Elevated PI3K signaling drives multiple Breast Cancer subtypes
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".