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Treatment-associated Fatigue in Cancer Patients Treated with Immune Checkpoint Inhibitors; a Systematic Review and Meta-analysis

Clinical Oncology, In Press, Corrected Proof, Available online 20 June 2016, Available online 20 June 2016

Abstract

Aims

Fatigue is one of the most prominent side-effects of immune checkpoint inhibition. Therefore, we assessed the risk of fatigue associated with inhibitors of the immune checkpoints.

Materials and methods

We examined data from the Medline and Google Scholar databases. We also examined original studies and review articles for cross-references. Eligible studies included randomised phase II and phase III trials of patients with cancer treated with ipilimumab, nivolumab, pembrolizumab and tremelimumab. The authors extracted relevant information on participants' characteristics, all-grade and high-grade fatigue and information on the methodology of the studies.

Results

In total, 17 trials were considered eligible for the meta-analysis. The odds ratio for all-grade fatigue for CTLA-4 inhibitors was 1.23 (95% confidence interval 1.07, 1.41; P = 0.003) and for high-grade fatigue was 1.72 (95% confidence interval 1.26, 2.33; P = 0.0005). Moreover, the odds ratio for all-grade fatigue for PD-1 inhibitors was 0.72 (95% confidence interval 0.62, 0.84; P < 0.0001) and for high-grade fatigue was 0.36 (95% confidence interval 0.23, 0.56; P < 0.00001).

Conclusions

The analysis of data showed that CTLA-4 inhibitors seem to be associated with a higher risk of all- and high-grade fatigue compared with control regimens, whereas PD-1 inhibitors seem to be associated with a lower risk of all- and high-grade fatigue compared with control regimens.

Highlights

  • Fatigue is one of the most prominent side-effects of immune checkpoint inhibition.
  • CTLA-4 inhibitors are associated with a higher risk of fatigue compared with controls.
  • PD-1 inhibitors are associated with a lower risk of fatigue compared with controls.

Key words: Fatigue, ipilimumab, nivolumab, pembrolizumab, tremelimumab.

Introduction

Stimulating our self-immune system to combat cancer cells has been a daunting dream for cancer researchers for decades. Until recently, there was no effective method to achieve this [1]. However, in 2010 the first phase III study using a so-called immune checkpoint inhibitor showed survival advantage in patients with advanced metastatic melanoma. Checkpoint inhibitors work by inhibiting the internal regulatory checkpoints that prevent over-activation of cytotoxic T cells and thereby allowing therapeutic anti-tumour T-cell responses. Currently, the target checkpoints that are inhibited by monoclonal antibodies in clinical practice are CTLA-4 and PD-1. Ipilimumab and tremelimumab are two CTLA-4-targeting antibodies that have shown promising efficacy in treating advanced melanoma [2] and [3], whereas PD-1-targeted agents include pembrolizumab and nivolumab, which have been approved in advanced melanoma and previously treated advanced non-small cell lung cancer (NSCLC) [4] and [5]. Moreover, nivolumab has been approved in the treatment of previously treated advanced renal cell carcinoma (RCC) [6]. Evaluation of these agents in many other cancers, including lymphoma and gastrointestinal cancers, is ongoing.

However, due to the novelty of their mechanism of action, they have been accompanied by immune-related adverse events, which are unique to this category of drugs [7]. Immune-related adverse events include cutaneous, hepatic, endocrine, gastrointestinal, renal and pulmonary toxicities [8], [9], [10], [11], and [12]. Moreover, an increased risk of fatigue has been observed. Fatigue is a well-known clinical problem in autoimmune diseases like lupus erythematosus and other connective tissue diseases. Therefore, it is not surprising to find fatigue as a potential immune-related adverse event reflecting some kind of induced autoimmunity. Consequently, there were concerns about the potential impact of checkpoint inhibitors on aggravating cancer-related fatigue. Therefore, this meta-analysis was conducted to determine the risk of treatment-associated fatigue in cancer patients who are being treated with inhibitors of the immune checkpoints.

