We are a multidisciplinary team with demonstrated competencies in bioinformatics, preclinical cancer modeling, high throughput genetic and chemical library screening, with a legacy of participation as well as leadership in large, multi-institutional research initiatives. Our proposal depicts workflows that systematically identify, validate, and assess druggability of novel targets in glioblastoma and other cancers to be forwarded by the Cancer Target Discovery and Development (CTD2) Network. Specifically in this application, TGen's In Silico Research Center of Excellence (ISRCE) provides working knowledge of The Cancer Genome Atlas (TCGA) on Glioblastoma Multiforme (GBM), and brings bioinformatic tools and workplans for database mining for tumor subgrouping and target identification. Systems biology expertise is provided by the Van Andel Research Institute (VARI) and Thompson Reuters (GeneGO). Candidate targets identified by informatics strategies (Aim 1) are further informed using 54 molecularly profiled human orthotopic primary GBM xenografts. These informatic platforms and their annotated Workflow Management Systems guide functional work in target and pathway validation (Aim 2) and tractability (Aim 3) at Sanford-Burnham Medical Research Institute (SBMRI). SBMRI's Center for Chemical Genomics [a Comprehensive Center in NIH's Molecular Libraries Probe Production Centers Network (MLPCN) and NCI's Chemical Biology Consortium] enable robust RNAi and small-molecule-based high-throughput assay development and screening for efficient large-scale functional validation of targets and pathways. Results will be utilized to iteratively enhance classification and prediction algorithms~ our refined bioinformatic tools are then available for identification of biologically significant targets in other tumor types forwarded by the CTD2 Network. Thus, the significance of this proposal stems from the unique integration of incisive in silico and laboratory technologies by leading biomedical research organizations for tractable target identification and validation in molecular subsets of cancers. The innovation of the project is underscored by our multi-disciplinary team iteratively interrogating well-characterized, clinically-relevant preclinical models of glioblastoma and other cancers. Overall, we describe a systematic approach that leverages top-tiered talent in the biology and modeling of glioblastoma, bioinformatic methodologies to identify tumor subgroups as well as targets and pathways, and an expansive repertoire of high throughput assays focused on key hallmarks of cancer suitable for efficient target validation in relevant preclinical models. Furthermore, our us of small-molecule compound screens against identified targets and pathways provides a potential fast-track for novel "perturbagens" against the validated targets. As such, the described project will impact the fields of informatics, cancer biology and drug discovery by demonstrating an efficient approach for tractable target identification and validation, thereby accelerating the translation of genomic discoveries into new treatments.

Public Health Relevance

The human genome project and allied molecular depictions of human cancer allow computer mining of possible vulnerabilities of cancer. We describe a workplan by which specific candidate therapy targets in cancer are identified, validated, then utilized to find molecules that could point to new cancer drugs.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZCA1-SRLB-V (J1))
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Gerhard, Daniela
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Translational Genomics Research Institute
United States
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Jung, Sungwon; Kim, Seungchan (2014) EDDY: a novel statistical gene set test method to detect differential genetic dependencies. Nucleic Acids Res 42:e60
Xie, Qian; Mittal, Sandeep; Berens, Michael E (2014) Targeting adaptive glioblastoma: an overview of proliferation and invasion. Neuro Oncol 16:1575-84
Jung, Sungwon; Verdicchio, Michael; Kiefer, Jeff et al. (2013) Learning contextual gene set interaction networks of cancer with condition specificity. BMC Genomics 14:110