65 YEARS OF THE DOUBLE HELIX: Exploiting insights on the RET receptor for personalized cancer medicine
Bibliographic record
Abstract
The focus of precision cancer medicine is the use of patient genetic signatures to predict disease occurrence and course and tailor approaches to individualized treatment to improve patient outcomes. The rearranged during transfection (RET) receptor tyrosine kinase represents a paradigm for the power of personalized cancer management to change cancer impact and improve quality of life. Oncogenic activation of RET occurs through several mechanisms including activating mutations and increased or aberrant expression. Activating RET mutations found in the inherited cancer syndrome multiple endocrine neoplasia 2 permit early diagnosis, predict disease course and guide disease management to optimize patient survival. Rearrangements of RET found in thyroid and lung tumors provide insights on potential disease aggressiveness and offer opportunities for RET-targeted therapy. Aberrant RET expression in a subset of cases is associated with tumor dissemination, resistance to therapies and/or poorer prognosis in multiple cancers. The potential of RET targeting through repurposing of small-molecule multikinase inhibitors, selective RET inhibitors or other novel approaches provides exciting opportunities to individualize therapies across multiple pathologies where RET oncogenicity contributes to cancer outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".