Abstract
Background
Cell cycle checkpoint kinase 2 (CHEK2) plays an essential role in the repair of DNA damage. Single nucleotide polymorphisms (SNPs) in DNA repair genes are thought to influence treatment effects and survival of cancer patients. This study aimed to investigate the relationship between polymorphisms in the CHEK2 gene and efficacy of platinum-based doublet chemotherapy in never-smoking Chinese female patients with advanced non-small-cell lung cancer (NSCLC).
Methods
Using DNA from blood samples of 272 Chinese advanced NSCLC non-smoking female patients treated with first-line platinum-based chemotherapy, we have analyzed the relationships between four SNPs in the CHEK2 gene and clinical outcomes.
Results
We found that overall survival (OS) was significantly associated with CHEK2 rs4035540 (Log-Rank P=0.020), as well as the CHEK2 rs4035540 dominant model (Log-Rank P=0.026), especially in the lung adenocarcinoma group. After multivariate analysis, patients with rs4035540 A/G genotype had a significantly better OS than those with the G/G genotype (HR =0.67, 95% CI, 0.48–0.93; P=0.016). In the toxicity analysis, it was observed that patients with the CHEK2 rs4035540 A/A genotype had a higher risk of gastrointestinal toxicity than the G/G genotype group (P=0.009). However, there are no significant associations between chemotherapy treatments and genetic variations.
Conclusions
Our findings indicate that SNPs in CHEK2 are related to Chinese advanced NSCLC never-smoking female patients receiving platinum-based doublet chemotherapy in China. Patients with rs4035540 A/G genotype have a better OS. And patients with rs4035540 A/A genotype have a higher risk of gastrointestinal toxicity. These results point to a direction for predicting the prognosis for Chinese never-smoking NSCLC female patients. However, there are no significant associations between chemotherapy treatments and SNPs in CHEK2, which need more samples to the further study.
Keywords: Cell cycle checkpoint kinase 2 (CHEK2), single nucleotide polymorphisms (SNPs), non-small-cell lung cancer (NSCLC), chemotherapy, never-smoking women
Introduction
In China, lung cancer is the most common cause of the deaths by malignancies. Studies show that the incidence of lung cancer is closely related to the ability to repair DNA damage caused by environmental exposure and other factors. Non-small-cell lung cancer (NSCLC) accounts for 80% of all lung cancer cases. As first-line therapy for advanced NSCLC, platinum-based combination chemotherapy has the effect of enhancing cancer patients’ overall survival (OS) and quality of life (1). However, different groups of patients still have different prognoses. It is essential to find precise and effective biomarkers in order to establish personal treatment regimens for each patient. There is arising overall trend toward an increasing incidence and mortality of NSCLC among females, especially in those who never smoked (2). Previous studies have researched the prognosis factors in lung cancer for women, including performance status (PS) and clinical stages of the disease (3).
Single nucleotide polymorphisms (SNPs) are common in the human genome. There is growing evidence that demonstrates that the presence of some SNPs can predict NSCLC patients’ response to chemotherapy (4-7). Evidence for genetic susceptibility to lung cancer will increase the chance to study prognosis factors for never-smoking women with NSCLC receiving chemotherapy.
There are some potential genes to be as the prediction biomarker for cancer till now, such as BRCA1 (8), XRCC1 (9,10), ABCB1 (11), and NDRG4 (12). High-lighting BRCA1 promoter methylation may be a biomarker for effect and better prognosis of DNA damaging agents for breast cancer (8), XRCC1 genetic variants may be the markers for predicting lung cancer susceptibility (9) and are associated to the OS of advanced NSCLC patients treated by gemcitabine/platinum (10). ABCB1 C3435T gene polymorphism may as a potential biomarker of progress free survival in breast cancer patients (11). And as highly expressed methylated NDRG4 gene in colorectal cancer (CRC) patients, the detection of methylated NDRG4 could be used as a novel diagnostic technique for CRC (12).
Since our team has reported some articles about the relationship between treatments efficacy and SNPs in different genes, such as RB1 (13), WEE1 (7), etc. And WEE1 as a G2/M checkpoint kinase can induce G2/M cell cycle arrest in the response to DNA damage. Cell cycle checkpoint kinase 2 (CHEK2) also is a G2/M checkpoint kinase, so we focused on it after WEE1 SNPs research selectively. The checkpoint kinase 2 (CHK2, or CHEK2) gene on chromosome 22 is still playing an important role in tumor suppression (14). DNA damage can cause CHEK2 phosphorylation (15), activated CHEK2 can lead to phosphorylation of the CDC25 family, BRCA1, p53, and other similar functional effectors in order to start the cell cycle checkpoint regulation (15-17). Results from previous studies have shown a relationship between the CHEK2 mutation and an increased risk for lung cancer (18,19), breast cancer (20,21), prostate cancer (22,23), colorectal cancer (24,25) and other cancers (26,27). In addition, there was a relationship between the decreased risk of endometrial cancer and the rs8135424 CHEK2 SNP (28). And there was a significant risk association between CHEK2 SNP rs17507066 and serous epithelial ovarian cancer (29). There are few previous studies which reported about the relationship about CHEK2 SNPs with lung cancer, especially the rs4035540 in CHEK2. We have added a table to summary about the lung cancer related SNPs CHEK2 gene (Table S1).
