Impact of antitumor regimens on the outcomes of cancer patients with COVID-19: a pooled analysis

Impact of antitumor regimens on the outcomes of cancer patients with COVID-19: a pooled analysis 肿瘤治疗方案对肿瘤合并新型冠状病毒肺炎患者临床结局的影响分析 1 equal first-author 2 equal 1 3 4 1 1 corresp Yihua WU, georgewu@zju.edu.cn Yihua WU, georgewu@zju.edu.cn georgewu@zju.edu.cn https://orcid.org/0000-0003-1645-9046 1 corresp Yihua WU, georgewu@zju.edu.cn Yihua WU, georgewu@zju.edu.cn georgewu@zju.edu.cn https://orcid.org/0000-0001-7952-4090 Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou310058, China Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou310058, China The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou310003, China The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou310003, China Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou310003, China Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou310003, China National Clinical Research Center for Infectious Diseases, Hangzhou310003, China National Clinical Research Center for Infectious Diseases, Hangzhou310003, China 不同的肿瘤治疗方案可能是肿瘤合并新型冠状病毒肺炎（COVID-19）患者预后较差的原因之一，但是目前相关研究却仍未有定论。因此，全面深入的分析可以辅助临床医生选择和制定新型冠状病毒流行期间的抗肿瘤治疗方案。我们通过从PubMed、Embase和Cochrane数据库检索得到的提供了每个患者临床信息的11项相关研究展开汇总分析。本研究发现化疗是这些COVID-19合并肿瘤的患者中最常见的抗肿瘤治疗。经过对混杂因素的调整，抗肿瘤治疗方案对患者结局的影响并不显著。值得关注的是，在对于化疗和激素治疗的研究中，高龄（≥65岁）会增加这类患者发生死亡（化疗的研究中比值比（OR）=2.36，95%的置信区间（95% CI）=1.11~5.01， P =0.025；激素治疗的研究中OR=2.29；95% CI=1.08~4.85， P =0.03）及严重事件（OR=2.72，95% CI=1.45~5.10， P =0.002）的可能性，心脑血管疾病病史则会增加这类患者发生严重事件的可能性（OR=1.91；95% CI=1.02~3.57， P =0.043）。在进一步的亚组分析中，我们发现免疫治疗可能会显著增加患有实体肿瘤的新型冠状病毒患者死亡（OR=2.66，95% CI=1.07~6.64， P =0.03）和发生严重事件（OR=5.04，95% CI=1.02~24.86， P =0.039）的可能性，而化疗则可能显著增加患有血液系统肿瘤的新型冠状病毒患者死亡的可能性（OR=4.11，95% CI=1.59~10.6， P =0.003）。综上所述，在多因素模型中，相较于不同的肿瘤治疗方案，影响肿瘤合并COVID-19患者预后的关键临床因素如年龄、心脑血管病等，可能更为关键。在亚组分析中，免疫治疗和化疗则可能分别对实体肿瘤患者和血液肿瘤患者有较为显著的影响。 COVID-19 肿瘤治疗方案 化疗，免疫治疗 汇总分析 Dajing XIA, dxia@zju.edu.cn Since the outbreak of coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) discovered in December 2019, the disease has emerged as a global pandemic ( Shi et al., 2020 Shi et al., 2020 ; World Health Organization, 2020 World Health Organization, 2020 ). Several studies have shown a higher incidence of COVID-19, as well as related poor outcomes in patients with malignancies as compared with those without them ( Liang et al., 2020 Liang et al., 2020 ; Tian et al., 2020 Tian et al., 2020 ). The impact of cancer on COVID-19 may be attri‑buted to the use of antitumor treatments that may disturb the host response to SARS-CoV-2 infection ( Wang et al., 2020 Wang et al., 2020 ), while the current studies on this topic have drawn controversial conclusions. Some implied that anticancer treatments might elevate the risk of death ( García-Suárez et al., 2020 García-Suárez et al., 2020 ; Liu et al., 2020 Liu et al., 2020 ). On the contrary, others pointed out that this association is not significant ( Brar et al., 2020 Brar et al., 2020 ; Lee et al., 2020a Lee et al., 2020a ). Although previous systematic reviews have investigated this important issue ( Wang and Huang, 2020 Wang and Huang, 2020 ), the heterogeneity of findings is obvious and the general conclusion has remained unclear. Considering this ambiguity, it is difficult for clinicians to make therapeutic decisions when facing patients with both cancer and COVID-19; therefore, a high-quality and accurate evaluation of the impact of anticancer treatments on COVID-19 patients is necessary. Accordingly, we conducted a pooled analysis with the original data of each patient for the first time to provide a comprehensive perspective into the association between anticancer regimens and the outcomes of cancer patients with COVID-19. The study was carried out according to a predefined protocol submitted to the PROSPERO registry (CRD42020209305). After performing a search on PubMed, Embase, and Cochrane library for the period of January 1, 2019 to October 17, 2020, 5306 citations were identified, of which 1374 duplicate records were excluded. The review of titles and abstracts led to the exclusion of 3762 irrelevant citations, leaving 170 for further assessment. After reviewing