COVID-19 in cancer patients: The impact of vaccination on outcomes early in the pandemic.

INTRODUCTION

With the rapid evolution of the severe acute respiratory syndrome coronavirus 2 (SARS‐COV‐2) pandemic, the development of effective and safe vaccines with immediate deployment became a key strategy to reduce the spread of the virus and protect against severe infections and death. 1 The first two vaccines against coronavirus disease 2019 (COVID‐19), Pfizer‐BioNTech's BNT162b2 and Moderna's mRNA‐1273 vaccines, were approved on December 11 and 18, 2020, respectively. 2 The emergency use authorizations were based on Phase 3 data that reported >94% and 100% efficacy in preventing symptomatic COVID‐19 and severe infections, respectively. 3 , 4 However, timing of COVID‐19 vaccinations and the number of needed doses in special populations, such as immunocompromised individuals and cancer patients, were uncertain due to lack of clinical data. 5

Immunocompromised patients with cancer and prior transplant are vulnerable to breakthrough infections and complications related to COVID‐19. 6 , 7 , 8 , 9 Severe COVID‐19, use of mechanical ventilation, and overall mortality are reportedly greater among cancer patients than among the general population. 6 , 7 , 8 , 9 , 10 , 11 , 12 Multiple risk factors have been identified, such as age, use of active chemotherapy, and type of cancer—with patients with hematologic malignancies (HM) having worse clinical outcomes than patients with solid tumors. 13 , 14 Despite the potential risk of breakthrough infections among cancer patients after COVID‐19 vaccination when compared to healthy controls, this preventive strategy may protect cancer patients against severe COVID‐19 and may improve overall survival. 15 , 16

Vaccinating cancer patients against influenza has proved safe and somewhat effective 15 and provided a basis to promote vaccination of cancer patients after the approval of the BNT162b2/Pfizer–BioNTech and mRNA‐1273/Moderna mRNA COVID‐19 vaccines. However, many studies measuring humoral responses after COVID‐19 vaccination in cancer patients raised concerns about reduced efficacy. 17 , 18 , 19 Reduced efficacy based on humoral response was more pronounced among HM patients compared with solid tumor patients 19 ; this poor serologic response and underlying immunocompromised status likely contributed to higher rates of breakthrough infections when compared to healthy individuals. 20 , 21 , 22 , 23

Breakthrough COVID‐19 in vaccinated cancer patients has been previously associated with poor outcomes. 16 , 19 , 24 Underlying HM, immunosuppressive drugs use, and prior hematopoietic cell transplant (HCT) were major risk factors for breakthrough infections. 16 , 24 , 25 , 26 Among the cohorts with varying immunocompromising conditions, breakthrough COVID‐19 was associated with greater rates of hospitalization and in‐hospital death. 12 , 27 Similar findings were noted when HM patients with breakthrough infections were compared with patients without cancer. 23 Among solid organ transplant recipients, breakthrough COVID‐19 in fully vaccinated patients was not clearly associated with worse outcomes. 28 On the other hand, earlier studies focusing on cancer patients did not demonstrate any clinical benefit, such as reduction in COVID‐19–related mortality among cancer patients who underwent COVID‐19 vaccination. 16

We aimed in this large cohort study to identify risk factors associated with COVID‐19–related outcomes such as rates of hospitalization, severe COVID‐19, and 30‐day COVID‐19 attributable mortality and to determine if any protective effects of COVID‐19 vaccination in cancer patients compared with unvaccinated cancer patients after the initial phase of vaccines rollout for immunocompromised patients during the Alpha and Delta waves or surges.

