Bibliographic record
Abstract
RAS GTPases are fundamental regulators of development and drivers of an extraordinary number of human cancers. RAS oncoproteins constitutively signal through downstream effector proteins, triggering cancer initiation, progression and metastasis. In the absence of targeted therapeutics to mutant RAS itself, inhibitors of downstream pathways controlled by the effector kinases RAF and PI3K have become tools in the treatment of RAS-driven tumours. Unfortunately, the efficacy of this approach has been greatly minimized by the prevalence of acquired drug resistance. Decades of research have established that RAS signalling is highly complex, and in addition to RAF and PI3K these small GTPase proteins can interact with an array of alternative effectors that feature RAS binding domains. The consequence of RAS binding to these effectors remains relatively unexplored, but these pathways may provide targets for combinatorial therapeutics. We discuss here three candidate alternative effectors: RALGEFs, RASSF5 and AFDN, detailing their interaction with RAS GTPases and their biological significance. The metastatic nature of RAS-driven cancers suggests more attention should be granted to these alternate pathways, as they are highly implicated in the regulation of cell adhesion, polarity, cell size and cytoskeletal architecture.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".