Advances and Challenges in KRAS Mutation Detection and Clinical Implications
Bibliographic record
Abstract
Aberrant activation of the RAS signaling pathway is a halmark of various cancers. This activation, is often caused by mutations in RAS genes or other pathway components and, drivesi uncontrolled cell growth and proliferation. Studies have demonstrated that certain codon mutations can significantly influence the clinical outcomes of cancer patients. Historically, KRAS was considered "undruggable"; however, recent advancements in drug discovery have led to the development of promising KRAS inhibitors. Accurately identifying the specific type of KRAS mutation in a patient is essential for making optimal treatment decisions. Several methods have been developed for detecting KRAS mutations to address this need, focusing on creating robust, rapid, sensitive, accurate, and cost-effective approaches, particularly for point-of-care applications. Starting with the Ras family and RASopathies, this review provides a comprehensive overview of KRAS mutation detection methods, ranging from research-use-only techniques to in vitro diagnostic-certified tests. Published results are critically evaluated in terms of accuracy, sensitivity, cost, throughput, and suitability for various sample types and clinical settings. This, offers researchers and clinicians an up-to-date resource for.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".