Autotaxin Inhibition with IOA-289 Decreases Breast Tumor Growth in Mice Whereas Knockout of Autotaxin in Adipocytes Does Not
Bibliographic record
Abstract
Breast cancer cells produce negligible quantities of autotaxin. Instead, previous work indicated that adipocytes in the inflamed adipose tissue adjacent to breast tumors are a major source of autotaxin secretion that drives breast tumor growth, metastasis, and the loss of efficacy for chemotherapy and radiotherapy. To test this hypothesis, we used mice with an adipocyte-specific knock out of autotaxin. The lack of autotaxin secretion from adipocytes failed to decrease the growth of orthotopic E0771 breast tumors in syngeneic C57BL/6 mice and the growth and lung metastasis of spontaneous breast tumors in MMTV-PyMT mice. However, the inhibition of autotaxin with IOA-289 decreased the growth of E0771 tumors, indicating that another source of autotaxin is responsible for tumor growth. Tumor-associated fibroblasts and leukocytes produce the majority of autotoxin transcripts in the E0771 breast tumors, and we hypothesize that they are the main sources of ATX that drive breast tumor growth. Autotaxin inhibition with IOA-289 increased the numbers of CD8α+-T-cells in the tumors. This was accompanied by decreases in the concentrations of CXCL10, CCL2, and CXCL9 in the plasma and LIF, TGFβ1, TGFβ2, and prolactin in the tumors. Bioinformatics analysis of human breast tumor databases showed that autotaxin (ENPP2) is expressed mainly in endothelial cells and fibroblasts. Autotaxin expression correlated significantly with increases in IL-6 cytokine receptor ligand interactions, signaling by LIF, TGFβ, and prolactin. This confirms the relevance of results from autotaxin inhibition in the mouse model. We propose that inhibiting autotaxin activity that is derived from cells presenting breast tumors such as fibroblasts, leukocytes, or endothelial cells changes the tumor micro-environment in such a way as to inhibit tumor growth.
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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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".