Targeting focal adhesion kinase signaling in tumor growth and metastasis
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Focal adhesion kinase (FAK), a crucial mediator of integrin and growth factor signaling, is a novel and promising target in cancer therapy. FAK resides within focal adhesions which are contact points between extracellular matrix (ECM) and cytoskeleton, and increased expression of the kinase has been linked with cancer cell migration, proliferation and survival. The aim of this review is to summarize the current research in the area and to assess the potential of different FAK-targeting strategies for cancer therapy. AREAS COVERED IN THIS REVIEW: We briefly examine the evidence pointing towards FAK as potential anti-cancer target since its discovery in 1992. Then, we summarize different approaches developed to interfere with FAK signaling and important results reported from these experiments. Finally, we discuss the potential of these strategies to accomplish inhibition of tumor growth and distant spread as well as potentially meaningful combinations with other therapeutic modalities in the context of the currently available evidence. WHAT THE READER WILL GAIN: The review emphasizes the link between FAK biology and the consequences of interference with FAK signaling. Based on this foundation an opinion is formed with regard to the future of FAK as therapeutic target. TAKE HOME MESSAGE: Inhibition of FAK harbours the potential to restrain malignant growth and progression with minimal side effects in normal tissues. Small molecule inhibitors of the kinase should be examined in further clinical studies and combinations with existing therapies need to be explored. More efforts are required to identify markers which predict response towards FAK inhibition.
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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 | 0.001 | 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) | 0.004 | 0.004 |
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".