Effects of Modulating Actin Dynamics on HER2 Cancer Cell Motility and Metastasis
Bibliographic record
Abstract
Amplification of HER2 leads to development of HER2-positive (HER2+) cancers with high rates of metastasis compared to other cancer subtypes. The goal of this study was to probe the vulnerability of HER2+ cancer cells to a filamentous actin (F-actin) severing and capping toxin. The growth and viability of human HER2+ breast cancer (HCC1954) and ovarian cancer (SKOV3) cell lines were significantly impaired upon treatment with the marine macrolide mycalolide B (Myc B) at doses above 100 nanomolar. Further testing of Myc B in combination with the antibody-drug conjugate Trastuzumab-emtansine (T-DM1) led to improved killing of SKOV3 cells compared to either treatment alone. At sub-lethal doses, treatment of HER2+ cancer cells with Myc B resulted in rapid loss of leading edge protrusions and formation of aggresomes containing F-actin and the actin regulatory protein Cortactin. This correlated with robust inhibition of HER2+ cancer cell motility and invasion with Myc B treatment. In SKOV3 tumor xenograft assays, intratumoral injections of Myc B impaired HER2+ tumor growth and metastasis, with maximal effects observed in combination with systemic delivery of Trastuzumab. Metastasis of SKOV3 cells to the lungs following tail vein injection was also reduced by Myc B. Together, these findings provide rationale for targeting F-actin in combination with existing therapies for HER2+ cancers to reduce metastasis.
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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".