CELL INTELLIGENCE · 肿瘤细胞治疗研究

BACKGROUND: Chimeric antigen receptor (CAR) T cells targeting CD19 achieve remarkable remissions in refractory B cell malignancies, yet deleterious side effects such as cytokine release syndrome (CRS) limit their broader application. Current preclinical assays on manufactured cell products do not predict human clinical function. We hypothesized that variability in the CAR proximal protein interaction networks that mediate CAR signal transduction may correlate with patient-to-patient differences in toxicity. METHODS: Using banked, preinfusion 41BB-CD3 CAR T cell products with known clinical outcomes, we applied quantitative multiplex co-immunoprecipitation (QMI) to profile 200 binary interactions among 21 key signaling proteins following CD19 stimulation. Bioinformatic analysis clustered interactions into functional modules, and correlated protein interaction patterns with clinical outcomes. RESULTS: Correlation network analysis, which clusters interactions into coregulated modules, identified a stimulation-responsive module with similar behavior in all products, and a second module that correlated with the presence of CRS. The CRS module was enriched for interactions among CD28, FYB, and the SRC family kinases LCK and FYN. In a head-to-head validation cohort, a similar CD28-FYB-kinase module again correlated with the presence of CRS. Using a combined dataset, a machine learning classifier trained on top QMI features retrospectively identified CRS samples with high accuracy. CONCLUSIONS: These data indicate that subtle, batch-to-batch differences in CAR signalosome assembly may correlate with CRS, and they support the further development of a preinfusion proteomic assay to forecast CRS risk in CAR T cell products.