Adaptive immunity to SARS-CoV-2 in cancer patients: The CAPTURE study

  1. Annika Fendler
  2. Lewis Au
  3. Laura Amanda Boos
  4. Fiona Byrne
  5. Scott T.C. Shepherd
  6. Ben Shum
  7. Camille L. Gerard
  8. Barry Ward
  9. Wenyi Xie
  10. Maddalena Cerrone
  11. Georgina H. Cornish
  12. Martin Pule
  13. Leila Mekkaoui
  14. Kevin W. Ng
  15. Richard Stone
  16. Camila Gomes
  17. Helen R. Flynn
  18. Ana Agua-Doce
  19. Phillip Hobson
  20. Simon Caidan
  21. Mike Howell
  22. Robert Goldstone
  23. Mike Gavrielides
  24. Emma Nye
  25. Bram Snijders
  26. James Macrae
  27. Jerome Nicod
  28. Adrian Hayday
  29. Firza Gronthoud
  30. Christina Messiou
  31. David Cunningham
  32. Ian Chau
  33. Naureen Starling
  34. Nicholas Turner
  35. Jennifer Rusby
  36. Liam Welsh
  37. Nicholas van As
  38. Robin Jones
  39. Joanne Droney
  40. Susana Banerjee
  41. Kate Tatham
  42. Shaman Jhanji
  43. Mary O’Brien
  44. Olivia Curtis
  45. Kevin Harrington
  46. Shreerang Bhide
  47. Tim Slattery
  48. Yasir Khan
  49. Zayd Tippu
  50. Isla Leslie
  51. Spyridon Gennatas
  52. Alicia Okines
  53. Alison Reid
  54. Kate Young
  55. Andrew Furness
  56. Lisa Pickering
  57. Sonia Ghandi
  58. Steve Gamblin
  59. Charles Swanton
  60. Emma Nicholson
  61. Sacheen Kumar
  62. Nadia Yousaf
  63. Katalin Wilkinson
  64. Anthony Swerdlow
  65. Ruth Harvey
  66. George Kassiotis
  67. Robert Wilkinson
  68. James Larkin
  69. Samra Turajlic

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

There is a pressing need to characterise the nature, extent and duration of immune response to SARS-CoV-2 in cancer patients and inform risk-reduction strategies and preserve cancer outcomes. CAPTURE is a prospective, longitudinal cohort study of cancer patients and healthcare workers (HCWs) integrating longitudinal immune profiling and clinical annotation. We evaluated 529 blood samples and 1051 oronasopharyngeal swabs from 144 cancer patients and 73 HCWs and correlated with >200 clinical variables. In patients with solid cancers and HCWs, S1-reactive and neutralising antibodies to SARS-CoV-2 were detectable five months post-infection. SARS-CoV-2-specific T-cell responses were detected, and CD4 + T-cell responses correlated with S1 antibody levels. Patients with haematological malignancies had impaired but partially compensated immune responses. Overall, cancer stage, disease status, and therapies did not correlate with immune responses. These findings have implications for understanding individual risks and potential effectiveness of SARS-CoV-2 vaccination in the cancer population.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.12.21.20248608: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    , APC anti-IgM (clone MHM-88, Biolegend) and PE anti-IgA (clone IS11-8E10, Miltenyi Biotech) for 30 min (all antibodies diluted 1:200 in FACS buffer).
    anti-IgM
    suggested: None
    anti-IgA
    suggested: None
    Virus plaques were visualized by immunostaining, as described previously for the neutralisation of influenza viruses using a rabbit polyclonal anti-NSP8 antibody used at 1:1000 dilution and anti-rabbit-HRP conjugated antibody at 1:1000 dilution and detected by action of HRP on a tetramethyl benzidine (TMB) based substrate.
    anti-NSP8
    suggested: (Acris Antibodies GmbH Cat# AP09089SU-N, RRID:AB_2035808)
    anti-rabbit-HRP
    suggested: (Kindle Biosciences Cat# R1006, RRID:AB_2800464)
    Compensation was performed with 20 µl antibody-stained anti-mouse Ig, k / negative control compensation particle set (BD Biosciences, UK). 1×106 live CD19-/CD14- cells were acquired per sample.
    antibody-stained anti-mouse Ig, k /
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    MFI in control SUP-T1 cells was subtracted from MFI in spike-expressing SUP-T1 cells, and resulting values were divided by MFI in control SUP-T1 cells to calculate the specific increase in MFI.
    SUP-T1
    suggested: BCRC Cat# 60191, RRID:CVCL_1714)
    Neutralising antibody assay: Confluent monolayers of Vero E6 cells were incubated with SARS-CoV-2 virus and twofold serial dilutions of heat-treated serum or plasma samples starting at 1:40 for 4 hrs at 37°C, 5% CO2, in duplicates.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    Samples were run on a Bio-Rad Ze5 analyser running Bio-Rad Everest software v2.4 and analysed using FlowJo v10
    Bio-Rad Everest
    suggested: None
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)

    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: We detected the following sentences addressing limitations in the study:
    There are limitations in this report, many of which will be addressed with longer follow up and planned analyses within the CAPTURE program. Our data so far reflect those who recovered from COVID-19 but included a broad representation of illness from asymptomatic to severe. We have in the majority recruited convalescent patients, and hence this interim report is focused on the immune protective response. Ongoing work will explore correlates of immunopathology during acute infection. These data provide insights into the branches of adaptive immunity against SARS-CoV-2, placed in the context of cancer and cancer therapy, which also bear relevance for the effectiveness of SARS-CoV-2 vaccines. Further questions that can only be assessed with longer follow up are the impact of treatment interruptions during the first wave of the pandemic on long term cancer outcomes, and the impact on quality of life for those with persistent symptoms following SARS-CoV-2 infection (Yelin et al., 2020). The prospective, longitudinal framework of this study integrating clinical outcomes and immune profiling will serve to generate data rapidly to address these and other emergent questions as the pandemic evolves, including immune responses to new SARS-CoV-2 variants (Kemp et al., 2020), and during the ensuing vaccination programme.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT03226886RecruitingTRACERx Renal CAPTURE Sub-study

    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.

    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.

  2. Version published to 10.1101/2020.12.21.20248608v1 on medRxiv