An exposure driven functional model of carcinogenesis

作者:Lund Eiliv*
来源:Medical Hypotheses, 2011, 77(2): 195-198.
DOI:10.1016/j.mehy.2011.04.009

摘要

The understanding of general models of carcinogenesis have not advanced substantially over the last years despite a rapidly increasing amount of detailed knowledge in molecular biology, genetics and epidemiology, and it has been difficult to come up with specific hypotheses in order to test existing models. Current multistage models consider mutations per se as the driving forces of carcinogenesis. In contrast, novel knowledge in epidemiology and basic research combined with upcoming large scale technologies of transcriptomics and epigenetics offers a new model - the exposure driven functional model. In this model exposures to carcinogenic substances are the determinants of the carcinogenesis leading to functional changes in gene regulation and to mutations. The diversity of exposures and functional changes could give not one, but many different cancer phenotypes. Under this novel model, cessation of exposure could arrest or reverse the carcinogenic process. To test hypotheses based on this novel model, studies with valid exposures measurements, biological material suitable for all "-omics" analyses, and a design that takes into account the time order principle of causality are a prerequisite. In epidemiology, such designs have been proposed, while in basic genetic research most designs only comply with some, but not all of these conditions. The strength of the exposure driven functional model is the potential for testing specific hypothesis of the effect of many exposures on different stages or mutations. An example of such design would be the testing of the effect of continuing or stopping exposures in relation to the last stage of carcinogenesis in a prospective or globolomic design. If successful the exposure driven carcinogenic model and the use of functional genomics could improve the understanding of carcinogenesis and concomitantly the assessment of causality as part of systems epidemiology.

  • 出版日期2011-8