Author
Christina Justenhoven
Other affiliations: Robert Bosch Stiftung, University of Stuttgart, Bosch
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.
Topics: Breast cancer, Population, Cancer, Breast disease, Genotype
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, Peter MacCallum Cancer Centre11, QIMR Berghofer Medical Research Institute12, 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, University of Tübingen34, Bosch35, 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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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
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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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University of Cambridge1, National Institutes of Health2, Princess Anne Hospital3, St Mary's Hospital4, Wellcome Trust Sanger Institute5, Science Applications International Corporation6, Fred Hutchinson Cancer Research Center7, Baylor College of Medicine8, University of Hawaii at Manoa9, University of Utah10, Marshfield Clinic11, American Cancer Society12, University of Copenhagen13, Hannover Medical School14, Russian Academy15, Seoul National University16, Leiden University17, Erasmus University Rotterdam18, Curie Institute19, Nofer Institute of Occupational Medicine20, University of Helsinki21, University of Melbourne22, QIMR Berghofer Medical Research Institute23, Netherlands Cancer Institute24, Carlos III Health Institute25, University of Cologne26, German Cancer Research Center27, Heidelberg University28, Technische Universität München29, University of Tübingen30, Bosch31, University of Ulm32, Karolinska Institutet33, University of Eastern Finland34, Mayo Clinic35, Cancer Council Victoria36, Harvard University37, Norwegian University of Science and Technology38, University of Minnesota39, Agency for Science, Technology and Research40, University of Sheffield41, Academia Sinica42, China Medical University (Taiwan)43, National Defense Medical Center44, University of California, Irvine45, University of Toronto46, Cancer Research UK47
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
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Montserrat Garcia-Closas, Fergus J. Couch1, Sara Lindström2, Kyriaki Michailidou3 +284 more•Institutions (82)
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
Cited by
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National Institutes of Health1, University of Chicago2, Duke University3, Harvard University4, University of Oxford5, GlaxoSmithKline6, Johns Hopkins University7, Yale University8, deCODE genetics9, Howard Hughes Medical Institute10, Princeton University11, Washington University in St. Louis12, University of California, Berkeley13, Stanford University14, University of Michigan15, Cornell University16, University of Washington17, University of Queensland18, Vanderbilt University19, North Carolina State University20, QIMR Berghofer Medical Research Institute21
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
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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
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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
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Wellcome Trust Centre for Human Genetics1, University of Oxford2, University of Michigan3, Fred Hutchinson Cancer Research Center4, Duke University5, University of Ottawa6, Tufts University7, Foundation for Research & Technology – Hellas8, Broad Institute9, Harvard University10, Boston Children's Hospital11
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