Skip to main content
Download PDF

Fatigue in breast cancer patients on chemotherapy: a cross-sectional study exploring clinical, biological, and genetic factors

BMC Cancer volume 22, Article number: 16 (2022) Cite this article

Abstract

Background

Cancer-related fatigue (CRF) is one of the most common and distressing complaints reported by cancer patients during chemotherapy considerably impacting all aspects of a patient’s life (physical, psychosocial, professional, and socioeconomic). The aim of this study was to assess the severity of cancer-related fatigue in a group of breast cancer patients undergoing chemotherapy and explore the association between fatigue scores and sociodemographic, clinical, biological, psychiatric, and genetic factors.

Methods

A cross-sectional pilot study carried out at the oncology outpatient unit of Hôtel-Dieu de France University Hospital recruited 67 breast cancer patients undergoing chemotherapy between November 2017 and June 2019 to evaluate fatigue using the EORTC QLQ-C30 scale (European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire). Genotyping for seven gene polymorphisms (COMT, DRD2, OPRM1, CLOCK, PER2, CRY2, ABCB1) was performed using the Lightcycler® (Roche).

Results

The prevalence of fatigue was 46.3%. Multivariable analysis taking the fatigue score as the dependent variable showed that a higher number of cycles and a lower hemoglobin level were significantly associated with higher odds of exhibiting fatigue. Moreover, having at least one C allele for DRD2 SNP (vs. TT) was significantly associated with a 4.09 higher odds of expressing fatigue compared to TT patients. Finally, patients with at least one C allele for CLOCK SNP tended to display higher fatigue levels than TT patients.

Conclusions

Our study showed that anemic breast cancer patients with a high number of chemotherapy cycles and those carrying at least one C allele for DRD2 and CLOCK SNPs are at greater risk of exhibiting fatigue. Since no previous research has reported such genetic results, future studies are necessary to confirm our findings.

Peer Review reports

Background

According to the 2020 global cancer burden, female breast cancer ranked among the most commonly diagnosed cancer [1]. In this context, chemotherapy and radiotherapy remain the mainstay of cancer treatment. Thus, every year more than 2.3 million women encounter numerous side effects with devastating consequences on their health [1, 2]. Cancer-related fatigue (CRF) is one of the most common and distressing complaints reported by cancer patients during chemotherapy [3, 4]. Described as a multidimensional physical and/or mental tiredness or exhaustion that interferes with motivation and usual functioning [5], CRF results in substantial impairment of health-related quality of life (HRQoL) in breast cancer survivors [2, 6, 7]. Studies have shown that fatigue experienced by cancer patients undergoing chemotherapy is persistent and may remain beyond the chemotherapy session, considerably impacting all aspects of a patient’s life: physical, psychosocial, professional, and socioeconomic [8].

Despite its burden and relatively high prevalence among breast cancer patients (ranging from 60 to 90%) [9], CRF remains underestimated and mistreated, and little is known about the underlying risk factors. Understanding the contributing factors would allow the implementation of adequate targeted interventions for better management and quality of care [2, 7, 10]. Several hypotheses have been suggested to identify the predisposing factors to higher sensitivity for tiredness, including neurobiological dysfunctions (alterations in the hypothalamic-pituitary-adrenal (HPA) axis [11] and the autonomic nervous system responsiveness [12, 13]), pro-inflammatory cytokines and cellular immune system dysregulations [14, 15], psychological disorders such as depression, anxiety, and sleep disorders [16, 17], cancer treatments (e.g., regimen type, chemotherapy agents, doses) [18], in addition to other factors related to physical adaptability, pain [19, 20], or genetic predisposition [21,22,23].

Regarding the genetic factors, most studies among cancer patients evaluated the potential contribution of single nucleotide polymorphisms (SNPs) in the immune and inflammatory pathways, such as pro-inflammatory cytokines IL-1b, IL-6, and TNF-α [21]. However, these studies yielded conflicting results, possibly due to cancer itself; treatments could trigger a cytokine storm that may differ according to the type of cancer, disease stage, and regimen (all of which induce an epigenetic regulation) [21]. Therefore, we hypothesized that other genetic factors might also have a contributing role but have been scarcely explored with CRF. These include genes involved in different pathways, such as dopamine neurotransmission, opioid circuits, circadian rhythms, in addition to genes affecting the transport of xenobiotics to the central nervous system (chemotherapy drugs or pro-inflammatory mediators) [18, 21, 24,25,26].

Regarding the dopamine pathway, this study will explore the eventual correlation between SNPs in genes encoding the dopamine receptor D2 (DRD2) and the metabolic enzyme catechol-O-methyl transferase (COMT). Indeed, Miller et al. have previously reported impaired dopaminergic striatal functioning in individuals with chronic fatigue syndrome [24]. Studies have shown that the SNP c.957C > T (rs6277) in DRD2 affects the striatal D2 receptor availability, leading to a decreased DRD2 mRNA stability and receptor synthesis, consequently altering dopamine’s signal transduction [25]. As for COMT, the studied SNP p.Val158Met (p.V158M; rs4680) leads to 3-to-4 times lower COMT enzymatic activity [26]; patients carrying the Met variant allele exhibit higher levels of catecholamines, such as epinephrine, which promotes a higher pain sensitivity by stimulating β2-adrenergic receptors in the central and peripheral nervous systems [27]. A study had demonstrated that breast cancer patients with Met/Met genotype exhibit higher fatigue and pain sensitivity after surgery (mastectomy or quadrantectomy), stating that higher pain intensity can predispose to increased CRF [22].

