Age and ligand specificity influence the outcome of pathogen engagement on preleukemic and leukemic B-cell precursor populations
Bibliographic record
Abstract
Common infections have long been proposed to play a role in the development of pediatric B-cell acute lymphoblastic leukemia (B-ALL). However, epidemiologic studies report contradictory effects of infection exposure on subsequent B-ALL risk, and no specific pathogen has been definitively linked to the disease. A unifying mechanism to explain the divergent outcomes could inform disease prevention strategies. We previously reported that the pattern recognition receptor (PRR) ligand Poly(I:C) exerted effects on B-ALL cells that were distinct from those observed with other nucleic acid-based PRR ligands. Here, using multiple double-stranded RNA (dsRNA) moieties, we show that the overall outcome of exposure to Poly(I:C) reflects the balance of opposing responses induced by its ligation to endosomal and cytoplasmic receptors. This PRR response biology is shared between mouse and human B-ALL and can increase leukemia-initiating cell burden in vivo during the preleukemia phase of B-ALL, primarily through tumor necrosis factor α signaling. The age of the responding immune system further influences the impact of dsRNA exposure on B-ALL cells in both mouse and human settings. Overall, our study demonstrates that potentially proleukemic and antileukemic effects can each be generated by the stimulation of pathogen recognition pathways and indicates a mechanistic explanation for the contrasting epidemiologic associations reported for infection exposure and B-ALL.
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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 | 0.000 | 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 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".