Trials to Overcome Drug Resistance to EGFR and ALK Targeted Therapies – Past, Present, and Future
Bibliographic record
Abstract
Molecularly targeted agents are changing the therapeutic landscape in advanced non-small cell lung cancer. Since the discovery of sensitizing mutations in the epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) domain, clinical investigations have focused on optimizing the efficacy of EGFR and ALK tyrosine kinase inhibitors by addressing therapeutic resistance that commonly develops within a year of treatment initiation. Here, we review the clinical trials of novel therapies and combination regimens that have been undertaken in response to our evolving understanding of the mechanisms of resistance to targeted therapy. The aim of these trials was to enhance the therapeutic efficacy of targeted therapies by improving blockade and/or inhibiting parallel or compensatory signaling pathways. We have documented the sequential conduct of EGFR and ALK biomarker-driven trials in order to highlight particular pitfalls and successes, which should be considered in the design of future trials. Although there remain significant challenges, substantial gains have been made in our understanding of cellular resistance. This knowledge will drive the design of future trials to the benefit of lung cancer patients.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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".