Molecular signatures associated with venous thromboembolism in children with acute lymphoblastic leukemia
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) is a frequent complication of childhood acute lymphoblastic leukemia (ALL). OBJECTIVES: We aimed to identify molecular markers and signatures of the leukemia microenvironment associated with VTE in childhood ALL by the dual-omics approach of gene expression and DNA methylation profiling. METHODS: Eligible children aged 1 to 21 years old with newly diagnosed ALL were enrolled in the Dana-Farber Cancer Institute 16-001 trial with available RNA sequencing data from bone marrow at diagnosis. The primary outcome was VTE requiring medical intervention, divided between early events (ETs), within 6 weeks from ALL diagnosis, or late events otherwise. We compared differential gene expression and DNA methylation in children with and without VTE and in the subgroup of children with ETs. The DNA methylation cis-regulation was explored by dual-omics integration. Functional gene set enrichment analyses were performed to assess dysregulated pathways associated with thrombosis. Gene expression profiling-based signature for the thrombosis-free interval was determined using the Kaplan-Meier estimator and log-rank tests. RESULTS: We included 248 patients (median age, 7.5 years; 78% precursor B-cell ALL), of whom 56 (23%) developed VTE. Genes and metabolic pathways involved in coagulation, platelet activation, and neutrophil extracellular trap formation were associated with ETs. Dual-omics analysis indicated that methylation reprogramming might be responsible for the overexpression of genes involved in neutrophil extracellular trap formation and coagulation in patients with ETs. A prothrombotic gene signature, based on VWF, PF4, and CXCL8 expression, predicted a thrombosis-free interval. CONCLUSION: This suggests that gene markers and epigenetic regulation of the leukemic microenvironment are drivers of VTE, notably ETs in childhood ALL.
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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 | 0.001 | 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".