A large-scale candidate gene approach identifies SNPs in SOD2 and IL13 as predictive markers of response to preoperative chemoradiation in rectal cancer

Abstract

Neoadjuvant radiochemotherapy followed by total mesorectal excision is now the standard treatment for locally advanced rectal cancer. However, tumor response to chemoradiation varies widely among individuals and cannot be determined before the final pathologic evaluation. The aim of this study was to identify germline genetic markers that could predict sensitivity or resistance to preoperative radiochemotherapy (RT-CT) in rectal cancer. We evaluated the predictive value of 128 single-nucleotide polymorphisms (SNPs) in 71 patients preoperatively treated by RT-CT. The selected SNPs were distributed over 76 genes that are involved in various cellular processes such as DNA repair, apoptosis, proliferation or immune response. The SNPs superoxide dismutase 2 (SOD2) rs4880 (P=0.005) and interleukin-13 (IL13) rs1800925 (P=0.0008) were significantly associated with tumor response to chemoradiation. These results reinforce the idea of using germline polymorphisms for personalized treatment.

Introduction

In locally advanced rectal cancer, surgery is now supported by the adjunction of preoperative radiochemotherapy (RT-CT), which has been shown to improve local control.1 In addition, preoperative therapy can induce tumor regression, thus allowing subsequent radical surgery in a previously non-resectable tumor,2 and increasing the probability of sphincter-saving procedures in low rectal tumors.3, 4 Moreover, pathological complete response, defined as the absence of any residual viable tumor cells in the surgical specimen, can be achieved in 7–31% of patients,5 and it has been shown that this subgroup of patients have a favorable outcome.6, 7 However, currently there are no means of predicting the individual response to preoperative RT-CT. Identification of predictive markers of cancer response to preoperative RT-CT is of clinical importance, as patients with a priori resistant tumor could be selected for alternative or more intensive treatment regimens aimed at improving their response.

Many translational studies have sought to identify predictive factors of tumor response after neoadjuvant RT-CT in rectal cancer.8, 9 These works focused mostly on the immunohistochemical analysis of a small panel of proteins involved in apoptosis (p53 and B cell CLL/lymphoma 2 (BCL2)), cell cycle arrest (p21), proliferation (Ki67), neo-angiogenesis (vascular endothelial growth factor (VEGF)), DNA repair (MLH1 (mutL homolog 1) and MSH2 (mutS homolog 2)) or 5-fluorouracil metabolism (thymidylate synthetase (TYMS)). So far, conflicting results have been obtained, possibly because of the heterogeneity of tumor expression of these markers,10 or the lack of standardization of immunohistochemistry.11 These issues could be addressed by alternative approaches, such as blood analysis of germline genetic variations.

Single-nucleotide polymorphisms (SNPs) are the most common type of germline genetic variation, and, with the completion of the HapMap project,12 millions of SNPs are now annotated. SNPs within genes may alter the function or the expression of the corresponding proteins and several pharmacogenetic studies have shown that SNPs may contribute to interindividual variability in the response to various anticancer treatments.13, 14

The aim of this study was to identify SNPs that are predictive of the response to neoadjuvant RT-CT in rectal cancer. For this purpose, we prospectively investigated 128 SNPs in 71 patients with stage II–IV rectal cancer treated by preoperative RT-CT. The selected SNPs were distributed over 76 genes involved in different cellular pathways such as DNA repair, apoptosis, proliferation or immune response.

Materials and methods

Patients

Between November 2005 and May 2008, 71 Caucasian patients with histologically confirmed rectal adenocarcinoma were recruited in a prospective study conducted at the Comprehensive Cancer Center of Montpellier. All patients underwent preoperative radiotherapy (total dose of 45 or 50 Gy, delivered in 25 fractions of 1.8–2 Gy over 5 weeks) with concurrent chemotherapy (1600 mg m–2 capecitabine daily or capecitabine +50 mg m–2 oxaliplatin weekly). Surgery was scheduled to be performed 6–8 weeks after completion of preoperative RT-CT. For all patients, informed consent for the use of clinical records and blood samples for research purposes was obtained. The protocol was approved by the ethical committee of Montpellier (France). Patient characteristics are summarized in Table 1.

