# Scaffolds Mimicking the Tumor Microenvironment for In Vitro Malignancy Models

## TL;DR

This paper reviews 3D scaffold models that mimic the tumor environment to improve cancer research and drug testing.

## Contribution

The paper provides a comprehensive review of biomimetic scaffolds for replicating the tumor microenvironment in vitro.

## Key findings

- Biomimetic scaffolds can replicate tumor extracellular matrix features like stiffness and porosity.
- Technologies like electrospinning and 3D bioprinting enhance the accuracy of tumor tissue modeling.
- Integration of vascular components and stromal cells improves the physiological relevance of tumor models.

## Abstract

The tumor microenvironment (TME) plays a crucial role in regulating cancer cell proliferation, invasion, and drug resistance. Traditional two-dimensional (2D) in vitro models and animal models often fail to replicate the biochemical and biophysical complexity of human tumors, leading to low predictive power in preclinical drug screening. In recent years, scaffold-based three-dimensional (3D) in vitro models have emerged as promising alternatives, offering a more physiologically relevant context for studying tumor behavior. Among these, biomimetic scaffolds capable of replicating the composition, stiffness, porosity, and signaling features of the tumor extracellular matrix (ECM) are of particular interest. This review provides a comprehensive overview of scaffold-based approaches for mimicking the TME in vitro. After outlining the key characteristics of the tumor ECM, we discuss various scaffold typologies, including those based on natural, synthetic, and hybrid biomaterials, as well as decellularized ECM. Recent advancements in fabrication technologies, such as electrospinning and 3D bioprinting, are also highlighted for their role in replicating the geometric and mechanical features of tumor tissues. Special attention is given to the integration of vascular components and stromal cells to recapitulate the complexity of the TME. Finally, we explore current limitations and future directions, emphasizing the need for standardized and reproducible models, particularly in the context of personalized cancer therapy.