Stable tumor vessel normalization with pO2 increase and endothelial PTEN activation by inositol trispyrophosphate brings novel tumor treatment
Bibliographic record
Abstract
Tumor hypoxia is a characteristic of cancer cell growth and invasion, promoting angiogenesis, which facilitates metastasis. Oxygen delivery remains impaired because tumor vessels are anarchic and leaky, contributing to tumor cell dissemination. Counteracting hypoxia by normalizing tumor vessels in order to improve drug and radio therapy efficacy and avoid cancer stem-like cell selection is a highly challenging issue. We show here that inositol trispyrophosphate (ITPP) treatment stably increases oxygen tension and blood flow in melanoma and breast cancer syngeneic models. It suppresses hypoxia-inducible factors (HIFs) and proangiogenic/glycolysis genes and proteins cascade. It selectively activates the tumor suppressor phosphatase and tensin homolog (PTEN) in vitro and in vivo at the endothelial cell (EC) level thus inhibiting PI3K and reducing tumor AKT phosphorylation. These mechanisms normalize tumor vessels by EC reorganization, maturation, pericytes attraction, and lowering progenitor cells recruitment in the tumor. It strongly reduces vascular leakage, tumor growth, drug resistance, and metastasis. ITPP treatment avoids cancer stem-like cell selection, multidrug resistance (MDR) activation and efficiently enhances chemotherapeutic drugs activity. These data show that counteracting tumor hypoxia by stably restoring healthy vasculature is achieved by ITPP treatment, which opens new therapeutic options overcoming hypoxia-related limitations of antiangiogenesis-restricted therapies. By achieving long-term vessels normalization, ITPP should provide the adjuvant treatment required in order to overcome the subtle definition of therapeutic windows for in vivo treatments aimed by the current strategies against angiogenesis-dependent tumors.
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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".