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Quantifying tumor specificity using Bayesian probabilistic modeling for drug and immunotherapeutic target discovery

  • 0Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA; Perlmutter Cancer Center, New York University Grossman School of Medicine, New York, NY, USA.
Cell Reports Methods +

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November 8, 2024

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November 8, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces BayesTS, a new computational method to identify safer therapeutic targets for diseases like cancer by analyzing gene and protein expression in healthy tissues. BayesTS improves target discovery and prioritization for drug development.

Area Of Science

  • Computational biology
  • Genomics
  • Drug discovery

Background

  • Therapeutic strategy development for diseases like cancer involves costly and potentially dangerous testing for off-target effects.
  • Identifying disease-specific targets is crucial for developing safer and more effective treatments.

Purpose Of The Study

  • To develop a systematic method for learning gene and protein disease specificity using molecular measurements from healthy tissues.
  • To improve the prioritization of therapeutic targets in oncology drug development.

Main Methods

  • A hierarchical Bayesian modeling approach (BayesTS) was used to integrate protein and gene expression data.
  • The method incorporates tunable parameters to adjust for tissue essentiality.
  • BayesTS was extended to include splicing antigens and combinatorial target pairs.

Main Results

  • BayesTS outperforms existing strategies in defining therapeutic targets.
  • The method successfully nominated previously unrecognized therapeutic targets.
  • Extensions of BayesTS yielded more specific targets for therapeutic applications.

Conclusions

  • BayesTS offers a robust computational framework for identifying safer and more effective therapeutic targets.
  • This approach can facilitate improved target prioritization in oncology, potentially leading to better drug development outcomes.
  • The method's flexibility allows for the integration of diverse molecular data modalities.

Keywords: