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Address correspondence to: Kord M. Kober, PhD, Department of Physiological Nursing, University of California, 2 Koret Way—N631Y, San Francisco, CA 94143-0610, USA.
Little is known about the phenotypic and molecular characteristics associated with changes over time in fatigue and lack of energy in patients with breast cancer.
Objectives
The aim of this study was to identify subgroups (i.e., latent classes) of women with distinct fatigue and energy trajectories; evaluate for differences in phenotypic characteristics between the latent classes for fatigue and energy; and evaluate for associations between polymorphisms in genes for pro- and anti-inflammatory cytokines, their receptors, and their transcriptional regulators and latent class membership.
Methods
Patients were enrolled before and followed for six months after breast cancer surgery. Latent class analyses were done to identify subgroups of patients with distinct fatigue and energy trajectories. Candidate gene analyses were done to identify cytokine genes associated with these two symptoms.
Results
For both fatigue and lack of energy, two distinct latent classes were identified. Phenotypic characteristics associated with the higher fatigue class were younger age, higher education, lower Karnofsky Performance Status score, higher comorbidity, higher number of lymph nodes removed, and receipt of chemotherapy (CTX). Polymorphisms in interleukin (IL) 1β and IL10 were associated with membership in the higher fatigue class. Phenotypic characteristics associated with the lower energy class included: a lower Karnofsky Performance Status score and a higher comorbidity score. A polymorphism in IL1R1 was associated with membership in the lower energy class.
Conclusion
Within each latent class, the severity of fatigue and decrements in energy were relatively stable over the first six months after breast cancer surgery. Distinct phenotypic characteristics and genetic polymorphisms were associated with membership in the higher fatigue and lower energy classes.
Although more than 90% of patients diagnosed with breast cancer will undergo surgery, only a limited number of longitudinal studies have evaluated for preoperative levels of fatigue and changes in fatigue after surgery.
Only one study was identified that used a type of growth mixture modeling (GMM) to identify subgroups of breast cancer patients (n = 290) with distinct fatigue trajectories.
Using fatigue assessments done before and at four and five months after surgery, groups of patients with persistently high (21%) and persistently low (79%) levels of fatigue were identified. Patients in the high group were more likely to report lower levels of physical activity and higher levels of anxiety. No demographic or clinical characteristics were associated with membership in the higher fatigue group. Although this study provides important information on persistent fatigue, only three assessments were done (i.e., four weeks before surgery and four and eight months after surgery) and the confidence intervals (CIs) were wide, which suggests that a larger sample could result in more reliable estimates.
In oncology, fatigue is defined as a distressing, persistent sense of physical, emotional, and/or cognitive tiredness for exhaustion related to cancer or cancer treatment that is not proportional to recent activity and interferes with usual functioning.
have separate scales for fatigue-inertia and energy-vigor. The energy subscale of the POMS evaluates the intensity of energy using a variety of descriptors (e.g., energetic, full of pep). Like the POMS, the Lee Fatigue Scale (LFS) has two subscales (i.e., a fatigue subscale with 13 items and an energy subscale with five items). The LFS asks participants to rate their level of energy using a 0 to 10 numeric rating scale (NRS) on five descriptors (i.e., energetic, active, vigorous, efficient, lively). The original psychometric evaluation of the LFS identified these two distinct subscales.
In this study, although 32% of the women reported clinically meaningful levels of fatigue, nearly 50% of the patients reported clinically meaningful decrements in energy levels before surgery. No studies were identified that evaluated for subgroups of patients with distinct energy trajectories from before to six months after breast cancer surgery.
Although some progress has been made in evaluating the role of inflammatory mediators in the development and maintenance of fatigue, recent reviews of fatigue in patients with cancer recommend that additional research be done to establish the underlying mechanisms for fatigue.
In all these reviews, emphasis is placed on understanding the molecular mechanisms that underlie the development of persistent fatigue in oncology patients.
Cytokines, their receptors, and transcriptional regulators are one class of polypeptides that mediate inflammatory processes (for reviews, see Fagundes et al.,
genetic and epigenetic mechanisms involved in inflammation were associated with fatigue in oncology patients. Only one study was found that reported on associations between polymorphisms in cytokine genes and decrements in energy in oncology patients undergoing radiation therapy and their family caregivers.
This preliminary evidence provides support for the role of molecular mechanisms involved in inflammatory processes, particularly cytokines, in the etiology of fatigue and decrements in energy in oncology patients.
Given the paucity of longitudinal studies that aimed to determine distinct phenotypes for fatigue and energy after breast cancer surgery and the need for more molecular-based studies, the purposes of this study, in a sample of women (n = 398) who were assessed before and monthly for six months after surgery were to: identify subgroups (i.e., latent classes) of women with distinct fatigue and energy trajectories, evaluate for differences in phenotypic characteristics between the latent classes for fatigue and energy, and evaluate for associations between polymorphisms in genes for pro- and anti-inflammatory cytokines, their receptors, and their transcriptional regulators and latent class membership. We hypothesized that at least two latent classes would be identified for each symptom using GMM and distinct phenotypic characteristics and genetic polymorphisms would be associated with higher fatigue and lower energy latent class membership.
Materials and Methods
Patients and Settings
This analysis is part of a larger, longitudinal study that evaluated neuropathic pain and lymphedema in women who underwent breast cancer surgery. The study methods are described in detail elsewhere.
In brief, patients were recruited from breast care centers located in a Comprehensive Cancer Center, two public hospitals, and four community practices.
Patients were eligible to participate if they were adult women (≥18 years) who were scheduled to undergo unilateral breast cancer surgery; were able to read, write, and understand English; agreed to participate; 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. A total of 516 patients were approached, 410 were enrolled (response rate 79.5%), and 398 completed the enrollment assessment. The most common reasons for refusal were too busy, overwhelmed with the cancer diagnosis, or insufficient time available to do the enrollment assessment before surgery.
Instruments
The demographic questionnaire obtained information on age, marital status, education, ethnicity, employment status, and living situation. Patients rated their functional status using the Karnofsky Performance Status (KPS) scale that ranged from 30 (I feel severely disabled and need to be hospitalized) to 100 (I feel normal; I have no complaints or symptoms).
Patients were asked to indicate if they had one of 13 common medical conditions; if they received treatment for it (proxy for disease severity); and did it limit their activities (indication of functional limitations). For each condition, a patient can receive a maximum of 3 points. The total SCQ score can range from 0 to 39 points. The SCQ has well-established validity and reliability.
Identification of candidate categories of the International Classification of Functioning Disability and Health (ICF) for a generic ICF core set based on regression modelling.
