St. Jude Cloud: A Pediatric Cancer Genomic Data-Sharing Ecosystem

Abstract

Effective data sharing is key to accelerating research to improve diagnostic precision, treatment efficacy, and long-term survival in pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data-sharing ecosystem for accessing, analyzing, and visualizing genomic data from >10,000 pediatric patients with cancer and long-term survivors, and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabytes are freely available, including 12,104 whole genomes, 7,697 whole exomes, and 2,202 transcriptomes. The resource is expanding rapidly, with regular data uploads from St. Jude's prospective clinical genomics programs. Three interconnected apps within the ecosystem—Genomics Platform, Pediatric Cancer Knowledgebase, and Visualization Community—enable simultaneously performing advanced data analysis in the cloud and enhancing the Pediatric Cancer knowledgebase. We demonstrate the value of the ecosystem through use cases that classify 135 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 pediatric cancer subtypes. Significance: To advance research and treatment of pediatric cancer, we developed St. Jude Cloud, a data-sharing ecosystem for accessing >1.2 petabytes of raw genomic data from >10,000 pediatric patients and survivors, innovative analysis workflows, integrative multiomics visualizations, and a knowledgebase of published data contributed by the global pediatric cancer community. This article is highlighted in the In This Issue feature, p. 995

Evidence of pioneer factor activity of an oncogenic fusion transcription factor

“…This result was robust across conditions of P3F knockdown or add-back, as well as when comparing FP-RMS versus FN-RMS cell lines ( Figure S3 F). On subsequent analyses of RNA sequencing (RNA-seq) profiles from a cohort of PAX3-FOXO1 + FP-RMS versus FN-RMS (embryonal, ERMS, subtype) patients ( Downing et al., 2012 ; McLeod et al., 2021 ), we once again found that relatively few genes associated with PAX3-FOXO1 binding were differentially expressed in patients based on PAX3-FOXO1 fusion status ( Figure S3 G). Across P3F-binding site categories, less than 30% of proximal genes were differentially expressed in patient samples (average 23.7%, fold change >2, adjusted p value < 0.05), and these genes showed similar likelihood of being up- or down-regulated in P3F+ patients.…”

“…PAX3-FOXO1 signals and peaks are directly comparable across cell lines and conditions, as they have been collectively normalized for this study. Clinical RNA-seq data for pediatric rhabdomyosarcoma patients used for analysis in this study were obtained from the St. Jude Cloud ( McLeod et al., 2021 ). This paper does not report original code.…”

“…Tumor tissue expression data (in bam format) from pediatric patients diagnosed with Alveolar Rhabdomyosarcoma (ARMS) and Embryonal Rhabdomyosarcoma (ERMS) were obtained from the St. Jude Cloud Genomics Platform ( Downing et al., 2012 ; McLeod et al., 2021 ). Reads were converted to gene level expression using featureCount (Rsubread package, version 2.4.3) with the Rsubread package built-in annotation (NCBI RefSeq annotation for hg38, build 38.2).…”

Pioneer activity of an oncogenic fusion transcription factor at inaccessible chromatin

“…This result was robust across conditions of P3F knockdown or add-back, as well as when comparing FP-RMS versus FN-RMS cell lines (Figure S3F). Subsequent analyses of RNA-seq profiles from a cohort of PAX3-FOXO1+ FP-RMS vs. FN-RMS (embryonal, ERMS, subtype) patients (Downing et al, 2012;McLeod et al, 2021), we once again found that relatively few genes associated with PAX3-FOXO1 binding were differentially expressed in patients based on PAX3-FOXO1 fusion status (Figure S3G). Across P3F binding site categories, less than 30% of proximal genes were differentially expressed in patient samples (average 23.7%, fold change > 2, adjusted p-value < 0.05), and these genes showed similar likelihood of being up-or down-regulated in P3F+ patients.…”

“…PAX3-FOXO1 signals and peaks are directly comparable across cell lines and conditions, as they have been collectively normalized for this study. Clinical RNA-seq data for pediatric rhabdomyosarcoma patients used for analysis in this study were obtained from the St. Jude Cloud (McLeod et al, 2021). Only publicly available algorithms were used in the analysis of datasets presented in this study.…”