Publications by authors named "Artitaya Lophatananon"

102 Publications

Functional annotation of the 2q35 breast cancer risk locus implicates a structural variant in influencing activity of a long-range enhancer element.

Am J Hum Genet 2021 Jul 18;108(7):1190-1203. Epub 2021 Jun 18.

Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.

A combination of genetic and functional approaches has identified three independent breast cancer risk loci at 2q35. A recent fine-scale mapping analysis to refine these associations resulted in 1 (signal 1), 5 (signal 2), and 42 (signal 3) credible causal variants at these loci. We used publicly available in silico DNase I and ChIP-seq data with in vitro reporter gene and CRISPR assays to annotate signals 2 and 3. We identified putative regulatory elements that enhanced cell-type-specific transcription from the IGFBP5 promoter at both signals (30- to 40-fold increased expression by the putative regulatory element at signal 2, 2- to 3-fold by the putative regulatory element at signal 3). We further identified one of the five credible causal variants at signal 2, a 1.4 kb deletion (esv3594306), as the likely causal variant; the deletion allele of this variant was associated with an average additional increase in IGFBP5 expression of 1.3-fold (MCF-7) and 2.2-fold (T-47D). We propose a model in which the deletion allele of esv3594306 juxtaposes two transcription factor binding regions (annotated by estrogen receptor alpha ChIP-seq peaks) to generate a single extended regulatory element. This regulatory element increases cell-type-specific expression of the tumor suppressor gene IGFBP5 and, thereby, reduces risk of estrogen receptor-positive breast cancer (odds ratio = 0.77, 95% CI 0.74-0.81, p = 3.1 × 10).
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http://dx.doi.org/10.1016/j.ajhg.2021.05.013DOI Listing
July 2021

Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24.

Prostate Cancer Prostatic Dis 2021 Jun 14. Epub 2021 Jun 14.

School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA.

Background: We previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ.

Materials And Methods: Genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC.

Results: CF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings.

Conclusion: We identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.
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http://dx.doi.org/10.1038/s41391-021-00403-7DOI Listing
June 2021

Gene-Environment Interactions Relevant to Estrogen and Risk of Breast Cancer: Can Gene-Environment Interactions Be Detected Only among Candidate SNPs from Genome-Wide Association Studies?

Cancers (Basel) 2021 May 14;13(10). Epub 2021 May 14.

Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, 2730 Herlev, Denmark.

In this study we aim to examine gene-environment interactions (GxEs) between genes involved with estrogen metabolism and environmental factors related to estrogen exposure. GxE analyses were conducted with 1970 Korean breast cancer cases and 2052 controls in the case-control study, the Seoul Breast Cancer Study (SEBCS). A total of 11,555 SNPs from the 137 candidate genes were included in the GxE analyses with eight established environmental factors. A replication test was conducted by using an independent population from the Breast Cancer Association Consortium (BCAC), with 62,485 Europeans and 9047 Asians. The GxE tests were performed by using two-step methods in GxEScan software. Two interactions were found in the SEBCS. The first interaction was shown between rs13035764 of NCOA1 and age at menarche in the GE|2df model (-2df = 1.2 × 10). The age at menarche before 14 years old was associated with the high risk of breast cancer, and the risk was higher when subjects had homozygous minor allele G. The second GxE was shown between rs851998 near ESR1 and height in the GE|2df model (-2df = 1.1 × 10). Height taller than 160 cm was associated with a high risk of breast cancer, and the risk increased when the minor allele was added. The findings were not replicated in the BCAC. These results would suggest specificity in Koreans for breast cancer risk.
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http://dx.doi.org/10.3390/cancers13102370DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156547PMC
May 2021

Common genetic and clinical risk factors: association with fatal prostate cancer in the Cohort of Swedish Men.

Prostate Cancer Prostatic Dis 2021 Mar 15. Epub 2021 Mar 15.

Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.

Background: Clinical variables-age, family history, genetics-are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death.

Methods: Genotype/phenotype data were obtained from a nested case-control Cohort of Swedish Men (n = 3279; 2163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests.

Results: Median age at last follow-up/prostate cancer death was 78.0 (IQR: 72.3-84.1) and 81.4 (75.4-86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95% CI 2.78-4.17]), family history (HR 1.72 [1.46-2.03]), alcohol (HR 1.74 [1.40-2.15]), diabetes (HR 0.53 [0.37-0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99-2.97]), family history (HR 1.73 [1.48-2.03]), alcohol (HR 1.45 [1.19-1.76]), diabetes (HR 0.62 [0.42-0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer (p < 10).

Conclusions: PHS had the most robust association with fatal prostate cancer in a multivariable model with common risk factors, including family history. Adding PHS to clinical variables may improve prostate cancer risk stratification strategies.
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http://dx.doi.org/10.1038/s41391-021-00341-4DOI Listing
March 2021

Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women.

N Engl J Med 2021 02 20;384(5):428-439. Epub 2021 Jan 20.

