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; Pablo G. Cámara

doi:10.1038/s41467-020-17659-7

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, Pablo G. Cámara

^*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

41 Citationer (Scopus)

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.

Originalsprog	Engelsk
Artikelnummer	3808
Tidsskrift	Nature Communications
Vol/bind	11
Udgave nummer	1
ISSN	2041-1723
DOI	https://doi.org/10.1038/s41467-020-17659-7
Status	Udgivet - 2020
Udgivet eksternt	Ja

Bibliografisk note

Funding Information:
We thank J. A. Vega and IUOPA (Servicio de Histopatología Molecular en Modelos Animales de Cáncer) for assistance with the histopathology of mouse tumors; R. Aubin, G. Carlsson, K. Govek, A. Iavarone, A. Lasorella, S. Lowe, F. Sanchez-Rivera, E. Troisi, and S. Woodhouse for scientific discussions; and S. Christen, D. Ramanan, I. Sagalovskiy, and S. Thuault-Restituito for technical and administrative support. R.R. acknowledges funding from NIH (U54 CA193313-01). P.G.C. acknowledges funding from Stand-Up-To-Cancer Convergence 2.0.

Publisher Copyright:
© 2020, The Author(s).

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Citationsformater

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title = "Identification of relevant genetic alterations in cancer using topological data analysis",

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.",

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AU - Rabadán, Raúl

AU - Mohamedi, Yamina

AU - Rubin, Udi

AU - Chu, Tim

AU - Alghalith, Adam N.

AU - Elliott, Oliver

AU - Arnés, Luis

AU - Cal, Santiago

AU - Obaya, Álvaro J.

AU - Levine, Arnold J.

AU - Cámara, Pablo G.

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AB - 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.

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DO - 10.1038/s41467-020-17659-7

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