Advances and Challenges in 3D Bioprinted Cancer Models: Opportunities for Personalized Medicine and Tissue

Sai Liu1, Pan Jin1

  • 1Health Science Center, Yangtze University, Jingzhou 434023, China.

Polymers
|April 12, 2025
PubMed

Insights

3D bioprinting creates realistic cancer models for personalized medicine. These models help identify effective anti-cancer drugs for specific patient groups, improving treatment outcomes.

Area of Science:

  • Oncology
  • Biotechnology
  • Regenerative Medicine

Background:

  • Cancer is a leading global cause of death, with treatment efficacy varying significantly between patients.
  • Personalized medicine, utilizing molecular and genetic profiling, is essential for tailoring cancer therapies.
  • Identifying patient subgroups likely to respond to specific anti-cancer drugs remains a challenge.

Purpose of the Study:

  • To explore the application of 3D bioprinting in creating patient-specific cancer models.
  • To evaluate the potential of these 3D bioprinted models for personalized anti-cancer drug screening.
  • To address the need for improved methods in stratifying cancer patients for targeted therapies.

Main Methods:

  • Utilizing computer-aided design to construct multi-layered 3D bioengineered tissues.
  • Incorporating patient-derived cancer and stromal cells, genetic material, extracellular matrix proteins, and growth factors.
  • Employing both natural and synthetic biopolymers to support cell growth and mimic tumor microenvironments.

Main Results:

  • 3D bioprinting enables the recreation of organotypic tumor structures that mimic human tissue and microenvironments.
  • These bioprinted models facilitate physiologically relevant cell-cell and cell-matrix interactions.
  • The models exhibit 3D heterogeneity, closely resembling actual human tumors.

Conclusions:

  • 3D bioprinting offers a promising platform for developing personalized cancer therapies.
  • Bioprinted cancer models can serve as effective tools for high-throughput screening of anti-cancer agents.
  • This technology has the potential to advance personalized medicine by predicting individual patient responses to treatment.

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