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. 2014 Mar 1;12(1):10–22. doi: 10.1089/lrb.2013.0024

Cytokine Candidate Genes Predict the Development of Secondary Lymphedema Following Breast Cancer Surgery

Geraldine Leung 1, Christina Baggott 1, Claudia West 1, Charles Elboim 4, Steven M Paul 1, Bruce A Cooper 1, Gary Abrams 2, Anand Dhruva 2, Brian L Schmidt 5, Kord Kober 1, John D Merriman 1, Heather Leutwyler 1, John Neuhaus 2, Dale Langford 1, Betty J Smoot 2, Bradley E Aouizerat 1,,3, Christine Miaskowski 1,*,✉
PMCID: PMC3961780  PMID: 24502445

Abstract

Background: Lymphedema (LE) is a frequent complication following breast cancer treatment. While progress is being made in the identification of phenotypic risk factors for the development of LE, little information is available on the molecular characterization of LE. The purpose of this study was to determine if variations in pro- and anti-inflammatory cytokine genes were associated with LE following breast cancer treatment.

Methods and Results: Breast cancer patients completed a number of self-report questionnaires. LE was evaluated using bioimpedance spectroscopy. Genotyping was done using a custom genotyping array. No differences were found between patients with (n=155) and without LE (n=387) for the majority of the demographic and clinical characteristics. Patients with LE had a significantly higher body mass index, more advanced disease, and a higher number of lymph nodes removed. Genetic associations were identified for three genes (i.e., interleukin (IL4) 4 (rs2227284), IL 10 (rs1518111), and nuclear kappa factor beta 2 (NFKB2 (rs1056890)) associated with inflammatory responses.

Conclusions: These genetic associations suggest a role for a number of pro- and anti-inflammatory genes in the development of LE following breast cancer treatment.

Introduction

Lymphedema (LE) is a frequent complication of breast cancer treatment. LE is caused by a disruption in the lymphatic system that results in the accumulation of fluid in the interstitial space.1 LE manifests as swelling of the affected limb and is associated with chronic pain, disfigurement, reduced mobility, functional impairment, predisposition to infections, and increased health care costs.2–5

The true incidence of breast cancer-related LE is unknown, though estimates range from 6% to 83%.6 This wide variation is due to differences in diagnostic criteria, measurement techniques, timing of measurements, duration of follow-up, and sample characteristics.7,8 In a recent review of 11 prospective cohort studies,9 the median incidence rate for LE within 3 years of breast cancer treatment was 20%. In the United States, this rate would mean that more than 500,000 breast cancer survivors are affected by this incurable condition.10

Research is often directed at identifying risk factors for LE with the hope of developing interventions to reduce its incidence.11 In our previous study,12 we identified both phenotypic and genotypic differences between women who did and did not develop LE following breast cancer treatment. The phenotypic characteristics associated with LE were increased body mass index (BMI), increased number of lymph nodes removed, higher stage of disease, and having had a sentinel lymph node biopsy (SLNB). In addition, a number of candidate genes in the lymphatic and angiogenesis pathways were identified as being associated with LE (i.e., lymphocyte cytosolic protein 2 (LCP2), neuropilin-2 (NRP2), protein tyrosine kinase (SYK), Forkhead box protein C2 (FOXC2), vascular cell adhesion molecule 1 (VCAM1), and vascular endothelial growth factor C (VEGFC)). While this study was novel in uncovering associations between LE and lymphatic and angiogenic candidate genes, further investigation is warranted to identify additional molecular pathways.

Several studies have suggested that cytokines may be involved in the pathophysiology of LE.13,14 Cytokines play a key role in modulating inflammatory responses, which may subsequently lead to lymphatic dysfunction and LE.13 In a study that used a specific bioassay and performed transcriptional microarray analysis on human skin,15 a number of cytokine genes (i.e., interleukin (IL) 4, IL6, IL10, IL13) were upregulated in LE specimens. In another study that investigated the role of inflammation in the regulation of fibrosis and lymphatic dysfunction,14 the blockade of T-helper 2 cytokines, including IL-4 and IL-13, prevented T-cell differentiation and its subsequent inflammatory response in a mouse-tail model of LE. This blockade resulted in less fibrosis and improved lymphatic function. Findings from these studies suggest that variations in cytokine genes may account for some of the differences in the development of LE. Therefore, the purpose of this study was to determine if variations in pro-and anti-inflammatory cytokine genes were associated with the development of LE following breast cancer treatment.

Methods

Study samples and procedures

Demographic, clinical, and genomic data from a cross-sectional study (i.e., LE Study (NR0101282)) and a longitudinal study (i.e., Breast Symptoms Study (CA107091 and CA118658)) were combined for these analyses. Both studies used the same subjective and objective measures. Both studies were approved by the Committee on Human Research at the University of California, San Francisco (UCSF) and the Clinical Translational Science Institute's (CTSI) Clinical Research Center Advisory Committee.

LE Study

The LE study used a cross-sectional design to evaluate for differences in phenotypic and genotypic characteristics in women with (n=70) and without (n=71) LE. Women who were ≥18 years of age and ≥6 months post-treatment for unilateral breast cancer, with or without upper extremity LE, were recruited. Women were excluded for bilateral breast cancer, current upper extremity infection, lymphangitis, preexisting LE, current breast cancer, or contraindications to bioimpedance spectroscopy (BIS) testing. Women were recruited through the National Lymphedema Network website, San Francisco Bay area hospitals, and breast cancer or LE support groups and conferences. Women were evaluated in the Clinical Research Center at UCSF. After obtaining written informed consent, women completed the study questionnaires. Following the completion of these questionnaires, the research staff performed the objective measurements: height, weight, and BIS. A blood sample was drawn for genomic analyses.

