Self-Reported Cancer-Related Cognitive Impairment in Patients With Breast Cancer Is Associated With Potassium Channel Gene Polymorphisms

Cancer-related cognitive impairment (CRCI) is reported by 57% of patients with breast cancer (Schmidt et al., 2016). CRCI can include difficulties with attention and concentration, decrements in motivation, an inability to recall names of familiar objects or people, and memory loss (Mayo et al., 2021). The molecular mechanisms that underlie CRCI are complex and not fully understood (Oppegaard et al., 2022). Therefore, progress in the development of prevention and mitigation strategies remains limited (Onzi et al., 2022).

As noted in a previously published scoping review (Oppegaard et al., 2022), limited information is available on associations between CRCI and a variety of molecular markers. Inflammatory biomarkers (e.g., circulating cytokines, inflammatory genes) have been the most frequently studied. This line of inquiry is logical because cancer can induce inflammatory processes through multiple pathways, including tumor-related factors (Singh et al., 2019), psychological stress (Han et al., 2016), and as a consequence of treatment(s) (Bagnall-Moreau et al., 2019). Given that inflammation occurs in response to and/or in concert with other biologic processes (Medzhitov, 2008), an evaluation of additional molecular mechanisms may provide new insights into the causes of CRCI.

Although not studied in relationship to CRCI, potassium channels are ion channels that are distributed throughout the central nervous system (e.g., frontal cortex, basal ganglia) (Alam et al., 2023). Evidence suggests that potassium channels are important mediators of inflammation (Di et al., 2018). For example, in response to inflammation, potassium channels located in brain endothelial cells increase the permeability of the blood–brain barrier and contribute to neuroinflammation (Bittner et al., 2014). Equally important, stress-induced inflammatory signaling triggers the opening of potassium channels, which leads to a reduction in neuronal firing and decrements in cognitive function (Arnsten et al., 2023). Given that potassium channel genes have the potential to serve as therapeutic targets (Humphries & Dart, 2015), an evaluation of associations between CRCI and potassium channel genes is warranted.

In the authors’ previous study of patients with breast cancer who were assessed prior to and for six months after surgery (i.e., seven assessments) (Merriman et al., 2014), self-reported CRCI was evaluated using the Attentional Function Index (AFI) (Cimprich et al., 2011). Using growth mixture modeling, the following three distinct attentional function profiles were identified: high (N = 165), moderate (N = 101), and low–moderate (N = 131). Because no previous studies have evaluated for associations between CRCI and potassium channel genes in patients with breast cancer, the purpose of this study, which used the profiles identified in the previous growth mixture modeling analysis (Merriman et al., 2014), was to evaluate for associations between the phenotypic extremes (i.e., the high class versus the low–moderate class) and polymorphisms for potassium channel genes.

Methods

Sample and Setting

The theoretical framework for the overall study was the theory of symptom management (Weiss et al., 2023). For the current analysis, symptom (i.e., CRCI) and person (i.e., demographic, clinical, and biologic characteristics) concepts were evaluated.

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 aged 18 years or older; were scheduled to undergo surgery on one breast; were able to read, write, and understand English; and gave written informed consent. Patients with distant metastases at the time of diagnosis were excluded. Of the 516 patients who were approached, 410 enrolled in the study (80% response rate), and 397 patients completed the enrollment assessment. The most common reasons for refusal were being too busy or feeling overwhelmed.

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 preoperative visits, a clinical staff member explained the study and invited patients to participate. Women who were willing to participate were introduced to a research nurse, who determined eligibility. After providing written informed consent, patients completed baseline questionnaires and had a blood sample drawn a mean of four days prior to surgery. Follow-up questionnaires were completed each month for six months after surgery (i.e., seven assessments during a six-month period). Medical records were reviewed for disease and treatment information.

Measures

Patients completed a demographic questionnaire that obtained information on age, gender, ethnicity, marital status, living arrangements, education, employment status, and income. In addition, patients rated their functional status using the Karnofsky Performance Status Scale, with scores ranging from 30 (“I feel severely disabled and need to be hospitalized”) to 100 (“I feel normal; I have no complaints or symptoms”) (Karnofsky, 1977). To evaluate multimorbidity, patients completed the Self-Administered Comorbidity Questionnaire. The Self-Administered Comorbidity Questionnaire consists of 13 common medical conditions simplified into language that can be understood without prior medical knowledge. Patients indicated whether they had the condition, if they received treatment for it (proxy for disease severity), and if it limited their activities (indication of functional limitations). For each condition, the patient can receive a maximum of three points. Total scores on the Self-Administered Comorbidity Questionnaire range from 0 to 39, with higher scores indicating a greater comorbidity burden (Sangha et al., 2003).

