PATRI, a Genomics Data Integration Tool for Biomarker Discovery

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.

Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver From Short-Term Rat Toxicity Studies

“…Thus, the development of an analysis environment that exploits both canonical pathways and new extended network interactions may improve our understanding of the significance of highly regulated DEGs within the context of liver pathological processes (Cerami et al, 2010; Khatri et al, 2012). There has been tremendous progress in the pathway curation and integration process during the past few years and that progress has resulted in novel pathway tools (Fabregat et al, 2017; Huang et al, 2017; Uppal et al, 2017; Wang et al, 2017; Forsberg et al, 2018; Pittman et al, 2018; Ukmar et al, 2018). However, these tools are still highly fragmented and not integrated into a single framework for optimal DEGs analysis.…”

Emerging targets in cancer drug resistance

“…Silencing of HMGB2, an interacting partner MIEN1 sensitizes head and neck squamous cell carcinoma against cisplatin and 5-fluorouracil [ 130 ] . MIEN1 showed increased expression in lapatinib-sensitive breast cancer cells compared to lapatinib-resistant breast cancer cells [ 131 ] . Colony growth in soft agar, invasion into collagen matrix and formation of large acinar structures in three-dimensional cell cultures experiment demonstrated that increased MIEN1 expression is highly associated with cell transformation including epithelial to mesenchymal transition and reduced expression of E-cadherin and keratin-8 [ 132 ] .…”

Mining potentially actionable kinase gene fusions in cancer cell lines with the KuNG FU database

“…Precision medicine strongly relies on research models 13,14 and cancer cell lines can represent widely accessible tools for the investigation and the functional characterization of therapeutically actionable KGF targets, paralleling the genetic alterations present in primary tumor samples 11,[15][16][17][18][19] . Extensive KGF molecular annotation would therefore greatly support the drug development pipeline and aid the parallel advancement of companion diagnostic tools for the assessment of patient treatment eligibility, such as multiplex amplicon approaches 20 or Anchored Multiplex PCR (AMP) 21 .…”