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Record W3111948799 · doi:10.3390/cancers12123650

TGF-β Mediated Immune Evasion in Cancer—Spotlight on Cancer-Associated Fibroblasts

2020· article· en· W3111948799 on OpenAlexaff
Parisa Ghahremanifard, Ayan Chanda, Shirin Bonni, Pinaki Bose

Bibliographic record

VenueCancers · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTGF-β signaling in diseases
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTumor microenvironmentCancer-Associated FibroblastsCarcinogenesisCancer researchExtracellular matrixTransforming growth factorImmune systemMetastasisTransforming growth factor betaCancerImmune checkpointSignal transductionCancer cellTumor progressionBiologyCell biologyImmunologyImmunotherapy

Abstract

fetched live from OpenAlex

Various components of the tumor microenvironment (TME) play a critical role in promoting tumorigenesis, progression, and metastasis. One of the primary functions of the TME is to stimulate an immunosuppressive environment around the tumor through multiple mechanisms including the activation of the transforming growth factor-beta (TGF-β) signaling pathway. Cancer-associated fibroblasts (CAFs) are key cells in the TME that regulate the secretion of extracellular matrix (ECM) components under the influence of TGF-β. Recent reports from our group and others have described an ECM-related and CAF-associated novel gene signature that can predict resistance to immune checkpoint blockade (ICB). Importantly, studies have begun to test whether targeting some of these CAF-associated components can be used as a combinatorial approach with ICB. This perspective summarizes recent advances in our understanding of CAF and TGF-β-regulated immunosuppressive mechanisms and ways to target such signaling in cancer.

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 categoriesnone
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 score0.036
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations83
Published2020
Admission routes1
Has abstractyes

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