In the context of pain regulation, OPRM1 represents a crucial candidate gene for CRF. It encodes for the μ-opioid receptor (MOR) that regulates the analgesic response to pain and plays an essential role in the rewarding system [28]. The SNP c.118A > G (rs1799971) is the most explored polymorphism in OPRM1, leading to an asparagine-to-aspartic acid substitution at residue 40 (p.Asn40Asp), with a reduced affinity for endogenous opioids. Patients who carry at least one G variant allele exhibit higher pain levels than AA patients [29]. Consequently, acknowledging that increased pain sensitivity is associated with a dysregulation in pro-inflammatory cytokines, it is hypothesized that an alteration in the opioid system could potentially contribute to CRF in breast cancer patients [30].

Furthermore, owing to the fact fatigue is biologically regulated by a sleep/wake homeostatic process [31, 32], our study evaluated three of the circadian rhythm regulation genes: the Circadian Locomotor Output Cycles Kaput CLOCK gene (SNP c.3111 T > C; rs1801260), the Period 2 (PER2) gene (rs934945; G > A), and the Cryptochrome circadian Regulator 2 (CRY2) gene (rs10838524; G > A). Studies exploring these polymorphisms in CRF are scarce, and none have been performed in breast cancer patients. Research had found that the minor allele A of PER2 rs934945 was associated with lower odds of fatigue in patients with gliomas [23]. Other studies in non-cancer patients have reported an association between the C-allele in the SNP rs1801260 of CLOCK with eveningness that could contribute to a lower morning physical activity [33, 34].

Finally, regarding the drug efflux transporters, our study will examine the SNP rs1045642 (c.3435 T > C) in ABCB1, the gene encoding the P-glycoprotein (P-gp). This SNP has been associated with functional changes in mRNA stability and P-gp expression. Patients with the variant T allele could potentially report more fatigue than those who carry the wild-type genotype due to a lower efflux at the blood-brain barrier (BBB) level and higher drug concentration in the brain, especially that almost all cytotoxic drugs for breast cancer are substrates of P-gp [35, 36]. Based on this hypothesis, various studies have previously demonstrated a significant association between CRF and three gene polymorphisms in ABCB1: c.2677G > A/T (rs2032582) in breast cancer patients receiving docetaxel [37], and c.1236C > T (rs1128503) and c.3435C > T (rs1045642) in patients with gynecologic cancers receiving paclitaxel and carboplatin [38]. However, no previous studies have identified a correlation between our studied SNP and CRF.

Therefore, this pilot study aimed to assess the severity of cancer-related fatigue in a group of breast cancer patients undergoing chemotherapy and explore the association between fatigue scores and clinical, biological, sociodemographic, psychiatric, and genetic factors.

Methods

Study design

This cross-sectional pilot study evaluated the effect of sociodemographic, clinical, biological, psychiatric, and genetic factors on fatigue among breast cancer patients undergoing chemotherapy at the oncology outpatient unit of Hôtel-Dieu de France (HDF) University Hospital between November 2017 and June 2019.

Ethics approval

The HDF ethics committee approved the study (reference number: CEHDF1016, July 2017), and all patients signed a written consent prior to inclusion. All methods were carried out in accordance with relevant guidelines and regulations.

Patient’s sociodemographic and clinical information

Included patients were women aged 18 and above, with a primary diagnosis of breast cancer, and admitted to the outpatient oncology unit at HDF for intravenous chemotherapy every 21 days (random cycle out of a maximum of 10 cycles).

Non-inclusion criteria consisted of patients with relapsed breast cancer/other types of cancer, receiving adjuvant hormone therapy at the moment of the evaluation, having brain metastasis, or any other medical/surgical CNS disorders that may affect their ability to complete the questionnaires or be assessed clinically [39,40,41].

Three trained pharmacists collected sociodemographic and clinical information from medical records or through interviews with the patients: age, gender, weight, and height (to calculate the body mass index, BMI), Body Surface Area (BSA, calculated using the Mosteller formula) [42, 43] ethnicity/nationality, marital status, education level, socioeconomic level, comorbidities (e.g., diabetes, hypertension, dyslipidemia), alcohol consumption, smoking, medical history of allergic reactions, and medications used other than chemotherapy. They also recorded biological values at baseline, including creatinine levels (to calculate the creatinine clearance ClCr using Cockcroft-Gault formula [44, 45]) and complete blood count (CBC), in addition to cancer-related data: metastases, the number of chemotherapy cycles, chemotherapy regimen (medications and doses/m2).

On the first day of admission to the outpatient oncology unit to receive chemotherapy (random cycle, recorded as the actual chemotherapy cycle number), patients completed a self-reported questionnaire that included several validated scales to evaluate fatigue, sleep, anxiety, depression, and pain. Pharmacists assisted them in completing it and made sure they answered all questions.

Outcomes and clinical assessments

Fatigue

The primary outcome was cancer-related fatigue. Fatigue was evaluated using three questions from the EORTC QLQ-C30 scale (European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire), a 30-item instrument that measures the quality of life (QOL) in cancer patients in three main domains: global health status, functional status, and cancer-related symptom status. The questions rated on a 4-point Likert scale from 0 (not at all) to 4 (very much) were: QLQ C10: “Do you need rest?”; QLQ C12: “Did you feel weak?” and QLQ C18: “Were you tired?” [46]. The raw value obtained for each participant was then transformed according to the EORTC QLQ-C30 scoring manual into a score ranging from 0 to 100, with higher scores indicating worse fatigue and thus lower QOL.

Pain

The visual analogue scale (VAS) was used to evaluate pain. This subjective tool enables patients to measure disease-related pain on a line ranging from 0 (no pain) to 10 (extreme pain) [47].

Sleep

Two screening tools were used to evaluate sleep disorders:

  1. a.

    The Insomnia Severity Index (ISI) is a 7-item scale designed to assess the perceived severity of insomnia during the past 2 weeks. Items are rated on a 5-point Likert scale. The total score ranges from 0 to 28, with higher scores indicating more severe insomnia [48].

  2. b.

