EGFR mutation testing and treatment decisions in patients progressing on first- or second-generation epidermal growth factor receptor tyrosine kinase inhibitors
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to investigate real-world EGFR mutation testing in patients with metastatic non-small cell lung cancer (NSCLC) upon progression on first-/second-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKI), and subsequent treatments received. METHODS: Flatiron Health electronic health records-derived database was used to identify adult patients with metastatic NSCLC treated with first-/second-generation EGFR-TKI from 11/2015-09/2017, with start of first EGFR-TKI defined as the index date. Patients were stratified by receipt of EGFR-TKI as first-line (1 L) or later-line (2 L+) treatment. Mutation testing and subsequent therapies following first-/second-generation EGFR-TKI were described. RESULTS: Overall, 782 patients (1 L = 435; 2 L+ =347) were included. Median age was 69.0 years, 63.6% were female, 56.3% were white, 87.1% were treated in community-based practices, and 30.1% of patients died during the study period; median follow-up was 309.0 days. Among the 294 (1 L = 160; 2L+ =134) patients who received subsequent therapies, treatments included chemotherapy only (1 L = 15.6%; 2L+ =21.6%), immunotherapy only (1 L = 13.8%; 2 L+ =41.0%), and targeted therapies (1 L = 70.0%; 2 L+ =36.6%). Specifically, 40 (25.0%) 1 L patients and 7 (5.2%) 2 L+ patients received osimertinib as subsequent therapy. Before the start of subsequent therapy, EGFR T790M resistance mutation testing was performed in 88 (29.9%) patients (1 L = 63 [39.4%]; 2 L+ =25 [18.7%]). Of these patients, 25 (28.4%) were T790M positive, among whom 24 (96.0%) received osimertinib. CONCLUSIONS: A third of patients received subsequent therapies on disease progression; only 30% of these were tested for EGFR-TKI resistance mutation, prior to receiving subsequent therapies. These results highlight the importance of choosing treatments in the 1 L setting that optimize benefits for patients with EGFR-mutated NSCLC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".