Breast Symptoms Study

The Breast Symptoms Study used a longitudinal design to evaluate neuropathic pain and LE following breast cancer surgery.12,16–18 Women 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 would undergo breast cancer surgery on one breast; were able to read, write, and understand English; agreed to participate; and gave written informed consent. Patients were excluded if they were having breast cancer surgery on both breasts and/or had distant metastasis at the time of diagnosis. A total of 516 patients were approached to participate, 410 were enrolled in the study (response rate 79.5%), and 398 completed the preoperative assessment. The major reasons for refusal were: too busy, overwhelmed with the cancer diagnosis, or insufficient time available to do the enrollment assessment prior to surgery.

During the patient's preoperative visit, a clinician explained the study, determined the patient's willingness to participate, and introduced the patient to the research nurse. The research nurse met with the woman, determined eligibility, and obtained written informed consent prior to surgery. After obtaining written informed consent, the patient completed the study questionnaires prior to surgery. Following the completion of the questionnaires, the research nurse performed the objective measurements: height, weight, and bioimpedance spectroscopy (BIS). A blood sample was drawn for genomic analyses. Patients were contacted 2 weeks after surgery to schedule the first post-surgical appointment. The research nurse met with the patients either in their home or in the Clinical Research Center at 1, 2, 3, 4, 5, 6, 8, 10, and 12 months after surgery. In the second through fifth years of the study, patients were seen every 4 months. During each of the study visits, the women completed the study questionnaires and had the objective measures done by the research nurse.

Subjective measures

A demographic questionnaire obtained information on age, marital status, education, ethnicity, employment status, living situation, and financial status. Functional status was evaluated using the Karnofsky Performance Status (KPS) scale that has well established validity and reliability.19,20 Patients rated their functional status using the 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 exercised on a regular basis (yes/no). Clinical information was obtained from patient interviews and medical record reviews.

The Self-administered Co-morbidity Questionnaire (SCQ) is a short and easily understood instrument that was developed to measure co-morbidity in clinical and health service research settings.21 The questionnaire consists of 13 common medical conditions that were simplified into language that could be understood without any prior medical knowledge. Patients were asked to indicate if they had the condition using a “yes/no” format. If they indicated that they had a condition, they were asked if they received treatment for it and did it limit their activities. Patients were given the option to add two additional conditions not listed on the instrument. For each condition, a patient can receive a maximum of 3 points. Because there are 13 defined medical conditions and 2 optional conditions, the maximum score totals 45 points if the open-ended items are used and 39 points if only the closed-ended items are used. The SCQ has well-established validity and reliability and has been used in studies of patients with a variety of chronic conditions.21–25

Objective measures

Bioimpedance Spectroscopy (BIS) of LE—BIS measurements of the affected and unaffected arms, were done using the procedures described by Cornish and colleagues.26–28 Patients were instructed not to exercise or take a sauna within 8 h of the assessment. In addition, they were asked to refrain from drinking alcohol for 12 h prior to the assessment. BIS measurements were taken using a single channel BIS device (i.e., SFB7 device; ImpediMed, San Diego, CA in the LE study, or the Quantum × Bioelectrical Impedance Device; RJL Systems, Clinton Township, MI in the Breast Symptoms Study).

Women removed all jewelry and their skin was prepped with an alcohol wipe prior to surface electrode placement. Patients lay supine on a massage table with their arms 30 degrees from the body and legs not touching for at least 10 min prior to the BIS measurements. Electrodes were placed on the dorsum of the wrists adjacent to the ulnar styloid process, the dorsum of the hands just proximal to the third metacarpophalangeal joint, anterior to the ankle joints between the malleoli, and over the dorsum of the feet over the third metatarsal bone just proximal to the third metatarsophalangeal joint. Two ‘measurement’ electrodes were placed at either end of the 40 cm length over which the circumference measurements were made and the ‘drive’ electrodes were placed 8–10 cm distal to these measurement electrodes. Two readings of resistance were obtained from the affected and unaffected arms and averaged for subsequent analyses.

While cases and non-cases of LE were known in the LE study, for the Breast Symptoms Study, LE cases were determined based on the procedures of Cornish and colleagues26–28 using all of the data obtained from each woman during her participation in the study. A woman was defined as a LE case if the resistance ratio for the untreated arm/treated arm was>1.139 or >1.066 for those women who had surgery on the dominant or nondominant side, respectively at any of the BIS assessments.

Methods of analysis for phenotypic data

Data were analyzed using SPSS Version 19.29 Descriptive statistics and frequency distributions were generated on the sample characteristics. Independent sample t-tests, Chi-square analyses, and Mann Whitney U tests were done to evaluate for differences in demographic and clinical characteristics between patients with and without LE.

Methods of analysis for genomic data

Gene selection

Cytokines and their receptors are classes of polypeptides that mediate inflammatory processes.30 These polypeptides are divided into pro- and anti-inflammatory cytokines. Pro-inflammatory cytokines promote systemic inflammation and include interferon gamma (IFNG) 1, IFNG receptor 1 (IFNGR1), IL1R1, IL2, IL8, IL17A, nuclear factor kappa beta (NFKB1), NFKB2, and tumor necrosis factor alpha (TNFA).30,31 Anti-inflammatory cytokines suppress the activity of pro-inflammatory cytokines and include IL1R2, IL4, IL10, and IL13.30,31 Of note, IFNG1, IL1B, and IL6 possess pro- and anti-inflammatory functions.31

Blood collection and genotyping

Genomic DNA was extracted from archived buffy coats using the PUREGene DNA Isolation System (Invitrogen, Carlsbad, CA). Of the 543 patients recruited for this study, DNA was recovered from the archive buffy coat of 407 patients (i.e., 110 with and 297 without LE) who provided a blood sample. Genotyping was performed blinded to LE status, and positive and negative controls were included. DNA was quantitated with a Nanodrop Spectrophotometer (ND-1000) and normalized to a concentration of 50 ng/μL (diluted in 10 mM Tris/1 mM EDTA). Samples were genotyped using the GoldenGate genotyping platform (Illumina, San Diego, CA) and processed according to the standard protocol using GenomeStudio (Illumina). Signal intensity profiles and resulting genotype calls for each single nucleotide polymorphism (SNP) were visually inspected by two blinded reviewers. Disagreements were adjudicated by a third reviewer.

