Submit an Article
Online ISSN: 1945-4589
Impact Journals, LLC. is the publisher of Aging: www.impactjournals.com.
Impact Journals is a member of the Wellcome Trust List of Compliant Publishers.
Impact Journals is a member of the Society for Scholarly Publishing.
Learn about our FREE Post-Publication Promotion Services
COVID-19 Research Paper Volume 14, Issue 5 pp 2062—2080

Association of active immunotherapy with outcomes in cancer patients with COVID-19: a systematic review and meta-analysis

Chang Cao1, *, , Xinyan Gan1, *, , Xiaolin Hu2, *, , Yonglin Su3, , Yu Zhang4, , Xingchen Peng3, ,

  • 1 State Key Laboratory of Oral Diseases, West China Hospital of Stomatology/Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
  • 2 Department of Nursing, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
  • 3 Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
  • 4 Affiliated Hospital of Chengdu University, Chengdu 610041, Sichuan, P.R. China
* Equal contribution

Received: November 13, 2021       Accepted: January 25, 2022       Published: March 10, 2022      

https://doi.org/10.18632/aging.203945
How to Cite

Copyright: © 2022 Cao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: During the COVID-19 pandemic, there are growing concerns about the safety of administering immunotherapy in cancer patients with COVID-19. However, current clinical guidelines provided no clear recommendation.

Methods: Studies were searched and retrieved from electronic databases. The meta-analysis was performed by employing the generic inverse-variance method. A random-effects model was used to calculate the unadjusted odds ratios (ORs) and adjusted ORs with the corresponding 95% CIs.

Results: This meta-analysis included 20 articles with 6,042 cancer patients diagnosed with COVID-19. According to the univariate analysis, the acceptance of immunotherapy within 30 days before COVID-19 diagnosis did not increase the mortality of cancer patients (OR: 0.92; 95% CI: 0.68-1.25; P=0.61). Moreover, after adjusting for confounders, the adjusted OR for mortality was 0.51, with borderline significance (95% CI: 0.25-1.01; P=0.053). Similarly, the univariate analysis showed that the acceptance of immunotherapy within 30 days before COVID-19 diagnosis did not increase the risk of severe/critical disease in cancer patients (OR: 1.07; 95% CI: 0.78-1.47; P=0.66). No significant between-study heterogeneity was found in these analyses.

Conclusions: Accepting immunotherapy within 30 days before the diagnosis of COVID-19 was not significantly associated with a higher risk of mortality or severe/critical disease of infected cancer patients. Further prospectively designed studies with large sample sizes are required to evaluate the present results.

Introduction

As of 17 September 2021, a total of 226,844,344 confirmed COVID-19 cases were reported globally, and 4,666,334 COVID-19-related deaths were reported [1]. Cancer is revealed to be independently associated with the risk of COVID-19 and is significantly related to an increased rate of severe disease and mortality [2]. SARS-CoV-2 has been shown to trigger cytokine storms, leading to the increment of the incidence of acute respiratory distress syndrome (ARDS). Thus, during the pandemic, some oncologists have growing concerns about the safety of immunotherapy because of the risk of the uncontrolled inflammatory response of patients who are infected with COVID-19 [3]. Due to the unclear impacts of immunotherapy on cancer patients who had concurrent COVID-19, oncologists are currently confronted with difficulties in the management and treatment of cancer patients [4]. Given this, the delay or cancellation of planned immunotherapy might occur, negatively affecting patients' prognosis [5].

Although most clinical trials of immunotherapy have precluded patients with active virus infection because of concerns about disease reactivation and immune-related adverse events [6], a few studies assessing the effects of implementing immunotherapy in cancer patients with active virus infection have shown that different viruses have different effects on prognosis. Regarding human immunodeficiency virus (HIV), a prior study indicated that immunotherapy was safe and feasible among patients with non-small-cell lung cancer (NSCLC) and active HIV infection, and the expression of programmed death-ligand 1 (PD-L1) was much higher in infected individuals than in their counterparts [7]. Moreover, in terms of human papillomavirus (HPV), the results of a phase 3 clinical trial focusing on unresectable or metastatic head and neck cancer showed that immunotherapy had more benefit on overall survival (OS) among PD-L1 expressors with HPV-positive tumours than their counterparts with HPV-negative tumours [8]. Similarly, Epstein–Barr virus (EBV) infection was found to be significantly associated with positive programmed death-1 (PD-1) staining in patients with locoregionally advanced nasopharyngeal carcinoma, and positive PD-1 staining was independently identified as a predictor for improved prognosis [9]. However, with regard to hepatitis B virus (HBV), a clinical trial in patients with B-cell lymphoma illustrated the risk of HBV reactivation when patients coinfected with HBV were receiving immunotherapy, which might lead to a poorer prognosis [10]. Additionally, Tapia Rico et al. [11] indicated that HBV-positive cancer patients who were treated with ICIs had the hazard of developing HBV reactivation, which could result in severe or critical immune-related adverse events.

