Abstract
Purpose
Recent evidence has suggested a relationship between the baseline quality of life (QOL) self-reported by patients with cancer and genetic disposition. We report an analysis exploring relationships among baseline QOL assessments and candidate genetic variations in a large cohort of patients with lung cancer.
Patients and Methods
QOL data were provided by 1,299 patients with non–small-cell lung cancer observed at the Mayo Clinic between 1997 and 2007. Overall QOL and subdomains were assessed by either Lung Cancer Symptom Scale or Linear Analog Self Assessment measures; scores were transformed to a scale of 0 to 10, with higher scores representing better status. Baseline QOL scores assessed within 1 year of diagnosis were dichotomized as clinically deficient (CD) or not. A total of 470 single nucleotide polymorphisms (SNPs) in 56 genes of three biologic pathways were assessed for association with QOL measures. Logistic regression with training/validation samples was used to test the association of SNPs with CD QOL.
Results
Six SNPs on four genes were replicated using our split schemes. Three SNPs in the MGMT gene (adjusted analysis, rs3858300; unadjusted analysis, rs10741191 and rs3852507) from DNA repair pathway were associated with overall QOL. Two SNPs (rs2287396 [GSTZ1] and rs9524885 [ABCC4]) from glutathione metabolic pathway were associated with fatigue in unadjusted analysis. In adjusted analysis, two SNPs (rs2756109 [ABCC2] and rs9524885 [ABCC4]) from glutathione metabolic pathway were associated with pain.
Conclusion
We identified three SNPs in three glutathione metabolic pathway genes and three SNPs in two DNA repair pathway genes associated with QOL measures in patients with non–small-cell lung cancer.
INTRODUCTION
Human genetic variation can modulate the risk of developing cancer, the risk of developing symptoms related to treatment, and the outcome of cancer disease processes and treatments.1 There is emerging evidence for a genetic basis of patient-reported quality-of-life (QOL) outcomes.2 Combining two disparate fields of endeavor like genetics and QOL assessment compounds the challenges each encounters, requiring careful attention to the capabilities, complexities, and limitations of both. The successes and challenges of QOL data have been well documented, including issues such as missing data, reliability, validity, and responsiveness to change.3 Similarly, genetic investigations have inherent validity and reliability challenges.4,5 For example, results of a report published in Science that purported to have identified 150 genetic variants of longevity came into question, because different genotyping platforms had been used to scan the genome.6 The point here is that combining two fields of scientific enquiry requires careful attention to the capabilities, complexities, and limitations of both.
Fortunately, there are guidelines and established procedures for undertaking sound research in both fields. In QOL, multiple international efforts have produced techniques to resolve issues surrounding missing data and psychometrics so that patient-reported outcome (PRO) measures are actually more scientifically sound than clinician ratings.7–10 In genetic research, multiple guidelines exist11,12 to ensure high-quality scientific procedures are followed.
The concept of exploring the genetic basis of QOL was first put forward by Sloan et al13 in a 2004 American Society of Clinical Oncology plenary presentation14 as an ancillary investigation of a practice-changing colorectal cancer clinical trial15 and subsequently published in Current Problems in Cancer.16 In this study, results indicated that among the limited number of genetic and QOL variables available, there was an unusually large number of relationships among these two influences. In particular, overall QOL and fatigue were found to have strong relationships with genetic polymorphisms of the dihydropyrimidine dehydrogenase and thymidylate synthetase variants. Subsequently, an international consortium (ie, GENEQOL; http://www.geneqol.org) was formed and began a concerted effort to review the state of the science and explore ways to move forward in a methodologic manner. The first publication of this group summarized the work to date in this area and provided the scientific background for the general field.17 Subsequently, a special issue of Quality of Life Research contained a series of articles summarizing the genetic background of common symptoms such as pain,18 fatigue,19 mood,2,20 and overall well-being.21 In addition, Sprangers et al21 presented an updated theoretic version of the classic Wilson and Cleary model that incorporated genetic variables into the structure of QOL assessment, interpretation, and manifestation.
At the same time, genetic studies in lung cancer have seen similar advances.22 Findings have included an understanding of the relationship between cytokines and lung cancer QOL23 as well as the impact of smoking and health promotion behaviors on QOL of patients with lung cancer.24 This combination of scientific advances and available resources allowed for the present investigation to explore the relationship between pathway-based genetic variations and multidomain QOL in a large cohort of patients with lung cancer.
