Cancer subtype identification using somatic mutation data

Cancer subtype identification using somatic mutation data www.nature.com/bjc ARTICLE Genetics and Genomics Cancer subtype identification using somatic mutation data 1,2 1,2,3 4 5 1,2,6 Marieke Lydia Kuijjer , Joseph Nathaniel Paulson , Peter Salzman , Wei Ding and John Quackenbush BACKGROUND: With the onset of next-generation sequencing technologies, we have made great progress in identifying recurrent mutational drivers of cancer. As cancer tissues are now frequently screened for specific sets of mutations, a large amount of samples has become available for analysis. Classification of patients with similar mutation profiles may help identifying subgroups of patients who might benefit from specific types of treatment. However, classification based on somatic mutations is challenging due to the sparseness and heterogeneity of the data. METHODS: Here we describe a new method to de-sparsify somatic mutation data using biological pathways. We applied this method to 23 cancer types from The Cancer Genome Atlas, including samples from 5805 primary tumours. RESULTS: We show that, for most cancer types, de-sparsified mutation data associate with phenotypic data. We identify poor prognostic subtypes in three cancer types, which are associated with mutations in signal transduction pathways for which targeted treatment options are available. We identify subtype–drug associations for 14 additional subtypes. Finally, we perform a http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png British Journal of Cancer Springer Journals

Cancer subtype identification using somatic mutation data

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Publisher
Springer Journals
Copyright
Copyright © 2018 by The Author(s)
Subject
Biomedicine; Biomedicine, general; Cancer Research; Epidemiology; Molecular Medicine; Oncology; Drug Resistance
ISSN
0007-0920
eISSN
1532-1827
D.O.I.
10.1038/s41416-018-0109-7
Publisher site
See Article on Publisher Site

Abstract

www.nature.com/bjc ARTICLE Genetics and Genomics Cancer subtype identification using somatic mutation data 1,2 1,2,3 4 5 1,2,6 Marieke Lydia Kuijjer , Joseph Nathaniel Paulson , Peter Salzman , Wei Ding and John Quackenbush BACKGROUND: With the onset of next-generation sequencing technologies, we have made great progress in identifying recurrent mutational drivers of cancer. As cancer tissues are now frequently screened for specific sets of mutations, a large amount of samples has become available for analysis. Classification of patients with similar mutation profiles may help identifying subgroups of patients who might benefit from specific types of treatment. However, classification based on somatic mutations is challenging due to the sparseness and heterogeneity of the data. METHODS: Here we describe a new method to de-sparsify somatic mutation data using biological pathways. We applied this method to 23 cancer types from The Cancer Genome Atlas, including samples from 5805 primary tumours. RESULTS: We show that, for most cancer types, de-sparsified mutation data associate with phenotypic data. We identify poor prognostic subtypes in three cancer types, which are associated with mutations in signal transduction pathways for which targeted treatment options are available. We identify subtype–drug associations for 14 additional subtypes. Finally, we perform a

Journal

British Journal of CancerSpringer Journals

Published: May 16, 2018

References

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