Identification of relevant genetic alterations in cancer using topological data analysis

Raúl Rabadán (), Yamina Mohamedi, Udi Rubin, Tim Chu, Adam N. Alghalith, Oliver Elliott, Luis Arnés, Santiago Cal, Álvaro J. Obaya, Arnold J. Levine () and Pablo G. Cámara ()
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Raúl Rabadán: Columbia University
Yamina Mohamedi: Universidad de Oviedo
Udi Rubin: Columbia University
Tim Chu: Columbia University
Adam N. Alghalith: University of Pennsylvania
Oliver Elliott: Columbia University
Luis Arnés: Columbia University
Santiago Cal: Universidad de Oviedo
Álvaro J. Obaya: IUOPA, Instituto Universitario de Oncologia, Oviedo
Arnold J. Levine: The Simons Center for Systems Biology, Institute for Advanced Study
Pablo G. Cámara: University of Pennsylvania

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract 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. We show that ADAMTS12−/− mice have a five-fold increase in the susceptibility to develop lung tumors, confirming the role of ADAMTS12 as a tumor suppressor gene. Our results demonstrate that data integration through topological techniques can increase our ability to identify previously unreported cancer-related alterations.

Date: 2020
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DOI: 10.1038/s41467-020-17659-7

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