Searching for Synergy: FAK Inhibition in Metastatic Breast Cancer Treatment
Bibliographic record
Abstract
Breast cancer is the most common cancer among Canadian women and 14-20% will develop lethal metastases within 5 years. A potential novel therapeutic target is Focal Adhesion Kinase (FAK), a cytoplasmic tyrosine kinase. FAK’s expression is inversely correlated with survival and is known to regulate cell migration, proliferation and invasion. While tyrosine kinase inhibitors are historically ineffective as single agents, they are commonly used as part of combination therapies. Therefore, given its central role in tumor cell biology and cell signaling, we hypothesized that inhibiting FAK in combination with pharmacological agents commonly used to treat metastatic breast cancer patients will result in enhanced anti-tumor activity. We combined a commercial FAK inhibitor (PF-562271) with a range of chemotherapeutic agents commonly used to treat metastatic breast cancer and searched for synergistic partners. Only DNA topoisomerase inhibitors showed potential to synergistically reduce cell viability when paired with low doses of the FAK inhibitor. However, the combination does not induce an increase in cell death or apoptosis. It was then discovered that both agents in isolation and in combination produce increased levels of ROS, a toxic metabolite. This, along with other more preliminary data, provides clues for a novel proposed mechanism of action for this interaction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".