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  • 1
    Keywords: CANCER ; CELLS ; LUNG ; MODEL ; lung cancer ; LUNG-CANCER ; COHORT ; EXPOSURE ; TIME ; CARCINOGENESIS ; STAGE ; prevention ; CIGARETTE-SMOKING ; smoking ; COUNTRIES ; RATES ; PARAMETERS ; TRANSFORMATION ; PROJECT ; EPIC ; nutrition ; mechanistic model ; INITIATION ; DEPENDENCE ; TOBACCO-SMOKE ; 2-MUTATION MODEL ; PROMOTION ; prospective ; EUROPEAN COUNTRIES ; ATOMIC-BOMB SURVIVORS ; EXPANSION ; CIGARETTE-SMOKE ; BRITISH DOCTORS DATA ; CLONAL EXPANSION MODEL ; P53 MUTATION SPECTRUM ; SOMATIC MUTATIONS ; STATE-VECTOR MODEL
    Abstract: A stochastic two-stage cancer model is used to analyse the relation between lung cancer and cigarette smoking. The model contains the main rate-limiting stages of carcinogenesis, which include initiation, promotion (clonal expansion of initiated cells), malignant transformation and a lag time for tumour formation. Various data sets were used to test the model. These include the data of a large prospective collaborative project carried out in 10 different European countries, the European Prospective Investigation into Cancer and Nutrition (EPIC). This new data set has not been modelled before. The model is also tested on other published data from CPS-II (Cancer Prevention Study II) of the American Cancer Society and the British doctors' study. The analyses indicate that the EPIC data are best described with smoking dependence on the rates of malignant transformation and clonal expansion. With increasing smoking rates, saturation effects in the two exposure rate-dependent model parameters were observed. The results find confirmation in the biological literature, where both mutational effects and promotional effects of cigarette smoke are documented
    Type of Publication: Journal article published
    PubMed ID: 16410261
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  • 2
    Keywords: CANCER ; human ; LUNG ; MODEL ; lung cancer ; LUNG-CANCER ; COHORT ; cohort study ; DISEASE ; DISEASES ; EXPOSURE ; GENE ; GENES ; COMPLEX ; COMPLEXES ; DNA ; REDUCTION ; ASSOCIATION ; FREQUENCY ; polymorphism ; POLYMORPHISMS ; SUSCEPTIBILITY ; VARIANTS ; FREQUENCIES ; NUMBER ; REPAIR ; leukemia ; ACUTE LYMPHOBLASTIC-LEUKEMIA ; BLADDER-CANCER ; REGION ; DNA repair ; DNA-REPAIR GENES ; VARIANT ; FUNCTIONAL-CHARACTERIZATION ; CATECHOL-O-METHYLTRANSFERASE ; METHYLENETETRAHYDROFOLATE REDUCTASE ; prospective ; LUNG-CANCER RISK ; VARIABLES ; metabolic gene polymorphisms ; METABOLISM GENES
    Abstract: It is becoming increasingly evident that single-locus effects cannot explain complex multifactorial human diseases like cancer. We applied the multi-factor dimensionality reduction (MDR) method to a large cohort study on gene-environment and gene-gene interactions. The study (case-control nested in the EPIC cohort) was established to investigate molecular changes and genetic susceptibility in relation to air pollution and environmental tobacco smoke (ETS) in non-smokers. We have analyzed 757 controls and 409 cases with bladder cancer (n = 124), lung cancer (n = 116) and myeloid leukemia (n = 169). Thirty-six gene variants (DNA repair and metabolic genes) and three environmental exposure variables (measures of air pollution and ETS at home and at work) were analyzed. Interactions were assessed by prediction error percentage and cross-validation consistency (CVC) frequency. For lung cancer, the best model was given by a significant gene-environment association between the base excision repair (BER) XRCC1-Arg399Gln polymorphism, the double-strand break repair (DSBR) BRCA2-Asn372His polymorphism and the exposure variable 'distance from heavy traffic road', an indirect and robust indicator of air pollution (mean prediction error of 26%, P 〈 0.001, mean CVC of 6.60, P = 0.02). For bladder cancer, we found a significant 4-loci association between the BER APE1-Asp148Glu polymorphism, the DSBR RAD52-3'-untranslated region (3'-UTR) polymorphism and the metabolic gene polymorphisms COMT-Val158Met and MTHFR-677C 〉 T (mean prediction error of 22%, P 〈 0.001, mean CVC consistency of 7.40, P 〈 0.037). For leukemia, a 3-loci model including RAD52-2259C 〉 T, MnSOD-Ala9Val and CYP1A1-Ile462Val had a minimum prediction error of 31% (P 〈 0.001) and a maximum CVC of 4.40 (P = 0.086). The MDR method seems promising, because it provides a limited number of statistically stable interactions; however, the biological interpretation remains to be understood
    Type of Publication: Journal article published
    PubMed ID: 16956909
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