METHODS

Study design

We performed a retrospective cohort study that included consecutive cancer patients who were diagnosed with COVID‐19 via polymerase chain reaction on nasal swabs from January 1, 2021 to July 30, 2021 (during the Alpha and Delta waves), regardless of their vaccination status at the time of diagnosis. During this time period, the CDC recommended 2 doses of either mRNA vaccine (Pfizer–BioNTech or Moderna) or 1 dose of the adenovirus vector‐based vaccine (Janssen) for immunocompromised patients; our study was conducted before the recommendation of booster doses. All cancer patients undergoing active follow‐up in our institution at any stage of treatment were included. Patients were excluded if they had no diagnosis of cancer or had precancerous syndrome (e.g., myelodysplastic syndrome), had no recorded date of vaccination despite documentation of vaccination before infection, or were originally diagnosed with COVID‐19 outside of the study period. Only the first episode of COVID‐19 was included for analysis, and multiple episodes per patient were excluded. Patients' demographics, vaccination history, admission data, laboratory data, oncologic history, and other comorbidities were collected. The study objectives were to determine COVID‐19–related complications, including severe COVID‐19, hospitalization for COVID‐19, and 30‐day COVID‐19 attributable mortality. Severe COVID‐19 was defined based on requirement of oxygen supplementation for hypoxia within 30 days of COVID‐19 diagnosis, in line with the National Institutes of Health's (NIH) definition of severe infection. 29 Vaccination status was categorized as unvaccinated, partially vaccinated, or fully vaccinated at the time of COVID‐19. Partially vaccinated was defined as having received only 1 dose of either mRNA vaccine before infection or being diagnosed with COVID‐19 within 14 days of the second dose. Fully vaccinated was defined as having received 2 doses of either mRNA vaccine or 1 dose of the adenovirus vector‐based vaccine more than 14 days before COVID‐19. This study was performed after our institutional review board's approval, and a waiver of consent was granted.

Data collection

Patients with COVID‐19 were identified using our institution's infection control database. Demographics, comorbidities, laboratory data within 7 days of diagnosis, and COVID‐19 directed therapies within 14 days of diagnosis were collected using Palantir Foundry (Syntropy) as previously described. 30 Oncologic data were collected via our tumor registry. The study objectives and patients' vaccination status were determined via review of patients' electronic medical records.

SARS‐CoV ‐2 testing and protocols

SARS‐CoV‐2 testing was performed using one of two real‐time polymerase chain reaction assays available at our institution: the Cobas SARS‐CoV‐2 assay performed on the Cobas 6800 system (Roche Diagnostics) or the Abbott SARS‐CoV‐2 test performed on the m2000 system. Patients were tested if they were symptomatic (cough, fever, signs of pneumonia on imaging, rhinorrhea, or shortness of breath), or before admission or procedures if asymptomatic. Vaccination status was not considered when testing patients for COVID‐19. Patients were treated with available therapeutics for COVID‐19 based on its severity. These included remdesivir and steroids for patients with hypoxia in addition to tocilizumab or anakinra for patients with severe hypoxia.

Statistical analysis

For the purpose of analyzing the study objectives, outcomes were considered binary variables. Vaccination status was also analyzed as a binary variable; partially vaccinated patients were considered unvaccinated for the purpose of the analysis. To identify the independent predictors of the patient outcomes and evaluate the independent impact of vaccination on the outcomes, we carried out logistic regression model analysis with propensity score adjustment on each outcome. First, propensity score was generated using logistic regression analysis by modeling the probability of receiving vaccination for each patient. This logistic regression model included all the variables with a p ‐value ≤0.25 on their univariate association analyses with vaccination (Figure S1 ). Then when we carried out a logistic regression to analyze the association with each patient outcome (COVID‐19‐related hospitalization, all‐cause 30‐day mortality, and severe VCOVID‐19), respectively. This propensity score was incorporated into the logistic regression analysis using stabilized inverse probability of treatment weighted (IPTW) method to reduce the baseline imbalance between patients who received vaccination and those who did not. Treatment for COVID‐19 was given based on severity using a standardized algorithm during the study period; these variables were not included in our analysis.

RESULTS

Patient cohort

We identified a total of 963 patients with COVID‐19 from the infection control database. From those, 155 patients were excluded for the following reasons: 51 were diagnosed outside the study period, 82 had no history of cancer or premalignant condition, 4 were considered duplicate as they tested positive for SARS‐CoV‐2 repeatedly within 14 days of the first positive test, 5 had no documented date of COVID‐19 diagnosis, and 13 had unknown COVID‐19 vaccination dates. In addition, 1 patient was admitted at an outside hospital for COVID‐19, and we could not ascertain the severity of COVID‐19. Overall, 808 cancer patients with COVID‐19 were included for analysis; 207 with HM and 601 with solid tumors. Most patients were unvaccinated at time of COVID‐19 diagnosis ( n = 635; 79%). Among the 114 fully and 59 partially vaccinated patients with breakthrough COVID‐19, most had received BNT162b2/Pfizer–BioNTech COVID‐19 vaccines (Table 1 ).

