Skip to main content
Download PDF

Clinical and genetic factors associated with self-reported cognitive deficits in women with breast cancer: the “CAGE-Cog” study

BMC Cancer volume 22, Article number: 996 (2022) Cite this article

Abstract

Background

Breast cancer patients undergoing chemotherapy treatment are at particular risk of experiencing acute cognitive impairment leading to daily challenges in decision-making and reduced quality of life and functional autonomy. The aim was to assess the relationship between clinical and genetic factors and cognitive function in a sample of patients with breast cancer undergoing chemotherapy.

Methods

A cross-sectional study was carried out between November 2017 and June 2019 on women (N = 112) treated for breast cancer by intravenous chemotherapy at the oncology outpatient unit of Hôtel-Dieu de France Hospital, Beirut. Patients were evaluated with the 37-item Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog). Other validated scales were also used to assess depression, anxiety, sleep disorders, pain, and fatigue. DNA was obtained by a buccal swab (FTA®technology) for genotyping of different genes (ABCB1, COMT, DRD2, OPRM1, CLOCK, CRY2, and PER2) using the Lightcycler®(Roche).

Results

The mean age of participants was 56.04 years. Multivariable analysis, taking the four FACT-Cog subscores as the dependent variables, showed that the mean cognitive score decreased with higher depression, anxiety, and insomnia scores. Patients with university education levels had better perceived cognitive abilities than those with primary education. Moreover, carrying the G allele for the OPRM1 polymorphism (c.118A > G;rs197791) was significantly associated with a better cognitive function compared to AA patients (B = 2.05; p = 0.038).

Conclusions

A comprehensive oncological care plan should include a personalized assessment of all factors related to cognitive functioning in cancer patients, particularly anxiety and depression, to achieve an optimal patient outcome.

Peer Review reports

Background

Cancer-related cognitive impairment (CRCI) is defined as impaired memory, learning, concentration, executive function, visual-spatial abilities, and information processing [1,2,3]. Breast cancer patients undergoing chemotherapy treatment are at particular risk of experiencing acute cognitive impairment leading to daily challenges in decision-making, adherence to treatment, reduced quality of life and functional autonomy, and shorter survival rates [4, 5]. Hence, studies designed to evaluate cognition reported that up to 75% of women with breast cancer experience cognitive changes after a standard dose of chemotherapy [3, 6]. Several factors may account for the patients’ susceptibility to cognitive decline during a chemotherapy course, including number of therapy cycle [7], direct and indirect neurotoxicity due to treatment (progenitor cell and mitochondrial damage, immune system dysregulation) [8, 9], psychological disorders (anxiety, depression, sleep problems) [10,11,12], and genetic susceptibility [13,14,15]. Moreover, cancer-related fatigue, anxiety, depression, and insufficient sleep are associated with negative health outcomes, including impaired cognition [16,17,18]. As for the genetic aspect, most studies have focused on genes regulating neural repair/plasticity, such as the genes encoding the brain-derived neurotrophic factor (BDNF) [19] or apolipoprotein E (ApoE) [20]. However, other less frequently explored genetic factors might also contribute to chemotherapy-induced cognitive changes. These include the low efficiency of membrane efflux transporters at the blood–brain barrier (BBB) (such as P-glycoprotein or P-gp encoded by the ABCB1 gene) [21], impaired neurotransmission (via dopamine pathways, known for their implication in aging and neurocognition processing, especially in the prefrontal cortex [22]), dysregulation in the brain regions known to regulate learning and memory functions (opioid system) [23,24,25], or dysregulation in circadian rhythms [26].

The first selected single nucleotide polymorphism (SNP) in our study included the c.3435 T > C (rs1045642) in the ABCB1 gene encoding the P-gp. This substitution leads to a reduced P-pg expression and transport of xenobiotics to the central nervous system (CNS). Hence, patients with the T allele might have higher cognitive impairment due to a higher drug concentration in the brain, mainly because most breast cancer chemotherapy regimens have at least one cytotoxic drug that is a substrate of P-gp [27, 28]. Genes encoding the catechol-O-methyl transferase (COMT) and the dopamine receptor D2 (DRD2) were also explored. The SNP rs4680 in COMT is of particular interest: it causes a valine to methionine substitution (p.Val158 Met), leading to a 3- to fourfold reduced catabolic activity of COMT [29, 30]. Patients with the Met allele have a higher level of adrenaline in the prefrontal cortex (PFC) and increased dopamine binding to the D1 receptor, which enhances the stability of the dopaminergic PFC networks; these patients have a better performance in cognitive tasks (retaining information in the working memory) but a decreased cognitive flexibility while updating memory with new information [31]. As for DRD2, the SNP rs6277 (c.957C > T) causes a change in the mRNA folding, thus reducing the receptor stability and synthesis and the dopamine binding capacity in vivo and in vitro [32]. All these changes might lead to dysregulation in cognitive functioning in cancer patients. Despite the potential role of the opioid system in memory functions, the effects of SNPs in OPRM1 (gene encoding the Mu-opioid receptor) on cognitive function in breast cancer patients have been rarely explored. Our team was the first to describe in palliative cancer patients that homozygous AA patients for the SNP rs1799971 (c.118A > G) of OPRM1 had a significantly lower cognitive function than the AG patients [33]. Thus, it was decided to replicate this finding using a validated tool for cognitive function. Finally, several SNPs in genes of the circadian rhythm unexplored with cognition previously were evaluated in cancer patients: rs1801260 in CLOCK (Circadian Locomotor Output Cycles Kaput), rs934945 in PER2 (Period circadian regulator 2), and rs10838524 CRY2 (Cryptochrome circadian regulator 2). The hypothesis was that these SNPs would induce a loss of coherence between the components of this system, which might negatively affect cognitive function [26, 34, 35].