Self-reported CRCI was assessed using the AFI (Cimprich et al., 2011). The AFI consists of 13 items designed to measure perceived effectiveness in daily activities supported by attention and working memory. Higher mean scores on a numeric rating scale ranging from 0 to 10 indicate greater capacity to direct attention. Scores are grouped into categories of attentional function (i.e., a score less than 5 indicates low function, scores of 5–7.5 indicate moderate function, and scores greater than 7.5 indicate high function). Its Cronbach’s alpha was 0.93.

Analysis of Phenotypic Data

Data were analyzed using IBM SPSS Statistics, version 29.0, and Mplus, version 6.11. Descriptive statistics and frequency distributions were generated for sample characteristics and AFI scores. As previously described (Merriman et al., 2014), growth mixture modeling with robust maximum likelihood estimation was used to identify three latent classes of patients with distinct attentional function profiles. The current study used an extreme phenotype approach to compare the high versus low–moderate classes. This approach assumes that individuals whose phenotypes are the most different from one another (e.g., low versus high levels of symptoms) should be grouped for study (Pérez-Gracia et al., 2010). Differences between the two classes in demographic and clinical characteristics were evaluated using parametric and nonparametric tests. A p value of less than 0.05 was considered statistically significant.

Analysis of Genomic Data

Blood collection and genotyping: Genomic DNA was extracted from peripheral blood mononuclear cells using the Puregene^® Genomic DNA Isolation System. Samples were genotyped using the GoldenGate^® Assay Workflow and processed according to a standard protocol using GenomeStudio Software.

Single nucleotide polymorphism selection: A combination of tagging single nucleotide polymorphisms (SNPs) and literature-driven SNPs was selected for analysis. Tagging SNPs were required to be common (defined as having a minor allele frequency of 0.05 or greater) in public databases. To ensure robust genetic association analyses, quality control filtering of SNPs was performed. SNPs with call rates of less than 95% or a Hardy–Weinberg p value of less than 0.001 were excluded.

As shown in Supplementary Table 1 online, a total of 155 SNPs among the 10 candidate genes passed all quality control filters and were included in the genetic association analyses. The SNPs among the 10 candidate genes were identified as follows:

• Voltage-gated potassium channel subfamily A member 1: one SNP
• Voltage-gated potassium channel subfamily D member 2: nine SNPs
• Voltage-gated potassium channel modifier subfamily S member 1: four SNPs
• Inwardly rectifying potassium channel subfamily J member 3 (KCNJ3): 28 SNPs
• Inwardly rectifying potassium channel subfamily J member 5 (KCNJ5): eight SNPs
• Inwardly rectifying potassium channel subfamily J member 6 (KCNJ6): 58 SNPs
• Inwardly rectifying potassium channel subfamily J member 9: two SNPs
• Two-pore domain potassium channel subfamily K member 2 (KCNK2): 22 SNPs
• Two-pore domain potassium channel subfamily K member 3 (KCNK3): six SNPs
• Two-pore domain potassium channel subfamily K member 9 (KCNK9): 17 SNPs

Statistical Analyses for Genetic Data

Allele and genotype frequencies were determined by gene counting. Hardy–Weinberg equilibrium was assessed using chi-square or Fisher’s exact tests. For the haplotype determinations, measures of LD (i.e., D’ and r²) were computed from the patients’ genotypes using Haploview, version 4.2. LD-based haplotype block definition was based on D’ confidence interval (Gabriel et al., 2002). For SNPs that were members of the same haploblock, haplotypes were constructed using PHASE, version 2.1 (Stephens et al., 2001). Ancestry informative markers were used to minimize confounding because of population stratification (Halder et al., 2008).

For association tests, the following three genetic models were assessed for each SNP: additive, dominant, and recessive using chi-square or Fisher’s exact tests. For the significant SNPs, the genetic model that best fit the data, by maximizing the significance of the p value, was selected for the multivariate analysis. Logistic regression analyses, which controlled for significant covariates as well as genomic estimates of and self-reported race and ethnicity, were used to evaluate the association between SNPs and haplotypes that were significant in the bivariate analyses and membership in the low–moderate attentional function class. A backward stepwise regression was used to create the most parsimonious model. Except for genomic estimates of and self-reported race and ethnicity, only predictors with a p value of less than 0.05 were retained in the final model. Genetic model fit and unadjusted and covariate-adjusted odds ratios were estimated using Stata, version 15.

