Genetic Alterations in Intramedullary Spinal Cord Tumors

Background

Spinal cord tumors are rare neoplasms that occur in children that can result in tremendous disability and eventually mortality. Given their rarity, our understanding of spinal cord tumors is extremely poor. As a result, clinicians are often forced to treat tumors arising in the spinal cord based on data generated from similar tumors arising in the brain. However, there are burgeoning data that suggests that while pediatric brain and spinal cord tumors look identical under a microscope, they are biologically and clinically very different. Our composite studies on the genetic basis of spinal cord astrocytomas and ependymomas demonstrates this quite clearly. Using large scale genetic
profiling, we have been able to identify key genes and pathways that appear to drive tumor formation in a number of pediatric brain and spinal cord tumors, including pleomorphic xanthoastrocytoma (PXA), pediatric glioblastoma (PGBM), spinal cord and intracranial ependymomas. Many of the findings from these studies, such as novel mutations in the gene ATRX in PGBM, has spurred investigation from many investigators around the world. Others, such as mutations in the mTOR pathway in PXA, have the potential for immediate clinical ramifications since targeted therapies already exist. We hope that by identifying the genetic underpinnings of these devastating tumors, more rationally designed diagnostic and therapeutic strategies can be formulated.

Project Goal

Our overarching goal at the completion of this grant is to have a comprehensive understanding of the genetic basis of the four of the most common spinal cord tumors: ependymomas, astrocytomas, sub-ependymomas and myxopapillary ependymomas. This understanding will form the foundation on which we can develop novel diagnostic and therapeutic strategies. In addition, since nearly all samples are from Johns Hopkins Hospital, we have de-identified clinical outcomes data linked to each sample. The genetic events will be linked with clinical outcomes to determine which alterations are prognostic and predictive. This will allow clinicians to better stratify and appropriately tailor therapeutic and monitoring strategies.