COVID ‐19–related outcomes in vaccinated and unvaccinated patients with cancer

Tables 1 and 2 depict the baseline characteristics of the cohort, stratified by vaccination status and whether hospitalized or not. Overall, 30% were hospitalized, 16% had severe COVID‐19, 9% required high‐flow nasal cannula or mechanical ventilation, and 5% died within 30 days of COVID‐19 diagnosis. Compared with unvaccinated patients, vaccinated patients were older, more likely to have hypertension and lymphoma, and less likely to have undergone HCT (Table 1 ). In addition, vaccinated patients had a higher absolute monocyte count (AMC) and absolute neutrophil count (ANC) (Table S1 ). More unvaccinated patients than vaccinated patients received bamlanivimab for their COVID‐19; otherwise the use of antiviral and anti‐inflammatory therapies were similar between vaccinated and unvaccinated patients. COVID‐19–related outcomes were similar in vaccinated and unvaccinated patients (Table 1 ). 30‐day mortality attributable to COVID‐19 was significantly higher in HM patients when compared to patients with solid tumors (Figure 1 ).

Hospitalization

On univariate analysis, risk factors for hospitalization included older age, former smoking status, and multiple comorbidities such as history of hypertension, diabetes mellitus, heart failure, chronic obstructive pulmonary disease (COPD), Stage 3–5 chronic kidney disease (CKD), end‐stage kidney disease, and having lower body mass index (BMI) (Table 2 ). In addition, patients with HM were more likely to be hospitalized; in particular, patients who had undergone HCT and/or had leukemia or lymphoma. Univariate analysis of laboratory correlatives is depicted in Table S2 .

The multivariate analysis using logistic regression model with propensity score adjustment (excluding laboratory variables) included 787 patients whose BMI measurements were available at the time of COVID‐19 diagnosis. Independent predictors of hospitalization included history of heart failure, diabetes mellitus, COPD, Stage 3–5 CKD, lymphoma, and leukemia (Table 3 ). A high BMI was protective against hospitalization.

The multivariate analysis that included laboratory values encompasses 741 patients who had albumin, ANC, AMC, absolute lymphocyte count (ALC), and aspartate transaminase (AST) measured at time of COVID‐19 diagnosis. Independent predictors of hospitalization included heart failure, diabetes mellitus, COPD, and Stage 3–5 CKD (Table 3 ). Other factors such as having leukemia, lymphoma, a reduced albumin, a reduced AMC, and an elevated AST were also independently associated with COVID‐19–related hospitalization. Vaccination was not protective against COVID‐19–related hospitalization in either model, although a trend was seen in the latter ( p = 0.068).

Severe COVID ‐19

The analysis with severe COVID‐19 as the outcome measure included 807 patients. On univariate analysis, older age, smoking history, and comorbidities such as hypertension, diabetes mellitus, heart failure, COPD, lung fibrosis, Stage 3–5 CKD, and end‐stage kidney disease were associated with severe COVID‐19 (Table 4 ). In addition, having an underlying HM, such as leukemia or lymphoma, was associated with severe COVID‐19 compared with patients with solid tumors. Having no smoking history was protective against severe COVID‐19. Abnormal laboratory findings associated with severe COVID‐19 included reduced white blood cell count, reduced albumin, reduced ALC, and reduced AMC (Table S2 ).

The multivariate analysis using logistic regression model analysis with propensity score adjustment (excluding laboratory variables) included 807 patients; age ≥ 60 years, diabetes mellitus, heart failure, COPD, lymphoma, and leukemia were independent predictors of severe COVID‐19 (Table 5 ). Notably, patients who were fully vaccinated were protected against severe COVID‐19 in this model.

On multivariate analysis with laboratory variables included (740 patients included), age ≥ 60 years, diabetes mellitus, heart failure, COPD, lymphoma, and leukemia remained independently associated with severe COVID‐19 (Table 5 ). Reduced albumin, reduced AMC, and elevated AST were also associated with severe COVID‐19 (Table S3 ). Vaccination was protective against severe COVID‐19 in this model as well.