The authors' affiliations are as follows: the Centre for Cancer Genetic Epidemiology, Departments of Public Health and Primary Care (L.D., S. Carvalho, J.A., K.A.P., Q.W., M.K.B., J.D., B.D., N. Mavaddat, K. Michailidou, A.C.A., P.D.P.P., D.F.E.) and Oncology (C.L., P.A.H., C. Baynes, D.M.C., L.F., V.R., M. Shah, P.D.P.P., A.M.D., D.F.E.), University of Cambridge, Cambridge, the Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine (A. Campbell, D.J.P.), and the Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology (D.J.P.), University of Edinburgh, the Cancer Research UK Edinburgh Centre (D.A.C., J.F.), and the Usher Institute of Population Health Sciences and Informatics, University of Edinburgh Medical School (A. Campbell, J.F.), Edinburgh, the Divisions of Informatics, Imaging, and Data Sciences (E.F.H.), Cancer Sciences (A. Howell), Population Health, Health Services Research, and Primary Care (A. Lophatananon, K. Muir), and Evolution and Genomic Sciences, School of Biological Sciences (W.G.N., E.M.V., D.G.E.), University of Manchester, the NIHR Manchester Biomedical Research Unit (E.F.H.) and the Nightingale Breast Screening Centre, Wythenshawe Hospital (E.F.H., H.I.), Academic Health Science Centre and North West Genomics Laboratory Hub, and the Manchester Centre for Genomic Medicine, St. Mary's Hospital, Manchester University NHS Foundation Trust (W.G.N., E.M.V., D.G.E.), Manchester, the School of Cancer and Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy's Campus, King's College London, London (E.J.S.), the Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham (I.T.), and the Wellcome Trust Centre for Human Genetics and Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford (I.T.) - all in the United Kingdom; the Human Genotyping-CEGEN Unit, Human Cancer Genetic Program (A.G.-N., M.R.A., N.Á., B.H., R.N.-T.), and the Human Genetics Group (V.F., A.O., J.B.), Spanish National Cancer Research Center, Centro de Investigación en Red de Enfermedades Raras (A.O., J.B.), Servicio de Oncología Médica, Hospital Universitario La Paz (M.P.Z.), and Molecular Oncology Laboratory, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (M. de la Hoya), Madrid, the Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela, Complejo Hospitalario Universitario de Santiago (A. Carracedo, M.G.-D.), and Centro de Investigación en Red de Enfermedades Raras y Centro Nacional de Genotipado, Universidad de Santiago de Compostela (A. Carracedo), Santiago de Compostela, the Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galeo de Saúde, Vigo (J.E.C.), and Servicio de Cirugía General y Especialidades, Hospital Monte Naranco, Oviedo (J.I.A.P.) - all in Spain; the Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund (C. Wahlström, J.V., M.L., T. Törngren, Å.B., A.K.), the Department of Oncology, Örebro University Hospital, Örebro (C. Blomqvist), and the Departments of Medical Epidemiology and Biostatistics (K.C., M.E., M.G., P. Hall, W.H., K.H.), Oncology, Södersjukhuset (P. Hall, S. Margolin), Molecular Medicine and Surgery (A. Lindblom), and Clinical Science and Education, Södersjukhuset (S. Margolin, C. Wendt), Karolinska Institutet, and the Department of Clinical Genetics, Karolinska University Hospital (A. Lindblom), Stockholm - all in Sweden; the Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD (M.T.P., C.F., G.C.-T., A.B.S.), the Cancer Epidemiology Division, Cancer Council Victoria (G.G.G., R.J.M., R.L.M.), the Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health (G.G.G., R.J.M., R.L.M.), and the Department of Clinical Pathology (M.C.S.), University of Melbourne, Anatomical Pathology, Alfred Hospital (C.M.), and the Cancer Epidemiology Division, Cancer Council Victoria (M.C.S.), Melbourne, VIC, and Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC (G.G.G., M.C.S., R.L.M.) - all in Australia; the Division of Molecular Pathology (R.K., S. Cornelissen, M.K.S.), Family Cancer Clinic (F.B.L.H., L.E.K.), Department of Epidemiology (M.A.R.), and Division of Psychosocial Research and Epidemiology (M.K.S.), the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, Division Laboratories, Pharmacy and Biomedical Genetics, Department of Genetics, University Medical Center, Utrecht (M.G.E.M.A.), the Department of Clinical Genetics, Erasmus University Medical Center (J.M.C., A.M.W.O.), and the Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute (B.A.M.H.-G., A. Hollestelle, M.J.H.), Rotterdam, the Department of Clinical Genetics, Maastricht University Medical Center, Maastricht (E.B.G.G.), the Departments of Human Genetics (I.M.M.L., M.P.G.V., P.D.), Clinical Genetics (C.J.A.), and Pathology (P.D.), Leiden University Medical Center, Leiden, the Department of Human Genetics, Radboud University Medical Center, Nijmegen (A.R.M.), and the Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen (J.C.O.) - all in the Netherlands; the Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute (B.D.), and the Division of Cancer Epidemiology and Genetics, National Cancer Institute (T.A., S.J.C., X.R.Y., M.G.-C.), National Institutes of Health, Bethesda, MD; the Department of Pathology, Brigham and Women's Hospital, Harvard Medical School (B.D.), and the Department of Nutrition, Harvard T.H. Chan School of Public Health (R.M.V.D.), Boston; the Departments of Clinical Genetics (K.A.), Oncology (C. Blomqvist), and Obstetrics and Gynecology (H.N., M. Suvanto), Helsinki University Hospital, University of Helsinki, Helsinki, and the Unit of Clinical Oncology, Kuopio University Hospital (P. Auvinen), the Institute of Clinical Medicine, Oncology (P. Auvinen), the Translational Cancer Research Area (J.M.H., V.-M.K., A. Mannermaa), and the Institute of Clinical Medicine, Pathology, and Forensic Medicine (J.M.H., V.-M.K., A. Mannermaa), University of Eastern Finland, and the Biobank of Eastern Finland, Kuopio University Hospital (V.-M.K., A. Mannermaa), Kuopio - both in Finland; the N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus (N.N.A., N.V.B.); the Department of Gynecology and Obstetrics and Institute of Clinical Molecular Biology, University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel, Kiel (N.A.), the Institute of Medical Biometry and Epidemiology (H. Becher) and Cancer Epidemiology Group (T.M., J.C.-C.), University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, the Department of Gynecology and Obstetrics (M.W.B., P.A.F., L.H.) and Institute of Human Genetics (A.B.E.), University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-European Metropolitan Region of Nuremberg, Erlangen, the Division of Cancer Epidemiology (S.B., A. Jung, P.M.K., J.C.-C.), Molecular Epidemiology Group, C080 (B. Burwinkel, H.S.), Division of Pediatric Neurooncology (A.F.), and Molecular Genetics of Breast Cancer (U.H., M.M., M.U.R., D.T.), German Cancer Research Center, Molecular Biology of Breast Cancer, University Women's Clinic Heidelberg, University of Heidelberg (B. Burwinkel, A.S., H.S.), Hopp Children's Cancer Center (A.F.), Faculty of Medicine, University of Heidelberg (P.M.K.), and National Center for Tumor Diseases, University Hospital and German Cancer Research Center (A.S., C.S.), Heidelberg, the Department of Radiation Oncology (N.V.B., M. Bremer, H.C.) and the Gynecology Research Unit (N.V.B., T.D., P. Hillemanns, T.-W.P.-S., P.S.), Hannover Medical School, Hannover, the Institute of Human Genetics, University of Münster, Münster (N.B.-M.), Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart (H. Brauch, W.-Y.L.), iFIT-Cluster of Excellence, University of Tübingen, and the German Cancer Consortium, German Cancer Research Center, Partner Site Tübingen (H. Brauch), and the University of Tübingen (W.-Y.L.), Tübingen, Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum, Bochum (T.B.), Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig (C.E.), Center for Hereditary Breast and Ovarian Cancer (E.H., R.K.S.) and Center for Integrated Oncology (E.H., R.K.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, the Department of Internal Medicine, Evangelische Kliniken Bonn, Johanniter Krankenhaus, Bonn (Y.-D.K.), the Department of Gynecology and Obstetrics, University of Munich, Campus Großhadern, Munich (A. Meindl), and the Institute of Pathology, Städtisches Klinikum Karlsruhe, Karlsruhe (T.R.) - all in Germany; the Gynecological Cancer Registry, Centre Georges-François Leclerc, Dijon (P. Arveux), and the Center for Research in Epidemiology and Population Health, Team Exposome and Heredity, INSERM, University Paris-Saclay, Villejuif (E.C.-D., P.G., T. Truong) - both in France; the Institute of Biochemistry and Genetics, Ufa Federal Research Center of the Russian Academy of Sciences (M. Bermisheva, E.K.), the Department of Genetics and Fundamental Medicine, Bashkir State University (E.K., D.P., Y.V.), and the Ufa Research Institute of Occupational Health and Human Ecology (Y.V.), Ufa, Russia; the Department of Genetics and Pathology (K.B., A. Jakubowska, J. Lubiński, K.P.) and the Independent Laboratory of Molecular Biology and Genetic Diagnostics (A. Jakubowska), Pomeranian Medical University, Szczecin, Poland; the Copenhagen General Population Study, the Department of Clinical Biochemistry (S.E.B., B.G.N.), and the Department of Breast Surgery (H.F.), Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, and the Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen (S.E.B., B.G.N.) - both in Denmark; the Division of Cancer Prevention and Genetics, European Institute of Oncology Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) (B. Bonanni), the Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano (S. Manoukian), the Genome Diagnostics Program, FIRC Institute of Molecular Oncology (P.P.), and the Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (P.R.), Milan; the Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet (A.-L.B.-D., G.I.G.A., V.N.K.), and the Institute of Clinical Medicine, Faculty of Medicine, University of Oslo (A.-L.B.-D., V.N.K.), Oslo; Medical Faculty, Universidad de La Sabana (I.B.), and the Clinical Epidemiology and Biostatistics Department (F.G.) and Institute of Human Genetics (D.T.), Pontificia Universidad Javeriana, Bogota, Colombia; the Department of Internal Medicine and Huntsman Cancer Institute, University of Utah (N.J.C., M.J.M., J.A.W.), and the Intermountain Healthcare Biorepository and Department of Pathology, Intermountain Healthcare (M.H.C.), Salt Lake City; the David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California, Los Angeles (P.A.F.), and Moores Cancer Center (M.G.-D., M.E.M.) and the Department of Family Medicine and Public Health (M.E.M.), University of California San Diego, La Jolla; the Departments of Medical Oncology (V.G., D.M.) and Pathology (M.T.), University Hospital of Heraklion, Heraklion, and the Department of Oncology, University Hospital of Larissa, Larissa (E.S.) - both in Greece; the Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital (G.G., I.L.A.), the Departments of Laboratory Medicine and Pathobiology (A.M.M.) and Molecular Genetics (I.L.A.), University of Toronto, and the Laboratory Medicine Program, University Health Network (A.M.M.), Toronto, and the Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, QC (J.S.) - both in Canada; the Department of Electron Microscopy and Molecular Pathology (A. Hadjisavvas, K.K., M.A.L.), the Cyprus School of Molecular Medicine (A. Hadjisavvas, K.K., M.A.L., K. Michailidou), and the Biostatistics Unit (K. Michailidou), Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus; the Saw Swee Hock School of Public Health (M. Hartman, R.M.V.D.) and the Department of Medicine, Yong Loo Lin School of Medicine (R.M.V.D.), National University of Singapore, the Department of Surgery, National University Health System (M. Hartman, J. Li), and the Human Genetics Division, Genome Institute of Singapore (J. Li), Singapore; the Department of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia (W.K.H.), and the Breast Cancer Research Programme, Cancer Research Malaysia (W.K.H., P.S.N., S.-Y.Y., S.H.T.), Selangor, and the Breast Cancer Research Unit, Cancer Research Institute (N.A.M.T.), and the Department of Surgery, Faculty of Medicine (N.A.M.T., P.S.N., S.H.T.), University Malaya, Kuala Lumpur - both in Malaysia; Surgery, School of Medicine, National University of Ireland, Galway (M.J.K., N. Miller); the Department of Surgery, Daerim Saint Mary's Hospital (S.-W.K.), the Department of Surgery, Ulsan University College of Medicine and Asan Medical Center (J.W.L.), the Department of Surgery, Soonchunhyang University College of Medicine and Soonchunhyang University Hospital (M.H.L.), Integrated Major in Innovative Medical Science, Seoul National University College of Medicine (S.K.P.), and the Cancer Research Institute, Seoul National University (S.K.P.), Seoul, South Korea; the Department of Basic Sciences, Shaukat Khanum Memorial Cancer Hospital and Research Center, Lahore, Pakistan (M.U.R.); and the National Cancer Institute, Ministry of Public Health, Nonthaburi, Thailand (S.T.).

