Using Pharmacokinetic Profiles and Digital Quantification of Stained Tissue Microarrays as a Medium-Throughput, Quantitative Method for Measuring the Kinetics of Early Signaling Changes Following Integrin-Linked Kinase Inhibition in an In Vivo Model of Cancer
Bibliographic record
Abstract
A small molecule inhibitor (QLT0267) targeting integrin-linked kinase is able to slow breast tumor growth in vivo; however, the mechanism of action remains unknown. Understanding how targeting molecules involved in intersecting signaling pathways impact disease is challenging. To facilitate this understanding, we used tumor tissue microarrays (TMA) and digital image analysis for quantification of immunohistochemistry (IHC) in order to investigate how QLT0267 affects signaling pathways in an orthotopic model of breast cancer over time. Female NCR nude mice were inoculated with luciferase-positive human breast tumor cells (LCC6(Luc)) and tumor growth was assessed by bioluminescent imaging (BLI). The plasma levels of QLT0267 were determined by LC-MS/MS methods following oral dosing of QLT0267 (200 mg/kg). A TMA was constructed using tumor tissue collected at 2, 4, 6, 24, 78 and 168 hr after treatment. IHC methods were used to assess changes in ILK-related signaling. The TMA was digitized, and Aperio ScanScope and ImageScope software were used to provide semi-quantitative assessments of staining levels. Using medium-throughput IHC quantitation, we show that ILK targeting by QLT0267 in vivo influences tumor physiology through transient changes in pathways involving AKT, GSK-3 and TWIST accompanied by the translocation of the pro-apoptotic protein BAD and an increase in Caspase-3 activity.
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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) | 0.001 | 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".