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Record W3211944402 · doi:10.1038/s41388-021-02107-7

Emergence of β1 integrin-deficient breast tumours from dormancy involves both inactivation of p53 and generation of a permissive tumour microenvironment

2021· article· en· W3211944402 on OpenAlexafffund
Tung Bui, Yu Gu, Frédéric Ancot, Virginie Sanguin‐Gendreau, Dongmei Zuo, William J. Muller

Bibliographic record

VenueOncogene · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsMcGill University Health CentreMcGill University
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchMcGill University
KeywordsBiologyIntegrinCancer researchDormancyExtracellular matrixStromal cellAnoikisCell biologyCancer cellCancerCellGenetics

Abstract

fetched live from OpenAlex

The molecular and cellular mechanisms underlying mammary tumour dormancy and cancer recurrence are unclear and remain to be elucidated. Here, we report that mammary epithelial-specific disruption of β1 integrin in a murine model of Luminal B human breast cancer drastically impairs tumour growth with proliferation block, apoptosis induction and cellular senescence. β1 integrin-deficient dormant lesions show activation of the tumour suppressor p53, and tumours that circumvent dormancy possess p53 mutation analogous to those in human disease. We further demonstrate that mammary epithelial deletion of p53 in β1 integrin-deficient mice fully rescues tumour dormancy and bypasses cellular senescence. Additionally, recurrent β1 integrin-deficient tumours exhibit fibrosis with increased cancer-associated fibroblast infiltration and extracellular matrix deposition, absent in fast-growing β1 integrin/p53-deficient lesions. Taken together, these observations argue that β1 integrin modulates p53-dependent cellular senescence resulting in tumour dormancy and that pro-tumourigenic stromal cues and intrinsic genetic mutation are required for dormancy exit.

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 score0.388

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 head0.027
GPT teacher head0.260
Teacher spread0.233 · 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

Citations31
Published2021
Admission routes2
Has abstractyes

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