Therefore, this study aimed to explore cognitive impairment in a group of breast cancer patients undergoing a chemotherapy course (using a validated tool for cancer patients) and evaluate the contribution of clinical, psychological, and genetic factors underlying treatment-related cognitive decline.

Methods

Study design and patients

The CAGE-Cog is a cross-sectional study conducted at Hôtel-Dieu de France (HDF) Hospital between November 2017 and June 2019 to evaluate the impact of Clinical And Genetic factors on cognitive function in women with primary breast cancer treated by chemotherapy.

Inclusion criteria were: adult women (> 18 years old), primary diagnosis of breast cancer (Stage 0 to IV), to be scheduled to receive a course of chemotherapy (random cycle). Exclusion criteria were: patients who refused to participate in the study, concurrent radiation/adjuvant hormone therapy, relapsed breast cancer/other types of cancer, any surgical intervention/disorder of the central nervous system (dementia, multiple sclerosis, epilepsy, Parkinson's disease, intellectual disability, and neurosurgeries) that may affect cognitive function, brain metastasis (due to their known effect on the cognitive decline) [36,37,38].

Ethical approval

The ethics committee of HDF hospital approved this study (Reference: CEHDF1016, July 2017), and all patients gave their written consent prior to inclusion.

Sociodemographic and clinical information

Several demographic and clinical data were collected (using patient interview and medical records): age, gender, weight and height (to calculate the body mass index, BMI), Body Surface Area (BSA, calculated using the Mosteller formula) [39, 40], ethnicity/nationality, marital status, education level, socioeconomic level, presence of comorbidities (diabetes, hypertension, dyslipidemia, others), current alcohol consumption (self-reported), current tobacco smoking (self-reported), and medications (other than chemotherapy). Cancer-related clinical features were retrieved from the patients' medical records, including disease stage, location of metastases (if present), chemotherapy cycle number at the time of data collection, and chemotherapy regimen.

Cognitive function assessment and other assessments

Patients were interviewed on the day they were admitted to the oncology outpatient unit to receive chemotherapy (day 1 of chemotherapy, random cycle). The chemotherapy cycle number was recorded.

Cognitive function was assessed using the Functional Assessment of Cancer Therapy—Cognitive Function (FACT-Cog, version 3; Licensing agreement granted on November 2, 2017). This self-assessment scale consists of 37 questions divided into four subscales: 1) Perceived cognitive impairments subscale (CogPCI); 2) Perceived cognitive abilities subscale (CogPCA); 3) Comments from others subscale (CogOTH), referring to the comments of other people on cognitive function of the patient; and 4) Impact of perceived cognitive impairments on quality of life subscale (CogQOL), including disrupted normal daily functioning and working capacity. The higher the FACT-Cog score/subscore, the better the cognitive function of patients and the lower the impact on their quality of life [41].

Sleep disorders were evaluated using two screening tools, the Pittsburgh Sleep Quality Index (PSQI) and the insomnia severity index (ISI). The PSQI is a 19-item scale designed to measure sleep in seven domains during the past month: subjective quality of sleep, sleep latency, sleep duration, sleep efficiency, sleep disorders, sleep medication, and daytime dysfunction. The component scores range from 0 (no difficulty) to 3 (severe difficulty) and allow the calculation of an overall score ranging from 0 to 21 [42]. The ISI is a 7-item tool to assess the perceived severity of insomnia during the past two weeks. Questions are rated on a 5-point Likert scale from 0 (very satisfied) to 4 (not at all satisfied). Total scores range from 0 to 28, with the highest scores representing more severe insomnia [43].

Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS). This self-report tool consists of two subscales designed to identify and quantify anxiety (HADS-A) and depression (HADS-D) in patients. Symptoms of the previous week are reported on a scale from 0 (not at all) to 3 (most of the time). Patients would be classified as “normal” (Score 0–7), having a “borderline anxiety/depression” (Score 8–10), or “clinical anxiety/depression” (Score 11–21) [44].

Fatigue was evaluated using three questions from the EORTC-QLQ C30 scale (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire. QLQ C10: “Do you need rest?”; QLQ C12: “Did you feel weak?”; and QLQ C18: “Were you tired?”. A raw score was obtained by summing the responses to the three questions, then transformed into a score ranging from 0 to 100, according to the EORTC scoring manual. Higher scores indicated worse fatigue and a poorer QOL [45].

Finally, pain was estimated by the visual analogue scale (VAS), ranging from 0 (absence of pain) to 10 (maximum of pain) [46].

DNA Sampling and Genotyping

DNA was obtained by a buccal swab (Whatman® FTA® card technology-GE Healthcare) as recommended by the manufacturer. Genotyping for selected SNPs in ABCB1 (rs1045642), OPRM1 (rs1799971), COMT (rs4680), DRD2 (rs6277), CLOCK (rs1801260), PER2 (rs934945), and CRY2 (rs10838524) was performed using the Lightcycler® 2.0 (Roche Diagnostics GmbH-Mannheim-Germany).

Supplementary Table 1 shows the polymerase chain reaction (PCR) protocol and conditions. Positive (defined by direct sequencing) and negative controls (water) were systematically included in experiments.

