Factors Affecting COVID-19 Outcomes in Cancer Patients: A First Report From Guy's Cancer Center in London

Abstract

Background: There is insufficient evidence to support clinical decision-making for cancer patients diagnosed with COVID-19 due to the lack of large studies.
Methods: We used data from a single large UK Cancer Center to assess the demographic/clinical characteristics of 156 cancer patients with a confirmed COVID-19 diagnosis between 29 February and 12 May 2020. Logistic/Cox proportional hazards models were used to identify which demographic and/or clinical characteristics were associated with COVID-19 severity/death.
Results: 128 (82%) presented with mild/moderate COVID-19 and 28 (18%) with a severe case of the disease. An initial cancer diagnosis >24 months before COVID-19 [OR: 1.74 (95% CI: 0.71–4.26)], presenting with fever [6.21 (1.76–21.99)], dyspnea [2.60 (1.00–6.76)], gastro-intestinal symptoms [7.38 (2.71–20.16)], or higher levels of C-reactive protein [9.43 (0.73–121.12)] were linked with greater COVID-19 severity. During a median follow-up of 37 days, 34 patients had died of COVID-19 (22%). Being of Asian ethnicity [3.73 (1.28–10.91)], receiving palliative treatment [5.74 (1.15–28.79)], having an initial cancer diagnosis >24 months before [2.14 (1.04–4.44)], dyspnea [4.94 (1.99–12.25)], and increased CRP levels [10.35 (1.05–52.21)] were positively associated with COVID-19 death. An inverse association was observed with increased levels of albumin [0.04 (0.01–0.04)].
Conclusions: A longer-established diagnosis of cancer was associated with increased severity of infection as well as COVID-19 death, possibly reflecting the effects a more advanced malignant disease has on this infection. Asian ethnicity and palliative treatment were also associated with COVID-19 death in cancer patients.

Full text

ORIGINAL RESEARCH
published: 22 July 2020
doi: 10.3389/fonc.2020.01279
Frontiers in Oncology | www.frontiersin.org 1July 2020 | Volume 10 | Article 1279
Edited by:
Thierry Olivier Philip,
Institut Curie, France
Reviewed by:
Abdelbaset Mohamed Elasbali,
Al Jouf University, Saudi Arabia
Francesca Gorini,
National Research Council (CNR), Italy
*Correspondence:
Mieke Van Hemelrijck
mieke.vanhemelrijck@kcl.ac.uk
†These authors have contributed
equally to this work and share first
authorship
‡These authors have contributed
equally to this work and share senior
authorship
Specialty section:
This article was submitted to
Cancer Epidemiology and Prevention,
a section of the journal
Frontiers in Oncology
Received: 01 June 2020
Accepted: 19 June 2020
Published: 22 July 2020
Citation:
Russell B, Moss C, Papa S, Irshad S,
Ross P, Spicer J, Kordasti S,
Crawley D, Wylie H, Cahill F, Haire A,
Zaki K, Rahman F, Sita-Lumsden A,
Josephs D, Enting D, Lei M, Ghosh S,
Harrison C, Swampillai A, Sawyer E,
D’Souza A, Gomberg S, Fields P,
Wrench D, Raj K, Gleeson M, Bailey K,
Dillon R, Streetly M, Rigg A, Sullivan R,
Dolly S and Van Hemelrijck M (2020)
Factors Affecting COVID-19
Outcomes in Cancer Patients: A First
Report From Guy’s Cancer Center in
London. Front. Oncol. 10:1279.
doi: 10.3389/fonc.2020.01279
Factors Affecting COVID-19
Outcomes in Cancer Patients: A First
Report From Guy’s Cancer Center in
London
Beth Russell 1†, Charlotte Moss 1† , Sophie Papa 2,3 , Sheeba Irshad 2,3, Paul Ross 2,
James Spicer 2,3 , Shahram Kordasti 2,4, Danielle Crawley 1, 2, Harriet Wylie 1, Fidelma Cahill 1,
Anna Haire 1, Kamarul Zaki 2, Fareen Rahman 2, Ailsa Sita-Lumsden 2, Debra Josephs 1,2 ,
Deborah Enting 1,2 , Mary Lei 2, Sharmistha Ghosh 2, Claire Harrison 2,4, Angela Swampillai 2,
Elinor Sawyer 2,3 , Andrea D’Souza 2, Simon Gomberg 2, Paul Fields 4, David Wrench 4,
Kavita Raj 4, Mary Gleeson 4, Kate Bailey 4, Richard Dillon 4,5 , Matthew Streetly 4,
Anne Rigg 2, Richard Sullivan 3, Saoirse Dolly 2‡ and Mieke Van Hemelrijck 1,2
*‡
1Translational Oncology and Urology Research (TOUR), School of Cancer and Pharmaceutical Sciences, King’s College
London, London, United Kingdom, 2Guy’s and St Thomas’ NHS Foundation Trust (GSTT), Medical Oncology, London,
United Kingdom, 3School of Cancer and Pharmaceutical Sciences, King’s College London, London, United Kingdom,
4Haematology Department, Guy’s and St Thomas’ NHS Foundation Trust (GSTT), London, United Kingdom, 5Department of
Medical and Molecular Genetics, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
Background: There is insufficient evidence to support clinical decision-making for
cancer patients diagnosed with COVID-19 due to the lack of large studies.
