Impaired neutralizing antibody response to COVID-19 mRNA vaccines in cancer patients

  1. Cong Zeng
  2. John P. Evans
  3. Sarah Reisinger
  4. Jennifer Woyach
  5. Christina Liscynesky
  6. Zeinab El Boghdadly
  7. Mark P. Rubinstein
  8. Karthik Chakravarthy
  9. Linda Saif
  10. Eugene M. Oltz
  11. Richard J. Gumina
  12. Peter G. Shields
  13. Zihai Li
  14. Shan-Lu Liu

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Abstract

There is currently a critical need to determine the efficacy of SARS-CoV-2 vaccination for immunocompromised patients. In this study, we determined the neutralizing antibody response in 160 cancer patients diagnosed with chronic lymphocytic leukemia (CLL), lung cancer, breast cancer, and various non-Hodgkin’s lymphomas (NHL), after they received two doses of mRNA vaccines. Serum from 46 mRNA vaccinated health care workers (HCWs) served as healthy controls. We discovered that (1) cancer patients exhibited reduced neutralizing antibody titer (NT 50 ) compared to HCWs; (2) CLL and NHL patients exhibited the lowest NT 50 levels, with 50-60% of them below the detection limit; (3) mean NT 50 levels in patients with CLL and NHL was ~2.6 fold lower than those with solid tumors; and (4) cancer patients who received anti-B cell therapy exhibited significantly reduced NT 50 levels. Our results demonstrate an urgent need for novel immunization strategies for cancer patients against SARS-CoV-2, particularly those with hematological cancers and those on anti-B cell therapies.

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  1. Version published to 10.1186/s13578-021-00713-2
  2. ScreenIT

    SciScore for 10.1101/2021.10.20.21265273: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablePatients were 47% (n=75) female and 53% (n=85) male.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Cell Culture: HEK293T cells (ATCC CRL-11268, CVCL_1926) and HEK293T-hACE2 cells (BEI NR-52511) were grown in DMEM (Gibco, 11965-092) supplemented with 10% (vol/vol
    HEK293T-hACE2
    suggested: RRID:CVCL_A7UK)
    Briefly, HEK293T cells were transfected with HIV-1 NL4.3-inGluc and pcDNA3.1-SARS-CoV2-S-C9 constructs in a 2:1 ratio using polyelthylenimine (PEI).
    HEK293T
    suggested: None
    Virus and serum mixture were incubated for 1 hr at 37°C, and then added to HEK293T-ACE2 cells seeded at 2 × 104 cells/well.
    HEK293T-ACE2
    suggested: None
    Recombinant DNA
    SentencesResources
    This construct is based on a ΔEnv pNL4.3 HIV-1 vector and contains an anti-sense Gaussia luciferase (Gluc) gene with a sense intron.
    pNL4.3 HIV-1
    suggested: None
    Briefly, HEK293T cells were transfected with HIV-1 NL4.3-inGluc and pcDNA3.1-SARS-CoV2-S-C9 constructs in a 2:1 ratio using polyelthylenimine (PEI).
    pcDNA3.1-SARS-CoV2-S-C9
    suggested: None
    Software and Algorithms
    SentencesResources
    Quantification and Statistical Analysis: Data were analyzed as mean with Standard Error of Mean (SEM), Statistical analyses were performed using GraphPad Prism 5.0 as follows: One-way Analysis of Variance (ANOVA) with Bonferroni’s post-tests was used to compute statistical significance (p values) between multiple groups for multiple comparison or t-test was used for two groups for single comparison.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    NT50 values were calculated from VNA output using a non-linear regression with least-squares fit in GraphPad Prism5 (GraphPad Software, San Diego, California USA, www.graphpad.com).
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.10.20.21265273v1 on medRxiv