Physicians and investigators at the hospital assessing cognitive function and other variables were not informed of the genotyping results prior to the end of the study to minimize potential bias.

Data and statistical analysis

The SPSS software version 25.0 was used for data analysis. Descriptive statistics were calculated for all variables in the study, including the mean and standard deviation for continuous measures and counts and percentages for categorical variables. Each of the cognitive sub-scores (CogPCI, CogPCA, CogOTH, CogQOL) was analyzed separately. The different cognitive scores did not follow a normal distribution (verified by the Kolmogorov–Smirnov test); therefore, nonparametric tests were used. The Kruskal–Wallis test was applied to compare several groups, the Mann–Whitney test to compare two groups, and the Spearman correlation test to compare two continuous variables. Multiple linear regressions were performed, taking each cognitive score as the dependent variable, respectively, and using variables that showed a p < 0.1 in the bivariate analysis as independent variables to reduce potential confounders. A value of p ≤ 0.05 was considered statistically significant.

Results

Sociodemographic and clinical characteristics

A total of 112 women with breast cancer were included in our study (mean age 56.04 ± 11.69 years and an average BMI of 25.9 ± 4.62 kg/m2). Among these patients, 8 had metastases and received palliative chemotherapy. Pain, as evaluated by the VAS, was relatively low, with a mean score of 1.68 ± 2.48 [0–9]. The average number of chemotherapy cycles was 4.45 ± 2.35 [1-12] (Table 1). More details are presented in Supplementary Table 2.

Table 1 Patients’ demographics and clinical characteristics (N = 112)1
Full size table

Bivariate analyses

Bivariate analyses were conducted with each FACT-Cog subscale score and the total instrument score as the dependent variable in the model (Table 2).

Table 2 Bivariate analysis of categorical variables associated with the cognition scores
Full size table

The results have shown that a significantly higher CogPCI score was found in patients who did not have diabetes compared to those who did. Patients taking adjuvant treatments (vs. palliative or neo-adjuvant regimens) had significantly higher CogOTH, QOL, and total scores. Patients who did not have metastases and those with the AG genotype for OPRM1 SNP (vs. AA) had a significantly higher CogQOL score (p = 0.005) (Table 2).

Higher insomnia severity, pain score, anxiety, and depression levels were significantly associated with lower CogPCI, whereas higher pain score was significantly associated with lower CogPCA scores. Higher anxiety and depression scores were significantly associated with higher CogOTH. Higher insomnia severity, worse sleep quality (higher PSQI scores), higher chemotherapy cycle number, higher pain scores, and higher anxiety and depression scores were significantly associated with lower Cog QOL and total FACT-Cog scores (Table 3).

Table 3 Bivariate analysis of continuous variables associated with the cognition scores
Full size table

Multivariable analysis

A first linear regression, taking the CogPCI score as the dependent variable, showed that participants with higher anxiety (B = -1.06) and those with diabetes (B = -8.94) had lower CogPCI scores (Table 4, Model 1).

Table 4 Multivariable analysis
Full size table

A second forward linear regression, taking the CogPCA score as the dependent variable, showed that having a university education level (B = 2.75) was significantly associated with higher CogPCA scores (Table 4, Model 2).

A third linear regression, taking the CogOTH score as the dependent variable, showed that a higher depression level (B = -0.25) was significantly associated with lower CogOTH, whereas taking an adjuvant chemotherapeutic treatment (B = 1.65) vs. a palliative regimen was significantly associated with higher CogOTH scores (Table 4, Model 3).

A fourth linear regression, taking the CogQOL score as the dependent variable, showed that a higher depression (B = -0.24) and insomnia severity (ISI; B = -0.17) were significantly associated with lower CogQOL scores, whereas taking an adjuvant chemotherapeutic treatment (B = 3.21) vs. a palliative regimen and having the OPRM1 AG genotype vs. AA (B = 2.05) were significantly associated with higher CogQOL scores (Table 4, Model 4).

A fifth linear regression, taking the total FACT-Cog score as the dependent variable, showed that higher depression (B = -1.91) and taking a neo-adjuvant chemotherapeutic treatment (B = -13.80) vs. a palliative regimen were significantly associated with lower total FACT-Cog scores (Table 4, Model 5).

These results were adjusted over all the variables that showed a p-value of more than 0.1 in the bivariate analyses (including age and education usually controlled when considering cancer-related effects on cognition).

When forcing all the genes into each model, the results remained the same.

Discussion

Our study explored the clinical and genetic factors associated with cognitive impairment in patients with breast cancer. The total FACT-Cog and subdomain scores were similar to those reported in other studies among breast cancer patients [20, 47], but lower than those reported in other populations after the completion of chemotherapy [10], likely due to the relatively short period of evaluation of cognitive function since the beginning of chemotherapy treatment. Regarding the genetic factors, the rs1799971 of OPRM1 (c.118A > G) showed to be significantly associated with cognitive function; patients carrying the G variant had significantly higher CogQOL scores compared to AA patients. To the best of our knowledge, this study is the first to report such an association between the opioid system and cognitive function, using a validated tool for patient-reported (subjective) cognitive function in cancer patients. A similar finding had been previously reported in a palliative cancer group of patients but using a less specific tool for cognitive functioning (EORTC-QOL of the World Health Organization WHO) [33]. Previous studies have explored the association of dynorphin/κ-opioid receptor (κOR), but not the mu-opioid receptor, with cognitive function, showing its implication in emotion and cognition. Furthermore, it has been demonstrated that the pharmacological blockade of κOR prevented impairments of memory performance, whereas its activation was linked to cognitive decline in mice [24, 25]. Therefore, it was hypothesized that AA patients for OPRM1, exhibiting a greater activation of the mu-receptor, would have cognitive deficits as described with the activation of the κOR, explaining their lower cognitive performance and the subsequent impact on their quality of life. Future studies are necessary to validate these results in replication cohorts.

