Constitutive Activation of RAS/MAPK Pathway Cooperates with Trisomy 21 and Is Therapeutically Exploitable in Down Syndrome B-cell Leukemia
Bibliographic record
Abstract
Abstract Purpose: Children with Down syndrome (constitutive trisomy 21) that develop acute lymphoblastic leukemia (DS-ALL) have a 3-fold increased likelihood of treatment-related mortality coupled with a higher cumulative incidence of relapse, compared with other children with B-cell acute lymphoblastic leukemia (B-ALL). This highlights the lack of suitable treatment for Down syndrome children with B-ALL. Experimental Design: To facilitate the translation of new therapeutic agents into clinical trials, we built the first preclinical cohort of patient-derived xenograft (PDX) models of DS-ALL, comprehensively characterized at the genetic and transcriptomic levels, and have proven its suitability for preclinical studies by assessing the efficacy of drug combination between the MEK inhibitor trametinib and conventional chemotherapy agents. Results: Whole-exome and RNA-sequencing experiments revealed a high incidence of somatic alterations leading to RAS/MAPK pathway activation in our cohort of DS-ALL, as well as in other pediatric B-ALL presenting somatic gain of the chromosome 21 (B-ALL+21). In murine and human B-cell precursors, activated KRASG12D functionally cooperates with trisomy 21 to deregulate transcriptional networks that promote increased proliferation and self renewal, as well as B-cell differentiation blockade. Moreover, we revealed that inhibition of RAS/MAPK pathway activation using the MEK1/2 inhibitor trametinib decreased leukemia burden in several PDX models of B-ALL+21, and enhanced survival of DS-ALL PDX in combination with conventional chemotherapy agents such as vincristine. Conclusions: Altogether, using novel and suitable PDX models, this study indicates that RAS/MAPK pathway inhibition represents a promising strategy to improve the outcome of Down syndrome children with B-cell precursor leukemia.
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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) | 0.001 | 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".