4.5 Review

Germline genetic variation and predicting immune checkpoint inhibitor induced toxicity

Journal

NPJ GENOMIC MEDICINE
Volume 7, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41525-022-00345-6

Keywords

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Funding

  1. Birmingham Cancer Research UK Centre
  2. Bowel Cancer UK
  3. Birmingham Cancer Research UK Centre
  4. Bowel Cancer UK

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Immune checkpoint inhibitor therapy has revolutionised cancer treatment, but can lead to serious autoimmune adverse events. Currently, it is challenging to predict which patients will experience these adverse events and how severe they will be. Research suggests that the host genome plays a crucial role in determining the risk of these events.
Immune checkpoint inhibitor (ICI) therapy has revolutionised the treatment of various cancer types. ICIs reinstate T-cell function to elicit an anti-cancer immune response. The resulting immune response can however have off-target effects which manifest as autoimmune type serious immune-related adverse events (irAE) in similar to 10-55% of patients treated. It is currently challenging to predict both who will experience irAEs and to what severity. Identification of patients at high risk of serious irAE would revolutionise patient care. While the pathogenesis driving irAE development is still unclear, host genetic factors are proposed to be key determinants of these events. This review presents current evidence supporting the role of the host genome in determining risk of irAE. We summarise the spectrum and timing of irAEs following treatment with ICIs and describe currently reported germline genetic variation associated with expression of immuno-modulatory factors within the cancer immunity cycle, development of autoimmune disease and irAE occurrence. We propose that germline genetic determinants of host immune function and autoimmune diseases could also explain risk of irAE development. We also endorse genome-wide association studies of patients being treated with ICIs to identify genetic variants that can be used in polygenic risk scores to predict risk of irAE.

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