Cell-Free DNA Next-Generation Sequencing Prediction of Response and Resistance to Third-Generation EGFR Inhibitor
Bibliographic record
Abstract
INTRODUCTION: The genomic alterations driving resistance to third-generation EGFR tyrosine kinase inhibitors (TKIs) are not well established, and collecting tissue biopsy samples poses potential complications from invasive procedures. Cell-free circulating DNA (cfDNA) testing provides a noninvasive approach to identify potentially targetable mechanisms of resistance. Here we utilized a 70-gene cfDNA next-generation sequencing test to interrogate pretreatment and progression samples from 77 EGFR-mutated non-small cell lung cancer (NSCLC) patients treated with a third-generation EGFR TKI. PATIENTS AND METHODS: Rociletinib was evaluated in advanced or metastatic (second line or higher) disease with EGFR T790M-positive NSCLC in the TIGER-X (NCT01526928) and TIGER-2 (NCT02147990) studies. Plasma samples were collected at baseline and at the time of systemic progression while receiving rociletinib. The critical exons in 70 genes were sequenced in cfDNA isolated from plasma samples to elucidate a comprehensive genomic profile of alterations for each patient. RESULTS: Plasma-based cfDNA analysis identified 93% of the initial EGFR activating and 85% of the EGFR T790M resistance mutations in pretreatment samples with detectable tumor DNA. Profiling of progression samples revealed significant heterogeneity, with different variant types (eg, mutations, amplifications, and fusions) detected in multiple genes (EGFR, MET, RB1) that may be driving resistance in patients. Novel alterations not previously described in association with resistance to third-generation TKIs were also detected, such as an NTRK1 fusion. CONCLUSION: cfDNA next-generation sequencing identified initial EGFR activating and secondary T790M resistance mutations in NSCLC patients with high sensitivity, predicted treatment response equivalent to tissue analysis, and identified multiple novel and established resistance alterations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".