30‐day COVID ‐19 attributable mortality

The analysis with 30‐day COVID‐19 attributable mortality as the outcome measure included 794 patients. On univariate analysis, advanced age, having comorbidities such as heart failure, COPD, and Stage 3–5 CKD were associated with 30‐day COVID‐19 attributable mortality (Table 6 ). In addition, having an underlying HM, such as lymphoma or leukemia, was associated with higher mortality compared to patients with solid tumors (Figure 1 and Table 6 ). Multiple laboratory variables were associated with higher 30‐day COVID‐19 attributable mortality, including reduced WBC, albumin, ALC, AMC, higher lactate dehydrogenase, and higher C‐reactive protein (Table S4 ).

The multivariate analysis using logistic regression model analysis with propensity score adjustment (excluding laboratory variables) included 808 patients; CKD stage 3–5, having lymphoma, and leukemia were independent predictors of 30‐day COVID‐19 attributable mortality (Table 7 ). On multivariate analysis with laboratory variables included (749 patients), having leukemia, elevated ALC and reduced albumin were independently associated with 30‐day mortality attributable to COVID‐19 (Table 7 ). Vaccination was not protective against 30‐day COVID‐19 attributable mortality in all models. On Kaplan–Meier survival analysis patients with HM had significantly higher 30‐day COVID‐19 attributable mortality when compared to patients with solid tumors (Figure 1 ).

DISCUSSION

Our study shows that cancer patients who were fully vaccinated against COVID‐19 were protected from severe infections during the early Alpha and Delta COVID‐19 waves. Yet it is unclear if vaccination was able to prevent hospitalization or 30‐day attributable mortality to COVID‐19. We identified key risk factors, such as age ≥ 60 years and multiple comorbidities such as diabetes mellitus, heart failure, COPD, and CKD, that are independently associated with COVID‐19–related hospitalization, severe COVID‐19, and 30‐day COVID‐19 attributable mortality regardless of vaccination status. In addition, patients with HMs, such as lymphoma and leukemia, had worse outcomes, such as severe COVID‐19 and higher 30‐day COVID‐19 attributable mortality, compared with patients with solid tumors regardless of their vaccination status. We also report a high hospitalization rate but less severe COVID‐19 and low 30‐day mortality attributable to COVID‐19 among the whole cohort, regardless of the vaccination status.

Our study is in line with prior studies in immunocompetent patients and solid organ transplant recipients. 28 , 31 , 32 A previous report by Schmidt et al. reported no benefit of vaccination among cancer patients. 16 However, the study's endpoints did not include severe COVID‐19 but instead used surrogate markers such as intensive care unit admission and use of mechanical ventilation. In our study, we defined severe COVID‐19 as patients' requiring oxygen supplementation for hypoxia within 30 days of COVID‐19 diagnosis, in line with the NIH's definition. 29 In addition, Schmidt et al. identified only 3% of patients who were fully vaccinated (14% of our cohort of cancer patients were fully vaccinated at the time of COVID‐19 diagnosis) and were unable to power their analysis to detect statistically significant differences between vaccinated and unvaccinated cancer patients. 16

The initial Phase 3 trials to assess the efficacy of the BNT162b2/Pfizer‐BioNTech and mRNA‐1273/Moderna COVID‐19 mRNA vaccines reported a 95% and 94% efficacy for prevention of symptomatic infections, respectively. 3 , 4 Severe COVID‐19 was observed in only 1 patient who was fully vaccinated with BNT162b2/Pfizer‐BioNTech. 3 However, real‐world experience during the first year after the release of the mRNA vaccines noted a higher rate of breakthrough COVID‐19 than observed in the Phase 3 trials. 22 , 33 , 34 , 35 , 36 Multiple factors may explain this discrepancy, such as the rise of the new delta variant and other variants of concern 22 , 35 , 37 during that time; increased infection rate among vaccinated elderly, immunocompromised, and cancer patients 33 , 35 , 36 ; and waning immunity from the COVID‐19 vaccines over time. 20 , 34 Nevertheless, COVID‐19 vaccination prevented severe infections, 20 , 22 , 34 , 35 as we found in our study in fully vaccinated cancer patients.

