Mixed Effects Modeling of Proliferation Rates in Cell-Based Models: Consequence for Pharmacogenomics and Cancer

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The International HapMap project has made publicly available extensive genotypic data on a number of lymphoblastoid cell lines (LCLs). Building on this resource, many research groups have generated a large amount of phenotypic data on these cell lines to facilitate genetic studies of disease risk or drug response. However, one problem that may reduce the usefulness of these resources is the biological noise inherent to cellular phenotypes. We developed a novel method, termed Mixed Effects Model Averaging (MEM), which pools data from multiple sources and generates an intrinsic cellular growth rate phenotype. This intrinsic growth rate was estimated for each of over 500 HapMap cell lines. We then examined the association of this intrinsic growth rate with gene expression levels and found that almost 30% (2,967 out of 10,748) of the genes tested were significant with FDR less than 10%. We probed further to demonstrate evidence of a genetic effect on intrinsic growth rate by determining a significant enrichment in growth-associated genes among genes targeted by top growth-associated SNPs (as eQTLs). The estimated intrinsic growth rate as well as the strength of the association with genetic variants and gene expression traits are made publicly available through a cell-based pharmacogenomics database, PACdb. This resource should enable researchers to explore the mediating effects of proliferation rate on other phenotypes.

Vyšlo v časopise: Mixed Effects Modeling of Proliferation Rates in Cell-Based Models: Consequence for Pharmacogenomics and Cancer. PLoS Genet 8(2): e32767. doi:10.1371/journal.pgen.1002525
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1002525

Souhrn

Zdroje

1. International HapMap Consortium 2003 The international hapmap project. Nature 426 789 96

2. International HapMap Consortium 2007 A second generation human haplotype map of over 3.1 million snps. Nature 449 851 61

3. Genomes Project Consortium 2010 A map of human genome variation from population-scale sequencing. Nature 467 1061 73

4. WelshMMangraviteLMedinaMTantisiraKZhangW 2009 Pharmacogenomic discovery using cell-based models. PHARMACOLOGICAL REVIEWS 61 413

5. GamazonERZhangWKonkashbaevADuanSKistnerEO 2010 Scan: Snp and copy number annotation. Bioinformatics 26 259 262

6. GamazonERDuanSZhangWHuangRSKistnerEO 2010 Pacdb: a database for cell-based pharmacogenomics. Pharmacogenet Genomics 20 269 273

7. ThornCFKleinTEAltmanRB 2005 Pharmgkb: the pharmacogenetics and pharma-cogenomics knowledge base. Methods Mol Biol 311 179 91

8. KleinTEChangJTChoMKEastonKLFergersonR 2001 Integrating genotype and phenotype information: an overview of the pharmgkb project. pharmacogenetics research network and knowledge base. The Pharmacogenomics Journal 1 167 70

9. WheelerHEGamazonERStarkALODonnellPHGorsicLK 2011 Genome-wide meta-analysis identifies variants associated with platinating agent susceptibility across populations. The Pharmacogenomics Journal

10. WenYGorsicLKWheelerHEZiliakDMHuangRS 2011 Chemotherapeutic- induced apoptosis: a phenotype for pharmacogenomics studies. Pharmacogenet Genomics 21

11. SmirnovDAMorleyMShinESpielmanRSCheungVG 2009 Genetic analysis of radiation-induced changes in human gene expression. Nature 459 587 591

12. StarkALZhangWMiSDuanSO'DonnellPH 2010 Heritable and non-genetic factors as variables of pharmacologic phenotypes in lymphoblastoid cell lines. The Pharmacogenomics Journal 10 505

13. StarkAZhangWZhouTO'DonnellPBeiswangerC 2010 Population differences in the rate of proliferation of international hapmap cell lines. The American Journal of Human Genetics

14. ChoyEYelenskyRBonakdarSPlengeRMSaxenaR 2008 Genetic analysis of human traits in vitro: Drug response and gene expression in lymphoblastoid cell lines. PLoS Genet 4 e1000287 doi:10.1371/journal.pgen.1000287

15. BrauerMHuttenhowerCAiroldiERosensteinRMateseJ 2008 Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. Molecular Biology of the Cell 19 352

16. ScottMGundersonCWMateescuEMZhangZHwaT 2010 Interdependence of cell growth and gene expression: origins and consequences. Science 330 1099 102

17. KlumppSZhangZHwaT 2009 Growth rate-dependent global effects on gene expression in bacteria. Cell 139 1366 1375

18. WhitfieldMGeorgeLGrantGPerouC 2006 Common markers of proliferation. Nat Rev Cancer 6 99 106

19. RossDScherfUEisenMPerouCReesC 2000 Systematic variation in gene expression patterns in human cancer cell lines. Nature genetics 24 227 235

20. RhodesDYuJShankerKDeshpandeNVaramballyR 2004 Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA 101 9309

21. StarmansMHWKrishnapuramBSteckHHorlingsHNuytenDSA 2008 Ro- bust prognostic value of a knowledge-based proliferation signature across large patient microarray studies spanning different cancer types. Br J Cancer 99 1884

