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. 2022 Mar 4;22:241. doi: 10.1186/s12885-022-09320-x

The impact of anti-tumor approaches on the outcomes of cancer patients with COVID-19: a meta-analysis based on 52 cohorts incorporating 9231 participants

Qing Wu 1, Shuimei Luo 1, Xianhe Xie 1,2,✉
PMCID: PMC8895689  PMID: 35246063

Abstract

Background

This study was designed to investigate the impact of anti-tumor approaches (including chemotherapy, targeted therapy, endocrine therapy, immunotherapy, surgery and radiotherapy) on the outcomes of cancer patients with COVID-19.

Methods

Electronic databases were searched to identify relevant trials. The primary endpoints were severe disease and death of cancer patients treated with anti-tumor therapy before COVID-19 diagnosis. In addition, stratified analyses were implemented towards various types of anti-tumor therapy and other prognostic factors. Furthermore, odds ratios (ORs) were hereby adopted to measure the outcomes with the corresponding 95% confidence intervals (CIs).

Results

As indicated in the study consisting of 9231 individuals from 52 cohorts in total, anti-tumor therapy before COVID-19 diagnosis could elevate the risk of death in cancer patients (OR: 1.21, 95%CI: 1.07–1.36, P = 0.0026) and the incidence of severe COVID-19 (OR: 1.19, 95%CI: 1.01–1.40, P = 0.0412). Among various anti-tumor approaches, chemotherapy distinguished to increase the incidence of death (OR = 1.22, 95%CI: 1.08–1.38, P = 0.0013) and severe COVID-19 (OR = 1.10, 95%CI: 1.02–1.18, P = 0.0165) as to cancer patients with COVID-19. Moreover, for cancer patients with COVID-19, surgery and targeted therapy could add to the risk of death (OR = 1.27, 95%CI: 1.00–1.61, P = 0.0472), and the incidence of severe COVID-19 (OR = 1.14, 95%CI: 1.01–1.30, P = 0.0357) respectively. In the subgroup analysis, the incidence of death (OR = 1.17, 95%CI: 1.03–1.34, P = 0.0158) raised in case of chemotherapy adopted for solid tumor with COVID-19. Besides, age, gender, hypertension, COPD, smoking and lung cancer all served as potential prognostic factors for both death and severe disease of cancer patients with COVID-19.

Conclusions

Anti-tumor therapy, especially chemotherapy, augmented the risk of severe disease and death for cancer patients with COVID-19, so did surgery for the risk of death and targeted therapy for the incidence of severe COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09320-x.

Keywords: Anti-tumor therapy, cancer, COVID-19, Chemotherapy, Solid tumor

Background

As is known to all, the sudden outbreak and global overrun of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) [1], have generated heavy burdens and great challenges to global public health since December 2019 [2]. Up to date, people all over the world have been fighting against the fatal disease, as reported in over 200 million infected individuals.

Cancer patients are generally in severe immunosuppressive status deriving from cancer itself and the anti-tumor regimens. Furthermore, they have to visit the hospital regularly for monitoring or anti-tumor treatment (such as chemotherapy, immunotherapy, endocrine therapy, targeted therapy, surgery and radiotherapy) leading to increasing exposure to virus.

A growing number of studies revealed that, during the pandemic, cancer patients with COVID-19 generally suffered from worse outcomes compared to patients with COVID-19 alone [3–7]. In addition, some investigations targeted at exploring whether anti-tumor therapy was an additional risk factor for adverse outcomes of COVID-19 and whether it was necessary to change therapeutic modalities to mitigate the risk [8–10].

As far as we know, accumulating prospective and retrospective studies were conducted to evaluate clinical characteristics of cancer patients with COVID-19, as well as the impact of anti-tumor therapy on clinical outcomes of COVID-19 [11–13]. Nevertheless, research findings remained to be a bit conflicting and inconclusive as for the impact of anti-tumor therapeutic approaches on the severity of COVID-19 [14–18]. Consequently, a comprehensive survey based on a larger scale (52 cohorts incorporating 9231 individuals) and diverse dimensions was hereby carried out to clarify the correlation between anti-tumor therapy and COVID-19 prognosis.

Methods

Data sources and literature searches

A systematic electronic literature retrieval was in place for study screening, searching for abstracts of relevant studies in the published literature. PubMed, Cochrane Library and EMBASE were all searched with data updated as of 27th March 2021. Basic search terms entered were as follows: “COVID-19”, “SARS-CoV2”, “SARS-CoV-2”, “2019-nCoV”, “novel coronavirus”, “cancer”, “neoplasm”, “malignancy”, “carcinoma” and “tumor” (the full search strategy as shown in Additional file 1: Appendix 1). In addition, full-text papers were scrutinized as for abstracts without substantial information, and the references of relevant articles were reviewed for additional studies. Data retrieval was completed in English, with reviews, editorials comments and case reports all excluded.