Each item was rated on a 0 to 10 NRS. Total fatigue and energy scores were calculated as the mean of the 13 fatigue items and the five energy items, with higher scores indicating greater fatigue severity and higher levels of energy. Patients were asked to rate each item based on how they felt “right now.” The LFS has been used with healthy individuals
In the present study, Cronbach alphas for the fatigue and energy scales were 0.96 and 0.93, 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 boards 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 before surgery. After obtaining consent, patients completed the enrollment questionnaires an average of four days before surgery. Patients completed the LFS at enrollment and monthly for six months (i.e., seven assessments). Medical records were reviewed for disease and treatment information.
Genomic Analyses
Gene Selection
Cytokines, their receptors, and their transcriptional regulators are classes of polypeptides that mediate pro- and anti-inflammatory processes. Cytokine dysregulation is associated with fatigue (for reviews, see Bower and Ganz,
). Pro-inflammatory genes promote systemic inflammation and include chemokine (C-C-C motif) ligand 8 (CXCL8, previous gene symbol interleukin 8 [IL8]), interferon gamma [IFNG], IFNG receptor 1 (IFNGR1), IL1 receptor 1 [IL1R1], IL2, IL17A, and members of the tumor necrosis factor (TNF) family (i.e., lymphotoxin alpha [LTA], TNF). Anti-inflammatory genes suppress the activity of pro-inflammatory cytokines and include IL1R2, IL4, IL10, and IL13. Of note, IFNG1, IL1B, and IL6 possess pro- and anti-inflammatory functions. Nuclear factor kappa beta 1 (NFKB1) and NFKB2 are transcriptional regulators of these cytokine genes.
All genes were identified according to the approved symbol stored in the Human Genome Organization Gene Nomenclature Committee database (http://www.genenames.org).
Blood Collection and Genotyping
Of the 398 patients who completed the enrollment assessment, 310 provided a blood sample from which DNA could be isolated from peripheral blood mononuclear cells. Genomic DNA was extracted from peripheral blood mononuclear cells using the PUREGene DNA Isolation System (Invitrogen, Carlsbad, CA). DNA was quantitated with a Nanodrop Spectrophotometer (ND-1000) and normalized to a concentration of 50 ng/L. Genotyping was performed blinded to clinical status, and positive and negative controls were included. Samples were genotyped using a custom array on the Golden Gate genotyping platform (Illumina, San Diego, CA) and processed according to the standard protocol using GenomeStudio (Illumina).
SNP Selection
A combination of tagging single-nucleotide polymorphism (SNPs) and literature-driven SNPs were selected for analysis. Tagging SNPs were required to be common (i.e., a minor allele frequency ≥0.05) in public databases. SNPs with call rates of less than 95% or Hardy-Weinberg P-values of <0.001 were excluded. As shown in Supplementary Table 1, 83 SNPs from a total of 104 SNPs among 16 candidate genes passed all the quality control filters and were included in the genetic association analyses. Localization of SNPs on the human genome was performed using the GRCh38 human reference assembly. Regional annotations were identified using the University of California Santa Cruz Human Genome Browser GRCh38/hg38 (http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg38). Potential regulatory involvement of SNPs was investigated using a number of Encode data tracks.
The fatigue and energy data were analyzed separately. Descriptive statistics and frequency distributions were generated for sample characteristics. Independent-sample t-tests, Mann-Whitney U tests, chi-squared analyses, and Fisher exact tests were used to evaluate for differences in demographic and clinical characteristics between the two latent classes. A P-value of <0.05 was considered statistically significant.
Unconditional GMM with robust maximum likelihood estimation was carried out to identify latent classes with distinct fatigue and energy trajectories using Mplus, version 5.21 (Muthén & Muthén, Los Angeles, CA). These methods are described in detail elsewhere.
In brief, a single growth curve that represented the “average” change trajectory was estimated for the whole sample. Then, the number of latent growth classes that best fit the data was identified using guidelines recommended in the literature.
Allele and genotype frequencies were determined by gene counting. Hardy-Weinberg equilibrium was assessed by the chi-squared or Fisher exact tests. Measures of LD (i.e., D′ and R2) were computed from the patients' genotypes with Haploview 4.2 (https://www.broadinstitute.org/haploview/haploview). The LD-based haplotype block definition was based on D′ CI.
For SNPs that were members of the same haploblock, haplotype analyses were conducted to localize the association signal within each gene and to determine if haplotypes improved the strength of the association with the phenotype. Haplotypes were constructed using the program PHASE, version 2.1.
To improve the stability of haplotype inference, the haplotype construction procedure was repeated five times using different seed numbers with each cycle. Only haplotypes that were inferred with probability estimates of 0.85 or more, across the five iterations, were retained for downstream analyses. Only inferred haplotypes that occurred with a frequency estimate of ≥15% were included in the association analyses, assuming a dosage model (i.e., analogous to the additive model).
Ancestry informative markers were used to minimize confounding because of population stratification.
A panel of ancestry informative markers for estimating individual biogeographical ancestry and admixture from four continents: utility and applications.
using HelixTree (GoldenHelix, Bozeman, MT). Briefly, the number of PCs was sought that distinguished the major racial/ethnic groups in the sample by visual inspection of scatter plots of orthogonal PCs (i.e., PC1 vs. PC2, PC2 vs. PC3). This procedure was repeated until no discernable clustering of patients by their self-reported race/ethnicity was possible (data not shown). The first three PCs were selected to adjust for potential confounding because of population substructure (i.e., race/ethnicity) by including them in all the logistic regression models. One hundred six ancestry informative markers were included in the analysis.
For association tests, three genetic models were assessed for each SNP: additive, dominant, and recessive. Barring trivial improvements (i.e., delta <10%), the genetic model that best fit the data, by maximizing the significance of the P-value, was selected for each SNP. Logistic regression analysis, that controlled for significant covariates, and genomic estimates of and self-reported race/ethnicity, was used to evaluate the associations between genotype and fatigue and energy class membership. Only those genetic associations identified as significant from the bivariate analyses were evaluated in the multivariate analyses. A backward 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 <0.05 were retained in the final model. Genetic model fit and both unadjusted and covariate-adjusted odds ratios were estimated using STATA, version 13.
Associations between cytokine genes and a symptom cluster of pain, fatigue, sleep disturbance, and depression in patients prior to breast cancer surgery.
Preliminary evidence of an association between an interleukin 6 promoter polymorphism and self-reported attentional function in oncology patients and their family caregivers.
and the implementation of rigorous quality controls for genomic data, the nonindependence 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 because of 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 the SNPs that passed quality control criteria in Supplementary Table 1, to allow for subsequent comparisons and meta-analyses.
Results
GMM Analysis for Fatigue
Two distinct latent classes of fatigue trajectories were identified using GMM (Fig. 1a). A two-class model was selected because its Bayesian Information Criterion was smaller than the one-class and three-class models (Table 1). As listed in Table 2, the majority of the patients were classified into the Higher Fatigue class (n = 244, 61.5%). These patients had estimated fatigue scores that were high before surgery (3.90) and remained high over the six months of the study. Patients in the Lower Fatigue class (n = 153, 38.5%) had estimated fatigue scores that were lower at enrollment (1.60) and that gradually decreased over time.
