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RESEARCH ARTICLE
Neutralizing antibodies and safety of a
COVID-19 vaccine against SARS-CoV-2
wild-type and Omicron variants in solid cancer
patients
Busyamas ChewaskulyongID
1
, Pattarapong Satjaritanun
1
, Thanika Ketpueak
1
,
Thatthan Suksombooncharoen
1
, Chaiyut Charoentum
1
, Nuttaphoom Nuchpong
2
,
Apichat TantraworasinID
3
*
1Division of Medical Oncology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University,
Chiang Mai, Thailand, 2Medical Oncology Outpatient Clinic, Maharaj Nakorn Chiang Mai Hospital, Chiang
Mai University, Chiang Mai, Thailand, 3Department of Surgery, General Thoracic Unit, Faculty of Medicine,
and Clinical Surgical Research Center, Chiang Mai University, Chiang Mai, Thailand
*Apichat.t@cmu.ac.th,ohm_med@hotmail.com
Abstract
Objective
The aim of this study was to assess the seroconversion rate and percent inhibition of neu-
tralizing antibodies against the wild-type and Omicron variants of SARS-CoV-2 in patients
with solid cancer who received two COVID-19 vaccine doses by comparing chemotherapy
and nonchemotherapy groups.
Methods
This prospective cohort study enrolled 115 cancer patients from Maharaj Nakorn Chiang
Mai Hospital, Sriphat Medical Center, Faculty of Medicine, Chiang Mai University, and
Chiang Mai Klaimor Hospital, Chiang Mai, Thailand, between August 2021 and February
2022, with data from 91 patients who received two COVID-19 vaccine doses analyzed. Par-
ticipants received vaccines as part of their personal vaccination programs, including various
mRNA and non-mRNA vaccine combinations. Blood samples were collected at baseline, on
day 28, and at 6 months post-second dose to assess neutralizing antibodies. The primary
outcome was the seroconversion rate against the wild-type and Omicron variants on day
28. Secondary outcomes included seroconversion at 6 months, factors associated with
seroconversion, and safety.
Results
Among the participants, 45% were receiving chemotherapy. On day 28, seroconversion
rates were 77% and 62% for the wild-type and Omicron variants, respectively. Chemother-
apy did not significantly affect seroconversion rates (p = 0.789 for wild type, p = 0.597 for
Omicron). The vaccine type administered was positively correlated with seroconversion,
with an adjusted odds ratio (95% confidence interval) of 25.86 (1.39–478.06) for the wild
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OPEN ACCESS
Citation: Chewaskulyong B, Satjaritanun P,
Ketpueak T, Suksombooncharoen T, Charoentum
C, Nuchpong N, et al. (2024) Neutralizing
antibodies and safety of a COVID-19 vaccine
against SARS-CoV-2 wild-type and Omicron
variants in solid cancer patients. PLoS ONE 19(11):
e0310781. https://doi.org/10.1371/journal.
pone.0310781
Editor: Harapan Harapan, Universitas Syiah Kuala,
INDONESIA
Received: July 13, 2024
Accepted: September 5, 2024
Published: November 7, 2024
Copyright: ©2024 Chewaskulyong et al. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The minimal data are
within the paper and its Supporting Information.
The full data set cannot be shared publicly due to
ethic committee law and are available upon
request, and the deidentified datasets are currently
available from the Medical Oncology Unit at the
Faculty of Medicine, Chiang Mai University, Chiang
Mai, Thailand.
type and 17.38 (3.65–82.66) for the Omicron variant with the primary heterologous vaccine
regimen. Grades 1 and 2 adverse events were observed in 34.0% and 19.7% of partici-
pants, respectively.
Conclusions
Despite the lower seroconversion rate against the Omicron variant, no significant difference
was observed between the chemotherapy and nonchemotherapy groups. COVID-19 vacci-
nations demonstrated good tolerability in this cohort. These findings highlight the impor-
tance of vaccine safety and immunogenicity in cancer patients and can inform tailored
vaccination strategies for this vulnerable population.
Introduction
COVID-19, an emerging infectious disease first reported in December 2019, is now a global
pandemic caused by SARS-CoV-2. SARS-CoV-2 entry into host cells triggers an immune
response, resulting in the release of inflammatory cytokines. This excessive inflammation
drives high morbidity and mortality [1,2]. In addition to wild-type viruses, novel variants sig-
nificantly impact disease transmissibility, severity and the immune response [3]. Five major
variants of concern (VOCs), including Alpha, Beta, Delta, Gamma and Omicron variants,
have been reported [4].
Reports indicate that COVID-19 outcomes are worse in individuals with comorbidities [5],
particularly in immunocompromised individuals such as cancer patients undergoing treat-
ments, especially chemotherapy. Generally, chemotherapy not only affects quality of life but
also dampens immunity, leading to increased susceptibility to and worse outcomes of infection
[6,7]. For COVID-19, cancer patients are more prone to severe infection outcomes, including
increased rates of intensive care unit (ICU) admission, mechanical ventilation, prolonged hos-
pital stays, and mortality [8,9].
Studies on cancer patients have revealed decreased humoral immunity after infection and
vaccination. Anti-spike antibodies and anti-nucleocapsid antibodies were once used as surro-
gate protective markers against SARS-CoV-2 infection in earlier studies [10]. Natural infection
leads to reduced nucleocapsid immunoglobulin G (N-IgG) and spike immunoglobulin G
(S-IgG) levels, especially after recent chemotherapy [11]. However, patients receiving immu-
notherapy presented increased antibody levels [12]. Similarly, mRNA-based vaccine studies
have shown lower seroconversion rates (proportions of patients who develop detectable pro-
tective antibodies [13]) in cancer patients (90–94% after two vaccine doses) [14–16], with
decreased neutralizing antibody levels against SARS-CoV-2 variants [17,18]. However,
humoral immunity declines over time, making a third booster dose necessary to maintain an
adequate level of immunity [19]. Owing to the poor prognosis of some cancers, which is influ-
enced by different factors, such as primary site, histological subtype, performance status, and
stage, patients may have a shorter estimated life expectancy [20,21], particularly those with
advanced or metastatic disease [22]. Achieving a higher seroconversion rate even after two vac-
cine doses should be a concern because prompt protective immunity may be beneficial in
these vulnerable patients to decrease susceptibility to SARS-CoV-2 infection and COVID-
19-related hospitalization [23]. Data from noncancer populations revealed that heterologous
prime-boosted vaccinations generated higher neutralizing antibody levels than did homolo-
gous vaccinations [24]. Further research is needed to obtain these data from cancer patients.
