Molecular Insights of Pathways Resulting from Two Common PIK3CA Mutations in Breast Cancer
Bibliographic record
Abstract
The PI3K pathway is activated in approximately 70% of breast cancers. PIK3CA gene mutations or amplifications that affect the PI3K p110α subunit account for activation of this pathway in 20% to 40% of cases, particularly in estrogen receptor alpha (ERα)-positive breast cancers. AKT family of kinases, AKT1-3, are the downstream targets of PI3K and these kinases activate ERα. Although several inhibitors of PI3K have been developed, none has proven effective in the clinic, partly due to an incomplete understanding of the selective routing of PI3K signaling to specific AKT isoforms. Accordingly, we investigated in this study the contribution of specific AKT isoforms in connecting PI3K activation to ERα signaling, and we also assessed the utility of using the components of PI3K-AKT isoform-ERα signaling axis as predictive biomarkers of response to PI3K inhibitors. Using a variety of physiologically relevant model systems with defined natural or knock-in PIK3CA mutations and/or PI3K hyperactivation, we show that PIK3CA-E545K mutations (found in ∼20% of PIK3CA-mutant breast cancers), but not PIK3CA-H1047R mutations (found in 55% of PIK3CA-mutant breast cancers), preferentially activate AKT1. Our findings argue that AKT1 signaling is needed to respond to estrogen and PI3K inhibitors in breast cancer cells with PIK3CA-E545K mutation, but not in breast cancer cells with other PIK3CA mutations. This study offers evidence that personalizing treatment of ER-positive breast cancers to PI3K inhibitor therapy may benefit from an analysis of PIK3CA-E545K-AKT1-estrogen signaling pathways. Cancer Res; 76(13); 3989-4001. ©2016 AACR.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".