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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'.

ECM stiffness is emerging as a prognostic marker of tumor aggression or potential for relapse. However, conflicting reports muddle the question of whether increasing or decreasing stiffness is associated with aggressive disease. This chapter discusses this controversy in more detail, but the fact that tumor stiffening plays a key role in cancer progression and in regulating cancer cell behaviors is clear. The impact of having in vitro biomaterial systems that could capture this stiffening during tumor evolution is very high. These cell culture platforms could help reveal the mechanistic underpinnings of this evolution, find new therapeutic targets to inhibit the cross talk between tumor development and ECM stiffening, and serve as better, more physiologically relevant platforms for drug screening.

Cancer spread (metastasis) is responsible for 90% of cancer-related fatalities. Informing patient treatment to prevent metastasis, or kill all cancer cells in a patient's body before it becomes metastatic is extremely powerful. However, aggressive treatment for all non-metastatic patients is detrimental, both for quality of life concerns, and the risk of kidney or liver-related toxicity. Knowing when and where a patient has metastatic risk could revolutionize patient treatment and care. In this review, we attempt to summarize the key work of engineers and quantitative biologists in developing strategies and model systems to predict metastasis, with a particular focus on cell interactions with the extracellular matrix (ECM), as a tool to predict metastatic risk and tropism. [ABSTRACT FROM AUTHOR]