the full texts of these citations, 11 studies were included for the final pooled analysis. The detailed inclusion criteria were listed in Appendix S1. The selection process is shown in Fig. 1 Fig. 1 , and the search strategy is detailed in Appendix S2. During the assessment of the full texts, the studies that possibly reported on the same patients were carefully checked by the reviewers. Potential duplicate patients who were identified according to the information provided by the articles were removed. Finally, 11 studies with 394 participants were included in our research ( Bertuzzi et al., 2020 Bertuzzi et al., 2020 ; Biernat et al., 2020 Biernat et al., 2020 ; Bogani et al., 2020 Bogani et al., 2020 ; Booth et al., 2020 Booth et al., 2020 ; Dai et al., 2020 Dai et al., 2020 ; Frey et al., 2020 Frey et al., 2020 ; Joharatnam-Hogan et al., 2020 Joharatnam-Hogan et al., 2020 ; Kalinsky et al., 2020 Kalinsky et al., 2020 ; Wu et al., 2020 Wu et al., 2020 ; Zhang HY et al., 2020 Zhang HY et al., 2020 ; Zhang L et al., 2020 Zhang L et al., 2020 ). In addition, 45 patients from three studies ( Booth et al., 2020 Booth et al., 2020 ; Kalinsky et al., 2020 Kalinsky et al., 2020 ; Zhang HY et al., 2020 Zhang HY et al., 2020 ) were excluded due to the uncertainty of the COVID-19 diagnostic results. The following individual clinical data from each included study were extracted using a prospectively designed electronic data collection sheet: demographic information (gender, age, smoking habits, and comorbidities), the pattern of cancers, the details of antitumor regimens, the interval between anticancer treatments and COVID-19 onset, the primary outcome (all-cause mortality), and the secondary outcome (severe events, i.e., death, admission to an intensive care unit (ICU), and utilization of mechanical ventilation). The details of the involved studies are shown in Table 1 Table 1 . NA, not available. * Gynecologic cancer in these studies includes primary malignancies in cancers of the ovary, vulva, cervix, uterus, and endometrium. Study Sample size Country Age (year) Gender Regimen Tumor type Bertuzzi et al., 2020 26 Italy 43–89 Male/female Chemotherapy, immunotherapy, hormonal therapy, and targeted therapy Solid tumor Bogani et al., 2020 19 Italy 49–84 Female Surgery, chemotherapy, and targeted therapy Gynecologic cancer * Booth et al., 2020 66 UK NA Male/female Chemotherapy, targeted therapy, and hormonal therapy Haematologic malignancy Dai et al., 2020 105 China 26–87 Male/female Surgery, chemotherapy, immunotherapy, targeted therapy, and radiotherapy Not specified Zhang HY et al., 2020 107 China 36–98 Male/female Surgery, chemotherapy, immunotherapy, targeted therapy, radiotherapy, and hormonal therapy Not specified Zhang L et al., 2020 28 China 24–80 Male/female Surgery, chemotherapy, immunotherapy, targeted therapy, and radiotherapy Solid tumor Biernat et al., 2020 10 Poland 30–69 NA Chemotherapy, targeted therapy, and hormonal therapy Haematologic malignancy Kalinsky et al., 2020 27 USA 32–80 Female Chemotherapy, immunotherapy, targeted therapy, and hormonal therapy Breast tumor Wu et al., 2020 11 China 29–73 Male/female Chemotherapy, immunotherapy, targeted therapy, and radiotherapy Not specified Frey et al., 2020 19 USA NA Female Surgery, chemotherapy, immunotherapy, targeted therapy, radiotherapy, and hormonal therapy Gynecologic cancer * Joharatnam-Hogan et al., 2020 26 UK 24–91 Male/female Surgery, chemotherapy, targeted therapy, radiotherapy, and hormonal therapy Not specified Among the finally included 394 participants, the most commonly used antitumor regimen was chemotherapy (109 patients, 27.7%), followed by targeted therapy (56 patients, 14.2%), hormonal therapy (39 patients, 9.9%), radiotherapy (27 patients, 6.9%), immunotherapy (26 patients, 6.6%), and surgery (25 patients, 6.3%) ( Fig. 2 Fig. 2 a). The subgroup analysis revealed that the use of chemotherapy was the most common in patients with solid tumors and patients from each of the three different regions (Figs. 2b 2b and 2c 2c ). The analysis results indicated that, regardless of tumor type or geographic region, chemotherapy was the most common antitumor