Multi-faceted computational assessment of risk and progression in oligodendroglioma uncovers crucial role of Notch and PI3K pathways Open Access

Halani, Sameer (Spring 2018)

Permanent URL: https://etd.library.emory.edu/concern/etds/dj52w4709?locale=en
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Abstract

Oligodendroglioma are diffusely infiltrative gliomas defined by IDH-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 (TCGA) to objectively identify molecular factors associated with clinical outcomes of oligodendroglioma.

We identified 169 patients with confirmed diagnosis of molecular oligodendroglioma that were collected and followed from 2006 to 2016, with whole-genome sequencing performed through TCGA. Deep learning neural networks were used to leverage the breadth of data of TCGA and NOTCH1 mutations were the 5th strongest predictor of poor outcomes; these mutations are exclusively found in oligodendroglioma. These findings were extended to study signaling pathways implicated in oncogenesis and clinical endpoints associated with glioma progression. Inhibition of the entire canonical Notch pathway was found to be associated with poor clinical outcomes, thereby suggesting that global inactivation of this pathway is associated with a more aggressive subtype of oligodendroglioma. Investigation of more clinically revelant features of disease progression revealed NOTCH1 mutations were enriched in tumors that: exhibited features of radiographic disease progression (P = 0.008); had greater tumor cell density (P = 0.0015); and had greater rates of malignant cell proliferation based on MKI67 gene expression (P = 0.095).

Beyond the NOTCH1 mutations, expression of downstream targets of the Notch pathway, including HES and HEY, were reduced in CE+ tumors (= 0.016 and 0.050, respectively), was negatively correlated with tumor cell density, and negatively correlated with cellular proliferation (P< 0.05 for both). Traditional survival analysis showed increased Notch pathway signaling was protective for both overall survival (HR = 0.34; 95% CI 0.18 to 0.64) and progression-free survival (HR = 0.41; 95% CI 0.23 to 0.72).

Our findings that Notch pathway inactivation is 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. 

Table of Contents

Introduction…………………………………….……………1

Background…………………………………….……………3

Methods…………………………………….…………......…5

Results……………………………………………………….11

Discussion…………….…………………….………….……18

References…………………………………………..….……23

Tables / Figures………………………………………….…..26

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