Beckers, Anneleen

Cannoodt, Robrecht

De Preter, Katleen

Saeys, Yvan

Van Cauwenbergh, Caroline

English

Ghent University Academic Bibliography

Differential Module Analysis in NeuroblastomaRegulatory NetworksRobrecht Cannoodt1, 2, 3, Caroline Van Cauwenbergh1, Anneleen Beckers1,Frank Speleman1, Katleen De Preter1, Yvan Saeys2, 31Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium2Inﬂammation Research Center, VIB, Ghent, Belgium3Department of Respiratory Medicine, Ghent University Hospital, Ghent, BelgiumBackground and aimNeuroblastoma (NB) is a tumour of the sympathetic nervous system, which arises fromthe undiﬀerentiated precursor cells of the neural crest. This tumour aﬄicts children at anearly age and is characterised by its clinical heterogeneity which varies from spontaneousregression to an aggressive phenotype and death. At least 40% of all children with NB areconfronted with an advanced stage of this disease [1]. The survival rates of a child withhigh risk NB is estimated at 50%, despite the intensive and multimodal treatments [2].The chromosomal characterisation of NB with arrayCGH has resulted in signiﬁcant con-tributions in constructing a classiﬁcation model. This model consists of three genomicsubtypes [3]: low risk subtype 1 and high risk subtypes 2A and 2B. The analysis gave adetailed insight into the recurring chromosomal abnormalities, as shown in Figure 1.Neuroblastoma subtype 1Neuroblastoma subtype 2ANeuroblastoma subtype 2BFigure 1: Overview of the chromosomal abnormalitiesfor neuroblastoma subtypes 1, 2A and 2B.0.000.250.500.751.000 500 1000 1500 2000 2500Time (days)Survival oddsSubtype1 (N=85)2A (N=55)2B (N=36)Figure 2: Survival plot of 176 neuroblastomapatients.The MYCN oncogene is highly ampliﬁed in NB subtype 2B tumors, in combination with ahigh MYCN overexpression. The identiﬁcation of MYCN as driver gene for NB subtype 2Btumors led to the identiﬁcation of some of the most abberated interactions [4]. Currently,there are no known drivers for NB subtype 2A.AimTo develop a method based on diﬀerential networks (DN) designed for unravelling thebiological processes which lead to tumour growth in speciﬁc NB subtypes.Diﬀerential networks aim to compare groups of patients – or more generally, organisms –by construction interaction networks for each of the groups. A subnetwork which is verycharacteristic and speciﬁc to one network in comparison to the other networks, might becausal for the formation of tumours of that subtype. Such condition-speciﬁc subnetworksmight contain interesting targets for the development of more personalised drug treatments.An example of a simple DN method is shown in Figure 3.G1 G3 G5G2 G4 G6NB 2A networkG1 G3 G5G2 G4 G6NB 2B networkG1 G3 G5G2 G4 G6Diﬀerential networkLegend— Common interaction— NB 2A speciﬁc— NB 2B speciﬁcPersonaliseddrug treatmentFigure 3: An example of a diﬀerential network method.MethodAn outline of the methodology is shown in Figure 4. We used expression data from theNeuroblastoma Research Consortium (NRC, a collaboration between several European NBresearch groups) in order to infer two regulatory networks, one for NB 2A patients and onefor NB 2B patients. We inferred the networks using a variant of the GENIE3 [5] algorithm.By grouping together strongly connected genes using MCL [6], we created a higher-orderstructure in the graph, which we call a module, each representing a biological mechanism.We gave each module in each of the networks a diﬀerentiality score. A score of 0 meansthis module is not diﬀerential at all; that there is a module in the other network which isexactly the same. A score of 1 means this module is very diﬀerential; there is no modulein the other network which is anything like the current module.Expression RegulatoryModulesdata networkNB2A→ →Find diﬀerential modules �NB2B→ →Figure 4: The concept of diﬀerential module analysis for NB subtypes 2A and 2B.ResultsFor both networks, we selected the top 3 most diﬀerential modules, and the top 3 leastdiﬀerential modules. We performed an Gene Ontology enrichment analysis on each of themodules, listed for each module the top 4 GO enrichments, and found that most moduleswere enriched for biological processes related to oncogenesis, neural development or theprocess of growth. This is not unexpected, as the samples were taken from neural tissueof children with high risk NB.There were two speciﬁc enrichments of interest. Firstly, the Wnt/β-catenin program haspreviously been reported to be deregulated in high-risk NB without MYCN ampliﬁca-tion [7], which matches the NB subtype 2A module in which this enrichment was found.Secondly, Trk receptors were also previously reported as a prognostic marker for NB. Ahigh expression of the TrkA neutrophin receptor is associated with a good prognosis, whilea high expression of the TrkB neutrophin receptor usually occurs in high risk NB [8].NB 2A