Finding recurrent copy number alterations preserving within-sample homogeneity

Morganella, Sandro; Pagnotta, Stefano Maria; Ceccarelli, Michele

doi:10.1093/bioinformatics/btr488

Cited by 30 publications

(29 citation statements)

References 37 publications

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“…Moreover, the entire list of 64 Helios candidates was significantly enriched for our compiled set of breast cancer drivers (16/64, p-value < 4e −15 ), a large improvement over the set of all genes in amplified regions identified by GISTIC2 (17/452, p-value > e −3 ) (TCGA, 2012) (Figure 3A). The performance of the method was also compared against two other algorithms, GAIA (Morganella et al, 2011) and DiNAMIC (Walter et al, 2011), outperforming both of them (18/768, p-value > e −3 and 185/10651, p-value > e −3 respectively). This demonstrates the significant improvement of our integrative approach over the state of the art.…”

Section: Resultsmentioning

confidence: 99%

Integration of Genomic Data Enables Selective Discovery of Breast Cancer Drivers

Sanchez-Garcia

Villagrasa

Matsui

et al. 2014

Cell

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Identifying driver genes in cancer remains a crucial bottleneck in therapeutic development and basic understanding of the disease. We developed Helios, a novel algorithm that integrates genomic data from primary tumors with data from functional RNAi screens to pinpoint driver genes within large recurrently amplified regions of DNA. Applying Helios to breast cancer data identified a set of candidate drivers highly enriched with known drivers (p-value < e−14). 9/10 top scoring Helios genes are known drivers of breast cancer and in vitro validation of 12 novel candidates predicted by Helios found 10 conferred enhanced anchorage independent growth, demonstrating Helios’s exquisite sensitivity and specificity. We extensively characterized RSF-1, a driver identified by Helios whose amplification correlates with poor prognosis, and found increased tumorigenesis and metastasis in mouse models. We have demonstrated a powerful approach for identifying novel driver genes and how it can yield important insights into cancer.

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Section: Resultsmentioning

confidence: 99%

Integration of Genomic Data Enables Selective Discovery of Breast Cancer Drivers

Sanchez-Garcia

Villagrasa

Matsui

et al. 2014

Cell

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“…We first compared RAIG with four other approaches: GAIA (Morganella et al , 2011), JISTIC (Sanchez-Garcia et al , 2010), GISTIC (Beroukhim et al , 2007) and GISTIC2 (Mermel et al , 2011) on simulated data. We used simulated data from Morganella et al (2011) that offers three SCNA scenarios and two different noise models of increasing complexity that model both uncertainty in the amplitude and the position of SCNAs (both amplifications and deletions) in samples.…”

Section: Resultsmentioning

confidence: 99%

“…We used simulated data from Morganella et al (2011) that offers three SCNA scenarios and two different noise models of increasing complexity that model both uncertainty in the amplitude and the position of SCNAs (both amplifications and deletions) in samples. A description of the parameters of these simulations is in the Supplementary Material.…”

Section: Resultsmentioning

confidence: 99%

“…Subsequently, numerous methods with more complex models were introduced (Ben-Dor et al , 2007; Beroukhim et al , 2007; Diskin et al , 2006; Magi et al , 2011; Mermel et al , 2011; Morganella et al , 2011; Niida et al , 2012; Sanchez-Garcia et al , 2010; Walter et al , 2011). While these methods differ in important details, they all use variations of two basic steps: (i) compute a score at each genomic locus (typically a probe in microarray datasets) indicating the recurrence; (ii) examine correlations between recurrences scores of nearby loci to separate the true target region from other close, high-scoring regions.…”

Section: Introductionmentioning

confidence: 99%

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Detecting independent and recurrent copy number aberrations using interval graphs

