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1. Plasma concentration of brominated flame retardants and postmenopausal breast cancer risk: a nested case-control study in the French E3N cohort

Author

Francesca Romana Mancini, German Cano-Sancho, Oceane Mohamed, Iris Cervenka, Hanane Omichessan, Philippe Marchand, Marie-Christine Boutron-Ruault, Patrick Arveux, Gianluca Severi, Jean-Philippe Antignac, and Marina Kvaskoff

Subjects
Breast cancer, Brominated flame retardants (BFRs), Pentabromodiphenyl ethers (PBDEs), Polybrominated biphenyls (PBBs), E3N cohort, Biomonitoring, Industrial medicine. Industrial hygiene, RC963-969, Public aspects of medicine, RA1-1270
Abstract

Abstract Background Brominated flame retardants (BFRs) are lipophilic substances with endocrine-disrupting properties. To date, only few investigations, mainly retrospective case-control studies, have explored the link between internal levels of BFRs and the risk of breast cancer, leading to conflicting results. We investigated the associations between plasma concentrations of two main groups of BFRs, PBDEs (pentabromodiphenyl ethers) and PBBs (polybrominated biphenyls), and the risk of breast cancer in a nested case-control study. Methods A total of 197 incident breast cancer cases and 197 controls with a blood sample collected in 1994–1999 were included. Plasma levels of PBDE congeners (BDE-28, BDE-47, BDE-99, BDE-100, BDE153, BDE-154) and of PBB-153 were measured by gas chromatography coupled to high-resolution mass spectrometry. Conditional logistic regression models, adjusted for potential confounders, were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Results Women were aged 56 years on average at blood draw. All cases, except for one, were diagnosed after menopause, with an average age at diagnosis of 68 years. Overall, we found no evidence of an association between plasma levels of PBDEs and PBB-153 and postmenopausal breast cancer risk (log-concentrations of BFRs yielding non-statistically significant ORs of 0.87 to 1.07). The analysis showed a non-linear inverse association for BDE-100 and BDE-153 and postmenopausal breast cancer risk; nevertheless, these findings were statistically significant only when the exposure was modeled as ng/L plasma (third vs. first quintile: OR = 0.42, 95%CI = 0.19–0.93 and OR = 0.42, 95%CI = 0.18–0.98, respectively) and not when modeled as ng/gr of lipids (OR = 0.58, 95%CI = 0.27–1.25 and OR = 0.53, 95%CI = 0.25–1.17). These results were unchanged in stratified analyses by tumor hormone receptor expression or body mass index. Conclusions Our results suggest no clear association between internal levels of PBDEs and PBB-153 and the risk of breast cancer in postmenopausal women. However, these findings need to be carefully interpreted, taking into account limitations due to the limited number of women included in the study, the lack of information concerning genetic susceptibility of cases, and the unavailability of exposure assessment during critical windows of susceptibility for breast cancer. More studies are warranted to further investigate the relationships between PBDE and PBB exposure and breast cancer risk.

Published
2020
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2. Mitochondrial DNA copy number variation, leukocyte telomere length, and breast cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study

Author

Daniele Campa, Myrto Barrdahl, Aurelia Santoro, Gianluca Severi, Laura Baglietto, Hanane Omichessan, Rosario Tumino, H. B(as). Bueno-de-Mesquita, Petra H. Peeters, Elisabete Weiderpass, Maria-Dolores Chirlaque, Miguel Rodríguez-Barranco, Antonio Agudo, Marc Gunter, Laure Dossus, Vittorio Krogh, Giuseppe Matullo, Antonia Trichopoulou, Ruth C. Travis, Federico Canzian, and Rudolf Kaaks

Subjects
Mitochondrial copy number, Telomere length, Breast cancer, Cancer epidemiology, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Leukocyte telomere length (LTL) and mitochondrial genome (mtDNA) copy number and deletions have been proposed as risk markers for various cancer types, including breast cancer (BC). Methods To gain a more comprehensive picture on how these markers can modulate BC risk, alone or in conjunction, we performed simultaneous measurements of LTL and mtDNA copy number in up to 570 BC cases and 538 controls from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. As a first step, we measured LTL and mtDNA copy number in 96 individuals for which a blood sample had been collected twice with an interval of 15 years. Results According to the intraclass correlation (ICC), we found very good stability over the time period for both measurements, with ICCs of 0.63 for LTL and 0.60 for mtDNA copy number. In the analysis of the entire study sample, we observed that longer LTL was strongly associated with increased risk of BC (OR 2.71, 95% CI 1.58–4.65, p = 3.07 × 10− 4 for highest vs. lowest quartile; OR 3.20, 95% CI 1.57–6.55, p = 1.41 × 10− 3 as a continuous variable). We did not find any association between mtDNA copy number and BC risk; however, when considering only the functional copies, we observed an increased risk of developing estrogen receptor-positive BC (OR 2.47, 95% CI 1.05–5.80, p = 0.04 for highest vs. lowest quartile). Conclusions We observed a very good correlation between the markers over a period of 15 years. We confirm a role of LTL in BC carcinogenesis and suggest an effect of mtDNA copy number on BC risk.