Methods: We used data from a single large UK Cancer Center to assess the
demographic/clinical characteristics of 156 cancer patients with a confirmed COVID-19
diagnosis between 29 February and 12 May 2020. Logistic/Cox proportional hazards
models were used to identify which demographic and/or clinical characteristics were
associated with COVID-19 severity/death.
Results: 128 (82%) presented with mild/moderate COVID-19 and 28 (18%) with a
severe case of the disease. An initial cancer diagnosis >24 months before COVID-19
[OR: 1.74 (95% CI: 0.71–4.26)], presenting with fever [6.21 (1.76–21.99)], dyspnea
[2.60 (1.00–6.76)], gastro-intestinal symptoms [7.38 (2.71–20.16)], or higher levels of
C-reactive protein [9.43 (0.73–121.12)] were linked with greater COVID-19 severity.
During a median follow-up of 37 days, 34 patients had died of COVID-19 (22%). Being
of Asian ethnicity [3.73 (1.28–10.91)], receiving palliative treatment [5.74 (1.15–28.79)],
having an initial cancer diagnosis >24 months before [2.14 (1.04–4.44)], dyspnea [4.94
(1.99–12.25)], and increased CRP levels [10.35 (1.05–52.21)] were positively associated
with COVID-19 death. An inverse association was observed with increased levels of
albumin [0.04 (0.01–0.04)].
Conclusions: A longer-established diagnosis of cancer was associated with increased
severity of infection as well as COVID-19 death, possibly reflecting the effects a more
advanced malignant disease has on this infection. Asian ethnicity and palliative treatment
were also associated with COVID-19 death in cancer patients.
Keywords: COVID-19, cancer, SARS-CoV-2, outcomes, directed acyclic graph

Russell et al. COVID-19 and Cancer
INTRODUCTION
In the context of cancer, the COVID-19 pandemic has led to
a series of challenging decisions that must be made (1,2).
Patient visits to the cancer clinic increase the potential risk
of infection when the alternative is self-isolation at home, and
some cancer treatments may predispose patients to moderate or
severe harmful effects of COVID-19 (3,4). Current precautionary
management decisions being made for cancer patients are
based on assumptions supported by limited evidence, based
on small case series from China and Italy (5–13) and larger
series from New York (14,15) and a recent consortium of
900 patients from over 85 hospitals in the USA, Canada, and
Spain (16). As a result of their limited sample sizes, most
studies were not able to distinguish between the effects of
age, cancer, and other comorbidities on COVID-19 outcomes
in this population (17,18). Moreover, the case series from
New York analyzed which patient characteristics are associated
with COVID-19 death, but only made a comparison with non-
cancer patients (14,15). The first results of the COVID-19
and Cancer Consortium provide insights from a large cohort
in terms of COVID-19 mortality, though a wide variety of
institutions with different COVID-19 testing procedures were
included (16). In addition, recently published prognostic studies
in COVID-19 positive patients have been judged to be at high
risk of bias, mainly due to non-representative selection of control
patients, exclusion of patients who had not experienced the
event of interest by the end of the study, high risk of model
overfitting, and limited information on model building strategies
used (19).
It can be difficult to confidently diagnose COVID-19
symptoms in cancer patients, as presenting features of the
infection are often similar to cancer symptoms and treatment-
related adverse events (17,20). This may result in a delayed
or missed COVID-19 diagnosis, which could lead to the
confounding of cases and infection mortality rates, as well
as late interventions for more life-threatening diseases (21).
In addition, COVID-19 may be a barrier to dignified and
humane end-of-life cancer care (17). Finally, the pandemic
is causing huge service reconfiguration for both curative
and palliative oncology care, resulting in fewer clinic visits
due to social distancing (22), cessation of screening, and
delays or changes in treatments that will inevitably have
serious impacts on cancer-related mortality and morbidity
(17,21). Our recent systematic review reported there is
currently no definitive evidence that specific cytotoxic drugs
are contraindicated in cancer patients infected with COVID-
19 (23).
Larger studies with multivariate models are urgently
warranted to further explore this intersection of COVID-19
and cancer in terms of clinical outcomes, so as to inform
oncological care during this outbreak and potential future
pandemics (24). Guy’s Cancer Center in South-East London,
which treats ∼8,800 patients annually, including 4,500 new
diagnoses, is one of the largest Comprehensive Cancer
Centers in the UK and is currently at the epicenter of the
UK COVID-19 epidemic.
METHODS
Study Population
Guy’s Cancer Cohort, a research ethics committee approved
research database (Reference number: 18/NW/0297) of all
routinely collected clinical data of cancer patients at Guy’s and
St Thomas’ NHS Foundation Trust (GSTT), forms the basis of
this observational study (25). The database contains routinely
collected prospective and retrospective demographic/clinical
data on all cancer patients treated at Guy’s Cancer Center.
We have an established clinical database for all cancer patients
tested for COVID-19 either in outpatient clinics or ward
settings since 29 February 2020. Using the unique hospital
number, these databases were merged prior to anonymization
for research purposes. We assessed outcomes included in the
core outcome sets currently being developed for COVID-19 to
ensure all relevant information is collected in our COVID specific
database (26).
