More is less: Inactivation and deletion events and the search for tumor suppressor genes
Bibliographic record
Abstract
Tumor suppressor genes are frequently inactivated in cancer by large-scale deletion events or epigenetic silencing, and experimental demonstration of such inactivation has historically been considered as support for assigning tumor suppressive function to a given gene. However, the discovery of a number of chromosomal domains wherein large deletions naturally occur at frequencies up to 100 times the average for the genome as a whole leads us to reevaluate the significance of sporadic deletions found within genes associated with these hotspots. Similarly, our recent demonstration that epigenetic chromatin silencing frequently spreads in cancer cells from gene-poor into gene-rich regions with apparent indifference to the gene content of the affected domain raises questions about the pertinence of inactivation as a criterion for ascribing tumor suppressor function to a given gene. We suggest that a number of putative suppressor genes for which inactivation and/or deletion events have been documented may simply be victims of collateral damage when these events occur, and the implication that these genes are being selected against during cancer progression should in some cases be reassessed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".