Background: Genetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking.

Methods: We used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity.

Results: Protein-truncating variants in 5 genes (, , , , and ) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (, , , and ) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in and , odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in , , , , , and , odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in , , and were associated with a risk of breast cancer overall with a P value of less than 0.001. For , , and , missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants.

Conclusions: The results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.).
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http://dx.doi.org/10.1056/NEJMoa1913948DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611105PMC
February 2021

Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer.

Prostate Cancer Prostatic Dis 2021 Jun 8;24(2):532-541. Epub 2021 Jan 8.

Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA.

Background: Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).

Materials And Method: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.

Results: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.

Conclusions: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.
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http://dx.doi.org/10.1038/s41391-020-00311-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157993PMC
June 2021

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Nat Genet 2021 01 4;53(1):65-75. Epub 2021 Jan 4.

Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.
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http://dx.doi.org/10.1038/s41588-020-00748-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8148035PMC
January 2021

Relationship of self-reported body size and shape with risk for prostate cancer: A UK case-control study.

PLoS One 2020 17;15(9):e0238928. Epub 2020 Sep 17.

Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.

Introduction: Previous evidence has suggested a relationship between male self-reported body size and the risk of developing prostate cancer. In this UK-wide case-control study, we have explored the possible association of prostate cancer risk with male self-reported body size. We also investigated body shape as a surrogate marker for fat deposition around the body. As obesity and excessive adiposity have been linked with increased risk for developing a number of different cancers, further investigation of self-reported body size and shape and their potential relationship with prostate cancer was considered to be appropriate.

Objective: The study objective was to investigate whether underlying associations exist between prostate cancer risk and male self-reported body size and shape.

Methods: Data were collected from a large case-control study of men (1928 cases and 2043 controls) using self-administered questionnaires. Data from self-reported pictograms of perceived body size relating to three decades of life (20's, 30's and 40's) were recorded and analysed, including the pattern of change. The associations of self-identified body shape with prostate cancer risk were also explored.

Results: Self-reported body size for men in their 20's, 30's and 40's did not appear to be associated with prostate cancer risk. More than half of the subjects reported an increase in self-reported body size throughout these three decades of life. Furthermore, no association was observed between self-reported body size changes and prostate cancer risk. Using 'symmetrical' body shape as a reference group, subjects with an 'apple' shape showed a significant 27% reduction in risk (Odds ratio = 0.73, 95% C.I. 0.57-0.92).

Conclusions: Change in self-reported body size throughout early to mid-adulthood in males is not a significant risk factor for the development of prostate cancer. Body shape indicative of body fat distribution suggested that an 'apple' body shape was protective and inversely associated with prostate cancer risk when compared with 'symmetrical' shape. Further studies which investigate prostate cancer risk and possible relationships with genetic factors known to influence body shape may shed further light on any underlying associations.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238928PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498010PMC
October 2020

African-specific improvement of a polygenic hazard score for age at diagnosis of prostate cancer.