22. DamascoCLemboASommaMPGattiMCuntoFD 2011 A signature inferred from drosophila mitotic genes predicts survival of breast cancer patients. PLoS ONE 6 e14737 doi:10.1371/journal.pone.0014737

23. DaiH 2005 A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. Cancer Res 65 4059 4066

24. RosenwaldAWrightGWiestnerAChanWCConnorsJM 2003 The pro-liferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. Cancer Cell 3 185 197

25. HuangRSDuanSBleibelWKistnerEZhangW 2007 A genome-wide approach to identify genetic variants that contribute to etoposide-induced cytotoxicity. Proceedings of the National Academy of Sciences 104 9758

26. HuangRSDuanSShuklaSJKistnerEOClarkTA 2007 Identification of genetic variants contributing to cisplatin-induced cytotoxicity by use of a genomewide approach. The American Journal of Human Genetics 81 427 437

27. ZhangWDuanSKistnerEBleibelWHuangRS 2008 Evaluation of genetic variation contributing to differences in gene expression between populations. The American Journal of Human Genetics 82 631 640

28. LeekJStoreyJ 2008 A general framework for multiple testing dependence. Proceedings of the National Academy of Sciences 105 18718

29. HuangDWShermanBTLempickiRA 2009 Systematic and integrative analysis of large gene lists using david bioinformatics resources. Nat Protoc 4 44 57

30. HuangDWShermanBTLempickiRA 2009 Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37 1 13

31. WirapatiPSotiriouCKunkelSFarmerPPradervandS 2008 Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res 10 R65

32. WhitfieldMSherlockGSaldanhaAMurrayJBallC 2002 Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Molecular Biology of the Cell 13 1977

33. ChangHSneddonJAlizadehASoodRWestR 2004 Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol 2 e7 doi:10.1371/journal.pbio.0020007

34. NicolaeDGamazonEZhangWDuanSDolanM 2010 Trait-associated snps are more likely to be eqtls: Annotation to enhance discovery from GWAS. PLoS Genet 6 e1000888 doi:10.1371/journal.pgen.1000888

35. GamazonERHuangRSCoxNJDolanME 2010 Chemotherapeutic drug susceptibility associated snps are enriched in expression quantitative trait loci. Proc Natl Acad Sci USA 107 9287 9292

36. NicaACMontgomerySBDimasASStrangerBEClaudeBeazley 2010 Candidate causal regulatory effects by integration of expression qtls with complex trait genetic associations. PLoS Genet 6 e1000895 doi:10.1371/journal.pgen.1000895

37. FehrmannRJansenRCVeldinkJHWestraHJArendsD 2011 Trans-eqtls reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the hla. PLoS Genet e1002197 doi:10.1371/journal.pgen.1002197

38. JaegerEWebbEHowarthKCarvajal-CarmonaLRowanA 2007 Common genetic variants at the crac1 (hmps) locus on chromosome 15q13. 3 inuence colorectal cancer risk. Nat Genet 40 26 28

39. HoJChoiSf LeeYHuiTChernyS 2011 Replication study of snp associations for colorectal cancer in hong kong chinese. Br J Cancer 104 369 375

40. TenesaAFarringtonSMPrendergastJGPorteousMEWalkerM 2008 Genome-wide association scan identifies a colorectal cancer susceptibility locus on 11q23 and replicates risk loci at 8q24 and 18q21. Nat Genet 40 631 637

41. UemuraTShiozakiKYamaguchiKMiyazakiSSatomiS 2009 Contribution of sialidase neu1 to suppression of metastasis of human colon cancer cells through desia-lylation of integrin 4. Oncogene

42. KnueselMTMeyerKDBerneckyCTaatjesDJ 2009 The human cdk8 subcomplex is a molecular switch that controls mediator coactivator function. Genes & Development 23 439 451

43. FiresteinRBassAJKimSYDunnIFSilverSJ 2008 Cdk8 is a colorectal cancer oncogene that regulates -catenin activity. Nature 455 547 551

44. ÇalşkanMCusanovichDAOberCGiladY 2011 The effects of ebv transformation on gene expression levels and methylation profiles. Human molecular genetics 20 1643 52

45. BatesDMaechlerMBolkerB 2011 lme4: Linear mixed-effects models using S4 classes. URL http://CRAN.R-project.org/package=lme4. R package version 0.999375-38

46. R Development Core Team 2010 R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/. ISBN 3-900051-07-0

47. LeekJT sva: Surrogate Variable Analysis. Key: sva.package Annotation: R package version 1.1.0

48. DabneyAStoreyJD with assistance from Gregory R Warnes 2010 qvalue: Q-value estimation for false discovery rate control. URL http://CRAN.R-project.org/package=qvalue. R package version 1.24.0

49. StoreyJDTibshiraniR 2003 Statistical significance for genomewide studies. Proc Natl Acad Sci USA 100 9440 5

50. WickhamH 2009 ggplot2: elegant graphics for data analysis. Springer New York. URL http://had.co.nz/ggplot2/book

51. PurcellSNealeBTodd-BrownKThomasLFerreiraMAR 2007 Plink: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81 559 575