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Record W3003195528 · doi:10.1038/s41416-019-0705-1

The life cycle of cancer-associated fibroblasts within the tumour stroma and its importance in disease outcome

2020· review· en· W3003195528 on OpenAlexafffund
Elisa D’Arcangelo, Nila C. Wu, Jose L. Cadavid, Alison P. McGuigan

Bibliographic record

VenueBritish Journal of Cancer · 2020
Typereview
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaCalifornia HIV/AIDS Research Program
KeywordsStromaCancerDiseaseMedicineOncologyPathologyCancer researchInternal medicineBiologyImmunohistochemistry

Abstract

fetched live from OpenAlex

The tumour microenvironment (TME) determines vital aspects of tumour development, such as tumour growth, metastases and response to therapy. Cancer-associated fibroblasts (CAFs) are abundant and extremely influential in this process and interact with cellular and matrix TME constituents such as endothelial and immune cells and collagens, fibronectin and elastin, respectively. However, CAFs are also the recipients of signals-both chemical and physical-that are generated by the TME, and their phenotype effectively evolves alongside the tumour mass during tumour progression. Amid a rising clinical interest in CAFs as a crucial force for disease progression, this review aims to contextualise the CAF phenotype using the chronological framework of the CAF life cycle within the evolving tumour stroma, ranging from quiescent fibroblasts to highly proliferative and secretory CAFs. The emergence, properties and clinical implications of CAF activation are discussed, as well as research strategies used to characterise CAFs and current clinical efforts to alter CAF function as a therapeutic strategy.

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)0.000
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.034
GPT teacher head0.346
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 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

Citations115
Published2020
Admission routes2
Has abstractyes

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