Enhanced Transformation by a Plasma Membrane-Associated Met Oncoprotein: Activation of a Phosphoinositide 3′-Kinase-Dependent Autocrine Loop Involving Hyaluronic Acid and CD44
Bibliographic record
Abstract
A Met-hepatocyte growth factor receptor oncoprotein, Tpr-Met, generated by chromosomal rearrangement, fuses a protein dimerization motif with the cytoplasmic domain of the Met receptor, producing a cytosolic, constitutively activated tyrosine kinase. Although both the Met receptor and the Tpr-Met oncoprotein associate with the same substrates, activating mutations of the Met receptor in hereditary papillary renal carcinomas have different signaling requirements for transformation than Tpr-Met. This suggests differential activation of membrane-localized pathways by oncogenic forms of the membrane-bound Met receptor but not by the cytoplasmic Tpr-Met oncoprotein. To establish which pathways might be differentially regulated, we have localized the constitutively activated Tpr-Met oncoprotein to the membrane using the c-src myristoylation signal. Membrane localization enhances cellular transformation, focus formation, and anchorage-independent growth and induces tumors with a distinct myxoid phenotype. This correlates with the induction of hyaluronic acid (HA) and the presence of a distinct form of its receptor, CD44. A pharmacological inhibitor of phosphoinositide 3' kinase (PI3'K), inhibits the production of HA, and conversely, an activated, plasma membrane-targeted form of PI3'K is sufficient to enhance HA production. Furthermore, the multisubstrate adapter protein Gab-1, which couples the Met receptor with PI3'K, enhances Met receptor-dependent HA synthesis in a PI3'K-dependent manner. These results provide a positive link to a role for HA and CD44 in Met receptor-mediated oncogenesis and implicate PI3'K in these events.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".