For SARS-CoV-2, many studies have been published, while only a marginal number of prior meta-analyses have focused on the efficiency and safety of administering immunotherapy for cancer patients during the pandemic, and solid evidence is still lacking [12, 13]. Because of the lack of evidence, current clinical practice guidelines provide no clear recommendation for administering immunotherapy in cancer patients who had concurrent COVID-19. The World Health Organization (WHO) recommended continuing or modifying previous treatment according to the patient's clinical condition [14]. Similarly, the American Society of Clinical Oncology (ASCO) and the European Society for Medical Oncology (ESMO) recommended making decisions on the basis of discreet evaluation of benefits and risks for cancer patients [4, 15].

Hence, considering the points mentioned above, we conducted the present meta-analysis, for evaluating the relationship between active immunotherapy and outcomes of cancer patients who had concurrent COVID-19 infection.

Materials and Methods

We conducted and reported the systematic review and meta-analysis, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [16]. The protocol of the systematic review and meta-analysis has been registered in the PROSPERO database (CRD42021274069).

Inclusion and exclusion criteria

The eligibility of the studies was assessed according to the following criteria. (1) The participants included cancer patients who were diagnosed with COVID-19 by RT–PCR and receiving active immunotherapy. (2) The intervention included immunotherapy within 30 days before the diagnosis of COVID-19. (3) The control group included cancer patients who did not receive immunotherapy within 30 days before the diagnosis of COVID-19. (4) The primary outcome was defined as mortality, and the secondary outcome was defined as the rate of severe/critical disease. The definition of severe/critical disease was in accordance with the WHO guidelines [14]. (5) All kinds of prospective and retrospective studies with extractable odds ratios (ORs), relative risks (RRs), or relevant statistics to calculate ORs and RRs were included. If the same cases from the same cohort were reported in more than one study, only the most recent study or the study reporting the most cases was included.

Studies were excluded based on the following exclusion criteria: (1) basic research, review, news, conference, guideline, editorial, comment, clinical experience, case report, and study protocol; (2) studies in which data were missing from a group of patients or data of patients receiving immunotherapy could not be separated from the whole patient group; (3) patients in whom cancer had been cured before the diagnosis of COVID-19; and (4) patients who were diagnosed with other viral pneumonias.

Search strategy

A comprehensive search strategy was designed and performed. Studies were searched and retrieved from databases, including Embase, the Cochrane Library, Web of Science, PubMed, and the China National Knowledge Infrastructure (CNKI). The published studies were searched from 01-Dec-2019 to 01-Aug-2021. Supplementary Table 1 presents the details of the search strategy for different databases. No language limitations were imposed. Moreover, the reference lists of the studies were reviewed, to search for relevant articles.

Two independent reviewers blinded to each other performed the screening process. The titles and abstracts of retrieved records were initially screened for eligibility. Following this, the full-text screening was performed to obtain eligible studies, in accordance with the inclusion and exclusion criteria. If any discrepancy between the reviewers emerged, it was solved by discussion and arbitration.

Data extraction

Data were extracted and collected from the included studies by two independent reviewers. Any discrepancy between the reviewers was solved by discussion and arbitration. The following datasets were extracted and collected by using a worksheet: name of the first author, publication year, country, study types (prospective or retrospective studies), total figure for participants, figure for males and females, median age or mean age, cancer types, number of patients receiving active immunotherapy, immunotherapy interval before COVID-19 diagnosis, and outcomes. The unadjusted and adjusted ORs were obtained from the articles. For adjusted ORs, adjusting variables for multivariate analyses were also extracted. If ORs were not provided, they were calculated based on original statistics, or RRs were extracted instead.

Quality assessment

The Newcastle–Ottawa Quality Assessment Scale (NOS) was employed to conduct the quality assessment. The scale for cohort studies was defined as 3 sections, encompassing selection (4 points), comparability (2 points), and outcome (3 points). Similarly, the scale for case-control studies was defined as 3 sections, encompassing selection (4 points), comparability (2 points), and exposure (3 points). A study was rated as low quality if it scored less than 5 points [17]. Two independent reviewers, blinded to each other, evaluated the risk of bias. If any disagreements emerged in the process, they were discussed or settled by the third reviewer.

Statistical analysis

The meta-analysis was performed by employing the generic inverse-variance method. ORs and their 95% confidence intervals (95% CIs) were calculated to compare the mortality and severe/critical disease rate between patients receiving active immunotherapy and the control patients. P<0.05 was considered statistically significant. As for heterogeneity analysis, the Cochran's Q test and inconsistency index (I2) were adopted, with I2>50% or P<0.1 deemed to indicate significant heterogeneity. We used random-effects model in the calculation of the pooled ORs and corresponding 95% CIs. Moreover, we conducted subgroup analyses according to study type, number of patients, cancer type, immunotherapy interval prior to the COVID-19 diagnosis, and number of patients receiving active immunotherapy.

We performed Egger's linear regression tests and Begg's rank correlation tests to estimate publication bias, in which P<0.1 indicated significant publication bias. Moreover, funnel plots were also provided to demonstrate the publication bias.

Furthermore, a sensitivity analysis was also performed by excluding an individual study each time to reflect whether any single study influenced the results.