However, for NSCLC never-smoking female patients receiving platinum-based doublet chemotherapy, there is not enough convincing evidence to prove that a relationship between the CHEK2 genetic polymorphisms and both the prognosis as well as the chemotherapeutic toxicity exists.
In this study, we have analyzed the association between four SNPs in the CHEK2 gene and the efficacy of chemotherapy, as well as the toxicity. We analyzed the data collected from 272 advanced NSCLC never-smoking female patients to try to find the new research direction to predict survival, efficacy, and/-or toxicity for Chinese never-smoking females with NSCLC.
Methods
Patients
In this study, we enrolled 272 female patients who had been diagnosed with clinical stages III–IV NSCLC and were receiving platinum-based (carboplatin or cisplatin) chemotherapy. Enrolled patients were from Shanghai Pulmonary Hospital, Shanghai Zhongshan Hospital, Shanghai Chest Hospital, and Shanghai Changhai Hospital (all China) and would provide their written informed consent before being included in this study. Detailed inclusion criteria included several specific conditions: (I) stages III–IV NSCLC confirmed by at least one diagnostic criteria; (II) inoperable only; (III) platinum-based (cisplatin or carboplatin) chemotherapy as the first-line treatment; (IV) confirmed primary NSCLC by the histological test; (V) the Eastern Cooperative Oncology Group (ECOG) (30) performance status (PS) from 0 to 2; (VI) no history of cancer in other organs; (VII) never received chemotherapy treatment previously; and (VIII) never smoking.
The ethics committee of Shanghai Pulmonary Hospital has approved this study. We have an approval number 2009FK31 from the Institution. Peripheral blood sample collection and the epidemiological information collection all had the informed consent of participants complying with the provisions of the ethics.
Treatment schedules and data collection
All enrolled patients received first-line platinum-based doublet chemotherapy with one of the following double chemotherapy regimens for 2 to 6 cycles: (I) carboplatin or cisplatin plus vinorelbine (NP/NC); (II) carboplatin or cisplatin plus gemcitabine (GP/GC); (III) carboplatin or cisplatin plus paclitaxel (TP/TC); or (IV) carboplatin or cisplatin plus docetaxel (DP/DC).
Following the Response Evaluation Criteria in Solid Tumors (RECIST) criteria 1.1 (31), patients’ responses to platinum-based therapy were assessed after the first two chemotherapy cycles. All responses were re-assessed at least four weeks after initial assessment using the same criteria. For data analyses, subjects achieving a stable disease (SD), partial response (PR) or complete responses (CRs) were grouped as responders; subjects with progressive disease (PD) were considered non-responders. OS was defined as the first day the patients received chemotherapy treatments to the final follow-up or day of death.
Following the National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events (CTCAE), version 3.0 (32), chemotherapy toxicity was evaluated twice weekly. A toxicity grade of 3 or 4 recorded during the initial two cycles of therapy was collected for analysis.
Tag SNPs selection and genotyping procedure
Four tag SNPs (rs4035540, rs5762746, rs2236141, and rs2236142) from the gene region (including 2 kb upstream) of the human CHEK2 (54163bp, 22q12.1) were selected from the Han Chinese population in Beijing (HCB) data downloaded from HapMap SNP databases (http://www.hapmap.org/) (Table 1). The tag SNPs were selected with a cutoff of 0.05 of a minor allele frequency (MAF) by Haploview (33) (http://www.broadinstitute.org/haploview/) or Hardy-Weinberg equilibrium P values <0.05 in the enrolled population. Haplotype blocks were concluded by the methods showed by Gabriel et al. (34).
Table 1. SNPs of CHEK2 gene.
| NCBI SNP ID | Location | Base change | Minor allele frequency | |
|---|---|---|---|---|
| Current data | HCB | |||
| rs4035540 | Intron | A/G | 0.292 | 0.279 |
| rs5762746 | Intron | G/A | 0.413 | 0.389 |
| rs2236141 | UTR-5 | G/A | 0.155 | 0.167 |
| rs2236142 | 5' flank; upstream variant 2KB | C/G | 0.399 | 0.389 |
SNP, single nucleotide polymorphism; CHEK2, cell cycle checkpoint kinase 2; UTR, untranslated region; HCB, Han Chinese Beijing population.
We have collected the blood 3 to 5 milliliters by anticoagulant blood collection tube from each patient before the chemotherapy. And 400 microliters of blood were used for DNA extraction by the Human QIAamp DNA Blood Maxi Kit (Qiagen GmbH, Hilden, Germany). DNA has been checked by NanoDrop 2000 (Thermo Scientific, USA). When the value of OD 260/280 nm was in the range from 1.8 to 2, we indicated that the DNA was suitable for high-throughput sequencing.
In order not to affect the statistical efficiency, we used MAF >0.05, genotyping call rate >0.95 and the GenCall score >0.2 as the filter criteria for SNP genotyping by using the iSelect HD Bead-Chip (Illumina, CA, USA). The silica beads with 3.1 µm diameter and oligonucleotide probes were made at first. Each bead could be combined with thousands of the same probes at the 5' end of sequences. With the use of microporous etched optical fiber technology on a chip, we could easily handle and add the beads to the chip. Beads were combined with fiber microporous in a disorderly manner. Only one bead was actually placed in each of the microprobe. Results could be read with the Illumina BeadScan machine.
