Induced Vascular Normalization—Can One Force Tumors to Surrender to a Better Microenvironment?
Bibliographic record
Abstract
Immunotherapy has changed the way many cancers are being treated. Researchers in the field of immunotherapy and tumor immunology are investigating similar questions: How can the positive benefits achieved with immunotherapies be enhanced? Can this be achieved through combinations with other agents and if so, which ones? In our view, there is an urgent need to improve immunotherapy to make further gains in the overall survival for those patients that should benefit from immunotherapy. While numerous different approaches are being considered, our team believes that drug delivery methods along with appropriately selected small-molecule drugs and drug candidates could help reach the goal of doubling the overall survival rate that is seen in some patients that are given immunotherapeutics. This review article is prepared to address how immunotherapies should be combined with a second treatment using an approach that could realize therapeutic gains 10 years from now. For context, an overview of immunotherapy and cancer angiogenesis is provided. The major targets in angiogenesis that have modulatory effects on the tumor microenvironment and immune cells are highlighted. A combination approach that, for us, has the greatest potential for success involves treatments that will normalize the tumor's blood vessel structure and alter the immune microenvironment to support the action of immunotherapeutics. So, this is reviewed as well. Our focus is to provide an insight into some strategies that will engender vascular normalization that may be better than previously described approaches. The potential for drug delivery systems to promote tumor blood vessel normalization is considered.
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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 | 0.000 | 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".