Selection of studies and definition

Initially, two investigators performed a screening of titles and abstracts respectively, then examined the full-text of articles to acquire eligible studies. Regarding the duplicate studies based on the same patients, only the latest or most comprehensive data were recruited as a whole.

Definition:

Anti-tumor therapy: patients receiving chemotherapy (cytotoxic chemotherapy), immunotherapy (immune checkpoint inhibitor), targeted therapy (molecular targeted therapy), surgery, radiotherapy, endocrine therapy (hormonal drugs) within the last 6 months before COVID-19 diagnosis.

Age: defined as “old” or “young” depending on each cut-off used to calculate the odds ratios (ORs) of age in the included studies.

Eastern Cooperative Oncology Group Performance Scale (ECOG PS): defined as “high” or “low” with a cut-off of 2.

Comorbidities: defined as “yes” or “no” to identify cancer patients with or without hypertension, diabetes, chronic obstructive pulmonary disease (COPD), cardiovascular disease, obesity status and smoking in the corresponding studies.

Blood parameters: defined as “high” or “normal” on the basis of each cut-off applied to calculate the ORs of white blood cell count, C-reactive protein (CRP), lymphocyte count, D-dimer, neutrophil to lymphocyte ratio (NLR), and creatine kinase in each included study.

Severe COVID-19: depending on respective definitions in the included studies, including infections requiring intensive care unit (ICU) admission, mechanical ventilation or even resulting in death.

Inclusion criteria

1) Prospective or retrospective studies to evaluate the impact of anti-tumor therapy on cancer patients with COVID-19; 2) patients pathologically confirmed as cancer; 3) patients diagnosed as COVID-19; 4) studies with data available for ORs and corresponding 95% confidence intervals (CIs) of severe COVID-19 and death rates in groups receiving anti-tumor treatments or not.

Data extraction

In this study, data extraction was implemented strictly according to the PRISMA guidelines (as shown in Additional file 2: Appendix 2). Meanwhile, all eligible studies involved the information as follows: the publication year and region, first author’s name, study type, number of patients, anti-tumor therapy, severe COVID-19 and/or death cases.

Quality assessment

The quality of included studies was assessed independently by two reviewers using the Newcastle-Ottawa Scale (NOS) for case-control and cohort studies, encompassing three dimensions of selection, comparability and exposure, with a full score of 9 points.

Statistical methods

The primary endpoints were composed of death and/or severe COVID-19 of cancer patients treated with anti-tumor therapy before COVID-19 diagnosis. Moreover, the correlation between anti-tumor therapy and the outcomes was determined by ORs with the corresponding 95%CIs. Subgroup analyses were further accomplished based on the type of anti-tumor therapy, type of cancer (solid cancer or haematological malignancy) and other prognostic factors. In addition, funnel plots and Egger’s test were applied to evaluate publication bias, and statistical analysis was realized via R 4.0 statistical software. Heterogeneity was assessed by means of I-square tests and chi-square, with remarkable heterogeneity in case of P < 0.1 or I2 > 40%. Furthermore, a random effect model was adopted to analyze the pooled data when heterogeneity existed; otherwise, a fixed effect model was employed accordingly.

Results

Selection of study

Initially, 9462 relevant articles were scrutinized intensively, of which 443 were filtered for duplication, and 8766 were excluded for digression after screening the titles and abstracts. After that, the full text of remaining 253 articles was thoroughly reviewed, among which 201 were excluded as they were reviews or case reports, not human research, not in English, without data for ORs and corresponding 95%CIs of severe COVID-19 and/or death in groups receiving anti-tumor therapy or not. Finally, a total of 52 cohorts [4, 6, 7, 11, 12, 14–60] incorporating 9231 participants were recruited in this study. See Fig. 1 for detailed procedures.

Fig. 1.

Fig. 1

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Flowchart on selection including trials in the meta-analysis

Study traits

As of 27th March 2021, altogether 9231 individuals in 52 cohorts were included with a sample size ranging from 12 to 1289, of which 45 were retrospective, 4 prospective and 3 retro-prospective. Meanwhile, ORs for severe COVID-19 and/or death were utilized to assess the impact of anti-tumor approaches on cancer patients with COVID-19. Among the foregoing studies, 41 cohorts witnessed death and 23 confronted with severe COVID-19. See Table 1 for principal characteristics.

Table 1.

