Pre-existing biochemical entities predict severity of cytokine release syndrome in B-cell lymphoma patients treated with CD19-directed CAR T-cells
Bibliographic record
Abstract
Abstract Potentially severe immune-related adverse events (irAEs) develop in relapsed/refractory large B cell lymphoma (R/R LBCL) patients during treatment with anti-CD19 CAR T-cell therapy. Therefore, prediction and better management of toxicities is critically required to improve patient outcomes. Cytokine release syndrome (CRS), one of the most notable acute irAEs, is the result of an inflammatory response of activated CAR T-cells and myeloid cells. Interestingly, high levels of glucose and low levels of certain amino acids are associated with pro-inflammatory immune activation in infectious and autoimmune diseases, but such associations have not been investigated in CAR T-cell therapy. Therefore, we employed mass spectrometry-based untargeted and targeted metabolomics analysis to identify pre-treatment host biological metabolites that predict rapid onset and severe CRS in 41 R/R LBCL patients treated with Axicabtagene Ciloleucel or Tisagenlecleucel. Analysis of plasma metabolites revealed significant associations of CRS with several metabolic classes, including carbohydrates, lipid, amino acids, dipeptides and nucleotides. Interestingly, high levels of the carbohydrates, glucose, mannose and 1,5-anhydroglucitol were observed in patients who experienced more rapid onset of CRS. In contrast, patients with greater severity of CRS had lower plasma abundance of the amino acids, glycine, proline and glutamate. Our data suggest that specific pre-existing plasma metabolites have the potential to serve as predictive biomarkers of CRS in patients undergoing anti-CD19 CAR T-cell therapy, and may be used for risk stratification and clinical management of irAEs in advance of treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".