Cellular mechanisms of RET receptor dysfunction in multiple endocrine neoplasia 2
Bibliographic record
Abstract
Abstract: Rearranged during transfection (RET) is a developmentally important receptor tyrosine kinase that has been identified as an oncogenic driver in a number of cancers. Activating RET point mutations gives rise to the inherited cancer syndrome multiple endocrine neoplasia type 2 (MEN2), characterized by medullary thyroid carcinoma. There are two MEN2 subtypes, MEN2A and MEN2B, that differ in tumour aggressiveness and the associated constellation of other disease features, which are caused by distinct patterns of RET amino acid substitution mutations. MEN2A-RET mutations affecting extracellular cysteine residues promote ligand-independent dimerization and constitutive RET activity, while MEN2B is caused by a single amino acid change in the tyrosine kinase domain of RET, releasing autoinhibition and producing a more active MEN2B-RET kinase that can promote signalling as monomers or dimers in the absence of a ligand. These mutations cause intrinsic biochemical changes in the RET structure and activation but also trigger extrinsic effects that alter RET cellular location, interactions and mechanisms of downregulation that can prolong or mislocate RET activity, changing or enhancing functional outcomes. Furthermore, changes in specific combinations of RET-mediated effects associated with different mutations give rise to the distinct MEN2 disease phenotypes. Here, we discuss the current understanding of the intrinsic and extrinsic characteristics of RET MEN2A cysteine and MEN2B mutants and how these contribute to transforming cellular processes and to the differences in tumour progression and disease aggressiveness.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".