Evaluation of the predictive capability of PETAL tool: a retrospective study on potential tyrosine kinases drug resistance targets

An ever-increasing number of tools and databases offers pieces of evidence and knowledge about gene function annotations, protein interactions, and experimentally validated biological pathways. These resources represent an excellent and essential instrument to facilitate pathway analysis. Among them, particular attention should be given to the one capable of finding alternative pathways or cross-talks events and potential drug target candidates. Here, we evaluated PETAL predictive capability, a Python tool that automatically explores and scans the relevant nodes within a KEGG pathway. Starting from three specific cancer scenarios (chronic myelogenous leukemia, non-small cell lung cancer, and head and neck squamous cell carcinoma) and related literature results about potential driver genes of drug resistance to EGFR tyrosine kinases inhibitors (SNCA, BCL-6 and YAP-1), we used PETAL to test its capability to identify in parallel these potential target genes involved in tumor progression and EGFR inhibitors resistance. By searching in-depth for ancestor and descendent nodes of SNCA, BCL-6 and YAP-1, across the EGFR pathway, we found that PETAL was able to detect the same targets investigated in the recent literature. Finally, this retrospective work emphasizes that PETAL could represent a powerful tool that can be used to improve the understanding of complex biological pathways and speed-up the identification of potential biomarkers and therapeutical candidates in cancer potentially in any other disease.