Materials and Methods

Sourcing the Data

A thorough literature review of Medline and Google scholar was conducted. The search was carried out until December 2015 and it was confined to human studies, published in English and carried out on cancer patients. The search was additionally confined to randomised controlled trials. The search terms included: ‘nivolumab’[Supplementary Concept] OR ‘tremelimumab’[Supplementary Concept] OR “ipilimumab’[Supplementary Concept] OR ‘pembrolizumab’[Supplementary Concept]. Trials were evaluated according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [13].

Selection Procedure of the Studies

Inclusion criteria for the clinical trials in the meta-analysis included randomised controlled trials in patients with cancer; participants were randomised to treatment with an immune checkpoint inhibitor and sample size and event rate are present for all- and high-grade treatment-associated fatigue; exclusion criteria included phase I studies.

Extraction of the Data

Review authors conducted extraction of the relevant data. The following data were assessed for each trial: name of the first author, year of publication, phase of the trial, treated cancer, treatment arms, number of participants and number of events (all- and high-grade fatigue). In the included trials, fatigue was assessed according to the Common Terminology Criteria of Adverse Events (CTCAE) version 4.0.

Implementation and Analysis of Data

Odds ratios and corresponding 95% confidence intervals of all-grade (grade 1–3) and high-grade (grade 3) fatigue were the principal measures. The number of events in participants randomised to inhibitors of the immune checkpoints was compared with those randomised to control treatment in each trial. Outcome heterogeneity between assessed studies in the study was evaluated through Cochrane's Q statistic. The fixed effect model was used in all of the sub-analyses because of the homogeneity of the results. Publication bias was assessed through the use of funnel plots. Analysis of the data was carried out using the RevMan 5.3 (Review Manager 5.3; Nordic Cochrane centre; Copenhagen, Denmark).

Results

Search Results

The search strategy revealed 271 potentially suitable records on inhibitors of the immune checkpoints from Medline and Google scholar databases. The reasons for study exclusion are clarified in Figure 1. Accordingly, 17 clinical trials were considered eligible for the analysis; this included 14 phase III trials and three phase II trials [2], [3], [4], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], and [27]. Six studies utilised ipilimumab, seven studies utilised nivolumab (two of which utilised the ipilimumab/nivolumab combination), one study utilised tremelimumab and three studies utilised pembrolizumab (one of which compared pembrolizumab with ipilimumab). Ten studies addressed malignant melanoma, four studies addressed NSCLC, one study addressed RCC, one study addressed castrate-resistant prostate cancer and one study addressed small cell lung cancer. The non-checkpoint inhibitor controls used in some of the studies included placebo, everolimus and chemotherapy (including dacarbazine and docetaxel).

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Fig 1

Study selection procedure.

 

Population Characteristics

In total, 10 562 patients were available for the analysis. Patients had adequate renal, hepatic and haematological parameters and the vast majority of patients had an ECOG performance score <3. The baseline patient characteristics and the number of events of treatment-related all- and high-grade fatigue are detailed in Table 1 and Table 2.

Table 1

Baseline characteristics of included trials

 

