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Record W2771171716 · doi:10.1080/08830185.2017.1403596

Viral subversion of APOBEC3s: Lessons for anti-tumor immunity and tumor immunotherapy

2017· review· en· W2771171716 on OpenAlexafffund
Faezeh Borzooee, Mahdi Asgharpour, Emma M. Quinlan, Michael D. Grant, Mani Larijani

Bibliographic record

VenueInternational Reviews of Immunology · 2017
Typereview
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsBiologyImmune systemContext (archaeology)Innate immune systemImmunotherapyCancer immunotherapyImmunityAcquired immune systemVirologyVirusImmunologyCancerGenetics

Abstract

fetched live from OpenAlex

APOBEC3s (A3) are endogenous DNA-editing enzymes that are expressed in immune cells including T lymphocytes. A3s target and mutate the genomes of retroviruses that infect immune tissues such as the human immunodeficiency virus (HIV). Therefore, A3s were classically defined as host anti-viral innate immune factors. In contrast, we and others showed that A3s can also benefit the virus by mediating escape from adaptive immune recognition and drugs. Crucially, whether A3-mediated mutations help or hinder HIV, is not up to chance. Rather, the virus has evolved multiple mechanisms to actively and maximally subvert A3 activity. More recently, extensive A3 mutational footprints in tumor genomes have been observed in many different cancers. This suggests a role for A3s in cancer initiation and progression. On the other hand, multiple anti-tumor activities of A3s have also come to light, including impact on immune checkpoint molecules and possible generation of tumor neo-antigens. Here, we review the studies that reshaped the view of A3s from anti-viral innate immune agents to host factors exploited by HIV to escape from immune recognition. Viruses and tumors share many attributes, including rapid evolution and adeptness at exploiting mutations. Given this parallel, we then discuss the pro- and anti-tumor roles of A3s, and suggest that lessons learned from studying A3s in the context of anti-viral immunity can be applied to tumor immunotherapy.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
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)0.0010.000

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 head0.112
GPT teacher head0.424
Teacher spread0.312 · 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

Citations16
Published2017
Admission routes2
Has abstractyes

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