Integration of Genomic Data Enables Selective Discovery of Breast Cancer Drivers

Sanchez-Garcia, Félix; Villagrasa, Patricia; Matsui, Junji; Kotliar, Dylan; Castro, Verónica; Akavia, Uri David; Chen, Bo Juen; Saucedo-Cuevas, Laura; Rodríguez-Barrueco, Ruth; Llobet‐Navas, David; Silva, José M.; Pe’er, Dana

doi:10.1016/j.cell.2014.10.048

Cited by 80 publications

(77 citation statements)

References 50 publications

(60 reference statements)

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“…The high frequency of recurring SCNAs suggests that some SCNAs may be cancer drivers and emphasizes the need to uncover driver genes within these regions (15). However, as amplified regions often encompass multiple genes, defining potential driver genes on SCNAs and distinguishing driver SCNA events are major challenges (16,17).…”

mentioning

confidence: 99%

Overexpression screens identify conserved dosage chromosome instability genes in yeast and human cancer

Duffy

Fam

Wang

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Somatic copy number amplification and gene overexpression are common features of many cancers. To determine the role of gene overexpression on chromosome instability (CIN), we performed genome-wide screens in the budding yeast for yeast genes that cause CIN when overexpressed, a phenotype we refer to as dosage CIN (dCIN), and identified 245 dCIN genes. This catalog of genes reveals human orthologs known to be recurrently overexpressed and/or amplified in tumors. We show that two genes, TDP1, a tyrosyl-DNA-phosphdiesterase, and TAF12, an RNA polymerase II TATA-box binding factor, cause CIN when overexpressed in human cells. Rhabdomyosarcoma lines with elevated human Tdp1 levels also exhibit CIN that can be partially rescued by siRNA-mediated knockdown of TDP1. Overexpression of dCIN genes represents a genetic vulnerability that could be leveraged for selective killing of cancer cells through targeting of an unlinked synthetic dosage lethal (SDL) partner. Using SDL screens in yeast, we identified a set of genes that when deleted specifically kill cells with high levels of Tdp1. One gene was the histone deacetylase RPD3, for which there are known inhibitors. Both HT1080 cells overexpressing hTDP1 and rhabdomyosarcoma cells with elevated levels of hTdp1 were more sensitive to histone deacetylase inhibitors valproic acid (VPA) and trichostatin A (TSA), recapitulating the SDL interaction in human cells and suggesting VPA and TSA as potential therapeutic agents for tumors with elevated levels of hTdp1. The catalog of dCIN genes presented here provides a candidate list to identify genes that cause CIN when overexpressed in cancer, which can then be leveraged through SDL to selectively target tumors.dosage chromosome instability | overexpression | synthetic dosage lethality | TDP1 | rhabdomyosarcoma

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mentioning

confidence: 99%

Overexpression screens identify conserved dosage chromosome instability genes in yeast and human cancer

Duffy

Fam

Wang

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…TBC1D16 and RAB27A) in melanoma via CONEXIC. The same group further developed Helios, an algorithm that identifies SMGs within large recurrently amplified regions of DNA by incorporating cancer genomics data into data from functional RNA interference (RNAi) screening studies [80]. They pinpointed a set of candidate SMGs in breast cancer and further experimentally validated that RSF-1-mediated tumorigenesis and metastasis in vivo.…”

Section: Data Integration-based Approachmentioning

confidence: 99%

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cheng¹,

Zhao²,

Zhao³

2015

Brief Bioinform

130

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Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in nextgeneration sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and wholetranscriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.

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“…Recurrent epigenetic alterations, especially DNA methylation [52], and large-scale functional screens [53][54][55][56] can also be used to identified novel candidate drivers. Then, these alterations or perturbations are integrative analyzed with gene expression variations (either cis-or trans-acting effects), the functional indications of genetic alterations [57][58][59][60][61].…”

Section: Regulatory Integrative Clusteringmentioning

confidence: 99%

Integrative clustering methods of multi‐omics data for molecule‐based cancer classifications

Wang

2016

Quant. Biol.

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One goal of precise oncology is to re-classify cancer based on molecular features rather than its tissue origin. Integrative clustering of large-scale multi-omics data is an important way for molecule-based cancer classification. The data heterogeneity and the complexity of inter-omics variations are two major challenges for the integrative clustering analysis. According to the different strategies to deal with these difficulties, we summarized the clustering methods as three major categories: direct integrative clustering, clustering of clusters and regulatory integrative clustering. A few practical considerations on data pre-processing, post-clustering analysis and pathway-based analysis are also discussed.

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Integration of Genomic Data Enables Selective Discovery of Breast Cancer Drivers

Cited by 80 publications

References 50 publications

Overexpression screens identify conserved dosage chromosome instability genes in yeast and human cancer

Overexpression screens identify conserved dosage chromosome instability genes in yeast and human cancer

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Integrative clustering methods of multi‐omics data for molecule‐based cancer classifications

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