Int J Cancer 2021 01 24;148(1):99-105. Epub 2020 Sep 24.

UMR Inserm 1134 Biologie Intégrée du Globule Rouge, INSERM/Université Paris Diderot-Université Sorbonne Paris Cité/INTS/Université des Antilles, Paris, France.

Polygenic hazard score (PHS) models are associated with age at diagnosis of prostate cancer. Our model developed in Europeans (PHS46) showed reduced performance in men with African genetic ancestry. We used a cross-validated search to identify single nucleotide polymorphisms (SNPs) that might improve performance in this population. Anonymized genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Ten iterations of a 10-fold cross-validation search were conducted to select SNPs that would be included in the final PHS46+African model. The coefficients of PHS46+African were estimated in a Cox proportional hazards framework using age at diagnosis as the dependent variable and PHS46, and selected SNPs as predictors. The performance of PHS46 and PHS46+African was compared using the same cross-validated approach. Three SNPs (rs76229939, rs74421890 and rs5013678) were selected for inclusion in PHS46+African. All three SNPs are located on chromosome 8q24. PHS46+African showed substantial improvements in all performance metrics measured, including a 75% increase in the relative hazard of those in the upper 20% compared to the bottom 20% (2.47-4.34) and a 20% reduction in the relative hazard of those in the bottom 20% compared to the middle 40% (0.65-0.53). In conclusion, we identified three SNPs that substantially improved the association of PHS46 with age at diagnosis of prostate cancer in men with African genetic ancestry to levels comparable to Europeans.
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http://dx.doi.org/10.1002/ijc.33282DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8135907PMC
January 2021

European polygenic risk score for prediction of breast cancer shows similar performance in Asian women.

Nat Commun 2020 07 31;11(1):3833. Epub 2020 Jul 31.

Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore, Singapore.

Polygenic risk scores (PRS) have been shown to predict breast cancer risk in European women, but their utility in Asian women is unclear. Here we evaluate the best performing PRSs for European-ancestry women using data from 17,262 breast cancer cases and 17,695 controls of Asian ancestry from 13 case-control studies, and 10,255 Chinese women from a prospective cohort (413 incident breast cancers). Compared to women in the middle quintile of the risk distribution, women in the highest 1% of PRS distribution have a ~2.7-fold risk and women in the lowest 1% of PRS distribution has ~0.4-fold risk of developing breast cancer. There is no evidence of heterogeneity in PRS performance in Chinese, Malay and Indian women. A PRS developed for European-ancestry women is also predictive of breast cancer risk in Asian women and can help in developing risk-stratified screening programmes in Asia.
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http://dx.doi.org/10.1038/s41467-020-17680-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395776PMC
July 2020

Prediction models for prostate cancer to be used in the primary care setting: a systematic review.

BMJ Open 2020 07 19;10(7):e034661. Epub 2020 Jul 19.

Division of Population Health, Health Services Research and Primary Care School of Health Sciences Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK

Objective: To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.

Design: Systematic review.

Data Sources: MEDLINE and Embase databases combined from inception and up to the end of January 2019.

Eligibility: Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Data Extraction And Synthesis: Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.

Results: An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.

Conclusion: Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.
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http://dx.doi.org/10.1136/bmjopen-2019-034661DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371149PMC
July 2020

Germline HOXB13 mutations p.G84E and p.R217C do not confer an increased breast cancer risk.

Sci Rep 2020 06 16;10(1):9688. Epub 2020 Jun 16.

Department of Gynecology and Obstetrics, University of Tübingen, Tübingen, Germany.

In breast cancer, high levels of homeobox protein Hox-B13 (HOXB13) have been associated with disease progression of ER-positive breast cancer patients and resistance to tamoxifen treatment. Since HOXB13 p.G84E is a prostate cancer risk allele, we evaluated the association between HOXB13 germline mutations and breast cancer risk in a previous study consisting of 3,270 familial non-BRCA1/2 breast cancer cases and 2,327 controls from the Netherlands. Although both recurrent HOXB13 mutations p.G84E and p.R217C were not associated with breast cancer risk, the risk estimation for p.R217C was not very precise. To provide more conclusive evidence regarding the role of HOXB13 in breast cancer susceptibility, we here evaluated the association between HOXB13 mutations and increased breast cancer risk within 81 studies of the international Breast Cancer Association Consortium containing 68,521 invasive breast cancer patients and 54,865 controls. Both HOXB13 p.G84E and p.R217C did not associate with the development of breast cancer in European women, neither in the overall analysis (OR = 1.035, 95% CI = 0.859-1.246, P = 0.718 and OR = 0.798, 95% CI = 0.482-1.322, P = 0.381 respectively), nor in specific high-risk subgroups or breast cancer subtypes. Thus, although involved in breast cancer progression, HOXB13 is not a material breast cancer susceptibility gene.
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http://dx.doi.org/10.1038/s41598-020-65665-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297796PMC
June 2020

Association of Nongenetic Factors With Breast Cancer Risk in Genetically Predisposed Groups of Women in the UK Biobank Cohort.

JAMA Netw Open 2020 04 1;3(4):e203760. Epub 2020 Apr 1.

Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, University of Manchester, Manchester, United Kingdom.

Importance: The association between noninherited factors, including lifestyle factors, and the risk of breast cancer (BC) in women and the association between BC and genetic makeup are only partly characterized. A study using data on current genetic stratification may help in the characterization.

Objective: To examine the association between healthier lifestyle habits and BC risk in genetically predisposed groups.

Design, Setting, And Participants: Data from UK Biobank, a prospective cohort comprising 2728 patients with BC and 88 489 women without BC, were analyzed. The data set used for the analysis was closed on March 31, 2019. The analysis was restricted to postmenopausal white women. Classification of healthy lifestyle was based on Cancer Research UK guidance (healthy weight, regular exercise, no use of hormone replacement therapy for more than 5 years, no oral contraceptive use, and alcohol intake <3 times/wk). Three groups were established: favorable (≥4 healthy factors), intermediate (2-3 healthy factors), and unfavorable (≤1 healthy factor). The genetic contribution was estimated using the polygenic risk scores of 305 preselected single-nucleotide variations. Polygenic risk scores were categorized into 3 tertiles (low, intermediate, and high).

Main Outcomes And Measures: Cox proportional hazards regression was used to assess the hazard ratios (HRs) of the lifestyles and polygenic risk scores associated with a malignant neoplasm of the breast.

