Understanding cancer-related fatigue: advancing the science

As the number of survivors increases, improving cancer patients’ quality of life has emerged as a vital priority [1]. Fatigue, which reduces quality of life, is the most pervasive and distressing side effect reported by cancer patients, especially during treatment. Regardless of cancer type and treatment modality, nearly all patients experience fatigue during cancer treatment and nearly a third report chronic fatigue that persists for years after treatment completion [2,3]. Cancer-related fatigue (CRF) is a general term for fatigue experienced by individuals with different cancer conditions and those who are receiving or have completed the various cancer treatments. CRF is characterized by consistent physical and mental tiredness, unrelated to activity and not relieved by rest. Additionally, these symptoms can be accompanied by depression and impairment in cognition and sleep [4].

Proposed etiologic mechanisms of CRF implicate immune system dysregulation, impaired nerve conduction, neuroendocrine and neurotransmitter dysregulation, and energy depletion [5–7]. Central mechanisms proposed include disruptions in basal ganglia and frontal lobe function, hypothalamic–pituitary–adrenal axis dysfunction, and enhanced proinflammatory cytokine release affecting neuronal metabolism [8–11]. Peripherally, impaired muscle contraction, peripheral nerve conduction, and other neuromuscular abnormalities are thought to contribute to CRF [12,13].

While the potential for involvement of these diverse mechanisms has been demonstrated, no biomarker has been consistently associated with CRF in large sample studies [5,6,10,14,15]. Furthermore, although a large variety of questionnaires to evaluate CRF have been developed, there is little consensus about what outcomes define this condition, hampering comparisons between trials [3]. To facilitate comparison across studies and better understand the pathophysiological processes associated with the CRF phenotype, clear contexts surrounding the CRF experiences of patient subsets should be considered. More specific definitions of CRF that account for the various causes and manifestations of CRF can improve CRF measurement, definition, and investigation. The recently proposed National Institutes of Health Symptom Science Model proposes that a clear phenotype of symptoms can direct better understanding and discovery of potential biomarkers [16]. This editorial provides support for this approach and suggests strategies to achieve this goal.

1. Measuring CRF

Repeatable and accurate assessments, including patient-reported information and objective measures, are critical for CRF characterization. Study objectives or design should dictate the appropriate unidimensional or multidimensional assessments of fatigue. For instance, a unidimensional measure of severity would be warranted for a drug trial while a multidimensional tool would better serve research into the varied components and manifestations of CRF [17]. Perhaps most importantly, a consensus must be reached in using clear and consistent interpretations of these tools for defining clinically meaningful scores.

Objective measurements, including assessments of skeletal muscle strength, endurance, sleep, and cognitive function, can be especially useful when combined with patient-reported outcomes. However, testing these parameters may be limited by expense and the relative difficulty to perform these tests in comparison to self-report tools in a community setting. Nonetheless, information on symptoms that co-occur with CRF, such as depressive symptoms, pain, cognitive dysfunction, and sleep impairment, are critical to assess because they likely originate from a shared mechanism [10]. Also, as with clinical and psychosocial data, such information may be retrieved from medical records and is useful in identifying the context in which fatigue occurs.

It is important to note that fatigue symptoms can be associated with environment and time, such as seasonal variability where patients report the most severe fatigue in December and January and the least severe in July [18,19].

2. Defining CRF

Heterogeneity among cancer patient populations can introduce covariates and is an important consideration for a context-based definition of CRF, especially in regard to specificity of disease and treatment. Cancer type and stage and the type or intensity of treatment may differentially impact the biology and phenotype of the CRF experience of patients [2]. Therefore, it might be more useful to investigate fatigue in subgroups based on disease type and treatment modality rather than to continue with the traditional, broadly encompassing view of CRF as a singular condition [17,20].

For instance, patients receiving either chemotherapy or radiotherapy experience different trajectories of CRF. On chemotherapy, individuals report a cyclical fatigue experience while patients on radiotherapy generally report increasing fatigue that resolves with treatment completion [4,21]. Also, individuals receiving chemotherapy usually report more intense fatigue than those receiving radiotherapy [21]. Because these treatments work by distinct pathways and generate different CRF phenotypes, they likely cause fatigue through different mechanisms. Evidence for this is seen by the fact that while radiotherapy triggers an increased inflammatory response, chemotherapy tends to suppress any inflammatory response [8]. These distinctions also help illustrate why more specific definitions of CRF are important for further etiological investigations.

In addition to specifying the type/stage of disease and type/intensity of treatment, additional information such as comorbid conditions and concomitant medications can provide context to the fatigue experience of individuals. Various comorbid conditions (e.g. cardiovascular, infectious, inflammatory, psychiatric, physical deconditioning, and mitochondrial diseases) can offer perspective to biologic underpinnings of the fatigue experience. Concomitant medications (anxiolytics, antidepressants, hormones [thyroid, estrogen, testosterone deprivation], and antibiotics) can also predispose someone to fatigue. Other factors found to influence the fatigue experience include gender, age, race, economic status, social support, educational attainment, and psychological status [1,2]. Currently, all of these factors are important to the study of CRF and must be considered by researchers and clinicians.

3. Conclusions

Our understanding of the etiology of CRF remains limited. Practical suggestions are offered here to address challenges and advance the science in this area. Continuous discussion of these issues is indispensable to bridge gaps in knowledge and to enhance assessment and management of CRF.