DNA-damage response gene polymorphisms and therapeutic outcomes in ovarian cancer

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Epithelial ovarian cancer has a poor prognosis owing to late diagnosis and frequent relapse after first-line therapy. Analysis of individual genetic variability could aid in the identification of markers, which could help in stratifying patients with the aim of optimizing individual therapy. In this study we assessed polymorphisms in three genes important in drugs' response in 97 early and 235 late-stage ovarian cancer patients. The Asp1104His polymorphism in xpg, a gene important for removal of platinum adducts, was associated with progression-free survival in early- and late-stage ovarian cancer. Our data indicate that a simple diagnostic analysis such as xpg genotyping can help in predicting response, and extension to other possibly relevant genotypes could be useful in selecting patients with epithelial ovarian cancer for optimal therapy and hence increase the chance of response.


Epithelial ovarian cancer (EOC) has a poor prognosis as the majority of cases are diagnosed in advanced stages. This limits the possibility of a cure and the overall survival (OS) rate at 5 years is less than 30%,1, 2, 3, 4 even though the majority of patients respond to a first-line platinum-containing drug. After an initial response, a high percentage of patients relapse, and second- and third-line therapies have only a modest impact on survival.5 As DNA is the target of platinum-containing drug, several studies analyzed DNA-repair gene expression patterns, either at the RNA or protein level. Platinum-induced lesions are mostly repaired by nucleotide excision repair (NER), but also by homologous recombination, mismatch repair and Fanconi anemia.6, 7 Evidence indicates that NER is a critical determinant of platinum-containing drug activity in vitro, and cancer cells that have a proficient NER mechanism have a greater likelihood of repairing DNA lesions and surviving when treated. In the clinical setting, however, data are not as clear as in experimental models.8 An increasing amount of evidence indicates that genetic variants of certain genes, even if not associated with changes in the expression levels of the proteins they are encoding for, could alter the functionality of the protein and possibly have an important role in defining patients with a high/low probability of response. Functional variants in genes involved in the DNA-repair pathway could be important determinants of platinum response in women with EOC.9, 10 Excision repair cross-complementation group-1 (ERCC1) has a significant role in platinum–DNA adduct repair.11, 12, 13, 14 Several ercc1 polymorphisms have been identified. The nucleotide 19007 C/T polymorphism (rs11615) affects mRNA levels,15 and shows a significant association with OS16, 17 and tumor response18 in colorectal cancer. This single-nucleotide polymorphism (SNP) was not associated with OS in many tumors such as melanoma,19 lung20 and ovarian21 cancer, but was described as an independent predictor of reduced risk of platinum resistance.21 A second ercc1 SNP, C8092A (rs3212986), located in the 3′-untranslated region, enhances mRNA stability.22 This SNP was associated with a favorable outcome in head and neck cancer23 and better OS in non-small-cell lung cancer.20

Xeroderma pigmentosum Group-G (XPG), a component of the NER pathway, is a structure-specific endonuclease catalyzing the 3′ incision at DNA-damage sites.24, 25 The non-synonymous SNP in its coding region causes the amino-acid change Asp1104His (rs17655). The SNP that is required for the interaction between XPG is located in the XPG C-terminus and other components of the NER machinery.26 The Asp1104His amino-acid change could influence the protein–protein interaction and predispose individuals to cancer. The SNP 46 His/His of xpg (rs1047768) is induced by a C–T nucleotide substitution. Although the real function of this variant is still unknown, different studies suggest a role in cancer development and drug response. XPG has been associated with a good response to platinum-based chemotherapy in advanced non-small-cell lung cancer.27

MDM2 is an oncoprotein that binds to and inactivates p53,28 a key protein involved in DNA-damage response. MDM2 can inhibit the transcriptional activity of p53 through direct binding to the N-terminal transactivation domain29, 30 and also acts as an E3 ligase, which leads to both export of p53 to the cytoplasm and its proteasomal degradation.31 MDM2 overexpression is associated with cancer progression and lack of response to therapy in human cancers.32, 33

A T–G polymorphism in the MDM2 promoter region, at nucleotide 309 (rs2279744), increases the affinity for binding to stimulatory protein-1 and results in higher levels of MDM2 expression.34 In humans, MDM2 SNP309 was associated with increased tumor formation in both hereditary and sporadic cancers.35

The use of genetic tools to predict response to platinum-based chemotherapy and survival in EOC could improve patient outcome. In this context, the aim of this study was to evaluate associations between the above-mentioned genetic variants in ercc1, xpg and MDM2 genes, and clinical outcomes.