Published
2018
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3. Identifying and correcting epigenetics measurements for systematic sources of variation

Author

Flavie Perrier, Alexei Novoloaca, Srikant Ambatipudi, Laura Baglietto, Akram Ghantous, Vittorio Perduca, Myrto Barrdahl, Sophia Harlid, Ken K. Ong, Alexia Cardona, Silvia Polidoro, Therese Haugdahl Nøst, Kim Overvad, Hanane Omichessan, Martijn Dollé, Christina Bamia, José Marìa Huerta, Paolo Vineis, Zdenko Herceg, Isabelle Romieu, and Pietro Ferrari

Subjects
Epigenetics, PC-PR2, Normalization, Methylation, Smoking status, Medicine, Genetics, QH426-470
Abstract

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 R2 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.

Published
2018
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4. Computational tools to detect signatures of mutational processes in DNA from tumours: A review and empirical comparison of performance.

Author

Hanane Omichessan, Gianluca Severi, and Vittorio Perduca

Subjects
Medicine, Science
Abstract

Mutational signatures refer to patterns in the occurrence of somatic mutations that might be uniquely ascribed to particular mutational process. Tumour mutation catalogues can reveal mutational signatures but are often consistent with the mutation spectra produced by a variety of mutagens. To date, after the analysis of tens of thousands of exomes and genomes from about 40 different cancer types, tens of mutational signatures characterized by a unique probability profile across the 96 trinucleotide-based mutation types have been identified, validated and catalogued. At the same time, several concurrent methods have been developed for either the quantification of the contribution of catalogued signatures in a given cancer sequence or the identification of new signatures from a sample of cancer sequences. A review of existing computational tools has been recently published to guide researchers and practitioners through their mutational signature analyses, but other tools have been introduced since its publication and, a systematic evaluation and comparison of the performance of such tools is still lacking. In order to fill this gap, we have carried out an empirical evaluation of the main packages available to date, using both real and simulated data. Among other results, our empirical study shows that the identification of signatures is more difficult for cancers characterized by multiple signatures each having a small contribution. This work suggests that detection methods based on probabilistic models, especially EMu and bayesNMF, have in general better performance than NMF-based methods.

Published
2019
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5. Association of Selenoprotein and Selenium Pathway Genotypes with Risk of Colorectal Cancer and Interaction with Selenium Status

Author

Veronika Fedirko, Mazda Jenab, Catherine Méplan, Jeb S. Jones, Wanzhe Zhu, Lutz Schomburg, Afshan Siddiq, Sandra Hybsier, Kim Overvad, Anne Tjønneland, Hanane Omichessan, Vittorio Perduca, Marie-Christine Boutron-Ruault, Tilman Kühn, Verena Katzke, Krasimira Aleksandrova, Antonia Trichopoulou, Anna Karakatsani, Anastasia Kotanidou, Rosario Tumino, Salvatore Panico, Giovanna Masala, Claudia Agnoli, Alessio Naccarati, Bas Bueno-de-Mesquita, Roel C.H. Vermeulen, Elisabete Weiderpass, Guri Skeie, Therese Haugdahl Nøst, Leila Lujan-Barroso, J. Ramón Quirós, José María Huerta, Miguel Rodríguez-Barranco, Aurelio Barricarte, Björn Gylling, Sophia Harlid, Kathryn E. Bradbury, Nick Wareham, Kay-Tee Khaw, Marc Gunter, Neil Murphy, Heinz Freisling, Kostas Tsilidis, Dagfinn Aune, Elio Riboli, John E. Hesketh, and David J. Hughes

Subjects
selenium, selenium status, selenoprotein gene variation, selenium pathway, colorectal neoplasms, selenoprotein P, prospective cohort, colorectal cancer risk, genetic epidemiology, biomarkers, Nutrition. Foods and food supply, TX341-641
Abstract

Selenoprotein genetic variations and suboptimal selenium (Se) levels may contribute to the risk of colorectal cancer (CRC) development. We examined the association between CRC risk and genotype for single nucleotide polymorphisms (SNPs) in selenoprotein and Se metabolic pathway genes. Illumina Goldengate assays were designed and resulted in the genotyping of 1040 variants in 154 genes from 1420 cases and 1421 controls within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Multivariable logistic regression revealed an association of 144 individual SNPs from 63 Se pathway genes with CRC risk. However, regarding the selenoprotein genes, only TXNRD1 rs11111979 retained borderline statistical significance after adjustment for correlated tests (PACT = 0.10; PACT significance threshold was P < 0.1). SNPs in Wingless/Integrated (Wnt) and Transforming growth factor (TGF) beta-signaling genes (FRZB, SMAD3, SMAD7) from pathways affected by Se intake were also associated with CRC risk after multiple testing adjustments. Interactions with Se status (using existing serum Se and Selenoprotein P data) were tested at the SNP, gene, and pathway levels. Pathway analyses using the modified Adaptive Rank Truncated Product method suggested that genes and gene x Se status interactions in antioxidant, apoptosis, and TGF-beta signaling pathways may be associated with CRC risk. This study suggests that SNPs in the Se pathway alone or in combination with suboptimal Se status may contribute to CRC development.

