Abstract A79: Mouse models of breast cancer identify oncogene-associated stroma subtypes
Bibliographic record
Abstract
Abstract Breast Cancer is a highly heterogeneous disease involving complex crosstalk between tumor cells and their surrounding microenvironment (stroma). Previous work in our lab utilized laser capture microdissection and gene expression analysis to separate Breast Cancer stromal samples into distinct subtypes. Although this and other data demonstrate the importance of the stroma in tumor progression, the cause of these stromal changes are still poorly understood. We hypothesize that changes within stroma are related to individual oncogenic events within the tumor epithelium. Analyzing human data does not allow us to draw causative conclusions due to the presence of compounding factors such as genetic variance and living conditions. In an attempt to circumvent this challenge we used transgenic mouse models of breast cancer in the same strain of mice (FVB) with oncogenes driven by a mammary specific promoter (MMTV). By keeping these variables constant and only varying the oncogene: using either Neu-NDL-2-5, PyVMT or Wnt1 - allowed us to test the aforementioned hypothesis. Class discovery and distinction confirmed that the epithelial and stromal samples from each mouse model were distinct, which validated our hypothesis. Pathway analysis of the genes that distinguish the stroma of the mouse models identifies genes linked to recruitment and activation of different stromal cells. Interestingly our findings corroborate with results from other labs using these mouse models. Citation Format: Sadiq M. Saleh, Paul Savage, Julie Laferrière, Sean Cory, Nicholas Bertos, Margarita Souleimanova, Hong Zhao, Eldad Zacksenhaus, William Muller, Michael Hallett, Morag Park. Mouse models of breast cancer identify oncogene-associated stroma subtypes. [abstract]. In: Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; 2014 Feb 26-Mar 1; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(1 Suppl):Abstract nr A79. doi:10.1158/1538-7445.CHTME14-A79
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".