Association between TP53 codon 72 single-nucleotide polymorphism and radiation sensitivity of human fibroblasts

Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation

Introduction

The search for biomarkers to predict radiosensitivity is important not only to individualize radiotherapy of cancer patients but also to forecast radiation exposure risks. The aim of this study was to devise a machine-learning method to stratify radiosensitivity and to investigate its association with genome-wide copy number variations (CNVs) as markers of sensitivity to ionizing radiation.

Methods

We used the Affymetrix CytoScan HD microarrays to survey common CNVs in 129 fibroblast cell strains. Radiosensitivity was measured by the surviving fraction at 2 Gy (SF2). We applied a dynamic programming (DP) algorithm to create a piecewise (segmented) multivariate linear regression model predicting SF2 and to identify SF2 segment-related distinctive CNVs.

Results

SF2 ranged between 0.1384 and 0.4860 (mean=0.3273 The DP algorithm provided optimal segmentation by defining batches of radio-sensitive (RS), normally-sensitive (NS), and radio-resistant (RR) responders. The weighted mean relative errors (MRE) decreased with increasing the segments' number. The borders of the utmost segments have stabilized after partitioning SF2 into 5 subranges.

Discussion

The 5-segment model associated C-3SFBP marker with the most-RS and C-7IUVU marker with the most-RR cell strains. Both markers were mapped to gene regions (MCC and SLC1A6, respectively). In addition, C-3SFBP marker is also located in enhancer and multiple binding motifs. Moreover, for most CNVs significantly correlated with SF2, the radiosensitivity increased with the copy-number decrease.In conclusion, the DP-based piecewise multivariate linear regression method helps narrow the set of CNV markers from the whole radiosensitivity range to the smaller intervals of interest. Notably, SF2 partitioning not only improves the SF2 estimation but also provides distinctive markers. Ultimately, segment-related markers can be used, potentially with tissues' specific factors or other clinical data, to identify radiotherapy patients who are most RS and require reduced doses to avoid complications and the most RR eligible for dose escalation to improve outcomes.

Gender bias in individual radiosensitivity and the association with genetic polymorphic variations

PURPOSE

To assess the extent of variation in radiosensitivity between individuals, gender-related dissimilarity and impact on the association with single nucleotide polymorphisms (SNPs).

MATERIALS AND METHODS

Survival curves of 152 fibroblast cell strains derived from both gender were generated. Individual radiosensitivity was characterized by the surviving fraction at 2Gy (SF2). SNPs in 10 radiation responsive genes were genotyped by direct sequencing.

RESULTS

The wide variation in SF2 (0.12-0.50; mean=0.33) was significantly associated with 3 SNPs: TP53 G72C (P=0.007), XRCC1 G399A (P=0.002) and ATM G1853A (P=0.01). Females and males differed significantly in radiosensitivity (P=0.004) that impacted genetic association where only XRCC1 remained significant in both gender (P<0.05). Meanwhile, discordant association was observed for TP53 that was significant in females (P=0.012) and ATM that was significant in males (P=0.0006). When gender-specific SF2-mean (0.31 and 0.35 for females and males; respectively) was considered, further discordance was observed where XRCC1 turned out not to be associated with radiosensitivity in males (P>0.05).

CONCLUSIONS

Although the variation in individual radiosensitivity was associated with certain SNPs, gender bias for both endpoints was evident. Therefore, assessing the risk of radiation exposure in females and males should be considered separately in order to achieve the ultimate goal of personalized radiation medicine.

Among 45 variants in 11 genes, HDM2 promoter polymorphisms emerge as new candidate biomarker associated with radiation toxicity

Due to individual variations in radiosensitivity, biomarkers are needed to tailor radiation treatment to cancer patients. Since single nucleotide polymorphisms (SNPs) are frequent in human, we hypothesized that SNPs in genes that mitigate the radiation response are associated with radiotoxicity, in particular late complications to radiotherapy and could be used as genetic biomarkers for radiation sensitivity. A total of 155 patients with nasopharyngeal cancer were included in the study. Normal tissue fibrosis was scored using RTOG/EORTC grading system. Eleven candidate genes (ATM, XRCC1, XRCC3, XRCC4, XRCC5, PRKDC, LIG4, TP53, HDM2, CDKN1A, TGFB1) were selected for their presumed influence on radiosensitivity. Forty-five SNPs (12 primary and 33 neighboring) were genotyped by direct sequencing of genomic DNA. Patients with severe fibrosis (cases, G3–4, n = 48) were compared to controls (G0–2, n = 107). Results showed statistically significant (P < 0.05) association with radiation complications for six SNPs (ATM G/A rs1801516, HDM2 promoter T/G rs2279744 and T/A rs1196333, XRCC1 G/A rs25487, XRCC5 T/C rs1051677 and TGFB1 C/T rs1800469). We conclude that these six SNPs are candidate genetic biomarkers for radiosensitivity in our patients that have cumulative effects as patients with severe fibrosis harbored significantly higher number of risk alleles than the controls (P < 0.001). Larger cohort, independent replication of these findings and genome-wide association studies are required to confirm these results in order for SNPs to be used as biomarkers to individualize radiotherapy on genetic basis.

