3D Bioprinting for Breast Cancer Modelling, Diagnosis, Therapeutic Testing, and Clinical Translation
Victricia Aku Turkson, Eliana Díaz-Cruces, Cristina de-la-Macorra-García, Jorge Troconis, Oscar Casanova-Carvajal, Stephanie Marina Díaz-López, Juan José-Uriarte, Joan Manuel Rodríguez-Díaz, Frederico B. De Sousa, Camilo Zamora-LedezmaBreast cancer remains a highly heterogeneous disease, and conventional two-dimensional cell cultures and animal models do not fully reproduce the complexity of the human tumor microenvironment. More physiologically relevant in vitro platforms are therefore needed to improve disease modelling and preclinical therapeutic evaluation. In this review article, recent advances in three-dimensional (3D) bioprinting for breast cancer modelling, diagnosis, therapeutic testing, and clinical translation are discussed. A PRISMA ScR-guided search of Scopus was conducted for studies published between 2015 and July 2026, resulting in 74 selected sources. A complementary bibliometric analysis of 129 Scopus-indexed documents was also performed to identify publication trends and major research directions. The review also covers bioprinting techniques, bioink formulations, crosslinking strategies, and multicellular breast tumor models. Particular attention is given to extrusion-based approaches using alginate-, gelatin, collagen, fibrin, and composite-based bioinks, as well as light-based bioprinting strategies employing photocrosslinkable methacrylate bioinks, including but not limited to gelatin methacrylate (GelMA), collagen methacrylate (ColMA), and hyaluronic acid methacrylate (HAMA), decellularized extracellular matrix-based bioinks, as well as patient-derived, vascularized, and tumor-on-chip systems. Furthermore, it is demonstrated that 3D bioprinting provides improved control over tumor architecture, cell–matrix interactions, stromal organization, and therapeutic-response studies. Finally, standardization, regulation, governance, reproducibility, vascularization, immune integration, scalability, and clinical validation that remain as critical challenges for translation into precision oncology are discussed.