Ultra-sensitive <i>EGFR</i> T790M Detection as an Independent Prognostic Marker for Lung Cancer Patients Harboring <i>EGFR</i> del19 Mutations and Treated with First-generation TKIs
Bibliographic record
Abstract
Abstract Purpose: The detection of preexisting EGFRT790M subclones and the assessment of their clinical significance in the pretreatment of patients with EGFRT790M non–small cell lung cancer (NSCLC) remain unclear. Experimental Design: A total of 179 tumor samples from patients treated or not with a first-generation tyrosine kinase inhibitor (TKI) was analyzed. The presence of ultra-low levels of preexisting EGFRT790M mutation was evaluated using ultra-sensitive droplet digital PCR (ddPCR) and the clinical implication of these mutations on first-generation TKI efficiency assessed. Results: With a ddPCR linear performance of 0.999 and an analytical sensitivity of approximately 0.001%, we observed a 66% (99/150) overall incidence of ultra-low EGFRT790M mutation. Among 82 patients harboring EGFRactivating mutations, the presence of a preexisting EGFRT790M mutation prior to any treatment was significantly associated with a longer progression-free survival (PFS; P = 0.009; log-rank test). Interestingly, longer PFS was linked to concomitant EGFRdel19 and ultra-low EGFRT790M mutations. Moreover, the presence of both EGFRdel19 and ultra-low EGFRT790M mutations was identified as the best fit for predicting the clinical outcome of patients treated with TKI compared with an ultra-low EGFRT790M mutation status or an activating mutation alone (P = 0.042 and P = 0.0071, respectively). Conclusions: We demonstrate that the detection of the ultra-low EGFRT790M mutation in TKI-naïve patients is not a rare event. We suggest that ddPCR should be used in clinical practice to distinguish patients who may respond to first- or third-generation TKIs.
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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".