Extracellular matrix ageing as a biological variable in 3D cancer models: A framework for age-aware model design

Ageing is a major determinant of cancer risk and outcome, yet age-associated changes in the extracellular matrix (ECM) remain poorly represented in most preclinical cancer models. Throughout life, the ECM undergoes alterations in composition, architecture, biochemical modification, mechanics and turnover that influence cell behaviour and tissue function. Although advanced three-dimensional (3D) cancer models increasingly incorporate ECM components, ageing is rarely considered as an independent biological variable, and the terms aged, ageing-associated and age-mimetic ECM are often applied to biologically distinct experimental systems. Here, we review current evidence describing ECM remodelling across the lifespan and evaluate how age-associated matrix states have been incorporated into 3D cancer models. We propose a framework that distinguishes four principal modelling strategies: chronologically aged ECM models, defined age-associated ECM modification models, cell-mediated ECM remodelling models, and mixed multicomponent ageing models. These approaches differ substantially in the ageing-associated features they reproduce, the degree of biological fidelity they provide and the strength of mechanistic inference they permit. Analysis of published studies provides evidence that age-associated ECM states can influence tumour invasion, proliferation, signalling and therapeutic response across many cancer types. At the same time, the available evidence indicates that ECM ageing is a multidimensional process that no single molecular or mechanical surrogate can capture. Hence, we argue that age-aware cancer models should be classified and interpreted according to the specific ageing-associated ECM features they reproduce and experimentally validate. By establishing ECM ageing as a definable biological variable, this framework provides practical guidance for designing, validating, and interpreting next-generation age-aware 3D in vitro cancer models.