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Record W4414056195 · doi:10.1016/j.xcrm.2025.102322

Modulation of fibronectin extracellular matrix enhances anti-tumor efficacy of immune checkpoint blockade

2025· article· en· W4414056195 on OpenAlexafffund
Kabir A. Khan, Maresa Caunt Mitzner, William Cruz‐Muñoz, Grant Li, Patricia Himmels, Ping Xu, H Kuo, Rajiv Jesudason, Alvin Gogineni, Robby M. Weimer, Annabelle Chow, Robert Piskol, Iacovos P. Michael, Weilan Ye, Robert S. Kerbel

Bibliographic record

VenueCell Reports Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsSunnybrook Health Science Centre
FundersGenentechCanadian Institutes of Health ResearchSunnybrook Research Institute
KeywordsAtezolizumabExtracellular matrixFibronectinIntegrinBlockadeImmune systemImmunotherapyImmune checkpointExtracellular

Abstract

fetched live from OpenAlex

The success of immune checkpoint inhibitors is limited by multiple factors, including poor T cell infiltration and function within tumors, partly due to a dense extracellular matrix (ECM). Here, we investigate modulating the ECM by targeting integrin α5β1, a major fibronectin-binding and organizing integrin, to improve immunotherapy outcomes. Use of a function-blocking murinized α5β1 antibody reduces fibronectin fibril formation, enhances CD8 + T cell transendothelial migration, increases vascular permeability, and decreases vessel-associated collagen. These changes culminate in improving the effectiveness of PD-L1 blockade, alone or with chemotherapy, in the E0771 breast cancer model. Clinically, high integrin alpha 5 (ITGA5) expression correlates with worse survival in patients treated with atezolizumab as monotherapy or combined with chemotherapy or anti-angiogenic therapies in numerous clinical trials. Overall, our studies suggest that ECM-modulating approaches could be used as a future strategy to increase the proportion of patients who respond to immune checkpoint inhibition and other immunotherapies.

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 score0.096
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.284
Teacher spread0.274 · 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

Citations5
Published2025
Admission routes2
Has abstractyes

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