    The Pittsburgh Sleep Quality Index (PSQI) is a 19-item tool developed to measure seven domains over the past month: subjective quality of sleep, sleep latency, sleep duration, sleep efficiency, sleep disorders, sleep medication, and daytime dysfunction. The seven sub-scores are rated from 0 (no difficulty) to 3 (severe difficulty) and yield a total score ranging from 0 to 21. Higher scores indicate worse sleep quality [49].

Anxiety and depression

The self-report Hospital Anxiety and Depression Scale (HADS) was used to explore the level of anxiety (HADS-A) and depression (HADS-D) during the previous week. Symptoms were reported on a scale from 0 (not at all) to 3 (most of the time) [50]. Higher scores defined higher levels of anxiety/depression.

Data and statistical analysis

Three pharmacists collected the data and performed data entry. The SPSS software version 25.0 was used for statistical analysis, performed by one of the authors on de-identified data. Descriptive statistics were calculated for all variables in the study as means and standard deviations for continuous measures and counts and percentages for categorical variables. As the dependent variable was not normally distributed and the sample size was small (n = 67), non-parametric tests were used: the Mann-Whitney test to compare means between two groups, Kruskal-Wallis test to compare three or more groups (with post hoc analysis), and Spearman correlation to correlate between continuous, ordinal, or count variables. DNA sampling as well as genotyping assays were performed as previously prescribed [51]. The genotype alleles were taken once as three categories, then combined and checked for any significant association with the dependent variable. Variables that showed p < 0.1 in the bivariate analysis were taken as independent variables in the multivariable analyses to reduce confounding factors. In all cases, a value of p ≤ 0.05 was considered statistically significant.

As for multivariable analyses, logistic regression models were used after dichotomization of the fatigue scale: participants with scores > 39 were considered to have fatigue (39 is the defined threshold for clinical importance (TCIs) for the EORTC QLQ-C30 Computer Adaptive Testing Core measure) [52, 53]. Independent variables groups were subsequently included in the regression models, using the ENTER method: cycle number, cancer treatment, and biological measures. The results related to 7 genes were also used and added to the model with variables that showed a p < 0.10; the last step represented a global model of genetic, sociodemographic, and clinically related factors.

Results

A total of 67 women with breast cancer were included in the study (mean age = 56.22 ± 11.96; mean BSA = 1.76 ± 0.17). Most of our patients were married (85,5%), with a secondary level of education. Almost 46% had a clinically significant fatigue, with a mean fatigue score of 42.12 ± 32.10 (as evaluated by the EORTC QLQ-C30) (Table 1).

Table 1 Sociodemographic and baseline characteristics (N = 67)
Full size table

Bivariate analyses

Bivariate analyses taking the fatigue score (FA score) as the dependent variable showed that patients with metastases, particularly bone metastases, exhibited higher fatigue (mean score 80 ± 18.26 for bone metastases versus 38.70 ± 31.39 for the absence of metastases). Moreover, patients receiving palliative chemotherapy and those treated with a capecitabine-based regimen had higher fatigue scores (Table 2). When exploring the continuous variables, results have shown that patients with a lower blood cell count (hemoglobin, leukocytes, and platelets) had significantly higher fatigue scores. Finally, the higher the cycle number, the higher the fatigue score (p = 0.007). Pain was not significantly associated with the fatigue level (p = 0.124) (Table 3).

Table 2 FA score, sociodemographic characteristics, and risk factors
Full size table
Table 3 Correlation between FA score and continuous variables
Full size table

Neither genetic factors (Table 4) nor sleep and mental scales (Table 5) were significantly associated with the fatigue score.

Table 4 Fatigue and genetic characteristics
Full size table
Table 5 Sleep and mental scales correlations with FA score
Full size table

Multivariable analysis

The multivariable analysis, taking the dichotomized fatigue score as the dependent variable, showed that a higher cycle number and a lower hemoglobin level were significantly associated with higher odds of exhibiting fatigue (ORa of 1.51 and 0.67, respectively). As for genetic factors, our results have shown that having at least one C allele for DRD2 SNP (CC and CT) was significantly associated with 4.09 higher odds of expressing fatigue compared to TT patients (p = 0.047). The CLOCK SNP tended toward significance: patients with the TT genotype had lower risks of expressing fatigue than TC patients (Table 6).

Table 6 Multivariable analysis using logistic regressions
Full size table

Discussion

Breast cancer patients experience several long-term physical complications related to chemotherapy, including pain, lymphedema, and fatigue [2, 54, 55]. Despite being one of the most harmful conditions on health-related QOL (damaging outcomes on prognosis, psychosocial, and physical function, e.g., functional disability, social isolation, depression) [56,57,58], CRF is often overlooked, mainly because it is not correctly and timely evaluated. Identifying the triggers for CRF is paramount for implementing patient-tailored strategies for prevention and early detection [2, 7]. This study aimed to assess fatigue severity and associated factors in a sample of patients with breast cancer.

Our results showed that almost half of our patients reported clinically significant fatigue, with a mean fatigue score of 42.12 ± 32.10, similar to what was previously reported in other breast cancer populations using the same scale (43.92 ± 27.43 and 42.2 ± 30.9) [59,60,61].

Regarding the genetic aspect, our study revealed novel significant correlations between fatigue and genetic factors, particularly DRD2 rs6277 and CLOCK rs1801260. It could demonstrate that patients who carry at least one C allele (CC and CT) for the c.957C > T (rs6277) of DRD2 were four times more likely to develop fatigue than TT patients. This SNP affects DRD2 mRNA stability, thereby influencing the expression of dopaminergic receptors D2 in the brain [62], particularly in the striatal, thalamic, and neocortical areas [63], with a possible consequence on dopamine’s signal transduction [25]. The few studies that examined the effect of rs6277 on physical fatigue have not addressed cancer populations. One research exploring the effects of nicotine on newly exposed individuals has shown that men with TT genotype expressed a decreased fatigue compared to those with CT/CC genotypes [64]. The exact mechanism explaining this observation is yet to be explored.

