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Christina Justenhoven

Bio: Christina Justenhoven is an academic researcher from University of Tübingen. The author has contributed to research in topics: Breast cancer & Population. The author has an hindex of 34, co-authored 73 publications receiving 7497 citations. Previous affiliations of Christina Justenhoven include Robert Bosch Stiftung & University of Stuttgart.


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-Heijboer28, Hanne Meijers-Heijboer24, 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. McCredie11, Margaret R. E. McCredie31, Margaret R. E. McCredie32, Melissa C. Southey30, Melissa C. Southey11, Graham G. Giles33, Chris Schroen30, Christina Justenhoven34, Christina Justenhoven35, Hiltrud Brauch35, Hiltrud Brauch34, Ute Hamann36, Yon-Dschun Ko, Amanda B. Spurdle12, Jonathan Beesley12, Xiaoqing Chen12, _ 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: It is shown that reproductive factors and BMI are most clearly associated with hormone receptor-positive tumors and suggest that triple-negative or CBP tumors may have distinct etiology.
Abstract: BACKGROUND: Previous studies have suggested that breast cancer risk factors are associated with estrogen receptor (ER) and progesterone receptor (PR) expression status of the tumors. METHODS: We pooled tumor marker and epidemiological risk factor data from 35,568 invasive breast cancer case patients from 34 studies participating in the Breast Cancer Association Consortium. Logistic regression models were used in case-case analyses to estimate associations between epidemiological risk factors and tumor subtypes, and case-control analyses to estimate associations between epidemiological risk factors and the risk of developing specific tumor subtypes in 12 population-based studies. All statistical tests were two-sided. RESULTS: In case-case analyses, of the epidemiological risk factors examined, early age at menarche (≤12 years) was less frequent in case patients with PR(-) than PR(+) tumors (P = .001). Nulliparity (P = 3 × 10(-6)) and increasing age at first birth (P = 2 × 10(-9)) were less frequent in ER(-) than in ER(+) tumors. Obesity (body mass index [BMI] ≥ 30 kg/m(2)) in younger women (≤50 years) was more frequent in ER(-)/PR(-) than in ER(+)/PR(+) tumors (P = 1 × 10(-7)), whereas obesity in older women (>50 years) was less frequent in PR(-) than in PR(+) tumors (P = 6 × 10(-4)). The triple-negative (ER(-)/PR(-)/HER2(-)) or core basal phenotype (CBP; triple-negative and cytokeratins [CK]5/6(+) and/or epidermal growth factor receptor [EGFR](+)) accounted for much of the heterogeneity in parity-related variables and BMI in younger women. Case-control analyses showed that nulliparity, increasing age at first birth, and obesity in younger women showed the expected associations with the risk of ER(+) or PR(+) tumors but not triple-negative (nulliparity vs parity, odds ratio [OR] = 0.94, 95% confidence interval [CI] = 0.75 to 1.19, P = .61; 5-year increase in age at first full-term birth, OR = 0.95, 95% CI = 0.86 to 1.05, P = .34; obesity in younger women, OR = 1.36, 95% CI = 0.95 to 1.94, P = .09) or CBP tumors. CONCLUSIONS: This study shows that reproductive factors and BMI are most clearly associated with hormone receptor-positive tumors and suggest that triple-negative or CBP tumors may have distinct etiology.

