Mapping of copy-number alterations with massively parallel sequencing
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE13373 | GEO | 2008/11/14
SECONDARY ACCESSION(S): PRJNA109797
REPOSITORIES: GEO
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Project description:This SuperSeries is composed of the following subset Series: GSE12019: Fine-scale mapping of copy-number alterations with massively parallel sequencing GSE13372: High-resolution mapping of copy-number alterations with massively parallel sequencing Refer to individual Series
2008-11-13 | E-GEOD-13373 | biostudies-arrayexpress
Project description:Cancer results from somatic alterations in key genes, including point mutations, copy-number alterations and structural rearrangements. A powerful way to discover cancer-causing genes is to identify genomic regions that show recurrent copy-number alterations (gains and losses) in tumor genomes. Recent advances in sequencing technologies suggest that massively parallel sequencing may provide a feasible alternative to DNA microarrays for detecting copy-number alterations. Here we present: (i) a statistical analysis of the power to detect copy-number alterations of a given size; (ii) SegSeq, an algorithm to segment equal copy numbers from massively parallel sequence data; and (iii) analysis of experimental data from three matched pairs of tumor and normal cell lines. We show that a collection of approximately 14 million aligned sequence reads from human cell lines has comparable power to detect events as the current generation of DNA microarrays and has over twofold better precision for localizing breakpoints (typically, to within approximately 1 kilobase).
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Project description:In order to benchmark the reproducibility of Affymetrix 238K Sty arrays for detecting copy-number alterations. We performed replicate hybridizations of 3 tumor cell lines and 2 paired normal cell lines obtained from the American Type Culture Collection (ATCC). We calculated copy numbers at each SNP probeset by array pre-processing with the GISTIC algorithm (PMID: 18077431). For each SNP probeset, we calculated the median copy number across replicate arrays. The median copy number profile for each tumor cell line was segmented with the GLAD algorithm (PMID: 15381628) to partition the genome into regions of constant copy number. We compared the copy-number alterations detected by GLAD segmentation of these arrays with statistical analyses of short sequence reads obtained from the Illumina/Solexa 1G GenomeAnalyzer. Shotgun sequencing results can be found in the NCBI Short Read Archive, accession number SRP000246. Keywords: disease state analysis 77 replicates of HCC1143 (breast ductal carcinoma), 69 replicates of HCC1143BL (matched normal), 42 replicates of HCC1954 (breast ductal carcinoma), 36 replicates of HCC1954BL (matched normal), 1 replicate of NCI-H2347 (lung adenocarcinoma)
2008-11-13 | E-GEOD-12019 | biostudies-arrayexpress
Project description:In order to benchmark the reproducibility of Affymetrix Genome-Wide Human SNP Array 6.0 for detecting copy-number alterations, we performed replicate hybridizations of 3 tumor cell lines and 2 paired normal cell lines obtained from the American Type Culture Collection (ATCC). We calculated copy numbers at each SNP probeset by a custom copy-number pipeline (PMID: 18772890). For each cell line, copy number data from replicate arrays are supplied in the accompanying matrix files. For each SNP probeset, we calculated the median copy number across replicate arrays. We compared the copy-number alterations detected by Circular Binary Segmentation segmentation of these arrays with statistical analyses of short sequence reads obtained from the Illumina/Solexa 1G GenomeAnalyzer. Shotgun sequencing results can be found in the NCBI Short Read Archive, accession number SRP000246 Keywords: disease state analysis 21 replicates of HCC1143 (breast ductal carcinoma), 21 replicates of HCC1143BL (matched normal), 13 replicates of HCC1954 (breast ductal carcinoma), 11 replicates of HCC1954BL (matched normal), 1 replicate of NCI-H2347 (lung adenocarcinoma), 1 replicate of NCI-H2347BL (matched normal)
2008-11-13 | E-GEOD-13372 | biostudies-arrayexpress
Project description:In order to benchmark the reproducibility of Affymetrix Genome-Wide Human SNP Array 6.0 for detecting copy-number alterations, we performed replicate hybridizations of 3 tumor cell lines and 2 paired normal cell lines obtained from the American Type Culture Collection (ATCC). We calculated copy numbers at each SNP probeset by a custom copy-number pipeline (PMID: 18772890). For each cell line, copy number data from replicate arrays are supplied in the accompanying matrix files. For each SNP probeset, we calculated the median copy number across replicate arrays. We compared the copy-number alterations detected by Circular Binary Segmentation segmentation of these arrays with statistical analyses of short sequence reads obtained from the Illumina/Solexa 1G GenomeAnalyzer. Shotgun sequencing results can be found in the NCBI Short Read Archive, accession number SRP000246 Keywords: disease state analysis
2008-11-14 | GSE13372 | GEO
