CYTOKINE GENE POLYMORPHISMS ASSOCIATED WITH VARIOUS DOMAINS OF QUALITY OF LIFE IN WOMEN WITH BREAST CANCER

Patients and Settings

This analysis is part of a larger, longitudinal study whose methods are described in detail elsewhere.^18–21 Women were eligible to participate if they: were ≥18 years; were scheduled to undergo unilateral breast cancer surgery; were able to read, write, and understand English; and gave written informed consent. Patients were excluded if they were having bilateral breast cancer surgery or had distant metastasis at the time of diagnosis.

Instruments

Patients completed a demographic questionnaire, the Karnofsky Performance Status (KPS) scale²² and the Self-Administered Comorbidity Questionnaire (SCQ).²³ QOL was evaluated using the Quality of Life-Scale-Patient Version (QOL-PV).^24,25 The QOL-PV consists of 41 items that measure four domains of QOL in cancer patients (i.e., physical well-being, psychological well-being, social well-being, spiritual well-being). Items are rated on a 0 to 10 numeric rating scale. Patients were asked to rate each item based on their life “at this time”. Mean subscale and total scores were calculated. Higher scores indicate better QOL. The QOL-PV has well established validity and reliability.^24,25 Cronbach’s alphas for the QOL-PV physical well-being, psychological well-being, social well-being, and spiritual well-being, as well as the total QOL score, were: .80, .86, .80, .63, and .86, respectively.

Study Procedures

The study was approved by the Committee on Human Research at the University of California, San Francisco and by the Institutional Review Board at each of the study sites. During the preoperative visit, a clinician explained the study; determined the patient’s willingness to participate; and introduced her to the research nurse. The research nurse met with the woman, determined eligibility, and obtained written informed consent prior to surgery. After obtaining consent, patients completed the enrollment questionnaires an average of 4 days prior to surgery. Patients completed the QOL-PV at enrollment and monthly for 6 months (i.e., 7 assessments). Medical records were reviewed for disease and treatment information.

Genomic analyses

Statistical Analyses for the Phenotypic Data

Data were analyzed using SPSS version 23²⁸ and STATA Version 13.²⁹ Descriptive statistics and frequency distributions were generated for sample characteristics. Parametric and non parametric tests were used to evaluate for differences in demographic and clinical characteristics among the latent classes. A p-value of <0.05 was considered statistically significant.

Unconditional growth mixture modelling (GMM) with robust maximum likelihood estimation was carried out to identify latent classes with distinct QOL trajectories using Mplus Version 5.21. These methods are described in detail elsewhere.²¹ In brief, a single growth curve that represented the “average” change trajectory was estimated for the entire sample. Then, the number of latent growth classes that best fit the data was identified using guidelines recommended in the literature.^30–32

Statistical Analyses for the Genetic Data

Allele and genotype frequencies were determined by gene counting. Hardy-Weinberg equilibrium was assessed by Chi-square or Fisher Exact tests. Measures of linkage disequilibrium ((LD), i.e., D’ and r²) were computed from the patients’ genotypes with Haploview 4.2. The LD-based haplotype block definition was based on D’ confidence interval.³³ Haplotypes were constructed using the program PHASE version 2.1.³⁴ Only inferred haplotypes that occurred with a frequency of >15% were included in the association analyses.

Ancestry informative markers (AIMs) were used to minimize confounding due to population stratification.^35–37 Homogeneity in ancestry among patients was verified by principal component analysis,³⁸ using HelixTree (GoldenHelix, Bozeman, MT). The first three PCs were selected to adjust for potential confounding due to population substructure (i.e., race/ethnicity) by including them in all of the logistic regression models. One hundred and six AIMs were included in the analysis.

For association tests, three genetic models were assessed for each SNP: additive, dominant, and recessive. The genetic model that best fit the data (i.e., most significant p-value) was selected for each SNP. Logistic regression analyses, that controlled for significant covariates, as well as genomic estimates of and self-reported race/ethnicity, were used to evaluate the associations between genotype and QOL class membership. Only those genetic associations identified as significant from the bivariate analyses were evaluated in the multivariate analyses. A backwards stepwise approach was used to create a parsimonious model. Except for genomic estimates of and self-reported race/ethnicity, only predictors with a p-value of <.05 were retained in the final model. Genetic model fit and both unadjusted and covariate-adjusted odds ratios were estimated using STATA version 13.²⁹

As was done in our previous studies,^{18,20,39–52} based on the recommendations in the literature,^53,54 as well as the implementation of rigorous quality controls for genomic data, the non-independence of SNPs/haplotypes in LD, and the exploratory nature of the analyses, adjustments were not made for multiple testing. In addition, significant SNPs identified in the bivariate analyses were evaluated further using logistic regression analyses that controlled for differences in phenotypic characteristics, potential confounding due to population stratification, and variations in other SNPs/haplotypes within the same gene. Only those SNPs that remained significant were included in the final presentation of the results. Therefore, the significant independent associations reported are unlikely to be due solely to chance. Unadjusted (bivariate) associations are reported for all of the SNPs that passed quality control criteria in Supplementary Table 1, to allow for subsequent comparisons and meta-analyses.

