Human tissue models in cancer research: looking beyond the mouse

Samuel J Jackson1, Gareth J Thomas2

  • 1National Centre for the Replacement, Refinement and Reduction of Animals in Research, Gibbs Building, 215 Euston Road, London NW1 2BE, UK Sam.Jackson@nc3rs.org.uk.

Insights

Non-animal human tissue models offer a clinically relevant platform for cancer research, overcoming limitations of traditional animal models. Optimizing human tissue use can transform drug discovery and improve translational research outcomes.

Area of Science:

  • Oncology
  • Translational Research
  • Biomedical Science

Background:

  • Mouse models, including patient-derived xenografts, are standard in cancer research but exhibit flaws misrepresenting human tumor biology.
  • These limitations hinder the translational value of animal models for clinical applications.
  • Current research faces challenges in accurately modeling human cancer due to inherent differences between species.

Discussion:

  • Non-animal human tissue models present a clinically relevant alternative, maximizing the utility of human tissue resources like biobanks.
  • Barriers to adoption include inadequate infrastructure for tissue handling and a cultural reliance on established animal models.
  • Overcoming these obstacles requires addressing data compatibility concerns and fostering a shift towards innovative research methodologies.

Key Insights:

  • Developing and utilizing non-animal human tissue models can provide a more accurate platform for cancer studies.
  • Addressing infrastructure deficits and promoting collaborative initiatives are crucial for the widespread adoption of these models.
  • Standardizing biobanking practices and quality control are essential for reliable human tissue research.

Outlook:

  • Coordinated efforts in data sharing and collaboration can accelerate the development and implementation of human tissue models.
  • Integrating these models into research practices promises to enhance cancer drug discovery pipelines.
  • Reducing dependence on potentially less predictive animal models will improve the efficiency and success rate of translational cancer research.