Materials and methods

Patients and sample collection

Biopsies and blood specimens were collected at the Clinic of Obstetrics and Gynecology, San Gerardo Hospital (Monza, Italy). Fresh tumor tissues were minced and frozen with blood samples at −80 °C. The collection and use of the samples was approved by the local scientific ethics committee, and patients gave written informed consent.


DNA from blood was extracted using the Maxwell 16 DNA Purification kit (Promega, Milan, Italy). The rs2279744 polymorphism was genotyped using primers described by Ueda et al.36 DNA was amplified using Go Taq Hot Start Polymerase (Promega). The other four SNPs were genotyped using TaqMan SNP Genotyping assays (Applied Biosystems, Monza, Italy). PCR was performed in 384-well plates prepared with automatic liquid handling (epMotion 5075; Eppendorf, Milan, Italy).

RNA isolation and cDNA preparation

Tissues were homogenized by ultraturrax and total RNA was extracted using the SV Total RNA Isolation kit (Promega). RNA was then reverse-transcribed using the High Capacity cDNA Reverse Transcription kit (Applied Biosystems) and stored at −80 °C until use.

Primer design

The primer pairs for mdm2 (for.: 5′-TGGCGTGCCAAGCTTCTCTGTG, rev.: 5′-TGTACCTGAGTCCGATGATTCCTGCTG), ercc1 (for.: 5′-CCAACAGCATCATTGTGAGC-3′, rev.: 5′-TCTTGGCCCAGCACATAGTC), xpg (for.: 5′-TCTGGAAGCTGCTGGAGTG, rev.: 5′-GACAAAAGGAATGGCAGGAG) and β-actin (for.: 5′-TCACCCACA CTGTGCCCATCTACCA, rev.: 5′-CAGCGGAACCGCTCATTGCCAATGG) were chosen spanning splicing junctions using the PRIMER-3 software available online (http://frodo.wi.mit.edu/primer3/).

Real-time reverse transcription-PCR

Relative quantification of xpg, ercc1 and mdm2 gene expression was performed by real-time reverse transcription-PCR (ABI 7900; Applied Biosystems). PCR was performed by using the GoTaq qPCR Master Mix (Promega). Relative quantification of the expression levels of xpg, ercc1 and mdm2 was calculated by using the ΔΔCt method using β-actin as the internal control gene.

Statistical methods

A consecutive cohort of patients with ovarian cancer for which biological material was available was identified and retrospectively enrolled in this monocentric study. Baseline covariate distributions were summarized using descriptive statistics (median and range for continuous variables, and absolute and percentage frequencies for categorical variables); non-parametric tests (Wilcoxon–Mann–Whitney test for continuous covariates and Fisher's exact test for categorical covariates) were used to detect statistical association. Both progression-free survival (PFS, event: first progression of disease or death by any cause) and OS (event: death by any cause) were calculated considering as starting point the date of diagnosis of ovarian cancer; the time to event outcome parameters were estimated by the Kaplan–Meier product-limit method; log-rank test was used to compare survival in genotype groups; and a Cox proportional hazards model was adapted to estimate hazard ratios (HRs), to adjust for prognostic factors that influenced time to events and to evaluate the statistical power of our study in detecting a relevant difference. All statistical tests were two-sided and a P-value <0.05 was considered statistically significant. Statistical analysis was performed by using the SAS software (SAS Institute, Cary, NC, USA) (version 9.2). The statistical package Stata (StataCorp, College Station, TX, USA) (version 11.1) was used for Kaplan–Meier plots and dot plots.


