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Record W3208843957 · doi:10.3390/cells10112960

The Metabolic Control of Myeloid Cells in the Tumor Microenvironment

2021· review· en· W3208843957 on OpenAlexaff
Eloise Ramel, Sebastian Lillo, Boutaina Daher, Marina Fioleau, Thomas Daubon, Maya Saleh

Bibliographic record

VenueCells · 2021
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsMcGill University
FundersFondation ARC pour la Recherche sur le CancerAgence Nationale de la Recherche
KeywordsMyeloidTumor microenvironmentImmune systemImmunotherapyCancer researchBiologyMyeloid cellsCrosstalkMass cytometryMyeloid-derived Suppressor CellCarcinogenesisCancer immunotherapyImmunologyCancerPhenotypeSuppressorGeneticsGene

Abstract

fetched live from OpenAlex

Myeloid cells are a key determinant of tumor progression and patient outcomes in a range of cancers and are therefore being actively pursued as targets of new immunotherapies. The recent use of high-dimensional single-cell approaches, e.g., mass cytometry and single-cell RNA-sequencing (scRNA-seq) has reinforced the predominance of myeloid cells in the tumor microenvironment and uncovered their phenotypic diversity in different cancers. The cancerous metabolic environment has emerged as a critical modulator of myeloid cell functions in anti-tumor immunity versus immune suppression and immune evasion. Here, we discuss mechanisms of immune-metabolic crosstalk in tumorigenesis, with a particular focus on the tumor-associated myeloid cell's metabolic programs. We highlight the impact of several metabolic pathways on the pro-tumoral functions of tumor-associated macrophages and myeloid-derived suppressor cells and discuss the potential myeloid cell metabolic checkpoints for cancer immunotherapy, either as monotherapies or in combination with other immunotherapies.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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