FEB 27, 2019 06:00 AM PST

Discovering Cellular Mechanisms and Biomarkers in Anti-PD1 Non-Responders Using Ingenuity Pathway Analysis Software and OmicSoft OncoLand Datasets from QIAGEN

SPONSORED BY: QIAGEN
Speakers
  • Senior Field Application Scientist, QIAGEN Advanced Genomics
    Biography
      Devendra (Dev) received his PhD from University of California San Diego(UCSD) Biomedical Sciences graduate program and did postdoctoral studies under both academic and pharmaceutical settings. During his post-doctoral studies, he focused on cellular mechanisms regulating stem cells and cancer stem cells through next-gen sequencing data generation, analysis, interpretation and mining. Dev joined QIAGEN as a field application scientist in 2015. In the past 3 years, he has provided trainings to many pharma, biotech, academic and government investigators for different QIAGEN bioinformatics software. In addition, he has helped many of these users troubleshoot problems with their existing workflows and design new workflows.

    Abstract:

    The last two decades have seen an explosion in the volume of oncology data generated using next-generation sequencing (NGS) and multi-omics techniques. As a result, there is a growing need for computational tools that are powerful enough to generate meaningful hypotheses about the biological mechanisms underlying cancer. In this presentation, we demonstrate how two of QIAGEN’s bioinformatics solutions – Ingenuity Pathway Analysis (IPA) software and the OmicSoft OncoLand database – can be combined to generate testable hypotheses in an immuno-oncology case study (GSE67501). This study sought to understand the mechanism underlying the failure of anti-PD-1 therapy in advanced renal cell carcinoma patients. The study data, along with tens of thousands of other public-domain datasets (including TCGA, SRA, GEO, GTEx and others), have been reprocessed using OmicSoft’s bioinformatics pipeline, and are readily available in OmicSoft’s OncoLand platform. Analysis of these data in OncoLand revealed that UGT1 family members and immune genes were differentially regulated in responder vs non-responder populations, similar to as described in the original publication. These data were then interpreted in IPA, which has millions of findings from peer-reviewed publications. Immunologic, cell cycle, metabolite transport and solute related pathways and functions were found to be enriched. Using IPA’s analytic and predictive capabilities, along with the public data curated from TCGA and GEO and other sources present in OncoLand, we demonstrate how these QIAGEN solutions can be used to generate hypotheses for mechanism of action, and to discover potential therapeutic targets and biomarkers.

    Learning Objectives: 

    1. Introduction to databases backing Ingenuity Pathway Analysis and Oncoland
    2. Studying potential biomarkers and targets through Oncoland’s data mining and comparison tools
    3. Generation of peer-reviewed literature backed hypotheses through Ingenuity Pathway Analysis


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