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Funding: This research was funded by the Faculty
of Medicine, Chiang Mai University. (MED-2564-
08326, CoV6/2565). The funders had no role in
study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
In this study, our objective was to assess the humoral-mediated immune response in terms
of the seroconversion rate and percent inhibition of neutralizing antibodies against the wild-
type and Omicron variants of SARS-CoV-2 in patients with solid cancer who received two
COVID-19 vaccine doses, comparing chemotherapy and nonchemotherapy groups. Addition-
ally, we aimed to investigate factors associated with antibody seroconversion on day 28 after
completing vaccination and adverse events following immunization.
Materials and methods
Study design and participants
This observational prospective cohort study was designed to evaluate humoral immunogenic-
ity in terms of surrogate neutralizing antibodies against the wild-type and XBB Omicron vari-
ants, as well as safety, in patients with solid cancer who received two doses of the CoronaVac
vaccine. However, the protocol was adapted and amended later to allow different vaccine com-
binations because of vaccine shortages and uncertainties regarding vaccine management by
the Thai government. Combinations of vaccines on different platforms, including mRNA vac-
cines with mRNA boosters, non-mRNA vaccines with non-mRNA boosters, and non-mRNA
vaccines with mRNA boosters, were allowed by the Ministry of Public Health of Thailand.
Notably, vaccine procurement and administration were not included in our study. This study
included adult solid cancer patients aged 20 years or above with a confirmed diagnosis of can-
cer by histology or imaging at any stage and undergoing any treatments, including patients
with complete remission of their disease within 1 year. The patients were required to have an
estimated life expectancy of more than six months. All patients were followed at the medical
oncology clinic at Maharaj Nakorn Chiang Mai Hospital, Sriphat Medical Center, Faculty of
Medicine, Chiang Mai University, and Chiang Mai Klaimor Hospital (private hospital),
Chiang Mai, Thailand. Exclusion criteria included having a previous diagnosis of SARS-CoV-
2 infection based on RT–PCR or antigen test kit (ATK) results in the past three months; having
high-risk epidemiological factors within the past 14 days, for example, having close contact
with an individual diagnosed with COVID-19 or visiting/living in an outbreak area; receiving
prior COVID-19 vaccines; receiving other live attenuated vaccines in the past four weeks or
inactivated and subunit vaccines in the past two weeks; having known allergies to any vaccine
components; having signs and symptoms of active skin infection at the injection site; having
HIV infection; receiving immunosuppressive drugs; receiving blood components within the
past three months; being pregnant; having uncontrolled medical conditions; and having hema-
tologic malignancies. The withdrawal criteria included inability to attend follow-up visits after
receiving the vaccine and not completing the vaccination program. Patients who completed
three vaccine doses and who had SARS-CoV-2 infection were also included. This study was
approved by the Ethics Committee of the Faculty of Medicine, Chiang Mai University, with
study code MED-2564-08326, approval number 348/2021. All participants received verbal and
written information about the study and provided informed consent. The recruitment period
for this study was from August 18, 2021, to February 28, 2022. This study was registered with
the Thai Clinical Trials Registry (TCTR) ID: TCTR20230510001.
Procedures and materials
Blood and data collection. A total of 157 patients with solid cancer were screened; 115
patients were enrolled in this study between August 2021 and February 2022. Data from 91
patients who had completed two vaccine doses were analyzed for neutralizing antibody levels.
Demographic data were obtained, and 6 mL of blood was drawn for baseline analysis from par-
ticipants on the day of signing the informed consent form. Each participant received a vaccine
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as part of their personal vaccination programs provided by the government or private hospi-
tals, which included homologous and heterologous vaccine regimens.
Blood was obtained at 28 days and 6 months after the second vaccine dose. Third booster
doses were allowed. Information on adverse events following immunization was collected dur-
ing follow-up at the oncology clinic. Blood samples from participants were collected, centri-
fuged and stored as plasma samples in liquid nitrogen at the Division of Clinical Immunology,
Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai Uni-
versity, Chiang Mai, Thailand, until further use for neutralizing antibody analysis and will be
kept until five years after the completion of all analyses. Neutralizing antibody analysis was
performed from March to September 2023.
Neutralizing antibody analysis. A surrogate virus neutralization assay kit (MAGLUMI,
Shenzhen New Industries Biomedical Engineering Co., Ltd.) was used for the wild-type neu-
tralizing antibody assay, and another neutralizing antibody kit (cPass™, GenScript, XBB variant
spike protein) was used for the XBB variant neutralizing antibody assay. This method mimics
the interaction between the host hACE2 receptor and the virus binding site via the recombi-
nant SARS-CoV-2 receptor binding domain (RBD). This assay has shown 100% sensitivity
and 100% specificity in clinical samples with confirmed SARS-CoV-2 virus neutralization titer
(VNT50) values �20. Assay results included the antibody level in IU/mL units (wild type
only), the percent inhibition, and whether the antibody was detected or undetected. Detected
neutralizing antibody was defined as an antibody level greater than 121.6 IU/mL for the wild
type and a percent inhibition greater than 30% for the Omicron variant. The XBB subvariant
of the SARS-CoV-2 Omicron variant was chosen for analysis because it was the most common
variant circulating in late 2022 [25].
