Author
Chris Schroen
Bio: Chris Schroen is an academic researcher from University of Melbourne. The author has contributed to research in topics: Genome-wide association study & Cancer. The author has an hindex of 2, co-authored 2 publications receiving 2261 citations.
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, University of Otago31, Cancer Council New South Wales32, 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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TL;DR: There are strong familial correlations in postmenopausal SHBG, IGF-I, and to a lesser extent T, which are consistent with a genetic etiology and substantial nongenetic familial factors.
Abstract: Background: Serum concentrations of some hormones are risk factors for certain cancers, but little is known about their familial associations especially for females. Methods: We measured serum concentrations of estradiol (E2), testosterone (T), SHBG, prolactin, and IGF-I for 645 Australian female postmenopausal twins and their sisters [182 monozygotic (MZ) and 107 dizygotic (DZ) pairs and 67 nontwin sisters] using well-established immunoassays. After suitable transformation and adjusting for age, body mass index (BMI), and time since menopause, familial correlations and proportions of variance attributed to genetic (h2) and nongenetic factors common to sisterships (c2) were estimated under the classic twin multivariate normal model using FISHER. Results: For all serum concentrations except prolactin, MZ, DZ, and sister pairs were correlated (P < 0.001). MZ correlations were in the range 0.5–0.7, and for all serum concentrations, there were no differences between DZ and sister correlations. MZ correlations...
24 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, 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
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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
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TL;DR: There is now substantial evidence for the importance of FGF signalling in the pathogenesis of diverse tumour types, and clinical reagents that specifically target the FGFs or FGF receptors are being developed.
Abstract: Fibroblast growth factors (FGFs) and their receptors control a wide range of biological functions, regulating cellular proliferation, survival, migration and differentiation. Although targeting FGF signalling as a cancer therapeutic target has lagged behind that of other receptor tyrosine kinases, there is now substantial evidence for the importance of FGF signalling in the pathogenesis of diverse tumour types, and clinical reagents that specifically target the FGFs or FGF receptors are being developed. Although FGF signalling can drive tumorigenesis, in different contexts FGF signalling can mediate tumour protective functions; the identification of the mechanisms that underlie these differential effects will be important to understand how FGF signalling can be most appropriately therapeutically targeted.
2,211 citations
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King's College London1, Memorial Sloan Kettering Cancer Center2, Harvard University3, University of Pennsylvania4, Cedars-Sinai Medical Center5, City of Hope National Medical Center6, University of Texas MD Anderson Cancer Center7, Lund University8, University of Cologne9, Peter MacCallum Cancer Centre10, National Institute for Health Research11, AstraZeneca12
TL;DR: Findings from this phase 2 study provide positive proof of concept of the efficacy and tolerability of genetically targeted treatment with olaparib in BRCA-mutated advanced ovarian cancer.
2,119 citations