Clinical Utility of Patient-Derived Xenografts to Determine Biomarkers of Prognosis and Map Resistance Pathways in <i>EGFR</i>-Mutant Lung Adenocarcinoma
Bibliographic record
Abstract
PURPOSE: Although epidermal growth factor receptor (EGFR) -mutated adenocarcinomas initially have high response rates to EGFR tyrosine kinase inhibitors (TKIs), most patients eventually develop resistance. Patient-derived xenografts (PDXs) are considered preferred preclinical models to study the biology of patient tumors. EGFR-mutant PDX models may be valuable tools to study the biology of these tumors and to elucidate mechanisms of resistance to EGFR-targeted therapies. METHODS: Surgically resected early-stage non-small-cell lung carcinoma (NSCLC) tumors were implanted into nonobese diabetic severe combined immune deficient (NOD-SCID) mice. EGFR TKI treatment was initiated at tumor volumes of 150 μL. Gene expression analysis was performed using a microarray platform. RESULTS: Of 33 lung adenocarcinomas with EGFR activating mutations, only 6 (18%) engrafted and could be propagated beyond passage one. Engraftment was associated with upregulation of genes involved in mitotic checkpoint and cell proliferation. A differentially expressed gene set between engrafting and nonengrafting patients could identify patients harboring EGFR-mutant tumor with significantly different prognoses in The Cancer Genome Atlas Lung Adenocarcinoma datasets. The PDXs included models with variable sensitivity to first- and second-generation EGFR TKIs and the monoclonal antibody cetuximab. All EGFR-mutant NSCLC PDXs studied closely recapitulated their corresponding patient tumor phenotype and clinical course, including response pattern to EGFR TKIs. CONCLUSION: PDX models closely recapitulate primary tumor biology and clinical outcome. They may serve as important laboratory models to investigate mechanisms of resistance to targeted therapies, and for preclinical testing of novel treatment strategies.
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How this classification was reachedexpand
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".