodies appeared in quite a few of the control samples, suggesting they might not be as cancer-specific as expected.
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| 3. |
- Perrier, Flavie, et al.
(författare)
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Identifying and correcting epigenetics measurements for systematic sources of variation
- 2018
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Ingår i: Clinical Epigenetics. - London : BioMed Central. - 1868-7083 .- 1868-7075. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Background: Methylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features.In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis.Results: A sizeable proportion of systematic variability due to variables expressing 'batch' and 'sample position' within 'chip' was identified, with values of the partial R-2 statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals' methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to 'batch' (1.3%) and 'sample position' (0.6%), and in a diminished variability attributable to 'chip' within a batch (0.9%). After ComBat or the residuals' corrections, a larger number of significant sites (k = 600 and k = 427, respectively) were associated to smoking status than the SVA correction (k = 96).Conclusions: The three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation.
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