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bioRxiv · 10.1101/2020.08.10.244343

Data Integration with SUMO Detects Latent Relationships Between Patients in Lower-Grade Gliomas

Sienkiewicz, K.·Chen, J.·Chatrath, A.·Lawson, J. T.·Sheffield, N. C.·Zhang, L.·Ratan, A.
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Abstract

Joint analysis of multiple genomic data types can facilitate the discovery of complex mechanisms of biological processes and genetic diseases. We present a novel data integration framework based on non-negative matrix factorization that uses patient similarity networks. Our implementation supports continuous multi-omic datasets for molecular subtyping and handles missing data without using imputation, making it more efficient for genome-wide assays in large cohorts. Applying our approach to gene expression, microRNA expression, and methylation data from patients with lower grade gliomas, we identify a subtype with a significantly poorer prognosis. Tumors assigned to this subtype are hypomethylated genome-wide with a gain of AP-1 occupancy in the demethylated distal enhancers. These tumors genomic profiles are similar to Grade IV gliomas: they are enriched for somatic chr7 gain, chr10 loss, and other molecular events that have yet to be used in the diagnosis of lower-grade gliomas as per the current WHO guidelines.

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Sienkiewicz, K., Chen, J., Chatrath, A., Lawson, J. T., Sheffield, N. C., Zhang, L., Ratan, A.. 2020-08-10. Data Integration with SUMO Detects Latent Relationships Between Patients in Lower-Grade Gliomas. https://doi.org/10.1101/2020.08.10.244343

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