The principal characteristics and further details of eligible articles

Author Year Study design Region Number of patient Male Median age (IQR) (years) Diagnosis method for COVID-19 Cancer type Comparison group
Kuderer NM [6] 2020 Retro-prospective multi-national 928 468 66 (57–76) RT-PCR non-specific cancer patients with no treatment
Lee LYW [19] 2020 Prospective UK 800 449 69 (59–76) RT-PCR non-specific cancer patients with no treatment
Zhang L [14] 2020 Retrospective China 28 17 65 (56–70) RT-PCR solid tumor cancer patients with no treatment
Stroppa EM [20] 2020 Retrospective Italy 25 20 71 (mean) (50–84) RT-PCR non-specific cancer patients with no treatment
Yang K [7] 2020 Retrospective China 205 96 63 (56–70) RT-PCR non-specific cancer patients with no treatment
Zhang H [21] 2020 Retrospective China 107 60 66 (36–98) RT-PCR and/or radiology non-specific cancer patients with no treatment
Robilotti EV [22] 2020 Retrospective USA 423 212 NA RT-PCR non-specific cancer patients with no treatment
Yarza R [23] 2020 Prospective Spain 63 34 NA RT-PCR and/or radiology solid tumor cancer patients treated other options
Li Q [24] 2020 Retrospective China 59 31 63 (54–70) RT-PCR non-specific cancer patients with no treatment
Jee J [25] 2020 Retrospective USA 309 159 NA RT-PCR non-specific cancer patients with no treatment
Sanchez-Pina JM [26] 2020 Retrospective Spain 39 23 64 (mean) RT-PCR hematological malignancies cancer patients with no treatment
Pinato DJ [15] 2020 Retrospective multi-national 890 503 68 (mean) RT-PCR non-specific cancer patients with no treatment
Assaad S [27] 2020 Retrospective France 55 26 64 (mean) RT-PCR non-specific cancer patients with no treatment
Garassino MC [28] 2020 Retrospective multi-national 200 141 68 (61–75) RT-PCR Thoracic Cancer cancer patients with no treatment
Liang WH [29] 2020 Retrospective China 18 12 60 (47–87) RT-PCR non-specific cancer patients with no treatment
Ma J [30] 2020 Retrospective China 37 20 62 (IQR: 59–70) RT-PCR and/or antibody test solid tumor cancer patients with no treatment
Mehta V [11] 2020 Retrospective USA 218 127 69 (10–92) RT-PCR non-specific cancer patients with no treatment
Yu J [31] 2020 Retrospective China 12 10 66 (48–78) RT-PCR and/or CT solid tumor cancer patients with no active antitumor treatment
Tian J [4] 2020 Retrospective China 232 119 64 (58–69) RT-PCR non-specific cancer patients with surgery
Fox TA [32] 2020 Retrospective UK 55 38 63 (23–88) RT-PCR, CT, and clinical features hematological malignancies cancer patients with no treatment
Booth S [33] 2020 Prospective UK 66 41 73 (IQR: 63–81) RT-PCR, radiological, and clinical features hematological malignancies cancer patients with no treatment
Cattaneo C [34] 2020 Retrospective Italy 102 66 68 (mean) RT-PCR hematological malignancies cancer patients with no treatment
Lara OD [35] 2020 Retrospective USA 121 NA 64 (IQR: 51–73) RT-PCR and CT gynecologic cancer cancer patients with no treatment
Liu C [36] 2020 Retrospective China 216 113 63 (IQR: 57–70) RT-PCR solid tumor cancer patients with no treatment
Luo J [37] 2020 Retrospective USA 102 49 68 (IQR: 61–75) RT-PCR lung cancer cancer patients with no treatment
Mato AR [38] 2020 Retrospective multi-national 198 125 63 (35–92) RT-PCR chronic lymphocytic leukemia cancer patients with no treatment
