TNF-α induces leukemic clonal evolution ex vivo in Fanconi anemia group C murine stem cells
Bibliographic record
Abstract
The molecular pathogenesis of the myeloid leukemias that frequently occur in patients with Fanconi anemia (FA) is not well defined. Hematopoietic stem cells bearing inactivating mutations of FA complementation group C (FANCC) are genetically unstable and hypersensitive to apoptotic cytokine cues including IFN-gamma and TNF-alpha, but neoplastic stem cell clones that arise frequently in vivo are resistant to these cytokines. Reasoning that the combination of genetic instability and cytokine hypersensitivity might create an environment supporting the emergence of leukemic stem cells, we tested the leukemia-promoting effects of TNF-alpha in murine stem cells. TNF-alpha exposure initially profoundly inhibited the growth of Fancc-/- stem cells. However, longer-term exposure of these cells promoted the outgrowth of cytogenetically abnormal clones that, upon transplantation into congenic WT mice, led to acute myelogenous leukemia. TNF-alpha induced ROS-dependent genetic instability in Fancc-/- but not in WT cells. The leukemic clones were TNF-alpha resistant but retained their characteristic hypersensitivity to mitomycin C and exhibited high levels of chromosomal instability. Expression of FANCC cDNA in Fancc-/- stem cells protected them from TNF-alpha-induced clonal evolution. We conclude that TNF-alpha exposure creates an environment in which somatically mutated preleukemic stem cell clones are selected and from which unaltered TNF-alpha-hypersensitive Fancc-/- stem cells are purged.
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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 | 0.001 |
| 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".