Abstract P1-04-05: Role of the Rb and p53 Tumor Suppressor Pathways in Mammary Tumorigenesis
Bibliographic record
Abstract
Abstract The retinoblastoma (Rb) and p53 tumor suppressor pathways are frequently altered in human malignancies including breast cancer. To investigate the role of these pathways in mammary tumorigenesis, we crossed mice expressing Cre recombinase in the mammary epithelium (MMTV-Cre) with mice harboring Rb and p53 floxed alleles to generate MMTV-Cre:Rbf/f:p53f/f mutant mice. Combined somatic loss of Rb and p53 led to the formation of aggressive triple-negative tumors (ER−, PR−, HER2−) that often displayed features of an epithelial to mesenchymal transition (EMT) including sarcomatoid differentiation and high expression of the mesenchymal marker N-Cadherin. Molecular profiling revealed Rb/p53 deficient tumors shared similar gene expression profiles with mouse and human claudin-low breast cancers. Interstingly, limiting dilution transplantation analysis also suggests that Rb/p53 deficient claudin-low tumors are enriched for tumor-initiating cells (TICs). To investigate the cellular origin of claudin-low tumors, the Rbf/f and p53f/f alleles were deleted within the luminal and basal compartments of the mammary gland using a Cre-expressing adenovirus (Ad-Cre) and CD24-CD49f-based FACS analysis. The different cellular fractions were then transplanted into the cleared mammary fat pad of recipient mice which are currently being monitored for tumor development. Finally, primary cell lines isolated from Rb/p53 deficient mammary tumors were used in conjunction with high-throughput chemical and genetic screens to identify new therapeutic targets. A primary screen of 260 kinase inhibitors and 312 FDA-approved off-patent drugs has identified novel candidates and a genome wide negative selection (‘drop-out’) screen is currently in progress. Ultimately, the results of this research will provide important insight into the biology of triple-negative breast cancer and will lead to the identification of novel therapeutic targets for the treatment of patients living with this disease. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P1-04-05.
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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 | 0.001 | 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 | 0.003 |
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".