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CD8+ T cell differentiation status correlates with the feasibility of sustained unresponsiveness following oral immunotherapy

Author

Listed:
  • Abhinav Kaushik

    (Stanford University School of Medicine)

  • Diane Dunham

    (Stanford University School of Medicine)

  • Xiaorui Han

    (Stanford University School of Medicine)

  • Evan Do

    (Stanford University School of Medicine)

  • Sandra Andorf

    (Stanford University School of Medicine
    University of Cincinnati College of Medicine
    Cincinnati Children’s Hospital Medical Center)

  • Sheena Gupta

    (Stanford University School of Medicine)

  • Andrea Fernandes

    (Stanford University School of Medicine)

  • Laurie Elizabeth Kost

    (Stanford University School of Medicine)

  • Sayantani B. Sindher

    (Stanford University School of Medicine)

  • Wong Yu

    (Stanford University School of Medicine
    University of California)

  • Mindy Tsai

    (Stanford University School of Medicine
    Stanford University School of Medicine)

  • Robert Tibshirani

    (Stanford University)

  • Scott D. Boyd

    (Stanford University School of Medicine
    Stanford University School of Medicine)

  • Manisha Desai

    (Stanford University)

  • Holden T. Maecker

    (Stanford University School of Medicine
    Stanford University School of Medicine)

  • Stephen J. Galli

    (Stanford University School of Medicine
    Stanford University School of Medicine
    Stanford University School of Medicine)

  • R. Sharon Chinthrajah

    (Stanford University School of Medicine)

  • Rosemarie H. DeKruyff

    (Stanford University School of Medicine)

  • Monali Manohar

    (Stanford University School of Medicine)

  • Kari C. Nadeau

    (Stanford University School of Medicine)

Abstract

While food allergy oral immunotherapy (OIT) can provide safe and effective desensitization (DS), the immune mechanisms underlying development of sustained unresponsiveness (SU) following a period of avoidance are largely unknown. Here, we compare high dimensional phenotypes of innate and adaptive immune cell subsets of participants in a previously reported, phase 2 randomized, controlled, peanut OIT trial who achieved SU vs. DS (no vs. with allergic reactions upon food challenge after a withdrawal period; n = 21 vs. 30 respectively among total 120 intent-to-treat participants). Lower frequencies of naïve CD8+ T cells and terminally differentiated CD57+CD8+ T cell subsets at baseline (pre-OIT) are associated with SU. Frequency of naïve CD8+ T cells shows a significant positive correlation with peanut-specific and Ara h 2-specific IgE levels at baseline. Higher frequencies of IL-4+ and IFNγ+ CD4+ T cells post-OIT are negatively correlated with SU. Our findings provide evidence that an immune signature consisting of certain CD8+ T cell subset frequencies is potentially predictive of SU following OIT.

Suggested Citation

  • Abhinav Kaushik & Diane Dunham & Xiaorui Han & Evan Do & Sandra Andorf & Sheena Gupta & Andrea Fernandes & Laurie Elizabeth Kost & Sayantani B. Sindher & Wong Yu & Mindy Tsai & Robert Tibshirani & Sco, 2022. "CD8+ T cell differentiation status correlates with the feasibility of sustained unresponsiveness following oral immunotherapy," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34222-8
    DOI: 10.1038/s41467-022-34222-8
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    References listed on IDEAS

    as
    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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