GMM Analyses

The fit indices used for GMM class selection are listed in Table 1. The parameter estimates for the identified classes are listed in Table 2. For physical well-being, three classes were identified (Figure 1A). For psychological well-being, a one-class solution was selected because a model with a larger number of classes was not supported (data not shown; Figure 1B). The mean psychological well-being score at enrollment was 5.76 (±1.82). This score increased slightly over time. For social well-being, two classes were identified (Figure 1C). For spiritual well-being, a one-class solution was selected because a model with a larger number of classes was not supported (data not shown; 1D). The mean spiritual well-being score at enrollment was 5.72 (±1.84). These scores remained relatively stable over time. For the total QOL scores, two classes were identified (Figure 1E).

Physical well-being

For physical well-being, the largest group of patients was named the Higher physical well-being class (n=207, 52.4%). These patients had a mean score prior to surgery of 8.931 (±0.729) and their scores remained relatively stable overtime. The second largest group was named the Lower physical well-being class (n=112, 28.4%) who had a mean enrollment score of 6.220 (±1.588) that remained relatively stable overtime. The third group was named the Changing physical well-being class (n=76, 19.2%). These patients had a mean enrollment score of 8.070 (±1.172). Their mean scores decreased to 5.767 (±1.266) at three months and then increased to 7.382 (±1.126) at six months following surgery.

Compared to the Higher class, patients in the Changing and Lower physical well-being classes were younger; were more likely to have had an axillary lymph node dissection (ALND) and to have received adjuvant CTX; and were less likely to have gone through menopause (Table 3). Compared to the Higher and Changing classes, patients in the Lower physical well-being class had a lower KPS score. Compared to the Higher class, patients in the Lower physical well-being class had a higher SCQ score; a higher body mass index (BMI); and were less likely to be White; and were less likely to have received adjuvant radiation therapy (RT). Compared to the Changing class, patients in the lower physical well-being class were more likely to have a lower income, have a higher stage of disease at the time of diagnosis, and had received neoadjuvant CTX, and less likely to have received adjuvant CTX. Compared to the Higher class, patients in the Changing class were less likely to have received RT in the six months following surgery.

Social well-being

For social well-being, one group of patients was named the Higher social well-being class (n=212, 53.8%), they had a mean enrollment score of 8.099 (±1.196) and their mean scores were relatively stable overtime. The second subgroup was named the Lower social well-being class (n=182, 46.2%). They had a mean enrollment score of 5.604 (±1.905) and their scores decreased slightly overtime.

Compared to the Higher social well-being class, patients in the Lower class were younger; less likely to be White; had a lower KPS score; and were less likely to have gone through menopause. In addition, patients in the Lower social well-being class had a higher stage of disease at diagnosis, more likely to have received neoadjuvant CTX, more likely to have had an ALND, less likely to have received adjuvant RT, and more likely to have received adjuvant CTX.

Total QOL

For total QOL, one class was named the Higher total QOL class (n=169, 42.7%). These patients had a mean enrollment score of 7.054 (±1.074) and their scores increased slightly over time. A second group was named the Lower total QOL class (n=227, 57.3%). These patients had a mean enrollment score of 5.983 (±1.318) and their scores decreased slightly overtime.

Compared to the Higher total QOL class, patients in the Lower class were significantly younger, had a lower KPS score, and were less likely to have gone through menopause. In addition, patients in the Lower class had a higher stage of disease at diagnosis, were more likely to have had an ALND, were less likely to have received adjuvant CTX, and were more likely to have received adjuvant RT.

Candidate Gene Analyses

Candidate gene analyses are summarized in Supplementary Table 1.

Physical well-being

In the ordinal logistic regression analyses for physical well-being, after controlling for age, KPS score, BMI, receipt of CTX during the six months after surgery (i.e., adjuvant CTX), as well as self-reported and genomic estimates of race/ethnicity, and other significant variants in the same gene, only the models fit for CXCL8 rs4073, NFKB2 rs11574849, and TNFSF rs1800683 remained significant. Pairwise comparisons revealed that the relationship between subgroup membership and the CXCL8 genotype was due to differences in genotype frequencies between the Higher versus Changing physical well-being class (p = .002, Table 4, Figure 2A). Patients who were homozygous for the rare “A” allele had an 80% decrease in the odds of belonging to the Changing physical well-being class.

