The biomimetic extracellular matrix: a therapeutic tool for breast cancer research

Abstract

Background: A deeper knowledge of the functional versatility and dynamic nature of the ECM has improved the understanding of cancer biology. Translational Significance: This work provides an in-depth view of the importance of the ECM to develop more mimetic breast cancer models, which aim to recreate the components and architecture of tumor microenvironment. Special focus is placed on decellularized matrices derived from tissue and cell culture, both in procurement and applications, as they have achieved great success in cancer research and pharmaceutical sector. The extracellular matrix (ECM) is increasingly recognized as a master regulator of cell behavior and response to breast cancer (BC) treatment. During BC progression, the mammary gland ECM is remodeled and altered in the composition and organi-zation. Accumulated evidence suggests that changes in the composition and mechanics of ECM, orchestrated by tumor-stromal interactions along with ECM remodeling enzymes, are actively involved in BC progression and metastasis. Understanding how specific ECM components modulate the tumorigenic process has led to an increased interest in the development of biomaterial-based biomi-metic ECM models to recapitulate key tumor characteristics. The decellularized ECMs (dECMs) have emerged as a promising in vitro 3D tumor model, whose recent advances in the processing and application could become the biomaterial by excellence for BC research and the pharmaceutical industry. This review offers a detailed view of the contribution of ECM in BC progression, and highlights the appli-cation of dECM-based biomaterials as promising personalized tumor models that more accurately mimic the tumorigenic mechanisms of BC and the response to treatment. This will allow the design of targeted therapeutic approaches adapted to the specific characteristics of each tumor that will have a great impact on the preci-sion medicine applied to BC patients. (Translational Research 2022; 247:117-136)