PATIENTS AND METHODS
Starting in 1997, all patients with a pathologic diagnosis of primary lung cancer evaluated and treated at Mayo Clinic (Rochester, MN) were prospectively enrolled and observed for outcome research, using protocols approved by the Mayo Clinic Institutional Review Board; all participants provided written informed consent.25,26 Procedures for identifying and observing patients with lung cancer enrolled onto this program have been previously described.25,26 The follow-up process started within 6 months after diagnosis and continued annually until death. More than 90% of eligible patients with lung cancer participated. On enrollment, all patients completed baseline health-related surveys and were then mailed similar surveys on an annual basis. Information on demographics, previous or concurrent illnesses, tobacco usage and exposure, tumor staging, and cancer therapy were abstracted by study personnel from medical records and entered into a database. Participants self-identified their race on questionnaires. Baseline QOL assessment was defined as data derived from the first completed QOL questionnaire, and as such, there were no missing data issues for the questionnaires.
QOL Assessments
QOL was assessed using both the Lung Cancer Symptom Scale27,28 and a series of numeric linear analog self-assessment measures29 to capture overall QOL and relevant domains of physical, mental, emotional, and social QOL as well as symptom measures of pain, fatigue, coughing, and dyspnea. Each QOL domain was scored on a scale of 0 to 10. A cutoff of score 5 or higher indicated a clinically meaningful deficit in that particular domain.30 These assessments have been demonstrated to be valid and reliable for assessing the QOL of patients with cancer in numerous oncology clinical trials and observational studies.31 In fact, the linear analog self-assessment measures are the most often used measures for assessing QOL in cancer control trials supported by the National Cancer Institute Division of Cancer Prevention.32 Furthermore, these particular measures of overall QOL and domains were used in the pilot exploration of a relationship between QOL and genetic variables.16
Sample Preparation and Genotyping
The 175 SNPs in 56 genes included in the study were carefully selected in a two-step process using existing genotype data from the same parent cohort of patients.33–35 All patients who consented to blood DNA samples were genotyped using two custom-designed GoldenGate panels (Illumina, San Diego, CA): one with the 470-SNP panel and the other with the 1,025-SNP panel. One hundred seventy-five SNPs were included in both panels; these formed the basis for the current analyses (Appendix Table A1, online only). Between the two panels, 70 genes were selected from multiple pathways involved in chemotherapies (platinum, taxane, gemcitabine, and epidermal growth factor receptor inhibitor) after a review of the literature. Tag SNPs were identified via SNPApp, a tag SNP selection program developed by the Bioinformatics Core at the Mayo Clinic. SNPApp queries multiple public SNP data repositories (Hapmap, SeattleSNPs, and National Institute for Environmental Health Science SNPs) that contain information on known SNPs, using ldSelect (http://droog.gs.washington.edu/ldSelect.html) to identify tag SNPs for the genes or regions of interest. SNPs within 5 kb of each gene with a minor allele frequency (MAF) ≥ 0.05 for European populations were used as candidate SNPs, and tag SNPs were identified with a pairwise linkage disequilibrium threshold of r2 ≥ 0.8. For a given gene, the SNP selection procedure used the SNP data repository with the greatest number of SNPs with an MAF ≥ 0.05 and the greatest number of linkage disequilibrium bins that met GoldenGate assay (Illumina) quality score thresholds. Nonsynomyous SNPs were preferentially selected as tags when they were identified, provided that their metrics were equivalent to those of alternative tag SNPs.
Genotyping and quality control were performed in the Mayo Clinic Genomics Shared Resource. Concordance among the three genomic control DNA samples (parents, child, trio) in duplicates and within-plate replicates was 100%. All samples were successfully genotyped, with an average call rate of 99.1%; 111 SNPs failed genotyping. Of the SNPs with genotyping data, the call rate was > 95%, and the MAF was > 0.001 for all. SNPs with a Hardy-Weinberg equilibrium (HWE) test P < .001 (n = 16) and/or monomorphic (n = 17) were excluded. In developing this article, the authors reviewed the REMARK criteria for genetics studies to ensure that all the relevant criteria were met.
Statistical Methods
Patient cases were defined as those with lung cancer with QOL score > 5, and controls were patients with lung cancer with QOL ≤ 5. This cutoff has been validated and confirmed by our group and others.30,31 Patient case/control analysis was performed across the following QOL domains: overall, pain, fatigue, coughing, and dyspnea.
Before any analysis, we assessed whether the SNP distribution varied between patient cases and controls; the allele frequencies were well matched, with a correlation coefficient of > 0.98 for each patient case/control group. We also assessed the frequency distribution at each SNP locus for HWE under the allele Mendelian biallelic expectation using the χ2 test. To investigate the association of SNPs with poor (score ≤ 5) versus good QOL (score > 5), a logistic regression that included each SNP as an independent variable was used. We considered three genetic models: additive, dominant, and recessive. The additive genetic model assumed each SNP as a continuous measure (0, 1, 2), where 0, 1, or 2 was the number of copies of the minor allele. The dominant genetic model assumed each SNP as a discrete measure (0, 1), where 0 was the zero copy of the minor allele, and 1 was at least one copy of the minor allele. The recessive model was similar to the dominant model, except that 0 represented zero and one copy of the minor allele, and 1 represented two copies of the minor allele. We considered two scenarios to study the power of the study. The first scenario used one SNP with common allele frequency (Table 1; overall unadjusted model, rs3852507). The second scenario used one SNP with a smaller allele frequency (Table 1; fatigue unadjusted model, rs9524885). In the first case, the power was 57%, and in the second, 71%, considering a type I error of 0.05.
