Fanconi anemia type C and p53 cooperate in apoptosis and tumorigenesis
Bibliographic record
Abstract
Fanconi anemia (FA) is a recessive genomic instability syndrome characterized by developmental defects, progressive bone marrow failure, and cancer. FA is genetically heterogeneous, however; the proteins encoded by different FA loci interact functionally with each other and with the BRCA1, BRCA2, and ATM gene products. Although patients with FA are highly predisposed to the development of myeloid leukemia and solid tumors, the alterations in biochemical pathways responsible for the progression of tumorigenesis in these patients remain unknown. FA cells are hypersensitive to a range of genotoxic and cellular stresses that activate signaling pathways mediating apoptosis. Here we show that ionizing radiation (IR) induces modestly elevated levels of p53 in cells from FA type C (Fancc) mutant mice and that inactivation of Trp53 rescues tumor necrosis factor alpha-induced apoptosis in myeloid cells from Fancc-/- mice. Further, whereas Fancc-/- mice failed to form hematopoietic or solid malignancies, mice mutant at both Fancc and Trp53 developed tumors more rapidly than mice mutant at Trp53 alone. This shortened latency was associated with the appearance of tumor types that are found in patients with FA but not in mice mutant at Trp53 only. Collectively, these data demonstrate that p53 and Fancc interact functionally to regulate apoptosis and tumorigenesis in Fancc-deficient cells.
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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 | 0.000 |
| 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 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".