Cited by 484Cancer Genomics and DiagnosticsChromatin Remodeling and CancerGenomic variations and chromosomal abnormalities

An integrated approach to uncover drivers of cancer

Open access
Akavia, Uri David, Litvin, Oren, Kim, Jessica, Sanchez-Garcia, Felix, Kotliar, Dylan, Causton, Helen C., Pochanard, Panisa, Mozes, Eyal +2 more·2010·Cell

Abstract

Systematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remain poorly defined. We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma data set. Our analysis correctly identified known drivers of melanoma and predicted multiple tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify candidate drivers with biological, and possibly therapeutic, importance in cancer.

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Crossref · 10.1016/j.cell.2010.11.013

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  • CONEXIC uses a Bayesian network to integrate gene expression and copy number variation from tumour samples to identify driver mutations.

    “Similarly, CONEXIC (Akavia et al., 2010) also uses a BN to integrate gene expression and copy number variation from tumour samples to identify driver mutations.”

    From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Akavia et al., 2010

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Authors
Akavia, U. D., Litvin, O., Kim, J., Sanchez-Garcia, F., Kotliar, D., Causton, H. C., Pochanard, P., Mozes, E., Garraway, L. A., & Pe'er, D.
Affiliations
  • Columbia University (US)
  • Broad Institute (US)
Venue
Cell
Publisher
Elsevier BV
Published
2010-12
Volume
143
Issue
6
Pages
1005-1017
ISSN
0092-8674 (ISSN-L)
OpenAlex
W1968667083
Cited by
484
References
129
Citations by year
2026: 5 · 2025: 5 · 2024: 14 · 2023: 21 · 2022: 18 · 2021: 17 · 2020: 30 · 2019: 28 · 2018: 24 · 2017: 33 · 2016: 42 · 2015: 51 · 2014: 47 · 2013: 60 · 2012: 56
FWCI
14.99
Type
journal article
Language
en
Open access
Locations
  • Cell (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Burroughs Wellcome Fund
  • National Institutes of Health (DP2OD002414)
  • National Cancer Institute (U54CA121852)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
Keywords
cancer driver genes, melanoma, chromosomal copy number alterations, gene expression, tumor dependency, Rab27a, protein trafficking, tumor progression, therapeutic target
MeSH terms
rab27 GTP-Binding Proteins, Bayes Theorem, Humans, Melanoma, GTPase-Activating Proteins, rab GTP-Binding Proteins, Gene Expression Profiling, Protein Transport, Microphthalmia-Associated Transcription Factor

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