Table 1.
Association Results: Significant Findings for Relationships Between SNPs and QOL Variables
| Split | Chromosome | Pathway | Gene | SNP | Position | No. of Patients |
MAF |
OR | 95% CI | P | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total | Patient Cases | Controls | Patient Cases | Controls | |||||||||
| Overall random unadjusted* | |||||||||||||
| One third | 10 | DNA | MGMT | rs10741191 | 131197266 | 407 | 293 | 114 | 0.45 | 0.36 | 1.38 | 1.01 to 1.88 | .0407 |
| Two thirds | 10 | DNA | MGMT | rs10741191 | 131197266 | 892 | 664 | 228 | 0.45 | 0.38 | 1.34 | 1.08 to 1.67 | .0086 |
| Overall | 10 | DNA | MGMT | rs10741191 | 131197266 | 1299 | 957 | 342 | 0.45 | 0.37 | 1.36 | 1.13 to 1.622 | < .001 |
| One third | 10 | DNA | MGMT | rs3852507 | 131289606 | 407 | 293 | 114 | 0.55 | 0.46 | 1.38 | 1.03 to 1.87 | .03385 |
| Two thirds | 10 | DNA | MGMT | rs3852507 | 131289606 | 892 | 664 | 228 | 0.51 | 0.45 | 1.27 | 1.03 to 1.58 | .02746 |
| Overall | 10 | DNA | MGMT | rs3852507 | 131289606 | 1299 | 957 | 342 | 0.52 | 0.45 | 1.30 | 1.10 to 1.55 | .00282 |
| Overall random adjusted | |||||||||||||
| One third | 10 | DNA | MGMT | rs3858300 | 131232119 | 405 | 293 | 114 | 0.40 | 0.34 | 1.48 | 1.04 to 2.11 | .03029 |
| Two thirds | 10 | DNA | MGMT | rs3858300 | 131232119 | 892 | 664 | 228 | 0.41 | 0.36 | 1.29 | 1.01 to 1.64 | .04227 |
| Overall | 10 | DNA | MGMT | rs3858300 | 131232119 | 1297 | 957 | 342 | 0.41 | 0.36 | 1.34 | 1.10 to 1.64 | .00377 |
| Fatigue random unadjusted | |||||||||||||
| One third | 14 | GSH | GSTZ1 | rs2287396 | 76863945 | 404 | 210 | 194 | 0.15 | 0.10 | 1.53 | 1.02 to 2.30 | .03887 |
| Two thirds | 14 | GSH | GSTZ1 | rs2287396 | 76863945 | 877 | 441 | 436 | 0.15 | 0.11 | 1.45 | 1.09 to 1.92 | .01 |
| Overall | 14 | GSH | GSTZ1 | rs2287396 | 76863945 | 1281 | 651 | 630 | 0.15 | 0.11 | 1.48 | 1.17 to 1.86 | < .001 |
| One third | 13 | GSH | ABCC4 | rs9524885 | 94733590 | 404 | 210 | 194 | 0.11 | 0.16 | 0.66 | 0.45 to 0.98 | .03785 |
| Two thirds | 13 | GSH | ABCC4 | rs9524885 | 94733590 | 877 | 441 | 436 | 0.11 | 0.16 | 0.68 | 0.52 to 0.89 | .00487 |
| Overall | 13 | GSH | ABCC4 | rs9524885 | 94733590 | 1281 | 651 | 630 | 0.11 | 0.16 | 0.67 | 0.54 to 0.84 | < .001 |
| Pain random adjusted | |||||||||||||
| One third | 10 | GSH | ABCC2 | rs2756109 | 101548736 | 400 | 344 | 58 | 0.39 | 0.5 | 0.62 | 0.42 to 0.94 | .02259 |
| Two thirds | 10 | GSH | ABCC2 | rs2756109 | 101548736 | 885 | 759 | 126 | 0.44 | 0.49 | 0.74 | 0.55 to 0.98 | .0373 |
| Overall | 10 | GSH | ABCC2 | rs2756109 | 101548736 | 1285 | 1103 | 184 | 0.42 | 0.49 | 0.70 | 0.55 to 0.88 | .00275 |
| One third | 13 | GSH | ABCC4 | rs9524885 | 94733590 | 400 | 344 | 58 | 0.13 | 0.20 | 0.58 | 0.35 to 0.98 | .0406 |
| Two thirds | 13 | GSH | ABCC4 | rs9524885 | 94733590 | 885 | 759 | 126 | 0.13 | 0.19 | 0.70 | 0.49 to 0.99 | .04502 |
| Overall | 13 | GSH | ABCC4 | rs9524885 | 94733590 | 1285 | 1103 | 184 | 0.13 | 0.20 | 0.66 | 0.49 to 0.88 | .00503 |
Abbreviations: GSH, glutathione; MAF, minor allele frequency; OR, odds ratio; SNP, single nucleotide polymorphism.
