Targeting the KRAS Pathway in Non-Small Cell Lung Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
: Lung cancer remains the leading cause of cancer-related deaths worldwide. However, significant progress has been made individualizing therapy based on molecular aberrations (e.g., EGFR, ALK) and pathologic subtype. KRAS is one of the most frequently mutated genes in non-small cell lung cancer (NSCLC), found in approximately 30% of lung adenocarcinomas, and is thus an appealing target for new therapies. Although no targeted therapy has yet been approved for the treatment of KRAS-mutant NSCLC, there are multiple potential therapeutic approaches. These may include direct inhibition of KRAS protein, inhibition of KRAS regulators, alteration of KRAS membrane localization, and inhibition of effector molecules downstream of mutant KRAS. This article provides an overview of the KRAS pathway in lung cancer and related therapeutic strategies under investigation. IMPLICATIONS FOR PRACTICE: The identification of oncogene-addicted cancers and specific inhibitors has revolutionized non-small cell lung cancer (NSCLC) treatment and outcomes. One of the most commonly mutated genes in adenocarcinoma is KRAS, found in approximately 30% of lung adenocarcinomas, and thus it is an appealing target for new therapies. This review provides an overview of the KRAS pathway and related targeted therapies under investigation in NSCLC. Some of these agents may play a key role in KRAS-mutant NSCLC treatment in the future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it