Statistical analysis
SPSS Statistics 20.0 (SPSS Inc., Chicago, IL, USA) was used for all analyses. The OS of the enrolled patients was examined by the Kaplan-Meier and log-rank tests. Multivariate analyses were carried out using the Cox proportional hazard model by adjusting for clinical characteristics, such as age, TNM stage, pathologic types, adjuvant therapy and PS. Differences with P<0.05 were considered statistically significant. Multivariate logistic regression analysis was used to estimate the response to therapy and the toxicity risk for each SNP with adjustment for clinical characteristics.
Results
Association between clinical characteristics and survival in enrolled patients treated with platinum-based chemotherapy
The cohort consisted of 272 NSCLC non-smoking female patients with a median age of 57 years, ranging from 26 to 80 years old. All patients had been diagnosed as IIIa/IIIb or IV clinical stage according to the ECOG PS 1 or 2.
The relationship of clinical characteristics with OS was analyzed using Kaplan-Meier curve test. No significant correlation was observed between the OS and pathologic types, adjuvant therapy, or ECOG PS (score 1 or 2) (Table 2). Since we focus on never-smoking female NSCLC patients, the clinical variable which may have effects on OS is the factor analyzed. The log-rank test suggested that younger patients (≤57 years old) had a significant better OS than elderly ones (Table 2, Log-Rank P=0.012; Figure 1A) and patients with TNM stage III had a significantly better OS than those with stage IV (Table 2, Log-Rank P=0.021; Figure 1B).
Table 2. Clinical characteristics of the recruited non-smoking female patients with advanced NSCLC.
| Variables | N | mOS (95% CI) (m)a | PL-R |
|---|---|---|---|
| Overall | 272 | ||
| Age | 0.012 | ||
| ≤57 | 146 | 26.47 (21.76–31.18) | |
| >57 | 125 | 18.87 (16.15–21.59) | |
| ECOG PS | 0.180 | ||
| 0–1 | 238 | 22.80 (18.95–26.65) | |
| 2 | 31 | 20.67 (14.83–26.50) | |
| Pathologic type | 0.552 | ||
| Adenocarcinoma | 234 | 23.50 (19.53–27.47) | |
| Squamous carcinoma | 20 | 23.83 (4.20–43.47) | |
| Adenosquamous carcinoma | 3 | 15.30 (1.06–29.54) | |
| Others | 15 | 19.10 (18.68–19.52) | |
| Adjuvant therapy | 0.985 | ||
| GP/GC | 82 | 25.60 (18.65–32.55) | |
| TP/TC | 72 | 21.37 (15.00–27.74) | |
| NP/NC | 68 | 23.50 (14.44–32.56) | |
| DP/DC | 20 | 20.87 (15.99–25.74) | |
| Other platinum combinations | 30 | 21.43 (15.59–27.28) | |
| TNM stage | 0.021 | ||
| III | 84 | 24.90 (18.56–31.24) | |
| IV | 186 | 20.23 (16.426–24.04) |
a, survival derived from Kaplan-Meier analysis. NSCLC, non-small-cell lung cancer; ECOG PS, Eastern Cooperative Oncology Group performance status; TP/TC, carboplatin or cisplatin plus paclitaxel; NP/NC, carboplatin or cisplatin plus vinorelbine; GP/GC, carboplatin or cisplatin plus gemcitabine; DP/DC, carboplatin or cisplatin plus docetaxel; TNM, tumor-lymph-node metastasis; N, number; m, months; OS, overall survival; mOS, median time to OS; PL-R, Log-Rank P.
Figure 1.
OS curves of significantly associated clinical characteristics and polymorphism in all patients. (A) OS and age classes; (B) OS and TNM stage; (C) OS and rs4035540; (D) OS and rs4035540 dominant model. P values: from the Kaplan-Meier and log-rank tests. OS, overall survival.
Furthermore, the multivariate Cox’s proportional hazards regression analysis suggested that age classified according to a cutoff of 57 years old (Table 3, HR =1.62, 95% CI: 1.19–2.22; P=0.002) and TNM stage (Table 3, HR =1.63, 95% CI: 1.16–2.30; P=0.005) were independent predictive factors.
Table 3. Multivariate Cox’s regression analysis of prognostic factors for OS in the 272 advanced NSCLC never-smoking female patients treated with platinum-based chemotherapy.