Reference Type of study Patient numbers Dose of immune checkpoint inhibitor Therapeutic indication All-grade fatigue High-grade fatigue
Ipilimumab studies
Hodi et al. (2010) [2] Phase III Arm A: ipilimumab plus gp100 (380 patients)
Arm B: ipilimumab alone (131 patients) or
Arm C: gp100 alone (132 patients).
Ipilimumab was administered at a dose of 3 mg/kg, was administered with or without gp100 every 3 weeks for up to four treatments. Induction (± re-induction) therapy in previously-treated HLA-A*0201 patients, with unresectable stage III or IV melanoma. Fatigue was encountered in 137 patients (36.1%), 55 patients (42%) and 41 patients (31.1%) in arms A, B and C Grade 3 fatigue was encountered in 19 patients (5%), 9 patients (6.9%) and 4 patients (3%) in arms A, B and C
Lynch et al. (2012) [22] Randomised phase II Arm A: concurrent ipilimumab plus paclitaxel and carboplatin followed by two doses of placebo plus paclitaxel and carboplatin (71 patients).
Arm B: phased ipilimumab - two doses of placebo plus paclitaxel and carboplatin followed by four doses of ipilimumab plus paclitaxel and carboplatin (67 patients). Arm C: placebo plus paclitaxel and carboplatin control (65 patients)
Ipilimumab was administered at a dose of 10 mg/kg every 3 weeks for ≤ 18 weeks. Eligible patients could receive maintenance therapy of ipilimumab or placebo every 12 weeks. Induction ± maintenance therapy in chemotherapy-naive stage IIIB/IV NSCLC 14 (20%), 13 (19%) and 14 (22%) patients in the three arms. High-grade fatigue took place in 6 (8%) (5 grade 3 and 1 grade 4 AE), 3 grade 3 (5%) and 3 grade 3 (5%) patients in the three arms.
Robert et al. (2011) [3] Phase III Arm A: ipilimumab plus dacarbazine 247 patients.
Arm B: placebo plus dacarbazine 251 patients
Ipilimumab: 10 mg/kg Previously untreated metastatic melanoma 103 (41.7%) versus 98 (39%) 27 (11%) versus 12 (5%)
Eggermont et al. (2014) [20] Phase III Arm A: ipilimumab (471 patients)
Arm B: placebo (474 patients)
Ipilimumab 10 mg/kg (n = 475) or placebo (n = 476) every 3 weeks for four doses, then every 3 months for up to 3 years until completion, disease recurrence or unacceptable toxicity. Induction and maintenance treatment for high risk stage III completely resected melanoma 189 (40%) versus 143 (30%) 10 (2.3%) versus 7(2%)
Kwon et al. (2014) [14] Phase III Arm A: ipilimumab (393 patients)
Arm B: placebo (396 patients)
Either ipilimumab
10 mg/kg or placebo every 3 weeks for up to four doses. Non-progressing patients could continue to receive ipilimumab at 10 mg/kg or placebo as maintenance therapy every 3 months until disease progression, unacceptable toxic effect or death.
Men after radiotherapy with metastatic castration-resistant prostate cancer, with at least one bone metastasis, that
had progressed after docetaxel chemotherapy
150 (38%) versus 123 (31%) 40 (10.2%) versus 35 (8.8%)
Reck et al. (2013) [19] Phase II RCT Arm A: control regimen (44 patients)
Arm B: concurrent regimen (42 patients)
Arm C: phased regimen (42 patients)
Paclitaxel (175 mg/m2)/carboplatin (area under the curve = 6) with either placebo (control) or ipilimumab 10 mg/kg in two alternative
regimens, concurrent ipilimumab or
phased ipilimumab
First-line therapy in extensive disease-
small-cell lung cancer
11 (25%) versus 13 (31%) versus 12 (29%) Grade 3 events: 2 (5%) versus 3 (7%) versus 5 (12%)
Nivolumab studies
Robert et al. (2015) [16] Phase III Arm A: nivolumab (206 patients)
Arm B: dacarbazine (205 patients)
Nivolumab (at a dose of 3 mg/kg every 2 weeks and dacarbazine-matched placebo every 3 weeks) or dacarbazine (at a dose of 1000 mg/m2 of BSA every 3 weeks and nivolumab-matched placebo every 2 weeks. Previously untreated unresectable patients, who had stage III or IV melanoma without a BRAF mutation. 41 patients (19.9%) versus 30 patients (14.6%) 2 patients (1%) only in the dacarbazine cohort
Weber et al. (2015) [17] Phase III Arm A: nivolumab (268 patients)
Arm B: investigator's choice of chemotherapy (102 patients)
Nivolumab (3 mg/kg IV every 2 weeks) Patients with advanced melanoma who progressed on or after anti-CTLA-4 therapy and a BRAF inhibitor in case of BRAF V600 mutation positive disease 67 (25%) versus 34 (34%) 3 (1%) versus 4 (4%)
Brahmer et al. (2015) [15] Phase III Arm A: nivolumab (131 patients)
Arm B: docetaxel
(129 patients)
Nivolumab (at a dose of 3 mg/kg every 2 weeks) versus docetaxel at a dose of 75 mg/m2 every 3 weeks Advanced squamous cell non-small cell lung cancer 21 (16%) versus 42 (33%) 1 (1%) versus 10 (8%)
Borghaei et al. (2015) [18] Phase III Arm A: nivolumab (292 patients)
Arm B: docetaxel (290 patients)
Arm A: nivolumab 3 mg/kg of body weight every 2 weeks
Arm B: docetaxel 75 mg/m2 every 3 weeks
Advanced non squamous cell non-small cell lung cancer 46(16%) versus 78 (29%) 3(1%) versus 13 (5%)
Motzer et al. (2015) [23] Phase III 821 patients were randomly assigned (in a 1:1 ratio) to
Arm A: nivolumab.
Arm B: everolimus.
(a) Nivolumab: 3 mg/kg
(b) Everolimus: 10 mg tab. once daily.
Advanced clear-cell renal cell carcinoma for which they had received previous treatment with one or two regimens of antiangiogenic therapy 134 (33%) versus 134 (34%) 10 (2%) versus 11 (3%)
Tremelimumab studies
Ribas et al. (2013) [21] Phase III Arm A: tremelimumab 325 patients
Arm B: physician's choice of standard-of-care chemotherapy (either single-agent DTIC or single-agent temozolomide) 319 patients
Tremelimumab dose: 15 mg/kg once every 90 days for up to four cycles. Treatment-naive unresectable stage IIIC or IV melanoma 106 (33%) versus 118 (37%) 19 (6%) versus 5 (2%)
Pembrolizumab studies
Ribas et al. (2015) [24] Randomised phase II Arm A: pembrolizumab (low dose): 179 patients
Arm B: pembrolizumab (high dose): 178 patients
Arm C: chemotherapy control: 171 patients
Pembrolizumab low dose: 2 mg/kg
Pembrolizumab high dose: 10 mg/kg
Ipilimumab-refractory advanced melanoma 38 (21%) versus 51 (28%) versus 54 (32%) 2 (1%) versus 1 (<1%) versus 8 (5%)
Herbst et al. (2015) [25] Randomised phase II/III study Arm A: pembrolizumab (low dose): 344 patients
Arm B: pembrolizumab (high dose): 346 patients
Arm C: docetaxel: 343 patients
Pembrolizumab low dose: 2 mg/kg
Pembrolizumab high dose: 10 mg/kg
Previously treated NSCLC with PD-L1>1% 46 (14%) versus 49 (14%) versus 76 (25%) 4 (1%) versus 6 (2%) versus 11 (4%)

