irAEs and TRBV polymorphisms - Interview with Timothy Looney, PhD

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Timothy Looney, PhD - Senior Director, Immunology and Bioinformatics at Singular Genomics

By: Timothy Looney, PhD

Date: 9/12/2023

The topic at hand revolves around "Immune-Related Adverse Events (IRAEs)" in the context of cancer treatment using "Immune Checkpoint Inhibitors (ICIs)." Timothy Looney, a prominent researcher with a PhD, discusses the significance of IRAEs in cancer immunotherapy. IRAEs are adverse events that occur when therapeutic agents like ICIs activate T cells, causing them to mistakenly target healthy tissues, leading to damage or destruction. These events, which can range from mild to life-threatening, are a major concern in cancer immunotherapy due to the lack of effective predictive biomarkers.

The conversation also explores how T cell receptor beta variable (TRBV) gene polymorphisms influence T cell function and autoantigen recognition. Studying these polymorphisms in relation to IRAEs has historically been challenging due to the repetitive nature of TRBV genes and the presence of pseudogenes resembling them in the genome. Timothy Looney's research introduces a novel method, "long amplicon TCR beta chain sequencing," to accurately analyze TRBV polymorphisms in cancer immunotherapy patients. This approach identifies specific TRBV allele haplotypes associated with the risk of severe IRAEs, potentially transforming personalized cancer treatment.

The major findings of this study reveal a strong correlation between certain TRBV allele haplotypes and the risk of severe IRAEs. Patients can be categorized into different haplotype groups based on their TRBV allele profiles, allowing predictions about their likelihood of experiencing adverse events during immunotherapy. This breakthrough could revolutionize patient management and treatment strategies, offering personalized approaches to cancer care. Additionally, these findings have broader implications for autoimmune disease research, potentially aiding in the identification of predictive biomarkers for such conditions.