Results

Growth Mixture Modeling Analysis for Attentional Function

As previously described (Merriman et al., 2014), three classes with distinct attentional function profiles were identified in patients with breast cancer who were assessed prior to and for six months after surgery using growth mixture modeling. Patients in the high attentional function (high) class (N = 165, 42%) had an estimated AFI score of 7.78 at enrollment, which increased and remained high during the next six months. Patients in the moderate attentional function (moderate) class (N = 101, 25%) had an estimated AFI score of 6.58 at enrollment, which decreased and then increased significantly but remained moderate during the next six months. Patients in the low–moderate attentional function (low–moderate) class (N = 131, 33%) had an estimated AFI score of 5.23 at enrollment, which did not change significantly during the next six months. In the current study, which used an extreme phenotype approach (Pérez-Gracia et al., 2010), differences between patients in the high (N = 165, 56%) and low–moderate (N = 131, 44%) classes were evaluated (see Figure 1).

Demographic and Clinical Characteristics

Compared to the high class, the low–moderate class was aged younger and had a lower annual income, a higher body mass index, a higher comorbidity burden, and a lower functional status. In addition, they were more likely to have received neoadjuvant therapy (see Table 1).

Candidate Gene Analysis

As shown in Supplemental Table 1 online, genotype frequencies were significantly different between the attentional function classes for 15 SNPs and 4 haplotypes (i.e., inwardly rectifying potassium channel subfamily J member 3 [KCNJ3]: 1 SNP, KNCJ5: 1 SNP, KCNJ6: 6 SNPs and 2 haplotypes, KCNK2: 4 SNPs and 1 haplotype, two-pore domain potassium channel subfamily K member 3 [KCNK3]: 1 SNP and 1 haplotype, and KCNK9: 2 SNPs).

Regression Analyses

To better estimate the magnitude (odds ratio) and precision (confidence interval) of genotype on attentional function class membership, multivariate logistic regression models were fit. In the final regression analyses of the phenotypic characteristics that were evaluated (i.e., age, annual income, body mass index, functional status, comorbidity burden, and receipt of neoadjuvant treatment), which included self-reported and genomic estimates of race and ethnicity, the only ones that were retained in the final model were age, comorbidity burden, and functional status. Six SNPs in four different genes remained significant in the logistic regressions (see Table 2).

For KCNJ5 rs2846700, carrying one or two doses of the rare allele (AA versus AG + GG) was associated with a 57% decrease in the odds of belonging to the low–moderate class. For KCNJ6 rs1399596, carrying two doses of the rare allele (TT + TC versus CC) was associated with a 77% decrease in the odds of belonging to the low–moderate class. For KCNJ6 rs2835945, carrying two doses of the rare allele (GG + GA versus AA) was associated with a 2.53 fold increase in the odds of belonging to the low–moderate class (see Figure 2).

For KCNK2 rs12757222, carrying one or two doses of the rare allele (AA versus AG + GG) was associated with a 62% decrease in the odds of belonging to the low–moderate class. For KCNK2 rs12080135, carrying one or two doses of the rare allele (TT versus TG + GG) was associated with a 2.05 fold increase in the odds of belonging to the low–moderate class. For KCNK9 rs3780051, carrying two doses of the rare allele (AA + AG versus GG) was associated with a 3.1 fold increase in the odds of belonging to the low–moderate class (see Figure 3).

Discussion

This study is the first to report on associations between self-reported CRCI and polymorphisms for potassium channel genes in patients with breast cancer who were assessed prior to and for six months after surgery. The latent classes were named based on the instrument’s name (i.e., AFI), with its emphasis on attention. However, in addition to attention, the AFI assesses perceived effectiveness in performing daily activities that are supported by working memory and executive functions (e.g., setting goals, planning, carrying out tasks) (Cimprich et al., 2011). Therefore, it is a valid and reliable measure of self-reported CRCI.

In the final logistic regression model, risk factors associated with membership in the low–moderate class included younger age, a higher comorbidity burden, and a lower functional status. As noted in one systematic review (Kim et al., 2020), although the association between age and CRCI is among the most frequently evaluated characteristics, results are inconclusive. For example, in one study of cancer survivors (Schmidt et al., 2016), younger age was associated with the occurrence of self-reported CRCI in the bivariate analysis, but it did not remain significant in the multivariate analysis. The fact that a higher comorbidity burden and lower functional status remained significant in the multivariate model is not surprising given that CRCI is frequently associated with the presence of comorbid conditions (Zhou et al., 2024). In addition, the presence of multiple comorbid conditions is associated with worse functional status in patients with breast cancer (Chia et al., 2021).