A meta-regression was performed to investigate the effects of any potential sources of heterogeneity (study type, number of patients, number of patients receiving active immunotherapy, cancer type, and interval between immunotherapy and diagnosis of COVID-19). P<0.1 was considered statistically significant. The permutation test was also performed to validate the robustness of meta-regression. The data synthesis was performed by using RevMan, version 5.4 (The Nordic Cochrane Center, The Cochrane Collaboration, Denmark). Publication bias was calculated by using Stata, version 13.1 (Stata Corp, College Station, USA). Meta-regression and permutation tests were performed using the metafor package in R, version 5.1.1 (The R Foundation for Statistical Computing).

Data availability statement

Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as online Supplementary Information.

Results

Study selection

Figure 1 displays the flow diagram of study selection process. Overall, 6,236 articles were initially retrieved from the electronic databases, including 2,895 articles from EMBASE, 202 articles from the Cochrane Library, 1,728 articles from PubMed, 1,258 articles from Web of Science, and 153 articles from CNKI. Then, 1,064 duplicate studies were excluded, followed by the screening process. Fifty-four potentially eligible articles remained and were assessed for eligibility by full-text screening. Finally, 20 articles fully meeting the inclusion criteria were included in subsequent meta-analysis [18–37]. Supplementary Table 2 lists the articles assessed for eligibility by full-text screening, in which reasons why studies were excluded are also shown.

PRISMA flow diagram of study inclusion.

Figure 1. PRISMA flow diagram of study inclusion.

Study main characteristics

Table 1 demonstrates the baseline characteristics of the included studies. Overall, five of the 20 included studies were prospective cohort studies [22, 29, 30, 35, 37]. The remainder were retrospectively designed [18–21, 23–28, 31–34, 36]. As a whole, 6,042 cancer patients diagnosed with COVID-19 were included, with 464 patients receiving active immunotherapy. Among the included 20 studies, five of them included patients with solid cancer [20, 23, 29, 33, 35], and four studies included patients with haematological malignancies [19, 30, 32, 37]. With regard to the remaining 11 studies, the included cancer types were nonspecific [18, 21, 22, 24–28, 31, 34, 36].

Table 1. Characteristics of the included studies.

Study IDStudy typeCountryNumber of patientsM/FaMedian age (IQR)(years)bCancer typeIntervalc(days)Number of patientsd
Assaad 2020RetrospectiveFrance302144/15858.2#Non-specific3026
Fox 2020RetrospectiveUK5538/1763(23-88)Hematological malignancies149
Garassino 2020RetrospectiveInternational200141/5968(61.8-75)Thoracic cancer7 (median)34
García-Suárez 2020ProspectiveSpain697e413/27772 (60-79)Hematological malignancies3044
Jee 2020RetrospectiveUS309119/150NAfNon-specific3518
Lee 2020ProspectiveUK800g449/34969(59-76)Non-specific2844
Lievre 2020RetrospectiveFrance1289795/49467(19-100)Solid cancer2862
Mehta 2020RetrospectiveUS218127/9169(10-92)Non-specific305
Mehta 2021RetrospectiveIndia186105/8152(42–58.75)Non-specific3011
Nakamura 2021RetrospectiveJapan3222/1074.5(24–90)Non-specific303
Ozer 2021RetrospectiveUS6837/3172(23-91)Non-specific282
Pinato 2020RetrospectiveInternational890503/38768#Non-specific19 (mean)56
Provencio 2021ProspectiveSpain447332/11567.1#Lung cancerNA91
Sanchez-Pina 2020ProspectiveSpain3923/1664.7#Hematological malignanciesNA3
Stroppa 2020RetrospectiveItaly2520/571.64#Non-specificNA4
Wang 2020RetrospectiveUS5830/2867Multiple myelomaNA32
Yang F 2020RetrospectiveChina5228/2463(34–98)Solid cancer301
Yang KY 2020RetrospectiveChina20596/10963(56–70)Non-specific284
Yarza 2020ProspectiveSpain6334/2966#Solid cancer288
Zhang 2020RetrospectiveChina10760/4766(36-98)Non-specific306
aM means males and F means females.
bIQR means interquartile range.
cInterval of immunotherapy before diagnosis of COVID-19.
dNumber of cancer patients receiving immunotherapy within 30 days before COVID-19 diagnosis.
eData are missing for 7 patients.
fNA means data not available.
g2 patients did not identify as either male or female.
#Mean age.

Supplementary Table 3 mirrors the results of the quality assessment. The NOS score of the included articles ranged between 6 and 8. None of the included studies was rated as low quality.

The effects of immunotherapy on cancer patients with COVID-19

According to the univariate analysis, the acceptance of active immunotherapy was not in relation to the increased mortality of cancer patients (OR: 0.92; 95% CI: 0.68-1.25; P=0.61), with no significant between-study heterogeneity found (I2=4%; P=0.41). A forest plot of the unadjusted OR for the relationship between active immunotherapy and mortality of cancer patients who had concurrent COVID-19 is shown in Figure 2. Moreover, after adjusting for confounders, the adjusted OR was 0.51, with borderline significance (95% CI: 0.25-1.01; P=0.053), as shown in Figure 3. No significant heterogeneity was observed between the studies providing adjusted results of mortality (I2=0%; P=0.55).

