Riding the storm: managing cytokine-related toxicities in CAR-T cell therapy
Bibliographic record
Abstract
The advent of chimeric antigen receptor T cells (CAR-T) has been a paradigm shift in cancer immunotherapeutics, with remarkable outcomes reported for a growing catalog of malignancies. While CAR-T are highly effective in multiple diseases, salvaging patients who were considered incurable, they have unique toxicities which can be life-threatening. Understanding the biology and risk factors for these toxicities has led to targeted treatment approaches which can mitigate them successfully. The three toxicities of particular interest are cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), and immune effector cell-associated hemophagocytic lymphohistiocytosis (HLH)-like syndrome (IEC-HS). Each of these is characterized by cytokine storm and hyperinflammation; however, they differ mechanistically with regard to the cytokines and immune cells that drive the pathophysiology. We summarize the current state of the field of CAR-T-associated toxicities, focusing on underlying biology and how this informs toxicity management and prevention. We also highlight several emerging agents showing promise in preclinical models and the clinic. Many of these established and emerging agents do not appear to impact the anti-tumor function of CAR-T, opening the door to additional and wider CAR-T applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".