treatment. Therefore, among the various anticancer regimens, chemotherapy was attributed important status. We calculated the crude and adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) to evaluate the impacts of antitumor regimens on the risks of primary (all-cause mortality) and secondary (severe events) outcomes. Based on the previous major studies ( Lee et al., 2020a Lee et al., 2020a ; Liu et al., 2021 Liu et al., 2021 ; Rüthrich et al., 2021 Rüthrich et al., 2021 ), age, sex, smoking status, and comorbidities were regarded as potential confounders. According to our univariate analysis below, the covariables which were identified as significant risk factors were further included in multivariate analysis for adjustment. Moreover, we conducted a subgroup analysis by tumor type, gender, age, and the time period between treatments and the onset of COVID-19. In total, 366 cancer patients with COVID-19 (ten studies) had a record of survival status, of which 91 patients (24.9%) died from any cause. The univariate analysis revealed that receiving chemotherapy (OR, 1.88; 95% CI, 1.13‒3.13) and hormonal therapy (OR, 2.98; 95% CI, 1.51‒5.89) ( Fig. 3 Fig. 3 ) were associated with an increased risk of all-cause mortality. Besides, in the univariate analysis of 153 patients with relevant data ( Bertuzzi et al., 2020 Bertuzzi et al., 2020 ; Dai et al., 2020 Dai et al., 2020 ; Kalinsky et al., 2020 Kalinsky et al., 2020 ), age and a history of cardiovascular and cerebrovascular diseases were identified as significant variables ( P < 0.05; Table S1) and were further included in multivariate analysis, which was performed for cases from six studies ( Bertuzzi et al., 2020 Bertuzzi et al., 2020 ; Bogani et al., 2020 Bogani et al., 2020 ; Dai et al., 2020 Dai et al., 2020 ; Kalinsky et al., 2020 Kalinsky et al., 2020 ; Wu et al., 2020 Wu et al., 2020 ; Zhang HY et al., 2020 Zhang HY et al., 2020 ) that provided complete relevant clinical information. In the multivariate analysis, we found no association between chemotherapy or hormonal therapy and increased risk of death ( Fig. 4 Fig. 4 ). It seemed that the clinical characteristics, such as advan‑cing age, or cardiovascular and cerebrovascular diseases, played a more important role in disease outcomes than the antitumor regimens. The OR for immunotherapy was significantly associated with an increased risk of all-cause mortality in solid tumor patients (OR, 2.66; 95% CI, 1.07‒6.64; 10 studies; Fig. 5 Fig. 5 ). In patients with haematologic malignancies, chemotherapy increased the risk of death (OR, 4.11; 95% CI, 1.59–10.60; 10 studies; Fig. 5 Fig. 5 ). When stratified according to gender, hormonal therapy was significantly associated with death in male patients (OR, 8.09; 95% CI, 2.09‒31.30; 9 studies; Fig. 5 Fig. 5 ). As for age, both chemotherapy (OR, 3.47; 95% CI, 1.20‒10.04; 8 studies) and immunotherapy (OR, 8.00; 95% CI, 1.78‒35.89; 8 studies) were associated with increased mortality in patients younger than 65 years, but not in those older than 65 years ( Fig. 5 Fig. 5 ). The impact of immunotherapy on mortality seemed to be more evident in younger patients. In elder patients, this impact could be diluted by other well-known factors, such as age and comorbidities. The time interval between recent anticancer treatments and the onset of COVID-19 was divided into "within 3 months" and "within 50 d" before the onset of COVID-19. For the former, the risk of all-cause mortality was increased in patients who had gone through chemotherapy (OR, 2.34; 95% CI, 1.25–4.36; 6 studies), targeted therapy (OR, 2.37; 95% CI, 1.15–4.88; 6 studies), and hormonal therapy (OR, 6.43; 95% CI, 2.28–18.15; 6 studies) ( Fig. 5 Fig. 5 ). For the antitumor regimens which were administered within 50 d before the onset of COVID-19, immunotherapy significantly increased the risk of all-cause mortality (OR, 3.57; 95% CI, 1.41–9.01; 5 studies; Fig. 5 Fig. 5 ), which was consistent with the overall result of multivariate analysis. Overall, the subgroup analysis demonstrated that the adverse impacts of chemotherapy mainly occurred in patients with haematologic malignancies. However, for patients with solid tumors, immunotherapy