Leukemia-initiating cells in human T-lymphoblastic leukemia exhibit glucocorticoid resistance
Bibliographic record
Abstract
T-cell acute lymphoblastic leukemia (T-ALL) is associated with a significant risk of disease relapse, but the biological basis for relapse is poorly understood. Here, we identify leukemiainitiating cells (L-ICs) on the basis of functional assays and prospective isolation and report a role for L-ICs in T-ALL disease and relapse. Long-term proliferation in response to NOTCH1 activating signals in OP9-DL1 coculture system or capacity to initiate leukemia in xenografts by the CD7(+)CD1a(-) subset of primary T-ALL samples was superior to other subsets, refining the identity of T-ALL L-ICs. T-ALL engraftment was improved in nonobese diabetic/severe combined immunodeficiency (NOD/scid)IL2Rγ(null) (NSG) mice compared with NOD/scid with anti-CD122 treatment (NS122), but both showed changes in leukemia immunophenotype. Clonal analysis of xenografts using the TCRG locus revealed the presence of subclones of T-ALL L-ICs, some of which possess a selective growth advantage and correlated with the capacity of CD7(+)CD1a(+) xenograft cells to engraft secondary NSG mice. Treatment of high-risk T-ALL xenografts eliminated CD1a(+) T-ALL cells, but CD1a(-) cells were resistant and their number was increased. Our results establish that primary CD1a(-) T-ALL cells are functionally distinct from CD1a(+) cells and that the CD7(+)CD1a(-) subset is enriched for L-IC activity that may be involved in mediating disease relapse after therapy.
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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 | 0.000 |
| 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".