Towards a more functional concept of causality in cancer research.

Advances in molecular technologies challenge the different concepts of causality in biology, epidemiology and multistage mathematical models. The lack of integration of the different aspects of causality into a common framework could postpone our attempts to build a human causal model of carcinogenesis. We present here some aspects of differences in methodology, terminology and traditions between the scientific disciplines and propose a research strategy using functional analyses of the transcriptome and epigenetics to illuminate causality in complex biological systems. Overcoming the challenges of biological material collection suitable for such analyses into a prospective design, this could give unique opportunities for verification of mechanistic information from basic biological research in a human model system. The ultimate goal is to obtain a dynamic causal description of the different carcinogenesis stages. The success of this novel approach depends on the biological relationship between the gene expression of the somatic driver mutations or co-expressed genes in tumours and the gene expressions mirrored in peripheral blood along the different stages of carcinogenesis. The use of gene expression profiles and epigenetics could produce a functional concept of causality to explain the human multistage carcinogenic process.

[1]  Hong Zhong,et al.  Novel Blood Biomarkers of Human Urinary Bladder Cancer , 2006, Clinical Cancer Research.

[2]  J. Chen,et al.  Global Gene Expression Profiling in Whole-Blood Samples from Individuals Exposed to Metal Fumes , 2004, Environmental health perspectives.

[3]  Paolo Vineis,et al.  The impact of new research technologies on our understanding of environmental causes of disease: the concept of clinical vulnerability , 2009, Environmental health : a global access science source.

[4]  H. Stefánsson,et al.  Genetics of gene expression and its effect on disease , 2008, Nature.

[5]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[6]  William C Hahn,et al.  Functional genetics and experimental models of human cancer. , 2004, Trends in molecular medicine.

[7]  N. Day,et al.  Multistage models and primary prevention of cancer. , 1980, Journal of the National Cancer Institute.

[8]  Bruce Potter Necessary but Not Sufficient , 2010, IEEE Secur. Priv..

[9]  D. Freedman,et al.  Multistage models for carcinogenesis. , 1989, Environmental health perspectives.

[10]  Sally A. Amundson,et al.  Identification of Potential mRNA Biomarkers in Peripheral Blood Lymphocytes for Human Exposure to Ionizing Radiation , 2000, Radiation research.

[11]  D. Altshuler,et al.  Genetic variation at the CYP19A1 locus predicts circulating estrogen levels but not breast cancer risk in postmenopausal women. , 2007, Cancer research.

[12]  H. Horvitz,et al.  MicroRNA expression profiles classify human cancers , 2005, Nature.

[13]  Jun Ma,et al.  The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool. , 2006, The Journal of laboratory and clinical medicine.

[14]  W. Ollier,et al.  EBV Immortalization of human B lymphocytes separated from small volumes of cryo-preserved whole blood. , 2008, International journal of epidemiology.

[15]  J. V. Moran,et al.  Initial sequencing and analysis of the human genome. , 2001, Nature.

[16]  N. Rothman,et al.  Discovery of Novel Biomarkers by Microarray Analysis of Peripheral Blood Mononuclear Cell Gene Expression in Benzene-Exposed Workers , 2005, Environmental health perspectives.

[17]  M. Stratton,et al.  The cancer genome , 2009, Nature.

[18]  G. Colditz,et al.  What can be learnt from models of incidence rates? , 2006, Breast Cancer Research.

[19]  D. Mccormick Sequence the Human Genome , 1986, Bio/Technology.

[20]  Denis Noble,et al.  Genes and causation , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[21]  Siobhan M. Dolan,et al.  Assessment of cumulative evidence on genetic associations: interim guidelines. , 2008, International journal of epidemiology.

[22]  O. Pereira-smith,et al.  The minimal set of genetic alterations required for conversion of primary human fibroblasts to cancer cells in the subrenal capsule assay. , 2005, Neoplasia.

[23]  A Morabia,et al.  On the origin of Hill's causal criteria. , 1991, Epidemiology.

[24]  Gregory Nuel,et al.  Deciphering Normal Blood Gene Expression Variation—The NOWAC Postgenome Study , 2010, PLoS genetics.

[25]  D. Coppola,et al.  Microarray-based identification and RT-PCR test screening for epithelial-specific mRNAs in peripheral blood of patients with colon cancer , 2006, BMC Cancer.

[26]  Graham A. Colditz,et al.  Merging and emerging cohorts: Not worth the wait , 2007, Nature.

[27]  Sandra B. Munro,et al.  Detection of Cancer with Serum miRNAs on an Oligonucleotide Microarray , 2009, PloS one.

[28]  M. Cotreau,et al.  Molecular classification of Crohn's disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells. , 2006, The Journal of molecular diagnostics : JMD.

[29]  T. Peakman,et al.  Levels of 5' RNA tags in plasma and buffy coat from EDTA blood increase with time. , 2008, International journal of epidemiology.

[30]  Mark S. Clements,et al.  Multistage Carcinogenesis and Lung Cancer Mortality in Three Cohorts , 2005, Cancer Epidemiology Biomarkers & Prevention.

[31]  John D Potter,et al.  Epidemiology, cancer genetics and microarrays: making correct inferences, using appropriate designs. , 2003, Trends in genetics : TIG.

