A p53-regulated apoptotic gene signature predicts treatment response and outcome in pediatric acute lymphoblastic leukemia
Bibliographic record
Abstract
Abstract: Gene signatures have been associated with outcome in pediatric acute lymphoblastic leukemia (ALL) and other malignancies. However, determining the molecular drivers of these expression changes remains challenging. In ALL blasts, the p53 tumor suppressor is the primary regulator of the apoptotic response to genotoxic chemotherapy, which is predictive of outcome. Consequently, we hypothesized that the normal p53-regulated apoptotic response to DNA damage would be altered in ALL and that this alteration would influence drug response and treatment outcome. To test this, we first used global expression profiling in related human B-lineage lymphoblastoid cell lines with either wild type or mutant TP53 to characterize the normal p53-mediated transcriptional response to ionizing radiation (IR) and identified 747 p53-regulated apoptotic target genes. We then sorted these genes into six temporal expression clusters (TECs) based upon differences over time in their IR-induced p53-regulated gene expression patterns, and found that one cluster (TEC1) was associated with multidrug resistance in leukemic blasts in one cohort of children with ALL and was an independent predictor of survival in two others. Therefore, by investigating p53-mediated apoptosis in vitro, we identified a gene signature significantly associated with drug resistance and treatment outcome in ALL. These results suggest that intersecting pathway-derived and clinically derived expression data may be a powerful method to discover driver gene signatures with functional and clinical implications in pediatric ALL and perhaps other cancers as well. Keywords: pediatric acute lymphoblastic leukemia, p53, gene expression signature, outcomes analysis
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".