# Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data

Savage, RS and Ghahramani, Z and Griffin, JE and Kirk, P and Wild, DL (2013) Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data. International Conference on Machine Learning (ICML) 2012: Workshop on Machine Learning in Genetics and Genomics.

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## Abstract

We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowing for both agreement and disagreement between their underlying clustering structure. It includes feature selection and infers the most likely number of disease subtypes, given the data. We apply the method to 277 glioblastoma samples from The Cancer Genome Atlas, for which there are gene expression, copy number variation, methylation and microRNA data. We identify 8 distinct consensus subtypes and study their prognostic value for death, new tumour events, progression and recurrence. The consensus subtypes are prognostic of tumour recurrence (log-rank p-value of $3.6 \times 10^{-4}$ after correction for multiple hypothesis tests). This is driven principally by the methylation data (log-rank p-value of $2.0 \times 10^{-3}$) but the effect is strengthened by the other 3 data types, demonstrating the value of integrating multiple data types. Of particular note is a subtype of 47 patients characterised by very low levels of methylation. This subtype has very low rates of tumour recurrence and no new events in 10 years of follow up. We also identify a small gene expression subtype of 6 patients that shows particularly poor survival outcomes. Additionally, we note a consensus subtype that showly a highly distinctive data signature and suggest that it is therefore a biologically distinct subtype of glioblastoma. The code is available from https://sites.google.com/site/multipledatafusion/

Item Type: Article q-bio.GN q-bio.GN stat.ML UNSPECIFIED Div F > Computational and Biological Learning Cron Job 07 Mar 2014 11:59 10 Mar 2014 17:04