Autotaxin in the crosshairs: Taking aim at cancer and other inflammatory conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it