Random means individuals were randomly assigned to testing and validating sets.
For this analysis, we randomly split our sample into test (approximately one third of the patients) and replication samples (approximately two thirds of the patients). SNPs with P values less than .05 in the test sample were assessed for validation in the replication sample. Pooled analyses of the test and replication samples were performed on SNPs that replicated (P < .05 in each sample). Both univariable and multivariable analyses were performed. The multivariable analyses included age, sex, smoking status (never, former, current), surgery (yes or no), radiation therapy (yes or no), chemotherapy (yes or no), and performance score as covariates. Disease stage was not included in the multivariable analysis because of preliminary collinearity checks, which indicated that stage and treatment were highly correlated, and treatment was a stronger predictor than stage for QOL. Hence, only treatment was included in the multivariable analysis.
RESULTS
Patient characteristics are summarized in Table 2. This study included 1,299 patients (47% female; 53% male) who were 65 ± 10 years of age (± standard deviation). A majority were former (51%) or current (30%) smokers. Primary histologies included adenocarcinoma (51%) and squamous cell carcinoma (20%). Stage breakdown was as follows: stages I (32%), II (9%), III/limited (32%), and IV/extensive (27%). Eighty-eight percent of patients had a performance score ≤ 1. QOL domains included overall (74% with score > 5), pain (86%, score > 5), fatigue (51%, score > 5), coughing (79%, score > 5), and dyspnea (62%, score > 5). Descriptions of the QOL data are summarized in Table 3.
Table 2.
Patient Demographics and Clinical Characteristics
| Characteristic | No. | % |
|---|---|---|
| Age at diagnosis, years | ||
| No. of patients | 1,299 | |
| Mean | 64.8 | |
| SD | 10.35 | |
| Range | 18.0-93.0 | |
| Sex | ||
| Female | 605 | 46.6 |
| Male | 694 | 53.4 |
| Cigarette smoking status | ||
| Never | 248 | 19.1 |
| Former | 664 | 51.1 |
| Current | 387 | 29.8 |
| Cell type | ||
| Missing | 0 | |
| Adenocarcinoma/BAC | 659 | 50.7 |
| Squamous cell | 264 | 20.3 |
| Small cell | 135 | 10.4 |
| Other NSCLC | 179 | 13.8 |
| Carcinoid/salivary | 62 | 4.8 |
| Stage | ||
| Missing | 6 | |
| I | 409 | 31.6 |
| II | 120 | 9.3 |
| III/limited | 415 | 32.1 |
| IV/extensive | 349 | 27 |
| First performance status | ||
| No. of patients | 1,297 | |
| Mean | 0.72 | |
| SD | 0.72 | |
| Median | 1 | |
| Range | 0.0-4.0 | |
| Surgery | ||
| No | 565 | 43.5 |
| Yes | 734 | 56.5 |
| Chemotherapy | ||
| No | 400 | 30.8 |
| Yes | 899 | 69.2 |
| Radiation | ||
| No | 774 | 59.6 |
| Yes | 525 | 40.4 |
Abbreviations: BAC, bronchioloalveolar carcinoma; NSCLC, non–small-cell lung cancer; SD, standard deviation.
Table 3.
QOL Results for 1,299 Patients Reported Within 1 Year of Diagnosis
| QOL | No. | % |
|---|---|---|
| Overall > 5 | ||
| Missing | 0 | |
| No | 342 | 26.3 |
| Yes | 957 | 73.7 |
| Pain > 5 | ||
| Missing | 12 | |
| No | 184 | 14.3 |
| Yes | 1,103 | 85.7 |
| Fatigue > 5 | ||
| Missing | 18 | |
| No | 630 | 49.2 |
| Yes | 651 | 50.8 |
| Coughing > 5 | ||
| Missing | 12 | |
| No | 277 | 21.5 |
| Yes | 1,010 | 78.5 |
| Dyspnea > 5 | ||
| Missing | 14 | |
| No | 494 | 38.4 |
| Yes | 791 | 61.6 |
Abbreviation: QOL, quality of life.
From the 470 SNPs used in the analysis, six SNPs on four genes were replicated using our split schemes. Three SNPs in the MGMT gene (adjusted analysis, rs3858300; unadjusted analysis, rs10741191 and rs3852507) from DNA repair pathway were associated with overall QOL. Two SNPs (rs2287396 [GSTZ1] and rs9524885 [ABCC4]) from glutathione metabolic pathway were associated with fatigue in unadjusted analysis. Two SNPs (rs2756109 [ABCC2] and rs9524885 [ABCC4]) from the glutathione metabolic pathway were associated with pain in adjusted analysis.
