Predicting Neuroblastoma Using Developmental Signals and a Logic-Based Model
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ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE115166
PROVIDER: PRJNA473995 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7241934.fastq.gz | Fastqsanger.gz | |||
| SRR7241935.fastq.gz | Fastqsanger.gz | |||
| SRR7241936.fastq.gz | Fastqsanger.gz |
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Similar Datasets
Project description:Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic indicators of neuroblastoma, and find differences in the number and probability distribution of steady state outcomes. We validate the mechanistic model assumptions using RNAseq of the SHSY5Y human neuroblastoma cell line to define the input states and confirm the predicted outcome with antibody staining. Lastly, we apply input gene signatures from 77 published human patient samples and show that our model makes more accurate disease outcome predictions for early stage disease than any current neuroblastoma gene list. These findings highlight the predictive strength of a logic-based model based on developmental genes and offer a better understanding of the molecular network interactions during neuroblastoma disease progression.
2018-05-31 | GSE115166 | GEO
Project description:Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic indicators of neuroblastoma, and find differences in the number and probability distribution of steady state outcomes. We validate the mechanistic model assumptions using RNAseq of the SHSY5Y human neuroblastoma cell line to define the input states and confirm the predicted outcome with antibody staining. Lastly, we apply input gene signatures from 77 published human patient samples and show that our model makes more accurate disease outcome predictions for early stage disease than any current neuroblastoma gene list. These findings highlight the predictive strength of a logic-based model based on developmental genes and offer a better understanding of the molecular network interactions during neuroblastoma disease progression.
| S-EPMC6016551 | biostudies-literature
Project description:Developmental programs in neuroblastoma
| PRJNA686093 | ENA
Project description:Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning-the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system. [Data is also available from http://bugs.sgul.ac.uk/E-BUGS-132]
2012-11-04 | E-BUGS-132 | biostudies-arrayexpress
Project description:Neuroblastoma is a pediatric tumor of the developing sympathetic nervous system. We investigate developmental origins of neuroblastoma, the role of oncogenic MYCN in blocking normal differentiation and evaluate therapeutic interventions to overcome differentiation blocks. Here, we provide single cell expression profiles of IMR5/75-shMYCN cells upon knockdown of MYCN and control.
2020-12-18 | GSE163429 | GEO
Project description:Pulsed SILAC approaches allow measurement of protein dynamics, including protein translation and degradation. However, its use in quantifying acute changes has been limited due the low labeled peptide stoichiometry. Here, we describe the use of instrument logic to select peaks of interest via targeted mass differences (TMD) for overcoming this limitation. Comparing peptides artificially mixed at low heavy-to-light stoichiometry measured using standard data dependent acquisition with or without TMD revealed 2-3 fold increases in identification without significant loss in quantification precision for both MS2 and MS3 methods. Our benchmarked method approach increases throughput by reducing the necessary machine time. We anticipate that all pulsed SILAC measurements, if combined with TMT or not, would greatly benefit from instrument logic based approaches.
2020-05-29 | PXD018445 | Pride
Project description:Epigenetic subtypes of neuroblastoma [neuroblastoma]
| PRJNA559905 | ENA
Project description:Few families of signaling factors have been implicated in the control of development. Here we identify the neuropeptides nociceptin and somatostatin, a neurotransmitter and neuroendocrine hormone, as a class of developmental signals in chick and zebrafish. We show that signals from the anterior mesendoderm are required for the formation of anterior placode progenitors with one of the signals being somatostatin. Somatostatin controls ectodermal expression of nociceptin and both peptides regulate Pax6 in lens and olfactory progenitors. Consequently, loss of somatostatin and nociceptin signaling leads to severe reduction of lens formation. Our findings not only uncover these neuropeptides as developmental signals, but also identify a long-sought-after mechanism that initiates Pax6 in placode progenitors and may explain the ancient evolutionary origin of neuropeptides, pre-dating a complex nervous system.
2014-04-25 | GSE48116 | GEO
Project description:Core regularity transcription factors (CR TFs) define cell identity and lineage through an exquisitely precise and logical order during embryogenesis and development. These CR TFs regulated one another in three-dimensional space via distal enhancers that serve as logic gates embedded in their TF recognition sequences. Aberrant chromatin organization resulting in miswired circuitry of enhancer logic is a newly recognized feature in many cancers. Here, we report that PAX3-FOXO1 expression is driven by a translocated FOXO1 distal super enhancer (SE). ChIP-seq in tumors bearing rare PAX translocations implicate enhancer miswiring is a pervasive feature across all FP-RMS tumors. Therefore, our data reveal a mechanism of a translocated hijacked enhancer which disrupts the normal CR TF logic during skeletal muscle development (PAX3 to MYOD to MYOG), replacing it with an infinite loop logic that makes rhabdomyosarcoma cells unable to exit the undifferentiated proliferating stage.
2019-12-25 | GSE136799 | GEO
Project description:This study was undertaken to assess heterogeneity within neuroblastoma cell populations, and to assess whether the integrin CD49b can distinguish different neuroblastoma phenotypes. Methods: We performed bulk RNA sequencing on Neuro-2a and SH-SY5Y neuroblastoma cells sorted based on CD49b expression, and CUT&RUN for H3K4me1 and H3K27ac on Neuro-2a cells sorted based on CD49b expression. Results: We find that CD49b expression identifies transcriptionally distinct neuroblastoma cell populations. High CD49b expression marks cells expressing mesenchymal genes, while lack of CD49 denotes cells expression neuronal genes. CUT&RUN analysis shows that CD49b distinguishes cells with distinct enhancer and super enhancer profiles. Conclusion: RNA sequencing and CUT&RUN demonstrate that neuroblastoma cell lines include heterogeneous populations.
2023-12-05 | GSE207516 | GEO