RCT, randomised controlled trial; gp100, glycoprotein 100; NSCLC, non-small cell lung cancer.

Table 2

Direct comparison among different immune checkpoint inhibitors in the risk of fatigue

 

Reference Type of the study Patients, number Dose of the Immune checkpoint inhibitor Therapeutic indication All-grade fatigue High-grade fatigue
Larkin et al. (2015) [27] Phase III Arm A: nivolumab combined with placebo (316 patients).
Arm B: ipilimumab combined with nivolumab (314 patients).
Arm C: ipilimumab combined with placebo (315 patients).
(a) Nivolumab 3 mg/kg combined with placebo.
(b) Ipilimumab (3 mg/kg) combined with nivolumab (1 mg/kg).
(c) Ipilimumab (3 mg/kg) combined with placebo.
Advanced melanoma 107 (34%) versus 110 (35%) versus 87 (28%) 4 (1.3%) versus 13 (4.2%) versus 3 (1%)
Postow et al. (2015) [26] Phase III Arm A: ipilimumab (3 mg/kg) combined with nivolumab (1 mg/kg) (94 patients)
Arm B: ipilimumab (3 mg/kg) combined with placebo (46 patients)
(a) ipilimumab (3 mg/kg) combined with nivolumab (1 mg/kg)
(b) ipilimumab (3 mg/kg) combined with placebo
Advanced melanoma 37 (39%) versus 20 (43%) 5 (5%) versus 0
Robert et al. (2015) [4] Phase III 834 patients with advanced melanoma in a 1:1:1 ratio to receive:
Arm A: pembrolizumab every 2 weeks (279 patients)
Arm B: pembrolizumab every 3 weeks (277 patients)
Arm C: ipilimumab (278 patients)
Arm A: pembrolizumab 10 mg/kg every 2 weeks.
Arm B: pembrolizumab 10 mg/kg every 3 weeks
Arm C: ipilimumab 3 mg/kg
Advanced melanoma 58 (20.9%) versus 53 (19.1%) versus 39 (15.2%) 0 versus 1 (0.4%) versus 3 (1.2%)