We have included cancer patients who received a diagnosis
of COVID-19 from a positive PCR test from 29th February
to 12th May 2020. Until 30 April 2020, a COVID-19 test was
ordered for cancer patients if they presented with symptoms
necessitating hospitalization or if they were scheduled to undergo
a cancer-related treatment. From 1 May 2020, COVID-19 testing
was introduced as a part of standard care, with about 25% of
patients being swabbed daily depending on staff and testing kit
availability. A total of 1,507 patients were tested between 29
February and 12 May 2020, of whom 156 had COVID-19 (10%).
Statistical Methods
In this analysis of our data, we had three aims:
1) To describe demographic and clinical characteristics of
COVID-19 positive cancer patients, in terms of their COVID-
19 and cancer diagnoses.
2) To identify which demographic and/or clinical factors were
associated with COVID-19 severity in cancer patients.
3) To identify which demographic and/or clinical factors were
associated with COVID-19 death in cancer patients.
Descriptive statistics were used to address the first aim. Most
variables had several categories for the purpose of these
descriptive analyses but were collapsed for the purpose of
regression analyses due to the sample size of our cohort. Socio-
economic status (low, middle, high) was determined based on
the English Indices of Multiple Deprivation for postcodes (27).
Lymphocyte count (×109) was categorized as ≤0.5, 0.6–0.8, 0.9–
1.2, and >1.2 based on the Common Terminology Criteria for
Adverse Events v.5 (CTCAE). For the other laboratory variables,
we created tertiles instead of clinical cut-offs due to cancer
patients already having abnormal values for most of these blood
markers (Ferritin, C-reactive protein, and albumin). Radical
treatment was defined as those patients with a chance of long-
term survival or cure.
For the second aim, we conducted logistic regression analyses.
Mild/moderate COVID-19 was defined as pneumonia with or
without sepsis (i.e., those patients managed on the ward),
whereas severe COVID-19 was defined as acute respiratory
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Russell et al. COVID-19 and Cancer
distress syndrome (ARDS) or septic shock (i.e., those patients
where severity reached the criteria for Intensive Care Unit
admission, if deemed clinically appropriate). These definitions
were based on the WHO COVID-19 classification (28). The
models used to quantify the association between each factor and
COVID-19 severity were defined through a directed acyclic graph
(DAG) (Figure A1). Each factor was individually set as the main
exposure variable in the model in order to determine the minimal
adjustments required for each factor (Table A1).
The third research aim was addressed with Cox proportional
hazards regression analyses, whereby the models were defined
as above (Table A1). Follow-up was defined from the date of
COVID testing until death or 12 May 2020.
All statistical analyses were conducted with STATA
version 15.1.
RESULTS
Demographic and Clinical Characteristics
of COVID-19 Positive Cancer Patients
One hundred and twenty-eight patients (82%) presented with
mild/moderate COVID-19 and 28 patients (18%) with severe
COVID-19 (Table 1). More patients were male (58%) and aged
60+(68%; median age: 67). However, 14% of the cancer
population was aged <50 years (n=21; median age: 41). When
stratified by COVID grade, more male cancer patients presented
with severe disease (68%). Most patients were from a lower socio-
economic background (81%). With respect to ethnicity, about
half were White, 22% were Black (n=32), and 4% were of
Asian (n=6) origin. When stratified by COVID grade, a slightly
larger proportion of patients from a white ethnic background
had severe COVID (57%). Hypertension was the most reported
comorbidity (47%), followed by diabetes mellitus (22%), renal
impairment (19%), and cardiovascular disease (19%). However,
benign lung conditions were more commonly reported for those
who presented with severe COVID-19 (29 vs. 13% in those with
mild COVID-19).
The most frequently reported tumor types were
urological/gynecological (29%), followed by hematological
(18%) and breast (15%) (Table 2). The first group (n=45)
comprised of 21 prostate, 8 renal, 5 bladder, and 11 gynecological
cancers. Of the 28 hematological malignancies, four (14.3%)
were myeloid and 24 (85.7%) were lymphoid. Of all cancer
patients tested for COVID-19, 80 were positive after their
cancer-related hospital admission (51%), of which 61 were
solid tumors (76%) and 19 were hematological cancers (24%).
When stratified by COVID-19 severity, the largest proportion
of cancers presenting with severe COVID were hematological
(36%). A large proportion of patients had advanced cancer (40%
stage IV) and were diagnosed with their malignancy in the last
12 months (46%).
Overall, 39% of patients were receiving palliative treatment,
25% were receiving radical treatment, and 12% were treatment
naive. Treatment distributions were reasonably comparable
between COVID-19 severity groups. Of the 81 patients on
systemic treatment within the last 2 years, 54 were in a palliative
setting; of these, 50% were 1st line, 33% 2nd line, and 13% on
≥3rd treatment line. However, the majority of severe COVID-19
patients were on third line metastatic treatment. Table 2 provides
further details on the cancer characteristics.
Forty six percentage of the cancer patients diagnosed with
COVID-19 in this cohort presented with a cough and 52% had
a fever. Most patients were molecularly diagnosed within seven
days of their initial symptoms (58%) (Table 3). More patients in
the severe COVID-19 group presented with C-reactive protein
(CRP) values in the highest tertile (46 vs. 22% for mild/moderate
disease). Similarly, they had a lower lymphocyte count [43 vs.