Results: Mean (SD) age of the 2728 women with BC was 60.1 (5.5) years, and mean age of the 88 489 women serving as controls was 59.4 (4.9) years. The median follow-up time for the cohort was 10 years (maximum 13 years) (interquartile range, 9.44-10.82 years). Women with BC had a higher body mass index (relative risk [RR], 1.14; 95% CI, 1.05-1.23), performed less exercise (RR, 1.12; 95% CI, 1.01-1.25), used hormonal replacement therapy for longer than 5 years (RR, 1.23; 95% CI, 1.13-1.34), used more oral contraceptives (RR, 1.02; 95% CI, 0.93-1.12), and had greater alcohol intake (RR, 1.11; 95% CI, 1.03-1.19) compared with the controls. Overall, 20 657 women (23.3%) followed a favorable lifestyle, 60 195 women (68.0%) followed an intermediate lifestyle, and 7637 women (8.6%) followed an unfavorable lifestyle. The RR of the highest genetic risk group was 2.55 (95% CI, 2.28-2.84), and the RR of the most unfavorable lifestyle category was 1.44 (95% CI, 1.25-1.65). The association of lifestyle and BC within genetic subgroups showed lower HRs among women following a favorable lifestyle compared with intermediate and unfavorable lifestyles among all of the genetic groups: women with an unfavorable lifestyle had a higher risk of BC in the low genetic group (HR, 1.63; 95% CI, 1.13-2.34), intermediate genetic group (HR, 1.94; 95% CI, 1.46-2.58), and high genetic group (HR, 1.39; 95% CI, 1.11-1.74) compared with the reference group of favorable lifestyle. Intermediate lifestyle was also associated with a higher risk of BC among the low genetic group (HR, 1.40; 95% CI, 1.09-1.80) and the intermediate genetic group (HR, 1.37; 95% CI, 1.12-1.68).

Conclusions And Relevance: In this cohort study of data on women in the UK Biobank, a healthier lifestyle with more exercise, healthy weight, low alcohol intake, no oral contraceptive use, and no or limited hormonal replacement therapy use appeared to be associated with a reduced level of risk for BC, even if the women were at higher genetic risk for BC.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.3760DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7182796PMC
April 2020

Identification of novel breast cancer susceptibility loci in meta-analyses conducted among Asian and European descendants.

Nat Commun 2020 03 5;11(1):1217. Epub 2020 Mar 5.

Departments of Health Research and Policy, School of Medicine, Stanford University, California, CA, USA.

Known risk variants explain only a small proportion of breast cancer heritability, particularly in Asian women. To search for additional genetic susceptibility loci for breast cancer, here we perform a meta-analysis of data from genome-wide association studies (GWAS) conducted in Asians (24,206 cases and 24,775 controls) and European descendants (122,977 cases and 105,974 controls). We identified 31 potential novel loci with the lead variant showing an association with breast cancer risk at P < 5 × 10. The associations for 10 of these loci were replicated in an independent sample of 16,787 cases and 16,680 controls of Asian women (P < 0.05). In addition, we replicated the associations for 78 of the 166 known risk variants at P < 0.05 in Asians. These findings improve our understanding of breast cancer genetics and etiology and extend previous findings from studies of European descendants to Asian women.
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http://dx.doi.org/10.1038/s41467-020-15046-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7057957PMC
March 2020

Association between genetic polymorphisms and endometrial cancer risk: a systematic review.

J Med Genet 2020 09 17;57(9):591-600. Epub 2020 Feb 17.

Division of Cancer Sciences, University of Manchester, Manchester, UK

Introduction: Endometrial cancer is one of the most commonly diagnosed cancers in women. Although there is a hereditary component to endometrial cancer, most cases are thought to be sporadic and lifestyle related. The aim of this study was to systematically review prospective and retrospective case-control studies, meta-analyses and genome-wide association studies to identify genomic variants that may be associated with endometrial cancer risk.

Methods: We searched MEDLINE, Embase and CINAHL from 2007 to 2019 without restrictions. We followed PRISMA 2009 guidelines. The search yielded 3015 hits in total. Following duplicate exclusion, 2674 abstracts were screened and 453 full-texts evaluated based on our pre-defined screening criteria. 149 articles were eligible for inclusion.

Results: We found that single nucleotide polymorphisms (SNPs) in , , , , and were strongly associated with incident endometrial cancer. Nineteen variants were reported with genome-wide significance and a further five with suggestive significance. No convincing evidence was found for the widely studied variant rs2279744. Publication bias and false discovery rates were noted throughout the literature.

Conclusion: Endometrial cancer risk may be influenced by SNPs in genes involved in cell survival, oestrogen metabolism and transcriptional control. Larger cohorts are needed to identify more variants with genome-wide significance.
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http://dx.doi.org/10.1136/jmedgenet-2019-106529DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7476276PMC
September 2020

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.

Nat Genet 2020 01 7;52(1):56-73. Epub 2020 Jan 7.

Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.
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http://dx.doi.org/10.1038/s41588-019-0537-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6974400PMC
January 2020

Re-evaluating genetic variants identified in candidate gene studies of breast cancer risk using data from nearly 280,000 women of Asian and European ancestry.

EBioMedicine 2019 Oct 16;48:203-211. Epub 2019 Oct 16.

Department of Epidemiology, Cancer Prevention Institute of California, Fremont, CA, USA; Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.

Background: We previously conducted a systematic field synopsis of 1059 breast cancer candidate gene studies and investigated 279 genetic variants, 51 of which showed associations. The major limitation of this work was the small sample size, even pooling data from all 1059 studies. Thereafter, genome-wide association studies (GWAS) have accumulated data for hundreds of thousands of subjects. It's necessary to re-evaluate these variants in large GWAS datasets.

Methods: Of these 279 variants, data were obtained for 228 from GWAS conducted within the Asian Breast Cancer Consortium (24,206 cases and 24,775 controls) and the Breast Cancer Association Consortium (122,977 cases and 105,974 controls of European ancestry). Meta-analyses were conducted to combine the results from these two datasets.

Findings: Of those 228 variants, an association was observed for 12 variants in 10 genes at a Bonferroni-corrected threshold of P < 2·19 × 10. The associations for four variants reached P < 5 × 10 and have been reported by previous GWAS, including rs6435074 and rs6723097 (CASP8), rs17879961 (CHEK2) and rs2853669 (TERT). The remaining eight variants were rs676387 (HSD17B1), rs762551 (CYP1A2), rs1045485 (CASP8), rs9340799 (ESR1), rs7931342 (CHR11), rs1050450 (GPX1), rs13010627 (CASP10) and rs9344 (CCND1). Further investigating these 10 genes identified associations for two additional variants at P < 5 × 10, including rs4793090 (near HSD17B1), and rs9210 (near CYP1A2), which have not been identified by previous GWAS.

Interpretation: Though most candidate gene variants were not associated with breast cancer risk, we found 14 variants showing an association. Our findings warrant further functional investigation of these variants. FUND: National Institutes of Health.
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http://dx.doi.org/10.1016/j.ebiom.2019.09.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6838373PMC
October 2019

Two truncating variants in FANCC and breast cancer risk.

Sci Rep 2019 08 29;9(1):12524. Epub 2019 Aug 29.

Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia.

