Clustered Genomic Alterations in Chromosome 7p Dictate Outcomes and Targeted Treatment Responses of Lung Adenocarcinoma With<i>EGFR</i>-Activating Mutations
Bibliographic record
Abstract
PURPOSE: Although epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have been proven more effective for patients with lung adenocarcinoma with EGFR-activating mutation rather than wild type, the former group still includes approximately 30% nonresponders. The molecular basis of this substantial response heterogeneity is unknown. Our purpose was to seek molecular aberrations contributing to disease progression at the genome-wide level and identify the prognostic signature unique to patients with EGFR-activating mutation. PATIENTS AND METHODS: We first investigated the molecular differences between tumors with EGFR-activating mutation and wild-type tumors by conducting high-density array comparative genomic hybridization on a collection of 138 adenocarcinoma tissues. We then used an independent group of 114 patients to validate the clinical relevance of copy-number alterations (CNAs) in predicting overall and disease-free survival. Finally, focusing on 23 patients with EGFR mutation receiving EGFR-TKI treatment, we investigated the association between CNAs and response to EGFR-TKIs. RESULTS: We identified chromosome regions with differential CNAs between tumors with EGFR-activating mutation and wild-type tumors and found the aberration sites to cluster highly on chromosome 7p. A cluster of six representative chromosome 7p genes predicted overall and disease-free survival for patients with EGFR-activating mutation but not for those with wild type. Importantly, simultaneous presence of more genes with increased CNAs in this cluster correlated with less favorable response to EGFR-TKIs in patients with EGFR-activating mutation. CONCLUSION: Our results shed light on why responses to EGFR-TKIs are heterogeneous among patients with EGFR-activating mutation. They may lead to better patient management in this population.
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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 | 0.001 |
| 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".