Depicting the genetic architecture of pediatric cancers through an integrative gene network approach

• Published in: Scientific reports Vol. 10; no. 1; p. 1224
• Main Authors: Savary, Clara, Kim, Artem, Lespagnol, Alexandra, Gandemer, Virginie, Pellier, Isabelle, Andrieu, Charlotte, Pagès, Gilles, Galibert, Marie-Dominique, Blum, Yuna, de Tayrac, Marie
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.01.2020; Nature Publishing Group
• Subjects: 38; 38/39; 45; 631/208/199; 631/67/69; 639/705/1042; 692/4028/67/2332; Biomarkers, Tumor - genetics; Blood cancer; Cancer; Cell cycle; Child; Child, Preschool; Children; Computational Biology - methods; Computer Simulation; Databases, Genetic; DNA repair; Etiology; Female; Gene expression; Gene Expression - genetics; Gene Expression Profiling - methods; Gene Expression Regulation - genetics; Gene Expression Regulation, Neoplastic - genetics; Gene Regulatory Networks - genetics; Genes; Genetic Predisposition to Disease - genetics; Genomics - methods; Humanities and Social Sciences; Humans; Life Sciences; Male; Mathematical models; Models, Statistical; multidisciplinary; Neoplasms - etiology; Neoplasms - genetics; Pediatrics; Post-transcription; Protein Interaction Mapping - methods; Protein Interaction Maps - genetics; Protein Interaction Maps - physiology; Science; Science (multidisciplinary); Statistical analysis; Systems Integration; Therapeutic applications; Transcriptome - genetics; Tumorigenesis
• DOI: 10.1038/s41598-020-58179-0
• ISSN: 2045-2322

The genetic etiology of childhood cancers still remains largely unknown. It is therefore essential to develop novel strategies to unravel the spectrum of pediatric cancer genes. Statistical network modeling techniques have emerged as powerful methodologies for enabling the inference of gene-disease relationship and have been performed on adult but not pediatric cancers. We performed a deep multi-layer understanding of pan-cancer transcriptome data selected from the Treehouse Childhood Cancer Initiative through a co-expression network analysis. We identified six modules strongly associated with pediatric tumor histotypes that were functionally linked to developmental processes. Topological analyses highlighted that pediatric cancer predisposition genes and potential therapeutic targets were central regulators of cancer-histotype specific modules. A module was related to multiple pediatric malignancies with functions involved in DNA repair and cell cycle regulation. This canonical oncogenic module gathered most of the childhood cancer predisposition genes and clinically actionable genes. In pediatric acute leukemias, the driver genes were co-expressed in a module related to epigenetic and post-transcriptional processes, suggesting a critical role of these pathways in the progression of hematologic malignancies. This integrative pan-cancer study provides a thorough characterization of pediatric tumor-associated modules and paves the way for investigating novel candidate genes involved in childhood tumorigenesis.