Outcomes. The primary outcome was the seroconversion rate of neutralizing antibodies
at 28 days after completing two vaccine doses against SARS-CoV-2 infection for both the wild-
type and XBB Omicron variants. The secondary outcomes included the percent inhibition of
neutralizing antibodies, seroconversion rates at 6 months, factors associated with seroconver-
sion on day 28, and adverse events following immunization.
Statistical analysis. The required sample size was calculated to be 91 on the basis of previ-
ous seroconversion data. The data were analyzed per the protocol with the aim of reflecting
data on vaccine efficacy. Descriptive data are reported as numbers and percentages, means and
standard deviations (SDs), and medians and interquartile ranges. Chi-square tests and Fisher’s
exact tests were used to compare the baseline characteristics between the chemotherapy and
nonchemotherapy groups. The percent inhibition and seroconversion rates of neutralizing
antibodies are reported as the means with 95% confidence intervals and were analyzed via
repeated-measures mixed models across three time points (baseline, day 28, and month 6) and
SARS-CoV-2 variants (wild-type and Omicron variants). Univariable logistic regression analy-
sis was used to identify factors potentially associated with seroconversion. Factors with P <0.1
were further investigated via multivariable logistic regression analysis. Statistical significance
in each analysis was defined as P <0.05. All the statistical analyses were performed via
STATA/MP software version 17 (StataCorp LLC. College Station, TX, USA).
Results
Baseline characteristics
A total of 157 patients with solid cancer who planned to receive the COVID-19 vaccine were
screened, among whom 115 were ultimately enrolled in this study. Data from 91 patients who
had completed two vaccine doses were analyzed for neutralizing antibodies. The common rea-
sons for failed enrollment were the absence of vaccination (n = 21/157, 13.3%) and death
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before completion of the vaccine course (n = 18/157, 11.4%), one of which was a COVID-
19-related death. The failed screening and participant withdrawal data are depicted in Fig 1,
which shows the study flow. chart The vaccines included homologous mRNA-based vaccines
(mRNA+mRNA), homologous non-mRNA-based vaccines (non-mRNA+non-mRNA), and
heterologous vaccines (non-mRNA+mRNA) in 41 (45%), 31 (34%), and 19 (21%) participants,
respectively. Twenty-one of these individuals (23.0%) received a third-dose booster vaccine.
All patients were then classified into chemotherapy (n = 41, 45%) and nonchemotherapy
(n = 50, 55%) groups for further exploratory analysis, which were not prespecified subgroups.
Baseline demographic and disease characteristics are presented in Table 1. According to an
observational-only study, some parameters between groups were not well balanced. In the che-
motherapy group, male patients predominated (n = 26, 63.41%), whereas female patients pre-
dominated in the nonchemotherapy group (n = 31, 62.00%). The mean age and body mass
index (BMI) were 60.76 years and 23.44 kg/m
2
, respectively, for all participants. The most
common primary cancers were gastrointestinal (GI) cancer (n = 21, 51.2%) in the chemother-
apy group and breast cancer (n = 20, 40.0%) in the nonchemotherapy group. The baseline
white blood cell, neutrophil, and lymphocyte counts tended to be lower, and hematologic
adverse events during follow-up were more common in the chemotherapy group.
The vaccine regimens and their combinations are reported in Tables 2and 3, respectively.
Neutralizing antibody analysis
Seroconversion rate. The baseline seroconversion rate before vaccination was 0% in all
populations for both the wild-type and Omicron variants, confirming a seronegative status for
SARS-CoV-2 in all participants. The seroconversion rate of the surrogate neutralizing anti-
body for the wild-type SARS-CoV-2 variant at 28 days after completing vaccination in all par-
ticipants was 77% (95% CI: 67–85%). There was no significant difference in seroconversion
rate between the chemotherapy and nonchemotherapy groups (76% vs. 78%, p = 0.789). In
contrast, the seroconversion rates of the neutralizing antibody for the Omicron variant were
62% (95% CI, 51–72%), 59% (95% CI, 42–74%), and 64% (95% CI, 49–77%) in all participants
and in the chemotherapy and nonchemotherapy groups, respectively, with no difference
among the groups (p = 0.597). The seroconversion rate for the Omicron variant was lower
Fig 1. Study flow.
https://doi.org/10.1371/journal.pone.0310781.g001
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than that for the wild type in both treatment groups (p = 0.008 for both the chemotherapy and
nonchemotherapy groups). The seroconversion rates at 6 months did not differ from those on
day 28 for either variant and are provided in Tables 4and 5.
Table 1. Baseline demographic and disease characteristics.