Table 1 Patients’ characteristics

Response evaluation

Tumor response to preoperative RT-CT was evaluated by histopathological examination of the surgically resected rectal carcinoma according to the tumor regression grade (TRG) system proposed by Dworak et al.15 The entire primary tumor was paraffin embedded and tumor regression was semiquantitatively determined by assessing the amount of residual carcinoma cells versus the amount of fibrosis or mucin pools. TRG ranges from 0 when no regression is observed to 4 when no viable tumor cells are detected (complete regression). TRG 1 corresponds to presence of a dominant tumor mass and obvious fibrosis or mucin, TRG 2 to a tumor mass characterized by dominantly fibrotic or mucinous changes with few tumor cells or groups and TRG 3 to specimens with very few remaining tumor cells in fibrotic or mucinous tissue. Patients with TRG 0, 1 or 2 were defined as nonresponders, whereas those with TRG 3 or 4 were classified as responders.

Selection of genes and polymorphisms

A total of 128 SNPs distributed over 76 genes were examined in this study (Table 2). SNPs were chosen from the SNP500 Cancer database.16 The main selection criteria for SNPs inclusion were as follows: (1) substantial evidence in the literature that the candidate SNP is related to the risk of solid cancer, or that it may alter the expression/function of a gene involved in the cell response to ionizing radiation; and (2) >5% minor allele frequency according to the dbSNP database of the NCBI (National Center for Biotechnology Information).

Table 2 Candidate genes and SNPs

DNA extraction and SNP genotyping

Whole blood was collected at the time of patients’ enrollment and genomic DNA was extracted from peripheral lymphocytes using the QIAamp DNA blood maxi kit (Qiagen, Courtaboeuf, France). The majority of the SNPs were genotyped by IntegraGen (Evry, France) with the use of the SNPlex Genotyping System (Applied Biosystems, Courtaboeuf, France) or the TaqMan allelic discrimination assay (Applied Biosystems). Six SNPs (rs603965, rs11543848, rs712829, rs712830, rs17335738 and rs17290169) were characterized in our laboratory by PCR and restriction-fragment length polymorphism or direct sequencing (protocol available on demand).

Statistical analyses

All statistical analyses were performed using STATA 10.0. (StataCorp, College Station, TX, USA) and R, version 2.8.1 (R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0, http://www.R-project.org). Linkage disequilibrium was evaluated using Lewontin's coefficient D′ and the correlation coefficient r2. The association between individual SNPs and response to preoperative RT-CT was tested by logistic regression using the R package SNPassoc.17 For each SNP, genotypes were analyzed not only as a three-group categorical variable (co-dominant model) but also according to the dominant (combining the heterozygous and the rare homozygous variants) and recessive (combining the heterozygous and the homozygous including the most frequent allele variants) models. The best model was chosen based on the Akaike Information Criteria. For the clinicopathological features, univariate analyses to compare responders and nonresponders were performed using Pearson's χ2 or Fisher's exact test for categorical variables and the two-sample Wilcoxon test for all continuous variables. Differences were considered statistically significant when P<0.05. To account for multiple testing, we controlled the false discovery rate according to the Storey and Tibshirani method.18 A measure of statistical significance (the q-value) was estimated for each tested feature using the R q-value package (http://genomics.princeton.edu/storeylab/qvalue/). The calculated q-values take into account the fact that multiple SNPs were simultaneously tested, based on the observed distribution of their P-values. In this study, SNPs with q<0.10 were considered as true discoveries. Factors that were significant in univariate analyses were included in a multivariate logistic regression analysis to identify independent predictors of the response to RT-CT.

Results

Pathological responses to RT-CT

Out of 71 patients, 32 (45.1%) showed a good response to preoperative RT-CT. In 9 patients (12.7%), complete regression (TRG 4) was observed and in 23 patients (32.4%) only microscopic residual tumors (TRG 3) were detected. In all, 39 (54.9%) patients were considered nonresponders, including 2 (2.8%) patients with TRG 0, 16 (22.5%) patients with TRG 1 and 21 (29.6%) patients with TRG 2.

