The Quest for a Tumor Suppressor Gene Phenotype
Bibliographic record
Abstract
Our current definitions of the tumor suppressor gene (TSG) have been guided by the identification of the prototypical gene, RB1, a TSG that is implicated in the development of both the inherited and sporadic forms of retinoblastoma. The hallmark feature of this TSG is loss of function in tumoral cells, which can be restored by reintroduction of a normally functioning protein with concomitant reversion of tumorigenicity. Key to this discovery was that loss of function is often achieved by deletion of a normal copy of the TSG and retention of a mutated allele, which was either inherited or acquired. Suppression of tumorigenicity and the loss-of-function concept of TSGs was also demonstrated in early studies where normal cellular growth was achieved when tumorigenic cells were fused with normal cells. Thus loss of genetic content and restoration of gene function has guided studies aimed at the discovery of novel TSGs. Here we review the successes of TSG discovery using three approaches that are based on the genetic analysis of inherited predisposition to cancer, tumors that display chromosome loss, and tumorigenic cells that display a suppression of tumorigenicity as a result of transfer of normal chromosomes. Based on a review of the literature we conclude that the discovery of TSGs has been highly successful in the genetic analysis of inherited predisposition to cancer with a dominant mode of inheritance. In contrast, the latter two approaches have yielded a paucity of TSGs that exhibit features similar to the prototypical RB1 in that they are rarely inactivated by somatic mutations in tumors displaying LOH, although decreased gene expression is observed. Nevertheless, some of these genes have been shown to suppress tumorigenicity when normal function is restored in tumorigenic cells consistent with the loss-of-function concept. These observations continue to challenge our current definition of TSG.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".