Risk and Outcome of Breakthrough COVID-19 Infections in Vaccinated Patients With Cancer: Real-World Evidence From the National COVID Cohort Collaborative

Abstract

Purpose: To provide real-world evidence on risks and outcomes of breakthrough COVID-19 infections in vaccinated patients with cancer using the largest national cohort of COVID-19 cases and controls.

Methods: We used the National COVID Cohort Collaborative (N3C) to identify breakthrough infections between December 1, 2020, and May 31, 2021. We included patients partially or fully vaccinated with mRNA COVID-19 vaccines with no prior SARS-CoV-2 infection record. Risks for breakthrough infection and severe outcomes were analyzed using logistic regression.

Results: A total of 6,860 breakthrough cases were identified within the N3C-vaccinated population, among whom 1,460 (21.3%) were patients with cancer. Solid tumors and hematologic malignancies had significantly higher risks for breakthrough infection (odds ratios [ORs] = 1.12, 95% CI, 1.01 to 1.23 and 4.64, 95% CI, 3.98 to 5.38) and severe outcomes (ORs = 1.33, 95% CI, 1.09 to 1.62 and 1.45, 95% CI, 1.08 to 1.95) compared with noncancer patients, adjusting for age, sex, race/ethnicity, smoking status, vaccine type, and vaccination date. Compared with solid tumors, hematologic malignancies were at increased risk for breakthrough infections (adjusted OR ranged from 2.07 for lymphoma to 7.25 for lymphoid leukemia). Breakthrough risk was reduced after the second vaccine dose for all cancers (OR = 0.04; 95% CI, 0.04 to 0.05), and for Moderna's mRNA-1273 compared with Pfizer's BNT162b2 vaccine (OR = 0.66; 95% CI, 0.62 to 0.70), particularly in patients with multiple myeloma (OR = 0.35; 95% CI, 0.15 to 0.72). Medications with major immunosuppressive effects and bone marrow transplantation were strongly associated with breakthrough risk among the vaccinated population.

Conclusion: Real-world evidence shows that patients with cancer, especially hematologic malignancies, are at higher risk for developing breakthrough infections and severe outcomes. Patients with vaccination were at markedly decreased risk for breakthrough infections. Further work is needed to assess boosters and new SARS-CoV-2 variants.

FIG 1.

CONSORT diagram. The whole N3C-vaccinated population was screened according to the N3C data released on August 27, 2021. The exclusion criteria were based on scientific needs (excluding patients who had COVID-19 infections before vaccinations) and data availability (excluding JNJ-78436735), completeness (excluding COVID-19 infections after May 31, 2021), and quality. N3C, National COVID Cohort Collaborative.

FIG 2.

The effects of recent cancer treatments on breakthrough infection. The forest plot of logistic linear regression analyses is shown for (A) recent use (6 months before vaccination) of 15 NCI-derived drug categories in breakthrough infection cases, (B) recent use (6 months before vaccination) of drugs with v without major immunosuppressive effects in breakthrough infection cases, and (C) bone marrow transplantation in breakthrough infection cases. Age, sex, race and ethnicity, smoking status, vaccination doses, vaccination types, and vaccination date were included in all logistic regression models. Results from 14 separate logistic regression analyses, each tested a single medication category with its own reference group, are summarized in (A). The P values and 95% CIs shown in (A) were adjusted using FDR for multiple testing. *P < .05, **P < .01, ***P < .001. CAR, chimeric antigen receptor; FDR, false discovery rate; OR, odds ratio; ref, reference.

FIG A1.

The mRNA vaccination effectiveness in protecting patients with cancer from COVID-19 infection. The forest plots of logistic linear regression analyses are shown for (A) all features in the overall breakthrough cases and negative controls; (B) major cancer types in the overall breakthrough cases and negative controls; (C) major hematologic malignancies (breakthrough cases ≥ 20) in patients with cancers; and (D) fully vaccinated versus partially vaccinated cases and mRNA-1273 versus BNT162b2 vaccines in four individual patient groups with multiple myeloma, lymphoma, lymphoid leukemia, and myeloid leukemia. Age, sex, race and ethnicity, smoking status, vaccination doses, vaccination types, and vaccination date were included in all logistic regression models. Individual logistic regression analyses were performed for each hematologic malignancy type, and the results are summarized in (D). *P < .05, **P < .01, ***P < .001. OR, odds ratio; ref, reference.

FIG A2.

The mRNA vaccination effectiveness in reducing the risk of severe COVID-19 outcomes in patients with cancer. The forest plot of logistic linear regression analyses is shown for (A) all features in the overall breakthrough cases; (B) major cancer types in the overall breakthrough cases; (C) major hematologic malignancies (breakthrough cases ≥ 20) in breakthrough patients with cancers; and (D) fully vaccinated versus partially vaccinated cases and mRNA-1273 versus BNT162b2 vaccines in four individual patient groups with multiple myeloma, lymphoma, lymphoid leukemia, and myeloid leukemia. Age, sex, race and ethnicity, smoking status, adjusted CCI, vaccination doses, vaccination types, and vaccination date were included in all logistic regression models. Individual logistic regression analyses were performed for each hematologic malignancy type, and the results are summarized in (D). *P < .05, **P < .01, ***P < .001. CCI, Charlson Comorbidity Index; OR, odds ratio; ref, reference.