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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA184898-06
Application #
9695175
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Li, Jerry
Project Start
2014-07-01
Project End
2021-05-31
Budget Start
2019-06-01
Budget End
2021-05-31
Support Year
6
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02142
Wilson, Jennifer L; Kefaloyianni, Eirini; Stopfer, Lauren et al. (2018) Functional Genomics Approach Identifies Novel Signaling Regulators of TGF? Ectodomain Shedding. Mol Cancer Res 16:147-161
Kedaigle, Amanda J; Fraenkel, Ernest (2018) Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data. Methods Mol Biol 1711:13-26
Archer, Tenley C; Sengupta, Soma; Pomeroy, Scott L (2018) Brain cancer genomics and epigenomics. Handb Clin Neurol 148:785-797
Köksal, Ali Sinan; Beck, Kirsten; Cronin, Dylan R et al. (2018) Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data. Cell Rep 24:3607-3618
Waszak, Sebastian M; Northcott, Paul A; Buchhalter, Ivo et al. (2018) Spectrum and prevalence of genetic predisposition in medulloblastoma: a retrospective genetic study and prospective validation in a clinical trial cohort. Lancet Oncol 19:785-798
Archer, Tenley C; Ehrenberger, Tobias; Mundt, Filip et al. (2018) Proteomics, Post-translational Modifications, and Integrative Analyses Reveal Molecular Heterogeneity within Medulloblastoma Subgroups. Cancer Cell 34:396-410.e8
Chung, Chee Yeun; Khurana, Vikram; Yi, Song et al. (2017) In Situ Peroxidase Labeling and Mass-Spectrometry Connects Alpha-Synuclein Directly to Endocytic Trafficking and mRNA Metabolism in Neurons. Cell Syst 4:242-250.e4
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
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
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

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