Common and distinct features of mammary tumors driven by Pten-deletion or activating Pik3ca mutation
Bibliographic record
Abstract
// Jeff C. Liu 1 , Dong-Yu Wang 2,3 , Sean E. Egan 4,5 and Eldad Zacksenhaus 1,6 1 Division of Advanced Diagnostics, Toronto General Research Institute - University Health Network, Toronto, Ontario, Canada 2 Princess Margaret Cancer Center, Toronto, Ontario, Canada 3 Campbell Family Institute for Breast Cancer Research, Princess Margaret Hospital, Toronto, Ontario, Canada 4 Program in Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, Ontario, Canada 5 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada 6 Department of Medicine, University of Toronto, Toronto, Ontario, Canada Correspondence to: Jeff C. Liu, email: // Eldad Zacksenhaus, email: // Keywords : PTEN, PIK3CA, breast cancer, bioinformatics, mouse models Received : September 27, 2015 Accepted : January 18, 2016 Published : January 22, 2016 Abstract PTEN loss and PIK3CA activation both promote the accumulation of phosphatidylinositol (3, 4, 5)-trisphosphate (PIP3). While these proteins also have distinct biochemical functions, beyond the regulation of PIP3, little is known about the consequences of these differences in vivo . Here, we directly compared cancer signalling in mammary tumors from MMTV-Cre:Pten f/f and MMTV-Cre:Pik3ca LSL-H1047R mice. Using unsupervised hierarchical clustering we found that whereas MMTV-Cre:Pik3ca LSL-H1047R -derived tumors fall into two separate groups, designated squamous-like Ex and class14 Ex , MMTV-Cre:Pten f/f tumors cluster as one group together with PIK3CA H1047R class14 Ex , exhibiting a ‘luminal’ expression profile. Gene Set Enrichment Analysis (GSEA) of Pten ∆ and PIK3CA H1047R class14 Ex tumors revealed very similar profiles of signalling pathways as well as some interesting differences. Analysis of 18 signalling signatures revealed that PI3K signalling is significantly induced whereas EGFR signalling is significantly reduced in Pten ∆ versus PIK3CA H1047R tumors. Thus, Pten ∆ and PIK3CA H1047R tumors exhibit discernable differences that may impact tumorigenesis and response to therapy.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".