An Integrated Approach to Uncover Drivers of Cancer

By U. Akavia, O. Litvin, Jessica J. Kim, Felix Sanchez-Garcia, Dylan Kotliar, Helen C. Causton, Panisa Pochanard, Eyal Mozes, Levi A. Garraway, Dana Pe'er. Cell, 2010

495 citations recorded by the source corpus.

SUMMARY 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 remains 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 dataset. Our analysis correctly identified known drivers of melanoma and predicted multiple novel 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 novel candidate drivers with biological, and possibly therapeutic, importance in cancer.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b0222597a51787fdaf168f849e1d7d56aa72b937

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Citations in the corpus

More Is Better: Recent Progress in Multi-Omics Data Integration Methods (2017, methodology): “It is a Bayesian network-based method to integrate CNV and gene expression data (Akavia et al., 2010).”
Methods for the integration of multi-omics data: mathematical aspects (2016, background): “For example, Conexic [11] is tailored for DNA copy number variations (CNV) and gene expression data, while iCluster [12] can be in principle used for the analysis of any combination of omics encoded as quantitative values on the same set of samples (Table 1).”
Pathway and Network Analysis of Cancer Genomes (2015, unknown): “The approach was applied to predict and experimentally validate multiple cancer driver genes in melanoma and glioblastoma 16,60 .”
Computational solutions for omics data (2013, methodology): “For example, CONEXIC compares gene expression data in cancer tissue and normal tissue by extending the causal graphical model 76 , which connects gene sets with edges to represent their interactions, to elucidate dysregulated gene modules from cancer genomic data 88 .”
Efficient Dimensionality Reduction for High-Dimensional Network Estimation (2014, background): “Learning the gene regulatory network from expression data is an effective strategy to identify novel disease mechanisms (Akavia et al., 2010; TCGA, 2012).”
Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets (2018, methodology): “While applications of factor models for integrating different data types were reported previously (Lanckriet et al , 2004; Shen et al , 2009; Akavia et al , 2010; Mo et al , 2013), MOFA provides unique features (Materials and Methods, Appendix Table S3) that enable the interpretable reconstruction…”
Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis (2015, methodology): “All microarray data were processed and normalized as described previously (Akavia et al., 2010).”