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
British Journal of Cancer – Springer Journals
Published: May 16, 2018
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