Forest plot of the univariate analysis for the association between active immunotherapy and mortality. CI, confidence interval; IV, inverse variance; SE, standard error.

Figure 2. Forest plot of the univariate analysis for the association between active immunotherapy and mortality. CI, confidence interval; IV, inverse variance; SE, standard error.

Forest plot of the multivariate analysis for the association between active immunotherapy and mortality. Adjusted variables for the study by Lee 2020 [22]: age, sex, and presence of comorbidities; adjusted variables for the study by Mehta 2021 [24]: age and presence of comorbidities; and adjusted variables for the study by Yarza 2020 [35]: age, sex, Eastern Cooperative Oncology Group score (ECOG), presence of metastasis, previous venous thromboembolic event (VTE), and presence of chronic obstructive pulmonary disease (COPD). CI, confidence interval; IV, inverse variance; SE, standard error.

Figure 3. Forest plot of the multivariate analysis for the association between active immunotherapy and mortality. Adjusted variables for the study by Lee 2020 [22]: age, sex, and presence of comorbidities; adjusted variables for the study by Mehta 2021 [24]: age and presence of comorbidities; and adjusted variables for the study by Yarza 2020 [35]: age, sex, Eastern Cooperative Oncology Group score (ECOG), presence of metastasis, previous venous thromboembolic event (VTE), and presence of chronic obstructive pulmonary disease (COPD). CI, confidence interval; IV, inverse variance; SE, standard error.

Subgroup analyses for mortality of cancer patients co-diagnosed with COVID-19 were performed, of which the unadjusted ORs are mirrored in Table 2. As reflected in the subgroup analyses, no variables investigated were related to significant ORs (P>0.05), revealing that active immunotherapy was not in relation to increased mortality of cancer patients, regardless of confounders.

Table 2. Meta-analyses and subgroup analyses for mortality.

Patients receiving immunotherapy vs. control patientsN of studiesPooled OR (95%CI) aI2 (%) bPP for interaction
Overall140.92 (0.68, 1.25)4%0.61
Study type
Prospective30.67 (0.43, 1.04)0%0.070.054
Retrospective111.19 (0.81, 1.73)0%0.38
Number of patients
<10060.64 (0.28, 1.49)0%0.300.39
>10080.97 (0.63, 1.49)34%0.90
Cancer type
Hematological malignancies30.72 (0.27, 1.91)0%0.510.48
Solid tumor31.10 (0.69, 1.76)36%0.68
Non-specific cancer80.72 (0.39, 1.33)8%0.29
Immunotherapy interval before the COVID-19 diagnosis (days)
>2080.98 (0.56, 1.69)25%0.940.44
<2021.23 (0.54, 2.76)0%0.62
Number of patients receiving active immunotherapy
<1070.75 (0.27, 2.08)22%0.580.66
>1070.95 (0.70, 1.28)0%0.73
aCalculated by using the random-effect model.
bI2 means the inconsistency across studies.

For the meta-analysis of the severe/critical disease rate, the definition of WHO guidelines was employed [14]. Figure 4 shows a forest plot of the OR for the relationship between active immunotherapy and the rate of severe/critical disease in cancer patients who had concurrent COVID-19. According to the univariate analysis, active immunotherapy was not in relation to increased risk of severe/critical disease of cancer patients (OR: 1.07; 95% CI: 0.78-1.47; P=0.66), with nonsignificant between-study heterogeneity found (I2=0%; P=0.92).

Forest plot of the univariate analysis for the association between active immunotherapy and severe/critical disease rate. CI, confidence interval; IV, inverse variance; SE, standard error.

Figure 4. Forest plot of the univariate analysis for the association between active immunotherapy and severe/critical disease rate. CI, confidence interval; IV, inverse variance; SE, standard error.

Supplementary Tables 4, 5 mirrors the results of meta-regression. None of the tested covariates could yield heterogeneity, with P>0.1. Permutation tests showed that the results of meta-regression were reliable.

Sensitivity analysis and publication bias

In order to evaluate the stability of the present results, sensitivity analyses were performed by excluding an individual study each time to reflect whether any single study influenced the results. The results of the sensitivity analysis indicated that the pooled ORs were not significantly influenced by excluding any single study. Furthermore, Egger's linear regression tests and Begg's rank correlation tests were conducted through which nonsignificant publication bias was shown in the studies regarding mortality and the studies regarding severe/critical disease, as illuminated in Supplementary Figures 1–4. Supplementary Figures 5, 6 are funnel plots of included studies.

