Abstract

THREE-DIMENSIONAL TUMOR MODELS IN ONCOLOGY: ENGINEERING PHYSIOLOGICAL COMPLEXITY TO BRIDGE THE PRECLINICAL–CLINICAL TRANSLATIONAL GAP

Kotana Jahnavi*, Bandam Varshitha, Varikuti Janardhana, Chandrika Mamidi, Routhu Prathyusha, Prof. K. Eswar Kumar

ABSTRACT

Two-dimensional monolayer culture and murine xenografts have historically anchored preclinical oncology, yet neither adequately reproduces the three-dimensional architecture, biophysical gradients, stromal composition, and immune context of human tumors. This gap is widely implicated in the persistently low rate at which oncology compounds that succeed in preclinical testing go on to succeed in the clinic. Over the past decade, three-dimensional (3D) tumor models, multicellular spheroids, patient-derived organoids (PDOs), biomaterial-engineered scaffolds, 3D-bioprinted constructs, and microfluidic tumor-on-a-chip systems, have matured from proof-of-concept curiosities into validated platforms increasingly embedded in precision-oncology pipelines and, following the 2022 U.S. FDA Modernization Act 2.0, in regulatory-facing preclinical packages. This review synthesizes the current literature across these platform classes, tracing their biophysical rationale, fabrication strategies, and applications in drug and immunotherapy screening, metastasis modeling, and biomarker discovery. Distinct from prior platform-siloed reviews, we integrate three cross-cutting themes that increasingly determine translational success: (i) the reproducibility concerns surrounding animal-derived matrices such as Matrigel and the shift toward chemically defined synthetic and semisynthetic hydrogels; (ii) the convergence of 3D culture with artificial intelligence (AI) and machine learning (ML) for label-free phenotyping, network-based biomarker discovery, and the emerging concept of patient-specific digital tumor twins; and (iii) the regulatory and clinical-trial evidence base beginning to validate organoid- and chip-based drug-response prediction against real-world patient outcomes. We conclude with a roadmap toward standardized, immune-competent, vascularized, and computationally integrated tumor models capable of supporting genuinely individualized cancer therapeutics.

Keywords: Three-dimensional cell culture; tumor spheroids; patient-derived organoids; tumor-on-a-chip; 3D bioprinting; tumor microenvironment; precision oncology; drug screening; cancer immunotherapy; machine learning.