Oligodendrogliomas 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 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.

少突胶质细胞瘤是由IDH突变和 1p/19q 共缺失定义的弥漫性浸润性神经胶质瘤。它们的临床病程差异很大，生存期从 6 个月到 20 多年不等，但人们对其进展所涉及的途径或分层风险的最佳标志物知之甚少。我们利用机器学习方法和来自癌症基因组图谱的基因组数据来客观地识别与少突胶质细胞瘤临床结果相关的分子因素，并将这些发现扩展到研究与肿瘤发生和与神经胶质瘤进展相关的临床终点相关的信号通路。我们的多方面计算方法揭示了与少突胶质细胞瘤疾病进展和较短生存期相关的关键遗传改变，并特别确定了 Notch 通路失活和 PI3K 通路激活与晚期疾病和不良临床结果的 MRI 和病理学结果最密切相关。我们的研究结果表明，Notch 通路失活和 PI3K 通路激活与晚期疾病和生存风险相关，这将为疾病进展的临床相关标志物和治疗靶标铺平道路，以改善临床结果。此外，我们的方法展示了机器学习和计算方法在识别大数据和精准医学时代对疾病进展至关重要的遗传事件方面的优势。