Fig. 1Observed and estimated a) fatigue and b) energy trajectories for patients in each of the latent classes and the mean fatigue and energy scores for the total sample.
Two-class model was selected. The BIC was smaller than for the one-class and three-class models, and the BLRT indicated that the two-class solution fit the data better than the one-class solution.
Two-class model was selected. The BIC was smaller than for the one-class and three-class models, and the BLRT indicated that the two-class solution fit the data better than the one-class solution.
AIC = Akaike information criterion; BIC = Bayesian information criterion; BLRT = parametric bootstrapped likelihood ratio test for K-1 (H0) vs. K classes; CFI = comparative fit index; GMM = growth mixture model; LL = log-likelihood; n/a = not applicable; ns = not significant; RMSEA = root mean squared error of approximation; VLMR = Vuong-Lo-Mendell-Rubin likelihood ratio test for K-1 (H0) vs. K classes.
a Latent growth curve with linear and quadratic components; χ2 = 34.60, 19 df, P < 0.02, CFI = 0.99, RMSEA = 0.045.
b Two-class model was selected. The BIC was smaller than for the one-class and three-class models, and the BLRT indicated that the two-class solution fit the data better than the one-class solution.
c P < 0.05.
d P < 0.01.
e Latent growth curve with linear and quadratic components; χ2 = 50.99, 24 df, P = 0.001, CFI = 0.964, RMSEA = 0.053.
Differences in Demographic and Clinical Characteristics Between the Fatigue Classes
As summarized in Table 3, patients in the Higher Fatigue class were significantly younger and had more education, a lower KPS score, a higher SCQ score, and a higher number of lymph nodes removed. In addition, a higher percentage of patients in the Higher Fatigue class had received neoadjuvant chemotherapy (CTX) before surgery and adjuvant CTX during the first six months after breast cancer surgery.
Table 3Differences in Demographic and Clinical Characteristics between the Lower Fatigue (n = 153) and Higher Fatigue (n = 244) Classes
Characteristic
Lower Fatigue Class, n = 153 (38.4%) Mean (SD)
Higher Fatigue Class, n = 244 (61.3%) Mean (SD)
Statistic and P Value
Age (years)
57.8 (11.9)
53.1 (11.0)
t = 4.09, P < 0.0001
Education (years)
15.3 (2.5)
15.9 (2.8)
t = −2.02, P = 0.04
Karnofsky Performance Status score
96.6 (7.0)
91.1 (11.4)
t = 5.86, P < 0.0001
Self-administered Comorbidity Questionnaire score
3.8 (2.6)
4.6 (3.0)
t = −2.64, P = 0.009
Fatigue severity score at enrollment
1.6 (1.6)
4.1 (2.2)
t = −12.55, P < 0.0001
Number of breast biopsies in past year
1.5 (.8)
1.5 (.8)
U, P = 0.47
Number of positive lymph nodes
0.8 (1.9)
1.0 (2.4)
t = −0.88, P = 0.38
Number of lymph nodes removed
4.8 (5.1)
6.4 (7.5)
t = −2.43, P = 0.016
n (%)
n (%)
Ethnicity
χ2 = 2.82, P = 0.42
White
100 (65.8)
155 (63.8)
Black
19 (12.5)
21 (8.6)
Asian/Pacific Islander
17 (11.2)
32 (13.2)
Hispanic/mixed ethnic background/other
16 (10.5)
35 (14.4)
Married/partnered (% yes)
64 (42.1)
100 (41.5)
FE, P = 0.92
Work for pay (% yes)
71 (46.4)
118 (49.0)
FE, P = 0.68
Lives alone (% yes)
40 (26.5)
54 (22.4)
FE, P = 0.40
Gone through menopause (% yes)
96 (63.6)
151 (64.3)
FE, P = 0.91
Stage of disease
U, P = 0.13
0
29 (19.0)
44 (18.0)
I
66 (43.1)
85 (34.8)
IIA and IIB
48 (31.4)
92 (37.7)
IIIA, IIIB, IIIC, and IV
10 (6.5)
23 (9.4)
Surgical treatment
FE, P = 1.00
Breast conservation
123 (80.4)
195 (79.9)
Mastectomy
30 (19.6)
49 (20.1)
Sentinel node biopsy (% yes)
130 (85.0)
197 (80.7)
FE, P = 0.34
Axillary lymph node dissection (% yes)
50 (32.7)
98 (40.3)
FE, P = 0.14
Breast reconstruction at the time of surgery (% yes)
33 (21.7)
53 (21.7)
FE, P = 1.00
Neoadjuvant chemotherapy (% yes)
21 (13.7)
58 (23.9)
FE, P = 0.014
Radiation therapy during the first 6 months (% yes)
87 (56.9)
137 (56.1)
FE, P = 0.92
Chemotherapy during the first 6 months (% yes)
36 (23.5)
97 (39.8)
FE, P = 0.001
FE = Fisher exact test; SD = standard deviation; U = Mann-Whitney U test.
As summarized in Supplementary Table 1, no associations with fatigue latent class membership were observed for SNPs in CXCL8, INFGR1, IL1R2, IL2, IL4, IL6, IL13, 1L17 A, NFKB1, NFKB2, or members of the TNF family (i.e., LTA, TNF). However, genotype frequencies were significantly different between the two latent classes for eight SNPs spanning three genes: IFNG rs2069718, IL1B rs1143629, IL1B rs1143627, IL1B rs16944, IL1B rs1143623, IL10 rs3024496, IL10 rs1878672, and IL10 rs3024491.
Regression Analyses for IFNG, IL1B, and IL10 Genotypes and Lower vs. Higher Fatigue Classes
To better estimate the magnitude (i.e., odds ratio) and precision (95% CI) of genotype on the odds of belonging to the higher compared with the Lower fatigue class, multivariate logistic regression models were fit. In these regression analyses, that included genomic estimates of and self-reported race/ethnicity, the only phenotypic characteristics that remained significant in the multivariate model were age (in five-year increments), KPS score (in 10 unit increments), SCQ score, and receipt of CTX in the six months after surgery.
Two SNPs spanning two different genes remained significant in the multivariate logistic regression analyses (Table 4, Figs. 2a and 2b). For IL1B rs16944 and IL10 rs3024496, a recessive model fit the data best (P = 0.002). In the regression analysis for IL1B rs16944, carrying two doses of the rare A allele (i.e., GG + GA vs. AA) was associated with a 2.98-fold higher odds of belonging to the Higher Fatigue class. In the regression analysis for IL10 rs3024496, carrying two doses of the rare C allele (i.e., TT + TC vs. CC) was associated with a 66% decrease in the odds of belonging to the Higher Fatigue class.