Besides the genetic factors, our study has shown that patients who have anxiety or depression report more cognitive impairments (lower CogPCI scores for anxiety. and lower CogOTH, CogQOL, and total FACT-Cog scores for depression). These results are in line with several previous reports showing a significant association between reported psychological distress and subjective/objective cognitive disturbances in cancer patients [10,11,12, 17, 48]. Indeed, psychological and emotional distress from cancer diagnosis and cancer-related treatments (chemotherapy, radiotherapy, or surgery) might cause changes in the hypothalamic-pituitary axis [49]. This hypothesis suggests that emotional states can trigger biological changes, such as the release of pro-inflammatory cytokines and biochemical alterations in neuronal plasticity, which leads to cognitive decline [8].

Another psychological factor that emerged in the multivariable analyses is the insomnia severity as evaluated by the insomnia severity index. The higher the ISI scores, the lower the CogQOL scores due to cognitive decline. This finding is similar to what was previously reported about the association between sleep disorders, cognitive dysfunction, and the detrimental impact on quality of life [50,51,52]. Interestingly, some researchers identified neuro-inflammatory processes [18, 53] and reduced hippocampal volumes in areas involved in working memory among patients with sleep disturbances [54].

Pain was significantly associated with cognition in the bivariate but not multivariable analyses. Previous studies exploring pain in cancer and non-cancer patients highlighted that patients reporting ongoing/persistent moderate to severe pain might be at risk for cognitive decline. A possible explanation for why this relevant factor did not remain significant in the multivariable analyses could be the relatively low pain levels reported in our study (mean VAS score of 1.68) or the relatively small sample size.

Hence, psychological disorders, pain, fatigue, sleep disorders, and cognitive dysfunction seem to be interrelated and might share a common pathophysiology, suggesting the concept of a “symptom cluster” [4, 16, 32, 43, 55].

Furthermore, patients with diabetes reported more cognitive impairment as described elsewhere [56, 57]. Several hypotheses have been suggested to explain the pathophysiology of diabetes-related cognitive dysfunction. In that context, it was found that insulin receptors are expressed in the neuronal soma and synaptic terminals, making them essential components for the preservation of memory in the hippocampus [56]. This metabolic disorder can cause a chronic inflammatory state and oxidative stress that affect neurons, inducing apoptosis and increasing cerebral damage [58]. These biochemical modifications could, therefore, potentially lead to cognitive impairments.

Having a university level of education was significantly associated with higher CogPCA scores. Several studies had previously reported the correlation between higher education and cognitive reserve [59,60,61]. Indeed, higher education is associated with better mental adaptation and superior coping skills following chemotherapy-induced brain damage, thus protecting against cognitive impairment [60]. A study about the impact of education level on cognitive function in breast cancer patients has reported that patients with higher education levels performed better on the verbal memory task than those who are less educated [61].

Finally, chemotherapy regimens were also associated with different subscores of the cognitive evaluation. Patients who were offered adjuvant chemotherapy following breast cancer surgery had better CogOTH/CogQOL scores than those who received a neoadjuvant or palliative regimen. A possible explanation for such a result could be the more severe/advanced cancer stages in patients who did not receive adjuvant chemotherapy.

Limitations and strengths

Our study has several limitations related to its design. In this cohort of patients, cognitive function was not evaluated at baseline before starting any initial treatment (chemotherapy/surgery). Therefore, it was not possible to detect changes over time, and the cognitive assessment could have been biased as some patients might exhibit cognitive impairment since the beginning of their treatment. Moreover, although a validated tool for cancer patients was used, cognition was still subjectively reported and not objectively measured using clinical batteries for objective neurocognitive functioning assessment. A selection bias is also possible since patients were recruited from one hospital. An information bias is also likely since symptoms of depression, anxiety, and insomnia might have been over or underestimated by patients. Residual confounding bias might also be present since not all factors associated with cognitive function were considered in this study.

Finally, the sample size is considered relatively small for genetic analyses. However, to the best of our knowledge, this study is the first to explore several clinical and genetic factors, including genes for the circadian rhythm, not previously evaluated with cognitive function. Furthermore, multivariable analyses were performed, taking into account all confounding factors.

Conclusion

Our findings highlight the importance of identifying clinical and genetic markers to improve the diagnosis of cognitive impairment and implement personalized medicine approaches to mitigate detrimental health outcomes in women with breast cancer. Patients with higher susceptibility to cognitive impairment could benefit from a personalized cognitive assessment as part of a comprehensive oncological supportive care plan. Management strategies, including physical activity/exercise, behavioral interventions, or other targeted pharmacological agents, that are still currently under investigation, would be interesting to implement once stronger evidence supporting their relevance is identified. Thus, further research on a larger sample is necessary to confirm our findings, allow generalization to the breast cancer population, and implement the adequate management plan.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to the fact that the study is still ongoing on other cancer populations (other than breast cancer), but are available from the corresponding author on reasonable request.

