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Updated: Feb 16, 2026

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
B cells as biomarkers: predicting immune checkpoint therapy adverse events
Abstract:
Immune checkpoint inhibitors are becoming a cornerstone of cancer immunotherapy as a result of their clinical success in relieving immune suppression and driving durable antitumor T cell responses in certain subsets of patients. Unfortunately, checkpoint inhibition is also associated with treatment-related toxicities that result in a myriad of side effects, ranging from mild and manageable to severe and debilitating. In this issue of the JCI, Das and colleagues report an association between early therapy-induced changes in circulating B cells and an increased risk of high-grade immune-related adverse events (IRAEs) in patients treated with checkpoint inhibitors that target cytotoxic T lymphocyte-associated antigen-4 (CTLA4) and programmed cell death protein 1 (PD1). These findings identify potential predictive biomarkers for high-grade IRAEs that may be leveraged to improve patient monitoring and may prompt new treatment strategies to prevent IRAEs.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

