CAR T-Related Toxicities Based on Dynamic Proteomic Profiles Identifies Risk Factors for Cytokine Release Syndrome (CRS) and Immune Effector Cell -Associated Neurotoxicity Syndrome (ICANS)
Bibliographic record
Abstract
TK and SM are Co-first authors INTRODUCTION Treatment with chimeric antigen receptor (CAR) T-cells significantly improved outcomes in relapsed/refractory non-Hodgkin lymphoma (NHL) and multiple myeloma (MM). CAR-T activation and anti-tumor cytotoxicity are associated with bystander inflammatory reactions resulting in CRS and/or ICANS. Due to complex cytokine profiles, disease heterogeneity, and variability between commercial CAR-T products, identification of risk factors associated with CRS and/or ICANS has been challenging. In this study, we used plasma proteomic profiling at different timepoints to identify possible inflammatory mediators associated with CRS and ICANS METHODS We prospectively collected plasma samples from patients who received CAR-T cells therapy between 9/2021 to 12/2022 at several time points - before lymphodepletion chemotherapy on day -5 (relative to CAR-T cell infusion), prior to CAR T-cell infusion on day 0, and post CAR T-cell therapy on days 1, 2, 3, and 7. Protein profiling analyses were conducted at Eve Technologies (Calgary, Alberta, Canada) using an assay measuring 71 total cytokines and chemokines. Proteins levels were compared across different time points used Wilcoxon rank test, while features associated with CRS/ICANS were identified using logistic regression. Receiver operating characteristic (ROC) analysis used to identify variables predictive for CRS. Area under the curve (AUC) of at least 0.8 was used and best cutoffs were determined according to Youden index. P-values <0.05 were considered statistically significant. This study was supported in part by The Frederick A. Deluca Foundation. RESULTS Overall, 56 patients with available cytokine assays at all time points were included. The median age was 65 years (IQR: 57-74) and 70% were men. Of all patients, 26 (46%) had diffuse large B-cell lymphoma (DLBCL), 23 (41%) MM, 4 (7%) mantle cell lymphoma, and 3 (6) follicular lymphoma. Ide-cel (39%), liso-cel (36%), and axi-cel (17%) were the most used CAR-T cell products. All patients received lymphodepleting chemotherapy with fludarabine/cyclophosphamide. In total, 35 (63%) patients developed CRS (grade 1, 89%; grade 2, 8%; grade 3, 3%) and 18 (32%) patients developed ICANS (grade 1, 72%; grade 2, 22%; grade 3, 6%). Compared to patients who did not develop CRS, patients with CRS had lower median absolute lymphocyte counts at day -5 (0.02 x10 9/L vs. 0.05, p=0.0146), higher baseline CRP (13 vs. 4 mg/L, p=0.0005), and higher ferritin (914 vs. 442 mg/L, p=0.048). No differences in the type of CAR-T products (p=0.090), percentages of DLBCL or MM (p=0.270) were observed between CRS and no CRS cohorts ( Panel-A). First, we investigated the proteomic profiles at baseline for CRS odds. Hemoglobin (odd ratio [OR]: 0.6, 95%CI: 0.4-0.8) was associated with lower odds for CRS while IL6 (2.0, 1.2-3.3) and stem cell factor (scf 2.2, 1.2-4.2) were associated with higher odds of CRS. We then analyzed the differences in cytokine levels between day 0 and day 3 to select cytokines with significant changes for further analysis ( Panel-B). At day 3, groa (1.9, 1.1-3.3), IL3 (1.6, 1.2-2.1), IL5 (1.5, 1.2-1.9), IL6 (1.7, 1.3-2.3), IL10 (2.0, 1.3-3.0), TNFα (2.0, 1.1-3.6), and mcp2 (2.5, 1.2-5.3) were all associated with higher odds for CRS. Based on ROC analysis at day 3, best cutoff points to estimate CRS (value, sensitivity/specificity) for IL3 (3, 80%/90%), IL5 (197, 74%/85%), IL6 (11, 70%/85%), and IL10 (53, 74%/85%) were identified. Based on that, elevated IL3 (OR:24, 95%CI: 6-105), IL5 (11, 3-40), IL6 (21, 5-95), and IL10 (12, 3-46) were associated with higher odds for CRS. For ICANS, day 3 IL3 (1.5, 1.2-1.9), IL6 (1.2, 1.1-1.5), IL8 (2.1, 1.4-3.3), and IL10 (1.7, 1.3-2.4) were associated with higher odds for ICANS. Best cutoff points to estimate ICANS at day 3 (value, sensitivity/specificity) for IL3 (5, 78%/76%), IL6 (115, 78%/78%), IL10 (130, 81%/80%), and IL8 (21, 83%/81%) were identified. Based on that, elevated IL3 (OR:10, 95%CI: 3-37), IL6 (11, 3-43), IL10 (13, 3-51), and IL8 (19, 4-81) were associated with higher odds for ICANS. CONCLUSIONS In our comprehensive plasma proteomic profiles analysis, we identified cutoffs for IL3, IL6, IL5 and IL10 that may be predictive for CRS and ICANS regardless of CAR-T cell product. Our results are clinically applicable and may be used to recognize patients at risk for CRS and/or ICANS who may be eligible for prophylactic therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".