SNP selection

A combination of tagging SNPs and literature driven SNPs were selected for analysis. Tagging SNPs were required to be common (i.e., estimated to have a minor allele frequency ≥0.05) in public databases (e.g., HapMap). In order to ensure robust genetic association analyses, quality control filtering of SNPs was performed. SNPs with call rates of <95% or Hardy-Weinberg p values of<0.001 were excluded.

As shown in Table 1, a total of 86 SNPs among the 15 candidate genes (IFNG1: 5 SNPs, IFNGR1: 1 SNP; IL1B: 12 SNPs; IL1R1: 4 SNPs; IL1R2: 3 SNPs; IL2: 5 SNPs; IL4: 3 SNPs; IL6: 9 SNPs; IL8: 3 SNPs; IL10: 8 SNPs; IL13: 4 SNPs; IL17A: 5 SNPs; NFKB1: 11 SNPs; NFKB2: 4 SNPs; TNFA: 9 SNPs) passed all quality control filters and were included in the genetic association analyses. Potential functional roles of SNPs associated with LE were examined using PUPASuite 2.0,32 a comprehensive search engine that tests a series of functional effects.

Table 1.

Cytokine Genes and Single Nucleotide Polymorphisms Analyzed for Lymphedema Versus no Lymphedema

Gene SNP Position Chr MAF Alleles Chi Square p-value Model
IFNG1 rs2069728 66834051 12 .101 G>A 0.606 .739 A
IFNG1 rs2069727 66834490 12 .397 A>G 0.369 .831 A
IFNG1 rs2069718 66836429 12 .489 C>T 0.719 .698 A
IFNG1 rs1861493 66837463 12 .278 A>G 0.615 .735 A
IFNG1 rs1861494 66837676 12 .285 T>C 1.192 .551 A
IFNG1 rs2069709 66839970 12 .002 G>T n/a n/a n/a
IFNG1 HapA3         0.685 .710  
IFNG1 HapA5         0.412 .814  
IFNGR1 rs9376268 137574444 6 .262 G>A 0.387 .824 A
IL1B rs1071676 106042060 2 .189 G>C 2.856 .240 A
IL1B rs1143643 106042929 2 .385 G>A 2.190 .335 A
IL1B rs1143642 106043180 2 .080 C>T 0.918 .632 A
IL1B rs1143634 106045017 2 .187 C>T 2.776 .250 A
IL1B rs1143633 106045094 2 .393 G>A 2.876 .238 A
IL1B rs1143630 106046282 2 .110 C>A 0.332 .847 A
IL1B rs3917356 106046990 2 .457 G>A 0.622 .733 A
IL1B rs1143629 106048145 2 .380 T>C 2.479 .290 A
IL1B rs1143627 106049014 2 .386 T>C 3.397 .183 A
IL1B rs16944 106049494 2 .380 G>A 4.658 .097 A
IL1B rs1143623 106050452 2 .278 G>C 1.003 .606 A
IL1B rs13032029 106055022 2 .455 C>T 0.590 .745 A
IL1B HapA1         3.917 .141  
IL1B HapA4         2.127 .345  
IL1B HapA6         2.964 .227  
IL1B HapB1         8.064 .018  
IL1B HapB6         1.013 .602  
IL1B HapB8         1.053 .591  
IL1R1 rs949963 96533648 2 .211 G>A 7.695 .021 A
IL1R1 rs2228139 96545511 2 .054 C>G 0.391 .823 A
IL1R1 rs3917320 96556738 2 .048 A>C n/a n/a n/a
IL1R1 rs2110726 96558145 2 .336 C>T 1.720 .423 A
IL1R1 rs3917332 96560387 2 .184 A>T 2.612 .271 A
IL1R1 HapA1         1.827 .401  
IL1R1 HapA2         2.792 .248  
IL1R1 HapA3         2.683 .261  
IL1R2 rs4141134 96370336 2 .378 T>C 2.388 .303 A
IL1R2 rs11674595 96374804 2 .254 T>C 4.848 .089 A
IL1R2 rs7570441 96380807 2 .411 G>A 2.978 .226 A
IL1R2 HapA1         2.406 .300  
IL1R2 HapA2         2.292 .318  
IL1R2 HapA4         4.803 .091  
IL2 rs1479923 119096993 4 .302 C>T 0.540 .763 A
IL2 rs2069776 119098582 4 .264 T>C 1.245 .536 A
IL2 rs2069772 119099739 4 .247 A>G 0.251 .882 A
IL2 rs2069777 119103043 4 .053 C>T 0.747 .688 A
IL2 rs2069763 119104088 4 .275 T>G 0.770 .680 A
IL2 HapA1         1.805 .406  
IL2 HapA2         0.245 .885  
IL2 HapA3         1.980 .372  
IL4 rs2243248 127200946 5 .087 T>G 1.061 .588 A
IL4 rs2243250 127201455 5 .244 C>T n/a n/a n/a
IL4 rs2070874 127202011 5 .224 C>T n/a n/a n/a
IL4 rs2227284 127205027 5 .366 C>A FE .010 R
IL4 rs2227282 127205481 5 .368 C>G n/a n/a n/a
IL4 rs2243263 127205601 5 .127 C>G 3.268 .195 A
IL4 rs2243266 127206091 5 .216 G>A n/a n/a n/a
IL4 rs2243267 127206188 5 .217 G>C n/a n/a n/a
IL4 rs2243274 127207134 5 .239 G>A n/a n/a n/a
IL6 rs4719714 22643793 7 .252 A>T 0.190 .910 A
IL6 rs2069827 22648536 7 .071 G>T 0.771 .680 A