Table 1 highlights the six significant SNPs and associated QOL measures by their correspondent odds ratios using an additive genetic model. Similar results were obtained using recessive and dominant genetic models (results not shown). Three SNPs (rs3858300, rs10741191, and rs10741191) on the MGMT gene were associated with 34%, 36%, and 30% higher risk of reporting a deficit in overall QOL, respectively. SNP rs2287396 on the GSTZ1 gene was associated with a 47% higher risk of reporting a deficit in fatigue. SNP rs9524885 on the ABCC4 gene was associated with a 33% lower risk of reporting clinically significant deficits in fatigue. SNPs associated with pain were rs2756109 and rs9524885 on the ABCC4 gene, with a 30% and 34% lower risk of pain, respectively.
DISCUSSION
We identified three SNPs in four glutathione metabolic pathway genes and three in two DNA repair pathway genes associated with QOL measures in patients with non–small-cell lung cancer. Our findings represent a step forward in advancing our knowledge regarding the relationship between genetic processes and PROs. In our study, overall QOL was related to SNPs on the DNA repair gene MGMT. This gene has previously been reported to be related to biologic function in patients with lung cancer.36 The proteins coded for by the MGMT gene protect cells against the somatic point mutations frequently observed in various human neoplasms. Inactivation of the MGMT gene by promoter hypermethylation is associated with increased frequency of G:C → A:T transitions in the KRAS and TP53 genes in lung cancers.36 The concept that these biologic functions translate into an impact on overall QOL is hence plausible and consistent with our previous research.
The ABCC2 and ABCC4 genes have been linked to chemotherapy resistance.37 These genes were also found to be related to fatigue in our previous work in patients with colorectal cancer.16 ABCC proteins transport various molecules across extra- and intracellular membranes and might be a means of identifying which patients with cancer are likely to experience debilitating toxicity from anticancer agents such as cisplatin. These genes transport organic anions, including protease inhibitors, and have a role in pain associated with statin therapy.38 Similarly, the GSTZ1 gene is involved in processing anticancer agents that result in fatigue. GSTZ1 is a member of the glutathione S-transferase super family, which encodes multifunctional enzymes important in the detoxification of electrophilic molecules, including carcinogens, mutagens, and several therapeutic drugs, by conjugation with glutathione.39 This enzyme also plays a role in the catabolism of phenylalanine and tyrosine. The relationship seen with fatigue in our study furthers the hypothesis that interactions at the cellular level can result in impaired signal processing, which translates at the macro level into overall fatigue.40
Several a priori hypotheses were born out by empirical study. Specifically, genetic components for patient-reported fatigue and overall QOL were identified, similar to those found in the previous work of our team and others. We previously found 21 SNPs in cytokine genes associated with symptom burden and QOL outcomes.13,14,16 Our investigation involving patients with colorectal cancer demonstrated relationships among patient-reported QOL domains and polymorphisms associated with the metabolization of anticancer chemotherapy agents.16
There are two obvious next steps. First, replication studies are needed to refine our specific genetic markers related to patient-reported QOL-related domains. Recent advances in the science of PRO assessment have shown that self-reported symptoms (phenotype) can be measured with high reliability. The GENEQOL consortium has generated from the literature a list of most likely candidate genes to pursue and has had preliminary success in validation of pain, fatigue, and overall QOL outcomes.2 This group has also presented a theoretic framework and identified biologic pathways to explain how QOL variables can have a genetic underpinning.17,21
The second step is to explore whether the newly identified genetic biomarkers can be used in a clinical setting to indicate that an individual is susceptible to experiencing deficits in QOL domains in response to cancer diagnosis, disease process, treatment, and recovery. This is similar to using other biomarkers such as CA125 or BRCA1 to identify patients at risk for cancer recurrence or identify which agent is more likely to be successful.41 There are numerous supportive care interventions available on identification of a QOL deficit risk. For example, if a patient is identified as being genetically predisposed to deficits in fatigue during cancer treatment, prophylactic monitoring can be initiated for hemoglobin levels, and patient-reported fatigue can be assessed. Interventions might include pharmacologics (eg, erythropoietin stimulating agents, ginseng), physical therapy (eg, exercise), and psychosocial initiatives (eg, stress management). One might consider less toxic regimens for such patients or have a priori protective dose modification plans to prevent or alleviate the expected fatigue. Identifying patients at high risk for QOL-related deficits can actually improve the course of treatment by maintaining patient QOL throughout the disease process. Earlier, we demonstrated that a psychosocial intervention could improve patient QOL.42 Recent work has indicated that deficits in QOL are related to increased mortality among a broad spectrum of patients with cancer.43,44
There are notable limitations of this study. The lung cancer survivors were drawn from a single Midwest institution with a population homogeneous for race, socioeconomic status, and treatment modalities. Although the sample used did represent a mixture of lung cancer histologic types, there was no evidence that QOL was histologic type specific, despite the fact that there was significant difference in survival, particularly among patients with carcinoids (typical or atypical).
