Network-based stratification of tumor mutations

By Matan Hofree, J. Shen, Hannah Carter, Andrew M. Gross, Trey Ideker. 2013

701 citations recorded by the source corpus.

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence.

Subfields: Cancer Research, Genetics, Molecular Biology

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Citations in the corpus

Integrated Genomic Characterization of Papillary Thyroid Carcinoma (2014, methodology): “Subtype NBS3 appears to be predominantly BRAFV600E-associated.”
The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible (2016, background): “They are essential for modern life sciences: protein networks are used to increase discovery power for noisy data sets by ‘network smoothing’ (10,11), help define drug efficiency by network-based ‘drug-disease proximity measures’ (12), help to interpret the results of genome-wide association…”
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Multi-platform analysis of 12 cancer types reveals molecular classification within and across tissues-of-origin (2014, methodology): “populated single-mutation space) to cluster samples based on somatic DNA variants (Hofree et al., 2013), we calculated a mutation-based clustering after first associating genes with pathways and then identifying clusters based on mutated pathways (Figure S1F and Data S1).”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, methodology): “Without integrating other types of data, such as pathway or molecular network information [12, 13, 14], GWAS data alone can struggle to identify meaningful patterns associated with the phenotype of interest.”
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