Aberrant CDKN1A transcriptional response associates with abnormal sensitivity to radiation treatment

Normal tissue reactions to radiation therapy vary in severity among patients and cannot be accurately predicted, limiting treatment doses. The existence of heritable radiosensitivity syndromes suggests that normal tissue reaction severity is determined, at least in part, by genetic factors and these may be revealed by differences in gene expression. To test this hypothesis, peripheral blood lymphocyte cultures from 22 breast cancer patients with either minimal (11) or very severe acute skin reactions (11) have been used to analyse gene expression. Basal and post-irradiation expression of four radiation-responsive genes (CDKN1A, GADD45A, CCNB1, and BBC3) was determined by quantitative real-time PCR in T-cell cultures established from the two patient groups before radiotherapy. Relative expression levels of BBC3, CCNB1, and GADD45A 2 h following 2 Gy X-rays did not discriminate between groups. However, post-irradiation expression response was significantly reduced for CDKN1A (P<0.002) in severe reactors compared to normal. Prediction of reaction severity of ∼91% of individuals sampled was achieved using this end point. Analysis of TP53 Arg72Pro and CDKN1A Ser31Arg single nucleotide polymorphisms did not show any significant association with reaction sensitivity. Although these results require confirmation and extension, this study demonstrates the possibility of predicting the severity of acute skin radiation toxicity in simple tests

Linkage disequilibrium pattern of the ATM gene in breast cancer patients and controls; association of SNPs and haplotypes to radio-sensitivity and post-lumpectomy local recurrence

<p>Abstract</p> <p>Background</p> <p>The ATM protein is activated as a result of ionizing radiation, and genetic variants of the <it>ATM </it>gene may therefore affect the level of radiation-induced damage. Individuals heterozygous for <it>ATM </it>mutations have been reported to have an increased risk of malignancy, especially breast cancer.</p> <p>Materials and methods</p> <p>Norwegian breast cancer patients (272) treated with radiation (252 of which were evaluated for radiation-induced adverse side effects), 95 Norwegian women with no known history of cancer and 95 American breast cancer patients treated with radiation (44 of which developed ipsilateral breast tumour recurrence, IBTR) were screened for sequence variations in all exons of the <it>ATM </it>gene as well as known intronic variants by denaturating high performance liquid chromatography (dHPLC) followed by sequencing to determine the nature of the variant.</p> <p>Results and Conclusion</p> <p>A total of 56 variants were identified in the three materials combined. A borderline significant association with breast cancer risk was found for the 1229 T>C (Val>Ala) substitution in exon 11 (P-value 0.055) between the Norwegian controls and breast cancer patients as well as a borderline significant difference in haplotype distribution (P-value 0.06). Adverse side effects, such as: development of costal fractures and telangiectasias, subcutaneous and lung fibrosis, pleural thickening and atrophy were evaluated in the Norwegian patients. Significant associations were found for several of the identified variants such as rs1800058 (Leu > Phe) where a decrease in minor allele frequency was found with increasing level of adverse side effects for the clinical end-points pleural thickening and lung fibrosis, thus giving a protective effect. Overall our results indicate a role for variation in the <it>ATM </it>gene both for risk of developing breast cancer, and in radiation induced adverse side effects. No association could be found between risk of developing ipsilateral breast tumour recurrence and any of the sequence variants found in the American patient material.</p

SNPs in DNA repair or oxidative stress genes and late subcutaneous fibrosis in patients following single shot partial breast irradiation

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to evaluate the potential association between single nucleotide polymorphisms related response to radiotherapy injury, such as genes related to DNA repair or enzymes involved in anti-oxidative activities. The paper aims to identify marker genes able to predict an increased risk of late toxicity studying our group of patients who underwent a Single Shot 3D-CRT PBI (SSPBI) after BCS (breast conserving surgery).</p> <p>Methods</p> <p>A total of 57 breast cancer patients who underwent SSPBI were genotyped for SNPs (single nucleotide polymorphisms) in XRCC1, XRCC3, GST and RAD51 by Pyrosequencing technology. Univariate analysis (ORs and 95% CI) was performed to correlate SNPs with the risk of developing ≥ G2 fibrosis or fat necrosis.</p> <p>Results</p> <p>A higher significant risk of developing ≥ G2 fibrosis or fat necrosis in patients with: polymorphic variant <it>GSTP1 </it>(Ile105Val) (OR = 2.9; 95%CI, 0.88-10.14, <it>p </it>= 0.047).</p> <p>Conclusions</p> <p>The presence of some SNPs involved in DNA repair or response to oxidative stress seem to be able to predict late toxicity.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01316328">NCT01316328</a></p

A Bioinformatics Filtering Strategy for Identifying Radiation Response Biomarker Candidates

The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response