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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

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

Detection of gene fusions is important for discovery of cancer drivers and clinical oncology testing, but existing software tools for fusion detection usually take hours to run and may fail to find lowly expressed fusions. To overcome these limitations, we developed the Fuzzion2 program, which uses pattern matching to detect known gene fusions in unmapped paired-read RNA-Seq data. Given a set of patterns representing fusion transcript breakpoints, Fuzzion2 finds every read pair matching any of the patterns. Both exact and inexact (fuzzy) matches are detected; the fuzzy matching tolerates variations caused by sequencing errors, SNVs, and indels. By employing a novel index of frequency minimizers, Fuzzion2 needs only minutes to process a sample. We have also developed pipelines to produce patterns for Fuzzion2, from fusion contig sequences, from genomic breakpoints in DNA and RNA, and from fusion protein sequences. To evaluate its applicability in clinical testing, we ran Fuzzion2 on ~2,000 RNA-seq samples profiled by the St. Jude clinical genomics program and confirmed its sensitivity in identifying lowly expressed fusions, such as KIAA1549-BRAF in low-grade glioma, which are frequently missed by commonly used fusion detection programs. Notably, Fuzzion2 detected a subclonal BCR-ABL1 fusion expressed at 1% and 6% of the wild-type BCR and ABL1 transcription level, respectively, in a B-lineage ALL sample that also has an IGH-CRLF fusion. Processing RNA-seq data from BCR-ABL1 cell lines, K562 with p210 fusion and OP1 with p190 fusion, diluted at 1:10, 1:100, and 1:1000 showed that Fuzzion2 can detect the fusion at 1:10-1:100 dilution, achieving a sensitivity 10 times greater than that of other fusion detection programs. We also evaluated the performance of Fuzzion2 for large-scale data mining in a study to compare the prevalence of gene fusions in pediatric versus adult cancers. We assembled a set of 15,474 patterns representing 5,480 fusions identified in the Pediatric Cancer Genome Project, NCI TARGET, clinical sequencing, and the COSMIC database. Fuzzion2 was deployed to the NCI Cancer Genomics Cloud and analyzed 9,464 TCGA RNA-seq samples from adult solid and brain tumors. Processing took an average of 6 minutes at a cost of only US$0.16 per sample. Among the 105 recurrent fusions identified in pediatric cancers, only 11 were also found in adult cancers. These shared fusions can be classified into two categories: 1) gene fusions present in cancers that occur in both children and young adults, e.g., synovial sarcoma, papillary thyroid cancer, and fibrolamellar hepatocellular carcinoma; and 2) kinase fusions involving ABL1, NTRK, and FGFR. Our experience with Fuzzion2 demonstrates that it is a powerful tool for time-critical clinical application and large-scale data mining. It is publicly available at https://github.com/stjude/fuzzion2. Citation Format: Stephen V. Rice, Michael N. Edmonson, Liqing Tian, Michael Rusch, David A. Wheeler, Jennifer L. Neary, Scott Newman, Lu Wang, Patrick R. Blackburn, Michael Macias, Andrew Thrasher, Jian Wang, Mark R. Wilkinson, Xin Zhou, Jinghui Zhang. Fuzzion2: Fast, sensitive detection of known gene fusions by fuzzy pattern matching for clinical testing and large-scale data mining [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4092.

Effective data sharing is key to accelerating research that will improve the precision of diagnoses, efficacy of treatments and long-term survival of pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data sharing ecosystem developed via collaboration between St. Jude Children’s Research Hospital, DNAnexus, and Microsoft, for accessing, analyzing and visualizing genomic data from >10,000 pediatric cancer patients, long-term survivors of pediatric cancer and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabyes on St. Jude Cloud include 12,104 whole genomes, 7,697 whole exomes and 2,202 transcriptomes, which are freely available to researchers worldwide. The resource is expanding rapidly with regular data uploads from St. Jude’s prospective clinical genomics programs, providing public access as soon as possible rather than holding data back until publication. Three interconnected apps within the St. Jude Cloud ecosystem—Genomics Platform, Pediatric Cancer Knowledgebase (PeCan) and Visualization Community—provide a unique experience for simultaneously performing advanced data analysis in the cloud and enhancing the pediatric cancer knowledgebase. We demonstrate the value of the St. Jude Cloud ecosystem through use cases that classify 48 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 subtypes of pediatric cancer.