For NFKB2 rs11574849, pairwise comparisons revealed that the relationship between subgroup membership and genotype was due to differences in genotype frequencies between the Changing versus the Lower physical well-being classes (p = .016, Table 4, Figure 2B). Patients who were heterozygous or homozygous for the rare “A” allele had a 76% decrease in the odds of belonging to the Lower physical well-being class.

For TNFSF rs1800683, pairwise comparisons revealed that the relationship between subgroup membership and genotype was due to differences in the genotype frequencies between the Changing versus the Lower physical well-being classes (p = .013, Table 4, Figure 2C). Patients who were heterozygous or homozygous for the rare “A” allele had a 2.73 increased odds of belonging to the Lower physical well-being class.

Social well-being

In the regression analyses, after controlling for age, KPS score, receipt of adjuvant CTX, as well as self-reports and genomic estimates of race/ethnicity and other significant covariates in the same gene, the model fit for IL1B rs1143623 remained significant. Patients who were heterozygous or homozygous for the rare “C” allele had a 1.94 increased odds of belonging to the Lower social well-being class (p = .018, Table 5, Figure 3A).

Total QOL

In the regression analyses for total QOL, after controlling for age, KPS score, receipt of adjuvant CTX, as well as self-reports and genomic estimates of race/ethnicity and other significant variations in the same gene, the models fit for IL13 rs1881457 and NFKB1 rs4648068 remained significant. Patients who were heterozygous or homozygous for the rare “C” allele for IL13 rs1881457 had a 1.78 increased odds of belonging to the Lower total QOL class (p = .033, Table 6, Figure 3B). Patients who were homozygous for the rare “G” allele for NFKB1 rs4648068 had a 3.12 increased odds of belonging to the Lower total QOL class (p = .005, Table 6, Figure 3C).

Demographic and Clinical Characteristics Associated with Latent Class Memberships

Younger age, poorer functional status, and receipt of adjuvant CTX were the three phenotypic characteristics that remained significant in all the multivariable models. Consistent with previous reports,^61,62 younger women were more likely to be in the worse QOL classes. Perhaps younger cancer patients have more responsibilities⁶³ and/or experience a higher symptom burden during treatment.⁶⁴ However, in two studies of women following breast cancer surgery with a similar age profile,^8,55 older age was associated with Lower physical well-being scores. This inconsistent finding may be related to differences in comorbidities that can affect physical well-being.

Consistent with previous studies,^65,66 lower functional status was associated with membership in the Lower physical and social well-being as well as overall QOL classes. In our study, the mean KPS score for the patients in the lower physical well-being class indicates that they reported ‘some’ signs or symptoms of disease that required effort to carry on normal activity. In addition, the difference in KPS scores between the two physical well-being classes represents not only a statistically significant but a clinically meaningful difference in this score (i.e., Cohen’s d = 0.95).⁶⁷ While the differences in KPS scores between the social well-being and total QOL classes were statistically significant, they were not clinically meaningful. These inconsistent findings may be explained by the fact that the overall KPS score for the entire sample was high (i.e., 93.223 (±10.296)). An alternative explanation may be that even small decrements in functional status can have a differential impact on various domains, as well as overall QOL.⁶⁸

Consistent with previous reports,^6,8,69–71 receipt of adjuvant CTX was associated with membership in the lower QOL classes. The higher symptom burden associated with CTX may result in poorer physical and social well-being as well as overall QOL. For example, increased pain during intercourse from vaginal dryness as a side effect of CTX⁷¹ may lead to decreased satisfaction with sex life and decreased support from partners contributing to poorer social well-being.⁷²

BMI remained significant in only one of the ordinal logistic regression analyses, namely for physical well-being. Consistent with previous reports,^73,74 compared to the Higher class, patients in the Changing physical well-being class had a higher BMI. While the mean BMIs for both classes are in the ‘overweight’ category,⁷⁵ this finding suggests that even small decreases in BMI may improve the physical well-being of women following breast cancer surgery.

Polymorphims Associated with Latent Class Memberships

This study is the first to evaluate for associations between polymorphisms in cytokine genes and QOL outcomes in women following breast cancer surgery. Of note, across the three QOL outcomes, the genetic associations were completely different. For physical well-being, three genes (i.e., CXCL8, NFKB2, TNFSF) were associated with membership in lower QOL classes. For CXCL8 rs4073, patients who were homozygous for the rare “A” allele had a decreased risk of being in the Changing physical well-being class compared to the Higher physical well-being class. According to SNPinfo, this SNP is located in the promoter region of the gene and may alter gene expression by changing transcriptional factor binding sites (TFBS) for transcriptional factors involved in the transcription of DNA into ribonucleic acid (RNA). CXCL8 belongs to a family of chemokines that are responsible for the elimination of pathogens through recruitment and activation of leukocytes during inflammation.⁷⁶ Carrying the rare “A” allele for CXCL8 rs4073 is associated with a variety of conditions including cancer susceptibility,^77–79 infection,⁸⁰ and chronic inflammation.^81–83 Consistent with our findings, in a previous study,⁸⁴ patients who were homozygous for the rare “A” allele in CXCL8 rs4073 had a lower risk of CTX toxicities. Perhaps carriers of the rare “A” allele experience fewer symptoms or adverse effects from treatment that contribute to decrements in physical well-being.

