KRAS Mutation Status Is Associated with Enhanced Dependency on Folate Metabolism Pathways in Non–Small Cell Lung Cancer Cells
Bibliographic record
Abstract
KRAS gene mutation is linked to poor prognosis and resistance to therapeutics in non-small cell lung cancer (NSCLC). In this study, we have explored the possibility of exploiting inherent differences in KRAS-mutant cell metabolism for treatment. This study identified a greater dependency on folate metabolism pathways in KRAS mutant compared with KRAS wild-type NSCLC cell lines. Microarray gene expression and biologic pathway analysis identified higher expression of folate metabolism- and purine synthesis-related pathways in KRAS-mutant NSCLC cells compared with wild-type counterparts. Moreover, pathway analysis and knockdown studies suggest a role for MYC transcriptional activity in the expression of these pathways in KRAS-mutant NSCLC cells. Furthermore, KRAS knockdown and overexpression studies demonstrated the ability of KRAS to regulate expression of genes that comprise folate metabolism pathways. Proliferation studies demonstrated higher responsiveness to methotrexate, pemetrexed, and other antifolates in KRAS-mutant NSCLC cells. Surprisingly, KRAS gene expression is downregulated in KRAS wild-type and KRAS-mutant cells by antifolates, which may also contribute to higher efficacy of antifolates in KRAS-mutant NSCLC cells. In vivo analysis of multiple tumorgraft models in nude mice identified a KRAS-mutant tumor among the pemetrexed-responsive tumors and also demonstrated an association between expression of the folate pathway gene, methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), and antifolate activity. Collectively, we identify altered regulation of folate metabolism in KRAS-mutant NSCLC cells that may account for higher antifolate activity in this subtype of NSCLC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".