Multiomic Profiling of Central Nervous System Leukemia Identifies mRNA Translation as a Therapeutic Target
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Central nervous system (CNS) dissemination of B-precursor acute lymphoblastic leukemia (B-ALL) has poor prognosis and remains a therapeutic challenge. Here we performed targeted DNA sequencing as well as transcriptional and proteomic profiling of paired leukemia-infiltrating cells in the bone marrow (BM) and CNS of xenografts. Genes governing mRNA translation were upregulated in CNS leukemia, and subclonal genetic profiling confirmed this in both BM-concordant and BM-discordant CNS mutational populations. CNS leukemia cells were exquisitely sensitive to the translation inhibitor omacetaxine mepesuccinate, which reduced xenograft leptomeningeal disease burden. Proteomics demonstrated greater abundance of secreted proteins in CNS-infiltrating cells, including complement component 3 (C3), and drug targeting of C3 influenced CNS disease in xenografts. CNS-infiltrating cells also exhibited selection for stemness traits and metabolic reprogramming. Overall, our study identifies targeting of mRNA translation as a potential therapeutic approach for B-ALL leptomeningeal disease. Significance: Cancer metastases are often driven by distinct subclones with unique biological properties. Here we show that in B-ALL CNS disease, the leptomeningeal environment selects for cells with unique functional dependencies. Pharmacologic inhibition of mRNA translation signaling treats CNS disease and offers a new therapeutic approach for this condition. This article is highlighted in the In This Issue feature, p. 1
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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 | 0.000 | 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) | 0.000 | 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