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Record W4313826540 · doi:10.1242/jcs.260285

Intravital imaging of Wnt/β-catenin and ATF2-dependent signalling pathways during tumour cell invasion and metastasis

2023· article· en· W4313826540 on OpenAlexafffund
Konstantin Stoletov, Saray Sánchez, Irantzu Gorroño‐Etxebarria, Miriam Rábano, María dM Vivanco, Robert Kypta, John D. Lewis

Bibliographic record

VenueJournal of Cell Science · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicWnt/β-catenin signaling in development and cancer
Canadian institutionsUniversity of Alberta
FundersUniversity of AlbertaAlberta Cancer Foundation
KeywordsIntravasationWnt signaling pathwayBiologyMetastasisCancer researchCancer cellCateninBeta-cateninCellCancerCell biologySignal transductionGenetics

Abstract

fetched live from OpenAlex

Wnt signalling has been implicated as a driver of tumour cell metastasis, but less is known about which branches of Wnt signalling are involved and when they act in the metastatic cascade. Here, using a unique intravital imaging platform and fluorescent reporters, we visualised β-catenin/TCF-dependent and ATF2-dependent signalling activities during human cancer cell invasion, intravasation and metastatic lesion formation in the chick embryo host. We found that cancer cells readily shifted between states of low and high canonical Wnt activity. Cancer cells that displayed low Wnt canonical activity showed higher invasion and intravasation potential in primary tumours and in metastatic lesions. In contrast, cancer cells showing low ATF2-dependent activity were significantly less invasive both at the front of primary tumours and in metastatic lesions. Simultaneous visualisation of both these reporters using a double-reporter cell line confirmed their complementary activities in primary tumours and metastatic lesions. These findings might inform the development of therapies that target different branches of Wnt signalling at specific stages of metastasis.

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)0.000
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 score0.003
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.220
Teacher spread0.210 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

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