We propose an innovative, systems biology approach to uncover new therapeutic strategies for childhood embryonal tumors. Our project is a collaboration between labs in two separate Integrative Cancer Biology Program (ICBP) centers and a leading hospital-based translational research lab that is not within the ICBP network. Embryonal tumors are the most common central nervous system malignancies in childhood, and there is a pressing need for better therapies. Current survival rates range from 30 - 80%, and nearly all survivors have impaired neurological and neurocognitive function. Extensive genomic analysis of medulloblastomas, the most common embryonal tumors, failed to identify """"""""driver genes"""""""" that could explain the origin of most tumors or suggest new strategies. Nevertheless, these tumors can be grouped into a small number of subtypes that share transcriptional patterns and clinical outcomes. We believe that it is time for a fundamentally new approach that seeks oncogenic """"""""driver pathways"""""""" rather than """"""""driver genes."""""""" As many different genomic changes can all affect the same driver pathway, such pathways cannot be uncovered by looking for recurring genomic changes. Rather, we will use a systems biology approach to identify these oncogenic driver pathways. We will collect comprehensive datasets in human medulloblastoma tumors and cell lines by measuring mutations, copy number variations, mRNA expression, miRNA expression and epigenomic data. We will then construct network models identifying shared pathways altered across many patients within a subtype. Finally, we will functionally test driver pathways nominated from the network modeling. By merging these diverse genomic and transcriptional data collected from tumors of individual patients, we will have an unprecedented ability to uncover the root causes of cancer, providing new therapeutic strategies. The collective expertise of our collaboration provides a unique environment for solving this critical barrier in cancer, by combining strengths in analyzing genomic data, modeling signaling pathways and transcriptional regulatory networks and clinical expertise in embryonal brain tumors. Together, we will generate and merge all types of transcriptional, genomic and epigenomic data, extract biologically-relevant network models and experimentally validate novel drug targets.
There is a pressing need for better therapies for childhood brain tumors, where nearly all survivors suffer from impaired neurological function. We will use a combination of the latest experimental and computation techniques to take a holistic view of the molecular changes in these tumors and to search for better approaches for treatment.
|Akhmedov, Murodzhon; Kedaigle, Amanda; Chong, Renan Escalante et al. (2017) PCSF: An R-package for network-based interpretation of high-throughput data. PLoS Comput Biol 13:e1005694|
|Archer, Tenley C; Mahoney, Elizabeth L; Pomeroy, Scott L (2017) Medulloblastoma: Molecular Classification-Based Personal Therapeutics. Neurotherapeutics 14:265-273|
|Northcott, Paul A; Buchhalter, Ivo; Morrissy, A Sorana et al. (2017) The whole-genome landscape of medulloblastoma subtypes. Nature 547:311-317|
|Khurana, Vikram; Peng, Jian; Chung, Chee Yeun et al. (2017) Genome-Scale Networks Link Neurodegenerative Disease Genes to ?-Synuclein through Specific Molecular Pathways. Cell Syst 4:157-170.e14|
|Ursu, Oana; Gosline, Sara J C; Beeharry, Neil et al. (2017) Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens. PLoS One 12:e0185650|
|Milani, Pamela; Escalante-Chong, Renan; Shelley, Brandon C et al. (2016) Cell freezing protocol suitable for ATAC-Seq on motor neurons derived from human induced pluripotent stem cells. Sci Rep 6:25474|
|Kim, Jong Wook; Botvinnik, Olga B; Abudayyeh, Omar et al. (2016) Characterizing genomic alterations in cancer by complementary functional associations. Nat Biotechnol 34:539-46|
|Gosline, Sara J C; Gurtan, Allan M; JnBaptiste, Courtney K et al. (2016) Elucidating MicroRNA Regulatory Networks Using Transcriptional, Post-transcriptional, and Histone Modification Measurements. Cell Rep 14:310-9|
|Wilson, Jennifer L; Dalin, Simona; Gosline, Sara et al. (2016) Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia. Integr Biol (Camb) 8:761-74|
|Pirhaji, Leila; Milani, Pamela; Leidl, Mathias et al. (2016) Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat Methods 13:770-6|
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