Back

Inhibiting fibronectin assembly in the breast tumor microenvironment increases cell death and improves response to doxorubicin

Gari, M. K.; Lee, H.; Inman, D. R.; Burkel, B. M.; Highland, M. A.; Kwon, G. S.; Gupta, N.; Ponik, S. M.

2025-02-17 cancer biology
10.1101/2025.02.12.637963 bioRxiv
Show abstract

PurposeEffective therapies for solid tumors, including breast cancers, are hindered by several roadblocks that can be largely attributed to the fibrotic extracellular matrix (ECM). Fibronectin (FN) is a highly upregulated ECM component in the fibrotic tumor stroma and is associated with poor patient prognosis. This study aimed to investigate the therapeutic potential of an anti-fibrotic peptide that specifically targets FN and blocks the fibrillar assembly of FN. MethodsTo target FN, we used PEGylated Functional Upstream Domain (PEG-FUD), which binds to the 70 kDa N-terminal region of FN with high affinity, localizes to mammary tumors, and potently inhibits FN assembly in vitro and in vivo. Here, we used the 4T1 tumor model to investigate the efficacy and mechanisms of PEG-FUD to inhibit tumor growth. ResultsOur data demonstrates that PEG-FUD monotherapy reduces tumor growth without systemic toxicity. Analysis of the tumor microenvironment revealed that PEG-FUD effectively inhibited FN matrix assembly within tumors and reduced adhesion-mediated signaling through 5 integrin and FAK leading to enhanced tumor cell death. Notably, signaling through FAK has been associated with resistance mechanisms to doxorubicin (DOX). Therefore, we tested the combination of PEG-FUD and Dox, which significantly reduced tumor growth by 60% compared to vehicle control and 30% compared to Dox monotherapy. ConclusionsOur findings demonstrate that PEG-FUD significantly modifies the peritumoral ECM of breast cancer, leading to increased tumor cell death, and potentiates the efficacy of conventional breast cancer therapy.

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.