Ensemble inference by integrative cancer networks

Mora, Antonio M.; Taranta, Monia; Zaki, Nazar; Badidi, Elarbi; Cinti, Caterina; Capobianco, Enrico

doi:10.3389/fgene.2014.00059

Cited by 9 publications

(5 citation statements)

References 13 publications

(12 reference statements)

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“…At a methodological level, integrative cancer inference from networks is becoming increasingly popular [ 39 - 46 ], and advances in computational and visualization tools [ 45 ] make it feasible (no need of a model) and effective (ability to establish relationships). In our case, the data diversity in terms of experimental sources and conditions are not preventing both maps and charts to be displayed, thus driving our inference approach.…”

Section: Discussionmentioning

confidence: 99%

Time-course gene profiling and networks in demethylated retinoblastoma cell line

et al. 2015

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Retinoblastoma, a very aggressive cancer of the developing retina, initiatiates by the biallelic loss of RB1 gene, and progresses very quickly following RB1 inactivation. While its genome is stable, multiple pathways are deregulated, also epigenetically. After reviewing the main findings in relation with recently validated markers, we propose an integrative bioinformatics approach to include in the previous group new markers obtained from the analysis of a single cell line subject to epigenetic treatment. In particular, differentially expressed genes are identified from time course microarray experiments on the WERI-RB1 cell line treated with 5-Aza-2′-deoxycytidine (decitabine; DAC). By inducing demethylation of CpG island in promoter genes that are involved in biological processes, for instance apoptosis, we performed the following main integrative analysis steps: i) Gene expression profiling at 48h, 72h and 96h after DAC treatment; ii) Time differential gene co-expression networks and iii) Context-driven marker association (transcriptional factor regulated protein networks, master regulatory paths). The observed DAC-driven temporal profiles and regulatory connectivity patterns are obtained by the application of computational tools, with support from curated literature. It is worth emphasizing the capacity of networks to reconcile multi-type evidences, thus generating testable hypotheses made available by systems scale predictive inference power. Despite our small experimental setting, we propose through such integrations valuable impacts of epigenetic treatment in terms of gene expression measurements, and then validate evidenced apoptotic effects.

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Section: Discussionmentioning

confidence: 99%

Time-course gene profiling and networks in demethylated retinoblastoma cell line

et al. 2015

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“…We briefly summarize the data generation aspects relevant to the novel developments, and refer the readers for further details on HOS and RB treatments to our previous work (10,11). Here, we embrace networks for inference purposes, and following known techniques (12)(13)(14).…”

Section: Methodsmentioning

confidence: 99%

Immuno-regulated common markers but different network signatures in two associated cancers: evidences from epigenetic treatment

Capobianco

2016

Ann. Transl. Med.

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“…could help determining function diversity. Notably, cancer synergism is a crucial factor that would imply the possibility for some complex relationships to be pinpointed by targeting marker ensembles, such as complexes or modules, rather than at individual ones, 69 such as single genes or proteins. This difference may be particularly true with multiple cancers, despite the hallmark of cancer heterogeneity suggests to consider the distinct characteristics of each cancer.…”

Section: Methodsmentioning

confidence: 99%

Immunomediated Pan-cancer Regulation Networks are Dominant Fingerprints after Treatment of Cell Lines with Demethylation

2016

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Pan-cancer studies are particularly relevant not only for addressing the complexity of the inherently observed heterogeneity but also for identifying clinically relevant features that may be common to the cancer types. Immune system regulations usually reveal synergistic modulation with other cancer mechanisms and in combination provide insights on possible advances in cancer immunotherapies. Network inference is a powerful approach to decipher pan-cancer systems dynamics. The methodology proposed in this study elucidates the impacts of epigenetic treatment on the drivers of complex pan-cancer regulation circuits involving cell lines of five cancer types. These patterns were observed from differential gene expression measurements following demethylation with 5-azacytidine. Networks were built to establish associations of phenotypes at molecular level with cancer hallmarks through both transcriptional and post-transcriptional regulation mechanisms. The most prominent feature that emerges from our integrative network maps, linking pathway landscapes to disease and drug-target associations, refers primarily to a mosaic of immune-system crosslinked influences. Therefore, characteristics initially evidenced in single cancer maps become motifs well summarized by network cores and fingerprints.

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Ensemble inference by integrative cancer networks

Cited by 9 publications

References 13 publications

Time-course gene profiling and networks in demethylated retinoblastoma cell line

Time-course gene profiling and networks in demethylated retinoblastoma cell line

Immuno-regulated common markers but different network signatures in two associated cancers: evidences from epigenetic treatment

Immunomediated Pan-cancer Regulation Networks are Dominant Fingerprints after Treatment of Cell Lines with Demethylation

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