Rogado J [39] 2020 Retrospective Spain 45 30 71 (34–90) RT-PCR non-specific cancer patients with no treatment
Russell B [40] 2020 Retro-prospective UK 156 90 65 (mean) RT-PCR solid tumor cancer patients with no treatment
Scarfò L [41] 2020 Retrospective multi-national 190 126 72 (48–94) RT-PCR chronic lymphocytic leukemia cancer patients with no treatment
Vuagnat P [42] 2020 Retrospective France 58 NA 58 (IQR:48–68) RT-PCR and/or CT breast cancer cancer patients with no treatment
Wang BO [43] 2020 Retrospective USA 58 30 67 RT-PCR multiple myeloma cancer patients with no treatment
Wang J [44] 2020 Retrospective China 283 141 63 (IQR: 55–70) RT-PCR non-specific cancer patients with no treatment
Gonzalez-cao M [45] 2020 Retrospective Spain 50 27 69 (6–94) clinical or RT-PCR melanoma cancer patients with no treatment
De Melo AC [46] 2020 Retrospective Brazil 181 71 55 (2–88) RT-PCR non-specific cancer patients with no active antitumor treatment
Albiges L [47] 2020 Retrospective France 178 76 61 (52–71) RT-PCR and/or CT non-specific cancer patients with no treatment
Martínez-López J [48] 2020 Retrospective Spain 167 95 71 (IQR: 62–78) RT-PCR multiple myeloma (MM) cancer patients with no treatment
Martín-Moro F [49] 2020 Retrospective Spain 34 19 72.5 (35–94) RT-PCR and/or CT hematological malignancies cancer patients with no treatment
Lattenist R [50] 2021 Retrospective Belgium 13 10 70 (IQR: 59–79) RT-PCR and/or CT hematological malignancies cancer patients with no treatment
Nakamura S [51] 2020 Retrospective Japan 32 22 74.5 (24–90) RT-PCR non-specific cancer patients with no treatment
Rogiers A [52] 2021 Retrospective multi-national 110 72 63 (27–86) RT-PCR non-specific cancer patients with no treatment
Glenthøj A [16] 2021 Prospective Denmark 66 40 66.7 (25–91) hematological malignancies cancer patients with no treatment
Song C [17] 2020 Retrospective China 223 116 63 (56–71) RT-PCR non-specific cancer patients with discontinous treatment
Lunski MJ [18] 2020 Retrospective USA 312 142 NA RT-PCR non-specific cancer patients with no treatment
Nie L [53] 2020 Retrospective China 45 31 66 (58–74) RT-PCR lung cancer cancer patients with no treatment
Larfors G [54] 2020 Retrospective Sweden NA NA NA RT-PCR non-specific cancer patients with no treatment
H€ollein A [55] 2020 Retrospective Germany 17 8 73 (27–82) RT-PCR non-specific cancer patients with no treatment
Garnett C [56] 2020 Retrospective UK 32 21 72.5 (46–96) RT-PCR hematological malignancies cancer patients with no treatment
Hanna GJ [57] 2020 Retrospective USA 32 20 70 (38–91) RT-PCR head and neck cancer cancer patients with no treatment
Lie’vre A [58] 2020 Retro-prospective France 1289 795 67 (19–100) RT-PCR solid tumor cancer patients with no treatment
Smith M [59] 2020 Retrospective USA 86 NA 69 (mean) RT-PCR solid tumor cancer patients with no treatment
Wu YG [60] 2020 Retrospective China 14 9 37 (14–68) RT-PCR hematological malignancies cancer patients with no treatment
Yang F [12] 2020 Retrospective China 52 28 63 (34–98) RT-PCR solid tumor cancer patients with no treatment
Author Number of the control Anti-tumor therapy Chemotherapy Immunotherapy Targeted therapy Endocrine therapy Surgery Radiotherapy Outcome Required mechanical ventilation Severe COVID-19 Death