IL6 rs1800796 22649326 7 .123 C>G n/a n/a n/a
IL6 rs1800795 22649725 7 .316 C>G 0.357 .837 A
IL6 rs2069835 22650951 7 .061 T>C n/a n/a n/a
IL6 rs2066992 22651329 7 .124 G>T FE .023 D
IL6 rs2069840 22651652 7 .323 C>G 0.585 .746 A
IL6 rs1554606 22651787 7 .343 G>T 2.265 .322 A
IL6 rs2069845 22653229 7 .343 A>G 1.893 .388 A
IL6 rs2069849 22654236 7 .021 C>T n/a n/a n/a
IL6 rs2069861 22654734 7 .072 C>T 1.140 .566 A
IL6 rs35610689 22656903 7 .254 A>G 4.146 .126 A
IL6 HapA1         0.158 .924  
IL6 HapA2         0.285 .867  
IL6 HapB1         7.655 .022  
IL6 HapB2         4.402 .111  
IL6 HapB6         1.555 .460  
IL8 rs4073 70417508 4 .450 T>A 2.672 .263 A
IL8 rs2227306 70418539 4 .371 C>T 2.868 .238 A
IL8 rs2227543 70419394 4 .375 C>T 2.117 .347 A
IL8 HapA1         3.305 .192  
IL8 HapA4         2.564 .278  
IL10 rs3024505 177638230 1 .129 C>T 2.112 .348 A
IL10 rs3024498 177639855 1 .210 A>G 2.556 .279 A
IL10 rs3024496 177640190 1 .413 T>C 0.778 .678 A
IL10 rs1878672 177642039 1 .412 G>C 0.460 .795 A
IL10 rs3024492 177642438 1 .199 T>A 2.986 .225 A
IL10 rs1518111 177642971 1 .299 G>A FE .014 D
IL10 rs1518110 177643187 1 .296 G>T FE .010 D
IL10 rs3024491 177643372 1 .403 G>T 1.190 .552 A
IL10 HapA1         7.517 .023  
IL10 HapA2         4.372 .112  
IL10 HapA9         3.360 .186  
IL13 rs1881457 127184713 5 .229 A>C 1.229 .541 A
IL13 rs1800925 127185113 5 .243 C>T 1.163 .559 A
IL13 rs2069743 127185579 5 .017 A>G n/a n/a n/a
IL13 rs1295686 127188147 5 .259 G>A 0.654 .721 A
IL13 rs20541 127188268 5 .213 C>T 1.273 .529 A
IL13 HapA1         0.572 .751  
IL13 HapA4         1.067 .586  
IL17A rs4711998 51881422 6 .337 G>A 3.022 .221 A
IL17A rs8193036 51881562 6 .321 T>C 0.625 .732 A
IL17A rs3819024 51881855 6 .366 A>G 0.613 .736 A
IL17A rs2275913 51882102 6 .359 G>A 0.443 .801 A
IL17A rs3804513 51884266 6 .019 A>T n/a n/a n/a
IL17A rs7747909 51885318 6 .215 G>A 0.013 .994 A
NFKB1 rs3774933 103645369 4 .416 T>C 0.568 .753 A
NFKB1 rs170731 103667933 4 .362 A>T 0.480 .786 A
NFKB1 rs17032779 103685279 4 .009 T>C n/a n/a n/a
NFKB1 rs230510 103695201 4 .409 T>A 1.640 .441 A
NFKB1 rs230494 103706005 4 .434 A>G 0.043 .979 A
NFKB1 rs4648016 103708706 4 .007 C>T n/a n/a n/a
NFKB1 rs4648018 103709236 4 .015 G>C n/a n/a n/a
NFKB1 rs3774956 103727564 4 .437 C>T 0.023 .989 A
NFKB1 rs10489114 103730426 4 .015 A>G n/a n/a n/a
NFKB1 rs4648068 103737343 4 .359 A>G 1.605 .448 A
NFKB1 rs4648095 103746914 4 .052 T>C FE .853 A
NFKB1 rs4648110 103752867 4 .175 T>A 0.334 .846 A
NFKB1 rs4648135 103755716 4 .061 A>G FE .605 A
NFKB1 rs4648141 103755947 4 .174 G>A 1.474 .478 A
NFKB1 rs1609798 103756488 4 .339 C>T 1.789 .409 A
NFKB1 HapA1         1.435 .488  
NFKB1 HapA4         0.934 .627  
NFKB1 HapA9         0.248 .883  
NFKB2 rs12772374 104146901 10 .170 A>G 0.972 .615 A
NFKB2 rs7897947 104147701 10 .215 T>G 0.872 .647 A
NFKB2 rs11574849 104149686 10 .064 G>A 1.036 .596 A
NFKB2 rs1056890 104152760 10 .311 C>T FE .049 R
NFKB2 HapA1         1.759 .415  
NFKB2 HapA2         0.899 .638  
NFKB2 HapA3         0.723 .697  
TNFA rs2857602 31533378 6 .361 T>C 0.223 .894 A
TNFA rs1800683 31540071 6 .377 G>A 1.527 .466 A
TNFA rs2239704 31540141 6 .356 G>T 0.175 .916 A
TNFA rs2229094 31540556 6 .273 T>C 0.452 .798 A
TNFA rs1041981 31540784 6 .371 C>A 1.116 .572 A
TNFA rs1799964 31542308 6 .220 T>C 0.271 .873 A
TNFA rs1800750 31542963 6 .016 G>A n/a n/a n/a
TNFA rs1800629 31543031 6 .146 G>A 0.121 .941 A
TNFA rs1800610 31543827 6 .103 C>T 3.613 .164 A
TNFA rs3093662 31544189 6 .071 A>G 3.566 .168 A
TNFA HapA1         1.579 .664  
TNFA HapA6         0.683 .711  
TNFA HapA8         2.767 .251  
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A, additive model; Chr, chromosome; 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; TNFA, tumor necrosis factor alpha.