21% in the lowest category (≤0.5)] and lower albumin levels (39
vs. 22% in the lowest tertile).
Factors Associated With COVID-19
Severity in Cancer Patients
The odds ratios (ORs) for the associations between the various
demographic and clinical factors and COVID-19 severity status
are shown in Table 4. There was a non-statistically significant
indication that those patients who were diagnosed with cancer
more than 24 months ago were at a higher risk of presenting
with severe COVID-19 as compared to those diagnosed during
the last 24 months [OR: 1.74 (95% CI: 0.71–4.26)]. With
respect to symptom presentation, those presenting with a fever,
dyspnea, or gastro-intestinal symptoms were at a higher risk of
having severe COVID-19 as compared to those without these
symptoms [OR: 6.21 (1.76–21.99), 2.60 (1.00–6.76), and 7.38
(2.71–20.16), respectively].
Factors Associated With COVID-19 Death
in Cancer Patients
During a median follow-up of 37 days (IQR: 18–49), 34 cancer
patients had died of COVID-19 (22%) (Table 5). Several cancer
patient characteristics were found to be positively associated with
risk of COVID-19 death: being of Asian ethnicity [as compared
to white—HR: 3.73 (95% CI: 1.28–10.91)], receiving palliative
treatment [as compared to no active treatment—HR: 5.74 (95%
CI: 1.15–28.79)], time since cancer diagnosis >24 months [as
compared to ≤24 months—HR: 2.14 (95% CI: 1.04–4.44)],
presenting with dyspnea [as compared to no dyspnea—HR: 4.94
(95% CI: 1.99–12.25)], and having high CRP levels [3rd tertile
vs. 1st tertile—HR: 10.35 (95% CI: 1.05–52.21)]. In addition, an
inverse association with death from COVID-19 was observed
with normal albumin levels [3rd tertile vs. 1st tertile—HR: 0.04
(95% CI: 0.01–0.04)].
DISCUSSION
Using multivariate modeling based on a directed acyclic graph,
this study reports on a large cohort of COVID-19 positive
cancer patients from a single institution. Low SES, hypertension,
and diabetes were common in cancer patients with COVID-
19. Age, sex, ethnicity, SES, and current cancer treatment
were found to not be associated with severity of COVID-
19 infection in cancer patients. However, receipt of a cancer
diagnosis more than 24 months previously (as compared
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Russell et al. COVID-19 and Cancer
TABLE 1 | Demographic characteristics of COVID-19 positive cancer patients.
Total (n=156) WHO COVID grade
Mild/moderate (n=128) Severe (n=28)
n%n%n%
Sex
Male 90 57.70 71 55.50 19 67.90
Female 66 42.30 57 44.50 9 32.10
Age
<50 21 13.50 16 12.50 5 17.90
50–59 29 18.60 24 18.80 5 17.90
60–69 43 27.60 35 27.30 8 28.60
70–79 35 22.40 28 21.90 7 25.00
≥80 28 17.90 25 19.50 3 10.70
Mean (SD) 65.18 (14.80) 65.74 (14.39) 62.62 (16.41)
SES
Low 126 80.80 106 82.80 20 71.40
Medium 1 0.60 1 0.80 0 0.00
High 16 10.30 12 9.40 4 14
Missing 13 8.30 9 7.00 4 14.30
Ethnicity
White British 66 42.30 50 39.10 16 57.10
White other 12 7.70 9 7.00 3 10.70
Black Caribbean 8 5.10 8 6.30 0 0.00
Black African 15 9.60 14 10.90 1 3.60
Black other 12 7.70 9 7.00 3 10.70
Asian 6 3.80 4 3.10 2 7.10
Mixed 2 1.30 2 1.60 0 0.00
Other 5 3.20 4 3.10 1 3.60
Unknown 30 19.20 28 21.90 2 7.10
Comorbidities
Hypertension 74 47.40 63 49.20 11 39.30
Diabetes mellitus 35 22.40 31 24.20 4 14.30
Lung conditions 25 16.00 17 13.30 8 28.60
Renal impairment 30 19.20 26 20.30 4 14.30
Liver conditions 3 1.90 3 2.30 0 0.00
CVD 29 18.60 24 18.80 5 17.90
Frailty 10 6.40 6 4.70 4 14.30
Chronic steroid use 4 2.60 4 3.10 0 0.00
No. of comorbidities
0 43 27.60 34 26.60 9 32.10
1 48 30.80 40 31.30 8 28.60
2 33 21.20 28 21.90 5 17.90
3+32 20.50 26 20.30 6 21.40
Smoking history
Never 59 37.80 51 39.80 8 28.60
Current 11 7.10 9 7.00 2 7.10
Ex-smoker 39 25.00 31 24.20 8 28.60
Unknown 47 30.10 37 28.90 10 35.70
Medications
Polypharmacy 68 43.60 57 44.50 11 39.30
NSAIDs 20 12.80 16 12.50 4 14.30
ACE/ARB 33 21.20 29 22.70 4 14.30
Beta-blockers 24 15.40 20 15.60 4 14.30
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Russell et al. COVID-19 and Cancer
TABLE 2 | Tumor characteristics of COVID-19 positive cancer patients.
