Multi-faceted computational assessment of risk and progression in oligodendroglioma implicates NOTCH and PI3K pathways. Issue 1 (December 2018) - Halani, Sameer; Yousefi, Safoora; Vega, Jose; Rossi, Michael; Zhao, Zheng; Amrollahi, Fatemeh; Holder, Chad; Baxter-Stoltzfus, Amelia; Eschbacher, Jennifer; Griffith, Brent; Olson, Jeffrey; Jiang, Tao; et al.
Abstract Oligodendrogliomas are diffusely infiltrative gliomas defined byIDH -mutation and co-deletion of 1p/19q. They have highly variable clinical courses, with survivals ranging from 6 months to over 20 years, but little is known regarding the pathways involved with their progression or optimal markers for stratifying risk. We utilized machine-learning approaches with genomic data from The Cancer Genome Atlas to objectively identify molecular factors associated with clinical outcomes of oligodendroglioma and extended these findings to study signaling pathways implicated in oncogenesis and clinical endpoints associated with glioma progression. Our multi-faceted computational approach uncovered key genetic alterations associated with disease progression and shorter survival in oligodendroglioma and specifically identified Notch pathway inactivation and PI3K pathway activation as the most strongly associated with MRI and pathology findings of advanced disease and poor clinical outcome. Our findings that Notch pathway inactivation and PI3K pathway activation are associated with advanced disease and survival risk will pave the way for clinically relevant markers of disease progression and therapeutic targets to improve clinical outcomes. Furthermore, our approach demonstrates the strength of machine learning and computational methods for identifying genetic events critical to disease progression in the era of big data and precision medicine. Brain cancer: AI reveals pathways implicated in oligodendroglioma Using artificial intelligence, researchers identified two key signaling pathways involved in the progression of a brain cancer known as oligodendroglioma. A team led by Daniel Brat from Northwestern University Feinberg School of Medicine in Chicago, Illinois, and Lee Cooper from Emory University in Atlanta, Georgia, took a machine-learning approach to find links between genomic records, radiographic brain imaging data and digitized pathology slides from patients with oligodendroglioma, a type of tumor that develops from brain cells known as an oligodendrocytes. Their computational model identified an association between worse clinical outcomes and genetic alterations that either inactivate the Notch signaling pathway or activate the phosphoinositide 3-kinase pathway. The findings could yield potential therapeutic targets or prognostic biomarkers, although more work is needed to elucidate the exact proteins in these two pathways that drive disease progression.