Author
Rainer Fagerholm
Other affiliations: Helsinki University Central Hospital
Bio: Rainer Fagerholm is an academic researcher from University of Helsinki. The author has contributed to research in topics: Breast cancer & Genome-wide association study. The author has an hindex of 12, co-authored 22 publications receiving 3680 citations. Previous affiliations of Rainer Fagerholm include Helsinki University Central Hospital.
Papers
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University of Cambridge1, National Institutes of Health2, University of Southern California3, International Agency for Research on Cancer4, Academia Sinica5, Princess Anne Hospital6, St Mary's Hospital7, University of London8, The Breast Cancer Research Foundation9, Wellcome Trust Sanger Institute10, QIMR Berghofer Medical Research Institute11, Peter MacCallum Cancer Centre12, University of Copenhagen13, Curie Institute14, Nofer Institute of Occupational Medicine15, University of Helsinki16, Seoul National University17, University of Ulsan18, Harvard University19, Karolinska Institutet20, Agency for Science, Technology and Research21, Hannover Medical School22, Leiden University23, Erasmus University Rotterdam24, University of Minnesota25, University of Sheffield26, Mayo Clinic27, VU University Amsterdam28, Carlos III Health Institute29, University of Melbourne30, Cancer Council New South Wales31, University of Otago32, Cancer Council Victoria33, Bosch34, University of Tübingen35, German Cancer Research Center36, University of Eastern Finland37
TL;DR: To identify further susceptibility alleles, a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls was conducted, followed by a third stage in which 30 single nucleotide polymorphisms were tested for confirmation.
Abstract: Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r2.0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P,1027). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P,0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.
2,288 citations
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University of Sheffield1, University of Cambridge2, National Institutes of Health3, Curie Institute4, Nofer Institute of Occupational Medicine5, University of Melbourne6, University of Otago7, Cancer Council Victoria8, University of London9, University of Copenhagen10, German Cancer Research Center11, Bosch12, University of Tübingen13, University of Ulm14, Hannover Medical School15, University of Helsinki16, International Agency for Research on Cancer17, QIMR Berghofer Medical Research Institute18, University of Eastern Finland19, Mayo Clinic20, Netherlands Cancer Institute21, Seoul National University22, University of Ulsan23, Karolinska Institutet24, Agency for Science, Technology and Research25, Carlos III Health Institute26, University of Minnesota27
TL;DR: It is demonstrated that common breast cancer susceptibility alleles with small effects on risk can be identified, given sufficiently powerful studies, as well as the need for further studies to confirm putative genetic associations with breast cancer.
Abstract: The Breast Cancer Association Consortium (BCAC) has been established to conduct combined case-control analyses with augmented statistical power to try to confirm putative genetic associations with breast cancer. We genotyped nine SNPs for which there was some prior evidence of an association with breast cancer: CASP8 D302H (rs1045485), IGFBP3 -202 C --> A (rs2854744), SOD2 V16A (rs1799725), TGFB1 L10P (rs1982073), ATM S49C (rs1800054), ADH1B 3' UTR A --> G (rs1042026), CDKN1A S31R (rs1801270), ICAM5 V301I (rs1056538) and NUMA1 A794G (rs3750913). We included data from 9-15 studies, comprising 11,391-18,290 cases and 14,753-22,670 controls. We found evidence of an association with breast cancer for CASP8 D302H (with odds ratios (OR) of 0.89 (95% confidence interval (c.i.): 0.85-0.94) and 0.74 (95% c.i.: 0.62-0.87) for heterozygotes and rare homozygotes, respectively, compared with common homozygotes; P(trend) = 1.1 x 10(-7)) and weaker evidence for TGFB1 L10P (OR = 1.07 (95% c.i.: 1.02-1.13) and 1.16 (95% c.i.: 1.08-1.25), respectively; P(trend) = 2.8 x 10(-5)). These results demonstrate that common breast cancer susceptibility alleles with small effects on risk can be identified, given sufficiently powerful studies.
567 citations
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National Institutes of Health1, Karolinska Institutet2, University of Helsinki3, University of Cambridge4, University of Copenhagen5, Carlos III Health Institute6, Autonomous University of Madrid7, Bosch8, University of Tübingen9, German Cancer Research Center10, Ruhr University Bochum11, University of Bonn12, Hannover Medical School13, University of Eastern Finland14, Royal Brisbane and Women's Hospital15, Leiden University16, Erasmus University Rotterdam17, University of Hamburg18, University of Ulm19, Mayo Clinic20, Cancer Council Victoria21, University of Melbourne22, University of Southern California23, University of Hawaii at Manoa24, Harvard University25, Curie Institute26, Nofer Institute of Occupational Medicine27, Agency for Science, Technology and Research28, University of Sheffield29, Cancer Research UK30, International Agency for Research on Cancer31
TL;DR: The findings show that common genetic variants influence the pathological subtype of breast cancer and provide further support for the hypothesis that ER-positive and ER-negative disease are biologically distinct.
