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Sara Wedrén

Bio: Sara Wedrén is an academic researcher from Karolinska Institutet. The author has contributed to research in topics: Breast cancer & Population. The author has an hindex of 32, co-authored 55 publications receiving 6089 citations. Previous affiliations of Sara Wedrén include National University of Singapore & Genome Institute of Singapore.


Papers
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Journal ArticleDOI
Douglas F. Easton1, Karen A. Pooley1, Alison M. Dunning1, Paul D.P. Pharoah1, Deborah J. Thompson1, Dennis G. Ballinger, Jeffery P. Struewing2, Jonathan J. Morrison1, Helen I. Field1, Robert Luben1, Nicholas J. Wareham1, Shahana Ahmed1, Catherine S. Healey1, Richard Bowman, Kerstin B. Meyer1, Christopher A. Haiman3, Laurence K. Kolonel, Brian E. Henderson3, Loic Le Marchand, Paul Brennan4, Suleeporn Sangrajrang, Valerie Gaborieau4, Fabrice Odefrey4, Chen-Yang Shen5, Pei-Ei Wu5, Hui-Chun Wang5, Diana Eccles6, D. Gareth Evans7, Julian Peto8, Olivia Fletcher9, Nichola Johnson9, Sheila Seal, Michael R. Stratton10, Nazneen Rahman, Georgia Chenevix-Trench11, Georgia Chenevix-Trench12, Stig E. Bojesen13, Børge G. Nordestgaard13, C K Axelsson13, Montserrat Garcia-Closas2, Louise A. Brinton2, Stephen J. Chanock2, Jolanta Lissowska14, Beata Peplonska15, Heli Nevanlinna16, Rainer Fagerholm16, H Eerola16, Daehee Kang17, Keun-Young Yoo17, Dong-Young Noh17, Sei Hyun Ahn18, David J. Hunter19, Susan E. Hankinson19, David G. Cox19, Per Hall20, Sara Wedrén20, Jianjun Liu21, Yen-Ling Low21, Natalia Bogdanova22, Peter Schu¨rmann22, Do¨rk Do¨rk22, Rob A. E. M. Tollenaar23, Catharina E. Jacobi23, Peter Devilee23, Jan G. M. Klijn24, Alice J. Sigurdson2, Michele M. Doody2, Bruce H. Alexander25, Jinghui Zhang2, Angela Cox26, Ian W. Brock26, Gordon MacPherson26, Malcolm W.R. Reed26, Fergus J. Couch27, Ellen L. Goode27, Janet E. Olson27, Hanne Meijers-Heijboer24, Hanne Meijers-Heijboer28, Ans M.W. van den Ouweland24, André G. Uitterlinden24, Fernando Rivadeneira24, Roger L. Milne29, Gloria Ribas29, Anna González-Neira29, Javier Benitez29, John L. Hopper30, Margaret R. E. McCredie31, Margaret R. E. McCredie32, Margaret R. E. McCredie12, Melissa C. Southey30, Melissa C. Southey12, Graham G. Giles33, Chris Schroen30, Christina Justenhoven34, Christina Justenhoven35, Hiltrud Brauch35, Hiltrud Brauch34, Ute Hamann36, Yon-Dschun Ko, Amanda B. Spurdle11, Jonathan Beesley11, Xiaoqing Chen11, _ kConFab37, Arto Mannermaa37, Veli-Matti Kosma37, Vesa Kataja37, Jaana M. Hartikainen37, Nicholas E. Day1, David Cox, Bruce A.J. Ponder1 
28 Jun 2007-Nature
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