Characteristics All (n = 91) Nonchemotherapy (n = 50) Chemotherapy (n = 41) pValue
Sex, n (%)
Male 45 (49.45%) 19 (38.00%) 26 (63.41%) 0.016
Female 46 (50.55%) 31 (62.00%) 15 (36.59%)
Age, years, mean ±SD 60.76 ±11.78 59.48 ±12.16 62.48 ±11.62 0.234
BMI, mean ±SD 23.44 ±4.48 23.21 ±4.29 (n = 48) 23.50 ±4.76 0.843
Smoking, n (%) 19 (20.88%) 7 (14.00%) 12 (29.27%) 0.075
Primary cancer, n (%)
GI 32 (35.16%) 11 (22.00%) 21 (51.22%) 0.009
HBP 15 (16.58%) 8 (16.00%) 7 (17.07%)
Breast 25 (27.47%) 20 (40.00%) 5 (12.20%)
Prostate 5 (5.49%) 2 (4.00%) 3 (7.32%)
Others 14 (15.38%) 9 (18.00%) 5 (12.20%)
Metastatic disease, n (%) 44 (48.35%) 17 (34.00%) 27 (65.85%) 0.002
Comorbid, n (%)
DM 17 (18.68%) 9 (18.00%) 8 (19.51%) 0.854
HT 34 (37.46%) 15 (30.00%) 19 (46.34%) 0.109
Other 40 (43.96%) 20 (40.00%) 20 (48.78%) 0.401
Vaccines, n (%)
mRNA+mRNA 41 (45.05%) 21 (42.00%) 20 (48.78%) 0.688
Non-mRNA+mRNA 19 (20.88%) 12 (24.00%) 7 (17.07%)
Non-mRNA+non-mRNA 31 (34.07%) 17 (34.00%) 14 (34.15%)
Third vaccine booster 21 (23.08%) 12 (24.00%) 9 (21.95%) 0.817
WBC count, cells/mm
3
, median (IQR) 5985 (4400–7280) 6270 (4730–7540) 5040 (4160–6350) 0.085
Neutrophil count, cells/mm
3
, median (IQR) 3085 (2430–4190) 3560 (2520–4190) 2840 (2300–4560) 0.231
Lymphocyte count, cells/mm
3
, median (IQR) 1720 (1210–2270) 1870 (1370–2570) 1490 (950–1910) 0.057
Hematologic AE during follow up
Leukopenia 15 (24.19%) 4 (12.9%) 11 (35.48%) 0.038
Neutropenia 5 (8.06%) 0 5 (16.13%) 0.020
Lymphopenia 20 (32.26%) 7 (22.58%) 13 (41.94%) 0.103
BMI = body mass index (mg/m
2
), GI = gastrointestinal cancer, HBP = hepatobiliary-pancreatic cancer, DM = diabetes mellitus, HT = hypertension, WBC = white blood
cell, AE = adverse event
https://doi.org/10.1371/journal.pone.0310781.t001
Table 2. Vaccine regimens.
Company Vaccine Platform
Non-mRNA vaccines
AZD1222, ChAdOx1 nCoV-19 AstraZeneca Replication-deficient chimpanzee adenoviral vector
CoronaVac, SinoVac Sinovac Biotech Whole inactivated virus
BBIBP-CorV, BIBP vaccine Sinopharm Whole inactivated virus
mRNA vaccines
BTN162b2, Comirnaty Pfizer–BioNTech nucleoside-modified mRNA
mRNA-1273, Spikevax Moderna nucleoside-modified mRNA
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Percent inhibition. The percent inhibition was below the cutoff level for seroconversion
in all participants at baseline. At 28 days after vaccination, the mean percent inhibition for the
wild type was 62.25% (95% CI, 55.19–69.30), 61.79% (95% CI, 50.79–72.78), and 62.62% (95%
CI, 53.13–72.12) for all participants and for the chemotherapy and nonchemotherapy groups,
respectively (Table 3). There was no difference between the treatment groups (p = 0.907). For
the Omicron variant, the mean percent inhibition was 42.17 (95% CI, 36.90–47.44), 40.09
(95% CI, 32.19–47.99), and 43.87 (95% CI, 36.59–51.16) for all participants and for the chemo-
therapy and nonchemotherapy groups, respectively. Again, there was no difference between
the treatment groups (p = 0.476). However, the percent inhibition of the Omicron variant was
lower than that of the wild type (p<0.001 in both treatment groups). The percent inhibition at
6 months is provided in Tables 4and 5.
Factors associated with seroconversion. The univariable analysis of factors associated
with the seroconversion of neutralizing antibodies for both the wild-type and Omicron vari-
ants is shown in Table 6. The results of the multivariable analysis presented in Table 7 revealed
that the type of vaccine was the sole factor positively associated with seroconversion. For the
wild-type SARS-CoV-2 variant, the adjusted odds ratios for the homologous mRNA vaccine
(mRNA+mRNA) and heterologous vaccine (non-mRNA+mRNA) were 14.42 (95% CI 1.99–
104.24, p = 0.008) and 25.86 (95% CI 1.39–478.06, p = 0.029), respectively. For the Omicron
variant, the adjusted odds ratios were 8.90 (96% CI 2.93–26.94, p <0.001) and 17.38 (95% CI
2.65–82.66, p <0.001) for the homologous mRNA and heterologous vaccines, respectively.
Diabetes mellitus (DM) was another potential factor associated with reduced seroconversion
for the wild-type SARS-CoV-2 variant, with an adjusted odds ratio of 0.15 (95% CI: 0.02–1.02,
p = 0.053), but the threshold for statistical significance was not met.
Safety
Grades 1 and 2 adverse events following immunization occurred in 34.0% and 19.7% of all par-
ticipants, respectively, as shown in Table 8. The most common side effect was pain at the injec-
tion site, followed by fever and fatigue. There were no serious adverse events leading to
emergency department visits or hospitalizations.
Table 3. Vaccine combinations.
All (91) Nonchemotherapy (50) Chemotherapy (41)
mRNA + mRNA 41 21 20
Pfizer+ Pfizer 21 8 13
Moderna+ Moderna 19 12 7
Others 1 1 0
Non-mRNA+ mRNA 19 12 7
AstraZeneca+Pfizer 18 11 7
Others 1 1 0
Non-mRNA + non-mRNA 31 17 14
Sinopharm + Sinopharm 17 12 5
CoronaVac + AstraZeneca 9 4 5
AstraZeneca + AstraZeneca 3 0 3
Others 2 1 1
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Clinical correlation of seroconversion and SARS-CoV-2 infection
Four participants (n = 4/91, 4.39%) were confirmed to have SARS-CoV-2 infection after com-
pleting vaccinations; all of them tested negative for seroconversion for both the wild-type and
Omicron variants. The timing periods of infection ranged from 1 to 11 months after the sec-
ond vaccine dose. Among them, one patient suffered from severe COVID-19 pneumonia,
required mechanical ventilation, and experienced multiorgan failure, leading to death. In con-
trast, none of the seropositive participants were diagnosed with SARS-CoV-2 infection.
Table 4. Comparison between the chemotherapy and nonchemotherapy groups.