For NFKB2 rs11574849, patients who were homozygous for the rare “A” allele had a decreased risk of being in the Lower physical well-being class than in the Changing class. NFKB2 encodes for one-half of the NFKB protein, which is a central transcriptional modulator of inflammation.⁸⁵ NFKB2 rs11574849 is located in the intron region of the gene and has no known function. While no studies were identified that evaluated this SNP, it may be that this SNP is in LD with other functional variants of the same gene.

For TNFSF rs1800683, patients who were heterozygous or homozygous for the rare “A” allele had an increased risk of being in the Lower physical well-being class compared to the Changing physical well-being class. TNFSF rs1800683 is located in the coding region of the lymphotoxin-alpha (LTA) gene and is part of the promoter region of TNFSF. According to SNPinfo, this SNP may affect TNFSF expression by altering TFBS or disrupting messenger RNA (mRNA) splicing and affect LTA protein function. TNFSF and LTA are involved in a broad range of biologic functions including immune responses.⁸⁶ Consistent with the findings from this study, the rare “A” allele for TNFSF rs1800683 was associated with higher fatigue⁸⁷ and wake after sleep onset in adults living with HIV/AIDS,88 and less evening energy in oncology patients and their family caregivers.⁴³

Only one SNP was associated with social well-being class membership. For IL1B rs1143623, patients who were heterozygous or homozygous for the rare “C” allele had an increased risk of being in the Lower social well-being class. According to SNPinfo, IL1B rs1143623 is located in the promoter region of the gene and may alter gene expression by changing TFBS. IL1B is involved in a number of cellular activities including cell proliferation, differentiation, and apoptosis, as well as being an important mediator of acute and chronic inflammatory responses.⁸⁹ The rare “C” allele in this SNP was associated with: gout,⁹⁰ reduced drug efficacy in type 2 diabetes patients,⁹¹ cancer,^92,93 higher triglycerides and cholesterol,⁹⁴ and rheumatoid arthritis.⁹⁵ The relationship between this SNP and social well-being requires further exploration. However, the variety in the conditions associated with this SNP highlights the potential widespread involvement of this SNP in cellular activities.

Two SNPs were associated with overall QOL class membership. For IL13 rs1881457, patients who were heterozygous or homozygous for the rare “C” allele had an increased risk of being in the Lower total QOL class. According to SNPinfo, IL13 rs1881457 is located in the promoter region of the gene and may alter gene expression by affecting TFBS. Critical to immune responses to allergens, IL13 induces antibody synthesis.⁹⁶ In a study of patients with non-small cell lung cancer undergoing CTX,⁹⁷ decreased survival and greater disease recurrence were reported in carriers of the rare “C” allele for IL13 rs1881457. Our finding is partially supported by a review of the literature⁹⁸ that highlights the association between poorer QOL scores and poorer survival in cancer patients.

For NFKB1 rs4648068, patients who were homozygous for the rare “G” allele had an increased risk of being in the Lower total QOL class. NFKB1 rs4648068 is located in the intron region of NFKB1 and has no known function. In a limited number of studies, rs4648068 was associated with increased risk of ovarian⁹⁹ and gastric^100–102 cancer. This study is the first to identify an association between NFKB1 rs4648068 and outcomes other than cancer risk.

It should be noted that this study’s findings and conclusions are preliminary and require replication. The generalizability of our findings is limited to breast cancer patients. Indeed, none of the cytokine gene associations identified in our previous study of QOL outcomes in oncology patients and their FCs were significant in the current study.^16,17 Reasons for these differences may be related to: differences in participant characteristics such as the inclusion of FCs in the sample and/or differences in the impact or timing of different cancer treatments on QOL outcomes. Lastly, although our sample size was sufficient, larger samples may identify additional latent classes, as well as other phenotypic and genotypic associations.

Conclusions

Findings from this study confirm that women experience distinctly different physical well-being, social well-being, and total QOL outcomes during and following breast cancer surgery. Factors such as younger age, poorer functional status, higher BMI, and receipt of adjuvant CTX may place women at greater risk for poorer QOL. While most of these factors were reported previously, they should be used by clinicians to identify high risk patients prior to surgery. This type of risk profiling may allow for more tailored interventions to support these patients. Furthermore, understanding unique genomic markers would allow for earlier identification of cancer patients at higher risk for poorer QOL outcomes.