Genotypes and linkage disequilibrium

Three SNPs (rs5031039, rs11549467 and rs36017265) were monomorphic and were removed from the analysis. Moreover, strong linkage disequilibrium could be shown for the following SNPs: TP73 rs2273953 and rs1801173 (D′=1, r2=1), ERCC4 rs744154 and rs1799800 (D′=1, r2=1), ICAM5 rs1056538 and rs2228615 (D′=1, r2=1), NFKB1 rs3774937 and rs3774936 (D′=1, r2=1), XRCC5 rs1051677 and rs6941 (D′=0.99, r2=0.98) as well as TP53BP1 rs2602141 and rs560191 (D′=0.99, r2=0.96). In addition, BRCA1 rs16941 was in strong linkage disequilibrium with rs16942 (D′=1, r2=1), rs1799966 (D′=1, r2=1) and rs799917 (D′=0.99, r2=0.90). Therefore, among the linked SNPs, association analyses considered only the SNP with the highest number of successful genotypes.

Univariate analysis

No significant correlation was found between the pathologic response and pretherapeutic clinicopathological parameters, total irradiation dose or chemotherapy regimen (Table 1). As expected, the pathologic T stage was significantly correlated with the response: tumors that responded well to preoperative RT-CT according to the TRG evaluation were associated with a lower T stage.

Concerning the SNPs, CASP10 rs13010627, ERCC4 rs744154, IL13 rs1800925, MLH1 rs1800734, MBD4 rs10342, NOS2a rs2297518, SOD2 rs4880 and XPA rs3176658 were significantly associated with the response to neoadjuvant treatment (Table 3). After adjustment for multiple testing, only the associations with the superoxide dismutase 2 (SOD2) rs4880 (P=0.004; q=0.089) and interleukin-13 (IL13) rs1800925 (P<0.001; q=0.029) polymorphisms were maintained. For these two SNPs, the calculated allele frequency distributions were similar to the mean prevalence described in the NCBI SNP database for Caucasians.

Table 3 Univariate analysis of SNPs associated with response to RT-CT

Multivariate analysis

As no pretreatment clinicopathological parameter was found to be associated with the response to RT-CT, the multivariate analysis included only the two SNPs that were significant in univariate analysis (SOD2 rs4880 and IL13 rs1800925, q<0.1). The results indicated that these two SNPs remained independent predictive factors of the pathologic response (Table 4). Patients with the SOD2 rs4880 C/T or T/T genotypes had a significantly lower chance of response to preoperative RT-CT than patients with the C/C genotype (odds ratio 0.19; 95% confidence interval 0.06–0.64; P=0.005). Similarly, patients with the IL13 rs1800925 C/T and T/T genotypes had a significantly poorer response than those with the IL13 rs1800925 C/C genotype (odds ratio 0.14; 95% confidence interval 0.04–0.49; P=0.0008).

Table 4 Multivariate analysis of SNPs associated with response to RT-CT

Discussion

During the past decade, much effort has been put toward the identification of markers that can predict the response to preoperative radio- or radiochemotherapy in patients with rectal cancer. However, most published studies have been limited to a single or a small number of markers or genes of interest. Because of advances in high-throughput genotyping, it is now possible to conduct large-scale studies that analyze hundreds or thousands of SNP simultaneously. In this prospective study, which included 71 patients with rectal cancer homogeneously treated by preoperative RT-CT, we examined 128 SNPs of 76 genes using a large-scale candidate-gene approach. In univariate analysis, eight SNPs (CASP10 rs13010627, ERCC4 rs744154, IL13 rs1800925, MLH1 rs1800734, MBD4 rs10342, NOS2a rs2297518, SOD2 rs4880 and XPA rs3176658) were significantly associated with response to RT-CT. However, when a large number of SNPs are tested simultaneously, as it is the case here, the chance of false-positive findings is considerably increased. Several methods have been proposed to address this problem. A traditional approach is the Bonferroni correction, in which the original P-values are multiplied by the number of hypotheses tested.19 If the adjusted P-value is still below 0.05, the association is considered significant. This approach is a stringent multiple testing correction that greatly reduces the number of false positives. The tradeoff is that it also reduces the number of true discoveries. To overcome this drawback, we decided to use the Storey and Tibshirani method18 that takes into account the characteristics of the P-value distribution to estimate the adjusted P-values (or q-values). Whereas the P-value is a measure of significance in terms of the false-positive rate, the q-value is a measure of significance relative to the false discovery rate. Although a P-value of 0.05 means that 5% of all tests will be false positives, a q-value of 0.05 implies that 5% of the tests that are called significant are false discoveries. This is clearly a far smaller quantity. This method is less conservative than the Bonferroni correction, and provides a good balance between discovery of statistically significant associations and limitation of false-positive occurrences. Out of the eight SNPs initially selected by the univariate analysis, only two, SOD2 rs4880 and IL13 rs1800925, met the significance criterion of a q<0.10. Genome-wide association studies conventionally apply a threshold of q<0.05, but a higher threshold may be set for candidate gene studies, which use previous information to select investigated SNP.20