Table 4Multiple Logistic Regression Analyses for Cytokine Genes and Lower Fatigue vs. Higher Fatigue Classes
Predictor
Odds Ratio
Standard Error
95% CI
Z
P-Value
IL1B rs16944
2.98
1.215
1.336, 6.626
2.67
0.008
Age
0.96
0.012
0.936, 0.984
−3.17
0.002
KPS score
0.94
0.017
0.911, 0.978
−3.21
0.001
SCQ score
1.10
0.062
0.998, 1.243
1.73
0.083
Any chemotherapy
2.26
0.655
1.284, 3.992
2.83
0.005
Overall model fit: χ2 = 56.98, P < 0.0001
IL10 rs3024496
0.34
0.120
0.172, 0.682
−3.05
0.002
Age
0.95
0.013
0.930, 0.979
−3.58
<0.001
KPS score
0.95
0.017
0.916, 0.980
−3.09
0.002
SCQ score
1.12
0.063
1.007, 1.255
2.08
0.037
Any chemotherapy
2.32
0.675
1.315, 4.106
2.90
0.004
Overall model fit: χ2 = 60.96, P < 0.0001
Any chemotherapy = receipt of chemotherapy within six months after surgery; CI = confidence interval; IL1B = interleukin 1 beta; KPS = Karnofsky Performance Status; SCQ = Self-administered Comorbidity Questionnaire.
Multiple logistic regression analyses of candidate gene associations with Lower Fatigue vs. Higher Fatigue classes (n = 301). For each model, the first three principal components identified from the analysis of ancestry informative markers, and self-reported race/ethnicity, were retained in all models to adjust for potential confounding because of race/ethnicity (data not shown). For the regression analyses, predictors evaluated in each model included genotype (IL1B rs16944: GG + GA vs. AA; IL10 rs3024496: TT + TC vs. CC, age (five-year increments), functional status (KPS score in 10-unit increments), self-administered comorbidity questionnaire score, and receipt of chemotherapy within six months after surgery).
Fig. 2a) Differences between the fatigue latent classes in the percentages of patients who were homozygous or heterozygous for the common allele (GG + GA) or homozygous for the rare allele (AA) for rs16944 in interleukin 1 beta (IL1B). Values are plotted as unadjusted proportions with corresponding P-value. b) Differences between the fatigue latent classes in the percentages of patients who were homozygous or heterozygous for the common allele (TT + TC) or homozygous for the rare allele (CC) for rs3024496 in interleukin 10 (IL10). Values are plotted as unadjusted proportions with corresponding P-value.
Two distinct latent classes of energy trajectories were identified using GMM (Fig. 1b). A two-class model was selected because its Bayesian Information Criterion was smaller than the one-class and three-class models (Table 1). As listed in Table 2, the majority of the patients were classified into the Lower Energy class (n = 270, 68.0%). These patients had estimated energy scores that were low before surgery (4.35) and remained low over the six months of the study. Patients in the Higher Energy class (n = 127, 32.0%) had estimated energy scores that were higher at enrollment (5.82) and that gradually increased over time.
Differences in Demographic and Clinical Characteristics Between the Energy Classes
As summarized in Table 5, patients in the Lower Energy class had a lower KPS score and a higher SCQ score. In addition, the percentage of patients based on their stage of disease differed between the two energy classes. However, post hoc contrasts failed to identify the subgroups that differed between the classes.
Table 5Differences in Demographic and Clinical Characteristics between the Higher Energy (n = 127) and Lower Energy (n = 270) Classes
Post hoc contrasts of the difference in stage of disease between the Higher Energy and Lower Energy classes failed to identify the subgroups who differed between the classes (P < 0.0083).
0
29 (22.8)
44 (16.3)
I
51 (40.2)
100 (37.0)
IIA and IIB
39 (30.7)
101 (37.4)
IIIA, IIIB, IIIC, and IV
8 (6.3)
25 (9.3)
Surgical treatment
FE, P = 0.686
Breast conservation
100 (78.7)
218 (80.7)
Mastectomy
27 (21.3)
52 (19.3)
Sentinel node biopsy (% yes)
103 (81.1)
224 (83.0)
FE, P = 0.673
Axillary lymph node dissection (% yes)
40 (31.7)
108 (40.0)
FE, P = 0.120
Breast reconstruction at the time of surgery (% yes)
28 (22.2)
58 (21.5)
FE, P = 0.896
Neoadjuvant chemotherapy (% yes)
22 (17.5)
57 (21.1)
FE, P = 0.421
Radiation therapy during the first 6 months (% yes)
75 (59.1)
149 (55.2)
FE, P = 0.515
Chemotherapy during the first 6 months (% yes)
34 (26.8)
99 (36.7)
FE, P = 0.054
FE = Fisher exact test; SD = standard deviation; U = Mann-Whitney U test.
a Post hoc contrasts of the difference in stage of disease between the Higher Energy and Lower Energy classes failed to identify the subgroups who differed between the classes (P < 0.0083).
As summarized in Supplementary Table 1, no associations with energy class membership were found for SNPs in any gene except IL1R1. The genotype frequency was significantly different between the two latent classes for one SNP: IL1R1 rs2110726.
Regression Analyses for IL1R1 Genotype and Higher vs. Lower Energy Classes
To better estimate the magnitude (i.e., odds ratio) and precision (95% CI) of genotype on the odds of belonging to the Higher compared with the Lower Energy class, a multivariate logistic regression model was fit. In this regression analysis that included genomic estimates of and self-reported race/ethnicity, the only phenotypic characteristics that remained significant in the multivariate model were KPS score (in 10-unit increments) and receipt of CTX in the six months after surgery.
In the multivariate logistic regression analysis, the association between IL1R1 rs2110726 and the energy phenotype remained significant (Table 6, Fig. 3). For this SNP, a dominant model fit the data best (P = 0.021). In the regression analysis, carrying one or two doses of the rare T allele (i.e., CC vs. CT + TT) was associated with a 50% decrease in the odds of belonging to the Lower Energy class.
Table 6Multiple Logistic Regression Analysis for Cytokine Genes and Higher Energy vs. Lower Energy Classes
Predictor
Odds Ratio
Standard Error
95% CI
Z
P-Value
IL1R1 rs2110726
0.50
0.137
0.296, 0.859
−2.52
0.012
KPS score
0.96
0.015
0.931, 0.989
−2.70
0.007
Any chemotherapy
1.83
0.510
1.059, 3.159
2.17
0.030
Overall model fit: χ2 = 24.11, P = 0.004
Any chemotherapy = receipt of chemotherapy within six months after surgery; CI = confidence interval; IL1R1 = interleukin 1 receptor 1; KPS = Karnofsky Performance Status; SCQ = Self-administered Comorbidity Questionnaire.