Kuderer NM [6] 553 366 160 38 75 85 32 12 death 116 242 121
Lee LYW [19] 272 528 281 44 72 64 29 76 death NA 360 226
Zhang L [14] 22 6 3 1 2 NA NA 1 sever COVID-19 10 15 8
Stroppa EM [20] 13 12 8 4 NA NA NA NA death NA NA 9
Yang K [7] 128 54 31 4 12 NA 4 9 death 32 52 40
Zhang H [21] 70 37 NA 6 NA NA NA NA death NA 56 23
Robilotti EV [22] NA NA 191 31 NA NA 31 NA sever COVID-19 40 85 51
Yarza R [23] NA NA 36 8 7 10 NA NA sever COVID-19; death NA 24 16
Li Q [24] 43 16 12 NA 6 NA 1 1 death 27 35 16
Jee J [25] 43 170 102 18 49 NA NA NA sever COVID-19 NA 120 31
Sanchez-Pina JM [26] 15 24 4 NA 5 NA NA NA death NA 18 NA
Pinato DJ [15] 403 479 206 56 93 92 NA 33 sever COVID-19; death 97 565 299
Assaad S [27] 26 29 16 3 14 NA NA NA death NA NA 30
Garassino MC [28] 58 142 48 34 28 NA NA NA death 9 NA 66
Liang WH [29] 14 4 NA NA NA NA NA NA sever COVID-19 NA 9 NA
Ma J [30] 24 13 NA NA NA NA NA NA sever COVID-19 NA 20 5
Mehta V [11] NA NA 42 5 NA NA NA 49 death 45 NA 61
Yu J [31] 5 7 5 2 1 NA 1 4 sever COVID-19; death NA 3 3
Tian J [4] NA NA NA NA NA NA 119 NA sever COVID-19 NA 148 NA
Fox TA [32] NA NA 29 25 NA NA NA NA sever COVID-19; death NA 25 19
Booth S [33] 29 37 NA NA NA NA NA NA death NA NA 34
Cattaneo C [34] 43 59 20 28 NA NA NA NA death NA NA 40
Lara OD [35] NA NA NA NA NA NA NA NA death NA 20 NA
Liu C [36] 138 78 NA NA NA NA NA NA death NA NA 37
Luo J [37] 48 54 NA NA NA NA NA NA sever COVID-19; death 18 NA 25
Mato AR [38] 79 119 51 NA NA NA NA NA death 53 NA 66
Rogado J [39] 15 30 19 1 2 NA NA NA death NA 29 19
Russell B [40] 18 81 45 7 5 NA NA NA sever COVID-19; death NA 28 34
Scarfò L [41] 73 116 NA NA NA NA NA NA sever COVID-19; death NA 151 56
Vuagnat P [42] NA NA 29 NA 19 19 3 36 sever COVID-19 NA NA 4
Wang BO [43] 11 47 NA NA 28 NA NA NA death NA NA 14
Wang J [44] 188 95 46 NA 12 NA 23 NA sever COVID-19; death NA NA 50
Gonzalez-cao M [45] 12 38 NA 22 16 NA NA NA sever COVID-19; death NA 34 13
De Melo AC [46] 16 165 63 NA NA 20 12 10 death 34 NA 60
Albiges L [47] 61 117 66 19 30 16 NA NA sever COVID-19; death NA 47 31
Martínez-López J [48] NA NA 83 NA NA NA NA NA death 15 141 56
Martín-Moro F [49] NA 19 NA NA NA NA NA NA death 4 17 11
Lattenist R [50] 6 7 3 NA NA NA NA NA death NA NA 6
Nakamura S [51] 19 13 10 3 NA 4 13 NA death 3 NA 11
Rogiers A [52] NA NA 25 NA NA NA NA NA sever COVID-19; death NA 35 18
Glenthøj A [16] 10 9 NA NA NA NA NA NA sever COVID-19 NA 33 NA
Song C [17] 19 204 NA NA NA NA NA NA sever COVID-19 NA 159 NA
Lunski MJ [18] 256 56 12 4 9 44 5 2 death NA NA 66
Nie L [53] 34 11 4 4 NA NA 3 NA death 3 23 11
Larfors G [54] NA NA NA NA NA NA NA NA sever COVID-19; death NA NA NA
H€ollein A [55] 2 15 14 1 2 NA NA 1 death 3 NA 6
Garnett C [56] 10 22 NA NA NA NA NA NA death NA NA 18
Hanna GJ [57] 26 6 3 1 0 NA 4 1 death NA NA NA
Lie’vre A [58] NA NA 577 110 181 57 56 133 death 49 NA 370
Smith M [59] 47 39 NA NA NA NA NA NA sever COVID-19 NA 29 NA
Wu YG [60] NA NA 7 NA NA NA NA NA death NA NA 6
Yang F [12] NA NA 6 1 NA NA 2 NA sever COVID-19 NA 19 11
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Abbreviations: ICIs Immune checkpoint inhibitors, RT-PCR Reverse transcription-polymerase chain reaction, NA Not available, ICU Intensive Care Unit

