The RET signaling pathway: Linking developmental and neoplastic roles
Bibliographic record
Abstract
Proc Amer Assoc Cancer Res, Volume 46, 2005 SY27-1 Although many of the genes now recognized as contributing to neoplastic transformation have similar and predictable roles in cell growth, migration and proliferation in most cell types, a number of genes have now been recognized as having normal primary roles specifically in organismal development. Mutations of these genes contribute to tumorigenesis in specific characteristic cell types, however, in some cases the type of mutation, and the developmental window in which it occurs, can determine whether the genetic variant contributes to neoplastic changes or to an abnormality of normal development. One gene with two such diverse effects is the RET proto-oncogene. RET encodes a transmembrane receptor tyrosine kinase that is required for development of neural crest derivatives, the central and peripheral nervous systems and the kidney. Activating missense mutations of RET give rise to the inherited cancer syndrome multiple endocrine neoplasia type 2 (MEN 2) which is characterized by medullary thyroid carcinoma, the adrenal tumour pheochromocytoma and by parathyroid hyperplasia. In each case, these mutations result in constitutive activation of the receptor. Intriguingly however, mutations that result in inactivation of the RET receptor, or significant reduction in cell surface associated RET protein, lead to a common congenital abnormality Hirschsprung disease (HSCR), characterized by absence of the parasympathetic ganglia of varying extents of the distal colon. Comparisons of the diametrically opposite effects of MEN 2 and HSCR RET-mutations have suggested that many tissues are highly sensitive to relatively small changes in the levels of RET, but that different tissues have unique sensitivity to increased or decreased RET activity. Recent studies that have provided some insight into normal RET signal transduction and the mechanisms by which RET mutations contribute to disease phenotype will be discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".