SOD2 has a central role in the detoxification of reactive oxygen species, thereby protecting against oxidative stress, which is a hallmark of both radiotherapy and chemotherapy. SOD2 overexpression has been shown to inhibit reactive oxygen species-induced activation of NF-κB21 and as a consequence to induce radiosensitivity.22, 23 The SOD2 rs4880 SNP leads to incorporation of either one alanine (Ala) or one valine (Val) in the mitochondrial targeting sequence of the SOD2 precursor protein.24 The Ala-SOD2 precursor, which is encoded by the rs4880 C variant, generates a SOD2 protein that is 40% more active than the one produced by the Val-SOD2 precursor, because of a more efficient mitochondrial import.25 In our study, the SOD2 rs4880 C/C genotype was associated with a higher probability of response to RT-CT than the C/T or T/T genotypes. By conferring higher activity, the C/C genotype is more likely to inhibit reactive oxygen species-induced activation of NF-κB, thus favoring sensitivity to ionizing radiations.

The IL13 gene encodes a cytokine involved in tumor immunosurveillance downregulation, thus allowing tumor growth in the host.26, 27 The SNP IL13 rs1800925 is a functional polymorphism located in the promoter of the gene and the T allele is associated with increased IL13 transcription.28 In our study, patients harboring the T allele had poorer response to RT-CT. By killing tumor cells, RT-CT promotes the release of tumor-associated antigens, which are then taken up by dendritic cells and presented to cytolytic T cells. This cross-presentation is believed to enhance RT-CT effects.29, 30 Therefore, the T allele, by inducing higher IL13 transcription, could inhibit chemoradiation-induced tumor immunosurveillance and thereby decrease the RT-CT effects.

The biological basis of tumor response to RT-CT could be related to the tumor, the tumor microenvironment, and also to the host. In previous studies, we showed that germline genetic polymorphisms were potential prognostic factors in colorectal cancer.31, 32 Moreover, the potential value of SNPs as predictive markers has been highlighted in various cancer types, including pancreas,33 esophagus34 and lung tumors.35 In this study, we showed that the SOD2 rs4880 and IL13 rs1800925 polymorphims are independent predictive markers of the response to preoperative RT-CT in rectal cancer. Such markers may help discriminating rectal cancer patients who might benefit from neoadjuvant RT-CT from those who should be candidates for alternative treatments such as targeted therapy (for example, anti-epidermal growth factor receptor or anti-vascular endothelial growth factor). However, further clinical trials and larger samples of patients with rectal cancer are needed to confirm the relevance of our results.

In conclusion, our work identifies SOD2 rs4880 and IL13 rs1800925 as potential new markers that may allow personalization of rectal cancer therapy with a single blood sample test.

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Acknowledgements

This work was supported by Merck Santé and the ANRT (Association Nationale de la Recherche Technique).

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Correspondence to E Lopez-Crapez.

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The authors declare no conflict of interest.

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Ho-Pun-Cheung, A., Assenat, E., Bascoul-Mollevi, C. et al. A large-scale candidate gene approach identifies SNPs in SOD2 and IL13 as predictive markers of response to preoperative chemoradiation in rectal cancer. Pharmacogenomics J 11, 437–443 (2011). https://doi.org/10.1038/tpj.2010.62

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Keywords

  • rectal cancer
  • SNP
  • predictive marker
  • response to treatment
  • radiochemotherapy

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