Assessment of study quality and publication bias

Refer to Additional file 3: Appendix 3 for quality assessment of 52 recruited studies. Furthermore, no publication bias was defined via Egger’s tests in the pooled analyses for various anti-tumor approaches (see Additional file 4: Appendix 4) and supernumerary prognostic factors (see Additional file 5: Appendix 5).

Data analysis

In this study, regarding cancer patients treated with anti-tumor therapy before COVID-19 diagnosis, the pooled OR was 1.21 (95%CI: 1.07–1.36, P = 0.0026) (Fig. 2A) for death without publication bias (Fig. 2C, Egger’s test: P = 0.5516), and 1.19 (95%CI: 1.01–1.40, P = 0.0412) (Fig. 2B) for severe COVID-19 without publication bias (Fig. 2D, Egger’s test: P = 0.3930).

Fig. 2.

Fig. 2

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The impact of anti-tumor therapy on clinical outcomes of cancer patients with COVID-19. Forest plots of (A) death, B severe COVID-19 between groups divided by receiving anti-tumor therapy or not before COVID-19 diagnosis; Funnel plots of (C) death, D severe COVID-19 between groups divided by receiving anti-tumor therapy or not before COVID-19 diagnosis

The impact of anti-tumor therapy on death and severe disease of cancer patients with COVID-19

As for cancer patients with COVID-19, compared with patients without anti-tumor approaches, the incidence of death appeared to be higher in patients treated with chemotherapy (OR = 1.22, 95%CI: 1.08–1.38, P = 0.0013) (Fig. 3A) and surgery (OR = 1.27, 95%CI: 1.00–1.61, P = 0.0472) (Fig. 3B), but not in patients receiving radiotherapy (OR = 0.90, 95%CI: 0.75–1.09, P = 0.2817), targeted therapy (OR = 0.97, 95%CI: 0.76–1.23, P = 0.7914), endocrine therapy (OR = 0.95, 95%CI: 0.80–1.12, P = 0.5097), and immunotherapy (OR = 1.05, 95%CI: 0.90–1.22, P = 0.5412) (Additional file 6: Appendix 6).

Fig. 3.

Fig. 3

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The impact of various anti-tumor approaches on clinical outcomes of cancer patients with COVID-19. The impact of (A) chemotherapy and (B) surgery on death of cancer patients with COVID-19; The impact of (C) chemotherapy and (D) targeted therapy on severe disease of cancer patients with COVID-19

Compared with cancer patients without anti-tumor approaches, the incidence of severe COVID-19 was higher in patients receiving chemotherapy (OR = 1.10, 95%CI: 1.02–1.18, P = 0.0165) (Fig. 3C) and targeted therapy (OR = 1.14, 95%CI: 1.01–1.30, P = 0.0357) (Fig. 3D), but not in patients treated with surgery (OR = 1.15, 95%CI: 0.89–1.47, P = 0.2888) and immunotherapy (OR = 1.18, 95%CI: 0.97–1.45, P = 0.1034) (Additional file 6: Appendix 6).

Subgroup analysis

Patients were further divided into groups of solid tumor and haematological malignancy depending on the type of cancer, as listed in Table 2. Compared with patients without anti-tumor approaches, solid tumor patients with COVID-19 witnessed higher incidence of death after receiving chemotherapy (OR = 1.17, 95%CI: 1.03–1.34, P = 0.0158), but not the case in haematological malignancy patients with COVID-19 (OR = 1.41, 95%CI: 0.74–2.68, P = 0.2964).

Table 2.

Subgroup analysis of the impact of anti-tumor therapy on death and severe disease of cancer patients with COVID-19

Anti-tumor therapy Solid tumour Haematological malignancy
death severe COVID-19 death severe COVID-19
OR (95%CI) P OR (95%CI) P OR (95%CI) P OR (95%CI) P
Chemotherapy 1.17 (1.03–1.34) 0.0158 1.16 (0.81–1.66) 0.4072 1.41 (0.74–2.68) 0.2964 NA NA
Radiotherapy NA NA NA NA NA NA NA NA
Targeted therapy NA NA NA NA NA NA NA NA
Surgery NA NA NA NA NA NA NA NA
Endocrine therapy NA NA NA NA NA NA NA NA
Immunotherapy 0.91 (0.47–1.76) 0.7705 NA NA NA NA NA NA
Antitumor therapy 1.15 (0.94–1.42) 0.1815 1.08 (0.88–1.32) 0.4643 1.26 (0.91–1.75) 0.1597 NA NA
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Abbreviations NA Not available, OR Odds ratio, CI Confidence interval

Supernumerary prognostic factors for death and severe disease of cancer patients with COVID-19

The potential prognostic factors for the death of cancer patients with COVID-19 were as follows: age (OR = 1.15, 95%CI: 1.12–1.19, P < 0.0001) (Fig. 4A), gender (OR = 1.22, 95%CI: 1.11–1.34, P < 0.0001) (Fig. 4B), hypertension (OR = 1.32, 95%CI: 1.22–1.41, P < 0.0001) (Fig. 4C), diabetes (OR = 1.31, 95%CI: 1.20–1.42, P < 0.0001) (Fig. 4D), COPD (OR = 1.24, 95%CI: 1.08–1.41, P = 0.0016) (Fig. 4E), cardiovascular disease (OR = 1.33, 95%CI: 1.15–1.55, P = 0.0001) (Fig. 4F), smoking (OR = 1.29, 95%CI: 1.14–1.47, P < 0.0001) (Fig. 4G), ECOG PS (OR = 1.73, 95%CI: 1.47–2.03, P < 0.0001) (Fig. 4H), lung cancer (OR = 1.38, 95%CI: 1.05–1.81, P = 0.0200) (Fig. 4I), white blood cell count (OR = 1.86, 95%CI: 1.17–2.97, P = 0.0093) (Fig. 4J), and CRP (OR = 1.03, 95%CI: 1.00–1.05, P = 0.0298) (Fig. 4K). Nevertheless, obesity status (OR = 1.02, 95%CI: 0.91–1.15, P = 0.6827), lymphocyte count (OR = 1.24, 95%CI: 0.57–2.68, P = 0.5868), D-dimer (OR = 1.01, 95%CI: 0.98–1.05, P = 0.3981) and NLR (OR = 1.30, 95%CI: 0.64–2.64, P = 0.4763) were not highly correlated to the death of cancer patients with COVID-19 (Additional file 7: Appendix 7).

