Abstract A20: Insulin receptor signaling in mammary epithelial cells
Bibliographic record
Abstract
Abstract Obesity has recently emerged as a major adverse factor in the prevalence and severity of many types of cancers, including breast cancer (BC). Obesity is often accompanied by hyperinsulinemia and the development of type 2 diabetes. Elevated circulating insulin levels are thought to mediate some of the adverse prognostic effects of obesity in BC (Goodwin et al., 2002). Insulin receptor (IR) is expressed in many human BCs and the activation of the IR/receptor tyrosine kinase signaling pathway is characteristic of most BCs (Mulligan et al., 2007). We hypothesize that ectopic expression of IR in BCs sensitizes BC cells to the action of insulin, leading to signaling events specific to epithelial cells, functionally distinct from the insulin responses in physiological insulin-target tissues. To further understand the role of IR in BC we have implemented the recently developed Mammalian Membrane Two Hybrid (MaMTH) (Petschnigg et al., 2014) screen to identify IR interactors in human mammary epithelial MCF10A cells. The ongoing screen has to date identified 99 IR protein-protein interactions (PPIs), 51 of which are insulin-dependent. Coimmunoprecipitation and shRNA knockdown analyses are being used to validate the PPIs and provide insight into the functional significance of IR associations for growth and proliferation of MCF10A cells. Ultimately, the epithelial cell IR-interactome is meant to provide an insight into the insulin contribution to the activation of RTK signaling in BC, and provide a roadmap for further exploration of IR signaling in cancer. Citation Format: Yekaterina Poloz, Mariam Z. El Sheikh, Julia Petschnigg, Bella Groisman, Max Kotlyar, Igor Jurisica, Igor Stagljar, Vuk Stambolic. Insulin receptor signaling in mammary epithelial cells. [abstract]. In: Proceedings of the AACR Special Conference: Targeting the PI3K-mTOR Network in Cancer; Sep 14-17, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(7 Suppl):Abstract nr A20.
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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) | 0.001 | 0.001 |
| 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) | 0.009 | 0.005 |
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".