Another genetic factor that tended toward significance was the SNP of CLOCK: patients with TC genotype for the rs1801260 of CLOCK had higher risks of exhibiting fatigue than TT patients. To the best of our knowledge, this study is the first to demonstrate a direct association between this polymorphism and CRF in breast cancer patients. However, previous research among non-cancer patients could correlate the C-allele in the SNP rs1801260 of CLOCK with eveningness, leading to lower morning physical activity [33]. The SNP c.3111 T > C is located in the 3′-untranslated region; it modifies sleep homeostasis by altering the patient’s biological clock, resulting in abnormalities in physiological processes and sleep-wake cycles [65, 66]. Since fatigue is biologically regulated by sleep-wake homeostasis, sleep disruptions can be a potential risk factor for fatigue. Thus, our results are consistent with previous findings showing an association between fatigue and circadian rhythm disruptions [67, 68].

The study of the biological and clinical factors revealed that a lower hemoglobin level is associated with higher odds of expressing fatigue. Hematological toxicities are a considerable challenge in breast cancer management. They can be related to many factors, including chemotherapy-induced bone marrow suppression [69], nutritional deficiency, vomiting, and cancer/chemotherapy-induced inflammatory syndrome. Inflammatory cytokines produced in cancer patients lead to the upregulation of hepcidin, a protein that blocks the release of iron to transferrin (iron transporter) [70, 71]. This process results in anemia, with a reduced erythropoietic response to anemia and decreased oxygen transfer to tissues and muscles [72], explaining the subsequent fatigue. Therefore, in fatigue management, clinicians should highlight the importance of assessing correctly and treating anemia, whether by blood transfusions or erythropoietin (mainly epoetin alfa) as stated by international guidelines (American Society of Clinical Oncology (ASCO) and European Society for Medical Oncology (ESMO) [73, 74]). Such an approach considerably improves hemoglobin levels and quality of life during chemotherapy among breast cancer patients [75,76,77].

Finally, our results revealed that the risk of fatigue significantly increased with the number of chemotherapy cycles, in agreement with previous findings showing that breast cancer patients undergoing chemotherapy reported more fatigue over time compared to baseline [78,79,80,81]. This fatigue is consequent to cumulative factors precipitating CRF, e.g., chemotherapy [82], pain, depression, anxiety, emotional distress, sleep disturbance, cachexia, anemia [83, 84].

Limitations and strengths

Our study has several limitations, especially related to the small sample size for genetic analyses and the absence of baseline evaluation (first chemotherapy cycle) since patients could have exhibited fatigue even before starting chemotherapy regimens. Moreover, some modifiable determinants that could significantly influence fatigue were not reported, such as physical activity and nutritional status (even if the BMI can be a surrogate measure of nutrition status [3, 85]). Indeed, several studies reported that a good nutritional status and high physical functioning improve HRQoL in breast cancer patients [3, 85,86,87]. Finally, although fatigue was evaluated with a tool validated in cancer patients (EORTC QLQ-C30), the use of other extensive and specific scales, such as the recently Arabic validated EORTC QLQ-BR23 [88], the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) [89], or the Fatigue Inventory-20 (MFI-20) [90] would have allowed a better evaluation of all fatigue effects and aspects.

However, despite the small sample size, multivariable analyses identified several genetic factors that have been rarely explored for their association with fatigue.

Future longitudinal studies, using a larger sample and more specific scales to evaluate fatigue, are needed to confirm our preliminary findings and explore their potential translation into clinical practice.

Conclusions

Our study showed that anemic breast cancer patients with a high number of chemotherapy cycles and those carrying at least one C allele for DRD2 and CLOCK SNPs are at greater risk of exhibiting fatigue. Since no previous research has reported such genetic results, future studies are necessary to confirm our findings, allowing clinicians to prioritize the management of patients at higher risks of fatigue during chemotherapy and tailor physical/psychological/cognitive-behavioral interventions to mitigate CRF while improving the quality of life of patients and their families.

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

ASCO:

American Society of Clinical Oncology

BBB:

blood-brain barrier

BMI:

Body mass index

BSA:

Body Surface Area

CBC:

Complete blood count

ClCr:

Creatinine clearance

CLOCK :

Circadian Locomotor Output Cycles Kaput gene

COMT:

Catechol-O-methyl transferase (protein)

COMT :

Catechol-O-methyl transferase gene

CRF:

Cancer-related fatigue

CRY2 :

Cryptochrome circadian Regulator 2 gene

DRD2 :

dopamine receptor D2 gene

EORTC QLQ-C30:

The European Organization for Research and Treatment of Cancer quality of life questionnaire

ESMO:

European Society for Medical Oncology

FA:

Fatigue

FACIT:

Functional Assessment of Chronic Illness Therapy system of Quality of Life questionnaires

FACIT-F:

Functional Assessment of Chronic Illness Therapy - Fatigue

HADS-A:

Hospital Anxiety and Depression Scale; anxiety subscale

HADS-D:

Hospital Anxiety and Depression Scale; depression subscale

HDF:

Hôtel-Dieu de France

HPA:

hypothalamic-pituitary-adrenal

HRQoL:

Health-related quality of life

ISI:

Insomnia Severity Index

MFI-20:

Fatigue Inventory-20

MOR:

μ-opioid receptor

P-gp:

P-glycoprotein

PER2 :

Period 2 gene

PSQI:

Pittsburgh Sleep Quality Index

QOL:

Quality of life

SNP:

Single nucleotide polymorphism

TCI:

thresholds for clinical importance

VAS:

Visual analogue scale

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  Google Scholar 

  2. Invernizzi M, Kim J, Fusco N. Editorial: quality of life in breast cancer patients and survivors. Front Oncol. 2020;10:620574.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Berger AM, Walker SN. An explanatory model of fatigue in women receiving adjuvant breast cancer chemotherapy. Nurs Res. 2001;50(1):42–52.

