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Investigating the role of endothelial cell‐specific p110β isoform of PI3K as a potential target for anti‐angiogenic therapy

2019· article· en· W3175613224 on OpenAlexaffabout
Abul Kalam Azad, Pavel Zhabyeyev, Gavin Y. Oudit, Ronald B. Moore, Allan G. Murray

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsSunitinibAngiogenesisCancer researchLewis lung carcinomaReceptor tyrosine kinaseCD31Tumor microenvironmentMedicineMetastasisVascular endothelial growth factorTyrosine-kinase inhibitorCancerReceptorPharmacologyInternal medicineVEGF receptors

Abstract

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Introduction Angiogenesis‐inhibitor drugs targeting Vascular Endothelial Growth Factor (VEGF) signalling to the endothelial cell (EC) are used to treat various cancers. However, tumors become resistant to this therapy due to the recruitment of alternative growth factors, acting via cognate EC receptor tyrosine kinases (RTK) or g‐protein coupled receptors (GPCR), to cue neo‐angiogenesis. In ECs, the PI3 kinase p110β isoform is uniquely coupled to both RTKs and GPCRs. Endothelial‐specific p110β inactivation impairs angiogenic sprouting and tip cell marker gene expression in vitro . These data indicate that p110β mediates pro‐angiogenic signals. We hypothesize that EC PI3 kinase‐β activity mediates tumor angiogenesis escape from sunitinib therapy. Methods Mouse Lewis lung carcinoma (LLC1) or B16F10 melanoma cells were implanted subcutaneously in EC‐specific p110β knockout (ECβKO) or control mice. Sunitinib (40mg/kg/day) treatment was initiated when the tumors reached an average volume of 200 mm 3 , then tumor growth was monitored, and all mice were euthanized when the average tumor volume reached 1500 mm 3 . Second, to model metastasis, B16F10 cells were injected intravenously in ECβKO or control mice, then treated with sunitinib for 20 days. Pimonidazole was administrated 1 hour before euthanasia. Immunohistochemical analyses were performed for CD31‐positive vessels and pimonidazole‐positive hypoxic areas. Results EC‐specific p110β loss with sunitinib treatment decreases the growth of subcutaneous LLC1 and B16F10 melanoma cells among syngeneic mice vs the sunitinib alone‐treated control mice. Similarly, EC p110β loss with sunitinib decreases B16F10 metastases in the lung, and the overall tumor area in lung cross‐section, vs sunitinib‐treated control mice. Further, subcutaneous primary and metastatic tumors had a marked decrease in CD31‐positive microvessels in ECβKO vs control mice, accompanied by a significant reduction in tip cell marker gene expression. Surprisingly, pimonidazole‐positive hypoxic area in the tumors was normalized in ECβKO vs control mice. We found that inactivation of EC‐p110β increased NG2‐positive pericyte coverage of tumor microvessels and arterioles. Conclusions These findings demonstrate that EC‐specific inactivation of p110β in combination with sunitinib decreases primary tumor growth and tumor metastasis vs sunitinib treatment alone. The density of the tumor vasculature and tip cell gene expression is reduced, but tumor oxygen delivery is normalized. Inhibition of endothelial p110β may be useful as adjuvant therapy with sunitinib, and may facilitate delivery and/or response of the tumor to conventional chemotherapy agents. Support or Funding Information Canadian Cancer Society This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.230
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2019
Admission routes2
Has abstractyes

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