Algorithmic Reconstruction of GBM Network Complexity

Abstract

Glioblastoma (GBM) is a complex disease that is difficult to treat. Establishing the complex genetic interactions regulating cell fate decisions in GBM can help to shed light on disease aggressivity and improved treatments. Networks and data science offer novel approaches to study gene expression patterns from single-cell datasets, helping to distinguish genes associated with control of differentiation and thus aggressivity. Here, we applied a host of data theoretic techniques, including clustering algorithms, Waddington landscape reconstruction, trajectory inference algorithms, and network approaches, to compare gene expression patterns between pediatric and adult GBM, and those of adult GSCs (glioma-derived stem cells) to identify the key molecular regulators of the complex networks driving GBM/GSC and predict their cell fate dynamics. Using these tools, we identified critical genes and transcription factors coordinating cell state transitions from stem-like to mature GBM phenotypes, including eight transcription factors (OLIG1/2, TAZ, GATA2, FOXG1, SOX6, SATB2, YY1) and four signaling genes (ATL3, MTSS1, EMP1, and TPT1) as clinically targetable novel putative function interactions differentiating pediatric and adult GBMs from adult GSCs. Our study is among the first to provide strong evidence of the applicability of complex systems approaches for reverse-engineering gene networks from patient-derived single-cell datasets and inferring their complex dynamics, bolstering the search for new clinically-relevant targets in GBM.