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. 2018 Feb;55(2):334-350.e3.
doi: 10.1016/j.jpainsymman.2017.09.020. Epub 2017 Sep 23.

Cytokine Gene Polymorphisms Associated With Various Domains of Quality of Life in Women With Breast Cancer

Kimberly Alexander  1 , Yvette P Conley  2 , Jon D Levine  3 , Bruce A Cooper  3 , Steven M Paul  3 , Judy Mastick  3 , Claudia West  3 , Christine Miaskowski  4
Affiliations

Cytokine Gene Polymorphisms Associated With Various Domains of Quality of Life in Women With Breast Cancer

Kimberly Alexander et al. J Pain Symptom Manage. 2018 Feb.

Abstract

Context: Little is known about the phenotypic and molecular characteristics associated with various domains of quality of life (QOL) in women after breast cancer surgery.

Objectives: In a sample of women with breast cancer (n = 398), purposes were as follows: to identify latent classes with distinct trajectories of QOL from before surgery through six months after surgery and to evaluate for differences in demographic and clinical characteristics, as well as for polymorphisms in cytokine genes, between these latent classes.

Methods: Latent class analyses were done to identify subgroups of patients with distinct QOL outcomes. Candidate gene analyses were done to identify cytokine gene polymorphisms associated with various domains of QOL (i.e., physical, psychological, spiritual, social).

Results: One latent class was identified for the psychological and spiritual domains. Two latent classes were identified for the social domain and overall QOL scores. Three latent classes were identified for the physical domain. For the physical and social domains, as well as for the overall QOL scores, distinct phenotypic characteristics (i.e., younger age, poorer functional status, higher body mass index, and receipt of adjuvant chemotherapy) and a number of cytokine gene polymorphisms (CXCL8, NFKB2, TNFSF, IL1B, IL13, and NFKB1) were associated with membership in the lower QOL classes.

Conclusions: Findings suggest that women experience distinctly different physical well-being, social well-being, and total QOL outcomes during and after breast cancer surgery. The genetic associations identified suggest that cytokine dysregulation influences QOL outcomes. However, specific QOL domains may be impacted by different cytokines.

Keywords: Quality of life; breast cancer; cytokine genes; growth mixture modeling; polymorphism.

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Figures

Figure 1
Figure 1
Observed and estimated physical well-being (Figure 1A), psychological well-being (Figure 1B), social well-being (Figure 1C), spiritual well-being (Figure 1D), and total quality of life (QOL) (Figure 1E) trajectories for patients in each of the latent classes.
Figure 2
Figure 2
Figure 2A - Differences among the physical well-being (PWB) latent classes in the percentages of patients who were homozygous or heterozygous for the common allele TT+TA) or homozygous for the rare allele (AA) for rs4073 in chemokine (C-C-C motif) ligand 8 (CXCL8). Values are plotted as unadjusted proportions with corresponding p-value. Figure 2B - Differences among the PWB latent classes in the percentages of patients who were homozygous for the common allele (GG) or heterozygous or homozygous for the rare allele (GA+AA) for rs11574849 in nuclear factor kappa beta 2 (NFKB2). Values are plotted as unadjusted proportions with corresponding p-value. Figure 2C - Differences among the PWB latent classes in the percentages of patients who were homozygous for the common allele (GG) or heterozygous or homozygous for the rare allele (GA+AA) for rs1800683 in tumor necrosis factor super family (TNFSF). Values are plotted as unadjusted proportions with corresponding p-value.
Figure 3
Figure 3
Figure 3A - Differences between the social well-being (SWB) latent classes in the percentages of patients who were homozygous for the common allele (GG) or heterozygous or homozygous for the rare allele (GC+CC) for rs1143623 in interleukin 1 beta (IL1B). Values are plotted as unadjusted proportions with corresponding p-value. Figure 3B - Differences between the total quality of life (QOL) latent classes in the percentages of patients who were homozygous for the common allele (AA) or heterozygous or homozygous for the rare allele (AC+CC) for rs1881457 in interleukin 13 (IL13). Values are plotted as unadjusted proportions with corresponding p-value. Figure 3C - Differences between the total quality of life (QOL) classes in the percentages of patients who were homozygous or heterozygous for the common allele (AA+AG) or homozygous for the rare allele (GG) for rs4648068 in nuclear factor kappa beta 1 (NFKB1). Values are plotted as unadjusted proportions with corresponding p-value.
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