Total (n=156) WHO COVID grade
Mild/moderate (n=128) Severe (n=28)
n%n%n%
Cancer type
Urological/gynae 45 28.80 39 30.50 6 21.40
Gastro-intestinal 21 13.50 19 14.80 2 7.10
Hematological 28 17.90 18 14.10 10 35.70
Skin/head and neck/sarcoma 10 6.40 9 7.00 1 3.60
Central nervous system 11 7.10 10 7.80 1 3.60
Breast 24 15.40 21 16.40 3 10.70
Lung 17 10.90 12 9.40 5 17.90
Cancer stage
I 17 10.90 17 13.30 0 0.00
II 23 14.70 21 16.40 2 7.10
III 22 14.10 18 14.10 4 14.30
IV 63 40.40 49 38.30 14 50.00
Missing 31 19.90 23 18.00 8 28.60
Risk category* (n=4)
Low 1 25.00 0 0.00 1 33.33
Intermediate 2 50.00 1 100.00 1 33.33
High 1 25.00 0 0.00 1 33.33
Treatment paradigm
Treatment naive 18 11.50 16 12.50 2 7.10
Neoadjuvant 7 4.50 7 5.50 0 0.00
Adjuvant 8 5.10 8 6.30 0 0.00
Radical 38 24.40 28 21.90 10 35.70
Palliative 60 38.50 49 38.30 11 39.30
Watch and wait 7 4.50 7 5.50 0 0.00
Surveillance 12 7.70 10 7.80 2 7.10
Missing 6 3.80 3 2.30 3 10.70
Line of palliative treatment (N=54)
1 27 50.00 23 52.27 4 40.00
2 18 33.33 13 29.55 5 50.00
3 6 11.11 5 11.36 1 10.00
4 1 1.90 1 2.27 0 0.00
Missing 2 3.70 2 4.55 0 0.00
Systemic treatment (N=81)
Systemic chemotherapy 45 55.60 34 53.10 11 64.60
Immunotherapy 7 8.60 5 7.80 2 11.80
Biological 13 16.00 11 17.20 2 11.80
Targeted therapy 5 6.20 5 7.80 0 0.00
Combination therapy 11 13.60 9 14.10 2 11.80
Time since cancer diagnosis
<3 months 41 26.30 34 26.60 7 25.00
3–12 months 30 19.20 25 19.50 5 17.90
12–24 months 20 12.80 18 14.10 2 7.10
>24 months 57 36.50 45 35.20 12 42.90
Performance status
0 19 12.20 17 13.30 2 7.10
1 43 27.60 34 26.60 9 32.10
2 33 21.20 27 21.10 6 21.40
3 14 9.00 13 10.20 1 3.60
4 6 3.80 5 3.90 1 3.60
*For myeloid malignancies only.
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Russell et al. COVID-19 and Cancer
TABLE 3 | COVID-19 presentation of COVID-19 positive cancer patients.
Total (n=156) WHO COVID grade
Mild/moderate (n=128) Severe (n=28)
n%n%n%
Symptoms
Cough 72 46.20 57 44.50 15 53.60
Fever 81 51.90 59 46.10 22 78.60
Dyspnoea 55 35.30 41 32.00 14 50.00
Gastro-intestinal symptoms 25 16.00 13 10.20 12 42.90
Time between first symptom and diagnosis
<7 days 90 57.70 71 55.50 19 67.90
7–14 days 27 17.30 22 17.20 5 17.90
>14 days 14 9.00 11 8.60 3 10.70
Missing 25 16.00 24 18.80 1 3.60
Care setting
Outpatient 36 23.10 36 28.10 0 0.00
Inpatient 105 67.30 90 70.30 15 53.60
ITU 13 8.30 0 0.00 13 46.40
Missing 2 1.30 2 1.60 0 0.00
Laboratory values*
Ferritin (µg/L)
T1 (80–793) 18 11.50 14 10.90 4 14.30
T2 (891–1,442) 18 11.50 13 10.20 5 17.90
T3 (1,596–5,958) 17 10.90 10 7.80 7 25.00
Missing 103 66.00 91 71.10 12 42.90
CRP (mg/L)
T1 (3–41) 44 28.20 38 29.70 6 21.40
T2 (42–117) 39 25.00 30 23.40 9 32.10
T3 (126–508) 41 26.30 28 21.90 13 46.40
Missing 32 20.50 32 25.00 0 0.00
Lymphocytes (×109)
≤0.5 39 25.00 27 21.10 12 42.90
0.6–0.8 38 24.40 30 23.40 8 28.60
0.9–1.2 27 17.30 25 19.50 2 7.10
>1.2 27 17.30 21 16.40 6 21.40
Missing 25 16.00 25 19.50 0 0.00
Albumin (g/L)
T1 (20–32) 39 25.00 28 21.90 11 39.30
T2 (33–38) 43 27.60 32 25.00 11 39.30
T3 (39–57) 34 21.80 32 25.00 2 7.10
Missing 40 25.60 36 28.10 4 14.30
*Distribution shown in tertiles (T).
to within 24 months) and presenting with fever, dyspnea,
or gastro-intestinal symptoms were linked with higher odds
of developing severe illness as compared to mild/moderate
COVID-19. Higher levels of CRP and ferritin were also
associated with more severe COVID-19 disease in infected cancer
patients. During a median follow-up of 37 days, the following
cancer patient characteristics were found to be positively
associated with COVID-19 death: Asian ethnicity, palliative
treatment, initial cancer diagnosis >24 months, dyspnea at
presentation, and high CRP levels. Normal serum albumin
levels were inversely associated with death from COVID-19 in
cancer patients.