Published
2019
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6. Associations between plasma levels of brominated flame retardants and methylation of DNA from peripheral blood: A cross-sectional study in a cohort of French women

Author

Hanane Omichessan, Vittorio Perduca, Silvia Polidoro, Marina Kvaskoff, Thérèse Truong, German Cano-Sancho, Jean-Philippe Antignac, Laura Baglietto, Francesca Romana Mancini, and Gianluca Severi

Subjects
Cross-Sectional Studies, Halogenated Diphenyl Ethers, Humans, Female, DNA, DNA Methylation, Biochemistry, Methylation, Gas Chromatography-Mass Spectrometry, General Environmental Science, Flame Retardants
Abstract

Brominated flame retardants (BFRs) are organic compounds that are widespread in the environment. Because of their persistence, they are able to bioaccumulate with major impacts on human health. It has been hypothesized that the effect of BFRs on human health is mediated by alterations of DNA methylation.The aim of this study was to examine the association between methylation of DNA extracted from peripheral blood and circulating levels of BFRs measured in plasma.We conducted a methylation wide association study on 336 blood samples from a study within the E3N (Etude Epidémiologique auprès de femmes de l'Education Nationale) cohort, a long-term longitudinal cohort of French women. DNA methylation at more than 850 000 cytosine-guanine dinucleotide (CpG) sites was measured with the Illumina Infinium HumanMethylation - EPIC BeadChip. Circulating levels of seven BFRs (BDE-28, BDE-47, BDE-99, BDE-100, BDE-153, BDE-154 and PBB-153) were measured by gas chromatography coupled to high-resolution mass spectrometry in plasma samples. The association between DNA methylation and BFRs plasma levels was assessed through linear mixed-effects models followed by gene-set enrichment analyses (GSEA).We identified 253 CpG sites whose methylation levels were significantly associated with exposure to BFRs after Bonferroni correction. For 50 of these CpGs the p-values were less than 2.2x10Exposure to BFRs appears to be related to numerous alterations in DNA methylation. These findings, if replicated in independent studies, provide insights into the biological and health effects of BFRs.

