Integrative Modeling Reveals Annexin A2-mediated Epigenetic Control of Mesenchymal Glioblastoma
Artikel i vetenskaplig tidskrift, 2016

Glioblastomas are characterized by transcriptionally distinct subtypes, but despite possible clinical relevance, their regulation remains poorly understood. The commonly used molecular classification systems for GBM all identify a subtype with high expression of mesenchymal marker transcripts, strongly associated with invasive growth. We used a comprehensive data-driven network modeling technique (augmented sparse inverse covariance selection, aSICS) to define separate genomic, epigenetic, and transcriptional regulators of glioblastoma subtypes. Our model identified Annexin A2 (ANXA2) as a novel methylation-controlled positive regulator of the mesenchymal subtype. Subsequent evaluation in two independent cohorts established ANXA2 expression as a prognostic factor that is dependent on ANXA2 promoter methylation. ANXA2 knockdown in primary glioblastoma stem cell-like cultures suppressed known mesenchymal master regulators, and abrogated cell proliferation and invasion. Our results place ANXA2 at the apex of a regulatory cascade that determines glioblastoma mesenchymal transformation and validate aSICS as a general methodology to uncover regulators of cancer subtypes.

Glioblastoma

Mesenchymal transformation

Partial correlation based networks

Master regulators of cancer cell phenotypes

New methods for integrative data analysis

Brain tumor stem cells

Epigenetic regulation

Data integration

Annexin A2

Spare inverse covariance selection

Författare

Teresia Kling

Sahlgrenska akademin

Roberto Ferrarese

Universitat Freiburg im Breisgau

Darren Ó hAilín

Universitat Freiburg im Breisgau

Patrik Johansson

University of Uppsala Rudbeck Laboratory

Dieter Henrik Heiland

Universitat Freiburg im Breisgau

Fangping Dai

Universitat Freiburg im Breisgau

Ioannis Vasilikos

Universitat Freiburg im Breisgau

Astrid Weyerbrock

Universitat Freiburg im Breisgau

Rebecka Jörnsten

Chalmers, Matematiska vetenskaper, matematisk statistik

Göteborgs universitet

Maria Stella Carro

Universitat Freiburg im Breisgau

Sven Nelander

University of Uppsala Rudbeck Laboratory

EBioMedicine

2352-3964 (eISSN)

Vol. 12 72-85

Ämneskategorier

Bioinformatik och systembiologi

Cancer och onkologi

DOI

10.1016/j.ebiom.2016.08.050