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Record W4200374922 · doi:10.3389/fonc.2021.772305

Novel Pathways for Targeting Tumor Angiogenesis in Metastatic Breast Cancer

2021· review· en· W4200374922 on OpenAlexaff
Jordan A. Harry, Mark L. Ormiston

Bibliographic record

VenueFrontiers in Oncology · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsQueen's University
Fundersnot available
KeywordsAngiogenesisMetastasisCancer researchCancerMedicineBreast cancerAngiopoietinHedgehogSignal transductionVascular endothelial growth factorTumor microenvironmentImmunologyBiologyInternal medicineCell biology

Abstract

fetched live from OpenAlex

Breast cancer is the most common cancer affecting women and is the second leading cause of cancer related death worldwide. Angiogenesis, the process of new blood vessel development from pre-existing vasculature, has been implicated in the growth, progression, and metastasis of cancer. Tumor angiogenesis has been explored as a key therapeutic target for decades, as the blockade of this process holds the potential to reduce the oxygen and nutrient supplies that are required for tumor growth. However, many existing anti-angiogenic approaches, such as those targeting Vascular Endothelial Growth Factor, Notch, and Angiopoietin signaling, have been associated with severe side-effects, limited survival advantage, and enhanced cancer regrowth rates. To address these setbacks, alternative pathways involved in the regulation of tumor angiogenesis are being explored, including those involving Bone Morphogenetic Protein-9 signaling, the Sonic Hedgehog pathway, Cyclooxygenase-2, p38-mitogen-activated protein kinase, and Chemokine Ligand 18. This review article will introduce the concept of tumor angiogenesis in the context of breast cancer, followed by an overview of current anti-angiogenic therapies, associated resistance mechanisms and novel therapeutic targets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.348
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations46
Published2021
Admission routes1
Has abstractyes

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