Abbreviations

ApoE:

Apolipoprotein E

BBB:

Blood brain barrier

BDNF:

Brain-derived neurotrophic factor

BMI:

Body mass index

BSA:

Body Surface Area

CNS:

Central nervous system

COMT:

Catechol-O-methyl transferase

CogPCA:

Perceived cognitive abilities subscale

CogPCI:

Perceived cognitive impairments subscale

CogOTH:

Comments from others subscale

CogQOL:

Impact of perceived cognitive impairments on quality of life

EORTC-QLQ C30 scale:

European Organization for Research and Treatment of Cancer FACIT: Functional Assessment of Chronic Illness Therapy system of Quality of Life questionnaires

FACT-Cog:

Functional Assessment of Cancer Therapy-Cognitive Function

HADS-A:

Hospital Anxiety and Depression Scale; anxiety subscale

HADS-D:

Hospital Anxiety and Depression Scale; depression subscale

HDF:

Hôtel-Dieu de France

ISI:

Insomnia severity index

PCR:

Polymerase chain reaction

PFC:

Prefrontal cortex

P-gp:

P-glycoprotein

PSQI:

Pittsburgh Sleep Quality Index

SNP:

Single nucleotide polymorphism

VAS:

Visual analogue scale

WHO:

World Health Organization

References

  1. Ahles TA, Root JC, Ryan EL. Cancer- and cancer treatment-associated cognitive change: an update on the state of the science. J Clin Oncol. 2012;30(30):3675–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Dietrich J, Prust M, Kaiser J. Chemotherapy, cognitive impairment and hippocampal toxicity. Neuroscience. 2015;309:224–32.

    Article  CAS  PubMed  Google Scholar 

  3. Vannorsdall TD. Cognitive Changes Related to Cancer Therapy. Med Clin North Am. 2017;101(6):1115–34.

    Article  PubMed  Google Scholar 

  4. Janz NK, Mujahid M, Chung LK, Lantz PM, Hawley ST, Morrow M, Schwartz K, Katz SJ. Symptom experience and quality of life of women following breast cancer treatment. J Womens Health (Larchmt). 2007;16(9):1348–61.

    Article  Google Scholar 

  5. Alatawi Y, Hansen RA, Chou C, Qian J, Suppiramaniam V, Cao G. The impact of cognitive impairment on survival and medication adherence among older women with breast cancer. Breast Cancer. 2020;28(2):277–88.

    Article  PubMed  Google Scholar 

  6. Janelsins MC, Kesler SR, Ahles TA, Morrow GR. Prevalence, mechanisms, and management of cancer-related cognitive impairment. Int Rev Psychiatry. 2014;26(1):102–13.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Oh PJ. Predictors of cognitive decline in people with cancer undergoing chemotherapy. Eur J Oncol Nurs. 2017;27:53–9.

    Article  PubMed  Google Scholar 

  8. Cheung YT, Ng T, Shwe M, Ho HK, Foo KM, Cham MT, Lee JA, Fan G, Tan YP, Yong WS, et al. Association of proinflammatory cytokines and chemotherapy-associated cognitive impairment in breast cancer patients: a multi-centered, prospective, cohort study. Ann Oncol. 2015;26(7):1446–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Gaman AM, Uzoni A, Popa-Wagner A, Andrei A, Petcu EB. The Role of Oxidative Stress in Etiopathogenesis of Chemotherapy Induced Cognitive Impairment (CICI)-“Chemobrain.” Aging Dis. 2016;7(3):307.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Janelsins MC, Heckler CE, Peppone LJ, Kamen C, Mustian KM, Mohile SG, Magnuson A, Kleckner IR, Guido JJ, Young KL, et al. Cognitive Complaints in Survivors of Breast Cancer After Chemotherapy Compared With Age-Matched Controls: An Analysis From a Nationwide, Multicenter. Prospective Longitudinal Study J Clin Oncol. 2017;35(5):506–14.

    PubMed  Google Scholar 

  11. Berman MG, Askren MK, Jung M, Therrien B, Peltier S, Noll DC, Zhang M, Ossher L, Hayes DF, Reuter-Lorenz PA, et al. Pretreatment worry and neurocognitive responses in women with breast cancer. Health Psychol. 2014;33(3):222–31.

    Article  PubMed  Google Scholar 

  12. Von Ah D, Tallman EF. Perceived Cognitive Function in Breast Cancer Survivors: Evaluating Relationships With Objective Cognitive Performance and Other Symptoms Using the Functional Assessment of Cancer Therapy—Cognitive Function Instrument. J Pain Symptom Manage. 2015;49(4):697–706.

    Article  Google Scholar 

  13. Cheng H, Li W, Gan C, Zhang B, Jia Q, Wang K. The COMT (rs165599) gene polymorphism contributes to chemotherapy-induced cognitive impairment in breast cancer patients. Am J Transl Res. 2016;8(11):5087–97.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Ng T, Lee YY, Chae JW, Yeo AHL, Shwe M, Gan YX, Ng RCH, Chu PPY, Khor CC, Ho HK, et al. Evaluation of plasma brain-derived neurotrophic factor levels and self-perceived cognitive impairment post-chemotherapy: a longitudinal study. BMC Cancer. 2017;17(1):867.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Park JY, Lengacher CA, Reich RR, Alinat CB, Ramesar S, Le A, Paterson CL, Pleasant ML, Park HY, Kiluk J, et al. Translational genomic research: the role of genetic polymorphisms in MBSR program among breast cancer survivors (MBSR[BC]). Transl Behav Med. 2019;9(4):693–702.

