Abstract 2983: Stat3 regulates supernumerary centrosome clustering in cancer cells via Stathmin/PLK1
Bibliographic record
Abstract
Abstract Centrosome amplification and supernumerary centrosomal content are common features of cancer cells. Such cells must cluster their centrosomes to form a bipolar spindle and achieve productive mitosis. Targeting the mechanisms that allow cells to cluster extra centrosomes is considered a promising cancer therapeutic strategy. We have now identified Stat3, a protein that is frequently activated in many types of cancers and also regulates stem cell function, as a regulator of centrosome clustering in cancer cells. A high content chemical screen for the identification of inhibitors of centrosome clustering identified Stattic, a Stat3 inhibitor, as a centrosome clustering inhibitor. Stat3 depletion in cell lines as well as in tumors in vivo resulted in significant inhibition of centrosome clustering and in decreased tumor viability and growth. Interestingly, we identify a novel, transcription-independent mechanism for Stat3-mediated centrosome clustering that requires activities of Stathmin, a Stat3 interactor involved in microtubule depolymerisation, and polo-like kinase1 (PLK1). Furthermore, stem cell function in PLK4-driven centrosome amplified breast tumor cells is highly sensitive to Stat3 inhibitors, reflected in higher inhibitor sensitivity of tumors derived from these cells in vivo. We have therefore identified a novel role of Stat3 in the regulation of centrosome clustering, and this role of Stat3 may be critical in identifying tumor types that are sensitive to Stat3 inhibitors. Citation Format: Edward J. Morris, Eiko Kawamura, Jordan Gillespie, Paul McDonald, William Muller, Shoukat Dedhar. Stat3 regulates supernumerary centrosome clustering in cancer cells via Stathmin/PLK1. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2983.
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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) | 0.006 | 0.002 |
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".