Identification of Relevant Genetic Alterations in Cancer using Topological Data Analysis
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Large-scale cancer genomic studies enable the systematic identification of mutations that lead to the genesis and progression of tumors, uncovering the underlying molecular mechanisms and potential therapies. While some such mutations are recurrently found in many tumors, many others exist solely within a few samples, precluding detection by conventional recurrence-based statistical approaches. Integrated analysis of somatic mutations and RNA expression data across 12 tumor types reveals that mutations of cancer genes are usually accompanied by substantial changes in expression. We use topological data analysis to leverage this observation and uncover 38 elusive candidate cancer-associated genes, including inactivating mutations of the metalloproteinase ADAMTS12 in lung adenocarcinoma.
“Identification of Relevant Genetic Alterations in Cancer using Topological Data Analysis”
AUTHORS: Raúl Rabadán, Yamina Mohamedi, Udi Rubin, Tim Chu, Oliver Elliott, Luis Arnés, Santiago Cal, Álvaro J. Obaya, Arnold J. Levine, Pablo G. Cámara.
LINK TO PUBLICATION:
Nature Commun. 2020 July 30. doi: 10.1038/s41467-020-17659-7.