Combined Use of Gefitinib and Bevacizumab in Advanced Non-Small-Cell Lung Cancer with EGFR G719S/S768I Mutations and Acquired C797S Without T790M After Osimertinib: A Case Report and Literature Review
Bibliographic record
Abstract
Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are effective in non-small-cell lung cancer (NSCLC) with sensitizing mutations. However, patients with uncommon EGFR mutations show variable responses, and resistance often develops. The C797S mutation is a common resistance mechanism after third-generation EGFR-TKI osimertinib therapy, with no standard treatment established. A 37-year-old Chinese woman with advanced NSCLC harboring EGFR G719S/S768I mutations developed an acquired C797S mutation without T790M after second- and third-generation EGFR-TKI therapy. She was treated with a combination of gefitinib and bevacizumab, achieving a partial response, particularly in liver metastases. Her overall survival exceeded 60 months. Gefitinib combined with bevacizumab demonstrates efficacy in managing NSCLC with uncommon EGFR mutations and overcoming acquired C797S resistance. This combination therapy offers a promising treatment strategy for patients with limited options after resistance to second- and third-generation EGFR-TKIs.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".