Abstract SY07-02: Polarity proteins in morphogenesis and metastasis
Bibliographic record
Abstract
Abstract Loss of epithelial organization is a hallmark of carcinomas, but whether polarity regulates tumor growth and metastasis is poorly understood. A conserved set of polarity proteins, called PAR proteins, drives cell polarization in many contexts, and in Drosophila these proteins can function as tumor suppressors. However, to date, only Par4 (LKB1) has been implicated in human cancers. To determine whether other polarity proteins might have tumor suppressor functions in mammals, we have used lentiviruses to deliver oncogenes and shRNAs to primary murine mammary cells, which are then implanted into the cleared fat pads of isogenic host mice. Using this system, we silenced expression of the PAR3 polarity protein in the context of oncogenic Notch (NICD) or Ras. Depletion of Par3 dramatically reduced tumor latency in both models, producing tumors that were highly invasive and metastatic. Notably, the NICD/shPAR3 tumors retained epithelial character, with no evidence of epithelial-mesenchymal transition in either the primary tumors or the lung metastases. We have identified an unexpected signaling pathway that drives invasion in these tumors. Importantly, PAR3 protein expression is significantly reduced in ∼50% of human breast cancers, as compared to matched normal tissue. Together, these data provide a link between cell polarity, tissue organization, and metastasis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr SY07-02. doi:10.1158/1538-7445.AM2011-SY07-02
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.025 |
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".