Multiple logistic regression analyses of candidate gene associations with Higher Energy vs. Lower Energy classes (n = 301). For each model, the first three principal components identified from the analysis of ancestry informative markers, and self-reported race/ethnicity, were retained in all models to adjust for potential confounding because of race/ethnicity (data not shown). For the regression analysis, predictors evaluated in the model included genotype (IL1R1 rs2110726: CC vs. CT + TT, functional status (KPS score in 10-unit increments) and receipt of chemotherapy within six months after surgery.
Fig. 3Differences between the energy latent classes in the percentages of patients who were homozygous for the common allele (CC) or heterozygous or homozygous for the rare allele (CT + TT) for rss110726 in interleukin 1 receptor 1 (IL1R1). Values are plotted as unadjusted proportions with corresponding P-value.
Overlap Between the Fatigue and Energy Latent Classes
An analysis of membership in the fatigue and energy classes in this study revealed that of the 397 patients evaluated: 16.6% (n = 66) were in both the Lower Fatigue and Lower Energy classes; 21.9% (n = 87) were in both the Lower Fatigue and Higher Energy classes; 10.1% (n = 40) were in both the Higher Fatigue and Higher Energy classes; and 51.4% (n = 204) were in both the Higher Fatigue and Lower Energy classes.
Discussion
This study is the first to use GMM to identify subgroups of breast cancer patients with distinct trajectories of fatigue and energy and evaluate for associations between a number of cytokine genes and these distinct phenotypes. Because less is known about decrements in energy after breast cancer surgery, this section begins with a discussion of the fatigue findings. In the section that discusses the energy findings, similarities and differences between the two symptoms in phenotypic characteristics and molecular markers are described.
Fatigue Findings
In the present study, 62% of the patients reported fatigue scores at the clinically meaningful cutoff score that occurred before surgery and persisted for six months after surgery. This finding contrasts with the previous study by Bodtcher et al.
who reported that only 21% of their sample reported high levels of fatigue before surgery and that their fatigue decreased at three months and then returned to pretreatment levels at eight months after surgery. Although the demographic and clinical characteristics of the two samples were relatively similar, differences in the instruments used to assess fatigue and in the statistical approaches used to create the latent classes may explain the difference in percentages of patients in the higher fatigue class.
In terms of demographic characteristics, patients who were younger and had more years of education were more likely to be in the higher fatigue class. Although our findings are consistent with those of Bodtcher et al.
mediational analyses demonstrated that preoperative expectations of postoperative levels of fatigue accounted in part for the effects of age on the severity of postoperative fatigue. The authors commented that their findings could be explained by Social Learning Theory, which suggests that an individual's previous experiences with fatigue might shape one's expectancies for the symptom.
They hypothesized that older patients may have had previous experiences with surgery and/or fatigue that lowered their expectations for fatigue. An equally important consideration in all the studies cited above is that the mean age of the patients ranged from late 40s to late 50s. Therefore, additional studies are needed that evaluate the association between age and changes in fatigue severity after breast cancer surgery in older age groups.
Although two studies found no association between education and fatigue severity in breast cancer survivors and women who underwent adjuvant treatment,
who evaluated fatigue trajectories in women who were followed for 12 months after breast cancer surgery. Given these inconsistent findings and the relatively high levels of education in the current and previous studies,
additional research is warranted to determine the specific factors associated with educational attainment (e.g., different levels of social responsibility, different employment opportunities) that may contribute to variations in fatigue severity.
Across a series of studies, the evidence is clear that a higher number of comorbidities, poorer performance status, and higher fatigue severity before the initiation of cancer treatment are associated with worse fatigue trajectories or membership in the higher fatigue class (for review, see Bower and Ganz
). Although stage of disease and type of surgery were not associated with membership in the Higher Fatigue class, in our study, patients who had a higher number of lymph nodes removed and those who received neoadjuvant or adjuvant CTX were more likely to be in the Higher Fatigue class. An examination of the clinical characteristics that predicted higher fatigue severity in the four studies that evaluated patients after breast cancer surgery
reveal a rather disparate list of risk factors. These inconsistent findings can be partially explained by the number and types of demographic, clinical, symptom, and psychological characteristics that were placed in the various types of multivariate analyses. Future meta-analyses and larger studies with a more comprehensive list of potential predictors would provide insights into the characteristics that clinicians need to assess to identify breast cancer patients who are at higher risk for more severe fatigue.
IL1B is a pro-inflammatory cytokine that is synthesized in a variety of cells including circulating monocytes and tissue macrophages. In our study, patients who were homozygous for the rare A allele in IL1B rs16944 were more likely to be classified in the higher fatigue class. This SNP lies upstream of the IL1B gene. It lies in a region of histone modification that is suggestive of regulatory activity (i.e., H3K27Ac) and to a lesser extent in a region associated with promoters (i.e., H3K4Me1). However, based on available data in ENCODE, because this region does not contain a DNase Hypersensitivity Cluster or evidence of a transcription binding site, the location of any nearby regulatory DNA elements remains inconclusive. This SNP has been evaluated in a number of studies of fatigue in oncology patients. In contrast to our findings, in a study of 33 fatigue and 14 non-fatigued breast cancer survivors,
patients who were heterozygous or homozygous for the rare T allele (A allele in our study) were more likely to be in the non-fatigued group that was assessed using the Multidimensional Fatigue Symptom Inventory.
no associations were found with IL1B rs16944 and the fatigue phenotype. These inconsistent findings may be related to the instruments used to assess fatigue, the methods used to create the “fatigue phenotype” (e.g., dichotomization using a cutpoint vs. latent class analysis), and/or the timing of the assessment of fatigue in relationship to the patient's disease trajectory (e.g., active treatment vs. survivorship).
IL10 is an anti-inflammatory cytokine that regulates the growth and differentiation of B cells, natural killer cells, and cytotoxic, helper, and regulatory T cells.
In our study, patients who were homozygous for the rare C allele in IL10 rs3024496 were less likely to be in the higher fatigue class. This SNP is in a 3′ untranslated region of the fifth exon of IL10. It is upstream from four TargetScan miRNA that are predicted to be putative miRNA regulatory sites and may be involved in miRNA regulatory actions. However, based on ENCODE data, little support exists for transcription factor binding in this region. Consistent with the findings from the present study, this polymorphism was associated with increased production of IL10 in cell culture.
the IL10 pathway was found to be differentially expressed between patients with breast cancer who reported low compared with high levels of evening fatigue during CTX.
Energy Findings
In the present study, 68.0% of the patients reported energy scores that were below the cutoff score for clinically meaningful decrements in energy levels (i.e., ≤4.8). Of note, these decrements in energy levels persisted in the Lower Energy class over the six months of the study. Comparisons of these findings with our previous study of patients and family caregivers
are difficult because diurnal variations in energy levels were not evaluated in the present study.