Fig. 4.

Fig. 4

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The supernumerary prognostic factors for death of cancer patients with COVID-19. A Age (old vs. young); B Gender (male vs. female); C Hypertension (yes vs. no); D Diabetes (yes vs. no); E Chronic obstructive pulmonary disease (COPD) (yes vs. no); F Cardiovascular disease (yes vs. no); G Smoking (yes vs. no); H Eastern Cooperative Oncology Group Performance Scale (ECOG PS) (high vs. low); I Type of solid tumor (lung cancer vs. other solid tumor); J White blood cell count (high vs. normal); K C-reactive protein (high vs. normal)

Furthermore, the potential prognostic factors for severe disease of cancer patients with COVID-19 included age (OR = 1.10, 95%CI: 1.05–1.15, P < 0.0001) (Fig. 5A), gender (OR = 1.12, 95%CI: 1.04–1.21, P = 0.0017) (Fig. 5B), hypertension (OR = 1.22, 95%CI: 1.02–1.45, P = 0.0286) (Fig. 5C), COPD (OR = 1.20, 95%CI: 1.01–1.43, P = 0.0416) (Fig. 5D), smoking (OR = 1.21, 95%CI: 1.08–1.35, P = 0.0008) (Fig. 5E), and lung cancer (OR = 1.30, 95%CI: 1.08–1.56, P = 0.0055) (Fig. 5F). However, such factors as diabetes (OR = 1.03, 95%CI: 0.88–1.20, P = 0.7415), obesity status (OR = 1.00, 95%CI: 0.92–1.10, P = 0.9254), ECOG PS (OR = 1.39, 95%CI: 0.93–2.07, P = 0.1119), white blood cell count (OR = 1.90, 95%CI: 0.88–4.11, P = 0.1026), CRP (OR = 1.39, 95%CI: 0.77–2.50, P = 0.2735), lymphocyte count (OR = 1.02, 95%CI: 0.76–1.36, P = 0.9093), D-dimer (OR = 1.05, 95%CI: 0.98–1.13, P = 0.1387), and creatine kinase (OR = 1.52, 95%CI: 0.83–2.77, P = 0.1762) did not obviously influence the severe disease of cancer patients with COVID-19 (Additional file 7: Appendix 7).

Fig. 5.

Fig. 5

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The supernumerary prognostic factors for severe disease of cancer patients with COVID-19. A Age (old vs. young); B Gender (male vs. female); C Hypertension (yes vs. no); D COPD (yes vs. no); E Smoking (yes vs. no); F Type of solid tumor (lung cancer vs. other solid tumor)

Subgroup analysis

Depending on the type of cancer, patients were further assigned into groups of solid tumor and haematological malignancy, as listed in Additional file 8: Appendix 8.

The potential prognostic factors for the death of solid tumor patients with COVID-19 included age (OR = 1.01, 95%CI: 1.00–1.01, P = 0.0168), gender (OR = 1.22, 95%CI: 1.09–1.36, P = 0.0006), hypertension (OR = 1.20, 95%CI: 1.00–1.42, P = 0.0446), and smoking (OR = 1.19, 95%CI: 1.04–1.35, P = 0.0110).

Furthermore, age (OR = 1.37, 95%CI: 1.20–1.57, P < 0.0001), hypertension (OR = 1.20, 95%CI: 1.02–1.41, P = 0.0246) and diabetes (OR = 1.26, 95%CI: 1.03–1.53, P = 0.0245) ranked as the potential prognostic factors for the death of haematological malignancy patients with COVID-19.

