A novel screening approach comparing kinase activity of small molecule inhibitors with similar molecular structures and distinct biologic effects in triple-negative breast cancer to identify targetable signaling pathways
Bibliographic record
Abstract
Breast cancer affects women globally; the majority of breast cancer-related mortalities are due to metastasis. Acquisition of a mesenchymal phenotype has been implicated in the progression of breast cancer cells to an invasive, metastatic state. Triple-negative breast cancer (TNBC) subtypes have high rates of metastases, recurrence, and have poorer prognoses compared to other breast cancer types, partially due to lack of commonly targeted receptors. Kinases have diverse and pivotal functions in metastasis in TNBC, and discovery of new kinase targets for TNBC is warranted. We previously used a screening approach to identify intermediate-synthesis nonpotent, nonselective small-molecule inhibitors from the Published Kinase Inhibitor Set that reversed the mesenchymal phenotype in TNBC cells. Two of these inhibitors (GSK346294A and GSK448459A) are structurally similar, but have unique kinase activity profiles and exhibited differential biologic effects on TNBC cells, specifically on epithelial-to-mesenchymal transition (EMT). Here, we further interrogate these effects and compare activity of these inhibitors on transwell migration, gene (qRT-PCR) and protein (western blot) expressions, and cancer stem cell-like behavior. We incorporated translational patient-derived xenograft models in these studies, and we focused on the lead inhibitor hit, GSK346294A, to demonstrate the utility of our comparative analysis as a screening modality to identify novel kinase targets and signaling pathways to pursue in TNBC. This study introduces a new method for discovering novel kinase targets that reverse the EMT phenotype; this screening approach can be applied to all cancer types and is not limited to breast cancer.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".