    Article  PubMed  CAS  Google Scholar 

  4. Bardwell WA, Ancoli-Israel S. Breast cancer and fatigue. Sleep Med Clin. 2008;3(1):61–71.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Barsevick A, Frost M, Zwinderman A, Hall P, Halyard M, Consortium G. I’m so tired: biological and genetic mechanisms of cancer-related fatigue. Qual Life Res. 2010;19(10):1419–27.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Jaremka LM, Fagundes CP, Glaser R, Bennett JM, Malarkey WB, Kiecolt-Glaser JK. Loneliness predicts pain, depression, and fatigue: understanding the role of immune dysregulation. Psychoneuroendocrinology. 2013;38(8):1310–7.

    Article  PubMed  CAS  Google Scholar 

  7. Invernizzi M, de Sire A, Lippi L, Venetis K, Sajjadi E, Gimigliano F, et al. Impact of rehabilitation on breast cancer related fatigue: a pilot study. Front Oncol. 2020;10:556718.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bower JE. Cancer-related fatigue--mechanisms, risk factors, and treatments. Nat Rev Clin Oncol. 2014;11(10):597–609.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Palesh O, Scheiber C, Kesler S, Mustian K, Koopman C, Schapira L. Management of side effects during and post-treatment in breast cancer survivors. Breast J. 2018;24(2):167–75.

    Article  PubMed  Google Scholar 

  10. Takano T, Matsuda A, Ishizuka N, Ozaki Y, Suyama K, Tanabe Y, et al. Effectiveness of self-help workbook intervention on quality of life in cancer patients receiving chemotherapy: results of a randomized controlled trial. BMC Cancer. 2021;21(1):588.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bower JE, Ganz PA, Aziz N. Altered cortisol response to psychologic stress in breast cancer survivors with persistent fatigue. Psychosom Med. 2005;67(2):277–80.

    Article  PubMed  Google Scholar 

  12. Crosswell AD, Lockwood KG, Ganz PA, Bower JE. Low heart rate variability and cancer-related fatigue in breast cancer survivors. Psychoneuroendocrinology. 2014;45:58–66.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fagundes CP, Murray DM, Hwang BS, Gouin JP, Thayer JF, Sollers JJ 3rd, et al. Sympathetic and parasympathetic activity in cancer-related fatigue: more evidence for a physiological substrate in cancer survivors. Psychoneuroendocrinology. 2011;36(8):1137–47.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Bower JE, Ganz PA, Irwin MR, Kwan L, Breen EC, Cole SW. Inflammation and behavioral symptoms after breast cancer treatment: do fatigue, depression, and sleep disturbance share a common underlying mechanism? J Clin Oncol. 2011;29(26):3517–22.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Bower JE, Ganz PA, Aziz N, Fahey JL, Cole SW. T-cell homeostasis in breast cancer survivors with persistent fatigue. J Natl Cancer Inst. 2003;95(15):1165–8.

    Article  PubMed  Google Scholar 

  16. Jacobsen PB, Donovan KA, Weitzner MA. Distinguishing fatigue and depression in patients with cancer. Semin Clin Neuropsychiatry. 2003;8(4):229–40.

    PubMed  Google Scholar 

  17. Roscoe JA, Kaufman ME, Matteson-Rusby SE, Palesh OG, Ryan JL, Kohli S, et al. Cancer-related fatigue and sleep disorders. Oncologist. 2007;12(Suppl 1):35–42.

    Article  PubMed  Google Scholar 

  18. Jacobsen PB, Hann DM, Azzarello LM, Horton J, Balducci L, Lyman GH. Fatigue in women receiving adjuvant chemotherapy for breast cancer: characteristics, course, and correlates. J Pain Symptom Manag. 1999;18(4):233–42.

    Article  CAS  Google Scholar 

  19. Berger AM. Patterns of fatigue and activity and rest during adjuvant breast cancer chemotherapy. Oncol Nurs Forum. 1998;25(1):51–62.

    PubMed  CAS  Google Scholar 

  20. Collado-Hidalgo A, Bower JE, Ganz PA, Cole SW, Irwin MR. Inflammatory biomarkers for persistent fatigue in breast cancer survivors. Clin Cancer Res. 2006;12(9):2759–66.

    Article  PubMed  CAS  Google Scholar 

  21. Wang T, Yin J, Miller AH, Xiao C. A systematic review of the association between fatigue and genetic polymorphisms. Brain Behav Immun. 2017;62:230–44.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Fernandez-de-las-Penas C, Fernandez-Lao C, Cantarero-Villanueva I, Ambite-Quesada S, Rivas-Martinez I, del Moral-Avila R, et al. Catechol-O-methyltransferase genotype (Val158met) modulates cancer-related fatigue and pain sensitivity in breast cancer survivors. Breast Cancer Res Treat. 2012;133(2):405–12.

    Article  PubMed  CAS  Google Scholar 

  23. Armstrong TS, Vera E, Zhou R, Acquaye AA, Sullaway CM, Berger AM, et al. Association of genetic variants with fatigue in patients with malignant glioma. Neurooncol Pract. 2018;5(2):122–8.

    PubMed  Google Scholar 

  24. Miller AH, Jones JF, Drake DF, Tian H, Unger ER, Pagnoni G. Decreased basal ganglia activation in subjects with chronic fatigue syndrome: association with symptoms of fatigue. PLoS One. 2014;9(5):e98156.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Hirvonen MM, Laakso A, Nagren K, Rinne JO, Pohjalainen T, Hietala J. C957T polymorphism of dopamine D2 receptor gene affects striatal DRD2 in vivo availability by changing the receptor affinity. Synapse. 2009;63(10):907–12.

    Article  PubMed  CAS  Google Scholar 

  26. Park JY, Lengacher CA, Reich RR, Alinat CB, Ramesar S, Le A, 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 

  27. Khasar SG, Green PG, Miao FJ, Levine JD. Vagal modulation of nociception is mediated by adrenomedullary epinephrine in the rat. Eur J Neurosci. 2003;17(4):909–15.