Fanconi anemia (FA) is a genetically heterogeneous disorder with 22 disease-causing genes reported to date. In some FA genes, monoallelic mutations have been found to be associated with breast cancer risk, while the risk associations of others remain unknown. The gene for FA type C, FANCC, has been proposed as a breast cancer susceptibility gene based on epidemiological and sequencing studies. We used the Oncoarray project to genotype two truncating FANCC variants (p.R185X and p.R548X) in 64,760 breast cancer cases and 49,793 controls of European descent. FANCC mutations were observed in 25 cases (14 with p.R185X, 11 with p.R548X) and 26 controls (18 with p.R185X, 8 with p.R548X). There was no evidence of an association with the risk of breast cancer, neither overall (odds ratio 0.77, 95%CI 0.44-1.33, p = 0.4) nor by histology, hormone receptor status, age or family history. We conclude that the breast cancer risk association of these two FANCC variants, if any, is much smaller than for BRCA1, BRCA2 or PALB2 mutations. If this applies to all truncating variants in FANCC it would suggest there are differences between FA genes in their roles on breast cancer risk and demonstrates the merit of large consortia for clarifying risk associations of rare variants.
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http://dx.doi.org/10.1038/s41598-019-48804-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715680PMC
August 2019

Appraising the role of previously reported risk factors in epithelial ovarian cancer risk: A Mendelian randomization analysis.

PLoS Med 2019 08 7;16(8):e1002893. Epub 2019 Aug 7.

MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.

Background: Various risk factors have been associated with epithelial ovarian cancer risk in observational epidemiological studies. However, the causal nature of the risk factors reported, and thus their suitability as effective intervention targets, is unclear given the susceptibility of conventional observational designs to residual confounding and reverse causation. Mendelian randomization (MR) uses genetic variants as proxies for risk factors to strengthen causal inference in observational studies. We used MR to evaluate the association of 12 previously reported risk factors (reproductive, anthropometric, clinical, lifestyle, and molecular factors) with risk of invasive epithelial ovarian cancer, invasive epithelial ovarian cancer histotypes, and low malignant potential tumours.

Methods And Findings: Genetic instruments to proxy 12 risk factors were constructed by identifying single nucleotide polymorphisms (SNPs) that were robustly (P < 5 × 10-8) and independently associated with each respective risk factor in previously reported genome-wide association studies. These risk factors included genetic liability to 3 factors (endometriosis, polycystic ovary syndrome, type 2 diabetes) scaled to reflect a 50% higher odds liability to disease. We obtained summary statistics for the association of these SNPs with risk of overall and histotype-specific invasive epithelial ovarian cancer (22,406 cases; 40,941 controls) and low malignant potential tumours (3,103 cases; 40,941 controls) from the Ovarian Cancer Association Consortium (OCAC). The OCAC dataset comprises 63 genotyping project/case-control sets with participants of European ancestry recruited from 14 countries (US, Australia, Belarus, Germany, Belgium, Denmark, Finland, Norway, Canada, Poland, UK, Spain, Netherlands, and Sweden). SNPs were combined into multi-allelic inverse-variance-weighted fixed or random effects models to generate effect estimates and 95% confidence intervals (CIs). Three complementary sensitivity analyses were performed to examine violations of MR assumptions: MR-Egger regression and weighted median and mode estimators. A Bonferroni-corrected P value threshold was used to establish strong evidence (P < 0.0042) and suggestive evidence (0.0042 < P < 0.05) for associations. In MR analyses, there was strong or suggestive evidence that 2 of the 12 risk factors were associated with invasive epithelial ovarian cancer and 8 of the 12 were associated with 1 or more invasive epithelial ovarian cancer histotypes. There was strong evidence that genetic liability to endometriosis was associated with an increased risk of invasive epithelial ovarian cancer (odds ratio [OR] per 50% higher odds liability: 1.10, 95% CI 1.06-1.15; P = 6.94 × 10-7) and suggestive evidence that lifetime smoking exposure was associated with an increased risk of invasive epithelial ovarian cancer (OR per unit increase in smoking score: 1.36, 95% CI 1.04-1.78; P = 0.02). In analyses examining histotypes and low malignant potential tumours, the strongest associations found were between height and clear cell carcinoma (OR per SD increase: 1.36, 95% CI 1.15-1.61; P = 0.0003); age at natural menopause and endometrioid carcinoma (OR per year later onset: 1.09, 95% CI 1.02-1.16; P = 0.007); and genetic liability to polycystic ovary syndrome and endometrioid carcinoma (OR per 50% higher odds liability: 0.89, 95% CI 0.82-0.96; P = 0.002). There was little evidence for an association of genetic liability to type 2 diabetes, parity, or circulating levels of 25-hydroxyvitamin D and sex hormone binding globulin with ovarian cancer or its subtypes. The primary limitations of this analysis include the modest statistical power for analyses of risk factors in relation to some less common ovarian cancer histotypes (low grade serous, mucinous, and clear cell carcinomas), the inability to directly examine the association of some ovarian cancer risk factors that did not have robust genetic variants available to serve as proxies (e.g., oral contraceptive use, hormone replacement therapy), and the assumption of linear relationships between risk factors and ovarian cancer risk.

Conclusions: Our comprehensive examination of possible aetiological drivers of ovarian carcinogenesis using germline genetic variants to proxy risk factors supports a role for few of these factors in invasive epithelial ovarian cancer overall and suggests distinct aetiologies across histotypes. The identification of novel risk factors remains an important priority for the prevention of epithelial ovarian cancer.
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http://dx.doi.org/10.1371/journal.pmed.1002893DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6685606PMC
August 2019

Evaluation of a questionnaire to assess nutritional knowledge, attitudes and practices in a Thai population.

Nutr J 2019 07 10;18(1):35. Epub 2019 Jul 10.

Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.

Background: The rapid increase in non-communicable chronic diseases in people of working age has had a major effect on health care utilization, productivity and economy. Lifestyle and diet are recognized as being major risk determinants involved. Disease prevention strategies need to be based on people's understanding of nutritional knowledge, attitudes and practice. This study evaluates the validity of a new nutritional knowledge and practice questionnaire specifically developed for assessing individuals of working age in a Thai population.

Methods: The questionnaire was constructed and based on previous relevant literature and its content validity was scrutinized by an expert panel. An exploratory factor analysis (EFA) was performed to reduce the number of questions included. Subsequently, data from a cross-sectional study of 1,032 participants were used to evaluate the reliability and validity of this questionnaire. The validity of the questionnaire constructed for assessing knowledge and attitude was evaluated using Confirmatory Factor Analysis (CFA). For the practice component, set criteria were applied to determine the final variables used.

Results: CFA of the nutritional knowledge component suggested that all the variables in the model fitted with the data (χ = 80.17, df = 66, p > 0.05, CFI = 0.99, RMSEA = 0.01, SRMR = 0.02). The CFA final model for the nutritional knowledge included three factors (food recommendation, nutrients related to diseases, and healthy diet) with a total of 14 questions. For nutrition attitude, CFA also revealed a good fit (χ = 178.14, df = 93, p < 0.001, CFI = 0.99, RMSEA = 0.03, SRMR = 0.03). The final CFA model for nutritional attitude included three factors (food choice, healthy diet and food recommendation) with a total of 16 questions. For practice items, the number of questions was reduced from 76 to 60.