| Variables | HR (95% CI)a | P |
|---|---|---|
| Age | 0.002 | |
| ≤57 | 1 | R |
| >57 | 1.62 (1.19–2.22) | |
| ECOG PS | 0.838 | |
| 0–1 | 1 | R |
| 2 | 1.05 (0.649–1.70) | |
| Pathologic types | 0.334 | |
| Adenocarcinoma | 1 | R |
| Squamous carcinoma | 1.25 (0.65–2.39) | 0.509 |
| Adenosquamous carcinoma | 1.19 (0.29–4.89) | 0.814 |
| Others | 1.74 (0.93–3.26) | 0.082 |
| Adjuvant therapy | 0.827 | |
| GP/GC | 1 | R |
| TP/TC | 1.17 (0.79–1.74) | 0.428 |
| NP/NC | 1.10 (0.72–1.70) | 0.657 |
| DP/DC | 1.03 (0.52–2.02) | 0.943 |
| Other platinum combinations | 0.84 (0.48–1.48) | 0.555 |
| TNM stage | 0.005 | |
| III | 1 | R |
| IV | 1.63 (1.16–2.30) | |
| rs4035540 | 0.011 | |
| GG | 1 | R |
| AG | 0.67 (0.48–0.93) | 0.016 |
| AA | 1.34 (0.79–2.27) | 0.272 |
| rs5762746 | 0.322 | |
| AG | 1 | R |
| GG | 0.82 (0.45–1.48) | 0.506 |
| AA | 0.63 (0.32–1.26) | 0.192 |
| rs2236141 | 0.840 | |
| GG | 1 | R |
| AG | 0.93 (0.65–1.35) | 0.713 |
| AA | 1.36 (0.32–5.77) | 0.678 |
| rs2236142 | 0.376 | |
| CG | 1 | R |
| CC | 1.04 (0.71–1.53) | 0.826 |
| GG | 1.44 (0.86–2.38) | 0.163 |
aHRs, 95% CIs and their corresponding P values were calculated using multivariate Cox proportional hazard models, adjusted for other variables. OS, overall survival; NSCLC, non-small-cell lung cancer; HR, hazard ratio, CI, confidence interval; TP/TC, carboplatin or cisplatin plus paclitaxel; NP/NC, carboplatin or cisplatin plus vinorelbine; GP/GC, carboplatin or cisplatin plus gemcitabine; DP/DC, carboplatin or cisplatin plus docetaxel.
Association between CHEK2 tag SNPs and survival in enrolled patients treated with platinum-based chemotherapy
Analysis of the relationship by the log rank test between SNPs and OS showed that CHEK2 rs4035540 (Table 4, Log-Rank P=0.020; Figure 1C) was significantly related NSCLC patients’ OS. Furthermore, we also investigated the association between OS and both the CHEK2 rs4035540 dominant model, as well as recessive model. As a result, the dominant model was also found to be a significant factor contributing to the OS of the enrolled patients treated with platinum-based chemotherapy (Table 4, Log-Rank P=0.026; Figure 1D).
Table 4. Association between CHEK2 tag SNPs and OS in NSCLC patients.
| SNP and genotype | N | mOS (95% CI) (m)a | PL-R |
|---|---|---|---|
| rs4035540 | 0.020 | ||
| GG | 137 | 21.37 (15.80–26.93) | |
| AG | 104 | 24.37 (20.79–27.94) | |
| AA | 26 | 15.57 (13.94–17.19) | |
| Dominant model | 0.026 | ||
| GG + AG | 241 | 23.83 (19.89–27.78) | |
| AA | 26 | 15.57 (13.94–17.19) | |
| Recessive model | 0.315 | ||
| GG | 137 | 21.37 (15.80–26.93) | |
| AG + AA | 130 | 22.50 (19.37–25.63) | |
| rs5762746 | 0.898 | ||
| AG | 130 | 22.80 (18.10–27.50) | |
| GG | 90 | 23.83 (17.78–29.88) | |
| AA | 44 | 21.37 (12.99–29.75) | |
| Dominant model | 0.946 | ||
| GG + AG | 220 | 22.90 (19.08–26.72) | |
| AA | 44 | 21.37 (12.99–29.75) | |
| Recessive model | 0.680 | ||
| GG | 90 | 23.83 (17.78–29.88) | |
| AG + AA | 174 | 22.43 (17.90–26.97) | |
| rs2236141 | 0.919 | ||
| GG | 188 | 21.87 (18.44–25.29) | |
| AG | 70 | 25.83 (17.68–33.98) | |
| AA | 6 | 27.00 (18.28–35.72) | |
| Dominant model | 0.956 | ||
| GG+AG | 258 | 22.50 (18.91–26.09) | |
| AA | 6 | 27.00 (18.28–35.72) | |
| Recessive model | 0.702 | ||
| AG + AA | 76 | 26.90 (19.33–34.47) | |
| GG | 188 | 21.87 (18.44–25.29) | |
| rs2236142 | |||
| CG | 138 | 23.50 (19.26–27.75) | |
| CC | 94 | 23.83 (17.60–30.07) | |
| GG | 39 | 18.83 (13.63–24.03) | |
| Dominant model | 0.182 | ||
| CG + CC | 232 | 23.83 (19.72–27.95) | |
| GG | 39 | 18.83 (13.63–24.03) | |
| Recessive model | 0.657 | ||
| CG + GG | 177 | 22.50 (18.84–26.16) | |
| CC | 94 | 23.83 (17.60–30.07) |
a, survival derived from Kaplan-Meier analysis. N, number; m, months; CHEK2, cell cycle checkpoint kinase 2; SNP, single nucleotide polymorphism; OS, overall survival; NSCLC, non-small-cell lung cancer; mOS, median time to OS; PL-R, Log-Rank P.
Using multivariate Cox proportional hazard models, patients with rs4035540 A/G genotype had a significantly better OS than those with the G/G genotype (Table 3, HR =0.67, 95% CI, 0.48–0.93; P=0.016).