Discussion

The present study indicated that active immunotherapy was not associated with increased mortality or rate of severe/critical disease in cancer patients who had concurrent COVID-19 infection. Some discrepancies were found between the results yielded by the present study and those yielded by the previous studies, and those studies must be updated. In the results published by Liu et al. [13], immunotherapy was found to have a tendency of increasing the risk of mortality (RR: 1.20; 95% CI: 0.68-2.13) and severe/critical disease (RR: 1.24; 95% CI: 0.94-1.63). Moreover, Liu et al. [13] indicated that immunotherapy had higher risk compared with other anticancer treatments. The variation might partially derive from the inclusion of newly published studies in the present meta-analysis. The research of Liu et al. [13] was conducted in the comparatively earlier period of the pandemic, and the number of published studies regarding COVID-19 and cancer was limited. Furthermore, the results reported by Liu et al. [13] were all unadjusted; however, the accuracy and reliability of these unadjusted results might be affected by a series of confounding variables (e.g., age, sex, presence of comorbidities, smoking status, presence of metastasis), particularly in retrospective studies. In the study of Yekeduz et al. [12], immunotherapy was detected to have a potential risk of increasing mortality (OR: 1.12; 95% CI: 0.60-2.08) and the rate of severe/critical disease (RR: 1.60; 95% CI: 0.72-3.52). The discrepancy might be due to the inappropriate inclusion of the study published by Dai et al. [38], in which the comparison group was patients without cancer, resulting in significantly higher heterogeneity (I2>50%) [12].

Granted, some inconsistencies were observed, and there were still some studies supporting the present results. A previous study involving 522 patients with concurrent COVID-19 demonstrated that the number of T cells were drastically diminished in COVID-19 patients. Moreover, T cell exhaustion was also observed, concomitant with the higher expression of PD-1 and increased serum IL-6 and IL-10 in patients who were infected by COVID-19 [39]. In light of this immune response, immunotherapy administration might be conducive to patients with COVID-19 because it activates exhausted T cells by blocking PD-1/PD-L1 or CTLA-4 [40]. Furthermore, Yekeduz et al. [12] indicated that the use of immunotherapy, especially ICIs, was safe in cancer patients during the pandemic, which was in line with our results.

The present study has strengths, including comprehensive inclusion and a multivariate analysis. The figure for studies regarding immunotherapy included in this meta-analysis surpassed those in prior studies. In addition, the low publication bias and between-study heterogeneity contributed to more reliable and conservative results as well as higher quality of evidence. Moreover, in the special period of the COVID-19 outbreak, one of the major concerns is the safety of using immunotherapy to treat cancer patients, by virtue of the immune-related adverse events, which can probably lead to worsening prognosis of cancer patients who had concurrent COVID-19 infection [3]. However, the results derived from the present meta-analysis indicated that administering immunotherapy in cancer patients during the pandemic of COVID-19 was not associated with risk of death and severe/critical COVID-19. This provides more evidence for oncologists when managing cancer patients in the special era.

Some limitations should be addressed in the present research. First, the control groups were observed to be inconsistent in the included studies. Fifteen studies included cancer patients not receiving any active anticancer treatment in control groups, while the remaining five studies included cancer patients without active immunotherapy. Considering this, we conducted sensitivity analyses, of which the results showed that the exclusion of these five studies did not significantly alter the pooled results. Second, the definitions of severe/critical diseases were not totally consistent among included studies. This inconsistency might introduce the risk of bias to the present results. Third, due to the lack of adjusted results, we did not conduct a multivariate analysis of the severe/critical disease rate. The results of the univariate analysis may not mirror the real effects of immunotherapy on cancer patients as many confounders can affect the prognosis of cancer and COVID-19. Fourth, a majority of the included studies were retrospective. Fifth, some studies did not include sufficient patients on immunotherapy, which might introduce bias to the results. Although we did not find significant statistical heterogeneity or publication bias, the results yielded by the present meta-analysis should be discreetly interpreted in clinical practice, in combination with the assessment of specific patient conditions.

Conclusions

Accepting immunotherapy within 30 days before the diagnosis of COVID-19 was not significantly associated with a higher risk of mortality or severe/critical disease of infected cancer patients. Due to the limitations of the present study, the conclusions should be interpreted with discretion, and further prospectively designed studies with large sample sizes are required to evaluate the present results.

Author Contributions

Chang Cao, Xinyan Gan, and Xiaolin Hu contributed equally to this article. Chang Cao, Xinyan Gan and Xiaolin Hu completed full-text screening, data extraction, and quality assessment. Xiaolin Hu and Yonglin Su designed search strategy and searched studies in electronic databases. Yu Zhang and Xingchen Peng were responsible for the study concept and design. Authors were responsible for statistical analysis.

All authors confirm that data are available on reasonable request. All data relevant to the study are included in the article or uploaded as online Supplementary Information.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding

The work was supported by the National Natural Sciences Foundation of China (No. 81672386) and the Technology Innovation Project of Chengdu Science and Technology Bureau (No.2019-YF05-00459-SN).

