Autotaxin in the crosshairs: Taking aim at cancer and other inflammatory conditions
Bibliographic record
Abstract
Autotaxin is a secreted enzyme that produces most of the extracellular lysophosphatidate from lysophosphatidylcholine, the most abundant phospholipid in blood plasma. Lysophosphatidate mediates many physiological and pathological processes by signaling through at least six G-protein coupled receptors to promote cell survival, proliferation and migration. The autotaxin/lysophosphatidate signaling axis is involved in wound healing and tissue remodeling, and it drives many chronic inflammatory conditions from fibrosis to colitis, asthma and cancer. In cancer, lysophosphatidate signaling promotes resistance to chemotherapy and radiotherapy, and increases both angiogenesis and metastasis. Research into autotaxin inhibitors is accelerating, both as primary and adjuvant therapy. Historically, autotaxin inhibitors had poor bioavailability profiles and thus had limited efficacy in vivo. This situation is now changing, especially since the recent crystal structure of autotaxin is now enabling rational inhibitor design. In this review, we will summarize current knowledge on autotaxin-mediated disease processes including cancer, and discuss recent advancements in the development of autotaxin-targeting strategies. We will also provide new insights into autotaxin as an inflammatory mediator in the tumor microenvironment that promotes cancer progression and therapy resistance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".