could lead to life-threatening outcomes, especially when administered within 50 d. Both chemotherapy and immunotherapy were reported to affect SARS-CoV-2 antibody levels in patients and could influence the immune response to SARS-CoV-2 ( Yazaki et al., 2021 Yazaki et al., 2021 ). In fact, some previous studies even indicated that chemotherapy increased the risk of poor outcomes ( Grivas et al., 2021 Grivas et al., 2021 ; Sharafeldin et al., 2021 Sharafeldin et al., 2021 ), especially in patients with haematologic malignancies ( Lee et al., 2020b Lee et al., 2020b ), which is in agreement with our results. For patients with haematologic malignancies, myelosuppressive chemotherapy could disrupt the immune system and contribute to a more severe form of COVID-19 ( Lin et al., 2021 Lin et al., 2021 ). Furthermore, it was conferred that lymphocytopenia following myelosuppressive chemotherapy or lymphocyte-reducing chemotherapy in patients with haematologic malignancies correlated with a greater risk of severe or even a life-threatening form of COVID-19 ( Shi et al., 2020 Shi et al., 2020 ; Nakamura et al., 2021 Nakamura et al., 2021 ; Shumilov et al., 2021 Shumilov et al., 2021 ), which confirmed our finding for chemotherapy in patients with haematologic malignancies. Immunotherapy, including immune checkpoint blockage, chimeric antigen receptor (CAR) T-cell therapy, cancer vaccines, and tumor neoantigens, can reverse cancer immune evasion to eliminate cancer cells ( Yang, 2015 Yang, 2015 ). Insufficient signaling through the programmed death-1 (PD-1) pathway following the administration of immune checkpoint inhibitors, which is commonly done in solid tumors, might lead to immunopathology in acute virus infection ( Schönrich and Raftery, 2019 Schönrich and Raftery, 2019 ). The pathological characteristics in a fatal case of COVID-19 revealed CD8 T-cells with high cytotoxicity ( Xu et al., 2020 Xu et al., 2020 ), as observed in the toxicity of checkpoint inhibitors ( Kennedy and Salama, 2020 Kennedy and Salama, 2020 ). Therefore, it can be speculated that the interplay between SARS-CoV-2 infection and immunotherapy might result in poor outcomes among special patients. Considering the secondary outcome, 227 cancer patients with COVID-19 (four studies) had a record of severe events, in which 97 patients (42.7%) developed severe events during follow-up. In these studies, we carefully reviewed the defin‑itions of "severe events." Thus, in view of the similarity of the definition of "severe events" in these four studies ( Dai et al., 2020 Dai et al., 2020 ; Joharatnam-Hogan et al., 2020 Joharatnam-Hogan et al., 2020 ; Zhang HY et al., 2020 Zhang HY et al., 2020 ; Zhang L et al., 2020 Zhang L et al., 2020 ), for subsequent analysis, we defined severe events as death, admission to an ICU, and utilization of mechanical ventilation. The univariate analysis indicated that immunotherapy was significantly associated with the risk of severe events (OR, 5.75; 95% CI, 1.19–27.73; Fig. S1). In addition, sex, age, and history of cardiovascular and cerebrovascular diseases with P < 0.05 (Table S2) were included in multivariate analysis, which was conducted for cases in three studies ( Dai et al., 2020 Dai et al., 2020 ; Zhang HY et al., 2020 Zhang HY et al., 2020 ; Zhang L et al., 2020 Zhang L et al., 2020 ) providing sufficient relevant clinical information. When adjusted for the potential confounders (sex, age, and history of cardiovascular and cerebrovascular diseases), the effects of antitumor regimens on severe events became insignificant, while the effects of age and history of cardiovascular and cerebrovascular diseases remained significant (Fig. S2). Similar to the results of the primary outcome, the clinical characteristics seemed to be a more substantial risk factor in comparison with the antitumor regimens. A subgroup analysis was also performed, which indicated that, for tumor type, immunotherapy increased the risk of severe events in solid tumor patients (OR, 5.04; 95% CI, 1.02–24.86; 4 studies; Fig. 6 Fig. 6 ). Considering the interval between antitumor treatments and the onset of COVID-19, immunotherapy given within 50 d