Neurotoxicity of Immunotherapy: Immune Checkpoint Inhibitor-Related Encephalitis vs. Immune Effector Cell-Associated Neurotoxicity Syndrome
Bibliographic record
Abstract
Immune checkpoint inhibitors and engineered T-cell therapies such as chimeric antigen receptor T-cell (CAR-T) cells and bispecific T-cell engagers (BiTEs) have revolutionized oncology care, and with them came two neurologic syndromes that look deceptively alike at the bedside with confusion, seizures, and encephalopathy: immune checkpoint inhibitor-related encephalitis (irEncephalitis) and immune effector cell-associated neurotoxicity syndrome (ICANS). Several differential observations between the two syndromes motivated this review: 1) although loss of immune tolerance likely drives irEncephalitis, ICANS on the other hand is dominated by cytokine-endothelial-microglial cascades. The biology of both entities remains incompletely resolved and these lines blur in real patients, 2) irEncephalitis is uncommon in ICI recipients (∼ 0.1-1%), whereas ICANS is common after CAR-T (∼ 40% and generally lower with most T-cell engagers), 3) lack of diagnostic and grading systems, especially the absence of a dedicated irEncephalitis grading system, remains the key barrier to consistent outcomes and meaningful comparison across clinical trials, and 4) management philosophies are asymmetric (restoring immune tolerance with selective immunomodulation in irEncephalitis vs. rapidly suppressing cytokine-mediated neuroinflammation with corticosteroids as well as anti-cytokine agents in ICANS). Here we review the existing literature on pathophysiology and current landscape of the diagnostics, management, and clinical trials to gain further structured understanding of these intriguing disorders. In doing so, we conclude that: 1) although the syndromes share similar clinical features, their pathogenesis points to distinct management algorithms based on timing of onset and response profiles, making mechanism-informed intervention central to improving outcomes; 2) early T-cell engager trials hint at molecule-dependent ICANS risk and responsiveness, warranting standardized reporting and accumulation of platform-specific data; and 3) emerging biomarkers and targets that index microglial signaling and blood-brain barrier integrity promise more precise, effective management as the field matures. In this review, we adopt a mechanism-first, side-by-side comparison that links diagnosis, management, and evolution to bedside decisions, with the aim of enabling precise diagnosis and management for oncologists, neurologists, and trialists operating in the rapidly expanding era of T-cell-based immunotherapy.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".