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

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.

Shifts in Leukocyte Counts Drive the Differential Expression of Transcriptional Stroke Biomarkers in Whole Blood.

Our group recently identified a panel of ten genes whose RNA expression levels in whole blood have utility for detection of stroke. The purpose of this study was to determine the mechanisms by which these genes become differentially expressed during stroke pathology. First, we assessed the transcriptional distribution of the ten genes across the peripheral immune system by measuring their expression levels on isolated neutrophils, monocytes, B-lymphocytes, CD-4+ T-lymphocytes, CD-8+ T-lymphocytes, and NK-cells generated from the blood of healthy donors (n = 3). Then, we examined the relationship between the whole-blood expression levels of the ten genes and white blood cell counts in a cohort of acute ischemic stroke patients (n = 36) and acute stroke mimics (n = 15) recruited at emergency department admission. All ten genes displayed strong patterns of lineage-specific expression in our analysis of isolated leukocytes, and their whole-blood expression levels were correlated with white blood cell differential across the total patient population, suggesting that many of them are likely differentially expressed in whole blood during stroke as an artifact of stroke-induced shifts in leukocyte counts. Specifically, factor analysis inferred that over 50% of the collective variance in their whole-blood expression levels across the patient population was driven by underlying variance in white blood cell counts alone. However, the cumulative expression levels of the ten genes displayed a superior ability to discriminate between stroke patients and stroke mimics relative to white blood cell differential, suggesting that additional less prominent factors influence their expression levels which add to their diagnostic utility. These findings not only provide insight regarding this particular panel of ten genes, but also into the results of prior stroke transcriptomics studies performed in whole blood.

Leukocyte Dynamics Influence Reference Gene Stability in Whole Blood: Data-Driven qRT-PCR Normalization Is a Robust Alternative for Measurement of Transcriptional Biomarkers.

BACKGROUND: The use of reference genes for normalization of whole blood qRT-PCR data may be problematic in conditions such as stroke which induce alterations in white blood cell differential. In this study, we assessed the influence of stroke on the stability of commonly employed reference genes, and we evaluated data-driven normalization as an alternative. METHODS: Peripheral whole blood was sampled from 33 stroke patients and 29 controls, and qRT-PCR was used to measure the expression levels of 10 target genes whose transcripts are known stroke biomarkers. Target gene expression levels were normalized via those of 2 frequently cited reference genes (ACTB and B2M) as well as with the NORMA-Gene data-driven normalization algorithm. RESULTS: Whole blood expression levels of reference genes were significantly altered in stroke patients relative to controls. In comparison to normalization via reference genes, NORMA-Gene produced more robust target gene expression data in terms of differential expression dynamics, variance properties, and diagnostic performance. CONCLUSIONS: Our findings suggest that whole blood expression levels of commonly used reference genes may be sensitive to changes in white blood cell differential, and that data-driven qRT-PCR normalization approaches offer a powerful alternative.

Peripheral blood AKAP7 expression as an early marker for lymphocyte-mediated post-stroke blood brain barrier disruption.

Our group recently identified 16 genes whose peripheral blood expression levels are differentially regulated in acute ischemic stroke. The purpose of this study was to determine whether the early expression levels of any of these 16 genes are predictive for post-stroke blood brain barrier (BBB) disruption. Transcriptional expression levels of candidate genes were measured in peripheral blood sampled from ischemic stroke patients at emergency department admission, and BBB permeability was assessed at 24 hour follow up via perfusion-weighted imaging. Early heightened expression levels of AKAP7, a gene encoding a protein kinase A-binding scaffolding molecule, were significantly associated with BBB disruption 24 hours post-hospital admission. We then determined that AKAP7 is predominantly expressed by lymphocytes in peripheral blood, and strongly co-expressed with ITGA3, a gene encoding the adhesion molecule integrin alpha 3. Subsequent in vitro experiments revealed that heightened expression of AKAP7 and ITGA3 in primary human lymphocytes is associated with a highly adherent phenotype. Collectively, our results suggest that AKAP7 expression levels may have clinical utility as a prognostic biomarker for post-stroke BBB complications, and are likely elevated early in patients who later develop post-stroke BBB disruption due to the presence of an invasive lymphocyte population in the peripheral blood.

Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke.

Early and accurate diagnosis of stroke improves the probability of positive outcome. The objective of this study was to identify a pattern of gene expression in peripheral blood that could potentially be optimised to expedite the diagnosis of acute ischaemic stroke (AIS). A discovery cohort was recruited consisting of 39 AIS patients and 24 neurologically asymptomatic controls. Peripheral blood was sampled at emergency department admission, and genome-wide expression profiling was performed via microarray. A machine-learning technique known as genetic algorithm k-nearest neighbours (GA/kNN) was then used to identify a pattern of gene expression that could optimally discriminate between groups. This pattern of expression was then assessed via qRT-PCR in an independent validation cohort, where it was evaluated for its ability to discriminate between an additional 39 AIS patients and 30 neurologically asymptomatic controls, as well as 20 acute stroke mimics. GA/kNN identified 10 genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B and PLXDC2) whose coordinate pattern of expression was able to identify 98.4% of discovery cohort subjects correctly (97.4% sensitive, 100% specific). In the validation cohort, the expression levels of the same 10 genes were able to identify 95.6% of subjects correctly when comparing AIS patients to asymptomatic controls (92.3% sensitive, 100% specific), and 94.9% of subjects correctly when comparing AIS patients with stroke mimics (97.4% sensitive, 90.0% specific). The transcriptional pattern identified in this study shows strong diagnostic potential, and warrants further evaluation to determine its true clinical efficacy.