Targeting the KRAS Pathway in Non-Small Cell Lung Cancer
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".