PATRI, a Genomics Data Integration Tool for Biomarker Discovery

G Ukmar, G E M Melloni, L Raddrizzani, P Rossi, S Di Bella, M R Pirchio, M Vescovi, A Leone, M Callari, M Cesarini, A Somaschini, G Della Vedova, M G Daidone, M Pettenella, A Isacchi, R Bosotti

Research output: Contribution to journalArticle

Abstract

The availability of genomic datasets in association with clinical, phenotypic, and drug sensitivity information represents an invaluable source for potential therapeutic applications, supporting the identification of new drug sensitivity biomarkers and pharmacological targets. Drug discovery and precision oncology can largely benefit from the integration of treatment molecular discriminants obtained from cell line models and clinical tumor samples; however this task demands comprehensive analysis approaches for the discovery of underlying data connections. Here we introduce PATRI (Platform for the Analysis of TRanslational Integrated data), a standalone tool accessible through a user-friendly graphical interface, conceived for the identification of treatment sensitivity biomarkers from user-provided genomics data, associated with information on sample characteristics. PATRI streamlines a translational analysis workflow: first, baseline genomics signatures are statistically identified, differentiating treatment sensitive from resistant preclinical models; then, these signatures are used for the prediction of treatment sensitivity in clinical samples, via random forest categorization of clinical genomics datasets and statistical evaluation of the relative phenotypic features. The same workflow can also be applied across distinct clinical datasets. The ease of use of the PATRI tool is illustrated with validation analysis examples, performed with sensitivity data for drug treatments with known molecular discriminants.

Original languageEnglish
Pages (from-to)2012078
JournalBioMed Research International
Volume2018
DOIs
Publication statusPublished - 2018

Keywords

  • Biomarkers
  • Genomics
  • Humans
  • Neoplasms
  • Precision Medicine
  • Proteomics

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