    Article  PubMed  Google Scholar 

  28. Crist RC, Berrettini WH. Pharmacogenetics of OPRM1. Pharmacol Biochem Behav. 2014;123:25–33.

    Article  PubMed  CAS  Google Scholar 

  29. Bonenberger M, Plener PL, Groschwitz RC, Gron G, Abler B. Polymorphism in the micro-opioid receptor gene (OPRM1) modulates neural processing of physical pain, social rejection and error processing. Exp Brain Res. 2015;233(9):2517–26.

    Article  PubMed  CAS  Google Scholar 

  30. Bower JE. Cancer-related fatigue: links with inflammation in cancer patients and survivors. Brain Behav Immun. 2007;21(7):863–71.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Van Dongen HP, Caldwell JA Jr, Caldwell JL. Individual differences in cognitive vulnerability to fatigue in the laboratory and in the workplace. Prog Brain Res. 2011;190:145–53.

    Article  PubMed  Google Scholar 

  32. Guess J, Burch JB, Ogoussan K, Armstead CA, Zhang H, Wagner S, et al. Circadian disruption, Per3, and human cytokine secretion. Integr Cancer Ther. 2009;8(4):329–36.

    Article  PubMed  CAS  Google Scholar 

  33. Garaulet M, Sanchez-Moreno C, Smith CE, Lee YC, Nicolas F, Ordovas JM. Ghrelin, sleep reduction and evening preference: relationships to CLOCK 3111 T/C SNP and weight loss. PLoS One. 2011;6(2):e17435.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Ebisawa T. Circadian rhythms in the CNS and peripheral clock disorders: human sleep disorders and clock genes. J Pharmacol Sci. 2007;103(2):150–4.

    Article  PubMed  CAS  Google Scholar 

  35. 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  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  37. Jabir RS, Ho GF, Annuar M, Stanslas J. Association of allelic Interaction of single nucleotide polymorphisms of influx and efflux transporters genes with nonhematologic adverse events of docetaxel in breast cancer patients. Clin Breast Cancer. 2018;18(5):e1173–9.

    Article  PubMed  CAS  Google Scholar 

  38. de Castro CL, da Costa Junior LC, Lourenco LV, Seba KS, da Silva TSL, Vianna-Jorge R. Impact of gene polymorphisms on the systemic toxicity to paclitaxel/carboplatin chemotherapy for treatment of gynecologic cancers. Arch Gynecol Obstet. 2019;300(2):395–407.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  40. 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  PubMed  CAS  Google Scholar 

  41. Wagner LI, Gray RJ, Sparano JA, Whelan TJ, Garcia SF, Yanez B, 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  PubMed  PubMed Central  CAS  Google Scholar 

  42. Medscape, Body Surface Area Based Dosing, Retrieved October 17, 2021 from: https://reference.medscape.com/calculator/bsa-dosing.

  43. 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.

    Article  PubMed  CAS  Google Scholar 

  44. What is the Cockcroft-Gault formula for estimating creatinine clearance (CrCl) and how is it used in patients with chronic kidney disease (CKD)? Retrieved October 17, 2021 from: https://www.medscape.com/answers/238798-105315/what-is-the-cockcroft-gault-formula-for-estimating-creatinine-clearance-crcl-and-how-is-it-used-in-patients-with-chronic-kidney-disease-ckd.

  45. CrCl Cockroft-Gault calculator, Retrieved October 17, 2021 from: https://reference.medscape.com/calculator/51/crcl-cockroft-gault.

  46. Mierzynska J, Taye M, Pe M, Coens C, Martinelli F, Pogoda K, 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 

  47. 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 

  48. 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 

  49. 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 

  50. Hartung TJ, Friedrich M, Johansen C, Wittchen HU, Faller H, Koch U, 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 

  51. Hajj A, Hachem R, Khoury R, Nehme T, Hallit S, Nasr F, et al. Clinical and genetic factors associated with the breast cancer-related sleep disorders: the “CAGE-sleep” study-a cross-sectional study. J Pain Symptom Manag. 2021;62:e46.

    Article  Google Scholar 

  52. Giesinger JM, Loth FLC, Aaronson NK, Arraras JI, Caocci G, Efficace F, et al. Thresholds for clinical importance were established to improve interpretation of the EORTC QLQ-C30 in clinical practice and research. J Clin Epidemiol. 2020;118:1–8.

    Article  PubMed  Google Scholar 

  53. Giesinger JM, Loth FLC, Aaronson NK, Arraras JI, Caocci G, Efficace F, et al. Thresholds for clinical importance were defined for the European organisation for research and treatment of cancer computer adaptive testing core-an adaptive measure of core quality of life domains in oncology clinical practice and research. J Clin Epidemiol. 2020;117:117–25.

    Article  PubMed  Google Scholar 

  54. Invernizzi M, Lopez G, Michelotti A, Venetis K, Sajjadi E, De Mattos-Arruda L, et al. Integrating biological advances into the clinical management of breast cancer related lymphedema. Front Oncol. 2020;10:422.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Lim SM, Han Y, Kim SI, Park HS. Utilization of bioelectrical impedance analysis for detection of lymphedema in breast cancer survivors: a prospective cross sectional study. BMC Cancer. 2019;19(1):669.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Bower JE, Asher A, Garet D, Petersen L, Ganz PA, Irwin MR, et al. Testing a biobehavioral model of fatigue before adjuvant therapy in women with breast cancer. Cancer. 2019;125(4):633–41.

    Article  PubMed  Google Scholar 

  57. Puigpinos-Riera R, Serral G, Sala M, Bargallo X, Quintana MJ, Espinosa M, et al. Cancer-related fatigue and its determinants in a cohort of women with breast cancer: the DAMA cohort. Support Care Cancer. 2020;28(11):5213–21.