Association between CHEK2 tag SNPs and survival in NSCLC female patients classified by pathologic types
We further investigated the influence of SNPs on OS stratified by pathologic types (Table 5). The results suggested that the above rs4035540 (Log-Rank P=0.041) and rs2236142 (Log-Rank P=0.041), as well as the rs4035540 dominant model (Log-Rank P=0.025) were significantly associated with OS in non-smoking female patients with lung adenocarcinoma (Table 5, Figure 2).
Table 5. Association between CHEK2 tag SNPs and OS in enrolled patients with adenocarcinoma or non-adenocarcinoma NSCLC.
| Variables | Adenocarcinoma NSCLC patients | Non-adenocarcinoma NSCLC patients | |||||
|---|---|---|---|---|---|---|---|
| N | mOS (95% CI) (m)a | PL-R | N | mOS (95% CI) (m)a | PL-R | ||
| rs4035540 | 0.041 | 0.271 | |||||
| GG | 118 | 25.10 (17.05–33.15) | 19 | 18.87 (11.56–26.18) | |||
| AG | 88 | 24.37 (20.44–28.29) | 16 | 16.37 (0.00–39.21) | |||
| AA | 24 | 15.40 (13.81–16.99) | 2 | NA | |||
| Dominant model | 0.025 | 0.930 | |||||
| GG + AG | 206 | 25.10 (20.84–29.36) | 35 | 18.87 (14.98–22.76) | |||
| AA | 24 | 15.40 (13.81–16.99) | 2 | 21.37 | |||
| Recessive model | 0.627 | 0.126 | |||||
| GG | 118 | 25.10 (17.05–33.15) | 19 | 18.87 (11.56–26.18) | |||
| AG + AA | 112 | 22.80 (19.23–26.37) | 18 | 21.37 (11.44–31.30) | |||
| rs5762746 | 0.987 | 0.545 | |||||
| AG | 115 | 22.80 (18.11–27.49) | 15 | 16.23 (0.00–41.67) | |||
| GG | 75 | 26.47 (17.05–35.89) | 15 | 19.07 (12.88–25.25) | |||
| AA | 37 | 19.27 (6.34–32.20) | 7 | 21.37 (8.54–34.20) | |||
| Dominant model | 0.877 | 0.672 | |||||
| GG + AG | 190 | 24.37 (20.05–28.69) | 30 | 18.87 (12.80–24.93) | |||
| AA | 37 | 19.27 (6.34–32.20) | 7 | 21.37 (8.54–34.20) | |||
| Recessive model | 0.996 | 0.271 | |||||
| GG | 75 | 26.47 (17.05–35.89) | 15 | 19.07 (12.88–25.25) | |||
| AG + AA | 152 | 22.80 (18.11–27.49) | 22 | 16.37 (5.12–27.62) | |||
| rs2236141 | 0.452 | 0.275 | |||||
| GG | 164 | NA | 24 | NA | |||
| AG | 59 | NA | 11 | NA | |||
| AA | 5 | NA | 1 | NA | |||
| Dominant model | 0.901 | 0.866 | |||||
| GG + AG | 223 | NA | 35 | NA | |||
| AA | 5 | NA | 1 | NA | |||
| Recessive model | 0.235 | 0.113 | |||||
| AG + AA | 64 | 28.47 (24.91–32.02) | 12 | 16.37 (10.48–22.26) | |||
| GG | 164 | 22.50 (18.51–26.49) | 24 | 21.37 (15.44–27.30) | |||
| rs2236142 | 0.041 | 0.271 | |||||
| CG | 118 | 23.87 (19.90–27.83) | 20 | 19.10 (10.94–27.26) | |||
| CC | 81 | 26.90 (18.03–35.78) | 13 | 18.87 (14.38–23.35) | |||
| GG | 35 | 17.70 (13.57–21.83) | 4 | 21.43 (0.00–53.06) | |||
| Dominant model | 0.074 | 0.336 | |||||
| CG + CC | 199 | 25.10 (20.57–29.63) | 33 | 19.07 (15.396–22.74) | |||
| GG | 35 | 17.70 (13.57–21.83) | 4 | 21.433 (0.00–53.06) | |||
| Recessive model | 0.966 | 0.175 | |||||
| CG + GG | 153 | 22.80 (18.44–27.16) | 24 | 21.37 (13.79–28.94) | |||
| CC | 81 | 26.90 (18.03–35.78) | 13 | 18.87 (14.38–23.35) | |||
a, survival derived from Kaplan-Meier analysis. N, number; m, months; CHEK2, cell cycle checkpoint kinase 2; SNP, single nucleotide polymorphism; NSCLC, non-small-cell lung cancer; OS, overall survival; mOS, median time to OS; PL-R, Log-Rank P.
Figure 2.
OS curves of significantly associated rs4035540 in all patients classified by pathologic types. (A) OS and rs4035540 in enrolled patients with lung adenocarcinoma; (B) OS and rs4035540 in enrolled patients with lung non-adenocarcinoma. P values: from the Kaplan-Meier and log-rank tests. OS, overall survival.
