EGFR Mutation Subtypes Modulate Distinct Metabolic Profiles and Clinical Outcomes in Lung Cancer: A Retrospective Analysis
Bibliographic record
Abstract
ABSTRACT Lung cancer continues to be a leading contributor of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) as most prevalent cases. Identification of epidermal growth factor receptor (EGFR) mutations has profoundly enhanced our understanding and treatment of NSCLC, leading to the development of precision therapies, including EGFR tyrosine kinase inhibitors (TKIs). This retrospective study analyzed EGFR mutation distributions and their effect on overall survival (OS) using data accessed from TCGA. Our analysis revealed that EGFR mutations are most prevalent in lung cancer, with L858R appearing as the most frequent mutation, followed by E746_A750del and T790M. Remarkably, OS analysis exhibited that C797S mutations were linked with the least OS, with T790M, G719S, L861Q, and G719A also displaying significantly decreased OS compared to L858R mutations. Gene set enrichment analysis (GSEA) of T790M versus L858R cases revealed significant metabolic reprogramming in T790M mutants, noticeable by upregulation of oxidative phosphorylation (CPT1A, NDUFS1), lipid metabolism (HMGCR), and mTORC1 signaling. Metabolic adaptation observed in T790M indicates elevated bioenergetic flexibility and detoxification efficiency, possibly leading to therapeutic resistance. The work underscores EGFR mutation subtypes as distinct biological entities with distinctive metabolic needs, suggesting HMGCR (statin-targetable) and PPARα agonists as probable therapeutic paths for T790M-driven resistance. These understandings support for mutation-specific treatment approach and highlight the necessity to combine metabolic pathway targeting with EGFR blockade to augment responses in lung cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".