Similar to the fatigue findings, in the bivariate analyses, a lower KPS score, a higher SCQ score, and receipt of CTX during the six months after breast cancer surgery were associated with membership in the Lower Energy class. In addition, although stage of disease was associated with membership in the Lower Energy class (but not fatigue class membership) because of the relatively small sample sizes for the highest stage of disease, post hoc contrasts failed to identify subgroup differences. Again, although direct comparisons with our previous study are not possible,
a lower KPS score was associated with membership in the Low Morning and Moderate Evening energy classes. In both studies, KPS scores were approximately 90, which suggest that even with high functional status scores, some patients can experience significant decrements in energy levels.
Two additional characteristics warrant consideration. First, for both fatigue and energy, the receipt of adjuvant CTX was associated with membership in the class with the poorer outcomes. Although higher levels of fatigue are associated with the receipt of CTX,
this study is the first to document an association between receipt of adjuvant CTX and decrements in energy levels. Second, for both symptoms, clinically meaningful levels of fatigue and decrements in energy levels, before surgery, were associated with membership in the Higher Fatigue and Lower Energy classes, respectively. Taken together, these findings suggest that clinicians need to assess for both fatigue severity and decrements in energy levels before and after breast cancer surgery.
Only one SNP in the IL1R1 gene was associated with membership in the Lower Energy class. In this study, patients who were heterozygous or homozygous for the rare T allele in rs2110726 were less likely to be in the Lower Energy class. This SNP is located in the 3′ untranslated of the IL1R1 gene. It is 35 bases upstream from a polymorphic CpG methylation site (i.e., rs200426703). Although population data are not available for rs200426703 directly, rs2110726 is in high LD (R2 = 0.0232341, D′ = 1.0) with an SNP (i.e., rs3732134) that is 30 bases downstream from the methylated site (i.e., 65 bases downstream from rs2110726 and flanking rs200426703; see Supplementary Fig. 1). Methylated sites may be involved in gene regulation
The strong LD observed between the SNPs flanking this region including the methylated polymorphic site suggests that rs2110726 may be a proxy for the genotype at the methylated site (i.e., rs200426703). Therefore, polymorphisms in rs2110726 may be related to any gene regulation activity that may occur at the polymorphic methylated site. In a previous study with the same sample,
patients who were heterozygous or homozygous for the rare T allele had a lower odds of reporting preoperative breast pain. Taken together, these findings are consistent with studies of IL1 function in mice, in which removal of IL1R function or blockade of IL1A led to a decrease in inflammation and pain behaviors.
Additional functional studies are needed to determine if the minor allele of rs2110726 is associated with a decrease in IL1R1 function and, therefore, a decrease in the pro-inflammatory effects of IL1A.
Limitations
Several study limitations need to be acknowledged. Although a growing body of evidence suggests that diurnal variations in both fatigue and energy warrant consideration in future studies,
in the present study, patients were asked to evaluate their levels of fatigue and energy at the time they completed the questionnaire. In addition, because most patients had early-stage breast cancer, differences in fatigue and energy class memberships associated with stage of disease could not be evaluated. Third, no direct measurements of systemic levels of inflammatory markers were obtained to provide additional information on the underlying mechanisms for fatigue severity and decrements in energy. Finally, the genetic associations identified in this study warrant confirmation in future studies, and functional studies are needed to confirm the impact of these polymorphisms on inflammatory mediators.
Conclusions
Findings from this study suggest that within each latent class, the severity of fatigue and decrements in energy were relatively stable from before to six months after breast cancer surgery. In addition, distinct phenotypic characteristics and genetic polymorphisms were associated with membership in the higher fatigue and lower energy classes. Although these findings warrant confirmation in future studies, the phenotypic and genetic findings from this study support the growing body of literature that suggests that fatigue and energy are distinct but related symptoms.
Additional research is warranted to evaluate for differences in the underlying mechanisms for both symptoms. Future studies can focus on an evaluation of additional immune pathways and molecular markers of metabolic and neuroendocrine function.
A better understanding of the molecular mechanisms that underlie these two symptoms could lead to the earlier identification of high-risk patients and the development and testing of novel mechanistically based interventions.
Disclosures and Acknowledgments