Toxicity
After analyzing the possible association of chemotherapy toxicity with different treatment regimens, it was shown that CHEK2 rs4035540 was significantly associated with gastrointestinal toxicity. There was a significantly higher toxicity in the A/A genotype group than the G/G genotype group (adjusted OR =3.83, 95% CI, 1.36–10.56; P=0.009) (Table 6).
Table 6. Analysis of association between CHEK2 SNPs and chemotherapy toxicity.
| Toxicity (SNP and genotype) | GasTox | HemTox | LeuTox | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| No/yes (N) | OR (95% CI)a | Pa | No/yes (N) | OR (95% CI)a | Pa | No/yes (N) | OR (95% CI)a | Pa | |||
| rs4035540 | 0.023 | 0.277 | 0.298 | ||||||||
| GG | 115/17 | 1 | R | 91/40 | 1 | R | 107/26 | 1 | R | ||
| AG | 89/14 | 1.02 (0.46–2.25) | 0.960 | 78/23 | 0.60 (0.33–1.12) | 0.109 | 88/15 | 0.57 (0.27–1.19) | 0.135 | ||
| AA | 18/8 | 3.83 (1.39–10.56) | 0.009 | 19/7 | 0.82 (0.31–2.16) | 0.685 | 23/3 | 0.60 (0.16–2.27) | 0.453 | ||
| rs5762746 | 0.060 | 0.445 | 0.889 | ||||||||
| AG | 109/20 | 1 | R | 91/33 | 1 | R | 105/23 | 1 | R | ||
| GG | 80/7 | 0.42 (0.17–1.09) | 0.074 | 62/26 | 1.39 (0.72–2.66) | 0.326 | 73/14 | 1.21 (0.55–2.69) | 0.635 | ||
| AA | 34/10 | 1.54 (0.63–3.77) | 0.343 | 34/10 | 0.83 (0.36–1.95) | 0.671 | 37/7 | 1.14 (0.42–3.08) | 0.800 | ||
| rs2236141 | 1.000 | 0.349 | 0.303 | ||||||||
| GG | 156/29 | 1 | R | 137/46 | 1 | R | 158/28 | 1 | R | ||
| AG | 58/10 | 0.99 (0.43–2.26) | 0.984 | 44/22 | 1.56 (0.84–2.92) | 0.163 | 53/14 | 1.80 (0.84–3.87) | 0.132 | ||
| AA | 5/0 | NA | NA | 4/2 | 1.64 (0.28–9.54) | 0.582 | 5/1 | 1.85 (0.19–18.35) | 0.600 | ||
| rs2236142 | 0.056 | 0.644 | 0.446 | ||||||||
| CG | 117/20 | 1 | R | 99/34 | 1 | R | 116/20 | 1 | R | ||
| CC | 80/9 | 0.66 (0.28–1.58) | 0.354 | 65/25 | 1.28 (0.68–2.42) | 0.445 | 77/14 | 1.18 (0.54–2.59) | 0.673 | ||
| GG | 29/10 | 2.29 (0.93–5.63) | 0.071 | 26/13 | 1.38 (0.61–3.12) | 0.441 | 29/10 | 1.80 (0.73–4.47) | 0.204 | ||
aORs, 95% CIs and their corresponding P values were calculated using multivariate logistic regression analysis, adjusted for age, pathologic type, ECOG PS, adjuvant therapy and TNM stage. CHEK2, cell cycle checkpoint kinase 2; SNP, single nucleotide polymorphism; N, number; NA, not available; R, reference; NA, null data; OR, odds ratio; CI, confidence interval; GasTox, gastrointestinal toxicity; HemTox, hematological toxicity; LeuTox, leukotoxicity.
Chemotherapy response
There were no significant associations between chemotherapy treatments and genetic variations (Table S2). At the same time, we also found there was not any significant relationship between chemotherapy response and genetic variations grouped by the different regimens (Table S3).
Haploview analysis of CHEK2 tag SNPs
Among the four SNPs, the rs4035540 and rs5762746 were in strong LD (Linkage Disequilibrium) (│D’│=100%, r2=0.563). And rs2236141 and rs2236142 were in LD (│D’│=100%, r2=0.121). The results could be showed from the Haploview Software directly (Figure S1). Since only one tag SNP had the significance with the OS, there was no real meaning for analyzing the linkage disequilibrium between different SNPs.
Discussion
CHEK2 plays an essential role in the signaling pathway for DNA damage and cell checkpoint regulation. In order to maintain genomic stability when DNA damage occurs, CHEK2 is activated via phosphorylation of downstream genes (14). The cell cycle checkpoint is an important mechanism in maintaining genomic stability. A DNA damage checkpoint can detect the DNA damage in the cell cycle and lead to cell cycle arrest to provide enough time to repair the damage (35). Many studies have reported that CHEK2 gene mutations could increase the risk of some cancers (20,22,27,36-38). Many investigators have researched this area with special emphasis on CHEK2 1100delC.
There is a growing realization that genetic polymorphisms not only have effects on cancer development, but also on its prognosis. Numerous SNPs in CHEK2 have been screened to reveal possible relationships with various cancers. In a study by Lawrenson et al. (29), the results indicated a significant risk association between CHEK2 SNP rs17507066 and serous epithelial ovarian cancer (P=4.74E-7). Meanwhile, Gu et al. have reported that (39) about CHEK2 gene with the results showing that the rs738722 C/T polymorphism and the rs2236142 G/C polymorphism might be protective factors against the risk of lymph node metastasis of esophageal cancer in the Chinese population. However, whether the polymorphism of the CHEK2 gene is associated with the efficacy of platinum-based doublet chemotherapy in never-smoking NSCLC female patients has not yet been investigated.