The key aspect of this body of work is that the assessments have been well studied and established to be as reliable, valid, and reproducible as any other laboratory biomarker.45 Hence, given that we now know these valid PRO assessments can identify vulnerable subpopulations reliably, and we are beginning to uncover the biologic processes that lead to deficits in PRO QOL domains, we can think of these measures as just another surrogate end point biomarker (SEB). The source of the SEB may be a patient's cognitive report rather than a laboratory specimen, but psychometric research has demonstrated equal if not superior prognostic characteristics and less measurement error among PROs compared with laboratory-based alternatives. As with any SEB, PROs are not perfect, but they do not have to be perfect; they need to be useful and practical for improving cancer care. The evidence produced by this and other recent work for integrating PRO assessments into routine practice and other clinical and basic research as just another vital sign is mounting.
Appendix
Table A1.
175 SNPs Providing Basis for Current Analyses
| Pathway | Gene | Chromosome | Position | SNP |
|---|---|---|---|---|
| DNA | APEX1 | 14 | 19992989 | rs1760944 |
| DNA | ERCC1 | 19 | 50604576 | rs3212986 |
| DNA | ERCC1 | 19 | 50616202 | rs3212948 |
| DNA | ERCC2 | 19 | 50546759 | rs13181 |
| DNA | ERCC2 | 19 | 50547984 | rs1799787 |
| DNA | ERCC2 | 19 | 50549075 | rs238415 |
| DNA | ERCC2 | 19 | 50554355 | rs50871 |
| DNA | ERCC2 | 19 | 50563446 | rs1618536 |
| DNA | MGMT | 10 | 131163697 | rs1762429 |
| DNA | MGMT | 10 | 131197266 | rs10741191 |
| DNA | MGMT | 10 | 131209798 | rs12256667 |
| DNA | MGMT | 10 | 131218636 | rs11813056 |
| DNA | MGMT | 10 | 131232119 | rs3858300 |
| DNA | MGMT | 10 | 131255042 | rs511361 |
| DNA | MGMT | 10 | 131289606 | rs3852507 |
| DNA | MGMT | 10 | 131301610 | rs569235 |
| DNA | MGMT | 10 | 131339477 | rs2026976 |
| DNA | MGMT | 10 | 131342907 | rs4750761 |
| DNA | MGMT | 10 | 131364464 | rs7087131 |
| DNA | MGMT | 10 | 131379772 | rs11016879 |
| DNA | MGMT | 10 | 131394485 | rs11016884 |
| DNA | MGMT | 10 | 131396273 | rs12917 |
| DNA | MGMT | 10 | 131404381 | rs4750768 |
| DNA | MGMT | 10 | 131404460 | rs11016887 |
| DNA | MGMT | 10 | 131404852 | rs10764901 |
| DNA | MGMT | 10 | 131449800 | rs12243174 |
| DNA | MSH2 | 2 | 47500472 | rs4952887 |
| DNA | MSH3 | 5 | 80001292 | rs6151627 |
| DNA | MSH3 | 5 | 80052630 | rs181747 |
| DNA | MSH3 | 5 | 80061227 | rs6151735 |
| DNA | MSH3 | 5 | 80078031 | rs863221 |
| DNA | MSH3 | 5 | 80204693 | rs26279 |
| DNA | MSH3 | 5 | 80206890 | rs33003 |
| DNA | MSH6 | 2 | 47866350 | rs3136245 |
| DNA | MSH6 | 2 | 47872989 | rs2348244 |
| DNA | MSH6 | 2 | 47877906 | rs3136326 |
| DNA | MSH6 | 2 | 47878380 | rs3136329 |
| DNA | MSH6 | 2 | 47879198 | rs1800936 |
| DNA | OGG1 | 3 | 9763168 | rs2269112 |
| DNA | OGG1 | 3 | 9773140 | rs2072668 |
| DNA | RAD50 | 5 | 131981409 | rs17772583 |
| DNA | RAD50 | 5 | 131998784 | rs2237060 |
| DNA | RAD51 | 15 | 38775017 | rs3092981 |
| DNA | RAD51 | 15 | 38776313 | rs2619681 |
| DNA | RAD52 | 12 | 892713 | rs1051669 |
| DNA | RAD52 | 12 | 892940 | rs6413436 |
| DNA | RAD52 | 12 | 894855 | rs10744729 |
