Elevated V–ATPase Activity Following PTEN Loss Is Required for Enhanced Oncogenic Signaling in Breast Cancer
Bibliographic record
Abstract
Abstract PTEN loss-of-function contributes to hyperactivation of the PI3K pathway and to drug resistance in breast cancer. Unchecked PI3K pathway signaling increases activation of the mechanistic target of rapamycin complex 1 (mTORC1), which promotes tumorigenicity. Several studies have suggested that vacuolar (H+)–ATPase (V–ATPase) complex activity is regulated by PI3K signaling. In this study, we showed that loss of PTEN elevated V–ATPase activity. Enhanced V–ATPase activity was mediated by increased expression of the ATPase H+ transporting accessory protein 2 (ATP6AP2), also known as the prorenin receptor (PRR). PRR is cleaved into a secreted extracellular fragment (sPRR) and an intracellular fragment (M8.9) that remains associated with the V–ATPase complex. Reduced PTEN expression increased V–ATPase complex activity in a PRR-dependent manner. Breast cancer cell lines with reduced PTEN expression demonstrated increased PRR expression. Similarly, PRR expression became elevated upon PTEN deletion in a mouse model of breast cancer. Interestingly, concentration of sPRR was elevated in the plasma of patients with breast cancer and correlated with tumor burden in HER2-enriched cancers. Moreover, PRR was essential for proper HER2 receptor expression, localization, and signaling. PRR knockdown attenuated HER2 signaling and resulted in reduced Akt and ERK 1/2 phosphorylation, and in lower mTORC1 activity. Overall, our study demonstrates a mechanism by which PTEN loss in breast cancer can potentiate multiple signaling pathways through upregulation of the V–ATPase complex. Implications: Our study contributed to the understanding of the role of the V–ATPase complex in breast cancer cell tumorigenesis and provided a potential biomarker in breast cancer.
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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 | 0.000 | 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".