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Abstract: Cancer epidemiology has undergone marked development since the 1950s. One of the most spectacular and specific contributions was the demonstration of the massive effect of smoking on the occurrence of lung, larynx, and bladder cancer. Major chemical, physical, and biological carcinogenic agents have been identified in the working environment and in the overall environment. The chain of events from environmental exposures to cancer requires hundreds of polymorphic genes coding for proteins involved in the transport and metabolism of xenobiotics, or in repair, or in an immune or inflammatory response. The multifactorial and multistage characteristics of cancer create the theoretical conditions for statistical interactions that have been exceptionally detected. Over the last two decades, a considerable mass of data has been generated, mostly addressing the interactions between smoking and xenobiotic-metabolizing enzymes in smoking-related cancers. They were sometimes considered disappointing, but they actually brought a lot of information and raised many methodological issues. In parallel, the number of polymorphisms that can be considered candidate per function increased so much that multiple testing has become a major issue, and genome wide-screening approaches have more and more gained in interest. Facing the resulting complexity, some instruments are being set up: our studies are now equipped with carefully sampled biological collections, high-throughput genotyping systems are becoming available, work on statistical methodologies is ongoing, bioinformatics databases are growing larger and access to them is becoming simpler; international consortiums are being organized. The roles of environmental and genetic factors are being jointly elucidated. The basic rules of epidemiology, which are demanding with respect to sampling, with respect to the histological and molecular criteria for cancer classification, with respect to the evaluation of environmental exposures, their timeframes, quantification and covariables, with respect to study size and with respect to the rigor of multivariate analyses, are more pertinent than ever before. To cite this article: J. Clavel, C. R. Biologies 330 (2007). [Copyright &y& Elsevier]