Neutralizing Antibody Group Baseline Day 28 Month 6 pValue (Month 6 vs. Day
28)
Percent Inhibition (wild type) Nonchemotherapy N 48 50 45
Mean % (95%
CI)
6.28 (4.87–7.69) 62.63 (53.13–
72.12)
69.73 (59.95–
79.51)
0.170
Chemotherapy N 41 41 35
Mean % (95%
CI)
5.24 (4.01–6.47) 61.79 (50.79–
72.78)
67.31 (56.17–
78.44)
0.254
pValue (chemo vs. nonchemo) 0.907 0.791
Total N 89 91 80
Mean % (95%
CI)
5.80 (4.86–6.74) 62.25 (55.19–
69.30)
68.67 (61.48–
75.86)
0.074
Percent Inhibition
(Omicron variant)
Nonchemotherapy N 48 50 45
Mean % (95%
CI)
10.63 (8.72–
12.54)
43.87 (36.59–
51.16)
52.29 (42.99–
61.60)
0.052
Chemotherapy N 41 41 35
Mean % (95%
CI)
7.26 (5.45–9.07) 40.09 (32.19–
47.99)
42.88 (32.99–
52.77)
0.512
pValue (chemo vs. nonchemo) 0.476 0.180
Total N 89 91 80
Mean % (95%
CI)
9.08 (7.73–10.42) 42.17 (36.90–
47.44)
48.18 (41.45–
54.90)
0.061
Seroconversion rate (wild type) Nonchemotherapy N 48 50 45
Mean % (95%
CI)
0 (0–0.07) 0.78 (0.64–0.88) 0.80 (0.65–0.90) 0.778
Chemotherapy N 41 41 35
Mean % (95%
CI)
0 (0–0.09) 0.76 (0.60–0.88) 0.83 (0.66–0.93) 0.236
pValue (chemo vs. nonchemo) 0.789 0.743
Total N 89 91 80
Mean % (95%
CI)
0 (0–0.04) 0.77 (0.67–0.85) 0.81 (0.71–0.89) 0.365
Seroconversion rate (Omicron
variant)
Nonchemotherapy N 48 50 45
Mean % (95%
CI)
0 (0–0.07) 0.64 (0.49–0.77) 0.71 (0.56–0.84) 0.316
Chemotherapy N 41 41 35
Mean % (95%
CI)
0 (0–0.09) 0.59 (0.42–0.74) 0.60 (0.42–0.76) 0.875
pValue (chemo vs. nonchemo) 0.597 0.311
Total N 89 91 80
Mean % (95%
CI)
0 (0–0.04) 0.62 (0.51–0.72) 0.66 (0.55–0.76) 0.420
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Discussion
Our study was initially designed during the era of vaccine shortages in Thailand and world-
wide. According to government policy at that time, the procurement of COVID-19 vaccines
was disorganized, and access to vaccines relied on personal efforts. Therefore, vaccine combi-
nations with different platforms were expected to be heterogeneous unintentionally.
This study analyzed a real-world cohort of patients with solid cancer, which has a very high
mortality rate. Apart from suffering from their cancer, these patients are also vulnerable to
SARS-CoV-2 infection. Eleven percent of the participants (n = 18) died due to cancer-related
problems, including one death from COVID-19 pneumonia, and 3 percent (n = 4) were con-
firmed to be positive for SARS-CoV-2 infection before completing vaccination or blood analy-
sis. Given the grim prognosis of cancer, a higher rate of seroconversion to protective
antibodies against SARS-CoV-2 infection after two vaccine doses is still a concern, although a
third booster dose is crucial for maintaining immunity.
Compared with analysis of anti-RBD or anti-S antibodies, analysis of neutralizing antibodies
can serve as a more predictive tool for assessing protection against SARS-CoV-2 infection, as
these antibodies can bind to and neutralize the virus, aiding in viral control and clearance [26].
Anti-RBD and anti-S antibody levels are poor predictors of immunity for the wild-type and
novel variants, as increasing levels of anti-RBD do not necessarily imply the presence of neutral-
izing antibodies [27]. Data from previous reports may overestimate vaccine efficacy, highlight-
ing the importance of determining vaccine efficacy on the basis of neutralizing antibodies.
Surrogate neutralization assays offer an alternative effective method that does not require a bio-
safety level 3 laboratory and exhibits high sensitivity (95–100%) and specificity (99.93%) [28].
The test still performs acceptably after validation for the Omicron variant [29]. Our study
revealed a lower seroconversion rate of surrogate neutralizing antibodies (77% and 62% for the
wild-type and Omicron variants, respectively) than did previous reports on the seroconversion
Table 5. Comparison between wild-type and Omicron variants.
Strain Baseline Day 28 Month 6
Percent inhibition (nonchemotherapy) Wild type N 48 50 45
Mean % (95% CI) 6.28 (4.87–7.69) 62.63 (53.13–72.12) 69.73 (59.95–79.51)
Omicron variant N 48 50 45
Mean % (95% CI) 10.63 (8.72–12.54) 43.87 (36.59–51.16) 52.29 (42.99–61.60)
Wild type vs. Omicron, pValue <0.001 <0.001
Percent inhibition (chemotherapy) Wild type N 41 41 35
Mean % (95% CI) 5.24 (4.01–6.47) 61.79 (50.79–72.78) 67.31 (56.17–78.44)
Omicron variant N 41 41 35
Mean % (95% CI) 7.26 (5.45–9.07) 40.09 (32.19–47.99) 42.88 (32.99–52.77)
Wild type vs. Omicron, pValue <0.001 <0.001
Seroconversion rate (nonchemotherapy) Wild type N 48 50 45
Mean % (95% CI) 0 (0–0.07) 0.78 (0.64–0.88) 0.80 (0.65–0.90)
Omicron variant N 48 50 45
Mean % (95% CI) 0 (0–0.07) 0.64 (0.49–0.77) 0.71 (0.56–0.84)
Wild type vs. Omicron, pValue 0.008 0.103
Seroconversion rate (chemotherapy) Wild type N 41 41 35
Mean % (95% CI) 0 (0–0.09) 0.76 (0.60–0.88) 0.83 (0.66–0.93)
Omicron variant N 41 41 35
Mean % (95% CI) 0 (0–0.09) 0.59 (0.42–0.74) 0.60 (0.42–0.76)
Wild type vs. Omicron, pValue 0.008 0.005
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of anti-RBD and anti-S antibodies in patients with solid cancers (90–94%) [14,30] and in the
healthy population (97–99%) [31]. This result is consistent with a study on the pseudovirus neu-
tralization assay in cancer patients, where detectable neutralization antibodies for the wild-type
virus were found in 75–85% of the group receiving mRNA-based vaccines [18].
