Inhibition of proliferation and migration of luminal and claudin-low breast cancer cells by PDGFR inhibitors
Bibliographic record
Abstract
BACKGROUND: Platelet-derived growth factors (PDGFs) bind to two receptors, PDGFRα and PDGFRβ to mediate cell proliferation, migration and survival. Although epithelial cells typically do not express high levels of PDGFRs, their expression has been reported to increase in breast cancer cells that have undergone epithelial to mesenchymal transition. METHODS: PDGFR signaling was inhibited using Sunitinib malate, Imatinib mesylate or Regorafenib in murine and human luminal-like and claudin-low mammary tumor cell lines or Masitinib in only the human cell lines. A scratch wound assay was used to assess tumor cell migration while immunofluorescence for phosphorylated histone H3 or cleaved caspase 3 was used to determine tumor cell proliferation and apoptosis, respectively. RESULTS: Sunitinib and Regorafenib, but not Imatinib, were capable of significantly inhibiting the migration of both murine and human luminal-like and claudin-low breast cancer cells while Masitinib inhibited migration in both human breast cancer cell lines. Sunitinib but not Regorafenib or Imatinib also significantly suppressed tumor cell proliferation in all four cell lines tested while Masitinib had no significant effect on human breast cancer cell proliferation. None of the PDGFR inhibitors consistently regulated mammary tumor cell apoptosis. CONCLUSION: Sunitinib, Regorafenib and Masitinib may prove clinically useful in inhibiting breast cancer cell migration and metastasis while only Sunitinib (and possibly Regorafenib in some breast cancer subtypes) is effective at inhibiting both migration and proliferation of breast cancer cells.
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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 | 0.000 |
| 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) | 0.001 | 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".