| Autor: |
Ukmar G; NMS Oncology, Nerviano Medical Sciences Srl, Nerviano, Italy., Melloni GEM; University of Milano Bicocca, Milano, Italy., Raddrizzani L; NMS Oncology, Nerviano Medical Sciences Srl, Nerviano, Italy., Rossi P; Icona Srl, Cinisello Balsamo, Italy., Di Bella S; NMS Oncology, Nerviano Medical Sciences Srl, Nerviano, Italy., Pirchio MR; Icona Srl, Cinisello Balsamo, Italy., Vescovi M; Parametric Design Biotech, Gessate, Italy., Leone A; NMS Oncology, Nerviano Medical Sciences Srl, Nerviano, Italy., Callari M; Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy., Cesarini M; University of Milano Bicocca, Milano, Italy., Somaschini A; NMS Oncology, Nerviano Medical Sciences Srl, Nerviano, Italy., Della Vedova G; University of Milano Bicocca, Milano, Italy., Daidone MG; Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy., Pettenella M; Parametric Design Biotech, Gessate, Italy., Isacchi A; NMS Oncology, Nerviano Medical Sciences Srl, Nerviano, Italy., Bosotti R; NMS Oncology, Nerviano Medical Sciences Srl, Nerviano, Italy. |
| Abstrakt: |
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. |
