Identification of relevant genetic alterations in cancer using topological data analysis

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Identification of relevant genetic alterations in cancer using topological data analysis. / Rabadán, Raúl; Mohamedi, Yamina; Rubin, Udi; Chu, Tim; Alghalith, Adam N.; Elliott, Oliver; Arnés, Luis; Cal, Santiago; Obaya, Álvaro J.; Levine, Arnold J.; Cámara, Pablo G.

In: Nature Communications, Vol. 11, No. 1, 3808, 2020.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Rabadán, R, Mohamedi, Y, Rubin, U, Chu, T, Alghalith, AN, Elliott, O, Arnés, L, Cal, S, Obaya, ÁJ, Levine, AJ & Cámara, PG 2020, 'Identification of relevant genetic alterations in cancer using topological data analysis', Nature Communications, vol. 11, no. 1, 3808. https://doi.org/10.1038/s41467-020-17659-7

APA

Rabadán, R., Mohamedi, Y., Rubin, U., Chu, T., Alghalith, A. N., Elliott, O., Arnés, L., Cal, S., Obaya, Á. J., Levine, A. J., & Cámara, P. G. (2020). Identification of relevant genetic alterations in cancer using topological data analysis. Nature Communications, 11(1), [3808]. https://doi.org/10.1038/s41467-020-17659-7

Vancouver

Rabadán R, Mohamedi Y, Rubin U, Chu T, Alghalith AN, Elliott O et al. Identification of relevant genetic alterations in cancer using topological data analysis. Nature Communications. 2020;11(1). 3808. https://doi.org/10.1038/s41467-020-17659-7

Author

Rabadán, Raúl ; Mohamedi, Yamina ; Rubin, Udi ; Chu, Tim ; Alghalith, Adam N. ; Elliott, Oliver ; Arnés, Luis ; Cal, Santiago ; Obaya, Álvaro J. ; Levine, Arnold J. ; Cámara, Pablo G. / Identification of relevant genetic alterations in cancer using topological data analysis. In: Nature Communications. 2020 ; Vol. 11, No. 1.

Bibtex

@article{e80bde3e197e44d88006695a32a55031,

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.",

author = "Ra{\'u}l Rabad{\'a}n and Yamina Mohamedi and Udi Rubin and Tim Chu and Alghalith, {Adam N.} and Oliver Elliott and Luis Arn{\'e}s and Santiago Cal and Obaya, {{\'A}lvaro J.} and Levine, {Arnold J.} and C{\'a}mara, {Pablo G.}",

note = "Publisher Copyright: {\textcopyright} 2020, The Author(s).",

year = "2020",

doi = "10.1038/s41467-020-17659-7",

language = "English",

volume = "11",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "nature publishing group",

number = "1",

}

RIS

TY - JOUR

T1 - Identification of relevant genetic alterations in cancer using topological data analysis

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.

PY - 2020

Y1 - 2020

N2 - 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.

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.

U2 - 10.1038/s41467-020-17659-7

DO - 10.1038/s41467-020-17659-7

M3 - Journal article

C2 - 32732999

AN - SCOPUS:85088794915

VL - 11

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 3808

ER -

ID: 299824315

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