Nat Commun: Checkpoint multi-group prediction of adverse events during immunotherapy.

Immune-related adverse events (irAEs) during the treatment of anti-procedural death 1 (PD-1) or anti-procedural death ligand 1 (PD-L1) antibodies can affect any organ system and in some cases even kill due to the combination of immune activation and immunobalance disorders.
pneumonia, the most common fatal irAE, can lead to a 10% mortality rate, accounting for 35% of anti-PD-1/PD-L1-related deaths.
, the deadliest form of myocarditis, can cause about 50% of deaths.
, we need predictive biomarkers from iraEs to determine the benefit/risk ratio for patients treated with anti-PD-1/PD-L1.
T-cell population (TCR) diversity, CD8-T cell cloning dilation, and tumor mutation burden (TMB) have been recommended for possible use in predicting irAE, but they are based on a single factor or in a limited number of cases.
, so far, we lack a comprehensive way to identify the biomarkers of irAE.
, obtaining a patient sample queue with a sufficient sample size is challenging, and traditional methods can take years of multi-center research.
, researchers recently published a paper in the journal Nature Communications that looked at potential predictive factors for irAE risk in patients of 26 tumor types treated with anti-PD-1/PD-L1 by integrating real-world drug alert and molecular omics data.
researchers identified a two-variable linear regression model that accurately predicts Ir APAE's LCP1 and ADPGK, and validated the predictive potential of the LCP1-ADPGK model in a separate patient queue.
, this method provides a way to identify potential biomarkers of iraE in cancer immunotherapy using drug alert data and multi-group data.
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