References
- International Agency for Research on Cancer. GLOBOCAN 2012: Estimated cancer incidence, mortality and prevalence worldwide in 2012. Available from: http://globocan.iarc.fr/. Accessed June 22, 2017.
- Todd M, Shoag M, Cadman E. Survival of women with metastatic breast cancer at Yale from 1920 to 1980. J Clinl Oncol. 1983;1(6):406–408.
- Cancer in Norway 2012. Cancer incidence, mortality, survival and prevalence in Norway. Oslo: Cancer Registry of Norway, 2014. Available from: https://www.kreftregisteret.no/globalassets/cancer-in-norway/2012/cin_2012-web.pdf. Accessed June 22, 2017.
- Lipscomb J, Fleming ST, Trentham-Dietz A, et al; Centers for Disease Control and Prevention National Program of Cancer Registries Patterns of Care Study Group. What predicts an advanced-stage diagnosis of breast cancer? Sorting out the influence of method of detection, access to care, and biologic factors. Cancer Epidemiol Biomarkers Prev. 2016;25(4):613–623.
- Turajlic S, Swanton C. Metastasis as an evolutionary process. Science. 2016;352(6282):169–175.
- Vineis P, Schatzkin A, Potter JD. Models of carcinogenesis: an overview. Carcinogenesis. 2010;31(10):1703–1709.
- Rangarajan A, Weinberg RA. Opinion: comparative biology of mouse versus human cells: modelling human cancer in mice. Nat Rev Cancer. 2003;3(12):952–959.
- Of men, not mice. Nat Med. 2013;19(4):379.
- Gould SE, Junttila MR, de Sauvage FJ. Translational value of mouse models in oncology drug development. Nat Med. 2015;21(5):431–439.
- Freedman ML, Monteiro AN, Gayther SA, et al. Principles for the post-GWAS functional characterization of cancer risk loci. Nat Genet. 2011;43(6):513–518.
- Chadeau-Hyam M, Athersuch TJ, Keun HC, et al. Meeting-in-the-middle using metabolic profiling – a strategy for the identification of intermediate biomarkers in cohort studies. Biomarkers. 2011;16(1):83–88.
- Cava C, Bertoli G, Castiglioni I. Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential. BMC Syst Biol. 2015;9:62.
- Arnedos M, Vicier C, Loi S, et al. Precision medicine for metastatic breast cancer – limitations and solutions. Nat Rev Clin Oncol. 2015;12(12):693–704.
- Cesar ASM, Gradishar WJ. Changing treatment paradigms in metastatic breast cancer: lessons learned. JAMA Oncol. 2015;1(4):528–534; quiz 549.
- Lund E, Dumeaux V. Systems Epidemiology in Cancer. Cancer Epidemiol Biomarkers Prev. 2008;17(11):2954–2957.
- Lund E, Holden L, Bøvelstad H, et al. A new statistical method for curve group analysis of longitudinal gene expression data illustrated for breast cancer in the NOWAC postgenome cohort as a proof of principle. BMC Med Res Methodol. 2016;16:28.
- Lund E, Plancade S, Nuel G, Bøvelstad H, Thalabard JC. A processual model for functional analyses of carcinogenesis in the prospective cohort design. Med Hypotheses. 2015;85(4):494–497.
- Holden L. Time development of gene expression. NR note SAMBA/35/15, 2015. Available from: https://www.nr.no/files/samba/smbi/note2015SAMBA3515timeDevelopmentGenes.pdf. Accessed June 22, 2017.
- Holden L. Classify strata. NR note SAMBA/11/15, 2015. Available from: https://www.nr.no/files/samba/smbi/note2015SAMBA1115classifyStrata.pdf. Accessed June 22, 2017.
- Reiner A, Yekutieli D, Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics. 2003;19(3):368–375.
- Lund E, Dumeaux V, Braaten T, et al. Cohort profile: The Norwegian Women and Cancer Study–NOWAC–Kvinner og kreft. Int J Epidemiol. 2008;37(1):36–41.
- Dumeaux V, Borresen-Dale AL, Frantzen JO, Kumle M, Kristensen VN, Lund E. Gene expression analyses in breast cancer epidemiology: the Norwegian Women and Cancer postgenome cohort study. Breast Cancer Res. 2008;10(1):R13.
- Hofvind S, Geller B, Vacek PM, Thoresen S, Skaane P. Using the European guidelines to evaluate the Norwegian Breast Cancer Screening Program. Eur J Epidemiol. 2007;22(7):447–455.
- Holden M, Holden L. Statistical analysis of gene expression in blood before diagnosis of breast cancer. NR note SAMBA/07/16, 2016. Available from: https://www.nr.no/files/samba/smbi/note2016SAMBA0716BreastCancer.pdf. Accessed June 22, 2017.
- Lin SM, Du P, Huber W, Kibbe WA. Model-based variance-stabilizing transformation for Illumina microarray data. Nucleic Acids Res. 2008;36(2):e11.
- Du P, Kibbe WA, Lin SM. nuID: a universal naming scheme of oligonucleotides for Illumina, Affymetrix, and other microarrays. Biol Direct. 2007;2:16.
- Du P, Feng G, Kibbe W, Lin S (2016). lumiHumanIDMapping: Illumina Identifier mapping for Human. R package version 1.10.1.
- Lund E, Plancade S. Transcriptional output in a prospective design conditionally on follow-up and exposure: the multistage model of cancer. Int J Mol Epidemiol Genet. 2012;3(2):107–114.
- Spitz MR, Bondy ML. The evolving discipline of molecular epidemiology of cancer. Carcinogenesis. 2010;31(1):127–134.
- Berry D. Multiplicities in cancer research: ubiquitous and necessary evils. J Natl Cancer Inst. 2012;104(15):1124–1132.
- Weedon-Fekjær H, Lindqvist BH, Vatten LJ, Aalen OO, Tretli S. Estimating mean sojourn time and screening sensitivity using questionnaire data on time since previous screening. J Med Screen. 2008;15(2):83–90.
- Dumeaux V, Ursini-Siegel J, Flatberg A, et al. Peripheral blood cells inform on the presence of breast cancer: a population-based case-control study. Int J Cancer. 2015;136(3):656–667.
- Lappalainen I, Almeida-King J, Kumanduri V, et al. The European Genome-phenome Archive of human data consented for biomedical research. Nat Genet. 2015;47(7):692–695.
Reference
- Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–127.