Statistical analyses

Allele and genotype frequencies were determined by gene counting. Hardy-Weinberg equilibrium was assessed by the 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. LD-based haplotype block definition was based on D’ confidence interval.33

For SNPs that were members of the same haploblock, haplotype analyses were conducted in order 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 2.1.34 In order 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, across the five iterations, were retained for downstream analyses. Haplotypes were evaluated assuming a dosage model (i.e., analogous to the additive model).

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 Helix Tree (Golden Helix, Bozeman, MT). Briefly, the number of principal components (PCs) was sought which distinguished the major racial/ethnic groups in the sample by visual inspection of scatter plots of orthogonal PCs (i.e., PC 1 vs. PC2, PC2 vs. PC3). This procedure was repeated until no discernible clustering of patients by their self-reported race/ethnicity was possible (data not shown). One hundred and six AIMs were included in the analysis. The first three PCs were selected to adjust for potential confounding due to population substructure (i.e., race/ethnicity) by including the three covariates in all regression models.

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, as well as genomic estimates of and self-reported race/ethnicity, was used to evaluate the relationship between genotype and LE group membership. A backwards stepwise approach was used to create a parsimonious model. Genetic model fit and both unadjusted and covariate-adjusted odds ratios were estimated using STATA version 9.39

As was done in our previous studies,17,40,41 based on recommendations in the literature,42,43 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 regression analyses that controlled for differences in phenotypic characteristics, potential confounding due to population stratification, and variation 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 SNPs passing quality control criteria in Table 1 to allow for subsequent comparisons and meta-analyses.

Results

Differences in demographic and clinical characteristics

As shown in Table 2, no differences were found between patients with and without LE for the majority of the demographic and clinical characteristics.12 In the bivariate analyses, patients with LE had a significantly higher BMI and a lower KPS score, and were more likely to report lung disease. In addition, patients with LE had a higher number of lymph nodes removed, a higher number of positive nodes, more advanced disease at the time of diagnosis, were less likely to have had a SLNB, were more likely to have had an axillary lymph node dissection (ALND), had received chemotherapy (CTX) prior to or following surgery, and had received radiation therapy (RT) following surgery.

Table 2.

Differences in Demographic and Clinical Characteristics Between Patients with (n=155) and without (n=387) Lymphedema

Characteristic No lymphedema mean (SD) Lymphedema mean (SD) Statistics
Age (years) 54.9 (11.1) 56.2 (10.8) NS
Education (years) 16.0 (2.7) 15.8 (2.8) NS
Age at menopause (years) 47.8 (7.2) 46.7 (9.1) NS
Body mass index (kg/m2) 26.1 (5.6) 28.2 (6.7) P=0.001
Karnofsky Performance Status score 93.3 (9.7) 91.1 (11.1) P=0.028
Comorbidity score 4.0 (2.9) 4.5 (3.3) NS
Number of nodes removed 5.8 (6.3) 10.9 (9.0) P<0.0001
Number of positive nodes 0.7 (1.7) 1.7 (3.4) P=0.009
  % (n) % (n)  
Ethnicity
 White 68.8 (265) 72.9 (113)  
 Black 7.5 (29) 9.7 (15) NS
 Asian/Pacific Islander 13.0 (50) 7.1 (11)  
 Hispanic/Mixed ethnic background/Other 10.6 (41) 10.3 (16)  
Lives alone
Yes 23.0 (88) 28.9 (44) NS
No 77.0 (295) 71.1 (108)  
Married/partnered
 Yes 47.4 (182) 52.0 (79) NS
 No 52.6 (202) 48.0 (73)  
Employed
 Yes 51.4 (197) 49.7 (76) NS
 No 48.6 (186) 50.3 (77)  
Handedness
 Right 88.8 (341) 88.9 (136)  
 Left 8.1 (31) 9.2 (14) NS
 Both 3.1 (12) 2.0 (3)  
Occurrence of comorbid conditions (% and number of women who reported each co-morbid condition from the Self-Administered Comorbidity Questionnaire)
 Heart disease 5.6 (21) 6.0 (9) NS
 High blood pressure 27.0 (103) 34.9 (53) NS
 Lung disease 3.7 (14) 8.1 (12) p=0.04
 Diabetes 6.6 (25) 7.4 (11) NS
 Ulcer 3.7 (14) 4.7 (7) NS
 Kidney disease 1.6 (6) 2.0 (3) NS
 Liver disease 2.1 (8) 4.8 (7) NS
 Anemia 7.2 (27) 9.5 (14) NS
 Depression 21.8 (81) 26.7 (39) NS
 Osteoarthritis 19.2 (72) 26.7 (40) NS
 Back pain 29.3 (110) 31.5 (47) NS
 Rheumatoid arthritis 3.5 (13) 4.7 (7) NS
Diagnosed with mastitis
 Yes 13.1 (50) 11.3 (17) NS
 No 86.9 (332) 88.7 (134)  
Diagnosed with cystic breast disease
 Yes 21.5 (81) 23.3 (34) NS
 No 78.5 (295) 76.7 (112)  
Breastfed
 Yes 49.4 (190) 45.1 (69) NS
 No 50.6 (195) 54.9 (84)  
Surgery on affected breast not related to cancer
 Yes 9.3 (36) 14.8 (23) NS
 No 90.7 (351) 85.2 (132)  
Surgery to the affected arm not related to cancer
 Yes 3.1 (12) 5.2 (8) NS
 No 96.9 (375) 94.8 (147)  
Surgery on the affected hand not related to cancer
 Yes 5.2 (20) 7.1 (11) NS
 No 94.8 (367) 92.9 (144)  
Injury to the affected arm
 Yes 17.3 (67) 22.6 (35) NS
 No 82.7 (320) 77.4 (120)  
Injury to the affected hand
 Yes 17.1 (66) 17.4 (27) NS
 No 82.9 (321) 82.6 (128)  
Side of cancer surgery
 Dominant 49.9 (193) 41.9 (65) NS
 Nondominant 50.1 (194) 58.1 (90)  
Type of surgery
 Breast conservation 75.2 (291) 70.3 (109) NS
 Mastectomy 24.8 (96) 29.7 (46)  
Stage of disease
 Stage 0 18.1 (70) 5.2 (8)  
 Stage I 40.1 (155) 32.9 (51) p<0.0001
 Stage IIA and IIB 35.4 (137) 48.4 (75)  
 Stage IIIA, IIIB, IIIC, and IV 6.5 (25) 13.5 (21)  
Sentinel lymph node biopsy
 Yes 80.9 (313) 69.7 (108) p=0.006
 No 19.1 (74) 30.3 (47)  
Axillary lymph node dissection
 Yes 39.3 (152) 69.3 (106) p<0.0001
 No 60.7 (235) 30.7 (47)  
Reconstruction at the time of surgery
 Yes 21.6 (68) 22.2 (18) NS
 No 78.4 (247) 77.8 (63)  
Adjuvant chemotherapy
 Yes 36.7 (142) 59.7 (92) p<0.0001
 No 63.3 (245) 40.3 (62)  
Adjuvant radiation therapy
 Yes 57.1 (221) 71.0 (110) p<0.0001
 No 42.9 (166) 29.0 (45)  
Combinations of treatments
 Only surgery 23.8 (92) 8.4 (13)  
 Surgery and radiation therapy 39.5 (153) 32.3 (50) p<0.0001
 Surgery and chemotherapy 19.1 (74) 20.6 (32)  
 Surgery, radiation therapy, and chemotherapy 17.6 (68) 38.7 (60)  
Exercise on a regular basis
 Yes 73.7 (283) 75.2 (115) NS
 No 26.3 (101) 24.8 (38)  
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kg, kilograms; m2, meter squared; NS, not significant; SD, standard deviation. See Ref. 12.