Demographic and Cancer Characteristics
Several retrospective cohort studies published using data from
hospitals situated in Wuhan, China, Northern Italy, Canada, and
the USA have reported on the clinical characteristics of COVID-
19 positive cancer patients with sample sizes varying from 9 to
85, two slightly larger series of >200 patients, and a big data
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Russell et al. COVID-19 and Cancer
TABLE 4 | Odds Ratios and 95% confidence intervals for COVID-19 severity in
cancer patients.
OR* 95% CI
Sex
Male 1.00 Ref
Female 0.59 (0.25–1.40)
Age
≤60 1.00 Ref
>60 0.82 (0.35–1.93)
SES
Low 1.00 Ref
Middle N/A N/A
High 2.11 (0.51–8.67)
Ethnicity
White 1.00 Ref
Black 0.40 (0.13–1.28)
Asian 1.55 (0.26–9.16)
Other 0.52 (0.06–4.57)
Number of comorbidities
0 1.00 Ref
1 1.09 (0.29–4.07)
2 1.04 (0.23–4.68)
3+1.26 (0.29–5.41)
P for trend 0.776
Smoking history
Never 1.00 Ref
Ever 1.39 (0.36–5.33)
Cancer type
Solid 1.00 Ref
Hematological 3.14 (0.70–14.03)
Treatment paradigm
No active treatment 1.00 Ref
Radical/curative 2.76 (0.34–22.16)
Palliative 3.40 (0.50–23.20)
Time since cancer diagnosis
≤24 months 1.00 Ref
>24 months 1.74 (0.71–4.26)
Performance status
0–2 1.00 Ref
3+0.33 (0.04–2.64)
Symptoms
Cough 1.27 (0.53–3.01)
Fever 6.21 (1.76–21.99)
Dyspnea 2.60 (1.00–6.76)
GI symptoms 7.38 (2.71–20.16)
Time between first symptom
and diagnosis
<7 days 1.00 Ref
7–14 days 0.85 (0.28–2.54)
>14 days 1.02 (0.26–4.02)
CRP (mg/L)
T1 (3–41) 1.00 Ref
T2 (42–117) 1.95 (0.19–20.25)
(Continued)
TABLE 4 | Continued
OR* 95% CI
T3 (126–508) 9.43 (0.73–121.12)
Lymphocytes (×109)
≤0.5 1.00 Ref
0.6–0.8 0.49 (0.06–3.73)
0.9–1.2 0.27 (0.03–2.34)
>1.2 0.63 (0.07–5.84)
Albumin (g/L)
T1 (20–32) 1.00 Ref
T2 (33–38) 1.13 (0.18–7.00)
T3 (39–57) 0.07 (0.01–0.96)
*Adjustment as defined by the DAG (Table A1).
Consortium including over 85 institutions and 900 patients (5–
12,16). The median age reported in these studies was similar to
our study, with a range from 63 to 72 years. A larger proportion
of male patients has been observed. Lung cancer was the most
commonly reported cancer in the Zhang et al. (5), Yu et al.
(8), Yang et al. (10), and Stroppa et al. (11) studies, but only
accounted for 11% in our patient population. Zhang et al. (5)
estimated that in their cohort, 29% of patients tested positive
for COVID-19 following hospital admission, whereas this was
estimated at 51% in our cohort. Interpretation of this statistic is
difficult given the latency between exposure and manifestations
of infection, meaning patients diagnosed after admission may
have been infected outside of the hospital. Several studies (6,10)
noted that hypertension, diabetes, and coronary heart disease
were the most commonly reported comorbidities.
Our cancer cohort is similar in distribution of age, sex, and
comorbidities to the case series reported to date. The ethnicity
and SES of our COVID-19 positive cancer patients are most
likely a reflection of the catchment area of our Cancer Center
in South-East London (29), covering more deprived boroughs
(Lambeth and Southwark). Based on the number of cancer
patients treated at our Cancer Center in 2019, about 49% of
patients are of a White ethnic background. Variations observed
with the published data in terms of cancer type, stage, and
treatment may be a reflection of clinical practice (e.g., intensity
of treatment and frequency of hospital visits), of relative cancer
incidence, or of extent of treatment changes introduced as
mitigation in the face of the emerging pandemic. For example, the
most recently reported age-standardized lung cancer incidence
rates for males and females in Wuhan are 54.1 and 19.1 per
100,000, whereas these are estimated to be 37.5 and 24.3 per
100,000 in London (30). Early modification and prioritization
of treatment was introduced at our center, in accordance with
now-published guidance (31).
COVID-19 Characteristics and Severity
Comparably to our study, both the Zhang and the Du studies
also reported fever, cough, shortness of breath, and dyspnea as
common clinical features (5,6). As in the Chinese cohort of
85 fatal cases, our severe COVID-19 patients had comparable
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Russell et al. COVID-19 and Cancer
TABLE 5 | Hazard Ratios and 95 %Confidence intervals for COVID-19 death in
cancer patients.
