Decreased Lipid Phosphate Phosphatase 1/3 and Increased Lipid Phosphate Phosphatase 2 Expression in the Human Breast Cancer Tumor Microenvironment Promotes Tumor Progression and Immune System Evasion
Bibliographic record
Abstract
The LPP family is comprised of three enzymes that dephosphorylate bioactive lipid phosphates both intracellularly and extracellularly. Pre-clinical breast cancer models have demonstrated that decreased LPP1/3 with increased LPP2 expression correlates to tumorigenesis. This though has not been well verified in human specimens. In this study, we correlate LPP expression data to clinical outcomes in over 5000 breast cancers from three independent cohorts (TCGA, METABRIC, and GSE96058), investigate biological function using gene set enrichment analysis (GSEA) and the xCell cell-type enrichment analysis, and confirm sources of LPP production in the tumor microenvironment (TME) using single-cell RNA-sequencing (scRNAseq) data. Decreased LPP1/3 and increased LPP2 expression correlated to increased tumor grade, proliferation, and tumor mutational burden (all p < 0.001), as well as worse overall survival (hazard ratios 1.3–1.5). Further, cytolytic activity was decreased, consistent with immune system invasion. GSEA data demonstrated multiple increased inflammatory signaling, survival, stemness, and cell signaling pathways with this phenotype across all three cohorts. scRNAseq and the xCell algorithm demonstrated that most tumor LPP1/3 was expressed by endothelial cells and tumor-associated fibroblasts and LPP2 by cancer cells (all p < 0.01). Restoring the balance in LPP expression levels, particularly through LPP2 inhibition, could represent novel adjuvant therapeutic options in breast cancer treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".