Clinical and Biological Correlates of Neurotoxicity Associated with CAR T-cell Therapy in Patients with B-cell Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Abstract CD19-specific chimeric antigen receptor (CAR) T-cell therapy is highly effective against relapsed or refractory acute lymphoblastic leukemia (ALL), but is hindered by neurotoxicity. In 53 adult patients with ALL, we found a significant association of severe neurotoxicity with high pretreatment disease burden, higher peak CAR T-cell expansion, and early and higher elevations of proinflammatory cytokines in blood. Patients with severe neurotoxicity had evidence of blood–cerebrospinal fluid (CSF) barrier disruption correlating with neurotoxicity grade without association with CSF white blood cell count or CAR T-cell quantity in CSF. Proinflammatory cytokines were enriched in CSF during severe neurotoxicity with disproportionately high levels of IL6, IL8, MCP1, and IP10, suggesting central nervous system–specific production. Seizures, seizure-like activity, myoclonus, and neuroimaging characteristics suggested excitatory neurotoxicity, and we found elevated levels of endogenous excitatory agonists in CSF during neurotoxicity. Significance: We detail the neurologic symptoms and blood, CSF, and neuroimaging correlates of neurotoxicity associated with CD19 CAR T cells and identify neurotoxicity risk factors. Our findings implicate cellular components other than T cells and suggest novel links between systemic inflammation and characteristic neurotoxicity symptoms. Cancer Discov; 8(8); 958–71. ©2018 AACR. This article is highlighted in the In This Issue feature, p. 899
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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 | 0.001 |
| 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".