| DNA | RAD52 | 12 | 901715 | rs7962050 |
| DNA | RAD52 | 12 | 920515 | rs7311151 |
| DNA | RAD52 | 12 | 922749 | rs7307680 |
| DNA | RAD52 | 12 | 925713 | rs11064607 |
| DNA | RAD52 | 12 | 928949 | rs3748522 |
| DNA | RRM1 | 11 | 4072550 | rs12806698 |
| DNA | RRM1 | 11 | 4082585 | rs2268166 |
| DNA | XPA | 9 | 99484772 | rs2805835 |
| DNA | XPA | 9 | 99488438 | rs3176683 |
| DNA | XPA | 9 | 99499399 | rs1800975 |
| DNA | XPC | 3 | 14170683 | rs1124303 |
| DNA | XPC | 3 | 14174889 | rs2228000 |
| DNA | XPC | 3 | 14186105 | rs2733537 |
| DNA | XRCC1 | 19 | 48767476 | rs3213266 |
| GSH | ABCC1 | 16 | 15960474 | rs215101 |
| GSH | ABCC1 | 16 | 15992737 | rs152023 |
| GSH | ABCC1 | 16 | 15994167 | rs152022 |
| GSH | ABCC1 | 16 | 15996942 | rs17501331 |
| GSH | ABCC1 | 16 | 16016395 | rs1967120 |
| GSH | ABCC1 | 16 | 16018456 | rs3784863 |
| GSH | ABCC1 | 16 | 16062604 | rs17287570 |
| GSH | ABCC1 | 16 | 16077978 | rs4148350 |
| GSH | ABCC1 | 16 | 16089457 | rs2889517 |
| GSH | ABCC1 | 16 | 16106756 | rs16967755 |
| GSH | ABCC1 | 16 | 16110846 | rs3784867 |
| GSH | ABCC1 | 16 | 16113002 | rs3887893 |
| GSH | ABCC1 | 16 | 16133472 | rs212081 |
| GSH | ABCC2 | 10 | 101532568 | rs717620 |
| GSH | ABCC2 | 10 | 101537032 | rs2756105 |
| GSH | ABCC2 | 10 | 101548736 | rs2756109 |
| GSH | ABCC2 | 10 | 101556000 | rs11190291 |
| GSH | ABCC2 | 10 | 101582612 | rs4148398 |
| GSH | ABCC2 | 10 | 101584719 | rs7476245 |
| GSH | ABCC2 | 10 | 101595683 | rs3740065 |
| GSH | ABCC3 | 17 | 46080514 | rs4794173 |
| GSH | ABCC3 | 17 | 46088814 | rs4148412 |
| GSH | ABCC3 | 17 | 46090773 | rs739923 |
| GSH | ABCC3 | 17 | 46091402 | rs733392 |
| GSH | ABCC3 | 17 | 46092860 | rs1978153 |
| GSH | ABCC3 | 17 | 46101134 | rs879459 |
| GSH | ABCC3 | 17 | 46104521 | rs8075406 |
| GSH | ABCC3 | 17 | 46116104 | rs2277624 |
| GSH | ABCC3 | 17 | 46123485 | rs1051640 |
| GSH | ABCC4 | 13 | 94471792 | rs3742106 |
| GSH | ABCC4 | 13 | 94482701 | rs6492763 |
| GSH | ABCC4 | 13 | 94489513 | rs2182262 |
| GSH | ABCC4 | 13 | 94505114 | rs1189446 |
| GSH | ABCC4 | 13 | 94507073 | rs1750190 |
| GSH | ABCC4 | 13 | 94517495 | rs1189457 |
| GSH | ABCC4 | 13 | 94517795 | rs4148535 |
| GSH | ABCC4 | 13 | 94524074 | rs1189465 |
| GSH | ABCC4 | 13 | 94550690 | rs4148527 |
| GSH | ABCC4 | 13 | 94555944 | rs1729775 |
| GSH | ABCC4 | 13 | 94560006 | rs9561784 |
| GSH | ABCC4 | 13 | 94578020 | rs1189428 |
| GSH | ABCC4 | 13 | 94582157 | rs1729741 |
| GSH | ABCC4 | 13 | 94583722 | rs2766482 |
| GSH | ABCC4 | 13 | 94589729 | rs4148512 |
| GSH | ABCC4 | 13 | 94609600 | rs1750996 |
| GSH | ABCC4 | 13 | 94618853 | rs9561797 |
| GSH | ABCC4 | 13 | 94620762 | rs11568663 |
| GSH | ABCC4 | 13 | 94621240 | rs1729786 |
| GSH | ABCC4 | 13 | 94622104 | rs17189376 |
| GSH | ABCC4 | 13 | 94641435 | rs9524821 |
| GSH | ABCC4 | 13 | 94641576 | rs9524822 |
| GSH | ABCC4 | 13 | 94643273 | rs2487566 |
| GSH | ABCC4 | 13 | 94643632 | rs4148482 |
| GSH | ABCC4 | 13 | 94647911 | rs1751022 |
| GSH | ABCC4 | 13 | 94653501 | rs4773844 |
| GSH | ABCC4 | 13 | 94676843 | rs4636781 |
| GSH | ABCC4 | 13 | 94678484 | rs4773856 |
| GSH | ABCC4 | 13 | 94686672 | rs9634642 |
| GSH | ABCC4 | 13 | 94690357 | rs9524858 |
