Abstract PR14: Harnessing the power of big data to advance pediatric cancer care

Abstract

Abstract DNA and RNA sequencing is increasingly applied in clinical trials to find new therapeutic leads for children with incurable cancers. However, compared to similar studies in adults, these trials have yielded fewer new treatment options, because pediatric and adult malignancies are distinct biologically, and far less data are available on the genomics of pediatric tumors. Initiatives such as the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project, and the Medulloblastoma Advanced Genomics International Consortium (MAGIC) have generated large cohorts through collaboration, however they are limited to specific diseases. In addition, there is no mechanism to integrate these data with genomic data collected in prospective precision medicine trials. The failure to share data has meant that available genomic information is not being utilized to its full potential, resulting in missed therapeutic opportunities. The UC Santa Cruz Treehouse Childhood Cancer Project integrates genomic data generated by pediatric research studies, such as TARGET, MAGIC, the Pediatric Cancer Genome Project, and the Childhood Brain Tumor Tissue Consortium with genomic data generated by clinical trials. Treehouse also makes it possible to compare these data with large adult datasets, including The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and Stand Up to Cancer (SU2C). Together, these datasets provide access to the genomic information from over 15,000 individual tumors that can be used as context for real-time data interpretation from individual patients in clinical genomics trials. This work presents a case report that illustrates how integrating multiple pediatric and adult gene expression datasets with similar data collected from patients in a prospective clinical trial can provide new clinical leads for children with difficult-to-treat cancers. The data are integrated and analyzed using TumorMap, an unsupervised clustering and visualization approach that has been shown to reveal new clinical insights into adult cancers as part of the TCGA Pan-Cancer effort. The expression of individual genes and their relationship with phenotypic features in the combined cohort can be visualized using the UCSC Xena Bowser. New clinical leads can be recommended for individual patients based on the similarity of their molecular profiles to those of other cancers with available treatment options, as shown in the TumorMap. We propose to extend our case study to a framework of how genomic datasets collected from adult and pediatric patients in research and clinical settings can be used to inform the care of pediatric patients prospectively. This framework will provide new hope for children with difficult to treat cancers so that no therapeutic option is overlooked in the fight to save their lives. Citation Format: Olena Morozova, Yulia Newton, Melissa Cline, Stephen Yip, Arjun Rao, Josh Stuart, Ted Goldstein, Sofie Salama, Rebecca Deyell, S. Rod Rassekh, David Haussler. Harnessing the power of big data to advance pediatric cancer care. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Pediatric Cancer Research: From Mechanisms and Models to Treatment and Survivorship; 2015 Nov 9-12; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(5 Suppl):Abstract nr PR14.