Embryonal brain tumor networks
- Embryonal brain tumor networks
- Jill P. Mesirov
Scott L. Pomeroy
Our U01 is an innovative, systems biology approach designed to uncover new therapeutic strategies for childhood embryonal tumors. These 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% depending on subgroup, 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 alone. Rather, we use a systems biology approach based on a wide variety of omic methods to identify these oncogenic driver pathways.
We are creating comprehensive, genome-wide datasets from human medulloblastoma tumors and cell lines by measuring mutations, copy number variations, mRNA expression, proteomic (including enrichment for several types of post-translational modifications), metabolomic, and epigenomic data. We have developed innovative network models methods to unite these diverse data and thereby identify shared pathways altered across many patients within a subtype. Finally, we will test driver pathways nominated from the network modeling.
By merging these diverse data collected from tumors of individual patients, we will have an unprecedented ability to uncover the root causes of cancer, providing new therapeutic strategies. Concurrent with publication, we make all data and software tools developed during the course of this project available to the scientific community to further spark innovation and scientific advancements.
Jill P. Mesirov
Jill Mesirov is professor of medicine at UC San Diego and associate vice chancellor for computational health sciences. As associate vice chancellor, Mesirov is responsible for the overarching strategy for computational health sciences and research computing at UC San Diego School of Medicine. She is a member of the UCSD Moores Cancer Center, where she serves as co-lead for the cancer genomes and networks research program. Professor Mesirov is a computational biologist whose research focuses on analyzing molecular data to determine the underlying biological mechanisms of specific tumor subtypes, to stratify patients according to their relative risks of relapse, and to identify targets for new treatment regimens. In addition, Mesirov is committed to the development of practical, accessible software tools to bring these methods to the general biomedical research community. To this end, her lab has developed several popular analysis and visualization software packages, such as Gene Set Enrichment Analysis, the Molecular Signatures Database, GenePattern and the Integrative Genomics Viewer. This software is used by over 300,000 investigators worldwide.
Professor Fraenkel developed both experimental and computational expertise during early training in structural biology. As an independent research fellow at the Whitehead Institute, his group, together with Rick Young’s, published a map of the binding of over 200 regulators to the genome of Saccharomyces cerevisae. Since 2006, he has been a faculty member in the Department of Biological Engineering at MIT. The Fraenkel laboratory has published studies on transcriptional regulatory changes during evolution, in the normal developmental processes, and in diseases. Much of their current work involves diseases of the brain, including brain tumors and neurodegenerative diseases. They have developed integrative network models (http://fraenkel.mit.edu/software.html) that link diverse omic data in order to identify altered signaling pathways and propose therapeutic strategies, and have demonstrated the utility of these methods for genetic, transcriptional, epigenomic, proteomic, and metabolomic data.
Scott L. Pomeroy
Scott Pomeroy is a physician-scientist and currently is the Neurologist-in-Chief of Boston Children’s Hospital and the Director of the Intellectual and Developmental Disabilities Research Center of Boston Children’s Hospital and Harvard Medical School. The Pomeroy laboratory was among the first to employ genomic methods to molecularly characterize human cancers, and the first to demonstrate that embryonal brain tumors can be distinguished based on their molecular signatures. Using expression profiling, DNA copy number analysis and whole exome sequencing, they demonstrated that medulloblastomas consist of multiple clinically distinct subgroups that each have unique transcriptome and genetic basis. Outcome prediction models, based on these subgroups, are by far the most accurate predictors of medulloblastoma outcome currently available. These models have been incorporated into national multi-center therapeutic protocols conducted by the Children’s Oncology Group.