Investigation of genetic polymorphisms related to the outcome of radiotherapy for prostate cancer patients

The purpose of this study was to evaluate the association between ATM, TP53 and MDM2 polymorphisms in prostate cancer patients and morbidity after radiotherapy. The presence of ATM (rs1801516), TP53 (rs1042522, rs1800371, rs17878362, rs17883323, and rs35117667), and MDM2 (rs2279744) polymorphisms was assessed by direct sequencing of PCR fragments from 48 patients with histologically proven prostate adenocarcinoma and treated with external beam radiation. The side effects were classified according to the Radiation Therapy Oncology Group (RTOG) score. The results showed no association between clinical characteristics and the development of radiation toxicities (P > 0.05). The C>T transition in the position 16273 (intron 3) of TP53 (rs35117667) was significantly associated with the risk of acute skin toxicity (OR: 0.0072, 95% CI 0.0002–0.227, P = 0.003). The intronic TP53 polymorphism at position 16250 (rs17883323) was associated with chronic urinary toxicity (OR: 0.071, 95%CI 0.006–0.784, P = 0.032). No significant associations were found for the remaining polymorphisms (P > 0.05). The results show that clinical characteristics were not determinant on the developing of radiation sensitivity in prostate cancer patients, and intronic TP53 polymorphisms would be associated with increased acute and chronic radiation toxicities. These observations corroborate the importance of investigating the genetic profile to predict adverse side effects in patients undergoing radiotherapy.

Molecular characterization of TP53 gene in human populations exposed to low-dose ionizing radiation

Ionizing radiation, such as that emitted by uranium, may cause mutations and consequently lead to neoplasia in human cells. The TP53 gene acts to maintain genomic integrity and constitutes an important biomarker of susceptibility. The present study investigated the main alterations observed in exons 4, 5, 6, 7, and 8 of the TP53 gene and adjacent introns in Amazonian populations exposed to radioactivity. Samples were collected from 163 individuals. Occurrence of the following alterations was observed: (i) a missense exchange in exon 4 (Arg72Pro); (ii) 2 synonymous exchanges, 1 in exon 5 (His179His), and another in exon 6 (Arg213Arg); (iii) 4 intronic exchanges, 3 in intron 7 (C → T at position 13.436; C → T at position 13.491; T → G at position 13.511) and 1 in intron 8 (T → G at position 13.958). Alteration of codon 72 was found to be an important risk factor for cancer development (P = 0.024; OR = 6.48; CI: 1.29–32.64) when adjusted for age and smoking. Thus, TP53 gene may be an important biomarker for carcinogenesis susceptibility in human populations exposed to ionizing radiation.

Biological complexities in radiation carcinogenesis and cancer radiotherapy: impact of new biological paradigms

Although radiation carcinogenesis has been shown both experimentally and epidemiologically, the use of ionizing radiation is also one of the major modalities in cancer treatment. Various known cellular and molecular events are involved in carcinogenesis. Apart from the known phenomena, there could be implications for carcinogenesis and cancer prevention due to other biological processes such as the bystander effect, the abscopal effect, intrinsic radiosensitivity and radioadaptation. Bystander effects have consequences for mutation initiated cancer paradigms of radiation carcinogenesis, which provide the mechanistic justification for low-dose risk estimates. The abscopal effect is potentially important for tumor control and is mediated through cytokines and/or the immune system (mainly cell-mediated immunity). It results from loss of growth and stimulatory and/or immunosuppressive factors from the tumor. Intrinsic radiosensitivity is a feature of some cancer prone chromosomal breakage syndromes such as ataxia telangectiasia. Radiosensitivity is manifested as higher chromosomal aberrations and DNA repair impairment is now known as a good biomarker for breast cancer screening and prediction of prognosis. However, it is not yet known whether this effect is good or bad for those receiving radiation or radiomimetic agents for treatment. Radiation hormesis is another major concern for carcinogenesis. This process which protects cells from higher doses of radiation or radio mimic chemicals, may lead to the escape of cells from mitotic death or apoptosis and put cells with a lower amount of damage into the process of cancer induction. Therefore, any of these biological phenomena could have impact on another process giving rise to genome instability of cells which are not in the field of radiation but still receiving a lower amount of radiation. For prevention of radiation induced carcinogenesis or risk assessment as well as for successful radiation therapy, all these phenomena should be taken into account.

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 approximately 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.