This study was funded by grants from the National Cancer Institute (NCI, CA107091 and CA118658) and the Oncology Nursing Foundation. Dr. Christine Miaskowski is an American Cancer Society Clinical Research Professor and is supported by a K05 award from the NCI (CA168960). This project is supported by NIH/NCRR UCSF-CTSI (grant number UL1 RR024131). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. The authors declare no conflicts of interest.
Appendix
Supplementary Table 1Summary of Single-Nucleotide Polymorphisms Analyzed for Pro- and Anti-Inflammatory Cytokine Genes and the Growth Mixture Model Analysis for Fatigue and Energy Scores
Gene
SNP
Position
Chr
MAF
Alleles
Fatigue
Energy
χ2
P-Value
Model
χ2
P-Value
Model
IFNG1
rs2069728
68154003
12
0.110
G > A
1.023
0.6
A
2.337
0.311
A
IFNG1
rs2069727
68154442
12
0.384
A > G
0.138
0.933
A
0.051
0.975
A
IFNG1
rs2069718
68156381
12
0.494
C > T
FE
0.035
R
1.001
0.606
A
IFNG1
rs1861493
68157415
12
0.266
A > G
1.528
0.466
A
1.205
0.547
A
IFNG1
rs1861494
68157628
12
0.273
T > C
1.841
0.398
A
1.073
0.585
A
IFNG1
rs2069709
68159922
12
0.003
G > T
n/a
n/a
n/a
n/a
n/a
n/a
IFNG1
HapA3
1.618
0.445
1.374
0.503
IFNG1
HapA5
0.126
0.939
0.013
0.994
IFNGR1
rs9376268
137211613
6
0.254
G > A
1.053
0.591
A
1.974
0.373
A
IL1B
rs1071676
112829855
2
0.189
G > C
1.467
0.48
A
2.505
0.286
A
IL1B
rs1143643
112830724
2
0.383
G > A
0.478
0.787
A
1.647
0.439
A
IL1B
rs1143642
112830975
2
0.082
C > T
0.065
0.968
A
0.384
0.825
A
IL1B
rs1143634
112832812
2
0.187
C > T
1.22
0.543
A
2.183
0.336
A
IL1B
rs1143633
112832889
2
0.392
G > A
0.745
0.689
A
1.722
0.423
A
IL1B
rs1143630
112834077
2
0.115
C > A
2.466
0.291
A
3.457
0.178
A
IL1B
rs3917356
112834785
2
0.450
G > A
1.293
0.524
A
0.129
0.938
A
IL1B
rs1143629
112835940
2
0.389
T > C
FE
0.011
R
2.111
0.348
A
IL1B
rs1143627
112836809
2
0.397
T > C
FE
0.005
R
1.985
0.371
A
IL1B
rs16944
112837289
2
0.386
G > A
FE
0.002
R
1.898
0.387
A
IL1B
rs1143623
112838251
2
0.277
G > C
FE
0.028
D
1.045
0.593
A
IL1B
rs13032029
112842837
2
0.448
C > T
0.91
0.634
A
0.042
0.979
A
IL1B
HapA1
2.056
0.358
1.243
0.537
IL1B
HapA4
0.389
0.823
1.456
0.483
IL1B
HapA6
1.26
0.533
2.27
0.321
IL1B
HapB1
4.747
0.093
4.443
0.108
IL1B
HapB6
4.185
0.123
0.243
0.885
IL1B
HapB8
0.783
0.676
0.2
0.905
IL1R1
rs949963
102153325
2
0.223
G > A
2.084
0.353
A
3.995
0.136
A
IL1R1
rs2228139
102165188
2
0.053
C > G
0.721
0.697
A
3.402
0.182
A
IL1R1
rs3917320
102176414
2
0.047
A > C
n/a
n/a
n/a
n/a
n/a
n/a
IL1R1
rs2110726
102177821
2
0.317
C > T
1.581
0.454
A
FE
0.021
D
IL1R1
rs3917332
102180063
2
0.187
A > T
0.322
0.851
A
2.011
0.366
A
IL1R1
HapA1
1.487
0.475
5.074
0.079
IL1R1
HapA2
0.227
0.892
3.965
0.138
IL1R1
HapA3
0.296
0.863
1.974
0.373
IL1R2
rs4141134
101990063
2
0.362
T > C
0.508
0.776
A
0.035
0.983
A
IL1R2
rs11674595
101994529
2
0.258
T > C
0.111
0.946
A
1.184
0.553
A
IL1R2
rs7570441
102000532
2
0.408
G > A
0.241
0.886
A
0.027
0.986
A
IL1R2
HapA1
0.342
0.843
0.014
0.993
IL1R2
HapA2
FE
0.697
FE
0.076
IL1R2
HapA4
0.152
0.927
0.631
0.73
IL2
rs1479923
122449231
4
0.308
C > T
2.05
0.359
A
1.065
0.587
A
IL2
rs2069776
122450820
4
0.184
T > C
n/a
n/a
n/a
n/a
n/a
n/a
IL2
rs2069772
122451977
4
0.241
A > G
1.242
0.538
A
1.377
0.502
A
IL2
rs2069777
122455281
4
0.047
C > T
n/a
n/a
n/a
n/a
n/a
n/a
IL2
rs2069763
122456326
4
0.277
T > G
0.921
0.631
A
1.067
0.587
A
IL2
HapA1
0.767
0.681
0.005
0.997
IL2
HapA2
0.998
0.607
1.142
0.565
IL2
HapA3
1.242
0.538
1.377
0.502
IL4
rs2243248
132672951
5
0.086
T > G
3.529
0.171
A
3.142
0.208
A
IL4
rs2243250
132673461
5
0.269
C > T
n/a
n/a
n/a
n/a
n/a
n/a
IL4
rs2070874
132674017
5
0.245
C > T
n/a
n/a
n/a
n/a
n/a
n/a
IL4
rs2227284
132677032
5
0.387
C > A
n/a
n/a
n/a
n/a
n/a
n/a
IL4
rs2227282
132677486
5
0.390
C > G
n/a
n/a
n/a
n/a
n/a
n/a
IL4
rs2243263
132677606
5
0.124
C > G
2.202
0.333
A
0.503
0.778
A
IL4
rs2243266
132678096
5
0.237
G > A
n/a
n/a
n/a
n/a
n/a
n/a
IL4
rs2243267
132678193
5
0.237
G > C
n/a
n/a
n/a
n/a
n/a
n/a
IL4
rs2243274
132679139
5
0.261
G > A
n/a
n/a
n/a
n/a
n/a
n/a
IL4
HapA1
2.802
0.246
0.001
0.999
IL4
HapA3
4.5
0.105
1.053
0.591
IL4
HapX1
2.618
0.27
1.635
0.441
IL6
rs4719714
22721093
7
0.255
A > T
1.51
0.47
A
0.05
0.975
A
IL6
rs2069827
22725836
7
0.069
G > T
1.768
0.413
A
2.118
0.347
A
IL6
rs1800796
22726626
7
0.134
C > G
n/a
n/a
n/a
n/a
n/a
n/a
IL6
rs1800795
22727025
7
0.285
C > G
0.989
0.61
A
0.045
0.978
A
IL6
rs2069835
22728251
7
0.061
T > C
n/a
n/a
n/a
n/a
n/a
n/a
IL6
rs2066992
22728629
7
0.049
G > T
2.819
0.244
A
2.43
0.297
A
IL6
rs2069840
22728952
7
0.333
C > G
3.081
0.214
A
1.747
0.418
A
IL6
rs1554606
22729087
7
0.319
G > T
1.668
0.434
A
0.306
0.858
A
IL6
rs2069845
22730529
7
0.319
A > G
1.668
0.434
A
0.306
0.858
A
IL6
rs2069849
22731536
7
0.024
C > T
n/a
n/a
n/a
n/a
n/a
n/a
IL6
rs2069861
22732034
7
0.056
C > T
0.405
0.817
A
0.813
0.666
A
IL6
rs35610689
22734200
7
0.259
A > G
0.946
0.623
A
0.323
0.851
A
IL6
HapA1
0
1
0.118
0.943
IL6
HapA5
3.247
0.197
2.979
0.225
IL6
HapA8
1.254
0.534
0.476
0.788
CXCL8
rs4073
73740306
4
0.455
T > A
0.347
0.841
A
0.419
0.811
A
CXCL8
rs2227306
73741337
4
0.366
C > T
0.146
0.93
A
0.459
0.795
A
CXCL8
rs2227543
73742192
4
0.368
C > T
0.38
0.827
A
1.086
0.581
A
CXCL8
HapA1
0.347
0.841
0.419
0.811
CXCL8
HapA4
0.174
0.916
0.764
0.682
IL10
rs3024505
206766558
1
0.129
C > T
1.126
0.57
A
2.853
0.24
A
IL10
rs3024498
206768183
1
0.204
A > G
3.233
0.199
A
0.811
0.667
A
IL10
rs3024496
206768518
1
0.421
T > C
FE
0.007
R
3.195