significantly increased the risk of severe events (OR, 5.51; 95% CI, 1.14–26.72; Fig. 6 Fig. 6 ). Therefore, the use of immunotherapy significantly increased the risk of severe events in patients with solid tumors, especially when administered within 50 d, which was in accordance with the result of the primary outcome. In summary, our work provided a comprehensive review of anticancer regimens and their impacts on cancer patients with COVID-19. The findings showed that chemotherapy was the most common treatment in cancer patients with COVID-19. After adjustment, the effects of antitumor regimens on poor outcomes became insignificant, while the effects of advancing age and history of cardiovascular and cerebrovascular diseases remained significant, which indicated that crucial clinical characteristics such as age and comorbidity should deserve more attention. According to the subgroup analysis, chemotherapy increased the risk of all-cause mortality in patients with haematologic malignancies. Furthermore, immunotherapy was associated with all-cause mortality and severe events in patients with solid tumors, especially when given within 50 d before the onset of COVID-19. These findings can provide a basis for clinicians to tailor the regimens of anticancer treatments during the pandemic to minimize the impact of SARS-CoV-2 infection. The present analysis has several limitations. Firstly, the number of studies with individual original data was insufficient, and the sample size was limited. Secondly, the quality and standard of data from different studies could not be fully controlled. Consequently, a multicenter study with a larger sample size and a unified standard of clinical data is strongly recommended. In addition, it appears that the combinational utilization of different treatments could reduce the side effects ( Motz and Coukos, 2013 Motz and Coukos, 2013 ). However, limited by the available clinical data, we did not investigate the effects of the combination of anticancer treatments on patients. Furthermore, as discussed above, the time interval between the recent anticancer treatments and the onset of COVID-19 was not fully analyzed. A more accurate investigation in this respect should therefore be conducted. Contributions Statementauthor contribution statement 1 Yihua WU and Dajing XIA contributed to the conception and design of the study. Haohua LU, Yu SHI, Yihua WU, Kelie CHEN, and Yuequn NIU contributed to data extraction, statistical analysis, and data interpretation. Haohua LU, Yu SHI, and Yihua WU contributed to writing the manuscript. Dajing XIA, Zhi CHEN, and Haihong ZHU contributed to editing the manuscript. All authors have read and approved the final manuscript and, therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data. 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Ann Oncol , 31 ( 7 ): 894 - 901 . https://doi.org/10.1016/j.annonc.2020.03.296 https://doi.org/10.1016/j.annonc.2020.03.296 Clinical characteristics of COVID-19-infected cancer patients: a retrospective case study in three hospitals within Wuhan, China 10.1631/jzus.B2100151 1673-1581（2021）10-0876-09 This work was supported by the National Natural Science Foundation of China (Nos. 81773016 and 21976155), the Zhejiang Provincial Natural Science Foundation of China (No. LY18C060001), the Zhejiang Scientific and Technological Major Project under the 2020 Emergency (No. 2020C03124), the Zhejiang University Special Scientific Research Fund for COVID-19 Prevention and Control, and the Zhejiang Natural Project on Emergency Research about Community Prevention, Control, Early Warning and Prediction of the Novel Coronavirus Outbreak (No. LEZ20H260001). 2021-02-10 2021-07-19 2021-10-15 2022-05-09 2021-09-15 Copyright © Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2021 2021 浙江大学出版社 Appendixes S1 and S2; Tables S1 and S2; Figs. S1 and S2 2 Haohua LU, Yu SHI, Kelie CHEN, Zhi CHEN, Haihong ZHU, Yuequn NIU, Dajing XIA, and Yihua WU declare that they have no conflict of interest. This article does not contain any studies with human or animal subjects performed by any of the authors. Compliance with ethics guidelines 浙江大学学报（英文版）（B辑：生物医学和生物技术） 10 22