Project description:In order to benchmark the reproducibility of Affymetrix 238K Sty arrays for detecting copy-number alterations. We performed replicate hybridizations of 3 tumor cell lines and 2 paired normal cell lines obtained from the American Type Culture Collection (ATCC). We calculated copy numbers at each SNP probeset by array pre-processing with the GISTIC algorithm (PMID: 18077431). For each SNP probeset, we calculated the median copy number across replicate arrays. The median copy number profile for each tumor cell line was segmented with the GLAD algorithm (PMID: 15381628) to partition the genome into regions of constant copy number. We compared the copy-number alterations detected by GLAD segmentation of these arrays with statistical analyses of short sequence reads obtained from the Illumina/Solexa 1G GenomeAnalyzer. Shotgun sequencing results can be found in the NCBI Short Read Archive, accession number SRP000246. Keywords: disease state analysis
2008-11-14 | GSE12019 | GEO
Project description:The discovery of circulating tumour DNA molecules created a paradigm shift in tumour biomarkers as predictors of recurrence. Non-invasive prenatal testing (NIPT) to detect circulating cell-free foetal DNA in maternal plasma is increasingly recognised as a valuable substitute to perceive foetal copy number variation (CNV). This study aimed to determine whether the copy number detection in plasma samples using NIPT platform could be used as a prognostic biomarker in patients with gynaecological cancer. We conducted a prospective study using samples containing preoperative plasma from 100 women with gynaecological cancers. Samples were randomly rearranged and blindly sequenced using a low-coverage whole-genome sequencing plasma DNA, NIPT platform. The NIPT pipeline identified copy number alterations (CNAs) were counted in plasma as a gain or loss if they exceeded 10?Mb from the expected diploid coverage. Progression-free survival (PFS) and overall survival (OS) were analysed according to the presence of CNA in plasma using Kaplan-Meier analyses. The NIPT pipeline detected 19/100 cases of all gynaecological cancers, including 6/36 ovarian cancers, 3/11 cervical cancers, and 10/53 endometrial cancers. Patients with CNA in plasma had a significantly poorer prognosis in all stages concerning PFS and OS. Therefore, low-coverage sequencing NIPT platform could serve as a predictive marker of patient outcome.
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Project description:Benign metastasizing leiomyoma (BML) is a rare disease entity typically presenting as multiple extrauterine leiomyomas associated with a uterine leiomyoma. It has been hypothesized that the extrauterine leiomyomata represent distant metastasis of the uterine leiomyoma. To date, the only molecular evidence supporting this hypothesis was derived from clonality analyses based on X-chromosome inactivation assays. Here, we sought to address this issue by examining paired specimens of synchronous pulmonary and uterine leiomyomata from three patients using targeted massively parallel sequencing and molecular inversion probe array analysis for detecting somatic mutations and copy number aberrations. We detected identical non-hot-spot somatic mutations and similar patterns of copy number aberrations (CNAs) in paired pulmonary and uterine leiomyomata from two patients, indicating the clonal relationship between pulmonary and uterine leiomyomata. In addition to loss of chromosome 22q found in the literature, we identified additional recurrent CNAs including losses of chromosome 3q and 11q. In conclusion, our findings of the clonal relationship between synchronous pulmonary and uterine leiomyomas support the hypothesis that BML represents a condition wherein a uterine leiomyoma disseminates to distant extrauterine locations.
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Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2011-04-07 | E-GEOD-23056 | biostudies-arrayexpress
Project description:Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields of biology and medicine. However, the WGA-induced bias significantly limits sensitivity and specificity for CNVs detection. Addressing these limitations, we developed a practical bioinformatic methodology for CNVs detection at the single cell level using low coverage massively parallel sequencing. This method consists of GC correction for WGA-induced bias removal, binary segmentation algorithm for locating CNVs breakpoints, and dynamic threshold determination for final signals filtering. Afterwards, we evaluated our method with seven test samples using low coverage sequencing (4?9.5%). Four single-cell samples from peripheral blood, whose karyotypes were confirmed by whole genome sequencing analysis, were acquired. Three other test samples derived from blastocysts whose karyotypes were confirmed by SNP-array analysis were also recruited. The detection results for CNVs of larger than 1 Mb were highly consistent with confirmed results reaching 99.63% sensitivity and 97.71% specificity at base-pair level. Our study demonstrates the potential to overcome WGA-bias and to detect CNVs (>1 Mb) at the single cell level through low coverage massively parallel sequencing. It highlights the potential for CNVs research on single cells or limited DNA samples and may prove as a promising tool for research and clinical applications, such as pre-implantation genetic diagnosis/screening, fetal nucleated red blood cells research and cancer heterogeneity analysis.
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