SummaryXenografts are important models for cancer research and the presence of mouse reads in xenograft next generation sequencing data can potentially confound interpretation of experimental results. We present an efficient, cloud-based BAM-to-BAM cleaning tool called XenoCP to remove mouse reads from xenograft BAM files. We show application of XenoCP in obtaining accurate gene expression quantification in RNA-seq and tumor heterogeneity in WGS of xenografts derived from brain and solid tumors.Availability and ImplementationSt. Jude Cloud (https://pecan.stjude.cloud/permalink/xenocp) and St. Jude Github (https://github.com/stjude/XenoCP)

We developed cis-X, a computational method for discovering regulatory noncoding variants in cancer by integrating whole-genome and transcriptome sequencing data from a single cancer sample. cis-X first finds aberrantly cis-activated genes that exhibit allele-specific expression accompanied by an elevated outlier expression. It then searches for causal noncoding variants that may introduce aberrant transcription factor binding motifs or enhancer hijacking by structural variations. Analysis of 13 T-lineage acute lymphoblastic leukemias identified a recurrent intronic variant predicted to cis-activate the TAL1 oncogene, a finding validated in vivo by chromatin immunoprecipitation sequencing of a patient-derived xenograft. Candidate oncogenes include the prolactin receptor PRLR activated by a focal deletion that removes a CTCF-insulated neighborhood boundary. cis-X may be applied to pediatric and adult solid tumors that are aneuploid and heterogeneous. In contrast to existing approaches, which require large sample cohorts, cis-X enables the discovery of regulatory noncoding variants in individual cancer genomes.

Next-generation sequencing-based genomic profiling is now a mainstay of pediatric oncology research and clinical testing. Correlating genomic features of patient cancer genomes with curated data extracted from large reference cohorts is critical for identifying molecular subtypes and underlying mutagenesis processes. To facilitate such investigation, we developed two user-friendly workflows on St. Jude Cloud, a data sharing ecosystem hosting genomic data for >10,000 pediatric cancer patients and survivors. These workflows leverage St. Jude Cloud comprehensive pediatric cancer genomic data, including 1,616 RNA-seq of 135 cancer subtypes and 958 whole genome sequencing (WGS) of 35 subtypes, to enable user analysis of their data in the context of St. Jude Cloud cohorts without a need to download large datasets. The RNA-Seq Expression Classification workflow enables a user to compare their patient RNA-Seq gene expression data with blood (832), brain (456), and solid tumor (319) pediatric cancer reference cohorts and PDX models (45), enabling subtype classification using t-Distributed Stochastic Neighbor Embedding (t-SNE). Reference cohorts include curated subtype-defining somatic alterations integrating genomic variant data with expression profile. Resulting interactive t-SNE plots can be explored and annotated - with options to highlight cancer subtypes or samples and display sample information (age of onset, clinical diagnosis, molecular driver). To demonstrate, we analyze PAWNXH, a Children's Oncology Group AML sample with a novel ZBTB7A-NUTM1 fusion and find it clusters with AML samples harboring KMT2A re-arrangements suggesting a potential mechanism of pathogenesis. Integrating PDX samples enables model selection for functional experiments by connecting patient subtypes with mouse models. The Mutational Signatures workflow identifies and quantifies COSMIC mutational signatures in user-uploaded somatic VCF files for comparison to reference pediatric cancer cohorts. The interactive interface enables rapid identification of signatures within the query cohort and facilitates comparison to the reference using a cohort-level summary view. Identified signatures may also be explored at the sample-level for both query and reference cohorts, enabling the user to identify samples with signatures of interest for further analysis. We show an example comparison of mutational signatures identified in pediatric and adult AML samples. These workflows enable users to leverage curated pediatric cancer data to make discoveries in their own samples. Enabling point-and-click analysis in St. Jude Cloud removes the barrier for non-computational researchers and eliminates the need to download large reference datasets for local analysis. Both workflows utilize post-processed rather than raw genomic data, reducing transfer costs for uploading user data to the cloud. Citation Format: Andrew Thrasher, Michael Macias, Alexander M. Gout, Delaram Rahbarinia, Xin Zhou, Samuel W. Brady, Clay McLeod, Michael C. Rusch, Xiaolong Chen, Soheil Meshinchi, Michael A. Dyer, Suzanne J. Baker, Martine F. Roussel, Jinghui Zhang. Empowering point-and-click genomic analysis with large pediatric genomic reference data on St. Jude Cloud [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2289.

