Seeking genetic signature of radiosensitivity - a novel method for data analysis in case of small sample sizes

Abstract

BackgroundThe identification of polymorphisms and/or genes responsible for an organism's radiosensitivity increases the knowledge about the cell cycle and the mechanism of the phenomena themselves, possibly providing the researchers with a better understanding of the process of carcinogenesis.AimThe aim of the study was to develop a data analysis strategy capable of discovering the genetic background of radiosensitivity in the case of small sample size studies.ResultsAmong many indirect measures of radiosensitivity known, the level of radiation-induced chromosomal aberrations was used in the study. Mathematical modelling allowed the transformation of the yield-time curve of radiation-induced chromosomal aberrations into the exponential curve with limited number of parameters, while Gaussian mixture models applied to the distributions of these parameters provided the criteria for mouse strain classification. A detailed comparative analysis of genotypes between the obtained subpopulations of mice followed by functional validation provided a set of candidate polymorphisms that might be related to radiosensitivity. Among 1857 candidate relevant SNPs, that cluster in 28 genes, eight SNPs were detected nonsynonymous (nsSNP) on protein function. Two of them, rs48840878 (gene Msh3) and rs5144199 (gene Cc2d2a), were predicted as having increased probability of a deleterious effect. Additionally, rs48840878 is capable of disordering phosphorylation with 14 PKs. In silico analysis of candidate relevant SNP similarity score distribution among 60 CGD mouse strains allowed for the identification of SEA/GnJ and ZALENDE/EiJ mouse strains (95.26% and 86.53% genetic consistency respectively) as the most similar to radiosensitive subpopulationConclusionsA complete step-by-step strategy for seeking the genetic signature of radiosensitivity in the case of small sample size studies conducted on mouse models was proposed. It is shown that the strategy, which is a combination of mathematical modelling, statistical analysis and data mining methodology, allows for the discovery of candidate polymorphisms which might be responsible for radiosensitivity phenomena.

A review of radiation genomics: integrating patient radiation response with genomics for personalised and targeted radiation therapy

“…In addition, phosphorylation of MSH3 rs 48840878 was predicted to affect 14 different protein kinases, potentially altering the regulation of various downstream activities associated with radiotoxicity. 45 Mangoni et al, 44 also found an association between SNP rs26279 (G > A) and the development of acute skin reactions in breast cancer patients.…”

“…Using a G2 chromosomal radiosensitivity assay, mice inbred with SNP rs 48840878 (G>A) was predicted to have an increased probability of deleterious radiosensitivity effects, although no specific endpoint was determined. In addition, phosphorylation of MSH3 rs 48840878 was predicted to affect 14 different protein kinases, potentially altering the regulation of various downstream activities associated with radiotoxicity 45 . Mangoni et al, 44 also found an association between SNP rs26279 (G>A) and the development of acute skin reactions in breast cancer patients.…”

“…3 However, REQUITE which is an ongoing multicenter prospective observational study attempts to validate potential biomarkers in order to develop clinically useful statistical models to predict patient's risk of developing long-term side effects following radiation therapy alongside clinical and treatment planning parameters. 33,140 Several biomarkers (BRCA1/2, ATM, RAD51, nibrin (NBN), TP53, Cdh1, XRCC1/2/3, APE1, CHEK2, MLH1, MSH2/3, PTEN, EpCAM, FLCN) 1,[36][37][38][40][41][42][43][44][45][46][56][57][58][59]80,[141][142][143][144][145][146][147][148][149][150][151] have been identified for use in the field of radiation genomics and some are briefly presented here.…”

Advances in bioinformatics and biomedical engineering - special issue of IWBBIO 2013

“…The paper by J. Zyla et al [ 2 ] has as the main goal the development of a data analysis strategy capable of detecting the genetic background of radiosensitivity in case of studies in which the number of samples is small. In particular, the authors used a sample of 14 inbred mouse strains, being the number of biological replicates between 1 to 3.…”