Journal ArticleDOI
TL;DR: A subset of 64 genes was found to give an optimal separation of patients with good and poor outcomes, and the signature associated with prognosis and impact of adjuvant therapies was identified.
Abstract: Adjuvant breast cancer therapy significantly improves survival, but overtreatment and undertreatment are major problems. Breast cancer expression profiling has so far mainly been used to identify women with a poor prognosis as candidates for adjuvant therapy but without demonstrated value for therapy prediction. We obtained the gene expression profiles of 159 population-derived breast cancer patients, and used hierarchical clustering to identify the signature associated with prognosis and impact of adjuvant therapies, defined as distant metastasis or death within 5 years. Independent datasets of 76 treated population-derived Swedish patients, 135 untreated population-derived Swedish patients and 78 Dutch patients were used for validation. The inclusion and exclusion criteria for the studies of population-derived Swedish patients were defined. Among the 159 patients, a subset of 64 genes was found to give an optimal separation of patients with good and poor outcomes. Hierarchical clustering revealed three subgroups: patients who did well with therapy, patients who did well without therapy, and patients that failed to benefit from given therapy. The expression profile gave significantly better prognostication (odds ratio, 4.19; P = 0.007) (breast cancer end-points odds ratio, 10.64) compared with the Elston–Ellis histological grading (odds ratio of grade 2 vs 1 and grade 3 vs 1, 2.81 and 3.32 respectively; P = 0.24 and 0.16), tumor stage (odds ratio of stage 2 vs 1 and stage 3 vs 1, 1.11 and 1.28; P = 0.83 and 0.68) and age (odds ratio, 0.11; P = 0.55). The risk groups were consistent and validated in the independent Swedish and Dutch data sets used with 211 and 78 patients, respectively. We have identified discriminatory gene expression signatures working both on untreated and systematically treated primary breast cancer patients with the potential to spare them from adjuvant therapy.

792 citations

Journal ArticleDOI
Angela Cox1, Alison M. Dunning2, Montserrat Garcia-Closas3, Sabapathy P. Balasubramanian1, Malcolm W.R. Reed1, Karen A. Pooley2, Serena Scollen2, Caroline Baynes2, Bruce A.J. Ponder2, Stephen J. Chanock3, Jolanta Lissowska4, Louise A. Brinton3, Beata Peplonska5, Melissa C. Southey6, John L. Hopper6, Margaret R. E. McCredie7, Graham G. Giles8, Olivia Fletcher9, Nichola Johnson9, Isabel dos Santos Silva9, Lorna Gibson9, Stig E. Bojesen10, Børge G. Nordestgaard10, C K Axelsson10, Diana Torres11, Ute Hamann11, Christina Justenhoven12, Christina Justenhoven13, Hiltrud Brauch13, Hiltrud Brauch12, Jenny Chang-Claude11, Silke Kropp11, Angela Risch11, Shan Wang-Gohrke14, Peter Schürmann15, Natalia Bogdanova15, Thilo Dörk15, Rainer Fagerholm16, Kirsimari Aaltonen16, Carl Blomqvist16, Heli Nevanlinna16, Sheila Seal, Anthony Renwick, Michael R. Stratton, Nazneen Rahman, Suleeporn Sangrajrang, David J. Hughes17, Fabrice Odefrey17, Paul Brennan17, Amanda B. Spurdle18, Georgia Chenevix-Trench18, Jonathan Beesley18, Arto Mannermaa19, Jaana M. Hartikainen19, Vesa Kataja19, Veli-Matti Kosma19, Fergus J. Couch20, Janet E. Olson20, Ellen L. Goode20, Annegien Broeks21, Marjanka K. Schmidt21, Frans B. L. Hogervorst21, Laura J. van't Veer21, Daehee Kang22, Keun-Young Yoo22, Dong Young Noh22, Sei Hyun Ahn23, Sara Wedrén24, Per Hall24, Yen-Ling Low25, Jianjun Liu25, Roger L. Milne26, Gloria Ribas26, Anna González-Neira26, Javier Benitez26, Alice J. Sigurdson3, Alice J. Sigurdson27, Denise L. Stredrick3, Denise L. Stredrick27, Bruce H. Alexander27, Bruce H. Alexander3, Jeffery P. Struewing3, Jeffery P. Struewing27, Paul D.P. Pharoah2, Douglas F. Easton2 
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