Genomic Findings

Although specific functions vary by subtype, potassium channels regulate a number of biologic functions within the central nervous system, including the release of neurotransmitters, neuronal excitability, and plasticity (Djillani et al., 2019). Of the six significant SNPs identified in the current study, three were for inwardly rectifying potassium channel genes (i.e., KCNJ5 rs2846700, KCNJ6 rs1399596, and KCNJ6 rs2835945), and three were for two-pore domain potassium channel genes (i.e., KCNK2 rs12757222, KCNK2 rs12080135, and KCNK9 rs3780051). All of these SNPs are intron variants (Sherry et al., 2001). Although once believed to be noncoding and nonfunctional, evidence suggests that intron splicing is linked with the enhancement of transcription (Girardini et al., 2023). Of note, emerging evidence suggests that introns can regulate gene expression through intron-mediated enhancement of gene expression (Girardini et al., 2023).

Inwardly rectifying potassium channel genes: In terms of function, KCNJ5 and KCNJ6 are genes within the G-protein–gated inwardly rectifying potassium channel subfamily. G-protein–gated inwardly rectifying potassium channels regulate neuronal firing and excitability in the brain (Rifkin et al., 2017). Although no human studies were identified that reported findings related to rs2846700 and CRCI, in a preclinical study (Wickman et al., 2000), KCNJ5 knockout mice (i.e., mice lacking a functional KCNJ5 gene) had worse performance on tests of spatial learning and memory compared to wild-type mice (i.e., mice with a functional KCNJ5 gene).

An evaluation of expression quantitative trait loci for rs2846700 found associations with the pancreas (Lonsdale et al., 2013). Given that expression quantitative trait loci are a region of the chromosome where genetic variations are associated with the expression levels of nearby or distant genes (Zhang & Zhao, 2023), additional research is needed to understand this association. However, it is notable that in one study (Jongsma et al., 2011), decrements in cognitive function were worse in patients with chronic pancreatitis compared to healthy controls.

No studies were identified that reported findings for rs1399596 and rs2835945 and CRCI. However, in a preclinical model of Down syndrome (Kleschevnikov, 2022), triplication of the KCNJ6 gene resulted in the development of abnormal neural circuits that caused cognitive impairment. In a case report of a patient with Keppen-Lubinsky syndrome (van Midden et al., 2023), variations in the KCNJ6 gene were associated with a novel phenotype that included a mild intellectual disability. In terms of expression, the KCNJ6 gene regulates the excitability of dopaminergic neurons and is expressed in brain regions associated with attention deficit hyperactivity disorder (Ziegler et al., 2020).

Two-pore domain potassium channel genes: Two-pore domain potassium channels are found throughout the central nervous system (e.g., neurons, brain endothelial cells) (Bittner et al., 2014). Although no studies were identified that reported findings for rs12757222 or rs12080135 and CRCI, the KCNK2 gene encodes the potassium channel subfamily K member 2 (TREK-1). As noted in one review (Djillani et al., 2019), TREK-1 is expressed in the brain and has roles in a variety of clinical conditions (e.g., depression, ischemia, pain). In a mouse model of Alzheimer disease (Li et al., 2022), activation of TREK-1 channels with linolenic-a acid improved learning and memory deficits. In another preclinical study (Wang et al., 2020), knockout of TREK-1 expression in mice impaired the cellular structure and function of hippocampal pyramidal neurons. The authors concluded that cognitive impairment in conditions associated with aberrant expression of TREK-1 could be attributed to decrements in this potassium channel’s ability to regulate neuronal morphology, excitability, synaptic transmission, and plasticity (Wang et al., 2020).

Although no studies were identified that reported findings for rs3780051 and CRCI, a variant of the KCNK9 gene is associated with Birk-Barel syndrome (Zadeh & Graham, 2017), a condition that includes delayed intellectual development. The KCNK9 gene encodes a two-pore domain, acid-sensitive potassium channel (TASK3). As noted in one review (Bittner et al., 2010), TASK3 is critical for T-cell activation and subsequent inflammatory processes.

