Despite decades of research Glioblastoma (GBM) remains a uniformly fatal disease. Although the field has made advances in molecular classification of GBM that have led to a deeper understanding of the disease, there remains a lack of progress toward designing clinical applications that directly benefit patients. A growing body of evidence indicates that the cellular adaptation to therapy is in part governed by epigenetic regulators that work in concert with specific transcriptional networks to activate target genes to promote therapeutic resistance. Transcription factors (TFs) typically regulate gene expression by binding cis-acting regulatory elements such as promoters/enhancers and subsequently recruiting RNA polymerase II to activate gene expression. TFs and chromatin modifiers are frequently deregulated in human cancer and are a critical contributor to human malignancy. Thus, if chromatin modification at the cis-acting regulatory sites of genes is necessary to activate gene networks to initiate therapeutic adaptation, then the carefully mapping these modifications may allow us to identify DNA motifs and transcription networks associated with therapeutic resistance in GBM. Based on this, we are proposing to perform simultaneous epigenetic, and transcriptome profiling of the three different subtypes of GBMs in patient-derived xenograft models before and after therapy, as well as in freshly isolated patient samples (Aim 1). The goal of this aim is to interrogate the landscape of active chromatin which will allow us to identify gene and transcription networks that may be critical for cellular adaption during anti-glioma therapy. A significant limitation the proposed approach, and to similar research methods in the field, is the limited capacity to account for intratumoral heterogeneity. This is because bulk sequencing methods fail to detect subtle, but potentially biologically meaningful, differences between seemingly identical cells. To overcome this limitation, we are proposing to employ state of the art technologies such as Drop-sequencing and Nucleosome Occupancy and Methylome-sequencing to simultaneously measure the gene expression pattern as well as chromatin accessibility mapping at the single cell level during temozolomide-based therapy (Aim 2). We believe that these approaches will provide us a higher resolution understanding of tumor cell heterogeneity as well as mechanisms of therapeutic resistance during chemotherapy. Finally, we propose to employ the Drug Gene Interaction Database in concert with our single cell sequencing data to target novel outlier populations that emerge during conventional therapy (Aim 3). This aim will identify novel druggable targets that inhibit GBM cells ability to adapt to current standard of care therapies. In conclusion, this proposal will use state of the art techniques to generate a wealth of data at multiple cellular levels that can be used to further elucidate novel mechanisms of therapeutic resistance and GBM recurrence.
|Effective start/end date||9/1/19 → 6/30/24|
- National Institute of Neurological Disorders and Stroke (1R01NS112856-01)
Gene Regulatory Networks
RNA Polymerase II
Gene Expression Profiling
Standard of Care