    Article  PubMed  Google Scholar 

  16. Xu S, Thompson W, Ancoli-Israel S, Liu L, Palmer B, Natarajan L. Cognition, quality-of-life, and symptom clusters in breast cancer: Using Bayesian networks to elucidate complex relationships. Psychooncology. 2018;27(3):802–9.

    Article  PubMed  Google Scholar 

  17. Park JH, Jung YS, Jung YM, Bae SH. The role of depression in the relationship between cognitive decline and quality of life among breast cancer patients. Support Care Cancer. 2019;27(7):2707–14.

    Article  PubMed  Google Scholar 

  18. Zhu B, Dong Y, Xu Z, Gompf HS, Ward SA, Xue Z, Miao C, Zhang Y, Chamberlin NL, Xie Z. Sleep disturbance induces neuroinflammation and impairment of learning and memory. Neurobiol Dis. 2012;48(3):348–55.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ng T, Teo SM, Yeo HL, Shwe M, Gan YX, Cheung YT, Foo KM, Cham MT, Lee JA, Tan YP, et al. Brain-derived neurotrophic factor genetic polymorphism (rs6265) is protective against chemotherapy-associated cognitive impairment in patients with early-stage breast cancer. Neuro Oncol. 2016;18(2):244–51.

    Article  CAS  PubMed  Google Scholar 

  20. Lange M, Heutte N, Rigal O, Noal S, Kurtz JE, Levy C, Allouache D, Rieux C, Lefel J, Clarisse B, et al. Decline in Cognitive Function in Older Adults With Early-Stage Breast Cancer After Adjuvant Treatment. Oncologist. 2016;21(11):1337–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ahles TA, Saykin AJ. Candidate mechanisms for chemotherapy-induced cognitive changes. Nat Rev Cancer. 2007;7(3):192–201.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Savitz J, Solms M, Ramesar R. The molecular genetics of cognition: dopamine. COMT and BDNF Genes Brain Behav. 2006;5(4):311–28.

    Article  CAS  PubMed  Google Scholar 

  23. Jamot L, Matthes HW, Simonin F, Kieffer BL, Roder JC. Differential involvement of the mu and kappa opioid receptors in spatial learning. Genes Brain Behav. 2003;2(2):80–92.

    Article  CAS  PubMed  Google Scholar 

  24. Bilkei-Gorzo A, Mauer D, Michel K, Zimmer A. Dynorphins regulate the strength of social memory. Neuropharmacology. 2014;77:406–13.

    Article  CAS  PubMed  Google Scholar 

  25. Carey AN, Lyons AM, Shay CF, Dunton O, McLaughlin JP. Endogenous kappa opioid activation mediates stress-induced deficits in learning and memory. J Neurosci. 2009;29(13):4293–300.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Heyde I, Kiehn JT, Oster H. Mutual influence of sleep and circadian clocks on physiology and cognition. Free Radic Biol Med. 2018;119:8–16.

    Article  CAS  PubMed  Google Scholar 

  27. Clarke R, Leonessa F, Trock B. Multidrug resistance/P-glycoprotein and breast cancer: review and meta-analysis. Semin Oncol. 2005;32(6 Suppl 7):S9-15.

    Article  CAS  PubMed  Google Scholar 

  28. Zhou SF. Structure, function and regulation of P-glycoprotein and its clinical relevance in drug disposition. Xenobiotica. 2008;38(7–8):802–32.

    Article  CAS  PubMed  Google Scholar 

  29. Qin X, Peng Q, Qin A, Chen Z, Lin L, Deng Y, Xie L, Xu J, Li H, Li T, et al. Association of COMT Val158Met polymorphism and breast cancer risk: an updated meta-analysis. Diagn Pathol. 2012;7:136.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Small BJ, Rawson KS, Walsh E, Jim HS, Hughes TF, Iser L, Andrykowski MA, Jacobsen PB. Catechol-O-methyltransferase genotype modulates cancer treatment-related cognitive deficits in breast cancer survivors. Cancer. 2011;117(7):1369–76.

    Article  CAS  PubMed  Google Scholar 

  31. Schacht JP. COMT val158met moderation of dopaminergic drug effects on cognitive function: a critical review. Pharmacogenomics J. 2016;16(5):430–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bradberry JC, Droege M, Evans RL, Guglielmo J, Knapp D, Knapp K, Meyer S, Poirier T, Plaza C. Curricula Then and Now—An Environmental Scan and Recommendations Since the Commission to Implement Change in Pharmaceutical Education: Report of the 2006–2007 Academic Affairs Committee. Am J Pharm Educ. 2007;71(Suppl):S10.

    Article  PubMed Central  Google Scholar 

  33. Hajj A, Halepian L, Osta NE, Chahine G, Kattan J, Rabbaa Khabbaz L. OPRM1 c.118A>G Polymorphism and Duration of Morphine Treatment Associated with Morphine Doses and Quality-of-Life in Palliative Cancer Pain Settings. Int J Mol Sci. 2017;18(4):669.

    Article  PubMed Central  CAS  Google Scholar 

  34. Valdez P. Circadian Rhythms in Attention. Yale J Biol Med. 2019;92(1):81–92.

    PubMed  PubMed Central  Google Scholar 

  35. Waterhouse J. Circadian rhythms and cognition. Prog Brain Res. 2010;185:131–53.

    Article  PubMed  Google Scholar 

  36. Noh T, Walbert T. Brain metastasis: clinical manifestations, symptom management, and palliative care. Handb Clin Neurol. 2018;149:75–88.

