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Record W4409994213 · doi:10.1016/j.actbio.2025.04.061

Invasive phenotypes of triple-negative breast cancer-associated fibroblasts are mechanosensitive, AhR-dependent and correlate with disease state

2025· article· en· W4409994213 on OpenAlexafffund
Gabrielle Brewer, Paul Savage, Anne-Marie Fortier, Hong Zhao, Alain Pacis, Yu-Chang Wang, Dongmei Zuo, Monyse de Nóbrega, Annika Pedersen, Camille Cassel de Camps, Margarita Souleimanova, Valentina Muñoz Ramos, Jiannis Ragoussis, Morag Park, Christopher Moraes

Bibliographic record

VenueActa Biomaterialia · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsMcGill University and Génome Québec Innovation CentreMcGill Genome CentreMcGill UniversityMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Cancer SocietyMcGill UniversityGénome QuébecNational Natural Science Foundation of ChinaFondation du cancer du sein du QuébecCanadian Institutes of Health ResearchCancer Research SocietyMerck
KeywordsMechanosensitive channelsTriple-negative breast cancerPhenotypeBreast cancerCancer researchTriple negativeCancerMaterials scienceMedicineOncologyInternal medicineBiologyGeneticsGeneReceptor

Abstract

fetched live from OpenAlex

Cancer associated fibroblasts (CAFs) play a critically important role in facilitating tumour cell invasion during metastasis. They also modulate local biophysical features of the tumour microenvironment through the formation of fibrotic foci, which have been correlated with breast cancer aggression. However, the impact of the evolving three-dimensional biophysical tumour microenvironment on CAF function remains undefined. Here, by isolating CAFs from primary human triple-negative breast cancer tissue at the time of surgery, we find that their ability to remodel the local microenvironment and invade into a three-dimensional matrix correlates with disease state. We then engineered culture models to systematically deconstruct and recreate mechanical tissue features of early breast cancer fibrotic foci; and demonstrate that invasion is mechanically-activated only in CAFs from patients with no detectable pre-existing metastases, but is independent of mechanical cues in CAFs isolated from patients with later-stage axillary lymph node metastases. By comparing the differential transcriptional response of these cells to microenvironmental tissue stiffness, we identify the aryl hydrocarbon receptor (AhR) as being significantly upregulated in invasive sub-populations of both mechanically-activated and mechanically-insensitive CAFs. Increasing AhR expression in CAFs induced invasion, while suppressing AhR significantly reduced invasion in both mechanically-activated and mechanically-insensitive CAF populations, even on stiffnesses that recapitulate late-stage disease. This work therefore uses mechanobiological analyses to identify AhR as a mediator of CAF invasion, providing a potential stratification marker to identify those patients who might respond to future mechanics-based prophylactic therapies, and provides a targetable mechanism to limit CAF-associated metastatic disease progression in triple-negative breast cancer patients. STATEMENT OF SIGNIFICANCE: By designing a mechanically-tunable tissue-engineered model of fibroblastic foci, and using this to culture patient-derived cancer-associated fibroblasts, we demonstrate that these cells are differentially mechanosensitive, depending on disease stage of the patient. While comparing transcriptomic profiles of patient-derived cells produces too many pathways to screen, identifying the pathways activated by local tissue mechanics that were common across each patient allowed us to identify a specific target to limit fibroblast invasion. This broad discovery strategy may be useful across a variety of biomaterials-based tissue engineered models; and these specific findings suggest (1) a strategy to identify patients who might respond to CAF- or matrix-targeting therapies, and (2) a specific actionable target to limit CAF-associated metastatic disease progression.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.008
GPT teacher head0.243
Teacher spread0.235 · 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 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

Citations1
Published2025
Admission routes2
Has abstractno

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