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Record W2587227059 · doi:10.1093/neuonc/now212.844

TMIC-04. GLIOBLASTOMA-ASSOCIATED MYELOID CELLS DISPLAY NONPOLARIZED M0 MACROPHAGE PHENOTYPE

2016· article· en· W2587227059 on OpenAlexaff
Konrad Gabrusiewicz, Blanca Rodríguez, Jun Wei, Yuuri Hashimoto, Luke M. Healy, Sourindra Maiti, Ginu Thomas, Shouhao Zhou, Qianghu Wang, Ahmed Elakkad, Brandon Liebelt, Nasser K. Yaghi, Ravesanker Ezhilarasan, Neal Huang, Jeffrey S. Weinberg, Sujit S. Prabhu, Ganesh Rao, Raymond Sawaya, Lauren A. Langford, Janet Bruner, Gregory Fuller, Amit Bar‐Or, Wei Li, Rivka R. Colen, Michael A. Curran, Krishna Bhat, Jack P. Antel, Laurence J.N. Cooper, Erik P. Sulman, Amy B. Heimberger

Bibliographic record

VenueNeuro-Oncology · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicrogliaIntegrin alpha MImmune systemMyeloidFlow cytometryMyeloid-derived Suppressor CellCancer researchBiologyMacrophagePhenotypeTumor microenvironmentImmunologyPathologyInflammationMedicineCancerGeneSuppressorIn vitro

Abstract

fetched live from OpenAlex

The glioblastoma (GBM) microenvironment is commonly infiltrated with innate immune cells including microglia, macrophages, and myeloid-derived suppressor cells (MDSCs). Presumably these cells assume a tumor-supportive alternatively activated M2 phenotype, but no comprehensive phenotypic and/or genotypic studies have been conducted yet. Flow cytometry analysis was utilized to determine the expression of M1 and M2 markers in the GBM-associated myeloid cells (GAMs). Whole-genome microarray analysis was performed using RNA isolated from GAMs and matched GBM-patient and healthy-donor blood myeloid cells. Nanostring Technology was used to profile immune system- and cancer-related genes in GAMs, matched GBM-patient blood myeloid cells, healthy-donor blood myeloid cells, normal human microglia, and nonpolarized M0 and M1-, M2a-, M2c-polarized macrophages. Gene Set Enrichment Analysis (GSEA) was used to identify hallmarks of biological states or processes in immune cells derived from GBM-patient blood and tissue compared to healthy donors. Among CD11b+ cells, microglia and MDSCs constituted a higher percentage of GAMs compared to macrophages. In contrast, microglia were the most common CD11b+cells isolated from nonmalignant surgical brain samples, with macrophages constituting the remaining contributor to the brain parenchyma. Classical GBMs had significantly higher number of MDSCs than macrophages, while the mesenchymal subtype had more microglia than MDSCs and macrophages. The number of GAMs positively corresponded with the overall tumor size (precisely with edema) but not with overall patient survival. Flow cytometry studies and gene expression profile analysis revealed that GAMs express both anti-tumor M1 and tumor-supportive M2 markers. GSEA identified biological processes in these cells relative to matched blood cells, including signaling by KRAS, TGF-β, and TNF-α, epithelial-mesenchymal transition, angiogenesis, and hypoxia. Unsupervised analysis of gene expression profiles showed that GAMs aligned closely with nonpolarized M0 macrophages. New immunotherapeutic strategies for GBM are needed to redirect nonpolarized M0 GAMs towards the M1 phenotype.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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
Published2016
Admission routes1
Has abstractyes

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