Multiple variants of SARS-CoV-2 have evolved from the wild-type virus since the beginning
of the outbreak, mostly due to different mutations under selective pressure at the S and RBD
regions; furthermore, these variants also influence disease transmissibility, severity and immu-
nity after infection and vaccination [3]. Currently, there are five VOCs, including Alpha, Beta,
Delta, Gamma and Omicron variants [4]. The XBB subvariant of Omicron was the most preva-
lent variant worldwide in 2022, the study time period [25]. The lower seroconversion of the
Omicron variant (XBB) in this study is consistent with other reports in both cancer and
healthy populations [32,33]. Most COVID-19 vaccines were developed before the era of ongo-
ing novel VOCs, leading to challenging problems with vaccine effectiveness [34]. In the general
population, risk reductions for incidence and mortality are predominantly associated with the
Alpha variant but are diluted by the Delta and Omicron variants; these effects can be overcome
by booster doses [35]. In the capture study, seroconversion of neutralizing antibodies via live
virus microneutralization assays was found to be positive in 83, 61, 53, and 54% of patients
with cancer for the wild-type, Alpha, Beta, and Delta variants, respectively [17]. Another study
of lung cancer patients receiving mRNA vaccines revealed more than 50-fold decreased levels
of neutralizing antibodies to the Omicron variant compared with those to wild-type SARS--
CoV-2 in a live virus neutralization assay [32]. This finding is in concordance with that of our
study. Additionally, the mean percent inhibition decreased from 62.25% for the wild type to
42.17% for the Omicron variant. Thus, these findings suggest a limited efficacy of vaccines
against VOCs.
Active cancer treatments, including chemotherapy, targeted therapy, endocrine therapy,
and immunotherapy, have controversial outcomes with respect to seroconversion. Among
these agents, chemotherapy is considered an immunosuppressive agent that might cause leu-
kopenia and lead to infection. This concept has elicited interest in the potential detrimental
effects of chemotherapy on vaccine efficacy. Some studies have shown a reduced humoral
immune response in chemotherapy treatment groups [36–38]. In a large cohort study of U.S.
veterans, patients receiving chemotherapy within 3 months before vaccination had the lowest
vaccine effectiveness, which was 57% (95% CI: 23 to 90%), when compared with the endocrine
therapy and no systemic therapy groups (76%, 95% CI: 50 to 91%; and 85%, 95% CI: 29 to
100%, respectively) [39], whereas some studies have revealed no mitigatory effect of chemo-
therapy on humoral immunity in the context of COVID-19 vaccines [40,41]. Our study
revealed a decreasing trend but no statistically significant difference in either the seroconver-
sion rate or percent inhibition in patients receiving chemotherapy three months prior to or
during the vaccination period in terms of neutralization antibodies for both the wild-type and
Omicron variants, regardless of leukopenia, neutropenia, or lymphopenia. However, the over-
all effects of systemic treatments for cancer and vaccination on SARS-CoV-2 infection out-
comes remain unclear and inconsistent [42,43].
Differences in vaccine platforms result in unequal immunogenicity. Compared with non-
mRNA vaccines, mRNA-based vaccines generate greater amounts of RBD antibodies and neu-
tralizing antibodies in cancer patients [44,45]. The exploratory analysis of factors associated
with seroconversion from our study suggested that only the type of vaccine combination was
related. Compared with homologous non-mRNA vaccines, the primary heterologous vaccine
combination yielded the highest seroconversion outcome, with an adjusted odds ratio (ORR)
of 25.86 (95% CI 1.39–478.06, p = 0.029), followed by homologous mRNA vaccines, with an
adjusted ORR of 14.42 (95% CI 1.99–104.24, p = 0.008), for the wild type. This result was
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similar for the Omicron variant, with adjusted odds ratios of 17.38 (95% CI 2.65–82.66,
p<0.001) and 8.90 (96% CI 2.93–26.94, p <0.001) for heterologous and homologous mRNA
vaccines, respectively. Undoubtedly, booster doses after completing two COVID-19 vaccine
doses generate higher and longer-lasting neutralizing antibody levels [19,46,47]; conse-
quently, a third booster vaccine is essential and should be regarded as the standard of care. In a
large, matched control cancer cohort study in Singapore, the clinical benefit of vaccine for pre-
venting severe disease was even greater with a four-dose mRNA-based vaccine regimen [48].
To date, the clinical guidance for COVID-19 vaccination approved by the U.S>Centers for
Table 6. Univariable analysis of explored factors for seroconversion for the wild-type and Omicron variants.
