Induced B-Cell Receptor Diversity Predicts PD-1 Blockade Immunotherapy Response

Area of Science:

• Immunology
• Oncology
• Genomics

Background:

• Immune checkpoint inhibitors, like anti-PD-1 antibodies (aPD1), show efficacy in advanced cancers.
• Patient response to aPD1 therapy varies, with mechanisms of non-response not fully understood.

Purpose of the Study:

• To investigate the role of B-cell receptor (BCR) diversity in response to aPD1 therapy.
• To identify predictors of response and non-response to aPD1 therapy.

Main Methods:

• Single-cell RNA sequencing and spatial transcriptomics on tumors and lymph nodes.
• Spatial immunoreceptor profiling and long-term clinical follow-up.
• Machine learning model development for predicting BCR diversity from baseline RNA sequencing.

Main Results:

• Successful aPD1 response correlates with induced BCR clonal diversity post-treatment.
• Induced BCR clones co-localize with and activate T-cells, aiding tumor clearance.
• BCR diversity predicts response in basal cell carcinoma, glioblastoma, melanoma, and head and neck cancers.
• A gene expression signature predicts BCR diversity induction, enabling a machine learning model for prediction.

Conclusions:

• BCR diversity is a dynamic factor in immunotherapy response.
• BCR diversity serves as a generalizable prognostic marker for aPD1 therapy.
• Targeting B-cell diversity may improve outcomes for non-responders.