Conclusions: Questionnaire development should use a logical, systematic and structured approach. Results from our evaluation process demonstrates the construction validity of the nutritional knowledge and practice questionnaire developed. This questionnaire can be further modified for use in other countries within the region.
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http://dx.doi.org/10.1186/s12937-019-0463-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6621999PMC
July 2019

Evaluation of associations between genetically predicted circulating protein biomarkers and breast cancer risk.

Int J Cancer 2020 04 16;146(8):2130-2138. Epub 2019 Jul 16.

Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN.

A small number of circulating proteins have been reported to be associated with breast cancer risk, with inconsistent results. Herein, we attempted to identify novel protein biomarkers for breast cancer via the integration of genomics and proteomics data. In the Breast Cancer Association Consortium (BCAC), with 122,977 cases and 105,974 controls of European descendants, we evaluated the associations of the genetically predicted concentrations of >1,400 circulating proteins with breast cancer risk. We used data from a large-scale protein quantitative trait loci (pQTL) analysis as our study instrument. Summary statistics for these pQTL variants related to breast cancer risk were obtained from the BCAC and used to estimate odds ratios (OR) for each protein using the inverse-variance weighted method. We identified 56 proteins significantly associated with breast cancer risk by instrumental analysis (false discovery rate <0.05). Of these, the concentrations of 32 were influenced by variants close to a breast cancer susceptibility locus (ABO, 9q34.2). Many of these proteins, such as insulin receptor, insulin-like growth factor receptor 1 and other membrane receptors (OR: 0.82-1.18, p values: 6.96 × 10 -3.28 × 10 ), are linked to insulin resistance and estrogen receptor signaling pathways. Proteins identified at other loci include those involved in biological processes such as alcohol and lipid metabolism, proteolysis, apoptosis, immune regulation and cell motility and proliferation. Consistent associations were observed for 22 proteins in the UK Biobank data (p < 0.05). The study identifies potential novel biomarkers for breast cancer, but further investigation is needed to replicate our findings.
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http://dx.doi.org/10.1002/ijc.32542DOI Listing
April 2020

Consistent Biopsy Quality and Gleason Grading Within the Global Active Surveillance Global Action Plan 3 Initiative: A Prerequisite for Future Studies.

Eur Urol Oncol 2019 05 13;2(3):333-336. Epub 2018 Sep 13.

Kan-tonsspital Baden, Baden, Switzerland.

Within the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) initiative, 25 centers across the globe collaborate to standardize active surveillance (AS) protocols for men with low-risk prostate cancer (PCa). A centralized PCa AS database, comprising data of more than 15000 patients worldwide, was created. Comparability of the histopathology between the different cohorts was assessed by a centralized pathology review of 445 biopsies from 15 GAP3 centers. Grade group 1 (Gleason score 6) in 85% and grade group ≥2 (Gleason score ≥7) in 15% showed 89% concordance at review with moderate agreement (κ=0.56). Average biopsy core length was similar among the analyzed cohorts. Recently established highly adverse pathologies, including cribriform and/or intraductal carcinoma, were observed in 3.6% of the reviewed biopsies. In conclusion, the centralized pathology review of 445 biopsies revealed comparable histopathology among the 15 GAP3 centers with a low frequency of high-risk features. This enables further data analyses-without correction-toward uniform global AS guidelines for men with low-risk PCa. PATIENT SUMMARY: Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) initiative combines data from 15000 men with low-risk prostate cancer (PCa) across the globe to standardize active surveillance protocols. Histopathology review confirmed that the histopathology was consistent with low-risk PCa in most men and comparable between different centers.
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http://dx.doi.org/10.1016/j.euo.2018.08.017DOI Listing
May 2019

Genetically Predicted Levels of DNA Methylation Biomarkers and Breast Cancer Risk: Data From 228 951 Women of European Descent.

J Natl Cancer Inst 2020 03;112(3):295-304

Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.

Background: DNA methylation plays a critical role in breast cancer development. Previous studies have identified DNA methylation marks in white blood cells as promising biomarkers for breast cancer. However, these studies were limited by low statistical power and potential biases. Using a new methodology, we investigated DNA methylation marks for their associations with breast cancer risk.

Methods: Statistical models were built to predict levels of DNA methylation marks using genetic data and DNA methylation data from HumanMethylation450 BeadChip from the Framingham Heart Study (n = 1595). The prediction models were validated using data from the Women's Health Initiative (n = 883). We applied these models to genomewide association study (GWAS) data of 122 977 breast cancer patients and 105 974 controls to evaluate if the genetically predicted DNA methylation levels at CpG sites (CpGs) are associated with breast cancer risk. All statistical tests were two-sided.

Results: Of the 62 938 CpG sites CpGs investigated, statistically significant associations with breast cancer risk were observed for 450 CpGs at a Bonferroni-corrected threshold of P less than 7.94 × 10-7, including 45 CpGs residing in 18 genomic regions, that have not previously been associated with breast cancer risk. Of the remaining 405 CpGs located within 500 kilobase flaking regions of 70 GWAS-identified breast cancer risk variants, the associations for 11 CpGs were independent of GWAS-identified variants. Integrative analyses of genetic, DNA methylation, and gene expression data found that 38 CpGs may affect breast cancer risk through regulating expression of 21 genes.

Conclusion: Our new methodology can identify novel DNA methylation biomarkers for breast cancer risk and can be applied to other diseases.
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http://dx.doi.org/10.1093/jnci/djz109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073907PMC
March 2020

The functional ALDH2 polymorphism is associated with breast cancer risk: A pooled analysis from the Breast Cancer Association Consortium.

Mol Genet Genomic Med 2019 06 7;7(6):e707. Epub 2019 May 7.

Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.

Background: Epidemiological studies consistently indicate that alcohol consumption is an independent risk factor for female breast cancer (BC). Although the aldehyde dehydrogenase 2 (ALDH2) polymorphism (rs671: Glu>Lys) has a strong effect on acetaldehyde metabolism, the association of rs671 with BC risk and its interaction with alcohol intake have not been fully elucidated. We conducted a pooled analysis of 14 case-control studies, with individual data on Asian ancestry women participating in the Breast Cancer Association Consortium.

Methods: We included 12,595 invasive BC cases and 12,884 controls for the analysis of rs671 and BC risk, and 2,849 invasive BC cases and 3,680 controls for the analysis of the gene-environment interaction between rs671 and alcohol intake for BC risk. The pooled odds ratios (OR) with 95% confidence intervals (CI) associated with rs671 and its interaction with alcohol intake for BC risk were estimated using logistic regression models.