Our study has provided evidence for a relationship between the CHEK2 genetic polymorphisms and efficacy of platinum-based doublet chemotherapy in never-smoking Chinese NSCLC female patients. The A/G allele in rs4035540 in CHEK2 has been verified to be an independent protective factor for the prognosis for never-smoking female NSCLC patients. At the same time, TNM stages and age classes could also be the independent factors for predicting prognosis for these Chinese patients. Different histological types of NSCLC may have different biological behavior. After the subgroup analysis classified by pathologic types, we found that CHEK2 rs4035540 and rs2236142 may be significant in predicting OS in the never-smoking female patients lung adenocarcinoma group. Therefore, based on the findings of this study, we consider that CHEK2 polymorphisms may affect the efficacy of platinum-based chemotherapy, and may play valuable roles in predicting the prognosis for NSCLC with never-smoking female patients.
In addition, although platinum-based doublet chemotherapy has been proved to be effective for NSCLC patients, adverse side effects still exist because of the platinum-related DNA damage (40). We analyzed the incidence of drug toxicities, including gastrointestinal, hematological and leukotoxicities. As a result, in our patients, we found that CHEK2 rs4035540 A/A genotype group demonstrated higher gastrointestinal toxicity after treatment than the G/G genotype group. This finding suggests that it may help to identify patient subgroups with a strong risk for drug toxicity by testing for the presence of these polymorphisms for the selection of appropriate chemotherapy for the treatment of female NSCLC.
Our results suggest that genetic variations in the CHEK2 gene may play a role in predicting the toxicity and prognosis of NSCLC. The clinical significance of CHEK2 rs4035540 polymorphism in platinum-based chemotherapy of NSCLC has not been reported before. The screening of tagged SNPs of CHEK2 in this study may bring a new evidence of the importance of CHEK2 in NSCLC and the efficacy of treatments to clinical.
However, CHEK2 rs4035540 is located in the intron area of the CHEK2 gene. After the Haploview analysis, we made a very bold speculation that it may modify the expression levels of CHEK2 gene based on linkage disequilibrium with multiple SNPs. And this speculation about the complex signaling pathway of which CHEK2 is involved is our following plan to research. Determining whether the CHEK2 rs4035540 A/G genotype can be used a biomarker for the option of platinum-based regimen in NSCLC patients and the detailed mechanism are worth further studying in the future. The additional follow-up studies with a larger patient sample that includes different ethnic populations are warranted.
Conclusions
We demonstrated for the first time that polymorphisms in the CHEK2 gene may predict the clinical outcomes for advanced Chinese never-smoked NSCLC female patients following platinum-based doublet chemotherapy. Our study provides evidence of a useful molecular research direction which may be easily applied in clinical situation. Consequently, in addition to the clinical stages and the pathologic types classified by lung adenocarcinoma, it may help identify patient subgroups with the risk of poor diseases outcomes by testing for the presence of these polymorphisms; the information can be used to tailor personal therapies in certain populations. The mechanisms for the effects of these SNPs on CHEK2 biologic functions remain to be clarified in further studies to understand the role of the CHEK2 gene in determining NSCLC outcomes after platinum-based doublet chemotherapy.
Acknowledgements
Funding: This work was funded by National Natural Science Foundation of China (No. 81572269), Science and Technology Commission of Shanghai Municipality (No. 14411966400, No. 134119a3400), Shanghai Health Bureau Foundation (No. 201440397, XYQ2013115) and Med-Engineering Interdisciplinary Research Foundation (No. YG2015MS71), Shanghai Jiaotong University (No. 201440397).
Table S1. CHEK2 SNPs related to lung cancer.
| Study | SNPs | Effect size (95% CI) | P |
|---|---|---|---|
| Wang et al., 2014 (18) | rs17879961 | 0.38a | 1.27×10−13 |
| Brennan et al., 2007 (19) | rs17879961 | 0.44 (0.31–0.63) | <0.00001 |
| Zhang et al., 2010 (41) | rs2236141 | 0.73 (0.57–0.95) | 0.0018 |
a, the full data cannot been extracted from the study. CHEK2, cell cycle checkpoint kinase 2; SNP, single nucleotide polymorphism; CI, confidence interval.
