Active Cancer Infection & Immunity

Understanding the pathophysiology of auto-immunity through Immune Checkpoint Blockade

In plain English

AI plain-English summary

Immune checkpoint blockade cancer therapy can trigger severe autoimmune side effects in some patients, attacking healthy organs like the skin or joints while leaving others untouched, and no one knows why. This matters because these side effects—called immune-related adverse events—cause significant illness and can be fatal, yet doctors have no way to predict which organ will be affected or why. The underlying mechanisms remain a black box. The researcher will compare tissue samples from affected skin and joints with existing data from autoimmune diseases, then track patients over time to see how their immune cells change during treatment. If this work succeeds, it could transform how clinicians monitor patients on immunotherapy—spotting early warning signs before damage occurs. It might also reveal fundamental links between cancer immunotherapy and autoimmune disease, potentially opening new treatment avenues for both conditions. The deep learning models could eventually help predict individual patient risk, allowing doctors to tailor treatments or intervene earlier. This is primarily fundamental science into how the immune system goes awry, but the insights could reshape how millions of cancer patients are managed.

View original technical description
Cancer immunotherapy consisting of Immune Checkpoint Blockade (ICB) has reshaped management and outcomes for many cancers, but frequently elicits autoimmune side-effects (immune-related adverse events – irAEs) that are a source of significant morbidity, and are sometimes fatal. The determinants of irAEs are poorly understood, especially why divergent organ systems become involved in different patients. I will explore the relationship between patient T Cell receptor (TCR) repertoire, HLA and the mutations within the cancer to better understand precipitants of organ-specific irAEs and similarities with autoimmunity. To do this, I will perform spatial transcriptomics and TCR profiling of skin and knee joint irAEs and compare this with existing spatial datasets from autoimmune disease to investigate the similarities and differences in local immune environments. I will then move on to a longitudinal in-house cohort of patients to perform tumour exome and TCR sequencing, integrating this with TCR/RNA sequencing of peripheral lymphocytes at various timepoints in their ICB treatment, with the aim of understanding the mechanisms behind development of irAEs. Finally, I will interrogate large public cancer and autoimmune TCR-antigen datasets, building deep learning models to study how the interaction between TCR, HLA and putative peptide affects autoimmunity.

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Researchers

Esther Ng (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Exploring the clonal determinants of T-cell anti-cancer and autoimmune responses after immune checkpoint blockade
Understanding immune-related toxicities through multifacet profiling
Identifying critical pathways regulating autoimmunity in immuno-oncology and arthritis patients. Short title: IPADS (Immune PAthways for Drug Side-effects)
Imaging-based analysis of signaling pathways triggered by immune checkpoint receptors
Identifying autoimmune signatures in patients receiving checkpoint immunotherapy

Original classification

Early-Career Award

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