Inter-Platform comparability of microarrays in acute lymphoblastic leukemia

BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy and has been the poster-child for improved therapeutics in cancer, with life time disease-free survival (LTDFS) rates improving from <10% in 1970 to >80% today. There are numerous known genetic prognostic variables in ALL, which include T cell ALL, the hyperdiploid karyotype and the translocations: t(12;21)[TEL-AML1], t(4;11)[MLL-AF4], t(9;22)[BCR-ABL], and t(1;19)[E2A-PBX]. ALL has been studied at the molecular level through expression profiling resulting in un-validated expression correlates of these prognostic indices. To date, the great wealth of expression data, which has been generated in disparate institutions, representing an extremely large cohort of samples has not been combined to validate any of these analyses. The majority of this data has been generated on the Affymetrix platform, potentially making data integration and validation on independent sample sets a possibility. Unfortunately, because the array platform has been evolving over the past several years the arrays themselves have different probe sets, making direct comparisons difficult. To test the comparability between different array platforms, we have accumulated all Affymetrix ALL array data that is available in the public domain, as well as two sets of cDNA array data. In addition, we have supplemented this data pool by profiling additional diagnostic pediatric ALL samples in our lab. Lists of genes that are differentially expressed in the six major subclasses of ALL have previously been reported in the literature as possible predictors of the subclass. RESULTS: We validated the predictability of these gene lists on all of the independent datasets accumulated from various labs and generated on various array platforms, by blindly distinguishing the prognostic genetic variables of ALL. Cross-generation array validation was used successfully with high sensitivity and high specificity of gene predictors for prognostic variables. We have also been able to validate the gene predictors with high accuracy using an independent dataset generated on cDNA arrays. CONCLUSION: Interarray comparisons such as this one will further enhance the ability to integrate data from several generations of microarray experiments and will help to break down barriers to the assimilation of existing datasets into a comprehensive data pool

MINER: exploratory analysis of gene interaction networks by machine learning from expression data

<p>Abstract</p> <p>Background</p> <p>The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies.</p> <p>Results</p> <p>We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation.</p> <p>Conclusion</p> <p>Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.</p

Review of innovative immersive technologies for healthcare applications

Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications’ usability, innovation, and future work in the healthcare domain, and digital health data visual analytics

Alternative propulsor for mobile transportation and technological machines wood complex

Лесные машины, оборудованные альтернативным движителем, способны передвигаться по любым типам поверхностей (подготовленным дорогам, пахоте, болоту, песку, заснеженной местности и т.д.) с минимальным негативным воздействием.Forestry machines equipped alternative propulsors are capable to move on any types of land surfaces (the prepared roads, plowed land, bog, the sand, snow-covered land and etc.) with the minimal negative influence

A Wnt-BMP4 signaling axis induces MSX and NOTCH proteins and promotes growth suppression and differentiation in neuroblastoma

The Wnt and bone morphogenetic protein (BMP) signaling pathways are known to be crucial in the development of neural crest lineages, including the sympathetic nervous system. Surprisingly, their role in paediatric neuroblastoma, the prototypic tumor arising from this lineage, remains relatively uncharacterised. We previously demonstrated that Wnt/β-catenin signaling can have cell-type-specific effects on neuroblastoma phenotypes, including growth inhibition and differentiation, and that BMP4 mRNA and protein were induced by Wnt3a/Rspo2. In this study, we characterised the phenotypic effects of BMP4 on neuroblastoma cells, demonstrating convergent induction of MSX homeobox transcription factors by Wnt and BMP4 signaling and BMP4-induced growth suppression and differentiation. An immunohistochemical analysis of BMP4 expression in primary neuroblastomas confirms a striking absence of BMP4 in poorly differentiated tumors, in contrast to a high expression in ganglion cells. These results are consistent with a tumor suppressive role for BMP4 in neuroblastoma. RNA sequencing following BMP4 treatment revealed induction of Notch signaling, verified by increases of Notch3 and Hes1 proteins. Together, our data demonstrate, for the first time, Wnt-BMP-Notch signaling crosstalk associated with growth suppression of neuroblastoma

Evaluation of a functional epigenetic approach to identify promoter region methylation in phaeochromocytoma and neuroblastoma

The molecular genetics of inherited phaeochromocytoma have received considerable attention, but the somatic genetic and epigenetic events that characterise tumourigenesis in sporadic phaeochromocytomas are less well defined. Previously, we found considerable overlap between patterns of promoter region tumour suppressor gene (TSG) hypermethylation in two neural crest tumours, neuroblastoma and phaeochromocytoma. In order to identify candidate biomarkers and epigenetically inactivated TSGs in phaeochromocytoma and neuroblastoma, we characterised changes in gene expression in three neuroblastoma cell lines after treatment with the demethylating agent 5-azacytidine. Promoter region methylation status was then determined for 28 genes that demonstrated increased expression after demethylation. Three genes HSP47, homeobox A9 (HOXA9) and opioid binding protein (OPCML) were methylated in >10% of phaeochromocytomas (52, 17 and 12% respectively). Two of the genes, epithelial membrane protein 3 (EMP3) and HSP47, demonstrated significantly more frequent methylation in neuroblastoma than phaeochromocytoma. These findings extend epigenotype of phaeochromocytoma and identify candidate genes implicated in sporadic phaeochromocytoma tumourigenesis

Transcriptomic analyses of MYCN-regulated genes in anaplastic Wilms' tumour cell lines reveals oncogenic pathways and potential therapeutic vulnerabilities

The MYCN proto-oncogene is deregulated in many cancers, most notably in neuroblastoma, where MYCN gene amplification identifies a clinical subset with very poor prognosis. Gene expression and DNA analyses have also demonstrated overexpression of MYCN mRNA, as well as focal amplifications, copy number gains and presumptive change of function mutations of MYCN in Wilms’ tumours with poorer outcomes, including tumours with diffuse anaplasia. Surprisingly, however, the expression and functions of the MYCN protein in Wilms’ tumours still remain obscure. In this study, we assessed MYCN protein expression in primary Wilms’ tumours using immunohistochemistry of tissue microarrays. We found MYCN protein to be expressed in tumour blastemal cells, and absent in stromal and epithelial components. For functional studies, we used two anaplastic Wilms’ tumour cell-lines, WiT49 and 17.94, to study the biological and transcriptomic effects of MYCN depletion. We found that MYCN knockdown consistently led to growth suppression but not cell death. RNA sequencing identified 561 MYCN-regulated genes shared by WiT49 and 17.94 cell-lines. As expected, numerous cellular processes were downstream of MYCN. MYCN positively regulated the miRNA regulator and known Wilms’ tumour oncogene LIN28B, the genes encoding methylosome proteins PRMT1, PRMT5 and WDR77, and the mitochondrial translocase genes TOMM20 and TIMM50. MYCN repressed genes including the developmental signalling receptor ROBO1 and the stromal marker COL1A1. Importantly, we found that MYCN also repressed the presumptive Wilms’ tumour suppressor gene REST, with MYCN knockdown resulting in increased REST protein and concomitant repression of RE1-Silencing Transcription factor (REST) target genes. Together, our study identifies regulatory axes that interact with MYCN, providing novel pathways for potential targeted therapeutics for poor-prognosis Wilms’ tumour