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.
Description
Network-based stratification of tumor mutations : Nature Methods : Nature Research
%0 Journal Article
%1 hofree2013networkbased
%A Hofree, Matan
%A Shen, John P
%A Carter, Hannah
%A Gross, Andrew
%A Ideker, Trey
%D 2013
%I Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.
%J Nat Meth
%K cancer-research fulltext network-analysis
%N 11
%P 1108--1115
%T Network-based stratification of tumor mutations
%U http://dx.doi.org/10.1038/nmeth.2651
%V 10
%X 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.
@article{hofree2013networkbased,
abstract = {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.},
added-at = {2017-10-08T23:29:20.000+0200},
author = {Hofree, Matan and Shen, John P and Carter, Hannah and Gross, Andrew and Ideker, Trey},
biburl = {https://www.bibsonomy.org/bibtex/23a7948ca3f4288024942184d2ce9559f/marcsaric},
description = {Network-based stratification of tumor mutations : Nature Methods : Nature Research},
interhash = {e6f7c3a9bef37028752fd602df2f3923},
intrahash = {3a7948ca3f4288024942184d2ce9559f},
issn = {15487091},
journal = {Nat Meth},
keywords = {cancer-research fulltext network-analysis},
month = nov,
number = 11,
pages = {1108--1115},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
timestamp = {2017-10-08T23:29:20.000+0200},
title = {Network-based stratification of tumor mutations},
url = {http://dx.doi.org/10.1038/nmeth.2651},
volume = 10,
year = 2013
}