Elevated PI3K signaling drives multiple Breast Cancer subtypes
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Bibliographic record
Abstract
Jessica R. Adams 1,2 , Nathan F. Schachter 1,2 , Jeff C. Liu 3 , Eldad Zacksenhaus 3,4 and Sean E. Egan 1,2 1 Program in Developmental and Stem Cell Biology, The Hospital for Sick Children, 101 College St., East Tower 2 The Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada 3 Division of Cell and Molecular Biology, Toronto General Research Institute–University Health Network, Toronto, Ontario, Canada 4 The Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada Keywords: PIK3CA, Mouse models, Breast Cancer, PTEN, Akt, Metastasis Received: June 2, 2011; Accepted: June 2, 2011; Published: June 5, 2011; Correspondence: Sean E. Egan, e-mail: // // Abstract Most human breast tumors have mutations that elevate signaling through a key metabolic pathway that is induced by insulin and a number of growth factors. This pathway serves to activate an enzyme known as phosphatidylinositol 3’ kinase (PI3K) as well as to regulate proteins that signal in response to lipid products of PI3K. The specific mutations that activate this pathway in breast cancer can occur in genes coding for tyrosine kinase receptors, adaptor proteins linked to PI3K, catalytic and regulatory subunits of PI3K, serine/threonine kinases that function downstream of PI3K, and also phosphatidylinositol 3’ phosphatase tumor suppressors that function to antagonize this pathway. While each genetic change results in net elevation of PI3K pathway signaling, and all major breast cancer subtypes show pathway activation, the specific mutation(s) involved in any one tumor may play an important role in defining tumor subtype, prognosis and even sensitivity to therapy. Here, we describe mouse models of PI3K- breast cancer and how they may be used to guide development of novel therapeutics for treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it