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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Bayesian quantitative decision-making framework in cell and gene therapy development for rare diseases.

Rui Kang1, Yusuke Yamaguchi2, Cong Han2

  • 1Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA.

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|April 14, 2026
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Summary

This study introduces a Bayesian quantitative decision-making (QDM) framework for rare disease drug development, enhancing early-stage cell and gene therapy trials by incorporating external data for more robust decisions.

Keywords:
Bayesian statisticsQuantitative decision-makingcell and gene therapyproof-of-concept study

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Area of Science:

  • Drug Development
  • Biostatistics
  • Rare Diseases

Background:

  • Developing cell and gene therapies for rare diseases faces challenges due to small patient populations and limited clinical data.
  • Traditional quantitative decision-making (QDM) frameworks often yield inconclusive results in early-stage proof-of-concept (PoC) studies for rare diseases.

Purpose of the Study:

  • To present the first Bayesian QDM framework specifically designed for rare disease cell and gene therapy candidates.
  • To improve the robustness of QDM by systematically integrating external data sources.

Main Methods:

  • Developed a unified Bayesian QDM framework for controlled and uncontrolled PoC studies, utilizing hypothetical controls.
  • Integrated power priors with flexible information borrowing capabilities.
  • Provided practical implementation guidance via an open-source R Shiny application.

Main Results:

  • The proposed Bayesian QDM framework enables more informed decision-making in early-stage clinical trials.
  • Demonstrated potential for reduced trial durations and improved resource allocation.
  • Simulation and case studies illustrated the framework's practical application and benefits.

Conclusions:

  • The Bayesian QDM framework offers a significant advancement for rare disease drug development, particularly for cell and gene therapies.
  • Enhances decision-making robustness by leveraging external data and flexible information borrowing.
  • Facilitates accelerated development of critical therapies for rare diseases.