Cancer patients are highly encouraged to undergo COVID‐19 vaccination despite the scarcity of data. 10 , 38 Wu et al. had previously demonstrated that COVID‐19 vaccination was associated with lower rates of COVID‐19 among cancer patients and reported a 58% overall vaccine clinical efficacy after 2 doses of the vaccine. 10 However, patients with HM had the poorest vaccine clinical efficacy of 19%. The authors did not report on the protective impact of vaccination during infection. In our own study, we were able to demonstrate a reduction in severe COVID‐19 in vaccinated cancer patients, but 30‐day attributable mortality to COVID‐19 was similar between vaccinated and unvaccinated patients. Multiple confounders unique to our immunocompromised cancer patients may explain this discrepancy, such as advanced underlying cancer and transition to hospice care when further cancer treatment was thought to be futile.

Cancer patients have varied levels of immunosuppression. We found, as others have, that patients with HM are at greater risk for breakthrough COVID‐19 after vaccination and may have more COVID‐19–related complications than patients with solid tumors. 16 , 24 , 25 , 39 This is likely due to greater immunosuppression from underlying disease, chemotherapy or cellular therapy, or both—resulting in poor immunologic responses to COVID‐19 vaccination. 19 , 26 , 36 , 40 , 41 Fendler et al. reported a low seroconversion rate (59%) in HM patients after 2 doses of either BNT162b2/Pfizer‐BioNTech or AZD1222/AstraZeneca COVID‐19 vaccine. 41 This low response rate was particularly evident in HM patients receiving anti‐CD20 therapy. 19 In our study, patients with underlying leukemia and lymphoma remained at higher risk for hospitalization, severe COVID‐19, and 30‐day COVID‐19 attributable mortality compared to patients with other type of cancer regardless of their vaccination status. To better elicit immunologic responses in HM patients, multiple booster doses have been recommended based on recent studies. 40 Other strategies to prevent severe infections should be underscored in this vulnerable population, such as pre‐exposure prophylaxis with monoclonal antibodies when effective for the specific circulating SARS‐CoV‐2 variants or subvariants and early antiviral therapy in mildly symptomatic HM patients.

Our study has several limitations. We did not evaluate antibody responses to vaccination and SARS‐CoV‐2 sequencing in our cohort. Prior studies have measured humoral and cellular responses after COVID‐19 vaccination in immunocompromised patients, 19 , 26 , 36 , 40 , 41 but no clear quantitative thresholds that may protect against SARS‐CoV‐2 infection have been validated. In addition, there is no clear consensus on the timing of antibody measurement after vaccination. During the study period, a team led by Musser et al at a neighboring center in Houston, Texas assessed the circulating COVID‐19 variants from a large pool of samples from the greater Houston area. 42 , 43 The predominant variant in the community was Alpha between January 2021 and May 2021, 43 but cases of the Delta variant increased from April 2021 onward until almost 99.9% of tested samples were the Delta variant by July 2021. 42 Our cancer patients may have had either variant given the study period. This suggests that COVID‐19–related complications may be linked to poor host response and less to the type of circulating variant early into the pandemic. Our study did not measure associations of COVID‐19 with chemotherapy or stage of cancer. Nevertheless, given the large number of patients included in this analysis, we believe that the results provide a general overview of the different cancer subtypes. Lastly, our study did not assess vaccine efficacy in preventing symptomatic or asymptomatic COVID‐19 in cancer patients, as we did not include a comparative group of cancer patients with no COVID‐19 who were vaccinated.

Our study highlights the benefit of COVID‐19 vaccination in our cancer patients—specifically its protection against severe COVID‐19. Despite vaccination, breakthrough COVID‐19 was associated with high morbidity and mortality, especially among severely immunocompromised patients with underlying HM. Optimization of vaccination in cancer patients and the use of additional doses of vaccines, and pre‐exposure prophylactic strategies such as COVID‐19 monoclonal antibodies for severely immunocompromised patients should be determined in future studies.

