Tissue-Engineered “Metastases”: Treatment of Hepatic Colon Tumors with a Dual Action Autotaxin Inhibitor-Lysophosphatidic Acid Receptor Antagonist
Bibliographic record
Abstract
Lysophosphatidic acid (LPA) acts via G protein coupled receptors (GPCRs) to regulate critical cellular functions and pathophysiological levels of LPA or its receptors are linked to cancer initiation, progression and metastasis. LPA is biosynthesized by the lysophospholipase D activity of autotaxin(ATX/lysoPLD), a known factor for tumorigenesis. By attenuating both LPA signaling and LPA production, we expected to observe synergistic anti-cancer therapeutic effects. In vitro, treatment of human colon cancer cells (HCT 116) with BrP-LPA, a potent dual action ATX inhibitor and pan-LPA GPCR antagonist, significantly reduced cell proliferation, migration and invasion. Next, a tissue-engineered xenograft model to mimic hepatic metastasis of colon cancer was used to evaluate BrP-LPA efficacy in vivo. HCT 116 cells were suspended in Extracel™, a synthetic extracellular matrix (sECM), and injected directly into the livers of nude mice (n = 8). After 1 week, BrP-LPA in saline buffer was administered for two weeks by intraperitoneal injection (10 mg/kg) twice per week. Controls were injected with saline buffer only. The BrP-LPA treated group showed reduced liver tumor weight (p < 0.05) and reduced tumor volume (p < 0.05) relative to controls. This study is the first demonstration of the effects of a dual action ATX inhibitor/LPA antagonist on colon cancer cells, and the first example of a tissue-engineered hepatic colon cancer “metastases” as a platform for anti-cancer drug evaluation. The results suggest that attenuation of signaling through the LPA pathway offers a promising therapeutic target for reducing colon cancer growth and metastasis.
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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".