Candidate gene analyses for the development of LE

As summarized in Table 1, no associations with the occurrence of LE were found in the SNPs evaluated for INFG1, INFGR1, IL1R2, IL2, IL8, IL13, IL17, NFKB1, and TNFA. However, the genotype frequency was significantly different between those who did and did not develop LE for six SNPs and three haplotypes spanning six genes (i.e., IL1B, IL1R1, IL4, IL6, IL10, and NFKB2). One haplotype (HapB1, p=0.018) was identified for IL1B. For the SNP in IL1R1 (rs949963), an additive model fit the data best (p=0.021). For the SNP in IL4 (rs2227284), a recessive model fit the data best (p=0.010). One SNP (rs2066992) and 1 haplotype (HapB1, p=0.022) were identified in IL6. For rs2066992, a dominant model fit the data best (p=0.023). Two SNPs (rs1518111, rs1518110) and 1 haplotype (HapA1, p=0.023) were identified in IL10. For both SNPs, a dominant model fit the data best (p=0.014, 0.010, respectively). For the SNP in NFKB2 (rs1056890), a recessive model fit the data best (p=0.049).

Regression analyses of IL1B, IL4, IL6, IL10, and NFKB2 genotypes and haplotypes and the development of LE

In order to better estimate the magnitude (i.e., odds ratio, OR) and precision (95% confidence interval, CI) of genotype on the development of LE, multivariate logistic regression models were fit. As shown in Table 3, in addition to genotype, the phenotypic characteristics included in the regression models were ethnicity (i.e., White, Black, Asian, Hispanic/Mixed ethnic background/Other), BMI, stage of disease, having a SLNB, and number of lymph nodes removed. Receipt of CTX and RT, while not significant after the inclusion of genomic estimates of and self-reported race/ethnicity,12 were retained in all of the regression models for face validity.

Table 3.

Multiple Logistic Regression Analyses for IL4, IL10, and NFKB2 Genotypes to Predict the Development of Lymphedema

