Dental management in head and neck cancers: from intensity-modulated radiotherapy with photons to proton therapy.

INTRODUCTION: Despite reduction of xerostomia with intensity-modulated compared to conformal X-ray radiotherapy, radiation-induced dental complications continue to occur. Proton therapy is promising in head and neck cancers to further reduce radiation-induced side-effects, but the optimal dental management has not been defined. MATERIAL AND METHODS: Dental management before proton therapy was assessed compared to intensity-modulated radiotherapy based on a bicentric experience, a literature review and illustrative cases. RESULTS: Preserved teeth frequently contain metallic dental restorations (amalgams, crowns, implants). Metals blur CT images, introducing errors in tumour and organ contour during radiotherapy planning. Due to their physical interactions with matter, protons are more sensitive than photons to tissue composition. The composition of restorative materials is rarely documented during radiotherapy planning, introducing dose errors. Manual artefact recontouring, metal artefact-reduction CT algorithms, dual or multi-energy CT and appropriate dose calculation algorithms insufficiently compensate for contour and dose errors during proton therapy. Physical uncertainties may be associated with lower tumour control probability and more side-effects after proton therapy. Metal-induced errors should be quantified and removal of metal restorations discussed on a case by case basis between dental care specialists, radiation oncologists and physicists. Metallic amalgams can be replaced with water-equivalent materials and crowns temporarily removed depending on rehabilitation potential, dental condition and cost. Implants might contraindicate proton therapy if they are in the proton beam path. CONCLUSION: Metallic restorations may more severely affect proton than photon radiotherapy quality. Personalized dental care prior to proton therapy requires multidisciplinary assessment of metal-induced errors before choice of conservation/removal of dental metals and optimal radiotherapy.

Associating quantitative behavioral traits with gene expression in the brain: searching for diamonds in the hay.

UNLABELLED: Gene expression and phenotypic functionality can best be associated when they are measured quantitatively within the same experiment. The analysis of such a complex experiment is presented, searching for associations between measures of exploratory behavior in mice and gene expression in brain regions. The analysis of such experiments raises several methodological problems. First and foremost, the size of the pool of potential discoveries being screened is enormous yet only few biologically relevant findings are expected, making the problem of multiple testing especially severe. We present solutions based on screening by testing related hypotheses, then testing the hypotheses of interest. In one variant the subset is selected directly, in the other one a tree of hypotheses is tested hierarchical; both variants control the False Discovery Rate (FDR). Other problems in such experiments are in the fact that the level of data aggregation may be different for the quantitative traits (one per animal) and gene expression measurements (pooled across animals); in that the association may not be linear; and in the resolution of interest only few replications exist. We offer solutions to these problems as well. The hierarchical FDR testing strategies presented here can serve beyond the structure of our motivating example study to any complex microarray study. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combined application of behavior genetics and microarray analysis to identify regional expression themes and gene-behavior associations.

In this report we link candidate genes to complex behavioral phenotypes by using a behavior genetics approach. Gene expression signatures were generated for the prefrontal cortex, ventral striatum, temporal lobe, periaqueductal gray, and cerebellum in eight inbred strains from priority group A of the Mouse Phenome Project. Bioinformatic analysis of regionally enriched genes that were conserved across all strains revealed both functional and structural specialization of particular brain regions. For example, genes encoding proteins with demonstrated anti-apoptotic function were over-represented in the cerebellum, whereas genes coding for proteins associated with learning and memory were enriched in the ventral striatum, as defined by the Expression Analysis Systematic Explorer (EASE) application. Association of regional gene expression with behavioral phenotypes was exploited to identify candidate behavioral genes. Phenotypes that were investigated included anxiety, drug-naive and ethanol-induced distance traveled across a grid floor, and seizure susceptibility. Several genes within the glutamatergic signaling pathway (i.e., NMDA/glutamate receptor subunit 2C, calmodulin, solute carrier family 1 member 2, and glutamine synthetase) were identified in a phenotype-dependent and region-specific manner. In addition to supporting evidence in the literature, many of the genes that were identified could be mapped in silico to surrogate behavior-related quantitative trait loci. The approaches and data set described herein serve as a valuable resource to investigate the genetic underpinning of complex behaviors.

Transcriptional profiling with a blood pressure QTL interval-specific oligonucleotide array.

Although the evidence for a genetic predisposition to human essential hypertension is compelling, the genetic control of blood pressure (BP) is poorly understood. The Dahl salt-sensitive (S) rat is a model for studying the genetic component of BP. Using this model, we previously reported the identification of 16 different genomic regions that contain one or more BP quantitative trait loci (QTLs). The proximal region of rat chromosome 1 contains multiple BP QTLs. Of these, we have localized the BP QTL1b region to a 13.5-cM (20.92 Mb) region. Interestingly, five additional independent studies in rats and four independent studies in humans have reported genetic linkage for BP control by regions homologous to QTL1b. To view the overall renal transcriptional topography of the positional candidate genes for this QTL, we sought a comparative gene expression profiling between a congenic strain containing QTL1b and control S rats by employing 1) a saturated QTL1b interval-specific oligonucleotide array and 2) a whole genome cDNA microarray representing 20,465 unique genes that are positioned outside the QTL. Results indicated that 17 of the 231 positional candidate genes for this QTL are differentially expressed between the two strains tested. Surprisingly, >1,500 genes outside of QTL1b were differentially expressed between the two rat strains. Integrating the results from the two approaches revealed at least one complex network of transcriptional control initiated by the positional candidate Nr2f2. This network appears to account for the majority of gene expression differences occurring outside of the QTL interval. Further substitution mapping is currently underway to test the validity of each of these differentially expressed positional candidate genes. These results demonstrate the importance of using a saturated oligonucleotide array for identifying and prioritizing differentially expressed positional candidate genes of a BP QTL.

Cross-talk of expression quantitative trait loci within 2 interacting blood pressure quantitative trait loci.

Genetic dissection of the S rat genome has provided strong evidence for the presence of 2 interacting blood pressure quantitative trait loci (QTLs), termed QTL1 and QTL2, on rat chromosome 5. However, the identities of the underlying interacting genetic factors remain unknown. Further experiments targeted to identify the interacting genetic factors by the substitution mapping approach alone are difficult because of the interdependency of natural recombinations to occur at the 2 QTLs. We hypothesized that the interacting genetic factors underlying these 2 QTLs may interact at the level of gene transcription and thereby represent expression QTLs or eQTLs. To detect these interacting expression QTLs, a custom QTL chip containing the annotated genes within QTL1 and QTL2 was developed and used to conduct a transcriptional profiling study of S and 2 congenic strains that retain either 1 or both of the QTLs. The results uncovered an interaction between 2 transcription factor genes, Dmrta2 and Nfia. Furthermore, the "biological signature" elicited by these 2 transcription factors was differential between the congenic strain that retained Lewis alleles at both QTL1 and QTL2 compared with the congenic strain that retained Lewis alleles at QTL1 alone. A network of transcription factors potentially affecting blood pressure could be traced, lending support to our hypothesis.