Abstract 3136: Activated Src increases total Rac levels and requires Rac and IL6 for full neoplastic transformation
Bibliographic record
Abstract
Abstract Activated Src (vSrc) was previously shown to activate Rac through the exchange factors Vav2 and Tiam, and to require Rac for neoplastic transformation, but the exact mechanism remains unclear. Since Src requires the function of the Signal transducer and activator of transcription-3 (Stat3), a Rac target for transformation, we explored the potential role of the Rac/Stat3 axis in transformation by vSrc. In fact, we recently demonstrated a dramatic increase in the activity of Stat3 in breast carcinoma as well as normal epithelial cells and fibroblasts, as a consequence of cell to cell adhesion (Oncogene 23:2600). In light of these findings, we revisited the question of the role of Rac and Stat3 in vSrc transformation. The results showed that, as previously demonstrated, vSrc expressing mouse fibroblasts had higher Rac activity, at all densities examined. Unexpectedly, our results also demonstrate for the first time a dramatic surge in total Rac upon Src expression, which was at least in part responsible for the Stat3 activity increase. Moreover, Rac activation caused an increase in levels of cytokines of the IL6 family, which was required for Stat3 activation, as shown by the fact that knockdown of gp130, the common receptor subunit of this family, reduced Stat3 activation and neoplastic transformation. Besides transformation, the Rac/IL6/Stat3 axis was required for apoptosis inhibition upon vSrc expression. (supported by CIHR, CBCF-Ontario chapter, US Army breast cancer program, NSERC and Breast Cancer Action Kingston). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3136.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".