    Article  PubMed  CAS  Google Scholar 

  58. Barnes EA, Bruera E. Fatigue in patients with advanced cancer: a review. Int J Gynecol Cancer. 2002;12(5):424–8.

    Article  PubMed  CAS  Google Scholar 

  59. Oei SL, Thronicke A, Matthes H, Schad F. Assessment of integrative non-pharmacological interventions and quality of life in breast cancer patients using real-world data. Breast Cancer. 2021;28:608.

    Article  PubMed  Google Scholar 

  60. Waldmann A, Pritzkuleit R, Raspe H, Katalinic A. The OVIS study: health related quality of life measured by the EORTC QLQ-C30 and -BR23 in German female patients with breast cancer from Schleswig-Holstein. Qual Life Res. 2007;16(5):767–76.

    Article  PubMed  CAS  Google Scholar 

  61. Matias M, Baciarello G, Neji M, Di Meglio A, Michiels S, Partridge AH, et al. Fatigue and physical activity in cancer survivors: a cross-sectional population-based study. Cancer Med. 2019;8(5):2535–44.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Backstrom D, Eriksson Domellof M, Granasen G, Linder J, Mayans S, Elgh E, et al. Polymorphisms in dopamine-associated genes and cognitive decline in Parkinson's disease. Acta Neurol Scand. 2018;137(1):91–8.

    Article  PubMed  CAS  Google Scholar 

  63. Hirvonen MM, Lumme V, Hirvonen J, Pesonen U, Nagren K, Vahlberg T, et al. C957T polymorphism of the human dopamine D2 receptor gene predicts extrastriatal dopamine receptor availability in vivo. Prog Neuro-Psychopharmacol Biol Psychiatry. 2009;33(4):630–6.

    Article  CAS  Google Scholar 

  64. Perkins KA, Lerman C, Coddington S, Jetton C, Karelitz JL, Wilson A, et al. Gene and gene by sex associations with initial sensitivity to nicotine in nonsmokers. Behav Pharmacol. 2008;19(5–6):630–40.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Ozburn AR, Purohit K, Parekh PK, Kaplan GN, Falcon E, Mukherjee S, et al. Functional implications of the CLOCK 3111T/C single-nucleotide polymorphism. Front Psychiatry. 2016;7:67.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Lee KY, Song JY, Kim SH, Kim SC, Joo EJ, Ahn YM, et al. Association between CLOCK 3111T/C and preferred circadian phase in Korean patients with bipolar disorder. Prog Neuro-Psychopharmacol Biol Psychiatry. 2010;34(7):1196–201.

    Article  CAS  Google Scholar 

  67. Rogers VE, Zhu S, Mandrell BN, Ancoli-Israel S, Liu L, Hinds PS. Relationship between circadian activity rhythms and fatigue in hospitalized children with CNS cancers receiving high-dose chemotherapy. Support Care Cancer. 2020;28(3):1459–67.

    Article  PubMed  Google Scholar 

  68. O'Higgins CM, Brady B, O'Connor B, Walsh D, Reilly RB. The pathophysiology of cancer-related fatigue: current controversies. Support Care Cancer. 2018;26(10):3353–64.

    Article  PubMed  CAS  Google Scholar 

  69. Matikas A, Foukakis T, Moebus V, Greil R, Bengtsson NO, Steger GG, et al. Dose tailoring of adjuvant chemotherapy for breast cancer based on hematologic toxicities: further results from the prospective PANTHER study with focus on obese patients. Ann Oncol. 2019;30(1):109–14.

    Article  PubMed  CAS  Google Scholar 

  70. Abdel-Razeq H, Hashem H. Recent update in the pathogenesis and treatment of chemotherapy and cancer induced anemia. Crit Rev Oncol Hematol. 2020;145:102837.

    Article  PubMed  Google Scholar 

  71. Rodgers GM 3rd, Becker PS, Blinder M, Cella D, Chanan-Khan A, Cleeland C, et al. Cancer- and chemotherapy-induced anemia. J Natl Compr Cancer Netw. 2012;10(5):628–53.

    Article  Google Scholar 

  72. Sobrero A, Puglisi F, Guglielmi A, Belvedere O, Aprile G, Ramello M, et al. Fatigue: a main component of anemia symptomatology. Semin Oncol. 2001;28(2 Suppl 8):15–8.

    Article  PubMed  CAS  Google Scholar 

  73. Bohlius J, Bohlke K, Castelli R, Djulbegovic B, Lustberg MB, Martino M, et al. Management of cancer-associated anemia with erythropoiesis-stimulating agents: ASCO/ASH clinical practice guideline update. J Clin Oncol. 2019;37(15):1336–51.

    Article  PubMed  CAS  Google Scholar 

  74. Aapro M, Beguin Y, Bokemeyer C, Dicato M, Gascon P, Glaspy J, et al. Management of anaemia and iron deficiency in patients with cancer: ESMO clinical practice guidelines. Ann Oncol. 2018;29(Suppl 4):iv96–iv110.

    Article  PubMed  CAS  Google Scholar 

  75. Bekes I, Eichler M, Singer S, Friedl TWP, Harbeck N, Rack B, et al. Impact of granulocyte colony-stimulating factor (G-CSF) and Epoetin (EPO) on hematologic toxicities and quality of life in patients during adjuvant chemotherapy in early breast cancer: results from the multi-center randomized ADEBAR trial. Clin Breast Cancer. 2020;20(6):439–47.

    Article  PubMed  CAS  Google Scholar 

  76. Hudis CA, Vogel CL, Gralow JR, Williams D, Procrit Study G. Weekly epoetin alfa during adjuvant chemotherapy for breast cancer: effect on hemoglobin levels and quality of life. Clin Breast Cancer. 2005;6(2):132–42.

    Article  PubMed  CAS  Google Scholar 

  77. Lyman GH. Benefits of early intervention with erythropoiesis- stimulating proteins in chemotherapy-induced anemia. Oncology (Williston Park). 2006;20(8 Suppl 6):16–20.