| GSH | ABCC4 | 13 | 94691788 | rs4283094 |
| GSH | ABCC4 | 13 | 94701436 | rs4148434 |
| GSH | ABCC4 | 13 | 94709901 | rs7984157 |
| GSH | ABCC4 | 13 | 94716064 | rs870004 |
| GSH | ABCC4 | 13 | 94731671 | rs7324283 |
| GSH | ABCC4 | 13 | 94733590 | rs9524885 |
| GSH | ABCC4 | 13 | 94740493 | rs7330673 |
| GSH | ABCC4 | 13 | 94749594 | rs17189561 |
| GSH | GCLC | 6 | 53474948 | rs670548 |
| GSH | GCLC | 6 | 53484050 | rs642429 |
| GSH | GCLC | 6 | 53484607 | rs542914 |
| GSH | GCLC | 6 | 53490329 | rs600033 |
| GSH | GCLC | 6 | 53509062 | rs4712035 |
| GSH | GCLM | 1 | 94126037 | rs7549683 |
| GSH | GCLM | 1 | 94145466 | rs2301022 |
| GSH | GPX2 | 14 | 64478262 | rs2737844 |
| GSH | GPX2 | 14 | 64478957 | rs1800669 |
| GSH | GPX3 | 5 | 150380794 | rs870406 |
| GSH | GPX3 | 5 | 150381489 | rs3805435 |
| GSH | GPX3 | 5 | 150387649 | rs8177447 |
| GSH | GPX5 | 6 | 28607215 | rs440481 |
| GSH | GPX6 | 6 | 28585874 | rs4713167 |
| GSH | GPX7 | 1 | 52842102 | rs3753753 |
| GSH | GPX7 | 1 | 52847120 | rs1047635 |
| GSH | GSR | 8 | 30703446 | rs8190893 |
| GSH | GSS | 20 | 32989068 | rs7265992 |
| GSH | GSS | 20 | 32993427 | rs2273684 |
| GSH | GSS | 20 | 33002660 | rs6060127 |
| GSH | GSS | 20 | 33006266 | rs6088659 |
| GSH | GSTA2 | 6 | 52723374 | rs6577 |
| GSH | GSTA2 | 6 | 52725690 | rs2180314 |
| GSH | GSTA3 | 6 | 52873719 | rs557135 |
| GSH | GSTA3 | 6 | 52874942 | rs512795 |
| GSH | GSTA4 | 6 | 52950740 | rs405729 |
| GSH | GSTA4 | 6 | 52951388 | rs3734431 |
| GSH | GSTA4 | 6 | 52954861 | rs6904769 |
| GSH | GSTA4 | 6 | 52959938 | rs3756980 |
| GSH | GSTA5 | 6 | 52811355 | rs4715352 |
| GSH | GSTA5 | 6 | 52811725 | rs4715353 |
| GSH | GSTA5 | 6 | 52816756 | rs4715354 |
| GSH | GSTM4 | 1 | 110000250 | rs1010167 |
| GSH | GSTM4 | 1 | 110001883 | rs560018 |
| GSH | GSTM4 | 1 | 110003103 | rs650985 |
| GSH | GSTM5 | 1 | 110060539 | rs3768490 |
| GSH | GSTM5 | 1 | 110062265 | rs11807 |
| GSH | GSTO1 | 10 | 106003435 | rs2164624 |
| GSH | GSTO1 | 10 | 106015248 | rs1147611 |
| GSH | GSTO2 | 10 | 106023893 | rs10491045 |
| GSH | GSTO2 | 10 | 106027884 | rs157077 |
| GSH | GSTP1 | 11 | 67110155 | rs1138272 |
| GSH | GSTZ1 | 14 | 76860625 | rs8016187 |
| GSH | GSTZ1 | 14 | 76861793 | rs8004558 |
| GSH | GSTZ1 | 14 | 76862577 | rs2270422 |
| GSH | GSTZ1 | 14 | 76863945 | rs2287396 |
| GSH | GSTZ1 | 14 | 76866511 | rs8177573 |
Abbreviations: GSH, glutathione; SNP, single nucleotide polymorphism.
Footnotes
Supported by National Institutes of Health Research Grants No. CA77118, CA80127, and CA84354 (P.Y.).
Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.
AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
The author(s) indicated no potential conflicts of interest.
AUTHOR CONTRIBUTIONS
Conception and design: Jeff A. Sloan, Mariza de Andrade, Paul Decker, Matthew Clark, Ping Yang
Financial support: Ping Yang
Administrative support: Ping Yang
Provision of study materials or patients: Ping Yang
Collection and assembly of data: Paul Decker, Jason Wampfler, Curtis Oswold, Ping Yang
Data analysis and interpretation: All authors
Manuscript writing: All authors
Final approval of manuscript: All authors
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