Overall Incidence of Treatment-associated Fatigue

For analysis of the incidence, only arms receiving an immune checkpoint inhibitor were considered. The incidence of all-grade treatment-associated fatigue varied from 14 to 42%; whereas the incidence of high-grade treatment-associated fatigue varied from 1 to 11%. The absolute incidences in individual studies varied, however, according to dosing of the specific agent (10 mg/kg versus 3 mg/kg), the schedule (every 2 weeks versus every 3 weeks) as well as the use of combination regimens versus single agent immune checkpoint inhibitors. These differences are detailed in Table 1.

Odds Ratios of All-grade and High-grade Fatigue

For evaluating the odds ratios for all- and high-grade fatigue, only studies evaluating immune checkpoint inhibitors compared with other agents were considered. The results were subcategorised according to the type of agent used (i.e. CTLA-4 inhibitors versus PD-1 inhibitors). The fixed effects model was utilised in reporting all- and high-grade fatigue.

CTLA-4 inhibitors

The odds ratio for all-grade fatigue for CTLA-4 inhibitors was 1.23 (95% confidence interval 1.07, 1.41; P = 0.003) and for high-grade fatigue was 1.72 (95% confidence interval 1.26, 2.33; P = 0.0005) (Figure 2a,b). We also conducted a separate analysis for fatigue reported in the three studies evaluating ipilimumab monotherapy versus control and the odds ratio for all-grade fatigue was 1.48 (95% confidence interval 1.23, 1,79; P < 0.0001) (Figure 2c). It has to be noted that ipilimumab dosing in the studies included in this analysis was 10 mg/kg (except 3 mg/kg in [2]).

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Fig 2

Forest plot for odds ratios of (a) all-grade and (b) high-grade fatigue for cancer patients receiving immune checkpoint inhibitors compared with control (subgrouped by the type of drug used). (c) All-grade fatigue for cancer patients receiving ipilimumab monotherapy versus control.

 

PD-1 inhibitors

The odds ratio for all-grade fatigue for PD-1 inhibitors was 0.72 (95% confidence interval 0.62, 0.84; P < 0.0001) and for high-grade fatigue was 0.36 (95% confidence interval 0.23, 0.56; P < 0.00001) (Figure 2a,b).

Moreover, a test of subgroup differences was statistically significant between both CTLA-4 and PD-1 inhibitors for both all-grade and high-grade fatigue (Figure 2a,b).

Similar toxicity results and differences were found when using random effects instead of the fixed effects model (data not shown).

Thus, CTLA-4 inhibitors (ipilimumab 10 mg/kg and tremelimumab) are accompanied by a higher risk of all- and high-grade fatigue compared with control regimens; whereas PD-1 inhibitors (nivolumab and pembrolizumab) are accompanied by a lower risk of all- and high-grade fatigue compared with control regimens.

Other Relevant Comparisons

The odds ratio of all-grade fatigue with nivolumab/ipilimumab combination versus ipilimumab monotherapy (evaluated in two studies) was 1.29 (95% confidence interval 0.95, 1.75; P = 0.11) (Figure 3a). However, these data have to be interpreted cautiously because subtle differences may not be statistically significant with a small number of studies.

