Autotaxin and Lysophosphatidate Signaling: Prime Targets for Mitigating Therapy Resistance in Breast Cancer
Bibliographic record
Abstract
Overcoming and preventing cancer therapy resistance is the most pressing challenge in modern breast cancer management. Consequently, most modern breast cancer research is aimed at understanding and blocking these therapy resistance mechanisms. One increasingly promising therapeutic target is the autotaxin (ATX)-lysophosphatidate (LPA)-lipid phosphate phosphatase (LPP) axis. Extracellular LPA, produced from albumin-bound lysophosphatidylcholine by ATX and degraded by the ecto-activity of the LPPs, is a potent cell-signaling mediator of tumor growth, invasion, angiogenesis, immune evasion, and resistance to cancer treatment modalities. LPA signaling in the post-natal organism has central roles in physiological wound healing, but these mechanisms are subverted to fuel pathogenesis in diseases that arise from chronic inflammatory processes, including cancer. Over the last 10 years, our understanding of the role of LPA signaling in the breast tumor microenvironment has begun to mature. Tumor-promoting inflammation in breast cancer leads to increased ATX production within the tumor microenvironment. This results in increased local concentrations of LPA that are maintained in part by decreased overall cancer cell LPP expression that would otherwise more rapidly break it down. LPA signaling through six G-protein-coupled LPA receptors expressed by cancer cells can then activate virtually every known tumorigenic pathway. Consequently, to target therapy resistance and tumor growth mediated by LPA signaling, multiple inhibitors against the LPA signaling axis are entering clinical trials. In this review, we summarize recent developments in LPA breast cancer biology, and illustrate how these novel therapeutics against the LPA signaling pathway may be excellent adjuncts to extend the efficacy of evolving breast cancer treatments.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".