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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".