Endothelial Activation and Blood–Brain Barrier Disruption in Neurotoxicity after Adoptive Immunotherapy with CD19 CAR-T Cells
Bibliographic record
Abstract
Abstract Lymphodepletion chemotherapy followed by infusion of CD19-targeted chimeric antigen receptor–modified T (CAR-T) cells can be complicated by neurologic adverse events (AE) in patients with refractory B-cell malignancies. In 133 adults treated with CD19 CAR-T cells, we found that acute lymphoblastic leukemia, high CD19+ cells in bone marrow, high CAR-T cell dose, cytokine release syndrome, and preexisting neurologic comorbidities were associated with increased risk of neurologic AEs. Patients with severe neurotoxicity demonstrated evidence of endothelial activation, including disseminated intravascular coagulation, capillary leak, and increased blood–brain barrier (BBB) permeability. The permeable BBB failed to protect the cerebrospinal fluid from high concentrations of systemic cytokines, including IFNγ, which induced brain vascular pericyte stress and their secretion of endothelium-activating cytokines. Endothelial activation and multifocal vascular disruption were found in the brain of a patient with fatal neurotoxicity. Biomarkers of endothelial activation were higher before treatment in patients who subsequently developed grade ≥4 neurotoxicity. Significance: We provide a detailed clinical, radiologic, and pathologic characterization of neurotoxicity after CD19 CAR-T cells, and identify risk factors for neurotoxicity. We show endothelial dysfunction and increased BBB permeability in neurotoxicity and find that patients with evidence of endothelial activation before lymphodepletion may be at increased risk of neurotoxicity. Cancer Discov; 7(12); 1404–19. ©2017 AACR. See related commentary by Mackall and Miklos, p. 1371. This article is highlighted in the In This Issue feature, p. 1355
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".