Table S2. Analysis of association between CHEK2 SNPs and therapy response.
| SNP and genotype | Response/no response (N) | OR (95% CI)a | Pa |
|---|---|---|---|
| rs4035540 | 0.504 | ||
| GG | 106/26 | 1 | R |
| AG | 78/24 | 1.22 (0.63–2.37) | 0.559 |
| AA | 18/7 | 1.80 (0.65–4.97) | 0.254 |
| rs5762746 | 0.417 | ||
| AG | 94/32 | 1 | R |
| GG | 72/16 | 0.62 (0.30–1.29) | 0.199 |
| AA | 35/9 | 0.96 (0.4–2.33) | 0.935 |
| rs2236141 | 0.730 | ||
| GG | 136/44 | 1 | R |
| AG | 57/13 | 0.74 (0.36–1.55) | 0.428 |
| AA | 6/0 | NA | NA |
| rs2236142 | 0.895 | ||
| CG | 103/32 | 1 | R |
| CC | 71/19 | 0.85 (0.43–1.70) | 0.649 |
| GG | 30/7 | 1.00 (0.38–2.62) | 0.995 |
aORs, 95% CIs and their corresponding P values were calculated using multivariate logistic regression analysis, adjusted for age, ECOG PS, pathologic type, adjuvant therapy and TNM stage. CHEK2, cell cycle checkpoint kinase 2; SNP, single nucleotide polymorphism; N, number; NA, not available; R, reference; OR, odds ratio; CI, confidence interval.
Table S3. Stratification analysis of association between CHEK2 SNPs and therapy response selected by adjuvant therapy.
| SNP and genotype | GP/GC group | TP/TC group | NP/NC group | DP/DC group | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Response/no response (N) | OR (95% CI)a | Pa | Response/no response (N) | OR (95% CI)a | Pa | Response/no response (N) | OR (95% CI)a | Pa | Response/no response (N) | OR (95% CI)a | Pa | ||||
| rs4035540 | 0.539 | 0.165 | 0.257 | ||||||||||||
| GG | 30/8 | 1 | 32/9 | 1 | 23/5 | 1 | 10/0 | NA | NA | ||||||
| AG | 24/6 | 0.52 (0.13–2.06) | 0.349 | 17/7 | 1.56 (0.45–5.42) | 0.483 | 27/3 | 0.49 (0.11–2.28) | 0.362 | 3/4 | NA | NA | |||
| AA | 10/1 | 0.40 (0.04–3.85) | 0.429 | 3/4 | 5.30 (0.94–29.81) | 0.058 | 3/2 | 2.93 (0.38–22.46) | 0.300 | 0 | NA | NA | |||
| rs5762746 | 0.458 | 0.351 | 0.463 | ||||||||||||
| AG | 28/10 | 1 | 21/7 | 27/6 | 1 | 10/3 | NA | NA | |||||||
| GG | 19/4 | 0.72 (0.17–3.04) | 0.658 | 22/8 | 1.26 (0.35–4.51) | 0.723 | 18/1 | 0.27 (0.03–2.39) | 0.237 | 4/0 | NA | NA | |||
| AA | 17/1 | 0.25 (0.03–2.27) | 0.219 | 7/5 | 3.09 (0.64–14.83) | 0.160 | 8/2 | 1.13 (0.19–6.70) | 0.897 | 0/1 | NA | NA | |||
| rs2236141 | 0.986 | 0.840 | NA | ||||||||||||
| GG | 47/12 | 1 | 33/13 | 1 | 34/7 | 1 | 10/4 | NA | NA | ||||||
| AG | 16/3 | 1.14 (0.24–5.36) | 0.868 | 14/7 | 1.44 (0.43–4.78) | 0.555 | 18/2 | 0.52 (0.10–2.80) | 0.446 | 3/0 | NA | NA | |||
| AA | 1/0 | NA | NA | 3/0 | NA | NA | 0 | NA | NA | 1/0 | NA | NA | |||
| s2236142 | 0.933 | 0.886 | 0.788 | 0.608 | |||||||||||
| CG | 36/9 | 1 | 25/11 | 1 | 26/5 | 1 | 9/2 | 1 | |||||||
| CC | 21/5 | 0.98 (0.23–4.12) | 0.974 | 19/7 | 0.77 (0.23–2.54) | 0.665 | 17/2 | 0.59 (0.10–3.53) | 0.562 | 4/1 | 1.421 (0.09–23.79) | 0.807 | |||
| GG | 8/1 | 0.65 (0.07–6.42) | 0.709 | 8/2 | 0.73 (0.12–4.43) | 0.732 | 11/3 | 1.13 (0.20–6.35) | 0.886 | 1/1 | 5.771 (0.18–181.6) | 0.319 | |||
aORs, 95% CIs and their corresponding P values were calculated using multivariate logistic regression analysis, adjusted for age, ECOG PS, pathologic type and TNM stage. CHEK2, cell cycle checkpoint kinase 2; SNP, single nucleotide polymorphism; N, number; NA, not available; R, reference; NP/NC, carboplatin or cisplatin plus vinorelbine; GP/GC, carboplatin or cisplatin plus gemcitabine; TP/TC, carboplatin or cisplatin plus paclitaxel; DP/DC, carboplatin or cisplatin plus docetaxel; OR, odds ratio; CI confidence interval.
Figure S1.
Linkage disequilibrium map of genotyped CHEK2 SNPs. D’ values (%) showed in this figure and the deeper the color represents the stronger LD. The figure could be showed from the Haploview Software directly. CHEK2, cell cycle checkpoint kinase 2; SNP, single nucleotide polymorphism; LD, linkage disequilibrium.
Ethical Statement: The study was approved by the ethics committee of Shanghai Pulmonary Hospital (No. 2009FK31) and written informed consent was obtained from all patients.
Footnotes
Conflicts of Interest: The authors have no conflicts of interest to declare.
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