Clinical and histopathological characteristics

From September 1979 to December 2004, 359 patients with ovarian cancer were identified. A total of 27 (7.5%) patients with borderline grading were not considered for the study; out of the remaining patients 97 (29.2%) had FIGO stage-I and II disease, and 235 (70.8%) patients had FIGO stage-III and IV disease.

For the stage-I/II population the median age at diagnosis was 52.1 years (range: 16.5–81.8 years); 82 (84.5%) patients had FIGO stage-I; and the predominant histology was serous subtype (number of patients: 35, 36.1%) and poorly differentiated grade (number of patients: 43, 44.3%). Sixty-four patients (66.0%) received an adjuvant cisplatin-based therapy. After a median follow-up of 10.0 years, 67 (69.1%) patients were alive, of whom 62 (92.5%) were progression-free. Age at diagnosis was the only baseline covariate statistically associated to PFS (HR=1.04, 95% confidence interval (CI): 1.00–1.07; P=0.024); age at diagnosis (HR=1.04, 95% CI: 1.01–1.08; P=0.019) and cancer grading (HR(G3vsG1)=4.39, 95% CI: 1.01–19.10; P=0.049) were baseline covariates statistically associated to OS.

For the stage-III/IV population the median age at diagnosis was 54.6 years (range: 13.2–79.1 years); 208 (88.5%) patients had FIGO stage-III disease; residual tumor size was more than 2 cm in 154 (65.5%) patients; and the predominant histology was serous subtype (number of patients: 182, 77.4%) and poorly differentiated grade (number of patients: 155, 66.0%). All patients received platinum-based therapy. After a median follow-up of 11.3 years, 57 (24.3%) patients were alive, of whom 44 (77.2%) were progression-free; median PFS was 2.2 years (95% CI: 1.8–2.6 years); and median OS was 3.6 years (95% CI: 3.2–4.2 years). Age at diagnosis (HR=1.02, 95% CI: 1.01–1.03; P=0.004), residual tumor size (HR=1.75, 95% CI: 1.27–2.40; P0.001), histotype (HR(Mucinous vs Serous)=2.78, 95% CI: 1.36–5.68; P= 0.005) and grading (HR(G2 vs G1)=2.48, 95% CI: 1.18–5.20; P=0.017/HR(G3vsG1)=2.38, 95% CI: 1.16–4.86; P=0.018) were the baseline covariates statistically correlated to PFS, globally or at least comparing single risk factor categories; the same correlations were detected considering OS as endpoint: age at diagnosis (HR=1.02, 95% CI: 1.01–1.03; P0.001), residual tumor size (HR=2.25, 95% CI: 1.61–3.17; P0.001), histotype (HR(MucinousvsSerous)=2.20, 95% CI: 1.03–4.70; P=0.043) and grading (HR(G2vsG1)=3.20, 95% CI: 1.37–7.46; P=0.007/HR(G3vsG1)=2.86, 95% CI: 1.26–6.49; P=0.012).

The clinical and histopathological characteristics of the population are summarized in Table 1 ; survival estimates are plotted in Figure 1; and risk estimates of baseline covariates are reported in Table 2 .

Table 1 Clinical and histopathological characteristics
Figure 1

Survival estimate plots for early (left) and late (right)-stage disease.

PowerPoint slide

Table 2 Prognostic evaluation of clinical and histopathological characteristics

Correlation between Mdm2 polymorphism, and clinical and histopathological characteristics of ovarian cancer patients

The genotype frequency found in our cohort of patients was equal to that predicted by the Entrez SNP database (http://www.ncbi.nlm.nih.gov/sites/entrez?db=snp) (Table 3 ). For both the early- and advanced-stage population, genotype was not statistically significantly correlated to any clinical and histopathological characteristic (Table 4 ); in particular genotype did not predict prognosis (survival estimates plotted in Supplementary Figures 1a–d, and relative risk estimates reported in Tables 5 and 6 ).