References

  • 1. WHO. WHO coronavirus disease (COVID-19) dashboard. 2021. https://covid19.who.int/.
  • 2. Kuderer NM, Choueiri TK, Shah DP, Shyr Y, Rubinstein SM, Rivera DR, Shete S, Hsu CY, Desai A, de Lima Lopes G Jr, Grivas P, Painter CA, Peters S, et al, and COVID-19 and Cancer Consortium. Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study. Lancet. 2020; 395:1907–18. https://doi.org/10.1016/S0140-6736(20)31187-9 [PubMed]
  • 3. Rossi E, Schinzari G, Tortora G. Pneumonitis from immune checkpoint inhibitors and COVID-19: current concern in cancer treatment. J Immunother Cancer. 2020; 8:e000952. https://doi.org/10.1136/jitc-2020-000952 [PubMed]
  • 4. Curigliano G, Banerjee S, Cervantes A, Garassino MC, Garrido P, Girard N, Haanen J, Jordan K, Lordick F, Machiels JP, Michielin O, Peters S, Tabernero J, et al, and Panel members. Managing cancer patients during the COVID-19 pandemic: an ESMO multidisciplinary expert consensus. Ann Oncol. 2020; 31:1320–35. https://doi.org/10.1016/j.annonc.2020.07.010 [PubMed]
  • 5. Desai A, Sachdeva S, Parekh T, Desai R. COVID-19 and Cancer: Lessons From a Pooled Meta-Analysis. JCO Glob Oncol. 2020; 6:557–9. https://doi.org/10.1200/GO.20.00097 [PubMed]
  • 6. Kim ES, Bruinooge SS, Roberts S, Ison G, Lin NU, Gore L, Uldrick TS, Lichtman SM, Roach N, Beaver JA, Sridhara R, Hesketh PJ, Denicoff AM, et al. Broadening Eligibility Criteria to Make Clinical Trials More Representative: American Society of Clinical Oncology and Friends of Cancer Research Joint Research Statement. J Clin Oncol. 2017; 35:3737–44. https://doi.org/10.1200/JCO.2017.73.7916 [PubMed]
  • 7. Ostios-Garcia L, Faig J, Leonardi GC, Adeni AE, Subegdjo SJ, Lydon CA, Rangachari D, Huberman MS, Sehgal K, Shea M, VanderLaan PA, Cheng MP, Marty FM, et al. Safety and Efficacy of PD-1 Inhibitors Among HIV-Positive Patients With Non-Small Cell Lung Cancer. J Thorac Oncol. 2018; 13:1037–42. https://doi.org/10.1016/j.jtho.2018.03.031 [PubMed]
  • 8. Ferris RL, Blumenschein G Jr, Fayette J, Guigay J, Colevas AD, Licitra L, Harrington KJ, Kasper S, Vokes EE, Even C, Worden F, Saba NF, Docampo LC, et al. Nivolumab vs investigator’s choice in recurrent or metastatic squamous cell carcinoma of the head and neck: 2-year long-term survival update of CheckMate 141 with analyses by tumor PD-L1 expression. Oral Oncol. 2018; 81:45–51. https://doi.org/10.1016/j.oraloncology.2018.04.008 [PubMed]
  • 9. Cao C, Wei Q, Tang X, Jia Y, Sun X, Li W, Hu Q, Chen X. PD-1 and PD-L1 in locoregionally advanced nasopharyngeal carcinoma: Substudy of a randomized phase III trial. Head Neck. 2019; 41:1427–33. https://doi.org/10.1002/hed.25601 [PubMed]
  • 10. Kusumoto S, Arcaini L, Hong X, Jin J, Kim WS, Kwong YL, Peters MG, Tanaka Y, Zelenetz AD, Kuriki H, Fingerle-Rowson G, Nielsen T, Ueda E, et al. Risk of HBV reactivation in patients with B-cell lymphomas receiving obinutuzumab or rituximab immunochemotherapy. Blood. 2019; 133:137–46. https://doi.org/10.1182/blood-2018-04-848044 [PubMed]
  • 11. Tapia Rico G, Chan MM, Loo KF. The safety and efficacy of immune checkpoint inhibitors in patients with advanced cancers and pre-existing chronic viral infections (Hepatitis B/C, HIV): A review of the available evidence. Cancer Treat Rev. 2020; 86:102011. https://doi.org/10.1016/j.ctrv.2020.102011 [PubMed]
  • 12. Yekedüz E, Utkan G, Ürün Y. A systematic review and meta-analysis: the effect of active cancer treatment on severity of COVID-19. Eur J Cancer. 2020; 141:92–104. https://doi.org/10.1016/j.ejca.2020.09.028 [PubMed]
  • 13. Liu H, Yang D, Chen X, Sun Z, Zou Y, Chen C, Sun S. The effect of anticancer treatment on cancer patients with COVID-19: A systematic review and meta-analysis. Cancer Med. 2021; 10:1043–56. https://doi.org/10.1002/cam4.3692 [PubMed]
  • 14. WHO. Clinical management of COVID-19 patients: living guidance. 2021; https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2021-1.
  • 15. ASCO. ASCO special report: a guide to cancer care delivery during the covid-19 pandemic. 2021. https://www.asco.org/sites/new-www.asco.org/files/content-files/2020-ASCO-Guide-Cancer-COVID19.pdf.