Published
2021

7. Novel Common Genetic Susceptibility Loci for Colorectal Cancer

Author

Katja Butterbach, Julyann Pérez-Mayoral, Douglas F. Easton, Stefanie Brezina, Ben Zhang, Frank J. Manion, Hansong Wang, Sanford D. Markowitz, Liesel M. FitzGerald, Sun Ha Jee, Michelle Cotterchio, Daniel D. Buchanan, Timothy R. Church, Wolfgang Lieb, Xiao-Ou Shu, John L. Hopper, Stephanie A. Bien, Manuela Gago-Dominguez, Jian Gong, Laurence N. Kolonel, Graham G. Giles, Leon Raskin, Yingchang Lu, Kristen Anton, Charles S. Fuchs, Fränzel J.B. van Duijnhoven, Hermann Brenner, Yi Lin, Clicerio Gonzalez-Villalpando, Yong-Bing Xiang, Aaron K. Aragaki, Daniela Seminara, Stephanie M. Gogarten, Gad Rennert, Jose E. Castelao, Rocky Fischer, Antonio J. Molina, Jarmo Virtamo, Tabitha A. Harrison, Volker Arndt, Lee Soo Chin, Michael Hoffmeister, Mark A. Jenkins, Shu Chen Huang, Chris S. Carlson, Stéphane Bézieau, Rebecca D. Jackson, Bhramar Mukherjee, Darin Taverna, Bridget M. Riggs, Christopher K. Edlund, Christopher A. Haiman, Melissa C. Southey, Anna H. Wu, Marilena Melas, Antonia Trichopoulou, Hedy S. Rennert, Gianluca Severi, Stephen B. Gruber, Noralane M. Lindor, Gerhard A. Coetzee, Richard B. Hayes, Sarah J. Plummer, Keitaro Matsuo, Mathieu Lemire, Philipp Hofer, Neil Murphy, José María Huerta, Paul D.P. Pharoah, Erin M. Siegel, Sébastien Küry, Thomas J. Hudson, Annika Lindblom, Marcia Cruz Correa, Michael O. Woods, Sophia Harlid, Tilman Kuehn, David Shibata, Christopher I. Li, Stephen N. Thibodeau, Stephen J. Chanock, Jochen Hampe, Flavio Lejbkowicz, Jeroen R. Huyghe, Suminori Kono, Jenny Chang-Claude, Aung Ko Win, Brent W. Zanke, Alicja Wolk, David V. Conti, Elena M. Gonzalez-Villalpando, Christopher I. Amos, Shuo Jiao, Domenico Palli, Vicente Martín, Jesus P. Paredes Cotoré, Stephanie J. Weinstein, Andrea Gsur, William M. Grady, Koichi Matsuda, Loic Le Marchand, Hanane Omichessan, Marc J. Gunter, Graham Casey, Li Li, Zsofia K. Stadler, Eric J. Jacobs, Kevin McDonnell, Dallas R. English, Demetrius Albanes, Amit Joshi, Wei Zheng, Mariana C. Stern, Cecelia A. Laurie, Jing Ma, Cornelia M. Ulrich, Stephanie L. Schmit, Victor Moreno, John D. Potter, Chenxu Qu, Bette J. Caan, Heinz-Josef Lenz, M. Henar Alonso, Andrea Z. LaCroix, Christoph Mancao, John F. Harju, Yun Ru Liu, Jane C. Figueiredo, Gregory Idos, Kana Wu, Duncan C. Thomas, Motoki Iwasaki, W. James Gauderman, Thomas A. Sellers, David Van Den Berg, Barbara K. Fortini, David N. Levine, James M. Church, Ya Wen Cheng, Edith J. M. Feskens, Edward Giovannucci, Manish Gala, Polly A. Newcomb, Charles Kooperberg, Iona Cheng, David J. Hunter, Martha L. Slattery, Roger L. Milne, Lars G. Fritsche, Niha Zubair, Steven Gallinger, Yi Xin Zeng, Wei Shi, Andrew T. Chan, Fotios Loupakis, Vittorio Krogh, Clemens Schafmayer, Sun-Seog Kweon, Bethany van Guelpan, Amanda Bloomer, Kenneth Offit, Stephanie Tring, Shoichiro Tsugane, David Duggan, Fredrick R. Schumacher, Joseph Vijai, Weihua Jia, Marie-Christine Boutron-Ruault, Joel K. Greenson, Frank Luh, Ulrike Peters, Keith R. Curtis, Juergen Boehm, Robert E. Schoen, Sonja I. Berndt, Elizabeth L. Barry, Sebastian Stintzing, Li Hsu, Emily White, Conghui Qu, Peter T. Campbell, Caroline McNeil, and Yun Yen

Subjects
Oncology, Cancer Research, medicine.medical_specialty, Genotype, Nutrition and Disease, Colorectal cancer, Genome-wide association study, Biology, Polymorphism, Single Nucleotide, 03 medical and health sciences, 0302 clinical medicine, Càncer colorectal, Internal medicine, Voeding en Ziekte, Ethnicity, medicine, Genetic predisposition, Genetics, Humans, Life Science, Genetic Predisposition to Disease, Oncology & Carcinogenesis, Allele, Allele frequency, Genetic association, VLAG, Global Nutrition, Wereldvoeding, Oncology And Carcinogenesis, Case-control study, Articles, Prognosis, medicine.disease, United States, 3. Good health, Genetic Loci, Case-Control Studies, 030220 oncology & carcinogenesis, Colorectal Neoplasms, Genètica, Follow-Up Studies, Genome-Wide Association Study
Abstract

Background Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10−8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. Methods We conducted a GWAS in European descent CRC cases and control subjects using a discovery–replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10−8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. Results The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10−8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. Conclusions This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screening.

Published
2019

8. Association of Selenoprotein and Selenium Pathway Genotypes with Risk of Colorectal Cancer and Interaction with Selenium Status

