Elevated expression of wildtype RhoC promotes ErbB2- and Pik3ca-induced mammary tumor formation
Bibliographic record
Abstract
Abstract Copy number gains in genes coding for Rho activating exchange factors as well as losses affecting genes coding for RhoGAP proteins are common in breast cancer (BC), suggesting that elevated Rho signaling may play an important role. Extra copies and overexpression of RHOC also occur, although a role for RhoC overexpression in driving tumor formation has not been assessed in vivo. To this end, we report on the development of a Rosa26 (R26)-targeted Cre-conditional RhoC overexpression mouse (R26RhoC). This mouse was crossed to two models for ERBB2/NEU+ breast cancer: one based on expression of an oncogenic ErbB2/Neu cDNA downstream of the endogenous ErbB2 promoter (FloxNeoNeuNT), the other, a metastatic model that is based on high-level expression from MMTV regulatory elements (NIC). RhoC overexpression dramatically enhanced mammary tumor formation in FloxNeoNeuNT mice but showed a more subtle effect in the NIC line, which forms multiple mammary tumors after a very short latency. RhoC overexpression also enhanced mammary tumor formation in an activated Pik3ca model for breast cancer (Pik3caH1047R). The transforming effect of RhoC was associated with epithelial/mesenchymal transition (EMT) in ErbB2/NeuNT and Pik3caH1047R systems. Thus, our study reveals the importance of elevated wildtype Rho protein expression as a driver of breast tumor formation and highlights the significance of Copy Number Abberations that affect Rho signalling.
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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) | 0.001 | 0.001 |
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".