Variable Number of
deaths (n=34)
HR* 95% CI
Sex
Male 22 1.00 Ref
Female 12 0.73 (0.36–1.47)
Age
≤60 9 1.00 Ref
>60 25 1.34 (0.63–2.87)
SES
Low 26 1.00 Ref
Middle 0 N/A
High 3 1.03 (0.29–3.60)
Ethnicity
White 21 1.00 Ref
Black 5 0.51 (0.19–1.35)
Asian 4 3.73 (1.28–10.91)
Other 2 1.52 (0.35–6.49)
Number of comorbidities
0 9 1.00 Ref
1 8 1.20 (0.39–3.74)
2 8 1.92 (0.58–6.34)
3+9 1.14 (0.34–3.82)
P for trend 0.749
Smoking history
Never 15 1.00 Ref
Ever 9 1.00 (0.97–1.03)
Cancer type**
Solid 27 1.00 Ref
Hematological 7 0.22 (0.05–1.05)
Treatment paradigm
No active treatment 2 1.00 Ref
Radical/curative 6 1.35 (0.21–8.57)
Palliative 19 5.74 (1.15–28.79)
Time since cancer
diagnosis
≤24 months 15 1.00 Ref
>24 months 17 2.14 (1.04–4.44)
Performance status
0–2 20 1.00 Ref
3+5 0.56 (0.15–2.02)
Symptoms
Cough 16 1.00 (0.48–2.09)
Fever 21 1.63 (0.72–3.68)
Dyspnea 21 4.94 (1.99–12.25)
GI symptoms 8 1.44 (0.64–3.26)
Time between first
symptom and diagnosis
<7 days 23 1.00 Ref
7–14 days 6 0.86 (0.35–2.11)
>14 days 2 0.54 (0.13–2.29)
CRP (mg/L)
T1 (3–41) 4 1.00 Ref
(Continued)
TABLE 5 | Continued
Variable Number of
deaths (n=34)
HR* 95% CI
T2 (42–117) 9 2.87 (0.61–13.48)
T3 (126–508) 18 10.35 (1.05–52.21)
Lymphocytes (×109)
≤0.5 11 1.00 Ref
0.6–0.8 12 0.84 (0.21–3.38)
0.9–1.2 4 0.96 (0.20–4.57)
>1.2 4 0.75 (0.14–4.12)
Albumin (g/L)
T1 (20–32) 18 1.00 Ref
T2 (33–38) 8 0.50 (0.17–1.47)
T3 (39–57) 1 0.04 (0.01-0.42)
*Adjustment as defined by the DAG (Table A1).
**Unadjusted due to missingness not allowing to run fully adjusted model as per the DAG.
laboratory findings: decreased lymphocytes, increased CRP, and
decreased albumin (6).
Severe events were reported for 54% of the study population
and mortality for 29% in the Zhang study, as compared
to 18 and 22% in our cohort. Zhang et al. also reported
that recent treatment within 14 days was associated with
an increased risk of developing severe events (28 days) (5).
This difference with our observations may be attributed to
different definitions of severe events, as it was not entirely
clear how these were defined by Zhang et al. As highlighted
by Wynants et al. in their assessment of current statistical
models published for COVID-19 (19), there is a need for
consistent use of outcome definitions. However, our observations
of a positive association with CRP levels is in line with most
COVID-19 studies published to date (32). Apart from the
CCC-19 Consortium (16), no study to date has specifically
looked at COVID-19 severity at presentation in COVID-19
positive cancer patients and hence our observation of an
association with time since cancer diagnosis and presenting
symptoms needs further validation in other large cohorts with
homogenous definitions of inclusion criteria, testing strategies,
and outcome measures. However, it is possible that time since
cancer diagnosis is also a reflection of the extent of the disease and
progression along the palliative patient pathway from diagnosis
to death.
COVID-19 Death
The study by Yu et al. reported three deaths (25%) (8). In
the larger series from New York, Mehta et al. reported an
overall case fatality rate of 28%, with 37% for hematological
malignancies and 25% for solid tumors (14). The CCC-19
Consortium reported a 30-day mortality rate due to COVID-
19 of 13% (16). In our cohort, the overall case fatality rate
was 22%, with 25% for hematological cancers and 21% for
solid tumors. As more than 85 institutions were included in
the CCC-19 Consortium (16), it is possible that differences in
COVID-19 practice as well as cancer treatments between the
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Russell et al. COVID-19 and Cancer
numerous centers may explain the slightly lower death rate
as compared to reports from single center studies. Moreover,
our median follow-up is 37 days as compared to 21 days for
the Consortium. The heterogeneity between centers may also
explain why performance status was found to be associated with
COVID-19 outcomes, an observation not identified in our single
center cohort.
Our observations of Asian ethnicity being associated with
increased mortality from COVID-19 in cancer patients is of
interest, given the recent speculations about the disproportionate
effects of COVID-19 on Black, Asian, and minority ethnic
communities (33) as well as the confounding factor of vitamin D
deficiency (34). However, longer follow-up studies are required
to disentangle the association between ethnicity and COVID-19
death in cancer patients.