AUTHOR CONTRIBUTIONS

Fareed Khawaja: Conceptualization (equal); data curation (lead); formal analysis (supporting); investigation (lead); project administration (lead); visualization (equal); writing – original draft (lead). Georgios Angelidakis: Data curation (supporting); investigation (supporting); methodology (supporting); software (supporting); writing – original draft (supporting); writing – review and editing (supporting). Adina Feldman: Data curation (equal); investigation (equal); software (equal); writing – review and editing (supporting). Vinod Ravi: Conceptualization (supporting); data curation (supporting); software (supporting); visualization (supporting); writing – review and editing (supporting). Eric Woodman: Data curation (supporting); software (lead); supervision (lead); writing – review and editing (supporting). Micah Bhatti: Investigation (supporting); project administration (supporting); writing – review and editing (supporting). Ella Ariza‐Heredia: Conceptualization (supporting); methodology (supporting); writing – review and editing (supporting). Peter Elhajj: Visualization (supporting); writing – original draft (supporting); writing – review and editing (supporting). Amy Spallone: Supervision (supporting); writing – review and editing (supporting). Ying Jiang: Data curation (supporting); methodology (lead); software (supporting); writing – review and editing (supporting). Roy F. Chemaly: Conceptualization (equal); formal analysis (supporting); investigation (supporting); methodology (supporting); project administration (equal); supervision (equal); writing – review and editing (lead).

FUNDING INFORMATION

None.

CONFLICT OF INTEREST STATEMENT

FK: Honorarium from Medscape for educational material. Research support from Eurofins‐Viracor; GA: None; AF: None; PE: None; MB: None; EAH: Research support from MERCK; AS: None; JY: None; RFC: serves as a consultant, speaker, or advisor for ADMA Biologics, Pulmotec, Janssen, Merck/MSD, Partner Therapeutics, Takeda, Shinogi, Genentech, Astellas, Tether, Adagio Therapeutics, Oxford Immunotec, Karius, Eurofins‐Viracor, Aicuris, Moderna, and Ansun Pharmaceuticals. He received research grants paid to his institution from Merck, Karius, AiCuris, Ansun Pharmaceuticals, Takeda, Genentech, Oxford Immunotec, and Eurofins‐Viracor.

ETHICAL APPROVAL

This study was approved by the University of Texas MD Anderson Cancer Center IRB (Protocol PA15‐0002).

Supporting information

Data S1.

Click here for additional data file.

ACKNOWLEDGMENTS

We would like to thank Giordana De Las Pozas with the University of Texas MD Anderson Cancer Center tumor registry, and the members of D3CODE (Ashley Aaroe, Lee Andrews II, Kiran K Badami, Janna A Baganz, Pratibha Bajwa, Lori R Baker, Gregory R Barbosa, Hannah C Beird, Matt Bourgeois, Kristy Brock, Elizabeth M Burton, Juan Cata, Caroline Chung, Michael Cutherell, John Cuenca, Pierre B Cyr, Bouthaina Dabaja, Hiba Dagher, Kevin M Daniels, Mary Domask, Giulio Draetta, Sarah Fisher, Katy Elizabeth French, Andrew Futreal, Maria Gaeta, Christopher Gibbons, Myrna Godoy, Drew Goldstein, Jillian Gunther, Cristhiam Hernandez, Kate Hutcheson, David Jaffray, Jeff Jin, Teny Matthew John, Trey Kell, Mark Knafl, Anai Kothari, Rayson C Kwan, J Jack Lee, Yue Liao, Jennifer Litton, Alex Liu, Kevin W McEnery, Mary McGuire, Tego Musunuru, Mayoora Muthu, Joseph Nates, Craig S Owen, Priyadharshini Padmakumar, Melody Page, Nicholas Palaskas, Jay J Patel, Sabitha Prabhakaran, Pratibha Bajwa, Ludivine Russell, Bilja Sajith, Paul A Scheet, Stephanie Schmidt, Kenna R Shaw, Sanjay Shete, Daniel P Shoenthal, Lessley J Stoltenberg, Ishwaria Subbiah, Chuck Suitor, Hussein Tawbi, Phillip Thompson, Anastasia Turin, Samir Unni, Benju Vicknamparampil, Max C Weber, John Weinstein, Zoe Williams, Mark C Wozny, Carol Wu, Jia Wu, James C Yao, Chingyi Young, Emily Yu, Steven Zatorski) for their efforts. We also appreciate Bryan Tutt with the University of Texas MD Anderson's research medical library for editing this manuscript.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study cannot be shared based on institutional restrictions.

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