Predictor Odds Ratio Standard Error 95% CI Z P value
IL4 genotype 0.31 0.156 0.119, 0.829 −2.34 0.019
BMI 1.06 0.022 1.014, 1.102 2.61 0.009
Stage of disease
 Stage 0 versus I 3.22 1.927 0.996, 10.404 1.95 0.051
 Stage 0 versus II 4.27 2.714 1.229, 14.838 2.28 0.022
 Stage 0 versus III and IV 6.38 4.714 1.500, 27.145 2.51 0.012
SLNB 0.41 0.140 0.206, 0.796 −2.62 0.009
Number of nodes removed 1.09 0.022 1.047, 1.132 4.24 <0.0001
Any chemotherapy 1.11 0.344 0.604, 2.038 0.33 0.738
Any radiation therapy 1.23 0.366 0.685, 2.204 0.69 0.489
Overall model fit: χ2=83.69, p<0.0001, R2=0.1865
IL10 genotype 0.49 0.139 0.282, 0.857 −2.51 0.012
BMI 1.05 0.022 1.012, 1.099 2.53 0.011
Stage of disease
 tage 0 versus I 2.64 1.553 0.836, 8.359 1.65 0.098
 Stage 0 versus II 3.25 2.027 0.954, 11.039 1.88 0.059
 Stage 0 versus III and IV 5.78 4.227 1.378, 24.234 2.40 0.016
SLNB 0.40 0.138 0.204, 0.786 −2.66 0.008
Number of nodes removed 1.08 0.022 1.043, 1.128 4.07 <0.0001
Any chemotherapy 1.27 0.399 0.687, 2.354 0.77 0.444
Any radiation therapy 1.41 0.422 0.781, 2.531 1.14 0.256
Overall model fit: χ2=84.06, p<0.0001, R2=0.1876
NFKB2 genotype 3.06 1.338 1.299, 7.209 2.56 0.011
BMI 1.06 0.022 1.015, 1.103 2.69 0.007
Stage of disease
 Stage 0 versus I 2.91 1.725 0.912, 9.301 1.80 0.071
 Stage 0 versus II 3.81 2.406 1.108, 13.135 2.12 0.034
 Stage 0 versus III and IV 6.23 4.570 1.479, 26.233 2.49 0.013
SLNB 0.40 0.137 0.203, 0.783 −2.67 0.008
Number of nodes removed 1.08 0.021 1.043, 1.126 4.08 <0.0001
Any chemotherapy 1.15 0.361 0.624, 2.129 0.45 0.650
Any radiation therapy 1.36 0.406 0.755, 2.439 1.02 0.307
Overall model fit: χ2=84.16, p<0.0001, R2=0.1876
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For each model, the first three principal components identified from the analysis of ancestry informative markers as well as self-report race/ethnicity (i.e., White, Black, Asian/Pacific Islander, Hispanic/Mixed ethnic background/Other) were retained in all models to adjust for potential confounding due to race or ethnicity (data not shown). Predictors evaluated in each model included genotype (IL4 rs2227284: CC+CA versus AA; IL10 rs1518111: GG versus GA+AA; NFKB2 rs1056890: CC+CT versus TT), BMI (kilograms/meter squared), stage of disease, sentinel lymph node biopsy, number of lymph nodes removed, chemotherapy prior to or following surgery, and radiation therapy following surgery.

BMI, body mass index; CI, confidence interval; IL, interleukin; NFKB, nuclear factor kappa beta; SLNB, sentinel lymph node biopsy.

The only genetic associations that remained significant in the multivariate logistic regression analyses were for IL4 rs2227284, IL10 rs1518111, IL10 rs1518110, and NFKB2 rs1056890 (Table 3 and Fig. 1). In the regression analysis for IL4 rs2227284, carrying two doses of the rare allele (i.e., CC+CA vs. AA) was associated with a 69% decrease in the odds of developing LE (Figure 1A). In the regression analysis for IL10 rs1518111, carrying one or two doses of the rare allele (i.e., GG vs. GA+AA) was associated with 51% decrease in the odds of developing LE (Fig. 1B). The analyses for the second SNP in IL10, namely rs1518110, revealed that it is a perfect surrogate for IL10 rs1518111. IL10 rs1518111 was selected to represent the two surrogate SNPs. In the regression analysis for NFKB2 rs1056890, carrying two doses of the rare allele (i.e., CC+CT vs. TT) was associated with a 3.06-fold increase in the odds of developing LE (Fig. 1C).

FIG. 1.

FIG. 1.

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(A) Differences in the percentages of patient with and without lymphedema who were homozygous or heterozygous for the common allele (CC+CA) or homozygous for the rare allele (AA) for rs2227284 in interleukin 4 (IL4). Values are plotted as unadjusted proportions with the corresponding p value. (B) Differences in the percentages of patients with and without lymphedema who were homozygous for the common allele (GG) or heterozygous or homozygous for the rare allele (GA+AA) for rs1518111 in IL13. Values are plotted as unadjusted proportions with the corresponding p value. (C) Differences in the percentages of patients with and without lymphedema who were homozygous or heterozygous for the common allele (CC+CT) or homozygous for the rare allele (TT) for rs1056890 in nuclear factor kappa beta 2 (NFKB2). Values are plotted as unadjusted proportions with the corresponding p value.

Discussion

This study is the first to evaluate for variations in pro- and anti-inflammatory cytokine genes and the development of LE following breast cancer treatment. In brief, in the bivariate analyses (Table 2), the phenotypic predictors of LE included: a higher BMI, lower KPS score, having lung disease, increased number of lymph nodes removed, increased number of positive lymph nodes, a higher stage of disease at the time of diagnosis, not having a SLND, having an ALND, and receiving CTX or RT. However, in the multivariate analysis (Table 3), KPS score, having lung disease, number of positive nodes removed, and having an ALND were not retained in the final model (Table 3). In addition, when genomic estimates of and self-reported race/ethnicity were included in the multivariate logistic regression analysis,12 neither receipt of CTX nor receipt of RT remained significant predictors of LE.

The complex molecular pathways that underlie the development of LE following breast cancer treatment are being uncovered. In our previous study,12 variations in seven genes that play a role in lymphatic development and angiogenesis were associated with the development of LE. In this study, we extend this work and evaluated for variations in pro- and anti-inflammatory cytokine genes and their association with the development of LE.

Consistent with preclinical and clinical studies that identified a role for IL4 in the molecular pathway of LE development,14,15 patients who were homozygous for the rare allele in IL4 rs2227284 had a 69% decrease in the odds of developing LE. IL4 is a multifunctional cytokine that is known to induce T-helper 2 (Th2) cell immune responses in asthma and scleroderma. IL4 plays a regulatory role in apoptosis and cell proliferation, as well as in the expression of numerous genes in macrophages, lymphocytes, fibroblasts, endothelial cells, and epithelial cells.14,15 In addition, IL4 has the ability to activate macrophages differentially into M2 macrophages rather than M1 macrophages. M2 macrophages function in tissue repair, fibrosis, and the regulation of inflammation. A subset of M2 macrophages produce the chemokine CCL18, that has both direct effects on fibroblasts and indirect effects on T cells that result in fibrotic inflammatory diseases, including hypersensitivity pneumonitis and idiopathic pulmonary fibrosis.44 In addition, IL4 activated M2 macrophages increase the production of transforming growth factor beta (TGF-β), a tissue activator that leads to fibroblast production and collagen synthesis.45 One can hypothesize that dysregulation in the production of IL4 could lead to the development of soft tissue fibrosis and lymphatic dysfunction associated with LE. This hypothesis is supported by a recent preclinical study that demonstrated that inhibition of Th2 differentiation using IL4 prevented the initiation and progression of LE by decreasing tissue fibrosis and increasing lymphatic function.14