    Article  PubMed  Google Scholar 

  37. Lee PE, Tierney MC, Wu W, Pritchard KI, Rochon PA. Endocrine treatment-associated cognitive impairment in breast cancer survivors: evidence from published studies. Breast Cancer Res Treat. 2016;158(3):407–20.

    Article  CAS  PubMed  Google Scholar 

  38. Wagner LI, Gray RJ, Sparano JA, Whelan TJ, Garcia SF, Yanez B, Tevaarwerk AJ, Carlos RC, Albain KS, Olson JA Jr, et al. Patient-Reported Cognitive Impairment Among Women With Early Breast Cancer Randomly Assigned to Endocrine Therapy Alone Versus Chemoendocrine Therapy: Results From TAILORx. J Clin Oncol. 2020;38(17):1875–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Medscape, Body Surface Area Based Dosing, last assessed May 1, 2020 at: https://reference.medscape.com/calculator/bsa-dosing

  40. Verbraecken J, Van de Heyning P, De Backer W, Van Gaal L. Body surface area in normal-weight, overweight, and obese adults. A comparison study Metabolism. 2006;55(4):515–24.

    CAS  PubMed  Google Scholar 

  41. Lange M, Heutte N, Morel N, Eustache F, Joly F, Giffard B. Cognitive complaints in cancer: The French version of the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog), normative data from a healthy population. Neuropsychol Rehabil. 2016;26(3):392–409.

    Article  PubMed  Google Scholar 

  42. Akman T, Yavuzsen T, Sevgen Z, Ellidokuz H, Yilmaz AU. Evaluation of sleep disorders in cancer patients based on Pittsburgh Sleep Quality Index. Eur J Cancer Care (Engl). 2015;24(4):553–9.

    Article  Google Scholar 

  43. Gehrman PR, Garland SN, Matura LA, Mao J. Insomnia in breast cancer: Independent symptom or symptom cluster? Palliat Support Care. 2017;15(3):369–75.

    Article  PubMed  Google Scholar 

  44. Hartung TJ, Friedrich M, Johansen C, Wittchen HU, Faller H, Koch U, Brahler E, Harter M, Keller M, Schulz H, et al. The Hospital Anxiety and Depression Scale (HADS) and the 9-item Patient Health Questionnaire (PHQ-9) as screening instruments for depression in patients with cancer. Cancer. 2017;123(21):4236–43.

    Article  PubMed  Google Scholar 

  45. Mierzynska J, Taye M, Pe M, Coens C, Martinelli F, Pogoda K, Velikova G, Bjelic-Radisic V, Cardoso F, Brain E, et al. Reference values for the EORTC QLQ-C30 in early and metastatic breast cancer. Eur J Cancer. 2020;125:69–82.

    Article  PubMed  Google Scholar 

  46. Ovayolu O, Ovayolu N, Aytac S, Serce S, Sevinc A. Pain in cancer patients: pain assessment by patients and family caregivers and problems experienced by caregivers. Support Care Cancer. 2015;23(7):1857–64.

    Article  PubMed  Google Scholar 

  47. Park JH, Bae SH, Jung YS, Jung YM. The psychometric properties of the Korean version of the functional assessment of cancer therapy-cognitive (FACT-Cog) in Korean patients with breast cancer. Support Care Cancer. 2015;23(9):2695–703.

    Article  PubMed  Google Scholar 

  48. Bedillion MF, Ansell EB, Thomas GA. Cancer treatment effects on cognition and depression: The moderating role of physical activity. Breast. 2019;44:73–80.

    Article  PubMed  Google Scholar 

  49. Young EA, Abelson JL, Cameron OG. Effect of comorbid anxiety disorders on the hypothalamic-pituitary-adrenal axis response to a social stressor in major depression. Biol Psychiatry. 2004;56(2):113–20.

    Article  CAS  PubMed  Google Scholar 

  50. Walker WH 2nd, Borniger JC. Molecular Mechanisms of Cancer-Induced Sleep Disruption. Int J Mol Sci. 2019;20(11):2780.

    Article  CAS  PubMed Central  Google Scholar 

  51. Palesh O, Aldridge-Gerry A, Ulusakarya A, Ortiz-Tudela E, Capuron L, Innominato PF. Sleep disruption in breast cancer patients and survivors. J Natl Compr Canc Netw. 2013;11(12):1523–30.

    Article  PubMed  Google Scholar 

  52. Liou KT, Ahles TA, Garland SN, Li QS, Bao T, Li Y, Root JC, Mao JJ: The Relationship Between Insomnia and Cognitive Impairment in Breast Cancer Survivors. JNCI Cancer Spectrum 2019, 3(3):pkz041.

  53. van Dalfsen JH, Markus CR. The influence of sleep on human hypothalamic–pituitary–adrenal (HPA) axis reactivity: A systematic review. Sleep Med Rev. 2017;39:187–94.

    Article  PubMed  Google Scholar 

  54. Chee MW, Chuah LY, Venkatraman V, Chan WY, Philip P, Dinges DF. Functional imaging of working memory following normal sleep and after 24 and 35 h of sleep deprivation: Correlations of fronto-parietal activation with performance. Neuroimage. 2006;31(1):419–28.

    Article  PubMed  Google Scholar 

  55. Ho SY, Rohan KJ, Parent J, Tager FA, McKinley PS. A longitudinal study of depression, fatigue, and sleep disturbances as a symptom cluster in women with breast cancer. J Pain Symptom Manage. 2015;49(4):707–15.