Factors Wild Type Omicron Variant
Seroconversion n (%) OR (95% CI) pValue Seroconversion n (%) OR (95% CI) pValue
Sex
male 36 (80.00) (n = 45) 1.39 (0.53–3.64) 0.670 29 (64.44%) (n = 46) 1.26 (0.54–2.92) 0.578
female 34 (73.91%) (n = 46) ref - 27 (58.70%) (n = 45) ref -
Age
<65 yrs 47 (81.03%) (n = 58) 1.70 (0.63–4.57) 0.286 36 (62.07%) (n = 58) 1.22(0.52–2.88) 0.638
�65 yrs 25 (71.43%) (n = 35) ref - 20 (57.14%) (n = 35) ref -
Smoking (yes) 16 (84.21%) (n = 19) 1.68 (0.44–6.44) 0.447 12 (63.16%) (n = 19) 1.14 (0.40–3.23) 0.801
BMI
<18.5 (underweight) 9 (100%) (n = 9) 7.94 (0.42–148.19) 0.165 8 (88.89%) (n = 9) 3.73 (0.58–23.78) 0.163
18.5–22.9 (normal) 34 (79.07%) (n = 43) 1.51 (0.56–4.10) 0.410 24 (55.81%) (n = 43) 0.82 (0.34–1.98) 0.674
�23.0 (overweight) 27 (71.05%) (n = 38) ref - 23 (60.53%) (n = 38) ref -
Primary cancer, n (%)
GI vs. non-GI 25 (78.12%) (n = 32) 1.04 (0.37–2.91) 0.938 19 (59.38%) (n = 32) 0.92 (0.38–2.20) 0.857
HBP vs. non-HBP 13 (81.25%) (n = 16) 1.30 (0.33–5.07) 0.706 10 (62.50%) (n = 16) 1.09 (0.36–3.33) 0.867
Breast vs. nonbreast 18/26, 69.23% (n = 26) 0.53 (0.19–1.48) 0.229 14 (53.85%) (n = 26) 0.67 (0.27–1.69) 0.406
Prostate vs. nonprostate 3 (60.00%) (n = 5) 0.40 (0.06–2.61) 0.344 1 (20.00%) (n = 5) 0.14 (0.01–1.37) 0.093
Others vs. nonother 13 (92.86%) (n = 14) 4.40 (0.54–35.85) 0.166 13 (92.86%) (n = 14) 10.34 (1.28–82.92) 0.028
Metastasis 36 (81.82%) (n = 44) 1.72 (0.63–4.66) 0.286 28 (63.64%) (n = 44) 1.18 (0.50–2.76) 0.691
Active cancer treatment 51 (71.83%) (n = 71) 0.13 (0.01–1.07) 0.058 41 (57.75%) (n = 71) 0.45 (0.14–1.39) 0.167
Chemo vs. nonchemo 31 (75.61%) (n = 41) 0.87 (0.32–2.43) 0.788 24 (58.54%) (n = 41) 0.79 (0.34–1.85) 0.594
Targeted vs. nontargeted 11 (57.89%) (n = 19) 0.30 (0.10–0.90) 0.032 10 (52.63%) n = 19) 0.62 (0.22–1.74) 0.372
Hormonal vs. nonhormonal 8 (57.14%) (n = 14) 0.32 (0.09–1.07) 0.064 5 (35.71%) (n = 14) 0.28 (0.08–0.93) 0.038
No active cancer treatment 19 (95.00%) (n = 20) ref - 15 (75.00%) (n = 20) ref -
Comorbidity
DM 11 (64.71%) (n = 17) 0.45 (0.14–1.43) 0.181 10 (58.82%) (n = 17) 0.90 (0.30–2.63) 0.848
HT 24 (68.57%) (n = 35) 0.46 (0.17–1.24) 0.128 18 (51.43%) (n = 35) 0.52 (0.22–1.25) 0.148
Other comorbid 30 (73.175%) (n = 41) 0.66 (0.25–1.76) 0.414 26 (63.41%) (n = 41) 1.21 (0.52–2.82) 0.654
Vaccine type
mRNA+mRNA 40 (93.02%) (n = 43) 14.76 (4.06–53.65) <0.001 32 (74.42%) (n = 43) 6.99 (2.54–19.19) <0.001
non-mRNA+mRNA 19 (100.00%) (n = 19) 49.75 (2.76–895.38) 0.008 16 (84.21%) (n = 19) 11.66 (2.94–46.24) <0.001
Non-mRNA+non-mRNA 14 (43.75%) (n = 32) Reference 9 (28.12%) (n = 32) Reference
Leucopenia 11 (73.33%) (n = 15) 0.65 (0.16–2.52) 0.535 10 (66.67%) (n = 15) 1.35 (0.40–4.60) 0.624
Neutropenia 2 (40.00%) (n = 5) 0.14 (0.02–0.96) 0.046 2 (40.00%) (n = 5) 0.38 (0.06–2.51) 0.332
Lymphopenia 14 (70.00%) (n = 20) 0.46 (0.12–1.63) 0.234 12 (60.00%) (n = 20) 0.92 (0.31–2.74) 0.886
BMI = body mass index (mg/m
2
), GI = gastrointestinal cancer, HBP = hepatobiliary-pancreatic cancer, DM = diabetes mellitus, HT = hypertension
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Disease Control and Prevention (CDC) recommends three doses of mRNA vaccines for
immunocompromised individuals [49].
However, this study provides a novel report on the more robust immunogenicity of heterol-
ogous primary vaccines in patients with cancers, which is coherent with reports on healthy
populations [50] and a third heterologous prime-boost vaccine [24]. The potential mecha-
nisms include the reorientation of B-cell responses toward neutralizing sites of expressed epi-
topes encoded by mRNA vaccines [51] and the accompanying cellular and humoral responses
on different vaccine platforms [52]. Therefore, the heterologous vaccine strategy should be
encouraged as the second booster primary vaccine, particularly in populations that tend to
have lower seroconversion rates.
Diabetes Mellitus (DM) was another potential factor contributing to a weaker immune
response in patients with wild-type SARS-CoV-2 infection, with an adjusted odds ratio of
0.153 (95% CI: 0.023–1.022, p = 0.053); however, the threshold for statistical significance was
not met. DM is well known for its immunosuppressive state. Some systematic reviews in the
general population reported the inferiority of the immune response to the COVID-19 vaccine
in patients with DM, particularly those with poor glycemic control [53,54]. DM and glycemic
control in patients with cancer should be evaluated further regarding adverse correlations with
neutralizing antibodies.
Table 7. Multivariate analysis of factors in the wild-type subgroup and Omicron variant subgroup.
