KRAS: Biology, Inhibition, and Mechanisms of Inhibitor Resistance
Bibliographic record
Abstract
KRAS is a small GTPase that is among the most commonly mutated oncogenes in cancer. Here, we discuss KRAS biology, therapeutic avenues to target it, and mechanisms of resistance that tumors employ in response to KRAS inhibition. Several strategies are under investigation for inhibiting oncogenic KRAS, including small molecule compounds targeting specific KRAS mutations, pan-KRAS inhibitors, PROTACs, siRNAs, PNAs, and mutant KRAS-specific immunostimulatory strategies. A central challenge to therapeutic effectiveness is the frequent development of resistance to these treatments. Direct resistance mechanisms can involve KRAS mutations that reduce drug efficacy or copy number alterations that increase the expression of mutant KRAS. Indirect resistance mechanisms arise from mutations that can rescue mutant KRAS-dependent cells either by reactivating the same signaling or via alternative pathways. Further, non-mutational forms of resistance can take the form of epigenetic marks, transcriptional reprogramming, or alterations within the tumor microenvironment. As the possible strategies to inhibit KRAS expand, understanding the nuances of resistance mechanisms is paramount to the development of both enhanced therapeutics and innovative drug combinations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".