The transcriptional network for mesenchymal transformation of brain tumors

By M. S. Carro, W. Lim, Mariano J. Alvarez, R. Bollo, Xudong Zhao, E. Snyder, E. Sulman, Sandrine L. Anne, F. Doetsch, H. Colman and 4 more. Nature, 2009

594 citations recorded by the source corpus.

The inference of transcriptional networks that regulate transitions into physiological or pathological cellular states remains a central challenge in systems biology. A mesenchymal phenotype is the hallmark of tumour aggressiveness in human malignant glioma, but the regulatory programs responsible for implementing the associated molecular signature are largely unknown. Here we show that reverse-engineering and an unbiased interrogation of a glioma-specific regulatory network reveal the transcriptional module that activates expression of mesenchymal genes in malignant glioma. Two transcription factors (C/EBPβ and STAT3) emerge as synergistic initiators and master regulators of mesenchymal transformation. Ectopic co-expression of C/EBPβ and STAT3 reprograms neural stem cells along the aberrant mesenchymal lineage, whereas elimination of the two factors in glioma cells leads to collapse of the mesenchymal signature and reduces tumour aggressiveness. In human glioma, expression of C/EBPβ and STAT3 correlates with mesenchymal differentiation and predicts poor clinical outcome. These results show that the activation of a small regulatory module is necessary and sufficient to initiate and maintain an aberrant phenotypic state in cancer cells.

Subfields: Genetics, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/4a0563c5fd514ce1a93e96686b1ef4e630e143d9

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In Genetics

1. Jeremy N. Rich
2. Federico Manuel Giorgi
3. Alexander Lachmann

In Molecular Biology

1. C. Abate-Shen
2. Andrea Califano
3. Jeremy N. Rich
4. Federico Manuel Giorgi
5. M. Shen
6. Alexander Lachmann

In Oncology

1. C. Abate-Shen
2. Jeremy N. Rich
3. M. Shen

Citations in the corpus

• Molecular genetics of prostate cancer: new prospects for old challenges. (2010, background): “Alternatively, system biology approaches that identify master regulatory genes of disease progression may enable the effective stratification of patients, as has been applied for other cancer types (Carro et al. 2009).”
• Network-based inference of protein activity helps functionalize the genetic landscape of cancer (2016, methodology): “The experimentally silenced proteins, MYB, BCL6, STAT3, FOXM1, MEF2B and BCL6, were ranked as the 1 st , 1 st , 1 st , 2 nd , 3 rd , and 3 rd most significantly inactivated proteins among all those tested, respectively (Supplementary Fig.”
• Phosphorylation of EZH2 activates STAT3 signaling via STAT3 methylation and promotes tumorigenicity of glioblastoma stem-like cells (2013, background): “A recent study reported that STAT3 signaling is a master regulator of mesenchymal transformation of gliomas, and that STAT3 downstream genes are highly expressed in the mesenchymal GBM subtype (Carro et al., 2010; Verhaak et al., 2010).”
• Next-Generation Machine Learning for Biological Networks. (2018, unknown): “, 2015; Ballester andMitchell, 2010), the identification of key transcriptional drivers of cancer (Califano and Alvarez, 2017; Carro et al., 2010), predictions of metabolic functions in complex microbial communities (Langille et al.”