Author

Krasimira Aleksandrova, Leila Lujan-Barroso, Kim Overvad, Marc J. Gunter, Jeb Jones, Anna Karakatsani, Giovanna Masala, J. Ramón Quirós, John E. Hesketh, José María Huerta, Sophia Harlid, Aurelio Barricarte, Salvatore Panico, Anastasia Kotanidou, Kostas Tsilidis, Afshan Siddiq, Bas Bueno-de-Mesquita, Dagfinn Aune, Tilman Kühn, Therese Haugdahl Nøst, Guri Skeie, Mazda Jenab, Kay-Tee Khaw, Catherine Méplan, Marie-Christine Boutron-Ruault, Elio Riboli, Hanane Omichessan, Rosario Tumino, Antonia Trichopoulou, Wanzhe Zhu, Heinz Freisling, Claudia Agnoli, Nicholas J. Wareham, Alessio Naccarati, Kathryn E. Bradbury, Miguel Rodríguez-Barranco, Neil Murphy, Verena Katzke, Elisabete Weiderpass, Veronika Fedirko, Björn Gylling, Vittorio Perduca, Roel Vermeulen, Sandra Hybsier, Anne Tjønneland, David J. Hughes, Lutz Schomburg, Department of Medical and Clinical Genetics, Medicum, Faculty of Medicine, International Agency for Cancer Research (IACR), Experimental Endocrinology, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Imperial College London, Department of Clinical Epidemiology, Aarhus University Hospital, Institute of Cancer Epidemiology, Danish Cancer Society, Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Nutrition, hormones et cancer: épidémiologie et prévention (E3N), Epidémiologie, sciences sociales, santé publique (IFR 69), Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-École des hautes études en sciences sociales (EHESS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-École des hautes études en sciences sociales (EHESS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Gustave Roussy (IGR)-Université Paris-Sud - Paris 11 (UP11), Thermo Fisher Scientific, Thermo Fisher Scientific Inc., Division of Cancer Epidemiology, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Department of Epidemiology, Deutsches Institut für Ernahrungsforschung Potsdam-Rehbrücke (DIFE), Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School [Athens], Cancer Registry and Histopathology Unit, Civile - M.P.Arezzo Hospital, Department of Clinical and Experimental Medicine, Università degli studi di Napoli Federico II, Molecular and Nutritional Epidemiology Unit (ISPO), Cancer Research and Prevention Institute, Fondazione IRCCS Istituto Nazionale dei Tumori, National Institute for Public Health and the Environment [Bilthoven] (RIVM), Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], Institute of Community Medicine, University of Tromsø (UiT), Institut d'Investigació Biomèdica de Bellvitge [Barcelone] (IDIBELL), Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), CIBER de Epidemiología y Salud Pública (CIBERESP), Danish Cancer Society Research Center, MRC epidemiology Unit, Addenbrooke's Hospital, Clinical Gerontology Unit, University of Cambridge [UK] (CAM), Nutrition and Metabolism Section, University of Oxford [Oxford], Department of Epidemiology and Biostatistics, School of Cellular and Molecular Biosciences, Newcastle University [Newcastle], Charite Medical School Berlin, Aalborg Hospital-Aarhus University Hospital, Université Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-École des hautes études en sciences sociales (EHESS)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-École des hautes études en sciences sociales (EHESS)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Gustave Roussy (IGR)-Université Paris-Sud - Paris 11 (UP11), 'Civile M.P.Arezzo' hospital, Jones, Jeb S [0000-0001-9165-1658], Schomburg, Lutz [0000-0001-9445-1555], Perduca, Vittorio [0000-0003-0339-0473], Tumino, Rosario [0000-0003-2666-414X], Agnoli, Claudia [0000-0003-4472-1179], Weiderpass, Elisabete [0000-0003-2237-0128], Skeie, Guri [0000-0003-2476-4251], Lujan-Barroso, Leila [0000-0001-6224-1764], Huerta, José María [0000-0002-9637-3869], Rodríguez-Barranco, Miguel [0000-0002-9972-9779], Gylling, Björn [0000-0002-4688-8952], Harlid, Sophia [0000-0001-8540-6891], Freisling, Heinz [0000-0001-8648-4998], Hughes, David J [0000-0003-1668-8770], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, Male, Colorectal cancer, selenoprotein P, SUSCEPTIBILITY, Selenoprotein gene variation, Cohort Studies, Epidemiologia genètica, 0302 clinical medicine, GENETIC-VARIANTS, Genetic epidemiology, Prospective Studies, Selenoproteins, selenium, ComputingMilieux_MISCELLANEOUS, Genetics, chemistry.chemical_classification, ARCHITECTURE, Nutrition and Dietetics, integumentary system, Selenoprotein P, Middle Aged, VDP::Medical disciplines: 700::Basic medical, dental and veterinary science disciplines: 710, CROHNS-DISEASE, 3. Good health, European Prospective Investigation into Cancer and Nutrition, Näringslära, selenium pathway, 030220 oncology & carcinogenesis, selenium status, Female, HEALTH, Selenium status, 3143 Nutrition, Life Sciences & Biomedicine, lcsh:Nutrition. Foods and food supply, FRIZZLED-RELATED PROTEIN, Adult, MEGALIN, genetic epidemiology, Genotype, BIOMARKERS, prospective cohort, Nutritional Status, Environmental Sciences & Ecology, lcsh:TX341-641, Single-nucleotide polymorphism, [SDV.CAN]Life Sciences [q-bio]/Cancer, Biology, Polymorphism, Single Nucleotide, selenoprotein gene variation, Colorectal cancer risk, Article, Colorectal neoplasms, 03 medical and health sciences, Selenium, Seleni, Càncer colorectal, Selenium pathway, medicine, SNP, Humans, Genetic Predisposition to Disease, METAANALYSIS, Aged, Science & Technology, Nutrition & Dietetics, colorectal cancer risk, VDP::Medisinske Fag: 700::Basale medisinske, odontologiske og veterinærmedisinske fag: 710, colorectal neoplasms, Prospective cohort, medicine.disease, POLYMORPHISM, biomarkers, 030104 developmental biology, Frzb, chemistry, Gene Expression Regulation, 3121 General medicine, internal medicine and other clinical medicine, 1111 Nutrition and Dietetics, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Selenoprotein, Environmental Sciences, 0908 Food Sciences, Genètica, Biomarkers, Food Science
Abstract

