Raw RNA-sequencing data for deriving BCR sequences from the NIH U01 project: Finding the optimal balance of immunotherapy efficacy and toxicity.
IrAEs convey substantial morbidity, incur considerable costs, and in some cases may preclude further use of immune checkpoint blockades. Recent studies indicate that (1) up to 80% of individuals on ICIs experience some form of irAEs; (2) ~35% of patients require corticosteroid treatments to mitigate these events; and (3) up to 20% terminate their therapy due to irAEs. As immunotherapy use extends from major centers to smaller, isolated, and less experienced community sites, predicting the development of irAEs and incorporation of this prediction into ICI treatment considerations becomes extremely valuable. Despite the substantial efforts invested in machine learning models for predicting ICI treatment responses, few studies have focused on predictive modeling for irAEs. We address this unmet need by leveraging the unique patient resources collected at UTSW and developing a BCR-based biomarker for irAEs. Moreover, irAEs can happen as short as a few weeks or as long as a few years after ICI treatment. We deployed our model longitudinally to track if the evolution of autoreactive BCRs align with the timings of irAEs. Overall design: The dataset contains RNA sequencing profiles from peripheral blood samples of patients receiving immune checkpoint inhibitor (ICI) treatments at UT Southwestern Medical Center. Blood was collected at three points: baseline, during immune-related adverse events (irAEs), and at scheduled intervals throughout treatment. A total of 256 samples were gathered from 113 patients who received anti-PD1, anti-PDL1, and/or anti-CTLA4 therapies, either as single agents or in combination. The research examines B cell receptor (BCR) repertoire changes during ICI therapy and their relationship to clinical outcomes, with special attention to irAE development. The Illumina TruSeq® Stranded mRNA Library preparation protocol was used for RNA sequencing, with samples sequenced on the Illumina NovaSeq 6000 platform using PE-150 sequencing protocol. The processed data files contain BCR sequences extracted from RNA-seq data using MiXCR, including heavy chain sequences from each patient sample, clonotype information (groups of similar BCR sequences from the same B cell lineage), and clonal expansion data (abundance of each BCR clonotype). These receptor files were utilized to: calculate BCR binding scores toward various antigens using the Cmai model, monitor BCR repertoire changes during treatment, explore relationships between BCR binding patterns and irAEs, and develop an irAE risk score based on predicted autoantibody binding.