Limitations

Because this study is the first to evaluate for associations between self-reported CRCI and potassium channel genes in patients with breast cancer, the findings warrant confirmation in larger samples, as well as in men with prostate and testicular cancers, and in samples of patients with heterogeneous types of cancer. In addition, because CRCI was assessed using a self-report measure, future studies need to evaluate for associations between objective measures and potassium gene polymorphisms. Because a custom array was used in the parent study that evaluated other symptoms (e.g., pain), only a limited number of candidate genes were evaluated. Finally, given that no studies were identified that reported associations between CRCI and each of the significant SNPs in the current study, additional research is warranted on the role of various types of potassium channels in CRCI.

Implications for Nursing and Research

Although the findings from this research do not have immediate implications for clinical practice, they provide a foundation for ongoing research. First, the significant SNPs identified in this study were for either inwardly rectifying or two-pore domain potassium channel genes. Given the complex biologic roles of the various potassium channel genes, evaluation of other types of potassium channels, as well as other types of molecular analyses (e.g., gene expression, methylation), are warranted. Future studies should evaluate other genes that may affect cognitive function in patients with cancer (e.g., calcium channel genes [Baracaldo-Santamaría et al., 2023; Dhureja et al., 2023], sodium channel genes [Baumgartner et al., 2023; Noebels, 2019]). Genomic analyses that evaluate for associations with the subscales of the AFI (i.e., effective action, attentional lapses, and interpersonal effectiveness) may increase understanding of more specific CRCI phenotypes and/or mechanisms. Finally, future studies need to recruit a more diverse sample of patients, particularly in relation to various social determinants of health (e.g., neighborhood, insurance status, access to health care, occupation), which may affect cognitive function.

Conclusion

Because ion channels represent 19% of human genome–derived proteins targeted by drugs (Santos et al., 2017), additional research on associations between CRCI and a variety of ion channels may lead to the development of new and individualized therapies to prevent or treat this symptom. This study provides novel information on associations between self-reported CRCI and potassium channel genes in patients with breast cancer who were assessed prior to and for six months after surgery. These findings can be used to guide future research on the mechanisms that underlie CRCI. Equally important, they may lead to the identification of patients at increased risk for CRCI and the development of novel interventions.

About the Authors

Kate R. Oppegaard, RN, PhD, OCN^®, is a research fellow and Marilyn J. Hammer, PhD, DC, RN, FAAN, is the director of the Phyllis F. Cantor Center for Research in Nursing and Patient Care Services, both at the Dana-Farber Cancer Institute in Boston, MA; Yvette P. Conley, PhD, FAAN, is a professor and Carolyn S. Harris, PhD, RN, BMTCN^®, OCN^®, is a postdoctoral student, both in the School of Nursing at the University of Pittsburgh in Pennsylvania; Bruce A. Cooper, PhD, is a research data analyst III and senior statistician, and Steven M. Paul, PhD, is a research data analyst III, both in the Department of Physiological Nursing in the School of Nursing at the University of California, San Francisco; Joosun Shin, PhD, RN, OCN^®, is a research fellow at the Dana-Farber Cancer Institute; and Lisa Morse, MS, RN, CNS is a postdoctoral student in the School of Nursing, Gary M. Abrams, MD, is a professor in the School of Medicine, Jon D. Levine, PhD, MD, is a professor in the Department of Oral and Maxillofacial Surgery in the School of Dentistry, and Christine Miaskowski, PhD, RN, is a professor in the Department of Physiological Nursing in the School of Nursing, all at the University of California, San Francisco. This research was funded, in part, by grants from the National Cancer Institute (CA107091, CA118657). Oppegaard received postdoctoral fellowship support from the National Cancer Institute (CA156734). Harris received postdoctoral fellowship support from the National Institute of Nursing Research (NR009759). Shin received research funding from the Mittelman Family Fund Fellowship for Integrative Therapy. Hammer received research funding from GSK through Pack Health. Oppegaard and Miaskowski contributed to the conceptualization and design. Abrams and Miaskowski completed the data collection. Cooper, Paul, and Miaskowski provided statistical support. Oppegaard, Conley, Harris, Cooper, Levine, and Miaskowski provided the analysis. Oppegaard, Hammer, Conley, Harris, Shin, Morse, Abrams, Levine, and Miaskowski contributed to the manuscript preparation. Miaskowski can be reached at chris.miaskowski@ucsf.edu, with copy to ONFEditor@ons.org. (Submitted December 2023. Accepted February 14, 2024.)