Whole-genome sequencing (WGS) is invaluable for investigating genetic abnormalities contributing to the initiation, progression and long-term clinical outcome of pediatric cancer. St. Jude Cloud (https://www.stjude.cloud/) hosts 10,000 (10K) harmonized WGS samples generated from: 1) St. Jude/Washington University Pediatric Cancer Genome Project, 2) the Genomes for Kids Clinical Trial, 3) the St. Jude Lifetime Cohort Study, and 4) the Childhood Cancer Survivor Study. To enable on-the-cloud discovery and eliminate the need for data download, we developed GenomePaint, an interactive genomics browser, to explore the somatic and germline variants of the 10K genomes with rich annotation. Germline variants in cancer predisposition genes were annotated for pathogenicity. Using GenomePaint, users can compare pathogenic variants from a locus of interest across multiple cancers or test for association of a germline variant with a specific cancer type on the fly. By matching germline variants to somatic mutation hotspots from www.cancerhotspots.org, we annotated potential germline mosaic mutations including IDH1 R132H, FBXW7 R465C, and KRAS A146T. For noncoding variants, we investigated overlap with ATAC and DNase peaks in 50 cancer cell lines along with transcription factor motif change predictions. These features will enable exploration of the functional impact of genetic variations with potential clinical status such as genetic risk for a specific cancer type, genetic association with age of onset, or development of subsequent malignancies for pediatric cancer survivors. GenomePaint also provides an integrated view of somatic SNV/indel, copy number variation, loss-of-heterozygosity, structural variation, and gene fusion. These are shown together with tumor gene expression at the single tumor level. GenomePaint also presents allele-specific expression (ASE) and outlier expression as an indicator for assessing dysfunction of regulatory regions caused by genomic variants. Cloud-based on-the-fly ASE analysis is also available for user’s samples with paired DNA and RNA sequencing results. Such gene expression integration will drive novel insights about the functional aspects of somatic coding and noncoding mutations in pediatric cancer. The innovative visualization of whole-genome sequencing data generated from 10K pediatric cancer patients on the St. Jude Cloud enables genomic discovery by scientists and clinicians through exploration of this unprecedented resource. Citation Format: Xin Zhou, Clay Mcleod, Scott Newman, Zhaoming Wang, Michael Rusch, Kirby Birch, Michael Macias, Jobin Sunny, Gang Wu, Jian Wang, Edgar Sioson, Shaohua Lei, Robert J. Michael, Aman Patel, Michael N. Edmonson, Stephen V. Rice, Andrew Frantz, Ed Suh, Keith Perry, Carmen Wilson, Leslie L. Robinson, Yutaka Yasui, Kim E. Nichols, Gregory T. Armstrong, James R. Downing, Jinghui Zhang. Visualize 10,000 whole-genomes from pediatric cancer patients on St. Jude Cloud [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3671.

10019 Background: As tumor and germline genomic data from pediatric cancer patients is scarce in existing genomic databases, there is an urgent need for more comprehensive datasets. Such data will allow us to fully assess the actionable pediatric cancer genome, facilitate biomarker discovery, and identify new clinical associations. Methods: We sequenced 1002 tumor/normal pairs as part of a real-time clinical genomics service including whole genome, exome and transcriptome for 775 and exome/transcriptome for 227 samples. Tumor types were representative of the common and rare diseases treated at our institution (37% hematological, 31% brain and 32% solid tumors). A multidisciplinary team assessed every case, and after clinical reporting was complete, genomics data and basic clinical information (primary diagnosis, age, sex, ethnicity, primary/relapse/metastasis status), was made securely available online through St. Jude Cloud (www.stjude.cloud). Results: Based on analysis of 253 initial cases from the Genomes for Kids study, our multi-platform sequencing approach uncovered diagnostic, prognostic and/or therapeutically relevant findings in 78% of patients. We estimate 11-16% of clinically-relevant gene mutations could be missed by less comprehensive sequencing approaches. One quarter of patients had a potentially druggable mutation. This surprisingly high proportion was driven, in part, by BRAF fused low-grade gliomas and diverse JAK/STAT pathway alterations in B-Cell acute lymphoblastic leukemias. Whole genome/transcriptome sequencing allowed us to detect rare and novel gene fusions in 8% of cases and facilitated discovery of a new recurrent fusion gene in pediatric melanoma. All data is available online for others to mine and it is likely that additional clinically-relevant mutations can be uncovered. Conclusions: These data demonstrate the value of incorporating comprehensive sequencing into clinical diagnostics and patient care. We endeavor to make this large and richly annotated dataset available to others in real time rather than holding it back for months or years until publication. We anticipate adding approximately 500 additional cases per year at regular intervals, and as the resource grows, expect users to identify new targetable alterations that may be incorporated into patient care.