Table 3 Expected and observed polymorphisms prevalence (%)
Table 4 Correlations between clinical and histopathological parameters and genotypes
Table 5 Correlation between PFS, OS and polymorphisms in early stages
Table 6 Correlation between PFS, OS and polymorphisms in late stages

For 50 (13.9%) patients randomly sampled we determined, together with genotype, the expression of mdm2 mRNA in tumor samples and we correlated gene expression with genotype. Patients with the T/T genotype had a significantly lower expression of mdm2 compared with those with G/G and T/G genotypes (P=0.003; see Figure 2a). The difference remained statistically significant when the T/G and G/G genotypes were grouped and compared with the T/T genotype (P=0.019; see Figure 2b). These data correlate well with the evidence present in other tumor types that the G-containing genotypes result in a higher transcription of the gene. For this reason we decided to combine the G/G and the G/T genotypes in the analysis.

Figure 2

Correlation between genotypes and mdm2 mRNA expression in tumor samples (n=50). In panel a the three genotypes were considered separately, whereas in panel b, the T/G and G/G genotypes were grouped and compared with the T/T genotype.

PowerPoint slide

Correlation between ERCC1 polymorphism, and clinical and histopathological characteristics of ovarian cancer patients

For this gene we analyzed two polymorphisms, C8092A (rs3212986) and C19007T (rs11615), present in the 3′-untranslated region and the coding region of the gene, respectively. The genotype frequency found in our cohort of patients was as expected based on the available data (Table 3). The rs11615 polymorphism was not statistically significantly correlated to any clinical and histopathological characteristic of the early- and advanced-stage patients (Tables 4, 5, 6 and Supplementary Figures 2a–d); when compared with the A/A genotype, the group with the rs3212986 polymorphism C genotype was significantly associated to a major reduction in residual tumor size (P=0.037) and to more differentiated tumors (P=0.034) in advanced-stage patients as reported in Table 4; the group with the C genotype showed a 40% (95% CI: 63-2%, P=0.042) improvement in PFS and a 43% (95% CI: 65-7%, P=0.025) improvement in OS, but the assumption of proportional hazard was questionable as we can see from the crossing survival curves, and above all the result was not confirmed in the multivariate analysis (Table 6, and Figures 3c and d); no correlation was detected in early-stage patients (Tables 5, and Figures 3a and b).

Figure 3

Kaplan–Meier plots for PFS (a) and OS (b) in early, and PFS (c) and OS (d) in late stages, according to the ercc1 polymorphism (rs3212986).

PowerPoint slide

ercc1 tumor mRNA expression was determined for 85 (23.7%) patients. For both SNPs (P-value for rs11615: 0.156 and P-value for rs3212986: 0.568) we were unable to associate the genotype with ercc1 levels (Supplementary Figures 4a–d).

Correlation between ERCC5/XPG polymorphism, and clinical and histopathological characteristics of ovarian cancer patients

We analyzed two polymorphisms for this gene, one located at position 335, T335C (rs1047768), and the other at position 3507, G3507C (rs17655). The genotype frequency found in our cohort of patients was what we expected from the available data (Table 3). No statistically significant association was detected between the genotypes of rs1047768 polymorphism, and the clinical and histopathological characteristics of early- and advanced-stage patients (Tables 4, 5, 6 and Supplementary Figures 3a–d). The OS pattern for early-stage and the PFS pattern for late-stage disease with the rs17655 polymorphism were significantly different when comparing the genotype containing only C with the C/G and G/G genotypes, although the major risk category switches between the two endpoints (Figure 4). The correlation detected in univariate analysis (OS for early-stage malignancy: HR(C/C vs C/G, G/G)=0.38, 95% CI: 0.15–0.94; P=0.037; PFS for late-stage malignancy: HR(C/C vs C/G, G/G)=1.94, 95% CI: 1.03–3.68; P=0.041) was substantially confirmed in the multivariate analysis (OS for early-stage malignancy: HR(C/C vs C/G, G/G)=0.37, 95% CI: 0.14–0.96; P=0.041; PFS for late-stage malignancy: HR(C/C vs C/G, G/G)=2.12, 95% CI: 1.10–4.12; P=0.026). We investigated whether the different genotypes for both rs1047768 and rs17655 polymorphisms were associated with different mRNA levels in 85 (23.7%) tumors. No statistically significant differences were found between the groups (P-value for rs17655: 0.442 and P-value for rs1047768: 0.612; Supplementary Figures 4e–h).

