G-protein–coupled receptor GPR161 is overexpressed in breast cancer and is a promoter of cell proliferation and invasion
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) accounts for 20% of breast cancer in women and lacks an effective targeted therapy. Therefore, finding common vulnerabilities in these tumors represents an opportunity for more effective treatment. Despite the growing appreciation of G-protein-coupled receptor (GPCR)-mediated signaling in cancer pathogenesis, very little is known about the role GPCRs play in TNBC. Using genomic information of human breast cancer, we have discovered that the orphan GPCR, G-protein-coupled receptor 161 (GPR161) is overexpressed specifically in TNBC and correlates with poor prognosis. Knockdown of GPR161 impairs proliferation of human basal breast cancer cell lines. Overexpression of GPR161 in human mammary epithelial cells increases cell proliferation, migration, intracellular accumulation of E-cadherin, and formation of multiacinar structures in 3D culture. GPR161 forms a signaling complex with the scaffold proteins β-arrestin 2 and Ile Gln motif containing GTPase Activating Protein 1, a regulator of mammalian target of rapamycin complex 1 and E-cadherin. Consistently, GPR161 amplified breast tumors and cells overexpressing GPR161 activate mammalian target of rapamycin signaling and decrease Ile Gln motif containing GTPase Activating Protein 1 phosphorylation. Thus, we identify the orphan GPCR, GPR161, as an important regulator and a potential drug target for TNBC.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".