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Fig 3

Forest plot for odds ratios of (a) all-grade fatigue for cancer patients receiving nivolumab/ipilimumab combination compared with ipilimumab monotherapy; (b) all-grade fatigue for cancer patients receiving ipilimumab monotherapy versus anti-PD-1 monotherapy.

 

We conducted another relevant comparison evaluating ipilimumab monotherapy versus anti-PD-1 monotherapy (evaluated in two studies) and the odds ratio of all-grade fatigue was 0.70 (95% confidence interval 0.54, 0.91; P = 0.008) in favour of the ipilimumab arm (Figure 3b). The reason for the apparent discrepancy between the subgroup analysis (suggesting more fatigue with ipilimumab) above and the direct comparison evaluated here (suggesting less fatigue with ipilimumab) seems to be in the ipilimumab dosing. Most of the studies in the above analysis used an ipilimumab dose of 10 mg/kg, whereas in the direct comparison, the ipilimumab dose was 3 mg/kg (which is the current standard dose for ipilimumab). Another potential explanation may be in the control arms used with the two categories of agents; whereas chemotherapy was the control arm in all PD-1 inhibitor studies, placebo control was used in some CTLA-4 studies.

Moreover, the funnel plot did not show evidence of publication bias for the primary analysis (Figure 4a,b).

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Fig 4

Funnel plot for publication bias: (a) for CTLA-4 inhibitors; (b) for PD-1 inhibitors.

 

Subgroup Analysis

A subgroup analysis in accordance with the type of cancer treated (melanoma versus other cancers) was conducted and no significant difference among both subgroups was detected for either all-grade or high-grade fatigue (Figure 5a,b). This subgroup analysis was carried out using the random effects model.

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Fig 5

Forest plot for odds ratios of (a) all-grade and (b) high-grade fatigue for cancer patients receiving immune checkpoint inhibitors compared with control (subgrouped by the treated cancer).

 

Discussion

Fatigue is one of the main side-effects of checkpoint inhibitors. As far as we know, this is the most up to date analysis to provide an assessment of treatment-associated fatigue in cancer patients receiving inhibitors of the immune checkpoints. This data analysis showed that CTLA-4 inhibitors (ipilimumab 10 mg/kg and tremelimumab) are linked to a higher risk of all- and high-grade fatigue compared with control regimens, whereas PD-1 inhibitors (nivolumab and pembrolizumab) are linked to a lower risk of all- and high-grade fatigue compared with control regimens. Moreover, a subgroup difference based on the cancer treated cannot be detected.

Pembrolizumab, ipilimumab and nivolumab have been approved by the Food and Drug Administration for the treatment of advanced cases of malignant melanoma; pembrolizumab and nivolumab have also been approved for pretreated advanced NSCLC and nivolumab has been approved for advanced RCC.

Fatigue is one of the most distressing complaints in advanced cancer patients as well as an important cause for non-compliance of treatment. Interestingly, fatigue has been consistently reported as a side-effect to many chemotherapeutics and targeted agents [28] and [29].

Many patient-, cancer- and treatment-related factors collaborate to produce this complaint; and in most of the cases more than one factor contribute to the development of fatigue. This must to be born in mind when approaching cancer patients suffering from disabling fatigue; because focusing on one causative factor only and ignoring the other factors would not help the patients properly.

Some of the characteristic toxicities of immune checkpoint inhibitors may particularly contribute to the fatigue. These include immune-related endocrine disorders (including hypothyroidism, hypoadrenalism and hypopituitarism) [30]. Early detection and proper management of these endocrinopathies (with hormone replacement and/or immunosuppressive agents) may help to decrease significantly the incidence of fatigue with these agents.

Immune-related gastrointestinal (e.g. diarrhoea and colitis), hepatic (e.g. elevated transaminases), renal (presenting as nephritic or nephrotic syndrome) and pulmonary toxicities may have an important role in the development of fatigue with these agents. Proper management of these toxicities (with supportive care ± immunosuppressives) is expected to ameliorate the risk of fatigue with these agents. Some of the landmark phase III studies of immune checkpoint inhibitors have published dedicated guidelines to a grade-specific management of each of these toxicities [4]. These may be used as a useful guidance to the practicing physicians administering these agents.

