Genome Alteration Print (GAP): a tool to visualize and mine complex cancer genomic profiles obtained by SNP arrays

Popova, Tatiana; Manié, Élodie; Stoppa‐Lyonnet, Dominique; Rigaill, Guillem; Barillot, Emmanuel; Stern, Marc‐Henri

doi:10.1186/gb-2009-10-11-r128

Cited by 181 publications

(243 citation statements)

References 35 publications

(63 reference statements)

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“…GAP patterns (a two-dimensional plot of segmental copy number and allelic difference values; ref. 20) ensured the consistency and excluded noise artifacts (Fig. 1B).…”

Section: Resultsmentioning

confidence: 99%

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Ovarian Cancers Harboring Inactivating Mutations in CDK12 Display a Distinct Genomic Instability Pattern Characterized by Large Tandem Duplications

et al. 2016

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CDK12 is a recurrently mutated gene in serous ovarian carcinoma, whose downregulation is associated with impaired expression of DNA damage repair genes and subsequent hypersensitivity to DNA-damaging agents and PARP1/2 inhibitors. In this study, we investigated the genomic landscape associated with CDK12 inactivation in patients with serous ovarian carcinoma. We show that CDK12 loss was consistently associated with a particular genomic instability pattern characterized by hundreds of tandem duplications of up to 10 megabases (Mb) in size. Tandem duplications were characterized by a bimodal ($0.3 and $3 Mb) size distribution and overlapping microhomology at the breakpoints.This genomic instability, denoted as the CDK12 TD-plus phenotype, is remarkably distinct from other alteration patterns described in breast and ovarian cancers. The CDK12 TD-plus phenotype was associated with a greater than 10% gain in genomic content and occurred at a 3% to 4% rate in The Cancer Genome Atlas-derived and in-house cohorts of patients with serous ovarian carcinoma. Moreover, CDK12-inactivating mutations together with the TD-plus phenotype were also observed in prostate cancers. Our finding provides new insight toward deciphering the function of CDK12 in genome maintenance and oncogenesis. Cancer Res; 76(7); 1882-91. Ó2016 AACR.

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“…GAP patterns (a two-dimensional plot of segmental copy number and allelic difference values; ref. 20) ensured the consistency and excluded noise artifacts (Fig. 1B).…”

Section: Resultsmentioning

confidence: 99%

“…Absolute copy number and allelic contents were obtained using the Genome Alteration Print (GAP; ref. 20). DNA index was calculated as the averaged copy number.…”

Section: Snp-array Processingmentioning

confidence: 99%

Ovarian Cancers Harboring Inactivating Mutations in CDK12 Display a Distinct Genomic Instability Pattern Characterized by Large Tandem Duplications

et al. 2016

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“…3A; Supplementary Table S7). Moreover, ccrcc1 and ccrcc4 tumors showed a poorer PFS (13,8,19, and 24 months, respectively; P ¼ 0.0003) and OS (24,14,35, and 50 months, respectively; P ¼ 0.001) compared with ccrcc2 and ccrcc3 tumors ( Fig. 3B; Table 1; Supplementary Table S7).…”

Section: Molecular Subtypes and Sunitinib Responsementioning

confidence: 92%

“…Hybridization was performed by IntegraGen, according to the manufacturer's instructions. The absolute copy numbers and genotype status of segments were determined using the genome alteration print (GAP) method (14). Segments with an absolute copy number above (respectively below) the ploidy of the sample were considered as gains (respectively losses).…”

Section: Datasets and Preprocessingmentioning

confidence: 99%

Molecular Subtypes of Clear Cell Renal Cell Carcinoma Are Associated with Sunitinib Response in the Metastatic Setting

Beuselinck

Job

Becht

et al. 2015

Clinical Cancer Research

264

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Purpose: Selecting patients with metastatic clear-cell renal cell carcinoma (m-ccRCC) who might benefit from treatment with targeted tyrosine kinase inhibitors (TKI) is a challenge. Our aim was to identify molecular markers associated with outcome in patients with m-ccRCC treated with sunitinib.Experimental Design: We performed global transcriptome analyses on 53 primary resected ccRCC tumors from patients who developed metastatic disease and were treated with first-line sunitinib. We also determined chromosome copy-number aberrations, methylation status, and gene mutations in von Hippel-Lindau and PBRM1. Molecular data were analyzed in relation with response rate (RR), progression-free survival (PFS), and overall survival (OS). Validation was performed in 47 additional ccRCC samples treated in first-line metastatic setting with sunitinib.Results: Unsupervised transcriptome analysis identified 4 robust ccRCC subtypes (ccrcc1 to 4) related to previous molecular classifications that were associated with different responses to sunitinib treatment. ccrcc1/ccrcc4 tumors had a lower RR (P ¼ 0.005) and a shorter PFS and OS than ccrcc2/ccrcc3 tumors (P ¼ 0.001 and 0.0003, respectively). These subtypes were the only significant covariate in the multivariate Cox model for PFS and OS (P ¼ 0.017 and 0.006, respectively). ccrcc1/ccrcc4 tumors were characterized by a stem-cell polycomb signature and CpG hypermethylation, whereas ccrcc3 tumors, sensitive to sunitinib, did not exhibit cellular response to hypoxia. Moreover, ccrcc4 tumors exhibited sarcomatoid differentiation with a strong inflammatory, Th1-oriented but suppressive immune microenvironment, with high expression of PDCD1 (PD-1) and its ligands.Conclusions: ccRCC molecular subtypes are predictive of sunitinib response in metastatic patients, and could be used for personalized mRCC treatment with TKIs, demethylating or immunomodulatory drugs.

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“…The data were primarily interpreted by regarding each tumor as a single population of cells with uniform character. Despite the inherent limitation of this assumption, as shown by the widely reported tumor-normal mixing [17][18][19], large-scale inter-tumor comparisons have led to important new insights into significantly mutated genes [12,13], recurrently perturbed pathways [20], mutation signatures [16,21], tumor subtypes [22,23], molecular predictors of outcome, and commonalities or distinctions among different cancer types [24]. However, these studies are not designed to adequately investigate intra-tumor heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

A general framework for analyzing tumor subclonality using SNP array and DNA sequencing data

2014

Genome Biol

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Intra-tumor heterogeneity reflects cancer genome evolution and provides key information for diagnosis and treatment. When bulk tumor tissues are profiled for somatic copy number alterations (sCNA) and point mutations, it may be difficult to estimate their cellular fractions when a mutation falls within a sCNA. We present the Clonal Heterogeneity Analysis Tool, which estimates cellular fractions for both sCNAs and mutations, and uses their distributions to inform macroscopic clonal architecture. In a set of approximately 700 breast tumors, more than half appear to contain multiple recognizable aneuploid tumor clones, and many show subtype-specific differences in clonality for known cancer genes.

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Genome Alteration Print (GAP): a tool to visualize and mine complex cancer genomic profiles obtained by SNP arrays

Abstract: GAP, a method for analyzing complex cancer genome profiles from SNP arrays, performs well even with poor quality data and rearranged genomes

Cited by 181 publications

References 35 publications

Ovarian Cancers Harboring Inactivating Mutations in CDK12 Display a Distinct Genomic Instability Pattern Characterized by Large Tandem Duplications

Ovarian Cancers Harboring Inactivating Mutations in CDK12 Display a Distinct Genomic Instability Pattern Characterized by Large Tandem Duplications

Molecular Subtypes of Clear Cell Renal Cell Carcinoma Are Associated with Sunitinib Response in the Metastatic Setting

A general framework for analyzing tumor subclonality using SNP array and DNA sequencing data

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