Resistance to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors, T790M, and Clinical Trials
Bibliographic record
Abstract
Tumours with sensitizing mutations in the EGFR gene constitute a distinct molecular subgroup of non-small-cell lung cancers (NSCLCS) that benefit from precision medicine. First- and second-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIS) are recommended as upfront therapy for EGFR-mutated advanced NSCLC and, compared with chemotherapy, have resulted in superior progression-free survival, improved tumour response rates, and improved quality of life. However, resistance inevitably develops, and the third-generation tki osimertinib has been approved to target the gatekeeper EGFR mutation T790M, which is responsible for resistance in 60% of cases. Multiple drivers of tki resistance have now been identified, and many new drugs are in development. With respect to this rapidly evolving field, our review highlights the current status of treatment options for patients with EGFR-mutated advanced nsclc, focusing especially on identified causes of resistance, challenges, and clinical trials aiming to improve outcomes in this patient population.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".