Characterization of Leukemic Resistance to CD19-Targeted CAR T-cell Therapy through Deep Genomic Sequencing
Why this work is in the frame
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Bibliographic record
Abstract
Chimeric antigen receptor (CAR) T-cell therapy targeting CD19 has been a clinical breakthrough for pediatric B-cell acute lymphoblastic leukemia (B-ALL), and loss of the CD19 target antigen on leukemic cells represents a major mechanism of relapse. Previous studies have observed CD19 mutations specific to CD19- relapses, and we sought to clarify and strengthen this relationship using deep whole-exome sequencing in leukemic cells expanded in a patient-derived xenograft. By assessing pre-treatment and relapse cells from 13 patients treated with CAR T-cell therapy, 8 of whom developed CD19- relapse and 5 of whom developed CD19+ relapse, we demonstrate that relapse-specific single-nucleotide variants and small indels with high allele frequency combined with deletions in the CD19 gene in a manner specific to those patients with CD19- relapse. Before CAR T-cell infusion, one patient was found to harbor a pre-existing CD19 deletion in the context of genomic instability, which likely represented the first hit leading to the patient's subsequent CD19- relapse. Across patients, preexisting mutations and genomic instability were not significant predictors of subsequent CD19- relapse across patients, with sample size as a potential limiting factor. Together, our results clarify and strengthen the relationship between genomic events and CD19- relapse, demonstrating this intriguing mechanism of resistance to a targeted cancer immunotherapy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it