Extrachromosomal microDNA Signature as a Candidate Biomarker in Pediatric Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Despite improved therapies, refractory and relapsed ALL remain the leading cause of cancer-related mortality in children. There is a need for accessible biomarkers for frequent, minimally invasive disease monitoring and prompt intervention. MicroDNA is a novel extrachromosomal DNA that preferentially originates from gene segments with high transcriptional activity and/or increased chromatin accessibility. We investigated whether microDNA-producing genes repertoire changes in a disease-dependent manner. We characterized microDNAs in 52 paired bone marrow (BM) and plasma samples from pediatric patients with ALL at diagnosis, relapse, and remission. No difference in the length or number of microDNA was noted across stages, but comparative analysis of microDNA profiles led to the identification of microDNA gene panels associated with active disease. The relative distribution of these genes was significantly different from that expected by chance (P < 0.0001). Analyses of BM samples identified a signature comprising 289 distinct microDNA-producing genes present in multiple patients at diagnosis and relapse but absent in remission. The best biomarker candidates were 11 microDNA-producing genes identified also in plasma samples at diagnosis and overrepresented in patients who relapsed (P = 0.006). MicroDNA from the same genes was confirmed in relapse plasma samples. All signature genes are known to be involved in cancer proliferation or drug response. MicroDNA seems to be a candidate for a novel class of biomarkers for ALL, with the potential to improve precision diagnostics, particularly through their identification in plasma samples. Further validation in an independent cohort of patients is warranted. SIGNIFICANCE: Despite high cure rates, 10% to 15% of pediatric patients with ALL experience relapse. We identified a plasma-detectable microDNA signature from 11 genes that persists from diagnosis through relapse but disappears in remission. These findings demonstrate the potential of microDNA profiles as prognostic biomarkers in pediatric ALL, enabling noninvasive monitoring of disease status and risk stratification.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".