Lysophosphatidic Acid Receptor Signaling in the Human Breast Cancer Tumor Microenvironment Elicits Receptor-Dependent Effects on Tumor Progression
Bibliographic record
Abstract
Lysophosphatidic acid receptors (LPARs) are six G-protein-coupled receptors that mediate LPA signaling to promote tumorigenesis and therapy resistance in many cancer subtypes, including breast cancer. Individual-receptor-targeted monotherapies are under investigation, but receptor agonism or antagonism effects within the tumor microenvironment following treatment are minimally understood. In this study, we used three large, independent breast cancer patient cohorts (TCGA, METABRIC, and GSE96058) and single-cell RNA-sequencing data to show that increased tumor LPAR1, LPAR4, and LPAR6 expression correlated with a less aggressive phenotype, while high LPAR2 expression was particularly associated with increased tumor grade and mutational burden and decreased survival. Through gene set enrichment analysis, it was determined that cell cycling pathways were enriched in tumors with low LPAR1, LPAR4, and LPAR6 expression and high LPAR2 expression. LPAR levels were lower in tumors over normal breast tissue for LPAR1, LPAR3, LPAR4, and LPAR6, while the opposite was observed for LPAR2 and LPAR5. LPAR1 and LPAR4 were highest in cancer-associated fibroblasts, while LPAR6 was highest in endothelial cells, and LPAR2 was highest in cancer epithelial cells. Tumors high in LPAR5 and LPAR6 had the highest cytolytic activity scores, indicating decreased immune system evasion. Overall, our findings suggest that potential compensatory signaling via competing receptors must be considered in LPAR inhibitor 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 | 0.000 |
| 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 | 0.000 |
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".