Short-Term Responders of Non–Small Cell Lung Cancer Patients to EGFR Tyrosine Kinase Inhibitors Display High Prevalence of TP53 Mutations and Primary Resistance Mechanisms
Bibliographic record
Abstract
Non-small cell lung cancer (NSCLC) with activating EGFR mutations in exon 19 and 21 typically responds to EGFR tyrosine kinase inhibitors (TKI); however, for some patients, responses last only a few months. The underlying mechanisms of such short responses have not been fully elucidated. Here, we sequenced the genomes of 16 short-term responders (SR) that had progression-free survival (PFS) of less than 6 months on the first-generation EGFR TKI and compared them to 12 long-term responders (LR) that had more than 24 months of PFS. All patients were diagnosed with advanced lung adenocarcinoma and harbored EGFR 19del or L858R mutations before treatment. Paired tumor samples collected before treatment and after relapse (or at the last follow-up) were subjected to targeted next-generation sequencing of 416 cancer-related genes. SR patients were significantly younger than LR patients (P < .001). Collectively, 88% of SR patients had TP53 variations compared to 13% of LR patients (P < .001). Additionally, 37.5% of SR patients carried EGFR amplifications compared to 8% of LR patients. Other potential primary resistance factors were also identified in the pretreatment samples of 12 SR patients (75%), including PTEN loss; BIM deletion polymorphism; and amplifications of EGFR, ERBB2, MET, HRAS, and AKT2. Comparatively, only three LR patients (25%) were detected with EGFR or AKT1 amplifications that could possibly exert resistance. The diverse preexisting resistance mechanisms in SR patients revealed the complexity of defining treatment strategies even for EGFR-sensitive mutations.
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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) | 0.002 | 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".