Selenoprotein genetic variations and suboptimal selenium (Se) levels may contribute to the risk of colorectal cancer (CRC) development. We examined the association between CRC risk and genotype for single nucleotide polymorphisms (SNPs) in selenoprotein and Se metabolic pathway genes. Illumina Goldengate assays were designed and resulted in the genotyping of 1040 variants in 154 genes from 1420 cases and 1421 controls within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Multivariable logistic regression revealed an association of 144 individual SNPs from 63 Se pathway genes with CRC risk. However, regarding the selenoprotein genes, only TXNRD1 rs11111979 retained borderline statistical significance after adjustment for correlated tests (PACT = 0.10, PACT significance threshold was P &lt, 0.1). SNPs in Wingless/Integrated (Wnt) and Transforming growth factor (TGF) beta-signaling genes (FRZB, SMAD3, SMAD7) from pathways affected by Se intake were also associated with CRC risk after multiple testing adjustments. Interactions with Se status (using existing serum Se and Selenoprotein P data) were tested at the SNP, gene, and pathway levels. Pathway analyses using the modified Adaptive Rank Truncated Product method suggested that genes and gene x Se status interactions in antioxidant, apoptosis, and TGF-beta signaling pathways may be associated with CRC risk. This study suggests that SNPs in the Se pathway alone or in combination with suboptimal Se status may contribute to CRC development.

Published
2019

9. Stem cell replication, somatic mutations and role of randomness in the development of cancer

Author

Ludmil B. Alexandrov, Paolo Vineis, Cyrille Delpierre, Hanane Omichessan, Michelle Kelly-Irving, Mark P. Little, Vittorio Perduca, Gianluca Severi, Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Centre de recherche en épidémiologie et santé des populations (CESP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Somatic cell, Epidemiology, media_common.quotation_subject, 030204 cardiovascular system & hematology, Environment, medicine.disease_cause, 1117 Public Health and Health Services, 03 medical and health sciences, 0302 clinical medicine, Somatic mutations, Neoplasms, Replication (statistics), medicine, Humans, Cancer, Risk factors, Cell Division, Mutation, Risk Factors, Stem Cells, 030212 general & internal medicine, Randomness, ComputingMilieux_MISCELLANEOUS, SIGNATURES, media_common, Public, Environmental & Occupational Health, Science & Technology, business.industry, medicine.disease, RISK ACCURACY, 3. Good health, Luck, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Stem cell, SMOKING, Carcinogenesis, business, CHANCE, Neuroscience, Life Sciences & Biomedicine
Abstract

An intense scientific debate has recently taken place relating to the "bad luck" hypothesis in cancer development, namely that intrinsic random, and therefore unavoidable, mutagenic events would have a predominant role in tumorigenesis. In this article we review the main contributions to this debate and explain the reasons why the claim that cancer is mostly explained by intrinsic random factors is unsupported by data and theoretical models. In support of this, we present an analysis showing that smoking-induced mutations are more predictive of cancer risk than the lifetime number of stem cell cellular divisions.

Published
2018

10. Computational tools to detect signatures of mutational processes in DNA from tumours: a review and empirical comparison of performance

Author

Hanane Omichessan, Vittorio Perduca, Gianluca Severi, Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Gustave Roussy (IGR), Centre for Epidemiology and Biostatistics [Victoria, Australia], University of Melbourne-Melbourne School of Population and Global Health [Victoria, Australia], Cancer Epidemiology Centre & Cancer Council Victoria [Melbourne, Australia], University of Melbourne-Melbourne School for Population and Global Health, Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Perduca, Vittorio, and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
Empirical comparison, Computer science, Somatic cell, Science, DNA Mutational Analysis, [SDV.CAN]Life Sciences [q-bio]/Cancer, Computational biology, medicine.disease_cause, Genome, 03 medical and health sciences, chemistry.chemical_compound, Germline mutation, 0302 clinical medicine, [STAT.AP] Statistics [stat]/Applications [stat.AP], [SDV.CAN] Life Sciences [q-bio]/Cancer, Cancer genome, Neoplasms, medicine, Humans, Exome sequencing, 030304 developmental biology, [STAT.AP]Statistics [stat]/Applications [stat.AP], 0303 health sciences, Mutation, Multidisciplinary, Mutation Spectra, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], COSMIC cancer database, Genome, Human, Computational Biology, DNA, Neoplasm, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Identification (information), chemistry, 030220 oncology & carcinogenesis, Mutation (genetic algorithm), Medicine, DNA, Algorithms, Research Article
Abstract

