The nucleolus, the site for ribosome biogenesis contains hundreds of proteins and several types of RNA. The functions of many non-ribosomal nucleolar proteins are poorly understood, including Surfeit locus protein 6 (SURF6), an essential disordered protein with roles in ribosome biogenesis and cell proliferation. SURF6 co-localizes with Nucleophosmin (NPM1), a highly abundant protein that mediates the liquid-like features of the granular component region of the nucleolus through phase separation. Here, we show that electrostatically-driven interactions between disordered regions of NPM1 and SURF6 drive liquid-liquid phase separation. We demonstrate that co-existing heterotypic (NPM1-SURF6) and homotypic (NPM1-NPM1) scaffolding interactions within NPM1-SURF6 liquid-phase droplets dynamically and seamlessly interconvert in response to variations in molecular crowding and protein concentrations. We propose a mechanism wherein NPM1-dependent nucleolar scaffolds are modulated by non-ribosomal proteins through active rearrangements of interaction networks that can possibly contribute to the directionality of ribosomal biogenesis within the liquid-like nucleolus.

To discover driver fusions beyond canonical exon-to-exon chimeric transcripts, we develop CICERO, a local assembly-based algorithm that integrates RNA-seq read support with extensive annotation for candidate ranking. CICERO outperforms commonly used methods, achieving a 95% detection rate for 184 independently validated driver fusions including internal tandem duplications and other non-canonical events in 170 pediatric cancer transcriptomes. Re-analysis of TCGA glioblastoma RNA-seq unveils previously unreported kinase fusions (KLHL7-BRAF) and a 13% prevalence of EGFR C-terminal truncation. Accessible via standard or cloud-based implementation, CICERO enhances driver fusion detection for research and precision oncology. The CICERO source code is available at https://github.com/stjude/Cicero.

Pediatric therapy-related myeloid neoplasms (tMN) occur in children after exposure to cytotoxic therapy and have a dismal prognosis. The somatic and germline genomic alterations that drive these myeloid neoplasms in children and how they arise have yet to be comprehensively described. We use whole exome, whole genome, and/or RNA sequencing to characterize the genomic profile of 84 pediatric tMN cases (tMDS: n = 28, tAML: n = 56). Our data show that Ras/MAPK pathway mutations, alterations in RUNX1 or TP53, and KMT2A rearrangements are frequent somatic drivers, and we identify cases with aberrant MECOM expression secondary to enhancer hijacking. Unlike adults with tMN, we find no evidence of pre-existing minor tMN clones (including those with TP53 mutations), but rather the majority of cases are unrelated clones arising as a consequence of cytotoxic therapy. These studies also uncover rare cases of lineage switch disease rather than true secondary neoplasms.

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.

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