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This study introduces PATRI, a user-friendly platform for identifying drug sensitivity biomarkers. It analyzes genomic data to predict treatment response in preclinical and clinical samples, advancing precision oncology.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic datasets linked to clinical and drug sensitivity data are crucial for identifying therapeutic targets and biomarkers.
  • Integrating data from cell lines and clinical samples requires advanced analytical methods to uncover complex connections.

Purpose of the Study:

  • To introduce PATRI (Platform for the Analysis of TRanslational Integrated data), a standalone tool with a graphical interface.
  • To enable the identification of treatment sensitivity biomarkers from user-provided genomics data and associated sample characteristics.
  • To streamline translational analysis for drug discovery and precision oncology.

Main Methods:

  • PATRI statistically identifies baseline genomic signatures differentiating sensitive from resistant preclinical models.
  • It uses these signatures to predict treatment sensitivity in clinical samples via random forest categorization.
  • Statistical evaluation of phenotypic features and application across distinct clinical datasets are supported.

Main Results:

  • PATRI successfully identifies genomic signatures associated with drug sensitivity.
  • The tool demonstrates predictive capability for treatment sensitivity in clinical samples.
  • Validation examples confirm the utility of PATRI with known drug-treatment molecular discriminants.

Conclusions:

  • PATRI offers a user-friendly solution for translational analysis of genomic and clinical data.
  • The platform facilitates the discovery of novel drug sensitivity biomarkers and pharmacological targets.
  • PATRI supports advancements in precision oncology by integrating preclinical and clinical data insights.