IL4 rs2227284 is located in the intronic region of chromosome 5 in a region of the gene that undergoes DNA methylation. While no studies were identified that evaluated for an association between this SNP and the development of LE, in one study of Japanese women, individuals who were homozygous for the rare allele had a decreased risk for the development of rhinoconjunctivitis.46 In another study of Chinese children who were vaccinated for hepatitis B, the rare allele was associated with a poor humoral response to the vaccine.47 Taken together, these findings suggest that rs2227284 or a SNP(s) in linkage disequilibrium with rs2227284 may modulate a variety of inflammatory and immune responses. Additional research is warranted to confirm these findings in an independent cohort of breast cancer patients with LE.

In our study, patients who were heterozygous or homozygous for the rare allele in IL10 rs1518111 had a 51% decrease in the odds of developing LE. IL10 rs1518111 is located in the intronic region of chromosome 1 in a region that undergoes DNA methylation. In addition, this SNP is known to influence active transcription factor binding sites (i.e., PU-box binding protein (PU.1), RNA polymerase (Pol2)). The influence of IL10 rs1518111 on PU.1 binding is of particular interest because PU.1 plays a role in lymphangiogensis.48 In addition, this SNP was associated with ischemic stroke,49 benign prostate hyperplasia,50 and Behcet's disease (i.e., a chronic vasculitis that affects the skin, joints, lungs, and central nervous system51). Findings from these studies suggest that variations in the expression of IL10 may result in increased inflammation and contribute to these diseases. In addition, in a sample of healthy controls who were homozygous for the rare allele in IL10 rs1518111, mRNA expression and protein production of IL10 were decreased.52

Recent evidence has implicated IL10 in the development of LE.15 In addition to its anti-inflammatory effects, Shi et al.53 demonstrated, using human dermal fibroblasts, that IL10 has anti-fibrotic properties and can inhibit excessive deposition of collagen and the transformation of fibroblasts to myofibroblasts. In addition, polymorphisms in several candidate genes in IL10 and the IL10 receptor, which were not evaluated in this study, were associated with the development of LE following infection with filarial parasites.

Patients who were homozygous for the rare allele in NFKB2 rs1056890 had a 3.1-fold increase in the odds of developing LE. NF-κB transcription factors play a role in diverse cellular processes including the regulation of angiogenesis, metastasis, cell proliferation, tumor promotion, suppression of apoptosis, and inflammation.54 The NF-κB signaling pathway leads to the transcription of pro-inflammatory molecules, such as cytokines and chemokines. Alterations in NF-κB regulation are linked to diseases of chronic inflammation (e.g., Crohn's disease, rheumatoid arthritis, systemic lupus erythematosus). NF-κB2 (p52 and its precursor p100) is one of two subunits that contributes to dimeric NF-κB and is responsible for activating the non-canonical pathway of NF-κB.55 NF-κB2 functions within an autoregulatory loop in which the precursor protein p100 is processed to become the active NF-κB2 subunit known as p52, which can upregulate p100 expression. p100 can repress p52 activity, which acts as a negative feedback control loop.56 This autoregulatory loop is tightly controlled.

In one study, Yang et al.57 found that p52 transgenic mice that were deficient in the p100 precursor protein developed fatal lung inflammation characterized by diffuse alveolar damage with localized fibrosis. The lung tissue of the mice demonstrated high level induction of the Th1 cytokine IFN-γ and its inducible inflammatory chemokines, which are known to activate macrophages and result in a cycle of inflammatory processes and tissue damage. In addition, the transgenic mice displayed a significant increase in TNF-α which acts synergistically with IFN-γ to activate macrophages and regulate fibroblast proliferation and activation.

NFKB2 rs1056890 is located in the 3′ UTR region of NFKB2 on chromosome 10. In one study of Chinese patients with multiple myeloma, who were treated bortezomib,54 individuals who were heterozygous or homozygous for the rare allele had an overall lower response rate and decreased survival. Although rs1056890 has no known function, its association with response to bortezomib treatment,54 suggests that this SNP may be in LD with a unmeasured functional SNP(s).

Several limitations of this study need to be acknowledged. Although the sample size was relatively large, larger samples may reveal additional significant candidate gene associations. In addition, future studies need to confirm the functional effects of these polymorphisms.

Despite these limitations, the novel findings from this study suggest that genetic variations in pro- and anti- inflammatory cytokine genes may play a role in the development of secondary LE following breast cancer treatment. Although the pathophysiology of LE is complex and largely undetermined, the identified genetic associations may help with risk assessment and the development of targeted molecular therapy for this incurable condition.

Acknowledgments

This study was funded by grants from the National Cancer Institute (CA107091 and CA118658). Dr. Christine Miaskowski is an American Cancer Society Clinical Research Professor. Dr. Anand Dhruva is funded through NIH Mentored Patient-Oriented Research Career Development Award (K23 AT005340). Dr. Dale Langford is supported by a Department of Defense Breast Cancer Research Program Postdoctoral Fellowship. Mr. John Merriman is supported by an NINR fellowship (F31 NR012604), an ACS Doctoral Degree Scholarship (DSCN-10-087), an Oncology Nursing Society Doctoral Scholarship, and a UCSF Nursing Alumni Association Scholarship. Dr. Christina Baggott is funded by an American Cancer Society Mentored Research Scholar Award (MRSG 12-01-PCSM). Dr. Heather Leutwyler is funded by the KL2 Program (RR624130). 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 NIH.

Author Disclosure Statement

No competing financial interests exist.

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