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Record W2557168381 · doi:10.1093/carcin/bgw115

Hide-and-seek: the interplay between cancer stem cells and the immune system

2016· review· en· W2557168381 on OpenAlexafffund
Mohammad Sultan, Krysta M. Coyle, Dejan Vidovic, Margaret L. Thomas, Shashi Gujar, Paola Marcato

Bibliographic record

VenueCarcinogenesis · 2016
Typereview
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsImmune systemCancer stem cellBiologyAdjuvantImmunologyStem cellCancerImmunityCancer researchCancer cellCell biologyGenetics

Abstract

fetched live from OpenAlex

The enhanced ability of cancer stem cells (CSCs) to give rise to new tumors suggests that these cells may also have an advantage in evading immune detection and elimination. This tumor-forming ability, combined with the known plasticity of the immune system, which can play both protumorigenic and antitumorigenic roles, has motivated investigations into the interaction between CSCs and the immune system. Herein, we review the interplay between host immunity and CSCs by examining the immune-related mechanisms that favor CSCs and the CSC-mediated expansion of protumorigenic immune cells. Furthermore, we discuss immune cells, such as natural killer cells, that preferentially target CSCs and the strategies used by CSCs to evade immune detection and destruction. An increased understanding of these interactions and the pathways that regulate them may allow us to harness immune system components to create new adjuvant therapies that eradicate CSCs and improve patient survival.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.042
GPT teacher head0.328
Teacher spread0.286 · 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 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

Citations97
Published2016
Admission routes2
Has abstractyes

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