Abstract: A three-stage genome-wide association study recently identified single nucleotide polymorphisms (SNPs) in five loci (fibroblast growth receptor 2 (FGFR2), trinucleotide repeat containing 9 (TNRC9), mitogen-activated protein kinase 3 K1 (MAP3K1), 8q24, and lymphocyte-specific protein 1 (LSP1)) associated with breast cancer risk. We investigated whether the associations between these SNPs and breast cancer risk varied by clinically important tumor characteristics in up to 23,039 invasive breast cancer cases and 26,273 controls from 20 studies. We also evaluated their influence on overall survival in 13,527 cases from 13 studies. All participants were of European or Asian origin. rs2981582 in FGFR2 was more strongly related to ER-positive (per-allele OR (95%CI) = 1.31 (1.27-1.36)) than ER-negative (1.08 (1.03-1.14)) disease (P for heterogeneity = 10(-13)). This SNP was also more strongly related to PR-positive, low grade and node positive tumors (P = 10(-5), 10(-8), 0.013, respectively). The association for rs13281615 in 8q24 was stronger for ER-positive, PR-positive, and low grade tumors (P = 0.001, 0.011 and 10(-4), respectively). The differences in the associations between SNPs in FGFR2 and 8q24 and risk by ER and grade remained significant after permutation adjustment for multiple comparisons and after adjustment for other tumor characteristics. Three SNPs (rs2981582, rs3803662, and rs889312) showed weak but significant associations with ER-negative disease, the strongest association being for rs3803662 in TNRC9 (1.14 (1.09-1.21)). rs13281615 in 8q24 was associated with an improvement in survival after diagnosis (per-allele HR = 0.90 (0.83-0.97). The association was attenuated and non-significant after adjusting for known prognostic factors. Our findings show that common genetic variants influence the pathological subtype of breast cancer and provide further support for the hypothesis that ER-positive and ER-negative disease are biologically distinct. Understanding the etiologic heterogeneity of breast cancer may ultimately result in improvements in prevention, early detection, and treatment.
367 citations
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TL;DR: The results of this large meta-analysis of studies in populations of European ancestry confirm that germline genotype can provide prognostic information in addition to standard tumor prognostic factors.
Abstract: Background: Survival after a diagnosis of breast cancer varies considerably between patients, and some of this variation may be because of germline genetic variation We aimed to identify genetic markers associated with breast cancer-specific survival Methods: We conducted a large meta-analysis of studies in populations of European ancestry, including 37954 patients with 2900 deaths from breast cancer Each study had been genotyped for between 200000 and 900000 single nucleotide polymorphisms (SNPs) across the genome; genotypes for nine million common variants were imputed using a common reference panel from the 1000 Genomes Project We also carried out subtype-specific analyses based on 6881 estrogen receptor (ER)-negative patients (920 events) and 23059 ER-positive patients (1333 events) All statistical tests were two-sided Results: We identified one new locus (rs2059614 at 11q242) associated with survival in ER-negative breast cancer cases (hazard ratio HR = 195, 95% confidence interval CI = 155 to 247, P = 191 x 10-8) Genotyping a subset of 2113 case patients, of which 300 were ER negative, provided supporting evidence for the quality of the imputation The association in this set of case patients was stronger for the observed genotypes than for the imputed genotypes A second locus (rs148760487 at 2q242) was associated at genome-wide statistical significance in initial analyses; the association was similar in ER-positive and ER-negative case patients Here the results of genotyping suggested that the finding was less robust Conclusions: This is currently the largest study investigating genetic variation associated with breast cancer survival Our results have potential clinical implications, as they confirm that germline genotype can provide prognostic information in addition to standard tumor prognostic factors © 2015 © The Author 2015 Published by Oxford University Press
227 citations
01 Jan 2008
93 citations
Cited by
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University of Oxford1, Wellcome Trust Centre for Human Genetics2, University of Michigan3, Fred Hutchinson Cancer Research Center4, Duke University5, University of Ottawa6, Tufts University7, Foundation for Research & Technology – Hellas8, Boston Children's Hospital9, Broad Institute10, Harvard University11
TL;DR: This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
Abstract: The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
2,908 citations
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TL;DR: In this paper, the coding exons of the family of 518 protein kinases were sequenced in 210 cancers of diverse histological types to explore the nature of the information that will be derived from cancer genome sequencing.
Abstract: AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore
PL02-05
All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.
2,737 citations
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TL;DR: A variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours is reported.
Abstract: Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.
2,316 citations