Mechanisms of resistance to tyrosine kinase inhibitor‐targeted therapy and overcoming strategies
Bibliographic record
Abstract
Tyrosine kinase inhibitor (TKI)-targeted therapy has revolutionized cancer treatment by selectively blocking specific signaling pathways crucial for tumor growth, offering improved outcomes with fewer side effects compared with conventional chemotherapy. However, despite their initial effectiveness, resistance to TKIs remains a significant challenge in clinical practice. Understanding the mechanisms underlying TKI resistance is paramount for improving patient outcomes and developing more effective treatment strategies. In this review, we explored various mechanisms contributing to TKI resistance, including on-target mechanisms and off-target mechanisms, as well as changes in the tumor histology and tumor microenvironment (intrinsic mechanisms). Additionally, we summarized current therapeutic approaches aiming at circumventing TKI resistance, including the development of next-generation TKIs and combination therapies. We also discussed emerging strategies such as the use of dual-targeted antibodies and PROteolysis Targeting Chimeras. Furthermore, we explored future directions in TKI-targeted therapy, including the methods for detecting and monitoring drug resistance during treatment, identification of novel targets, exploration of dual-acting kinase inhibitors, application of nanotechnologies in targeted therapy, and so on. Overall, this review provides a comprehensive overview of the challenges and opportunities in TKI-targeted therapy, aiming to advance our understanding of resistance mechanisms and guide the development of more effective therapeutic approaches in cancer treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".