    Google Scholar 

  78. Donovan KA, Jacobsen PB, Andrykowski MA, Winters EM, Balducci L, Malik U, et al. Course of fatigue in women receiving chemotherapy and/or radiotherapy for early stage breast cancer. J Pain Symptom Manag. 2004;28(4):373–80.

    Article  Google Scholar 

  79. Liu L, Fiorentino L, Natarajan L, Parker BA, Mills PJ, Sadler GR, et al. Pre-treatment symptom cluster in breast cancer patients is associated with worse sleep, fatigue and depression during chemotherapy. Psychooncology. 2009;18(2):187–94.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Sultan A, Choudhary V, Parganiha A. Worsening of rest-activity circadian rhythm and quality of life in female breast cancer patients along progression of chemotherapy cycles. Chronobiol Int. 2017;34(5):609–23.

    Article  PubMed  Google Scholar 

  81. Ancoli-Israel S, Liu L, Rissling M, Natarajan L, Neikrug AB, Palmer BW, et al. Sleep, fatigue, depression, and circadian activity rhythms in women with breast cancer before and after treatment: a 1-year longitudinal study. Support Care Cancer. 2014;22(9):2535–45.

    Article  PubMed  PubMed Central  Google Scholar 

  82. de Alcantara BBR, Cruz FM, Fonseca FLA, da Costa Aguiar Alves B, Perez MM, Varela P, et al. Chemotherapy-induced fatigue is associated with changes in gene expression in the peripheral blood mononuclear cell fraction of patients with locoregional breast cancer. Support Care Cancer. 2019;27(7):2479–86.

    Article  PubMed  Google Scholar 

  83. Bower JE, Bak K, Berger A, Breitbart W, Escalante CP, Ganz PA, et al. Screening, assessment, and management of fatigue in adult survivors of cancer: an American Society of Clinical oncology clinical practice guideline adaptation. J Clin Oncol. 2014;32(17):1840–50.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Schmidt ME, Wiskemann J, Schneeweiss A, Potthoff K, Ulrich CM, Steindorf K. Determinants of physical, affective, and cognitive fatigue during breast cancer therapy and 12 months follow-up. Int J Cancer. 2018;142(6):1148–57.

    Article  PubMed  CAS  Google Scholar 

  85. Montagnese C, Porciello G, Vitale S, Palumbo E, Crispo A, Grimaldi M, et al. Quality of life in women diagnosed with breast cancer after a 12-month treatment of lifestyle modifications. Nutrients. 2020;13(1):136.

    Article  PubMed Central  Google Scholar 

  86. Lu Shin KN, Mun CY, Shariff ZM. Nutrition indicators, physical function, and health-related quality of life in breast cancer patients. Asian Pac J Cancer Prev. 2020;21(7):1939–50.

    Article  PubMed  Google Scholar 

  87. Lua PL, Salihah NZ, Mazlan N. Nutritional status and health-related quality of life of breast cancer patients on chemotherapy. Malays J Nutr. 2012;18(2):173–84.

    PubMed  CAS  Google Scholar 

  88. Jassim G, AlAnsari A. Reliability and validity of the Arabic version of the EORTC QLQ-C30 and QLQ-BR23 questionnaires. Neuropsychiatr Dis Treat. 2020;16:3045–52.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Functional Assessment of Chronic Illness Therapy - Fatigue (FACIT-F). Retrieved October 17, 2021 from: https://eprovide.mapi-trust.org/instruments/functional-assessment-of-chronic-illness-therapy-fatigue.

  90. Smets EM, Garssen B, Bonke B, De Haes JC. The multidimensional fatigue inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res. 1995;39(3):315–25.

Download references

Acknowledgements

The authors would like to thank all the physicians and students (namely, Dr. Fadi Nasr, Dr. Fadi El Karak, Dr. Aya Awad, Dr. Tamara Nehmé, Dr. Bashar ElJebbawi, Dr. Christina Chemaly) who helped recruit patients at the Hôtel-Dieu de France Hospital (Beirut, Lebanon). 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 (FPH71).

Author information

Author notes

  1. Aline Hajj and Rami Chamoun have 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, Rami Chamoun, Rita Khoury, Roula Hachem & Lydia Rabbaa Khabbaz

  2. Laboratoire de Pharmacologie, Pharmacie Clinique et Contrôle de Qualité des Médicaments, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon

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

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

    Pascale Salameh & Hala Sacre

  4. Faculty of Pharmacy, Lebanese University, Beirut, Lebanon

    Pascale Salameh

  5. University of Nicosia Medical School, Nicosia, Cyprus

    Pascale Salameh

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

    Georges Chahine & Joseph Kattan

Authors
  1. Aline Hajj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rami Chamoun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pascale Salameh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rita Khoury
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Roula Hachem
    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. Georges Chahine
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Joseph Kattan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Lydia Rabbaa Khabbaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Aline HAJJ (AH), Rami CHAMOUN (RC), Pascale SALAMEH (PS), Rita KHOURY (RK), Roula HACHEM (RH), Hala SACRE (HS), Georges CHAHINE (GC), Joseph KATTAN (JK), Lydia RABBAA KHABBAZ (LRK). AH designed the study. LRK contributed to the design. AH, RC and PS managed the literature search and analyses. RK, RH, GC and JK included the patients and performed the clinical assessment. PS undertook the statistical analysis. AH and RC wrote the first draft of the manuscript. HS edited the whole article for English language and intellectual content. LRK and JK supervised the whole process and critically reviewed the article. All authors contributed to the critically intellectual revision of the final manuscript and gave final approval of the version to be submitted/published.

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.

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., Chamoun, R., Salameh, P. et al. Fatigue in breast cancer patients on chemotherapy: a cross-sectional study exploring clinical, biological, and genetic factors. BMC Cancer 22, 16 (2022). https://doi.org/10.1186/s12885-021-09072-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-021-09072-0

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us