Discussion

A meta-analysis involving 15 studies demonstrated that chemotherapy could increase the risk of death from COVID-19 in cancer patients [61]. To our best knowledge, this study composed of 52 cohorts involving 9231 cancer patients with COVID-19, was so far the largest-scale investigation with respect to the impact of anti-tumor approaches on clinical outcomes of cancer patients with COVID-19, indicating that cancer patients with recent anti-tumor therapy (especially chemotherapy) were generally susceptible to develop into severe COVID-19, or even death.

Firstly, cancer patients with COVID-19 receiving chemotherapy were more likely to confront with severe disease and death, probably because patients treated with chemotherapy were susceptible to suffer from bone marrow suppression (including severe neutropenia or lymphocytopenia) and impaired immunity [62, 63], even respiratory infections (involving viral etiology) [64]. Furthermore, the recovery of immune system might take a long time after the weakening of immune functions by chemotherapy [65]. As a result, cancer patients with COVID-19 failed to effectively activate the immune system to eliminate the virus in a timely manner [66], that’s why they were more likely to trigger severe disease or even death.

Secondly, recent surgery might lead to increasing risk of death and a trend of severe disease in cancer patients with COVID-19, partially attributable to their frequent visits to hospital and postoperative negative nitrogen balance. Moreover, the stress and trauma caused by surgery could be clinically manifested as decreased immunity, since numerous studies revealed that the immunity of patients would reduce to a certain extent in a period of time after surgery [67].

Thirdly, patients administered with targeted therapy before COVID-19 diagnosis faced with elevated risk of severe disease. Despite targeted therapy seldomly impaired the immunity system of cancer patients, all those receiving maintenance targeted therapy suffered from advanced disease and many complications in general, giving rise to clinical worsening as a result.

Finally, tumor immunotherapy has played an increasingly crucial role in the field of anti-tumor treatment over the past decade [68]. As shown in our study, cancer patients with COVID-19 who received immunotherapy recently did not generate a higher rate of severe disease or death when comparing to those without immunotherapy.

In summary, this study aimed at providing clinicians with preliminary evidence for the safety of anti-tumor approaches during COVID-19. As to patients with COVID-19 who received anti-tumor approaches recently, especially chemotherapy, surgery and targeted therapy, clinicians should focus on disease progression and make intervention in a timely manner when necessary. Furthermore, intensive nursing and positive measures shall be taken to improve the prognosis and reduce the risk of death in practice.

Limitations

This study came up with four drawbacks as follows: firstly, limited studies related to radiotherapy, surgery and endocrine therapy might affect the accuracy of pooled results to some degree; secondly, 23 included studies failed to separate solid tumor from haematological malignancy for investigating the impact of anti-tumor approaches on the clinical outcomes, which might influence the accuracy of results; thirdly, bias might exist to some extent for excluding relevant studies published in non-English language; lastly, other forms of bias should be taken into account as follows: position bias (e.g. different health care systems and national policies in managing COVID-19) and time lag bias (time of study: start of pandemic vs. later phase of pandemic), which were not available in the included studies.

Conclusions

Anti-tumor therapy, especially chemotherapy, augmented the risk of severe disease and death for cancer patients with COVID-19, so did surgery for the risk of death and targeted therapy for the incidence of severe COVID-19.

Supplementary Information

Additional file 1. (14.2KB, docx)
Additional file 2. (31.8KB, docx)
Additional file 3. (25.4KB, docx)
Additional file 4. (16.8KB, docx)
Additional file 5. (18.8KB, docx)
Additional file 6. (1.1MB, docx)
Additional file 7. (2MB, docx)
Additional file 8. (19.1KB, docx)

Acknowledgments

None.

Code availability

Not applicable.

Registration and protocol

The review was not registered and the protocol was not prepared.

Abbreviations

COVID-19

Coronavirus disease 2019

ORs

Odds ratios

CIs

Confidence intervals

COPD

Chronic obstructive pulmonary disease

SARS-CoV-2

Severe acute respiratory syndrome-related coronavirus 2

NOS

Newcastle-Ottawa Scale

ECOG PS

Eastern Cooperative Oncology Group Performance Scale

NLR

Neutrophil to lymphocyte ratio

ICIs

Immune checkpoint inhibitors

T-PCR

Reverse transcription-polymerase chain reaction

NA

Not available

ICU

Intensive care unit

Authors’ contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Qing Wu, Shuimei Luo and Xianhe Xie. The first draft of manuscript was written by Qing Wu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

None.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional file 1. (14.2KB, docx)
Additional file 2. (31.8KB, docx)
Additional file 3. (25.4KB, docx)
Additional file 4. (16.8KB, docx)
Additional file 5. (18.8KB, docx)
Additional file 6. (1.1MB, docx)
Additional file 7. (2MB, docx)
Additional file 8. (19.1KB, docx)

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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