[32]  M. Fitó,et al.  Time course of changes in the expression of insulin sensitivity-related genes after an acute load of virgin olive oil. , 2009, Omics : a journal of integrative biology.

[33]  O. Opitz,et al.  In Vitro Transformation Models: Modeling Human Cancer , 2006, Cell cycle.

[34]  David Elashoff,et al.  Serum circulating human mRNA profiling and its utility for oral cancer detection. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[35]  A. Børresen-Dale,et al.  Gene expression profiling of peripheral blood cells for early detection of breast cancer , 2010, Breast Cancer Research.

[36]  M. Kumle,et al.  Cohort profile: The Norwegian Women and Cancer Study--NOWAC--Kvinner og kreft. , 2008, International journal of epidemiology.

[37]  M. Jucker,et al.  Koch’s postulates and infectious proteins , 2006, Acta Neuropathologica.

[38]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[39]  G. Klein Toward a genetics of cancer resistance , 2009, Proceedings of the National Academy of Sciences.

[40]  O. Kallioniemi,et al.  Genetic changes in intraductal breast cancer detected by comparative genomic hybridization. , 1997, The American journal of pathology.

[41]  Martin A. Nowak,et al.  Genetic Progression and the Waiting Time to Cancer , 2007, PLoS Comput. Biol..

[42]  Timothy B. Stockwell,et al.  The Sequence of the Human Genome , 2001, Science.

[43]  K. Marshall,et al.  Novel Blood-Based, Five-Gene Biomarker Set for the Detection of Colorectal Cancer , 2008, Clinical Cancer Research.

[44]  Lawrence W Green,et al.  Public health asks of systems science: to advance our evidence-based practice, can you help us get more practice-based evidence? , 2006, American journal of public health.

[45]  C. Davis,et al.  Evidence for dietary regulation of microRNA expression in cancer cells. , 2008, Nutrition reviews.

[46]  P. Rothberg,et al.  Oncogenes and cancer. , 1983, Cancer investigation.

[47]  D. Thelle STROBE and STREGA: instruments for improving transparency and quality of reporting scientific results , 2008, European Journal of Epidemiology.

[48]  Y. Lazebnik Can a biologist fix a radio? — or, what I learned while studying apoptosis , 2004, Biochemistry (Moscow).

[49]  S. Hanash,et al.  Mining the plasma proteome for cancer biomarkers , 2008, Nature.

[50]  C. Liew,et al.  The peripheral-blood transcriptome: new insights into disease and risk assessment. , 2007, Trends in molecular medicine.

[51]  Don P. Buesching,et al.  Necessary but not sufficient , 1994, Journal of General Internal Medicine.

[52]  T. Sellers,et al.  Epidemiology — identifying the causes and preventability of cancer? , 2006, Nature Reviews Cancer.

[53]  S. Teichmann,et al.  A HaemAtlas: characterizing gene expression in differentiated human blood cells , 2008, Blood.

[54]  Paolo Vineis,et al.  Molecular Epidemiology and Biomarkers in Etiologic Cancer Research: The New in Light of the Old , 2007, Cancer Epidemiology Biomarkers & Prevention.

[55]  A. Børresen-Dale,et al.  Gene expression analyses in breast cancer epidemiology: the Norwegian Women and Cancer postgenome cohort study , 2008, Breast Cancer Research.

[56]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[57]  A. B. Hill The Environment and Disease: Association or Causation? , 1965, Proceedings of the Royal Society of Medicine.

[58]  Paolo Vineis,et al.  A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25 , 2008, Nature.

[59]  M. Carbone,et al.  Modern Criteria to Establish Human Cancer Etiology , 2004, Cancer Research.

[60]  S. Friend,et al.  Signatures of environmental exposures using peripheral leukocyte gene expression: tobacco smoke. , 2004, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[61]  Pagona Lagiou,et al.  Causality in cancer epidemiology , 2005, European Journal of Epidemiology.

[62]  Rafael Meza,et al.  Analysis of lung cancer incidence in the nurses’ health and the health professionals’ follow-up studies using a multistage carcinogenesis model , 2008, Cancer Causes & Control.

[63]  Ryoiti Kiyama,et al.  Using a customized DNA microarray for expression profiling of the estrogen-responsive genes to evaluate estrogen activity among natural estrogens and industrial chemicals. , 2004, Environmental health perspectives.

[64]  A. Green,et al.  Microarrays and Epidemiology: Not the Beginning of the End but the End of the Beginning… , 2007, Cancer Epidemiology Biomarkers & Prevention.

[65]  A. Børresen-Dale,et al.  Gene expression profiling of whole-blood samples from women exposed to hormone replacement therapy , 2006, Molecular Cancer Therapeutics.

[66]  E Gabrielson,et al.  Genetic divergence in the clonal evolution of breast cancer. , 1996, Cancer research.

[67]  J. Potter Epidemiology informing clinical practice: from bills of mortality to population laboratories , 2005, Nature Clinical Practice Oncology.

[68]  Eiliv Lund,et al.  Systems Epidemiology in Cancer , 2008, Cancer Epidemiology Biomarkers & Prevention.

[69]  I. Tomlinson,et al.  What can we learn from the population incidence of cancer? Armitage and Doll revisited. , 2007, The Lancet. Oncology.