Strengths and Limitations
Whilst this is one of the largest single center COVID-19 positive
cancer cohorts to date, our sample size is still relatively modest
and hence confidence intervals for some statistically significant
observations are still wide. No firm conclusions in terms of
prognostic modeling can be drawn as of yet (19). Current
analyses aimed to generate further hypotheses on patient or
tumor characteristics indicative of severity or of death from
COVID-19 in the context of cancer. Our data for some of the
patient characteristics is limited; for example, smoking status
was missing for 29% of patients and hence likely underestimates
the proportion of smokers. COVID testing in the UK has only
been implemented gradually during the period of our data
collection, and there is selection bias in favor of patients being
tested as inpatients. Our analysis is likely to have missed cancer
outpatients under our care diagnosed with COVID-19 at other
hospitals—however this is most likely to be an even more
important issue for global Consortia with many hospitals only
adding a few cases to the overall dataset.
In light of these differences in COVID-19 management and
cancer treatments between centers, it is important to note that
our hospital has a specialized highly infectious disease unit
with extracorporeal membrane oxygenation (ECMO) facilities,
which ensured very experienced critical care management of
COVID patients. A standard clinical approach was used, with
concurrent antibiotics to cover bacterial infection and early
escalation of treatment decisions, including appropriateness of
ITU admission. No standard use of other agents (steroids or
antivirals) was applied unless within the context of a clinical trial.
Moreover, our general oncological approach was to maintain
standard anti-cancer treatment (including surgery, radiotherapy,
and systemic treatments) where it was safe and reasonable to do
so. Our Cancer Center managed to continue oncology services
throughout the COVID pandemic, whereas other Centers may
have redeployed staff that precluded this.
It is also a strength of our study that we used clearly defined
definitions of COVID-19 severity, as well as a DAG to develop the
different models, as to date very limited knowledge is available
regarding the intersection between COVID-19 and cancer (19).
Detailed information on our modeling will help comparison with
future studies with larger sample sizes and longer follow-ups.
CONCLUSION
Our analysis of one of the largest single center series of
COVID-19 positive cancer patients to date confirms a similar
distribution of age, sex, and comorbidities as reported for
other populations. Reflecting the general population, presenting
with fever, dyspnea, gastro-intestinal symptoms, higher levels
of CRP, or ferritin were also indicators of COVID-19 severity
in the cancer population. Similarly, we noted that dyspnea
at presentation, high CRP levels, and low levels of albumin
were associated with death from COVID-19. With respect to
cancer specific observations, patients who have lived longer
with their cancer were found to be more susceptible to a
greater infection severity, possibly reflecting the effect of a more
advanced malignant disease-as almost half of the severe cohort
were on third line metastatic treatment-or the impact of this
infection. The latter was also found to be associated with COVID-
19 death in cancer patients, as were being of Asian ethnicity
and receiving palliative treatment. Further validation will be
provided from other large case series, as well as from those
including longer follow-ups, to provide more definite guidance
for oncological care.
DATA AVAILABILITY STATEMENT
Data can be obtained by researchers via an application to the
Access Committee of Guy’s Cancer Cohort. An application form
can be obtained via Charlotte Moss, charlotte.moss@kcl.ac.uk.
ETHICS STATEMENT
Guy’s Cancer Cohort, a research ethics committee approved
research database (Reference Number: 18/NW/0297) of all
routinely collected clinical data of cancer patients at Guy’s and
St Thomas’ NHS Foundation Trust (GSTT), forms the basis of
this observational study.
AUTHOR CONTRIBUTIONS
BR, CM, PR, DC, HW, FC, AH, KZ, FR, AS-L, DJ, SD, ML, SGh,
ES, AD’S, SGo, DE, PF, DW, KR, MG, KB, RD, MS, and AS: data
collection. BR, CM, SP, SI, PR, JS, SD, and MVH: study design.
BR, CM, MVH, and SD: data analysis. MVH, BR, CM, SD, SP,
RS, PR, JS, SK, and CH: manuscript drafting. All authors: final
approval of manuscript.
FUNDING
This research was supported by the National Institute for Health
Research (NIHR) Biomedical Research Centre (BRC) based at
Guy’s and St Thomas’ NHS Foundation Trust and King’s College
London (IS-BRC-1215-20006). The authors are solely responsible
for study design, data collection, analysis, decision to publish, and
preparation of the manuscript. The views expressed are those of
the authors and not necessarily those of the NHS, the NIHR, or
the Department of Health. We also acknowledge support from
Frontiers in Oncology | www.frontiersin.org 9July 2020 | Volume 10 | Article 1279

Russell et al. COVID-19 and Cancer
Cancer Research UK King’s Health Partners Centre at King’s
College London and Guy’s and St Thomas’ NHS Foundation
Trust Charity Cancer Fund.
ACKNOWLEDGMENTS
We are grateful to Graham Roberts for providing us with
the descriptive statistics of the cancer patients treated at our
Cancer Centre. This article has been released as a preprint at
medRxiv (35).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fonc.
2020.01279/full#supplementary-material
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Russell, Moss, Papa, Irshad,Ross, Spicer, Kordasti, Crawley, Wylie,
Cahill, Haire, Zaki, Rahman, Sita-Lumsden, Josephs, Enting, Lei, Ghosh, Harrison,
Swampillai, Sawyer, D’Souza, Gomberg, Fields, Wrench, Raj, Gleeson, Bailey, Dillon,
Streetly, Rigg, Sullivan, Dolly and Van Hemelrijck. This is an open-access article
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The use, distribution or reproduction in other forums is permitted, provided the
original author(s) and the copyright owner(s) are credited and that the original
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