Pediatric cancer subtype characterization through network and multi-omics data analysis – Loredana Martignetti

Loredana Martignetti
Institut Curie, Paris
https://scholar.google.com/citations?user=uIq1vX4AAAAJ&hl=fr

Date(s) : 06/03/2017   iCal
14 h 00 min - 15 h 00 min

Cancers are typically classified depending on their tissue of origin. However, recent largescale genomic experiments are providing more detailed molecular characterizations of tumors, bringing the possibility of a more accurate stratification of tumor subtypes. To date, mainly owing to the maturity and availability of high throughput DNA- and RNA- based techniques, molecular classifications of complex diseases primarily focus on genomics and transcriptomics. Protein-level measurements are underutilized due to technical hurdles. Here, we present an integrative approach including deep proteomic measurements and transcriptomic profiles of pediatric brain tumors to decipher signaling pathways and molecular mechanisms underlying different tumor subtypes. This analysis yields a number of insights about quantitative concordance between mRNA expression and protein levels in tumor profiles. We applied a pathwaybased approach, called ROMA for Representation and quantification Of Module Activity [1], to capture biological information that is otherwise undetectable by focusing on individual genes. Our results uncover deregulations in some signaling pathways specific to poor outcome subgroups, possibly underlying mechanisms linked to bad prognosis.

[1] Martignetti L, Calzone L, Bonnet E, Barillot E, Zinovyev A. ROMA: Representation and Quantification of Module Activity from Target Expression Data. Front Genet. 2016 Feb 19;7:18

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