Hajirasouliha

Raphael

2014

Bioinformatics

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Motivation: Somatic copy number aberrations (SCNAs) are frequent in cancer genomes, but many of these are random, passenger events. A common strategy to distinguish functional aberrations from passengers is to identify those aberrations that are recurrent across multiple samples. However, the extensive variability in the length and position of SCNAs makes the problem of identifying recurrent aberrations notoriously difficult.Results: We introduce a combinatorial approach to the problem of identifying independent and recurrent SCNAs, focusing on the key challenging of separating the overlaps in aberrations across individuals into independent events. We derive independent and recurrent SCNAs as maximal cliques in an interval graph constructed from overlaps between aberrations. We efficiently enumerate all such cliques, and derive a dynamic programming algorithm to find an optimal selection of non-overlapping cliques, resulting in a very fast algorithm, which we call RAIG (Recurrent Aberrations from Interval Graphs). We show that RAIG outperforms other methods on simulated data and also performs well on data from three cancer types from The Cancer Genome Atlas (TCGA). In contrast to existing approaches that employ various heuristics to select independent aberrations, RAIG optimizes a well-defined objective function. We show that this allows RAIG to identify rare aberrations that are likely functional, but are obscured by overlaps with larger passenger aberrations.Availability: http://compbio.cs.brown.edu/software.Contact: braphael@brown.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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“…We compared RAIG with four existing algorithms: GAIA[2], JISTIC[3], GISTIC[4] and GISTIC2[5] on three simulated data sets, including a simple model of SCNAs with the introduction of spatial noise, a simulated model for examining the power of detecting secondary events, and a simulated model for demonstrating the power of separating two driver SCNAs that contain different fraction of overlap. The results demonstrate RAIG outperforms other approaches on all three simulated data sets.We used RAIG to perform a Pan-Cancer analysis ofSCNAs in 4,976 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) [6].…”

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A combinatorial algorithm to identify independent and recurrent copy number aberrations across cancer types

Hajirasouliha

Raphael

2014

2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)

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Somatic copy number aberrations (SCNAs) are frequent in cancer genomes, but many of these are random events that do not contribute to the cancer phenotype. A common strategy to distinguish functional, driver events from such random passenger events it to identify recurrent aberrations shared by multiple samples. However, the extensive variability in the length and position of SCNAs across samples makes the problem of identifying recurrent aberrations notoriously difficult. We developed an algorithm, RAIG (Recurrent Aberrations from Interval Graphs) [1], to identify independent and recurrentSCNAs across a set of samples as maximal cliques in an interval graph constructed from overlaps between aberrations. In contrast to existing approaches that deconvolve a recurrence score of SCNAs derived from individual markers (probes), RAIG analyzes the combinatorial structure of the underlying intervals, and thus explicitly models the dependencies between values of the recurrence score. RAIG uses a dynamic programming algorithm to optimize a rigorous objective function for the selection of recurrent aberrations. RAIG is very efficient, as maximal cliques in an interval graph can be efficiently enumerated. Also, RAIG is readily adaptable for both microarray and high-throughput sequencing data. We compared RAIG with four existing algorithms: GAIA[2], JISTIC[3], GISTIC[4] and GISTIC2[5] on three simulated data sets, including a simple model of SCNAs with the introduction ofspatial noise, a simulated model for examining the power of detecting secondary events, and a simulated model for demonstrating the power of separating two driver SCNAs that contain different fraction of overlap. The results demonstrate RAIG outperforms other approaches on all three simulated data sets. We used RAIG to perform a Pan-Cancer analysis of SCNAs in 4,976 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) [6]. Significantly recurrent SCNAs were observed in 112 regions, including 61 amplified regions and 51 deleted regions. 58 of these recurrent SCNAs were reported in recently published pan-cancer analysis [7], including amplifications of KRAS, CCND1, CDK4, MDM2, MDM4, FGFR3, PDGFRA, EGFR and MYC; and deletions of PTEN, RB1, NF1, ARID1A, CDKN2A, PTPRD and MLL3. RAIG also identified additional regions with known cancer genes, e.g. amplifications of ERBB3, SMARC4, MECOM and ESR1, and deletions of MAP2K4 and SLIT2. These results demonstrate that RAIG is a useful algorithm for identifying recurrent SCNAs across large patient cohorts.

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Finding recurrent copy number alterations preserving within-sample homogeneity

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 30 publications

References 37 publications

Integration of Genomic Data Enables Selective Discovery of Breast Cancer Drivers

Integration of Genomic Data Enables Selective Discovery of Breast Cancer Drivers

Detecting independent and recurrent copy number aberrations using interval graphs

A combinatorial algorithm to identify independent and recurrent copy number aberrations across cancer types

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