619 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 Brauch12, Hiltrud Brauch13, 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. Sigurdson27, Alice J. Sigurdson3, 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
Shahana Ahmed1, Gilles Thomas2, Maya Ghoussaini1, Catherine S. Healey1, Manjeet K. Humphreys1, Radka Platte1, Jonathan J. Morrison1, Melanie Maranian1, Karen A. Pooley1, Robert Luben1, Diana Eccles3, D. Gareth Evans4, Olivia Fletcher, Nichola Johnson, Isabel dos Santos Silva, Julian Peto, Michael R. Stratton5, Nazneen Rahman, Kevin B. Jacobs2, Kevin B. Jacobs6, Ross L. Prentice7, Garnet L. Anderson7, Aleksandar Rajkovic8, J. David Curb9, Regina G. Ziegler2, Christine D. Berg2, Saundra S. Buys10, Catherine A. McCarty11, Heather Spencer Feigelson12, Eugenia E. Calle12, Michael J. Thun12, W. Ryan Diver12, Stig E. Bojesen13, Børge G. Nordestgaard13, Henrik Flyger13, Thilo Dörk14, Peter Schürmann14, Peter Hillemanns14, Johann H. Karstens14, Natalia Bogdanova14, Natalia Antonenkova, Iosif V. Zalutsky, Marina Bermisheva14, S. A. Fedorova15, Elza Khusnutdinova, Daehee Kang16, Keun-Young Yoo16, Dong Young Noh16, Sei Hyun Ahn16, Peter Devilee17, Christi J. van Asperen17, R.A.E.M. Tollenaar17, Caroline Seynaeve18, Montserrat Garcia-Closas2, Jolanta Lissowska19, Louise A. Brinton2, Beata Peplonska20, Heli Nevanlinna21, Tuomas Heikkinen21, Kristiina Aittomäki21, Carl Blomqvist21, John L. Hopper22, Melissa C. Southey22, Letitia D. Smith23, Amanda B. Spurdle23, Marjanka K. Schmidt24, Annegien Broeks24, Richard van Hien24, Sten Cornelissen24, Roger L. Milne25, Gloria Ribas25, Anna González-Neira25, Javier Benitez25, Rita K. Schmutzler26, Barbara Burwinkel27, Barbara Burwinkel28, Claus R. Bartram28, Alfons Meindl29, Hiltrud Brauch30, Hiltrud Brauch31, Christina Justenhoven30, Christina Justenhoven31, Ute Hamann27, Jenny Chang-Claude27, Rebecca Hein27, Shan Wang-Gohrke32, Annika Lindblom33, Sara Margolin33, Arto Mannermaa34, Veli-Matti Kosma34, Vesa Kataja34, Janet E. Olson35, Xianshu Wang35, Zachary S. Fredericksen35, Graham G. Giles22, Graham G. Giles36, Gianluca Severi22, Gianluca Severi36, Laura Baglietto22, Laura Baglietto36, Dallas R. English22, Dallas R. English25, Susan E. Hankinson37, David G. Cox37, Peter Kraft37, Lars J. Vatten38, Kristian Hveem38, Merethe Kumle, Alice J. Sigurdson2, Michele M. Doody2, Parveen Bhatti2, Bruce H. Alexander39, Maartje J. Hooning18, Ans M.W. van den Ouweland18, Rogier A. Oldenburg18, Mieke Schutte18, Per Hall33, Kamila Czene33, Jianjun Liu40, Yuqing Li40, Angela Cox41, Graeme Elliott41, Ian W. Brock41, Malcolm W.R. Reed41, Chen-Yang Shen42, Chen-Yang Shen43, Jyh Cherng Yu44, Giu Cheng Hsu44, Shou Tung Chen, Hoda Anton-Culver45, Argyrios Ziogas45, Irene L. Andrulis46, Julia A. Knight46, Jonathan Beesley23, Ellen L. Goode35, Fergus J. Couch35, Georgia Chenevix-Trench23, Robert N. Hoover2, Bruce A.J. Ponder47, Bruce A.J. Ponder1, David J. Hunter37, Paul D.P. Pharoah1, Alison M. Dunning1, Stephen J. Chanock2, Douglas F. Easton1 
TL;DR: Strong evidence is found for additional susceptibility loci on 3p and 17q and potential causative genes include SLC4A7 and NEK10 on3p and COX11 on 17q.
Abstract: Genome-wide association studies (GWAS) have identified seven breast cancer susceptibility loci, but these explain only a small fraction of the familial risk of the disease. Five of these loci were identified through a two-stage GWAS involving 390 familial cases and 364 controls in the first stage, and 3,990 cases and 3,916 controls in the second stage. To identify additional loci, we tested over 800 promising associations from this GWAS in a further two stages involving 37,012 cases and 40,069 controls from 33 studies in the CGEMS collaboration and Breast Cancer Association Consortium. We found strong evidence for additional susceptibility loci on 3p (rs4973768: per-allele OR = 1.11, 95% CI = 1.08-1.13, P = 4.1 x 10(-23)) and 17q (rs6504950: per-allele OR = 0.95, 95% CI = 0.92-0.97, P = 1.4 x 10(-8)). Potential causative genes include SLC4A7 and NEK10 on 3p and COX11 on 17q.

480 citations

Journal ArticleDOI
TL;DR: SNPs at four loci were associated with ER-negative but not ER-positive breast cancer (P > 0.05), providing further evidence for distinct etiological pathways associated with invasive ER- positive and ER- negative breast cancers.
Abstract: Estrogen receptor (ER)-negative tumors represent 20-30% of all breast cancers, with a higher proportion occurring in younger women and women of African ancestry. The etiology and clinical behavior of ER-negative tumors are different from those of tumors expressing ER (ER positive), including differences in genetic predisposition. To identify susceptibility loci specific to ER-negative disease, we combined in a meta-analysis 3 genome-wide association studies of 4,193 ER-negative breast cancer cases and 35,194 controls with a series of 40 follow-up studies (6,514 cases and 41,455 controls), genotyped using a custom Illumina array, iCOGS, developed by the Collaborative Oncological Gene-environment Study (COGS). SNPs at four loci, 1q32.1 (MDM4, P = 2.1 × 10(-12) and LGR6, P = 1.4 × 10(-8)), 2p24.1 (P = 4.6 × 10(-8)) and 16q12.2 (FTO, P = 4.0 × 10(-8)), were associated with ER-negative but not ER-positive breast cancer (P > 0.05). These findings provide further evidence for distinct etiological pathways associated with invasive ER-positive and ER-negative breast cancers.

402 citations


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08 Oct 2009-Nature
TL;DR: This paper examined potential sources of missing heritability and proposed research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
Abstract: Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

7,797 citations

Journal ArticleDOI
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: It is concluded that multiple Imputation for Nonresponse in Surveys should be considered as a legitimate method for answering the question of why people do not respond to survey questions.
Abstract: 25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

3,216 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