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The lung extracellular matrix (ECM) maintains the structural integrity of the tissue and regulates the phenotype and functions of resident fibroblasts. Lung‐metastatic breast cancer alters these cell‐ECM interactions, promoting fibroblast activation. There is a need for bio‐instructive ECM models that match the ECM composition and biomechanics of the lung to study these cell‐matrix interactions in vitro. Here, a synthetic, bioactive hydrogel is synthesized that mimics the native lung modulus and includes a representative distribution of the most abundant ECM peptide motifs responsible for integrin‐binding and matrix metalloproteinase (MMP)‐mediated degradation in the lung, which enables quiescent culture of human lung fibroblasts (HLFs). Stimulation with transforming growth factor β1 (TGF‐β1), metastatic breast cancer conditioned media (CM), or tenascin‐C‐derived integrin‐binding peptide activated hydrogel‐encapsulated HLFs demonstrates multiple environmental methods to activate HLFs in a lung ECM‐mimicking hydrogel. This lung hydrogel platform is a tunable, synthetic approach to studying the independent and combinatorial effects of ECM in regulating fibroblast quiescence and activation.

 

Breast cancer is a leading cause of global cancer‐related deaths, and metastasis is the overwhelming culprit of poor patient prognosis. The most nefarious aspect of metastasis is dormancy, a prolonged period between primary tumor resection and relapse. Current therapies are insufficient at killing dormant cells; thus, they can remain quiescent in the body for decades until eventually undergoing a phenotypic switch, resulting in metastases that are more adaptable and drug resistant. Unfortunately, dormancy has few in vitro models, largely because lab‐derived cell lines are highly proliferative. Existing models address tumor dormancy, not cellular dormancy, because tracking individual cells is technically challenging. To combat this problem, a live cell lineage approach to find and track individual dormant cells, distinguishing them from proliferative and dying cells over multiple days, is adapted. This approach is applied across a range of different in vitro microenvironments. This approach reveals that the proportion of cells that exhibit long‐term quiescence is regulated by both cell intrinsic and extrinsic factors, with the most dormant cells found in 3D collagen gels. This paper envisions that this approach will prove useful to biologists and bioengineers in the dormancy community to identify, quantify, and study dormant tumor cells.

 

Bioengineers have built models of the tumour microenvironment (TME) in which to study cell–cell interactions, mechanisms of cancer growth and metastasis, and to test new therapies. These models allow researchers to culture cells in conditions that include features of the in vivo TME implicated in regulating cancer progression, such as extracellular matrix (ECM) stiffness, integrin binding to the ECM, immune and stromal cells, growth factor and cytokine depots, and a three-dimensional geometry more representative of the in vivo TME than tissue culture polystyrene (TCPS). These biomaterials could be particularly useful for drug screening applications to make better predictions of efficacy, offering better translation to preclinical models and clinical trials. However, it can be challenging to compare drug response reports across different biomaterial platforms in the current literature. This is, in part, a result of inconsistent reporting and improper use of drug response metrics, and vast differences in cell growth rates across a large variety of biomaterial designs. This study attempts to clarify the definitions of drug response measurements used in the field, and presents examples in which these measurements can and cannot be applied. We suggest as best practice to measure the growth rate of cells in the absence of drug, and follow our ‘decision tree’ when reporting drug response metrics. This article is part of a discussion meeting issue ‘Forces in cancer: interdisciplinary approaches in tumour mechanobiology’.