Figure 4

Kaplan–Meier plots for PFS (a) and OS (b) in early, and PFS (c) and OS (d) in late stages, according to the xpg/ercc5 polymorphism (rs17655).

PowerPoint slide


The search for biomarkers able to identify patients likely to respond to chemotherapy is a major challenge in oncology. Somatic tumor alterations in gene copy numbers, mutations and or deletions in crucial genes have been associated with response in several malignancies, including ovarian cancer.

Another important factor likely to influence response to treatment is the presence of polymorphic variants of genes. Genetic variants of defined genes have been associated with increased risk of cancer in several case–control studies. Increasing evidence is now correlating the activity and toxicity of anticancer agents with these variants, alone or in association with somatic tumor alterations.37

In ovarian cancer, first-line chemotherapy includes platinum-based drugs, which are known to cause DNA damage. The response of cells to platinum-based therapy depends on their ability to repair the lesions that have been induced. Preclinical evidence indicates that cells with low expression or with functional defects in DNA-repair genes have a better response to platinum-based treatments.38, 39, 40

In clinical practice the situation is more complex and somewhat conflicting results have been reported. A positive correlation between expression of the NER gene ercc1, and OS or PFS in ovarian cancer patients has been reported by some authors but not by others.41, 42, 43 Similarly, a positive correlation between XPG protein expression and response to therapy has been published, although in other studies this correlation at the RNA level was not found.44, 45

It is not surprising that response to therapy is not mediated by a single gene; the concomitant presence of several alterations could be more informative in this context. The determination of both somatic tumor alterations and patients' genetic variants could be more important for the identification of therapeutic response markers. In this context we tried to assess the role of polymorphisms in DNA-repair and response genes as determinants of response to therapy in ovarian cancer patients. The cohort of patients analyzed here includes a large proportion of early-stage (stage-I/II) patients, rarely available in other studies. This offers, together with the potential for long-term clinical follow-up, the unique possibility to determine the impact, if any, of the variants analyzed in the different subsets of patients on clinical outcome. Although we analyzed a large number of early-stage patients, our study was definitely underpowered to detect difference in relative risk that could be clinically interesting (HRClinical of at least 2.00–2.50). We tested the clinically relevant hypothesis of an HR between 2.00 and 2.50: consistent with the obtained estimates, this range could be rejected for three of the five SNPs investigated with an α error of no more than 0.08 for both early- and late-stage disease. For late stages, as reported in Table 7 , column ‘Inferior limit HR’, under the assumption of proportional hazard our study could detect with a power of 0.80 a 50% difference in PFS and OS risks among three of the five genotypes we compared; instead statistical significance was reached in the other two cases, the rs3212986 and rs17655 polymorphisms, for which the study was sufficiently powered (1-β=0.80) to detect a minimum relative risk of 2.14 and 2.35 in the PFS endpoint. Unfortunately as a consequence of multiple comparisons the probability that at least one spurious association giving P-value <0.05 could be found was estimated at 23% both for early and late stages; the 1% significance level could be considered more appropriate than the 5% one, but the study was definitely underpowered for the rs3212986 and rs17655 polymorphisms in advanced stages; our findings should be substantiated by an independent confirmatory study.46