speciﬁc (highly diﬀerential) module 1• reg. of morphogenesis of a branching structure (p=2.8e-5)• reg. of organ morphogenesis (p=6.4e-4)• bone mineralization (p=1.9e-3)• positive reg. of canonical Wnt signaling pathway (p=4.8e-3)NB 2A speciﬁc (highly diﬀerential) module 2• metal ion transport (p=1.8e-3)• cation transport (p=6.1e-3)• in utero embryonic development (p=7.5e-3)• negative reg. of intestinal cholesterol absorption (p=8.3e-3)NB 2A speciﬁc (highly diﬀerential) module 3• protein activation cascade (p=4.8e-10)• wound healing (p=1.4e-5)• coagulation (p=1.7e-5)• blood coagulation (p=1.7e-5)NB 2B speciﬁc (highly diﬀerential) module 1• cilium morphogenesis (p=5.2e-4)• mannose biosynthetic process (p=9.5e-3)• rRNA 5’-end processing (p=9.5e-3)• septin cytoskeleton organization (p=9.5e-3)NB 2B speciﬁc (highly diﬀerential) module 2• sperm motility (p=1.9e-3)• hexose biosynthetic process (p=3.4e-3)• monosaccharide biosynthetic process (p=4.2e-3)• hexitol catabolic process (p=5.5e-3)NB 2B speciﬁc (highly diﬀerential) module 3• ﬁbroblast growth factor receptor signaling pathway (p=1.4e-2)• transcription initiation from RNA polymerase III promoter (p=1.5e-2)• cellular response to ﬁbroblast growth factor stimulus (p=1.8e-2)• response to ﬁbroblast growth factor (p=1.8e-2)Common (highly similar) module 1• pos. reg. of transcr. from RNA polymerase II promoter (p=8.1e-10)• response to endogenous stimulus (p=7.8e-9)• reg. of apoptotic process (p=8.9e-9)• reg. of programmed cell death (p=1.0e-8)Common (highly similar) module 2• lung growth (p=9.1e-4)• reg. of neurotrophin TRK receptor signaling pathway (p=1.3e-3)• neg. reg. of peptidyl-threonine phosphorylation (p=2.2e-3)• neg. reg. of ﬁbroblast growth factor receptor sign. pathway (p=2.7e-3)Common (highly similar) module 3• reg. of immune system process (p=9.1e-47)• immune system process (p=1.1e-40)• leukocyte activation (p=4.5e-36)• lymphocyte activation (p=4.5e-36)Common (highly similar) module 4• response to cAMP (p=2.1e-6)• response to corticosterone (p=3.4e-6)• response to organophosphorus (p=6.4e-6)• muscle organ development (p=1.4e-4)Common (highly similar) module 5• single-organism transport (p=2.3e-3)• reg. of neurotransmitter secretion (p=9.8e-3)• trochlear nerve formation (p=1.6e-2)• trochlear nerve morphogenesis (p=1.6e-2)Common (highly similar) module 6• granzyme-mediated apoptotic signaling pathway (p=1.3e-2)• reg. of sequestering of zinc ion (p=1.3e-2)• synapse maturation (p=1.6e-2)• post-embryonic organ morphogenesis (p=1.9e-2)ConclusionWe performed a diﬀerential network analysis on NB expression data. Modules of interestwere analysed for Gene Ontology enrichment, after which NB experts immediately recog-nised biological processes related to oncogenesis. This analysis serves as a prototype forfurther diﬀerential network analyses for neuroblastoma research. Further research in the de-velopment of diﬀerential networks is needed, however. Currently, this approach was solelybased on expression data, but more of the available data types need to be integrated intothe network to get a better understanding of the underlying processes. Furthermore, eachstep in this analysis needs to be evaluated for robustness and performance, by adding slightperturbations to the dataset and observing the eﬀects of the perturbations downstream.References[1] Lars M Wagner and Mary K Danks. “New therapeutic targets for the treatment of high-risk neuroblastoma.” In: Journal of cellular biochemistry 107.1(May 2009), pp. 46–57.[2] John M Maris. “Recent advances in neuroblastoma.” In: The New England journal of medicine 362.23 (June 2010), pp. 2202–11.[3] Jo Vandesompele et al. “Unequivocal delineation of clinicogenetic subgroups and development of a new model for improved outcome prediction inneuroblastoma.” In: Journal of clinical oncology : oﬃcial journal of the American Society of Clinical Oncology 23.10 (Apr. 2005), pp. 2280–99.[4] Anneleen Beckers et al.“The MYCN/miR-26a-5p/LIN28B regulatory axis controls MYCN-driven LIN28B upregulation in neuroblastoma”. In: Advancesin Neuroblastoma Research 2014 (ANR 2014). Uniklinik Ko¨ln. 2014.[5] Vaˆn Anh Huynh-Thu et al. “Inferring Regulatory Networks from Expression Data Using Tree-Based Methods”. In: PLoS ONE 5.9 (Jan. 2010), e12776.[6] SM Van Dongen. “Graph clustering by ﬂow simulation”. PhD thesis. 2000.[7] X Liu et al. “Deregulated Wnt/beta-catenin program in high-risk neuroblastomas without MYCN ampliﬁcation.” In: Oncogene 27.10 (Feb. 2008),pp. 1478–88.[8] Alexander Schramm et al. “Biological eﬀects of TrkA and TrkB receptor signaling in neuroblastoma.” In: Cancer letters 228.1-2 (Oct. 2005), pp. 143–53.

Differential module analysis in neuroblastoma regulatory networks

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Differential module analysis in neuroblastoma regulatory networks

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