Abstract 200: Identification of proteins involved in c-Myc induced mammary epithelia apoptosis in 3D culture system
Bibliographic record
Abstract
Abstract Identification of proteins involved in c-Myc induced mammary epithelia apoptosis in 3D culture system We have previously discovered that depletion of a cell polarity gene Scribble in mammary epithelial cells disrupted cell polarity, apoptosis and morphogenesis in three dimensional cell culture as well as inducing dysplasia that progressed into tumor in mice. Loss of Scribble also inhibited c-Myc induced apoptosis to cause cell transformation in vitro and tumor formation in vivo. Scribble mutant with deficiency in membrane localization resulted in similar phenotypes. These results suggest that Scribble is a tumor suppressor in mammary gland epithelial cells and its functions depends on correct cellular localization. Furthermore, Scribble is required to inhibit Myc-induced tumorigenesis by blocking Myc-induced apoptosis. I am interested in identifying proteins that are involved in Scribble dependent apoptosis as well as discovering other polarity proteins that cooperate with c-Myc for mammary tumor progression. To address the two questions, I use MCF10A three dimensional culture system and RNAi technology to screen proteins participating in apoptosis during c-Myc induced mammary epithelial cell transformation. We will report a screen to identify proteins that are involved in c-Myc induced mammary epithelial cell apoptosis in 3D culture, using a lentiviral shRNA library. 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 200. doi:10.1158/1538-7445.AM2011-200
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".