AbstractGlioblastoma multiforme (GBM) is a highly aggressive malignant primary brain tumor, characterized by rapid growth, diffused infiltration of cells into both adjacent and remote brain regions, and a generalized resistance to currently available treatment modalities. To generate new insights into glioma biology, we performed systematic analysis of RNA-Seq data from various GBM cell lines that respond differently to chemotherapeutic agent Temozolomide and identified genes that can be used as markers for predictive and personalized medicine. Expression levels of selected genes was used to validate RNA-Seq data and predict TMZ response in FFPE samples using nanoString nCounter Analysis system.

RNA-Sequence Analysis Workflown Identify differentially expressed genes in various GBM cell lines classified

as responders and non-responders to chemotherapeutic agent Temozolomide using RNA-Seq.

n Focus on genes that are completely silenced (show no tags mapped to genes) in either responders or non-responders.

n Map identified genes to various pathways using Ingenuity’s knowledge base platform.

n Validate RNA-Seq data using nanoString nCounter platform.

n Determine expression levels of selected genes in tumor tissue to predict response to Temozolomide.

Mapping the Molecular Genomic Network of Glioblastoma MultiformeAnup Madan1, Aubree Hoover2, Juan Caballero3, Victor Cassen3, Dan Richards4, Doug Bassett4, Tanja Smith1,Karen Preusch1, Heather Collins1, Leila Shiraiwa1, Jeannette Nussbaum1, Mark Parrish1, David Henderson1,Sergey Stepaniants1, Lee Hood3 and Qiang Tian3 1Covance Genomics Laboratory, Seattle, WA; 2NanoString Technologies Inc., Seattle, WA; 3Institute for Systems Biology, Seattle, WA; 4Ingenuity Systems, Inc., Redwood City, CA (www.ingenuity.com)

Identified Genes Occupy Key Nodal Points that Link Various Networks

Validation of Next Gen Sequencing Data Using nanoString Technology

Expression of Selected Genes in a Panel of FFPE GBM Samples

Identified Genes were Mapped to 20 Different Key Networks Using Ingenuity

IPA Analysis ToolNetwork_ID Top Functions

1 Cellular Movement, Cellular Development, Cellular Growth and Proliferation

2 Carbohydrate Metabolism, Cell Death, Molecular Transport

3 Cell Death, Cancer, Cellular Growth and Proliferation

4 Developmental Disorder, Embryonic Development, Nervous System Development and Function

5 Cellular Movement, Cellular Growth and Proliferation, Cell Death

6 Molecular Transport, Digestive System Development and Function, Embryonic Development

7 Cancer, Gastrointestinal Disease, Hepatic System Disease

8 Reproductive System Disease, Endocrine System Development and Function, Lipid Metabolism

9 Cell Death, Gene Expression, Cell-To-Cell Signaling and Interaction

10 Free Radical Scavenging, Inflammatory Response, Cell-To-Cell Signaling and Interaction

11 Cellular Development, Cell Death, DNA Replication, Recombination, and Repair

12 Cardiovascular Disease, Post-Translational Modification, Hematological Disease

13 Cardiovascular Disease, Cell Signaling, Molecular Transport

14 Cell-To-Cell Signaling and Interaction, Cellular Assembly and Organization, Cellular Function a and Maintenance

15 Gene Expression, Cell Cycle, Cardiovascular System Development and Function

16 DNA Replication, Recombination, and Repair, Nucleic Acid Metabolism, Cardiovascular Disease

17 Dermatological Diseases and Conditions, Cellular Growth and Proliferation, Cancer

18 Cellular Assembly and Organization, Amino Acid Metabolism, Molecular Transport

19 Genetic Disorder, Neurological Disease, Cancer

20 Cell-To-Cell Signaling and Interaction, Tissue Development, Cellular Assembly and Organization

26,348 genes expressed in GBM cell lines

261 genes completely silenced in responders or

non-responders

50 Genes selected for follow-up studies

Filter Cascade

Mapping of genes on various networks allowed identification of genes that might play a crucial role in stratifying patients. As an example, key gene that connects Network 1 (Cellular growth and proliferation) with Networks 6, 7 and 12 (Transporters, Cancer progression, Post-Translational Modification) are silenced in cell lines that do not respond to TMZ therapy.

Differential Expression levels of majority of selected genes in various GBM cell lines correlated with response to therapy by either technology.

ConclusionsRNA-Seq data provides markers to predict response to TMZ therapy in GBM. These markers are being validated further.

n We are also exploring mechanism of differential expression by investigating aberrations in promoter regions of these genes.

n In addition to gene expression data, we have used exome sequencing to identify casual variants that can be used as predictive markers for response to therapy. The results of these analyses are presented in separate poster.

n The nanoString nCounter is useful for validating Next Gen sequencing results.

nanoString nCounter Assay

S Y S T E M SINGENUIT Y R

Network7

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Network15

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Network3

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Network1

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Network14

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Network7

Network2

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Network15

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Responders Non-Responders

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