Journal ArticleDOI
TL;DR: Current cigarette smokers are less likely to respond to methotrexate monotherapy and TNF inhibitors, and the overall cohort was similarly associated with a lower chance of a good response.
Abstract: Objective To determine whether cigarette smoking influences the response to treatment in patients with early rheumatoid arthritis (RA). Methods We retrieved clinical information about patients entering the Epidemiological Investigation of Rheumatoid Arthritis (EIRA) early RA cohort from 1996 to 2006 (n = 1,998) who were also in the Swedish Rheumatology Register (until 2007). Overall, 1,430 of the 1,621 registered patients were followed up from the time of inclusion in the EIRA cohort. Of these, 873 started methotrexate (MTX) monotherapy at inclusion, and 535 later started treatment with a tumor necrosis factor (TNF) inhibitor as the first biologic agent. The primary outcome was a good response according to the European League Against Rheumatism criteria at the 3-month visit. The influence of cigarette smoking (current or past) on the response to therapy was evaluated by logistic regression, with never smokers as the referent group. Results Compared with never smokers, current smokers were less likely to achieve a good response at 3 months following the start of MTX (27% versus 36%; P = 0.05) and at 3 months following the start of TNF inhibitors (29% versus 43%; P = 0.03). In multivariate analyses in which clinical, serologic, and genetic factors were considered, the inverse associations between current smoking and good response remained (adjusted odds ratio [OR] for MTX response 0.60 [95% CI 0.39–0.94]; adjusted OR for TNF inhibitor response 0.52 [95% CI 0.29–0.96]). The lower likelihood of a good response remained at later followup visits. Evaluating remission or joint counts yielded similar findings. Past smoking did not affect the chance of response to MTX or TNF inhibitors. Evaluating the overall cohort, which reflects all treatments used, current smoking was similarly associated with a lower chance of a good response (adjusted ORs for the 3-month, 6-month, 1-year, and 5-year visits 0.61, 0.65, 0.78, 0.66, and 0.61, respectively). Conclusion Among patients with early RA, current cigarette smokers are less likely to respond to MTX and TNF inhibitors.

232 citations

Journal ArticleDOI
TL;DR: A genome-wide association study of more than 2 million common variants in rheumatoid arthritis patients shows that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity.
Abstract: Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to antiTNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n=733), infliximab (n=894), or adalimumab (n=1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (DDAS) in the etanercept subset of patients (P=8610 28 ), but not in the infliximab or adalimumab subsets (P.0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 39 UTR of an immune-related gene,CD84, and thealleleassociated withbetterresponsetoetanerceptwas associatedwithhigherCD84 gene expression in peripheral blood mononuclear cells (P=1610 211 in 228 non-RA patients and P=0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P=0.02, n=210) and showed a non-significant trend for better DDAS in a subset of RA patients with gene expression data (n=31, etanercepttreated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA

154 citations


Cited by
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TL;DR: Gen expression profiles from 21 breast cancer data sets and identified 587 TNBC cases may be useful in biomarker selection, drug discovery, and clinical trial design that will enable alignment of TNBC patients to appropriate targeted therapies.
Abstract: Triple-negative breast cancer (TNBC) is a highly diverse group of cancers, and subtyping is necessary to better identify molecular-based therapies. In this study, we analyzed gene expression (GE) profiles from 21 breast cancer data sets and identified 587 TNBC cases. Cluster analysis identified 6 TNBC subtypes displaying unique GE and ontologies, including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem–like (MSL), and a luminal androgen receptor (LAR) subtype. Further, GE analysis allowed us to identify TNBC cell line models representative of these subtypes. Predicted “driver” signaling pathways were pharmacologically targeted in these cell line models as proof of concept that analysis of distinct GE signatures can inform therapy selection. BL1 and BL2 subtypes had higher expression of cell cycle and DNA damage response genes, and representative cell lines preferentially responded to cisplatin. M and MSL subtypes were enriched in GE for epithelial-mesenchymal transition, and growth factor pathways and cell models responded to NVP-BEZ235 (a PI3K/mTOR inhibitor) and dasatinib (an abl/src inhibitor). The LAR subtype includes patients with decreased relapse-free survival and was characterized by androgen receptor (AR) signaling. LAR cell lines were uniquely sensitive to bicalutamide (an AR antagonist). These data may be useful in biomarker selection, drug discovery, and clinical trial design that will enable alignment of TNBC patients to appropriate targeted therapies.

4,215 citations

Journal ArticleDOI
TL;DR: The increased understanding of the immune mechanisms of rheumatoid arthritis has led to the development of a considerable number of new therapeutic agents that alter the natural history of the disease and reduce mortality.
Abstract: The increased understanding of the immune mechanisms of rheumatoid arthritis has led to the development of a considerable number of new therapeutic agents that alter the natural history of the disease and reduce mortality.

3,975 citations

Journal ArticleDOI
TL;DR: Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
Abstract: Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.

3,646 citations

Journal ArticleDOI
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

Journal Article
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