International audience; Mutational signatures refer to patterns in the occurrence of somatic mutations that might be uniquely ascribed to particular mutational process. Tumour mutation catalogues can reveal mutational signatures but are often consistent with the mutation spectra produced by a variety of mutagens. To date, after the analysis of tens of thousands of exomes and genomes from about 40 different cancer types, tens of mutational signatures characterized by a unique probability profile across the 96 trinucleotide-based mutation types have been identified , validated and catalogued. At the same time, several concurrent methods have been developed for either the quantification of the contribution of catalogued signatures in a given cancer sequence or the identification of new signatures from a sample of cancer sequences. A review of existing computational tools has been recently published to guide researchers and practitioners through their mutational signature analyses, but other tools have been introduced since its publication and, a systematic evaluation and comparison of the performance of such tools is still lacking. In order to fill this gap, we have carried out an empirical evaluation of the main packages available to date, using both real and simulated data. Among other results, our empirical study shows that the identification of signatures is more difficult for cancers characterized by multiple signatures each having a small contribution. This work suggests that detection methods based on probabilistic models, especially EMu and bayesNMF, have in general better performance than NMF-based methods.

Published
2018
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11. Identifying and correcting epigenetics measurements for systematic sources of variation

Author

Christina Bamia, Paolo Vineis, Sophia Harlid, José María Huerta, Myrto Barrdahl, Alexei Novoloaca, Martijn E.T. Dollé, Kim Overvad, Alexia Cardona, Therese Haugdahl Nøst, Pietro Ferrari, Srikant Ambatipudi, Hanane Omichessan, Ken K. Ong, Akram Ghantous, Isabelle Romieu, Silvia Polidoro, Laura Baglietto, Zdenko Herceg, Flavie Perrier, Vittorio Perduca, Ong, Kenneth [0000-0003-4689-7530], Cardona, Alexia [0000-0002-7877-5565], Apollo - University of Cambridge Repository, Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Human Genetics Foundation (HuGeF), Department of Clinical Epidemiology, Aalborg Hospital-Aarhus University Hospital, National Institute for Public Health and the Environment [Bilthoven] (RIVM), WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Danish Cancer Society Research Center, Centre International de Recherche contre le Cancer - International Agency for Research on Cancer (CIRC - IARC), Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), and Aarhus University Hospital

Subjects
0301 basic medicine, lcsh:Medicine, EPIC, Epigenesis, Genetic, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics, Smoking status, ComputingMilieux_MISCELLANEOUS, Genetics (clinical), Mathematics, Oligonucleotide Array Sequence Analysis, Principal Component Analysis, VDP::Medisinske Fag: 700::Helsefag: 800::Samfunnsmedisin, sosialmedisin: 801, GENE-EXPRESSION DATA, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], CANCER, Regression, Normalization, normalization, Oncology, BIAS, DNA methylation, Principal component analysis, Epigenetics, Female, SMOKING, Life Sciences & Biomedicine, Normalization (statistics), lcsh:QH426-470, Breast Neoplasms, Methylation, PC-PR2, Molecular Biology, Genetics, Developmental Biology, 03 medical and health sciences, DNA METHYLATION ARRAY, MICROARRAY, Linear regression, REGRESSION, Humans, Cancer och onkologi, Science & Technology, epigenetics, lcsh:R, Methodology, Computational Biology, DNA Methylation, UNWANTED VARIATION, lcsh:Genetics, 030104 developmental biology, Cancer and Oncology, Case-Control Studies, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, CpG Islands, methylation, VDP::Medical disciplines: 700::Health sciences: 800::Community medicine, Social medicine: 801, FOLATE, smoking status
Abstract

Source at https://doi.org/10.1186/s13148-018-0471-6. © The Author(s). 2018 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 R2 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.

Published
2018

12. Mitochondrial DNA copy number variation, leukocyte telomere length, and breast cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study

Author

Laure Dossus, Antonio Agudo, Marc J. Gunter, Rudolf Kaaks, Miguel Rodríguez-Barranco, Maria-Dolores Chirlaque, Vittorio Krogh, Petra H.M. Peeters, Hanane Omichessan, Elisabete Weiderpass, Myrto Barrdahl, Ruth C. Travis, Federico Canzian, Antonia Trichopoulou, Laura Baglietto, Gianluca Severi, Hendrik B. Bueno-de-Mesquita, Giuseppe Matullo, Daniele Campa, Aurelia Santoro, Rosario Tumino, Campa, Daniele, Barrdahl, Myrto, Santoro, Aurelia, Severi, Gianluca, Baglietto, Laura, Omichessan, Hanane, Tumino, Rosario, Bueno-de-Mesquita, H.B(as)., Peeters, Petra H., Weiderpass, Elisabete, Chirlaque, Maria-Dolore, Rodríguez-Barranco, Miguel, Agudo, Antonio, Gunter, Marc, Dossus, Laure, Krogh, Vittorio, Matullo, Giuseppe, Trichopoulou, Antonia, Travis, Ruth C., Canzian, Federico, and Kaaks, Rudolf