The disease itself also has a pivotal role in the development of fatigue. This has been clear in the two placebo-controlled studies in this analysis [14] and [20]. In these studies, the placebo arms of the studies reported a strikingly high incidence of fatigue, which indicates the pivotal role played by the disease in the development of fatigue.

Background patient-related socio-psychological factors are also important in the development of fatigue. A higher incidence of depression and many other psychiatric disorders has been consistently reported with advanced cancer patients [31]. Thus, early and effective establishment of palliative care services stressing on the psychological and spiritual health of cancer patients is fundamental to the management of fatigue in cancer patients.

Nevertheless, fatigue itself can be an immune-related adverse event, which is related to the activation of the immune system. This phenomenon is well-known from autoimmune diseases. Permanent activation of the immune system may result in altered metabolism and increased pro-inflammatory cytokines, which can modulate brain function inducing fatigue.

Many international guidelines have been published to give an evidence-based management approach of fatigue in cancer patients. The American Society of Clinical Oncology has recently published a detailed assessment and treatment guidelines for fatigue in cancer patients [32]. These guidelines recommended that fatigue screening should be conducted for all cancer patients from the point of diagnosis onwards (even after completing the primary treatment and in the follow-up phase). The primary modalities of treatment proposed in this document include pharmacological treatments addressing any accompanying medical condition that is exacerbating the fatigue in addition to non-pharmacological treatments (moderate physical activity, psychosocial interventions as well as mind body interventions). Similar recommendations have been echoed by the National Comprehensive Cancer Network guidelines [33].

The disparity in the risk of fatigue among ipilimumab high dose, low dose and PD-1 inhibitors in the current analysis highlights the importance of not dealing with immune checkpoint inhibitors as a single entity; but rather categorising them according to their mechanism of action, dose and the combinations used, which would have profound effects on their efficacy and toxicity.

However, the results of this analysis have to be interpreted cautiously in view of a number of potential weaknesses associated with this analysis. These include the apparent heterogeneity in the immune checkpoint inhibitors and control regimens used as well as cancers treated. In order to overcome this weakness, multiple subgroup analyses have been carried out. Another confounding temporal factor is probably related to the time the different studies were conducted; the studies of CTLA-4 inhibitors were conducted at an earlier phase where adequate knowledge of the different toxicities of immune checkpoint inhibitors was not mature enough compared with the later studies of PD-1 inhibitors. This may have an indirect impact on the level of control of these toxicities and consequently the development of fatigue in treated patients. Moreover, the use of CTLA4 inhibitors is usually for a defined duration (over 12 weeks for ipilimumab); whereas treatment for PD-1 inhibitors is ongoing. This would affect the duration of the fatigue.

Conclusions

This analysis of data showed that CTLA-4 inhibitors (ipilimumab 10 mg/kg and tremelimumab) are linked to a higher risk of all- and high-grade fatigue compared with control regimens, whereas PD-1 inhibitors (nivolumab and pembrolizumab) are linked to a lower risk of all- and high-grade fatigue compared with control regimens.

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Footnotes

Clinical Oncology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt

OncoCentrum Zurich, Gastrointestinal Tumor Center Zurich (GITZ), Zurich, Switzerland

Surgical Center Zurich - Hirslanden Hospital Zurich, Switzerland

§ OncoCentrum Zurich, Swiss Tumor Immunology Institute (SwissTII), Zurich, Switzerland

Department of General, Visceral and Transplant Surgery, University of Heidelberg, Heidelberg, Germany

|| Department of Oncology, Center of Zug, Switzerland

∗∗ Department of HPB and Liver Transplantation, Rajhy Liver Hospital, Assiut University, Assiut, Egypt

Author for correspondence: O. Abdel-Rahman, Clinical Oncology Department, Faculty of Medicine, Ain Shams University, Lotfy Elsayed Street, Cairo 113331, Egypt.


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