  • 16. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; 372:n71. https://doi.org/10.1136/bmj.n71 [PubMed]
  • 17. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010; 25:603–5. https://doi.org/10.1007/s10654-010-9491-z [PubMed]
  • 18. Assaad S, Avrillon V, Fournier ML, Mastroianni B, Russias B, Swalduz A, Cassier P, Eberst L, Steineur MP, Kazes M, Perol M, Michallet AS, Rey P, et al. High mortality rate in cancer patients with symptoms of COVID-19 with or without detectable SARS-COV-2 on RT-PCR. Eur J Cancer. 2020; 135:251–9. https://doi.org/10.1016/j.ejca.2020.05.028 [PubMed]
  • 19. Fox TA, Troy-Barnes E, Kirkwood AA, Chan WY, Day JW, Chavda SJ, Kumar EA, David K, Tomkins O, Sanchez E, Scully M, Khwaja A, Lambert J, et al. Clinical outcomes and risk factors for severe COVID-19 in patients with haematological disorders receiving chemo- or immunotherapy. Br J Haematol. 2020; 191:194–206. https://doi.org/10.1111/bjh.17027 [PubMed]
  • 20. Garassino MC, Whisenant JG, Huang LC, Trama A, Torri V, Agustoni F, Baena J, Banna G, Berardi R, Bettini AC, Bria E, Brighenti M, Cadranel J, et al, and TERAVOLT investigators. COVID-19 in patients with thoracic malignancies (TERAVOLT): first results of an international, registry-based, cohort study. Lancet Oncol. 2020; 21:914–22. https://doi.org/10.1016/S1470-2045(20)30314-4 [PubMed]
  • 21. Jee J, Foote MB, Lumish M, Stonestrom AJ, Wills B, Narendra V, Avutu V, Murciano-Goroff YR, Chan JE, Derkach A, Philip J, Belenkaya R, Kerpelev M, et al. Chemotherapy and COVID-19 Outcomes in Patients With Cancer. J Clin Oncol. 2020; 38:3538–46. https://doi.org/10.1200/JCO.20.01307 [PubMed]
  • 22. Lee LY, Cazier JB, Angelis V, Arnold R, Bisht V, Campton NA, Chackathayil J, Cheng VW, Curley HM, Fittall MW, Freeman-Mills L, Gennatas S, Goel A, et al, and UK Coronavirus Monitoring Project Team. COVID-19 mortality in patients with cancer on chemotherapy or other anticancer treatments: a prospective cohort study. Lancet. 2020; 395:1919–26. https://doi.org/10.1016/S0140-6736(20)31173-9 [PubMed]
  • 23. Lièvre A, Turpin A, Ray-Coquard I, Le Malicot K, Thariat J, Ahle G, Neuzillet C, Paoletti X, Bouché O, Aldabbagh K, Michel P, Debieuvre D, Canellas A, et al, and GCO-002 CACOVID-19 collaborators/investigators. Risk factors for Coronavirus Disease 2019 (COVID-19) severity and mortality among solid cancer patients and impact of the disease on anticancer treatment: A French nationwide cohort study (GCO-002 CACOVID-19). Eur J Cancer. 2020; 141:62–81. https://doi.org/10.1016/j.ejca.2020.09.035 [PubMed]
  • 24. Mehta A, Vasudevan S, Parkash A, Sharma A, Vashist T, Krishna V. COVID-19 mortality in cancer patients: a report from a tertiary cancer centre in India. PeerJ. 2021; 9:e10599. https://doi.org/10.7717/peerj.10599 [PubMed]
  • 25. Mehta V, Goel S, Kabarriti R, Cole D, Goldfinger M, Acuna-Villaorduna A, Pradhan K, Thota R, Reissman S, Sparano JA, Gartrell BA, Smith RV, Ohri N, et al. Case Fatality Rate of Cancer Patients with COVID-19 in a New York Hospital System. Cancer Discov. 2020; 10:935–41. https://doi.org/10.1158/2159-8290.CD-20-0516 [PubMed]
  • 26. Nakamura S, Kanemasa Y, Atsuta Y, Fujiwara S, Tanaka M, Fukushima K, Kobayashi T, Shimoyama T, Omuro Y, Sekiya N, Imamura A. Characteristics and outcomes of coronavirus disease 2019 (COVID-19) patients with cancer: a single-center retrospective observational study in Tokyo, Japan. Int J Clin Oncol. 2021; 26:485–93. https://doi.org/10.1007/s10147-020-01837-0 [PubMed]
  • 27. Ozer M, Goksu SY, Mahdi M, Gandhi N. Characteristics and outcomes of cancer patients with covid-19 at a safety-net hospital. Cancer Treat Res Commun. 2021; 28:100418. https://doi.org/10.1016/j.ctarc.2021.100418 [PubMed]
  • 28. Pinato DJ, Zambelli A, Aguilar-Company J, Bower M, Sng C, Salazar R, Bertuzzi A, Brunet J, Mesia R, Segui E, Biello F, Generali D, Grisanti S, et al. Clinical portrait of the SARS-CoV-2 epidemic in European cancer patients. Cancer Discov. 2020; 10:1465–74. https://doi.org/10.1158/2159-8290.CD-20-0773 [PubMed]
