Inhibition of autotaxin activity with <scp>IOA</scp>‐289 decreases fibrosis in mouse <scp>E0771</scp> breast tumors
Bibliographic record
Abstract
Abstract Tumor‐associated fibrosis contributes to an immunosuppressive microenvironment that hinders effective anti‐tumor immune responses. This study investigates the potential of IOA‐289, a novel autotaxin (ATX) inhibitor, which blocks lysophosphatidate (LPA) production and signaling, in modulating fibrosis in breast tumors. Bioinformatic analysis of human breast tumors revealed a strong correlation between levels of LPA 1,‐4 receptors and extracellular matrix (ECM) genes. Interaction of ECM molecules and integrin β1/CD44 between myofibroblasts and other cell types had the highest contribution to cell–cell communication. We showed that LPA induced α‐smooth muscle actin mRNA in mouse mammary fibroblasts and increased expressions of collagen type‐I α1 chain ( COL1A1 ) and lamininγ1. IOA‐289 decreased the expressions of COL1A1 , fibronectin‐1, and transforming growth factor β1 (TGFβ1) in E0771 breast tumors in mice. Masson's trichrome staining revealed a marked decrease in collagen deposition within breast tumors of IOA‐289‐treated mice. Decreased tumor fibrosis aligns with previous findings that IOA‐289 enhanced the infiltration of CD8 + cytotoxic T cells and decreased fibrotic factors including leukemia inhibitory factor and transforming growth factor‐beta1 in tumors. We also demonstrated that E0771 cells express negligible ATX and LPA receptors. Therefore, ATX inhibition did not affect cancer cells directly in our model. These results underscore the potential of ATX inhibitors in reprogramming the tumor microenvironment to favor anti‐tumor immunity and attenuate fibrosis. ATX inhibitors are in clinical trials for treating idiopathic pulmonary fibrosis and pancreatic cancer. Our results support the development of ATX inhibitors as a strategy for improving the treatment of breast cancer and other diseases involving fibrosis.
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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".