MétaCan
Menu
Back to cohort

Immunometabolism in the Tumor Microenvironment

2020· article· en· W3112014428 on OpenAlexaff
Dominic G. Roy, Irem Kaymak, Kelsey S. Williams, H. Eric, Russell G. Jones

Bibliographic record

VenueAnnual Review of Cancer Biology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsMcGill University
Fundersnot available
KeywordsImmune systemTumor microenvironmentBiologyImmunotherapyImmunityCancer immunotherapyReprogrammingCancer cellCancer researchImmunologyCell biologyCancerCell

Abstract

fetched live from OpenAlex

Advances in immunotherapy have underscored the importance of antitumor immune responses in controlling cancer. However, the tumor microenvironment (TME) imposes several obstacles to the proper function of immune cells, including a metabolically challenging and immunosuppressive microenvironment. The increased metabolic activity of tumor cells can lead to the depletion of key nutrients required by immune cells and the accumulation of byproducts that hamper antitumor immunity. Furthermore, the presence of suppressive immune cells, such as regulatory T cells and myeloid-derived suppressor cells, and the expression of immune inhibitory receptors can negatively impact immune cell metabolism and function. This review summarizes the metabolic reprogramming that is characteristic of various immune cell subsets, discusses how the metabolism and function of immune cells are shaped by the TME, and highlights how therapeutic interventions aimed at improving the metabolic fitness of immune cells and alleviating the metabolic constraints in the TME can boost antitumor immunity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.860
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.290
Teacher spread0.278 · 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

Citations47
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAnnual Review of Cancer BiologySame topicImmune cells in cancerFrench-language works237,207