Multi-factor dimensionality reduction applied to a large prospective investigation on gene-gene and gene-environment interactions.

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.

Analysis of epidemiological cohort data on smoking effects and lung cancer with a multi-stage cancer model.

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.

Chronic pain as expression of neural substrates. Issues from the neuronal dynamics and mutual relations.

The Thalamo-Cortical somatosensory loop shows important synaptic re-organization in cases of chronic pain. Animal models exhibit severe functional distortions, potentially related to the anatomic rearrangements. Connectivity and information theoretic measurement represent important tools to quantify the functional disarrays. We performed electrophysiological experiments with multisite, multielectrode simultaneous recordings in the Thalamus and in the Somatosensory Cortex. The recurrent anomalies in the analytic estimates induce to hypothesize a potential neurodynamical explanation of the sensory context.

An evolutionary paradigm for carcinogenesis?

Mutations seem to be only one of the mechanisms involved in carcinogenesis; selection of mutated clones is a second crucial mechanism. An evolutionary (darwinian) theory of carcinogenesis can be useful to explain some contradictory observations of epidemiology, and to provide a common theoretical framework for carcinogenesis. In both the selection of species and in carcinogenesis (selection of mutated cells), mutation and selection can be interpreted as necessary and insufficient causes. Selection presupposes competition among clones-that is, survival advantage of the mutated species; without selective forces a mutation is mute, while the lack of mutations makes selective advantage impossible. The identification of carcinogen related fingerprints is ambiguous: it can suggest both a genuine mutational hotspot left by the carcinogenic stimulus (like in tobacco related p53 mutations), and selective advantage of clones whose mutations seem to be not exposure specific (like in the case of aflatoxin). We present several examples of exposures that can increase the risk of cancer in humans not via mutations but through a putative mechanism of clone selection.