Factors Adjusted OR (95% CI) pValue
Wild-type subgroup
Neutropenia 0.23 (0.02–2.12) 0.199
Diabetes mellitus 0.15 (0.02–1.02) 0.053
Vaccine type
Non-mRNA + non-mRNA Ref
mRNA+mRNA 14.42 (1.99–104.24) 0.008
Non-mRNA + mRNA 25.86 (1.39–478.06) 0.029
Omicron variant subgroup
Cancer type
Prostate 0.11 (0.01–1.34) 0.085
Other cancer 8.26 (0.81–84.08) 0.074
Vaccine type
Non-mRNA + non-mRNA Ref
mRNA+mRNA 8.90 (2.93–26.94) <0.001
Non-mRNA + mRNA 17.38 (3.65–82.66) <0.001
https://doi.org/10.1371/journal.pone.0310781.t007
Table 8. Adverse events.
Adverse events All Participants (n = 91) Nonchemotherapy (n = 50) Chemotherapy (n = 41)
Grade 1 n (%) Grade 2 n (%) Grade 1 n (%) Grade 2 n (%) Grade 1 n (%) Grade 2 n (%)
Any 31 (34.0%) 18 (19.7%) 22 (44.0%) 8 (16.0%) 9 (21.9%) 10 (24.3%)
Pain at injection
site
14 (15.3%) 10 (10.9%) 11 (22.0%) 3 (6.0%) 3 (7.3%) 7 (17.0%)
Fever 8 (8.7%) 5 (5.4%) 5 (10.0%) 2 (4.0%) 3 (7.3%) 3 (7.3%)
Fatigue 4 (4.3%) 3 (3.2%) 4 (8.0%) 3 (6.0%) 0 0
Malaise 3 (3.2%) 0 1 (2.0%) 0 2 (4.8%) 0
Diarrhea 1 (1.0%) 1 (1.0%) 1 (2.0%) 0 1 (2.4%) 1 (2.4%)
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Regarding safety concerns, vaccinations were well tolerated in both the chemotherapy and
nonchemotherapy groups. All the participants experienced minor reactions, such as pain at
the injection site, fever, and fatigue, which were self-limiting or alleviated by over-the-counter
drugs. None of the participants needed to seek medical attention or required hospitalization.
The strength of our study lies in providing data on COVID-19 vaccine safety and immuno-
genicity, specifically in terms of the seroconversion of surrogate neutralizing antibodies, in
solid cancer patients with and without active cancer treatments, especially chemotherapy for
advanced or metastatic disease. However, our study has several limitations. The population
size was small due to the high mortality rate of cancer patients, incomplete vaccination, and
lack of blood samples from some participants. Second, missing information on third, booster
vaccines during the follow-up period resulted in an inaccurate analysis of neutralizing antibod-
ies at 6 months. Hence, the longevity of the neutralizing antibodies could not be determined in
our study. In addition, this study did not include an analysis of the cellular immunity and
memory function of the adaptive immune response.
Conclusions
Our study revealed that in solid cancer patients, COVID-19 vaccination leads to substantial
immune responses, with seroconversion rates of 77% for the wild type and 62% for the Omi-
cron variant. Heterologous vaccines were more effective, and chemotherapy did not signifi-
cantly affect the seroconversion of neutralizing antibodies. The adverse events were mostly
mild, confirming the safety of the vaccines. Further studies on cell-mediated immunity, cur-
rent circulating variants and clinical benefits of COVID-19 vaccines beyond increasing neu-
tralizing antibody levels in cancer patients will provide additional valuable information.
Supporting information
S1 Dataset.
(XLSX)
Acknowledgments
We would like to extend our sincere gratitude to Watchara Kasinrerk, Witida Laopajon, Nuch-
jira Takheaw, and Supansa Pata for facilitating laboratory data collection and procedures.
Their dedication and assistance ensured the smooth execution of our experimental protocols.
We also gratefully acknowledge Antika Wongthani for her expert statistical analysis, which sig-
nificantly contributed to the rigor and interpretation of our study findings. We sincerely thank
Chanutchidchanok Jannakorn for her invaluable role in typing and coordinating efforts,
which greatly facilitated the organization and execution of this project. This study was partially
supported by the Clinical Surgical Research Center, Chiang Mai University, Chiang Mai,
Thailand.
Author Contributions
Conceptualization: Busyamas Chewaskulyong, Pattarapong Satjaritanun,
Apichat Tantraworasin.
Data curation: Busyamas Chewaskulyong, Pattarapong Satjaritanun.
Formal analysis: Busyamas Chewaskulyong, Pattarapong Satjaritanun,
Apichat Tantraworasin.
Funding acquisition: Busyamas Chewaskulyong.
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Neutralizing Antibody and Safety of COVID-19 Vaccine in Cancer Patients
PLOS ONE | https://doi.org/10.1371/journal.pone.0310781 November 7, 2024 13 / 17
Investigation: Busyamas Chewaskulyong, Pattarapong Satjaritanun.
Methodology: Busyamas Chewaskulyong, Pattarapong Satjaritanun, Apichat Tantraworasin.
Project administration: Busyamas Chewaskulyong, Pattarapong Satjaritanun.
Resources: Busyamas Chewaskulyong, Thanika Ketpueak, Thatthan Suksombooncharoen,
Chaiyut Charoentum, Nuttaphoom Nuchpong.
Supervision: Busyamas Chewaskulyong, Apichat Tantraworasin.
Validation: Busyamas Chewaskulyong, Pattarapong Satjaritanun, Apichat Tantraworasin.
Visualization: Busyamas Chewaskulyong.
Writing – original draft: Busyamas Chewaskulyong, Pattarapong Satjaritanun.
Writing – review & editing: Busyamas Chewaskulyong, Pattarapong Satjaritanun,
Thanika Ketpueak, Thatthan Suksombooncharoen, Chaiyut Charoentum,
Nuttaphoom Nuchpong, Apichat Tantraworasin.
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