Abstract 664: Insulin receptor signaling contributes to mammary tumorigenesis in mice
Bibliographic record
Abstract
Abstract A recent convergence of clinical and epidemiological evidence suggests that hyperinsulinemia associated with obesity and type 2 diabetes leads to higher incidence and poor outcome of breast cancer (BC). To probe the role of insulin signaling in BC, we created the MMTV-driven polyoma middle T BC model mice with an inducible mammary epithelium-specific deletion of the insulin receptor (INSR). Deletion of INSR in the mammary gland considerably reduced the mammary tumour burden in the INSR knockout mice as compared to the INSR wildtype controls, without affecting the tumour onset. The average weight of individual tumours was similar across the genotypes, suggesting that deletion of INSR affected the tumour initiation potential of the epithelial cells rather than tumour growth. Deletion of INSR also affected the metastasis of mammary tumours to the lung, with the INSR knockout mice presenting with half the number of metastases compared to the INSR wildtype controls. Our ongoing efforts are focused on mapping the signaling events downstream of the INSR that govern tumour initiation and metastasis in our mice, as well as modeling the impact of obesity on tumors in this model system, with the ultimate goal of identifying the role of INSR signaling in BC development and progression. Citation Format: Yekaterina Poloz, Samar Mouaaz, Sonya Lam, Vuk Stambolic. Insulin receptor signaling contributes to mammary tumorigenesis in mice. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 664.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".