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Record W2084773521 · doi:10.1158/0008-5472.can-14-2598

Mechanisms of Hypoxia-Mediated Immune Escape in Cancer

2014· article· en· W2084773521 on OpenAlexafffund
Ivraym B. Barsoum, Madhuri Koti, D. Robert Siemens, Charles H. Graham

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsQueen's University
FundersCanadian Institutes of Health Research
KeywordsImmune systemHypoxia (environmental)Cancer cellBiologyTumor progressionCancer researchTumor hypoxiaEffectorImmunityMetastasisAcquired immune systemImmunologyCancerImmunoeditingTumor microenvironmentImmunotherapyMedicineChemistryInternal medicine

Abstract

fetched live from OpenAlex

An important aspect of malignant progression is the acquired ability of tumor cells to avoid recognition and destruction by the immune system (immune escape). Clinical cancer progression is also associated with the development of tumor hypoxia, which is mechanistically linked to the acquisition of malignant phenotypes in cancer cells. Despite the well-established role of hypoxia in tumor cell invasion and metastasis, and resistance to therapy, relatively few studies have examined the contribution of hypoxia to cancer immune escape. Accumulating evidence reveals that hypoxia can impair anticancer immunity by altering the function of innate and adaptive immune cells and/or by increasing the intrinsic resistance of tumor cells to the cytolytic activity of immune effectors. Here, we discuss certain aspects of the contribution of hypoxia to tumor immune escape and provide evidence for a novel role of cyclic guanosine monophosphate (cGMP) signaling in the regulation of hypoxia-induced immune escape. Thus, we propose that activation of cGMP signaling in cancer cells may have important immunotherapeutic applications.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.044
GPT teacher head0.355
Teacher spread0.311 · 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 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

Citations198
Published2014
Admission routes2
Has abstractyes

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