0.202
A
IL10
rs1878672
206770367
1
0.416
G > C
FE
0.043
D
1.593
0.451
A
IL10
rs3024492
206770766
1
0.190
T > A
n/a
n/a
n/a
n/a
n/a
n/a
IL10
rs1518111
206771299
1
0.303
G > A
0.417
0.812
A
0.387
0.824
A
IL10
rs1518110
206771515
1
0.301
G > T
0.334
0.846
A
0.433
0.806
A
IL10
rs3024491
206771700
1
0.408
G > T
FE
0.043
D
1.588
0.452
A
IL10
HapA1
0.662
0.718
0.386
0.824
IL10
HapA2
2.763
0.251
1.535
0.464
IL10
HapA8
2.276
0.321
0.896
0.639
IL13
rs1881457
132656716
5
0.210
A > C
1.785
0.41
A
1.576
0.455
A
IL13
rs1800925
132657116
5
0.233
C > T
1.553
0.46
A
0.846
0.655
A
IL13
rs2069743
132657582
5
0.019
A > G
n/a
n/a
n/a
n/a
n/a
n/a
IL13
rs1295686
132660150
5
0.265
G > A
0.192
0.908
A
0.329
0.848
A
IL13
rs20541
132660271
5
0.212
C > T
2.79
0.248
A
0.12
0.942
A
IL13
HapA1
0.22
0.896
0.434
0.805
IL13
HapA4
2.713
0.258
0.055
0.973
IL17 A
rs4711998
52185554
6
0.346
G > A
1.467
0.48
A
0.579
0.749
A
IL17 A
rs8193036
52185694
6
0.327
T > C
0.515
0.773
A
4.504
0.105
A
IL17 A
rs3819024
52185987
6
0.372
A > G
1.807
0.405
A
0.585
0.746
A
IL17 A
rs2275913
52186234
6
0.361
G > A
1.41
0.494
A
1.466
0.48
A
IL17 A
rs3804513
52188398
6
0.023
A > T
n/a
n/a
n/a
n/a
n/a
n/a
IL17 A
rs7747909
52189450
6
0.217
G > A
1.944
0.378
A
1.28
0.527
A
NFKB1
rs3774933
102505181
4
0.409
T > C
1.182
0.554
A
0.182
0.913
A
NFKB1
rs170731
102527745
4
0.358
A > T
0.01
0.995
A
1.261
0.532
A
NFKB1
rs17032779
102545092
4
0.011
T > C
n/a
n/a
n/a
n/a
n/a
n/a
NFKB1
rs230510
102555008
4
0.410
T > A
1.51
0.47
A
0.505
0.777
A
NFKB1
rs230494
102565811
4
0.434
A > G
0.296
0.863
A
1.145
0.564
A
NFKB1
rs4648016
102568512
4
0.010
C > T
n/a
n/a
n/a
n/a
n/a
n/a
NFKB1
rs4648018
102569042
4
0.018
G > C
n/a
n/a
n/a
n/a
n/a
n/a
NFKB1
rs3774956
102587368
4
0.435
C > T
0.088
0.957
A
0.889
0.641
A
NFKB1
rs10489114
102590230
4
0.018
A > G
n/a
n/a
n/a
n/a
n/a
n/a
NFKB1
rs4648068
102597147
4
0.363
A > G
0.179
0.914
A
1.87
0.393
A
NFKB1
rs4648095
102606718
4
0.052
T > C
FE
0.569
A
FE
0.845
A
NFKB1
rs4648110
102612663
4
0.170
T > A
2.318
0.314
A
0.215
0.898
A
NFKB1
rs4648135
102615512
4
0.061
A > G
FE
1
A
FE
0.363
A
NFKB1
rs4648141
102615743
4
0.180
G > A
3.513
0.173
A
0.595
0.743
A
NFKB1
rs1609798
102616284
4
0.337
C > T
0.181
0.914
A
2.13
0.345
A
NFKB1
HapA1
1.352
0.509
0.375
0.829
NFKB1
HapA9
0.142
0.931
1.569
0.456
A
NFKB2
rs12772374
102397153
10
0.168
A > G
3.607
0.165
A
2.116
0.347
A
NFKB2
rs7897947
102397953
10
0.221
T > G
0.347
0.841
A
2.07
0.355
A
NFKB2
rs11574849
102399938
10
0.070
G > A
3.945
0.139
A
5.765
0.056
A
NFKB2
rs1056890
102403012
10
0.305
C > T
0.576
0.75
A
2.212
0.331
A
TNF SF
rs2857602
31565600
6
0.341
T > C
0.872
0.647
A
0.711
0.701
A
TNF SF
rs1800683
31572293
6
0.390
G > A
1.416
0.493
A
2.762
0.251
A
TNF SF
rs2239704
31572363
6
0.335
G > T
0.327
0.849
A
1.369
0.504
A
TNF SF
rs2229094
31572778
6
0.278
T > C
1.012
0.603
A
4.703
0.095
A
TNF SF
rs1041981
31573006
6
0.386
C > A
1.222
0.543
A
2.796
0.247
A
TNF SF
rs1799964
31574530
6
0.224
T > C
1.033
0.597
A
1.34
0.512
A
TNF SF
rs1800750
31575185
6
0.016
G > A
n/a
n/a
n/a
n/a
n/a
n/a
TNF SF
rs1800629
31575253
6
0.149
G > A
1.951
0.377
A
4.037
0.133
A
TNF SF
rs1800610
31576049
6
0.100
C > T
1.088
0.58
A
0.153
0.926
A
TNF SF
rs3093662
31576411
6
0.074
A > G
2.262
0.323
A
1.228
0.541
A
TNF SF
HapA1
0.28
0.869
2.259
0.323
TNF SF
HapA5
0.198
0.906
1.134
0.567
TNF SF
HapA6
0.017
0.991
2.877
0.237
A = additive model; Chr = chromosome (GRCh38 human reference assembly); D = dominant model; Hap = haplotype; IFNG = interferon gamma; IL = interleukin; MAF = minor allele frequency; n/a = not assayed because SNP violated Hardy-Weinberg expectations (P < 0.001) or because MAF was <.05; NFKB = nuclear factor kappa beta; R = recessive model; SNP = single-nucleotide polymorphism; TNF SF = tumor necrosis factor superfamily.
Supplementary Fig. 1Screenshot of the UCSC Genome Browser displaying a portion of the 3′ untranslated region of the IL1R1 gene on chromosome 2 of the hg19 (GRCh38) assembly. Assembly tracks show scale, scaffold and positions of the region, and gaps in the assembly. The gene model is provided by the “RefSeq Genes” track. The genomic loci of single-nucleotide polymorphism (SNP) rs200426703 in blue (annotated by dbSNP release 142) is shared with a CpG methylation site in orange (assayed by the Illumina HumanMethyl 450K bead array as part of the ENCODE project). This loci resides in a region flanked by SNP rs2110726 35 base pairs upstream and rs3732134 30 base pairs downstream. SNP loci for which population data is available from the 1000 Genomes project are identified in the “1000 Genomes Phase 1 Integrated Variant Calls” track in black.
Identification of candidate categories of the International Classification of Functioning Disability and Health (ICF) for a generic ICF core set based on regression modelling.
A panel of ancestry informative markers for estimating individual biogeographical ancestry and admixture from four continents: utility and applications.
Associations between cytokine genes and a symptom cluster of pain, fatigue, sleep disturbance, and depression in patients prior to breast cancer surgery.
Preliminary evidence of an association between an interleukin 6 promoter polymorphism and self-reported attentional function in oncology patients and their family caregivers.
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