Tolerance and Resistance to Targeted Therapy in NSCLC: Emerging Concepts and Strategies
Bibliographic record
Abstract
mutations two decades ago launched an era of rapid development and clinical application of targeted therapies in NSCLC. Today, increasing numbers of targeted therapies against somatic aberrations involving nine different genes have become available for treating patients with lung cancer and have improved their outcomes. However, acquired resistance and tumor tolerance to these therapies remains one of the biggest challenges in lung cancer treatment today. Most, if not all, targeted therapies have limited durability, which we now recognize is due to both genetic and non-genetic mechanisms of resistance. The state of our current understanding of resistance and new approaches to prevent or overcome resistance were recently presented at the International Association for the Study of Lung Cancer Hot Topics Meeting. Here, we summarize and discuss the emerging concepts and new strategies for combating drug tolerance and resistance in targeted therapies, including our understanding of the role of genetics, drug-tolerant persister cells, tumor plasticity and lineage transformation, spatial and temporal heterogeneity, microenvironmental influence, and novel therapeutic approaches.
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 | 0.001 |
| 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".