Table 7 Study power and clinically relevant hypothesis test

The mdm2 variant analyzed here could modulate p53 levels and activity.34 p53 expression has been associated with cellular response to platinum treatment.47 It has been reported that the mdm2 variant analyzed here is not only associated with an increased risk of tumor development for several types of tumor,36, 48, 49 but it is also correlated to clinical outcome in non-small-cell lung cancer.50 The analysis of the data of our cohort of patients does not suggest that this polymorphism has a role in determining PFS and/or OS. Similarly, mdm2 allelic variants were not associated with any clinical parameters in early or advanced stages.

ercc1 has been defined as a possible marker of response in ovarian cancer patients,51 and polymorphisms have been associated with response to platinum-based chemotherapy in some studies but not confirmed by others.21, 52, 53, 54 We found significant correlations between the ercc1 variant (rs3212986) and clinical parameters in both OS and PFS endpoints but only in univariate analysis; we also found a positive correlation between this polymorphism, and residual tumor and grading. These correlations explain why the ercc1 variant was not selected as an independent prognostic factor in multivariate analysis, but the data may indicate that this particular SNP could favor tumor metastasis and makes the cancer more difficult to eradicate. These results do not rule out a role for ERCC1 in determining response to platinum-based therapy, but rather suggest that the genotype in these two loci is not a determinant of ERCC1 activity. These findings, together with our observation, would be against a possible role of ERCC1 as biomarker for activity, in spite of the strong connection observed in ‘in vitro’ systems. It is worth mentioning that a strong correlation between ERCC1 and platinum response is observed in isogenic systems differing in the presence/absence of ERCC1.38 When different cell lines with different expressions of ercc1 are considered, the correlation is lost. Unless a clear relationship between ERCC1 protein expression and platinum response is found in clinical practice, we should consider ERCC1 as a determinant of platinum activity only when it is not present. Small amounts of ERCC1 could be sufficient to assure NER function as demonstrated in preclinical studies.

In our retrospective study, we found a statistically significant correlation between the xpg genotype (rs17655) in stage-I/II and stage-III/IV ovarian cancer with OS and PFS endpoints, respectively. Although not statistically significant, point estimates of PFS relative risk for early stages and OS relative risk for late stages are consistent with this result. The presence of a G-containing genotype is associated with a better prognosis in early-stage and with a worst prognosis in late-stage disease. This SNP is located in the C-terminus of the XPG protein, which is required for the interactions between XPG and other components of the NER machinery such as XPB, XPD, p62 and p44 subunits of the transcription factor IIH in the incision complex of NER. The amino-acid change from Asp to His could alter the protein binding and therefore modify NER efficiency. The physiopathology of early and late ovarian cancer seems to be different.55 It could be hypothesized that in early stages the presence of a more active NER does not influence the response to treatment, most likely because the tumor has been completely eradicated during surgery, but rather improves the ability of damaged cells to repair intrinsic DNA damage, limiting genomic instability. Late-stage ovarian cancer is often not completely eradicated during surgery and cancer cells are treated with a platinum-based compound, which is a substrate of NER. The presence of a more active NER would determine, in this case, a resistance to cell death in accordance with what is reported in the literature.56, 57

xpg expression has been widely reported to be associated with platinum activity in preclinical studies39 and has been suggested as a possible biomarker for ovarian cancer patients as its protein expression correlates with response to treatment. Our data increase the importance of the xpg gene, especially considering the fact that we have recently demonstrated that in a small but significant proportion of patients (roughly 20%) the xpg gene is methylated at the tumor level.58 The combination of genotypes and DNA methylation could be taken as an alternative to the determination of XPG protein expression. In fact DNA analysis is much easier and more reliable than determination of XPG protein expression. The use of a simple diagnostic analysis such as the xpg genotype and extension to other possibly relevant genotypes could be of use in stratifying patients for therapy.


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We gratefully acknowledge the generous contributions of the Nerina and Mario Mattioli Foundation. Dr S Piva revised the manuscript.

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Correspondence to M Marabese.

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Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website

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  • polymorphism
  • mdm2
  • ercc1
  • ercc5/xpg
  • ovarian cancer
  • SNP

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