GDNF and the RET Receptor in Cancer: New Insights and Therapeutic Potential
Bibliographic record
Abstract
The Glial cell line-derived neurotrophic Family Ligands (GFL) are soluble neurotrophic factors that are required for development of multiple human tissues, but which are also important contributors to human cancers. GFL signaling occurs through the transmembrane RET receptor tyrosine kinase, a well-characterized oncogene. GFL-independent RET activation, through rearrangement or point mutations occurs in thyroid and lung cancers. However, GFL-mediated activation of wildtype RET is an increasingly recognized mechanism promoting tumor growth and dissemination of a much broader group of cancers. RET and GFL expression have been implicated in metastasis or invasion in diverse human cancers including breast, pancreatic, and prostate tumors, where they are linked to poorer patient prognosis. In addition to directly inducing tumor growth in these diseases, GFL-RET signaling promotes changes in the tumor microenvironment that alter the surrounding stroma and cellular composition to enhance tumor invasion and metastasis. As such, GFL RET signaling is an important target for novel therapeutic approaches to limit tumor growth and spread and improve disease outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".