Subjects
0301 basic medicine, Oncology, Cancer Research, Mitochondrial copy number, Genome-wide association study, ADN mitocondrial, Cohort Studies, 0302 clinical medicine, Breast cancer, Risk Factors, Epidemiology of cancer, Leukocytes, Copy-number variation, Prospective Studies, Prospective cohort study, Telòmer, Middle Aged, Telomere, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Mitochondrial DNA, 3. Good health, European Prospective Investigation into Cancer and Nutrition, Europe, Quartile, 030220 oncology & carcinogenesis, Female, Research Article, Adult, medicine.medical_specialty, DNA Copy Number Variations, Breast Neoplasms, Biology, lcsh:RC254-282, DNA, Mitochondrial, Càncer de mama, 03 medical and health sciences, Cancer epidemiology, Internal medicine, medicine, Humans, Telomere length, Aged, VDP::Medical disciplines: 700::Clinical medical disciplines: 750::Oncology: 762, Cancer, Telomere Homeostasis, medicine.disease, VDP::Medisinske Fag: 700::Klinisk medisinske fag: 750::Onkologi: 762, 030104 developmental biology, Nutrition Assessment
Abstract

Source at https://doi.org/10.1186/s13058-018-0955-5. Licensed CC BY-NC-ND 4.0. Background: Leukocyte telomere length (LTL) and mitochondrial genome (mtDNA) copy number and deletions have been proposed as risk markers for various cancer types, including breast cancer (BC). Methods: To gain a more comprehensive picture on how these markers can modulate BC risk, alone or in conjunction, we performed simultaneous measurements of LTL and mtDNA copy number in up to 570 BC cases and 538 controls from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. As a first step, we measured LTL and mtDNA copy number in 96 individuals for which a blood sample had been collected twice with an interval of 15 years. Results: According to the intraclass correlation (ICC), we found very good stability over the time period for both measurements, with ICCs of 0.63 for LTL and 0.60 for mtDNA copy number. In the analysis of the entire study sample, we observed that longer LTL was strongly associated with increased risk of BC (OR 2.71, 95% CI 1.58–4.65, p = 3.07 × 10− 4 for highest vs. lowest quartile; OR 3.20, 95% CI 1.57–6.55, p = 1.41 × 10− 3 as a continuous variable). We did not find any association between mtDNA copy number and BC risk; however, when considering only the functional copies, we observed an increased risk of developing estrogen receptor-positive BC (OR 2.47, 95% CI 1.05–5.80, p = 0.04 for highest vs. lowest quartile). Conclusions: We observed a very good correlation between the markers over a period of 15 years. We confirm a role of LTL in BC carcinogenesis and suggest an effect of mtDNA copy number on BC risk.

Published
2018

14. Mutational and epigenetic signatures in cancer tissue linked to environmental exposures and lifestyle

Author

Gianluca Severi, Hanane Omichessan, Laura Baglietto, Vittorio Perduca, Mathématiques Appliquées à Paris 5 ( MAP5 - UMR 8145 ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National des Sciences Mathématiques et de leurs Interactions-Centre National de la Recherche Scientifique ( CNRS ), Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), and Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, APOBEC, DNA, DNA methylation, environmental exposures, epidemiology, mutational signatures, somatic mutations, tumour tissue, Animals, DNA Mutational Analysis, Genetic Predisposition to Disease, Humans, Mutation, Neoplasms, Environmental Exposure, Life Style, Oncology, Cancer Research, [SDV.CAN]Life Sciences [q-bio]/Cancer, Biology, [ SDV.CAN ] Life Sciences [q-bio]/Cancer, 03 medical and health sciences, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Ultraviolet light, medicine, Epigenetics, [ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS, Epigenomics, Genetics, Cancer, Environmental exposure, medicine.disease, 030104 developmental biology
Abstract

Purpose of review In this article, we describe how recent advances in the study of mutational and epigenetic signatures in tumours provide new opportunities to understand the role of the environment and lifestyle in cancer development. Recent findings Cancer-related mutational events have been investigated for decades but only recently the wide availability of genomic sequences and epigenomic data from thousands of cancer genomes has made it possible to identify numerous distinct mutational and epigenetic signatures through the application of advanced mathematical models. Some of these signatures have been linked to endogenous factors such as defective DNA repair or the action of APOBEC cytidine deaminases and to exogenous factors such as tobacco smoke, ultraviolet light, aflatoxins, aristolochic acid and ionizing radiation. More recently, it has been shown that exposure to factors such as tobacco smoke may also leave marks in the DNA methylation profile of both normal and tumour tissue in target organs. Summary The analysis of mutational and epigenetic signatures is a novel and useful tool to study cancer. Their application to experimental studies and to studies with detailed data on environmental exposures and lifestyle is likely to improve our understanding of how the environment and lifestyle influence cancer development and its evolution.

Published
2017

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