    Article  PubMed  Google Scholar 

  56. Zilliox LA, Chadrasekaran K, Kwan JY, Russell JW. Diabetes and Cognitive Impairment. Curr Diab Rep. 2016;16(9):87.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Black S, Kraemer K, Shah A, Simpson G, Scogin F, Smith A. Diabetes, Depression, and Cognition: a Recursive Cycle of Cognitive Dysfunction and Glycemic Dysregulation. Curr Diab Rep. 2018;18(11):118.

    Article  PubMed  Google Scholar 

  58. Rojas-Carranza CA, Bustos-Cruz RH, Pino-Pinzon CJ, Ariza-Marquez YV, Gomez-Bello RM, Canadas-Garre M. Diabetes-Related Neurological Implications and Pharmacogenomics. Curr Pharm Des. 2018;24(15):1695–710.

    Article  PubMed  CAS  Google Scholar 

  59. Van Arsdale A, Rosenbaum D, Kaur G, Pinto P, Kuo DY, Barrera R, Goldberg GL, Nevadunsky NS. Prevalence and factors associated with cognitive deficit in women with gynecologic malignancies. Gynecol Oncol. 2016;141(2):323–8.

    Article  PubMed  Google Scholar 

  60. Rimkus CM, Avolio IMB, Miotto EC, Pereira SA, Mendes MF, Callegaro D, Leite CDC. The protective effects of high-education levels on cognition in different stages of multiple sclerosis. Mult Scler Relat Disord. 2018;22:41–8.

    Article  PubMed  Google Scholar 

  61. Perrier J, Viard A, Levy C, Morel N, Allouache D, Noal S, Joly F, Eustache F, Giffard B. Longitudinal investigation of cognitive deficits in breast cancer patients and their gray matter correlates: impact of education level. Brain Imaging Behav. 2020;14(1):226–41.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank all physicians and students (namely Tamara Nehmé, Bashar ElJebbawi, Christina Chemaly) who helped recruit patients at Hôtel-Dieu de France Hospital (Beirut, Lebanon), and Dr. David Cella for granting permission to use the FACT-Cog (Licensing agreement granted on November 2, 2017). This work was supported by the financial support of the Saint-Joseph University (Conseil de la recherche: FPH71).

Funding

This work was supported by the “Conseil de la recherche” of the Saint-Joseph University of Beirut (FPH71).

Author information

Author notes

  1. Aline Hajj and Rita Khoury contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon

    Aline Hajj, Rita Khoury, Roula Hachem, Aya Awad & Lydia Rabbaa Khabbaz

  2. Laboratoire de Pharmacologie, Pharmacie Clinique Et Contrôle de Qualité Des Médicaments, Saint-Joseph University of Beirut, Beirut, Lebanon

    Aline Hajj, Rita Khoury, Roula Hachem & Lydia Rabbaa Khabbaz

  3. Faculty of Pharmacy, Université Laval, Québec, Canada

    Aline Hajj

  4. School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, P.O. Box 446, Jounieh, Lebanon

    Souheil Hallit

  5. Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon

    Souheil Hallit

  6. Psychology Department, College of Humanities, Effat University, Jeddah, 21478, Saudi Arabia

    Souheil Hallit

  7. INSPECT-LB (Institut National de Santé Publique, d’Épidémiologie Clinique Et de Toxicologie-Liban), Beirut, Lebanon

    Hala Sacre

  8. Department of Hemato-Oncology, Faculty of Medicine, Hôtel-Dieu de France Hospital, Saint-Joseph University of Beirut, Beirut, Lebanon

    Fady Nasr, Fadi El Karak, Georges Chahine & Joseph Kattan

Authors
  1. Aline Hajj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rita Khoury
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roula Hachem
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aya Awad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Souheil Hallit
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hala Sacre
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fady Nasr
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fadi El Karak
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Georges Chahine
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Joseph Kattan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lydia Rabbaa Khabbaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Aline HAJJ: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Writing—original draft. Rita KHOURY: Data curation, Investigation, Methodology, Writing—original draft. Roula HACHEM: Data curation, Investigation, Methodology, Writing—original draft. Aya AWAD: Investigation. Souheil HALLIT: Formal analysis, Software, Writing—review & editing. Hala SACRE: Writing—review & editing. Fady NASR: Investigation. Fadi El KARAK: Investigation. Georges CHAHINE: Investigation. Joseph KATTAN: Supervision, Investigation, Writing—review & editing. Lydia RABBAA KHABBAZ: Supervision, Writing—review & editing. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Aline Hajj.

Ethics declarations

Ethics approval and consent to participate

All experimental protocols were approved by Hôtel-Dieu de France Hospital ethical committee (HDF, CEHDF1016, July 2017). All methods were carried out in accordance with relevant guidelines and regulations. Participants were fully informed of the purpose and procedures of the study and had the adequate time to ask questions and ponder about their voluntary participation. A written informed consent was obtained from all patients before enrollment.

Consent for publication

Not applicable.

Competing interests

The authors have no conflicts of interest to disclose.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Supplementary Table 1. The PCR protocol and conditions for CLOCK, PER2, CRY2, COMT, DRD2, OPRM1, and ABCB1 genotyping. Supplementary Table 2.  Patients’ demographics and clinical characteristics (N = 112)*.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hajj, A., Khoury, R., Hachem, R. et al. Clinical and genetic factors associated with self-reported cognitive deficits in women with breast cancer: the “CAGE-Cog” study. BMC Cancer 22, 996 (2022). https://doi.org/10.1186/s12885-022-10077-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-022-10077-6

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us