Iron chelators for the treatment of cancer.

The study of iron chelators as anti-tumor agents is still in its infancy. Iron is important for cellular proliferation and this is demonstrated by observations that iron-depletion results in cell cycle arrest and also apoptosis. In addition, many iron chelators are known to inhibit ribonucleotide reductase, the iron-containing enzyme that is the rate-limiting step for DNA synthesis. Desferrioxamine is a well known chelator used for the treatment of iron-overload disease, but it has also been shown to possess anti-cancer activity. Another class of chelators, namely the thiosemicarbazones, have been shown to possess anti-cancer activity since the 1950's, although their mechanism(s) of action have only recently been more comprehensively elucidated. In fact, the redox activity of thiosemicarbazone iron complexes is thought to be important in mediating their potent cytotoxicity. Moreover, unlike typical iron chelators which simply act to deplete tumors of iron, several thiosemicarbazones (i.e., Bp44mT and Dp44mT) do not induce this effect, their anti-cancer efficacy being due to other mechanisms e.g., redox activity. Other reports have also shown that some thiosemicarbazones inhibit topoisomerase IIα, demonstrating that this class of agents have multiple molecular targets and act by various mechanisms. The most well characterized thiosemicarbazone iron chelator in terms of its assessment in humans is 3-aminopyridine-2-carboxaldehyde thiosemicarbazone (3-AP). Observations from these clinical trials highlight the less than optimal activity of this ligand and several side effects related to its use, including myelo-suppression, hypoxia and methemoglobinemia. The mechanisms responsible for these latter effects must be elucidated and the design of the ligand altered to minimize these problems and increase efficacy. This review discusses the development of chelators as unique agents for cancer treatment.

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.