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Deconstructing cancer in 3D: models, mechanisms, and personalized solutions

Zhao Huang1, Xirui Duan2, Jun Ji3

  • 1West China Institute of Preventive and Medical Integration for Major Diseases, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.

Molecular Cancer
|June 5, 2026
PubMed

Insights

Three-dimensional (3D) cancer models like patient-derived organoids (PDOs) offer superior recapitulation of tumor complexity compared to traditional methods. These advanced models are revolutionizing precision medicine and drug discovery by better predicting treatment response and modeling therapy resistance.

Area of Science:

  • Oncology
  • Biotechnology
  • Translational Medicine

Background:

  • Traditional 2D cancer models and patient-derived xenografts (PDXs) have limitations in mimicking tumor physiology and patient heterogeneity.
  • Three-dimensional (3D) cancer models, including patient-derived organoids (PDOs), offer enhanced recapitulation of tumor architecture and patient-specific characteristics.
  • These advanced models are crucial for overcoming the limitations of conventional preclinical systems in oncology research.

Purpose of the Study:

  • To review the technological landscape of 3D cancer models and their applications in oncology.
  • To highlight the role of 3D models in advancing functional precision medicine and drug discovery.
  • To emphasize the utility of 3D models in understanding and overcoming cancer therapy resistance.

Main Methods:

  • Exploration of various 3D cancer modeling technologies, including PDOs, spheroids, organ-on-a-chip, and 3D bioprinting.
  • Integration of 3D models with advanced techniques such as single-cell omics, CRISPR screening, and artificial intelligence.
  • Application of 3D models for predicting clinical drug response and modeling therapy resistance mechanisms.

Main Results:

  • 3D cancer models, particularly PDOs, significantly improve the recapitulation of tumor microenvironment (TME) and patient heterogeneity.
  • PDO avatars demonstrate utility in predicting clinical drug response, advancing functional precision medicine.
  • These models enable the induction, dissection, and therapeutic targeting of therapy-resistant cancer clones.

Conclusions:

  • 3D cancer models represent a significant advancement over traditional preclinical systems, offering higher physiological relevance.
  • The integration of 3D models with omics and AI technologies is ushering in a new era of predictive oncology.
  • Further validation and clinical translation of 3D models are essential for developing truly personalized and effective cancer therapies.
Keywords:
3D cancer modelsDrug resistancePatient-derived organoidsPrecision medicineTumor microenvironment

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