Coming of Age for Autotaxin and Lysophosphatidate Signaling: Clinical Applications for Preventing, Detecting and Targeting Tumor-Promoting Inflammation
Bibliographic record
Abstract
A quarter-century after the discovery of autotaxin in cell culture, the autotaxin-lysophosphatidate (LPA)-lipid phosphate phosphatase axis is now a promising clinical target for treating chronic inflammatory conditions, mitigating fibrosis progression, and improving the efficacy of existing cancer chemotherapies and radiotherapy. Nearly half of the literature on this axis has been published during the last five years. In cancer biology, LPA signaling is increasingly being recognized as a central mediator of the progression of chronic inflammation in the establishment of a tumor microenvironment which promotes cancer growth, immune evasion, metastasis, and treatment resistance. In this review, we will summarize recent advances made in understanding LPA signaling with respect to chronic inflammation and cancer. We will also provide perspectives on the applications of inhibitors of LPA signaling in preventing cancer initiation, as adjuncts extending the efficacy of current cancer treatments by blocking inflammation caused by either the cancer or the cancer therapy itself, and by disruption of the tumor microenvironment. Overall, LPA, a simple molecule that mediates a plethora of biological effects, can be targeted at its levels of production by autotaxin, LPA receptors or through LPA degradation by lipid phosphate phosphatases. Drugs for these applications will soon be entering clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".