A genome-wide view of the in vitro response to L-asparaginase in acute lymphoblastic leukemia
Bibliographic record
Abstract
To investigate the effect of L-asparaginase on acute lymphoblastic leukemia (ALL), we used cDNA microarrays to obtain a genome-wide view of gene expression both at baseline and after in vitro exposure to L-asparaginase in cell lines and pediatric ALL samples. In 16 cell lines, a baseline gene expression pattern distinguished L-asparaginase sensitivity from resistance. However, for 28 pediatric ALL samples, no consistent baseline expression pattern was associated with sensitivity to L-asparaginase. In particular, baseline expression of asparagine synthetase (ASNS) was not predictive of response to L-asparaginase. After exposure to L-asparaginase, 5 cell lines and 10 clinical samples exhibited very similar changes in the expression of a large number of genes. However, the gene expression changes occurred more slowly in the clinical samples. These changes included a consistent increase in expression of tRNA synthetases and solute transporters and activating transcription factor and CCAAT/enhancer binding protein family members, a response similar to that observed with amino acid starvation. There was also a consistent decrease in many genes associated with proliferation. Taken together, the changes seem to reflect a consistent coordinated response to asparagine starvation in both cell lines and clinical samples. Importantly, in the clinical samples, increased expression of ASNS after L-asparaginase exposure was not associated with in vitro resistance to L-asparaginase, indicating that ASNS-independent mechanisms of in vitro L-asparaginase resistance are common in ALL. These results suggest that targeting particular genes involved in the response to amino acid starvation in ALL cells may provide a novel way to overcome L-asparaginase resistance.
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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 | 0.001 | 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".