Results: The Lys/Lys genotype of rs671 was associated with increased BC risk (OR = 1.16, 95% CI 1.03-1.30, p = 0.014). According to tumor characteristics, the Lys/Lys genotype was associated with estrogen receptor (ER)-positive BC (OR = 1.19, 95% CI 1.05-1.36, p = 0.008), progesterone receptor (PR)-positive BC (OR = 1.19, 95% CI 1.03-1.36, p = 0.015), and human epidermal growth factor receptor 2 (HER2)-negative BC (OR = 1.25, 95% CI 1.05-1.48, p = 0.012). No evidence of a gene-environment interaction was observed between rs671 and alcohol intake (p = 0.537).

Conclusion: This study suggests that the Lys/Lys genotype confers susceptibility to BC risk among women of Asian ancestry, particularly for ER-positive, PR-positive, and HER2-negative tumor types.
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http://dx.doi.org/10.1002/mgg3.707DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6565553PMC
June 2019

Using prognosis to guide inclusion criteria, define standardised endpoints and stratify follow-up in active surveillance for prostate cancer.

BJU Int 2019 11 2;124(5):758-767. Epub 2019 Jun 2.

Department of Public Health and Epidemiology, University of Manchester, Manchester, UK.

Objectives: To test whether using disease prognosis can inform a rational approach to active surveillance (AS) for early prostate cancer.

Patients And Methods: We previously developed the Cambridge Prognostics Groups (CPG) classification, a five-tiered model that uses prostate-specific antigen (PSA), Grade Group and Stage to predict cancer survival outcomes. We applied the CPG model to a UK and a Swedish prostate cancer cohort to test differences in prostate cancer mortality (PCM) in men managed conservatively or by upfront treatment in CPG2 and 3 (which subdivides the intermediate-risk classification) vs CPG1 (low-risk). We then applied the CPG model to a contemporary UK AS cohort, which was optimally characterised at baseline for disease burden, to identify predictors of true prognostic progression. Results were re-tested in an external AS cohort from Spain.

Results: In a UK cohort (n = 3659) the 10-year PCM was 2.3% in CPG1, 1.5%/3.5% in treated/untreated CPG2, and 1.9%/8.6% in treated/untreated CPG3. In the Swedish cohort (n = 27 942) the10-year PCM was 1.0% in CPG1, 2.2%/2.7% in treated/untreated CPG2, and 6.1%/12.5% in treated/untreated CPG3. We then tested using progression to CPG3 as a hard endpoint in a modern AS cohort (n = 133). During follow-up (median 3.5 years) only 6% (eight of 133) progressed to CPG3. Predictors of progression were a PSA density ≥0.15 ng/mL/mL and CPG2 at diagnosis. Progression occurred in 1%, 8% and 21% of men with neither factor, only one, or both, respectively. In an independent Spanish AS cohort (n = 143) the corresponding rates were 3%, 10% and 14%, respectively.

Conclusion: Using disease prognosis allows a rational approach to inclusion criteria, discontinuation triggers and risk-stratified management in AS.
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http://dx.doi.org/10.1111/bju.14800DOI Listing
November 2019

The association between weight at birth and breast cancer risk revisited using Mendelian randomisation.

Eur J Epidemiol 2019 Jun 8;34(6):591-600. Epub 2019 Feb 8.

Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

Observational studies suggest that higher birth weight (BW) is associated with increased risk of breast cancer in adult life. We conducted a two-sample Mendelian randomisation (MR) study to assess whether this association is causal. Sixty independent single nucleotide polymorphisms (SNPs) known to be associated at P < 5 × 10 with BW were used to construct (1) a 41-SNP instrumental variable (IV) for univariable MR after removing SNPs with pleiotropic associations with other breast cancer risk factors and (2) a 49-SNP IV for multivariable MR after filtering SNPs for data availability. BW predicted by the 41-SNP IV was not associated with overall breast cancer risk in inverse-variance weighted (IVW) univariable MR analysis of genetic association data from 122,977 breast cancer cases and 105,974 controls (odds ratio = 0.86 per 500 g higher BW; 95% confidence interval 0.73-1.01). Sensitivity analyses using four alternative methods and three alternative IVs, including an IV with 59 of the 60 BW-associated SNPs, yielded similar results. Multivariable MR adjusting for the effects of the 49-SNP IV on birth length, adult height, adult body mass index, age at menarche, and age at menopause using IVW and MR-Egger methods provided estimates consistent with univariable analyses. Results were also similar when all analyses were repeated after restricting to estrogen receptor-positive or -negative breast cancer cases. Point estimates of the odds ratios from most analyses performed indicated an inverse relationship between genetically-predicted BW and breast cancer, but we are unable to rule out an association between the non-genetically-determined component of BW and breast cancer. Thus, genetically-predicted higher BW was not associated with an increased risk of breast cancer in adult life in our MR study.
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http://dx.doi.org/10.1007/s10654-019-00485-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497616PMC
June 2019

Author Correction: Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci.

Nat Genet 2019 02;51(2):363

Dame Roma Mitchell Cancer Research Centre, University of Adelaide, Adelaide, South Australia, Australia.

In the version of this article initially published, the name of author Manuela Gago-Dominguez was misspelled as Manuela Gago Dominguez. The error has been corrected in the HTML and PDF version of the article.
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http://dx.doi.org/10.1038/s41588-018-0330-6DOI Listing
February 2019

Review of non-clinical risk models to aid prevention of breast cancer.

Cancer Causes Control 2018 Oct 3;29(10):967-986. Epub 2018 Sep 3.

Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL, UK.

A disease risk model is a statistical method which assesses the probability that an individual will develop one or more diseases within a stated period of time. Such models take into account the presence or absence of specific epidemiological risk factors associated with the disease and thereby potentially identify individuals at higher risk. Such models are currently used clinically to identify people at higher risk, including identifying women who are at increased risk of developing breast cancer. Many genetic and non-genetic breast cancer risk models have been developed previously. We have evaluated existing non-genetic/non-clinical models for breast cancer that incorporate modifiable risk factors. This review focuses on risk models that can be used by women themselves in the community in the absence of clinical risk factors characterization. The inclusion of modifiable factors in these models means that they can be used to improve primary prevention and health education pertinent for breast cancer. Literature searches were conducted using PubMed, ScienceDirect and the Cochrane Database of Systematic Reviews. Fourteen studies were eligible for review with sample sizes ranging from 654 to 248,407 participants. All models reviewed had acceptable calibration measures, with expected/observed (E/O) ratios ranging from 0.79 to 1.17. However, discrimination measures were variable across studies with concordance statistics (C-statistics) ranging from 0.56 to 0.89. We conclude that breast cancer risk models that include modifiable risk factors have been well calibrated but have less ability to discriminate. The latter may be a consequence of the omission of some significant risk factors in the models or from applying models to studies with limited sample sizes. More importantly, external validation is missing for most of the models. Generalization across models is also problematic as some variables may not be considered applicable to some populations and each model performance is conditioned by particular population characteristics. In conclusion, it is clear that there is still a need to develop a more reliable model for estimating breast cancer risk which has a good calibration, ability to accurately discriminate high risk and with better generalizability across populations.
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http://dx.doi.org/10.1007/s10552-018-1072-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6182451PMC
October 2018