Genetically Targeted T Cells Eradicate Systemic Acute Lymphoblastic Leukemia Xenografts
Bibliographic record
Abstract
PURPOSE: Human T cells targeted to the B cell-specific CD19 antigen through retroviral-mediated transfer of a chimeric antigen receptor (CAR), termed 19z1, have shown significant but partial in vivo antitumor efficacy in a severe combined immunodeficient (SCID)-Beige systemic human acute lymphoblastic leukemia (NALM-6) tumor model. Here, we investigate the etiologies of treatment failure in this model and design approaches to enhance the efficacy of this adoptive strategy. EXPERIMENTAL DESIGN: A panel of modified CD19-targeted CARs designed to deliver combined activating and costimulatory signals to the T cell was generated and tested in vitro to identify an optimal second-generation CAR. Antitumor efficacy of T cells expressing this optimal costimulatory CAR, 19-28z, was analyzed in mice bearing systemic costimulatory ligand-deficient NALM-6 tumors. RESULTS: Expression of the 19-28z CAR, containing the signaling domain of the CD28 receptor, enhanced systemic T-cell antitumor activity when compared with 19z1 in treated mice. A treatment schedule of 4 weekly T-cell injections, designed to prolong in vivo T-cell function, further improved long-term survival. Bioluminescent imaging of tumor in treated mice failed to identify a conserved site of tumor relapse, consistent with successful homing by tumor-specific T cells to systemic sites of tumor involvement. CONCLUSIONS: Both in vivo costimulation and repeated administration enhance eradication of systemic tumor by genetically targeted T cells. The finding that modifications in CAR design as well as T-cell dosing allowed for the complete eradication of systemic disease affects the design of clinical trials using this treatment strategy.
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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 | 0.001 |
| 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".