  • 29. Provencio M, Mazarico Gallego JM, Calles A, Antoñanzas M, Pangua C, Mielgo Rubio X, Nadal E, Castro RL, López-Martín A, Del Barco E, Dómine M, Franco F, Diz P, et al. Lung cancer patients with COVID-19 in Spain: GRAVID study. Lung Cancer. 2021; 157:109–15. https://doi.org/10.1016/j.lungcan.2021.05.014 [PubMed]
  • 30. Sanchez-Pina JM, Rodríguez Rodriguez M, Castro Quismondo N, Gil Manso R, Colmenares R, Gil Alos D, Paciello ML, Zafra D, Garcia-Sanchez C, Villegas C, Cuellar C, Carreño-Tarragona G, Zamanillo I, et al. Clinical course and risk factors for mortality from COVID-19 in patients with haematological malignancies. Eur J Haematol. 2020; 105:597–607. https://doi.org/10.1111/ejh.13493 [PubMed]
  • 31. Stroppa EM, Toscani I, Citterio C, Anselmi E, Zaffignani E, Codeluppi M, Cavanna L. Coronavirus disease-2019 in cancer patients. A report of the first 25 cancer patients in a western country (Italy). Future Oncol. 2020; 16:1425–32. https://doi.org/10.2217/fon-2020-0369 [PubMed]
  • 32. Wang B, Van Oekelen O, Mouhieddine TH, Del Valle DM, Richter J, Cho HJ, Richard S, Chari A, Gnjatic S, Merad M, Jagannath S, Parekh S, Madduri D. A tertiary center experience of multiple myeloma patients with COVID-19: lessons learned and the path forward. J Hematol Oncol. 2020; 13:94. https://doi.org/10.1186/s13045-020-00934-x [PubMed]
  • 33. Yang F, Shi S, Zhu J, Shi J, Dai K, Chen X. Clinical characteristics and outcomes of cancer patients with COVID-19. J Med Virol. 2020; 92:2067–73. https://doi.org/10.1002/jmv.25972 [PubMed]
  • 34. Yang K, Sheng Y, Huang C, Jin Y, Xiong N, Jiang K, Lu H, Liu J, Yang J, Dong Y, Pan D, Shu C, Li J, et al. Clinical characteristics, outcomes, and risk factors for mortality in patients with cancer and COVID-19 in Hubei, China: a multicentre, retrospective, cohort study. Lancet Oncol. 2020; 21:904–13. https://doi.org/10.1016/S1470-2045(20)30310-7 [PubMed]
  • 35. Yarza R, Bover M, Paredes D, López-López F, Jara-Casas D, Castelo-Loureiro A, Baena J, Mazarico JM, Folgueira MD, Meléndez-Carmona MÁ, Reyes A, Lumbreras C, Paz-Ares L, et al. SARS-CoV-2 infection in cancer patients undergoing active treatment: analysis of clinical features and predictive factors for severe respiratory failure and death. Eur J Cancer. 2020; 135:242–50. https://doi.org/10.1016/j.ejca.2020.06.001 [PubMed]
  • 36. Zhang H, Wang L, Chen Y, Wu Q, Chen G, Shen X, Wang Q, Yan Y, Yu Y, Zhong Y, Wang X, Chua ML, Xie C. Outcomes of novel coronavirus disease 2019 (COVID-19) infection in 107 patients with cancer from Wuhan, China. Cancer. 2020; 126:4023–31. https://doi.org/10.1002/cncr.33042 [PubMed]
  • 37. García-Suárez J, de la Cruz J, Cedillo Á, Llamas P, Duarte R, Jiménez-Yuste V, Hernández-Rivas JÁ, Gil-Manso R, Kwon M, Sánchez-Godoy P, Martínez-Barranco P, Colás-Lahuerta B, Herrera P, et al, and Asociación Madrileña de Hematología y Hemoterapia (AMHH). Impact of hematologic malignancy and type of cancer therapy on COVID-19 severity and mortality: lessons from a large population-based registry study. J Hematol Oncol. 2020; 13:133. https://doi.org/10.1186/s13045-020-00970-7 [PubMed]
  • 38. Dai M, Liu D, Liu M, Zhou F, Li G, Chen Z, Zhang Z, You H, Wu M, Zheng Q, Xiong Y, Xiong H, Wang C, et al. Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multicenter Study during the COVID-19 Outbreak. Cancer Discov. 2020; 10:783–91. https://doi.org/10.1158/2159-8290.CD-20-0422 [PubMed]
  • 39. Diao B, Wang C, Tan Y, Chen X, Liu Y, Ning L, Chen L, Li M, Liu Y, Wang G, Yuan Z, Feng Z, Zhang Y, et al. Reduction and Functional Exhaustion of T Cells in Patients With Coronavirus Disease 2019 (COVID-19). Front Immunol. 2020; 11:827. https://doi.org/10.3389/fimmu.2020.00827 [PubMed]
  • 40. Hamashima R, Uchino J, Morimoto Y, Iwasaku M, Kaneko Y, Yamada T, Takayama K. Association of immune checkpoint inhibitors with respiratory infections: A review. Cancer Treat Rev. 2020; 90:102109. https://doi.org/10.1016/j.ctrv.2020.102109 [PubMed]
Download PDF
Corresponding Authors

Yu Zhang
tnt1057@outlook.com

Xingchen Peng
pxx2014@scu.edu.cn

Special Collection on COVID-19 Part 2
Special Collection
This article is included in:
Special Collection on COVID-19 Part 2
Keywords
COVID-19 cancer immunotherapy safety meta-analysis
Paper Sections