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.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".