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We describe a woman with the known pathogenic germline variant CHEK2:c.1100delC and synchronous diagnoses of both pelvic genital type leiomyosarcoma (LMS) and metastatic invasive ductal breast carcinoma. CHEK2 (checkpoint kinase 2) is a tumor-suppressor gene encoding a serine/threonine-protein kinase (CHEK2) involved in double-strand DNA break repair and cell cycle arrest. The CHEK2:c.1100delC variant is a moderate penetrance allele resulting in an approximately twofold increase in breast cancer risk. Whole-genome and whole-transcriptome sequencing were performed on the leiomyosarcoma and matched blood-derived DNA. Despite the presence of several genomic hits within the double-strand DNA damage pathway (CHEK2 germline variant and multiple RAD51B somatic structural variants), tumor profiling did not show an obvious DNA repair deficiency signature. However, even though the LMS displayed clear malignant features, its genomic profiling revealed several characteristics classically associated with leiomyomas including a translocation, t(12;14), with one breakpoint disrupting RAD51B and the other breakpoint upstream of HMGA2 with very high expression of HMGA2 and PLAG1 This is the first report of LMS genomic profiling in a patient with the germline CHEK2:c.1100delC variant and an additional diagnosis of metastatic invasive ductal breast carcinoma. We also describe a possible mechanistic relationship between leiomyoma and LMS based on genomic and transcriptome data. Our findings suggest that RAD51B translocation and HMGA2 overexpression may play an important role in LMS oncogenesis.
Pubmed ID: 28514723
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Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsAmerican company incorporated that develops, manufactures and markets integrated systems for the analysis of genetic variation and biological function. Provides a line of products and services that serve the sequencing, genotyping and gene expression and proteomics markets. Its headquarters are located in San Diego, California.
View all literature mentionsOriginal SAMTOOLS package has been split into three separate repositories including Samtools, BCFtools and HTSlib. Samtools for manipulating next generation sequencing data used for reading, writing, editing, indexing,viewing nucleotide alignments in SAM,BAM,CRAM format. BCFtools used for reading, writing BCF2,VCF, gVCF files and calling, filtering, summarising SNP and short indel sequence variants. HTSlib used for reading, writing high throughput sequencing data.
View all literature mentionsInternational functional genomics data collection generated from microarray or next-generation sequencing (NGS) platforms. Repository of functional genomics data supporting publications. Provides genes expression data for reuse to the research community where they can be queried and downloaded. Integrated with the Gene Expression Atlas and the sequence databases at the European Bioinformatics Institute. Contains a subset of curated and re-annotated Archive data which can be queried for individual gene expression under different biological conditions across experiments. Data collected to MIAME and MINSEQE standards. Data are submitted by users or are imported directly from the NCBI Gene Expression Omnibus.
View all literature mentionsIntegrated structural variant prediction software that can detect deletions, tandem duplications, inversions and translocations at single-nucleotide resolution in short-read massively parallel sequencing data. It uses paired-ends and split-reads to sensitively and accurately delineate genomic rearrangements throughout genome.
View all literature mentionsSoftware for somatic single nucleotide variant (SNV) and small indel detection from sequencing data of matched tumor-normal samples. The method employs a novel Bayesian approach which represents continuous allele frequencies for both tumor and normal samples, whilst leveraging the expected genotype structure of the normal. This is achieved by representing the normal sample as a mixture of germline variation with noise, and representing the tumor sample as a mixture of the normal sample with somatic variation. A natural consequence of the model structure is that sensitivity can be maintained at high tumor impurity without requiring purity estimates. The method has superior accuracy and sensitivity on impure samples compared to approaches based on either diploid genotype likelihoods or general allele-frequency tests.
View all literature mentionsA hidden Markov model (HMM) for predicting somatic loss of heterozygosity and allelic imbalance in whole tumour genome sequencing data.
View all literature mentionsA software suite using feature-based classifiers for somatic mutation prediction from paired tumour/normal next-generation sequencing data. mutationSeq has the advantages of integrating different features (e.g., base qualities, mapping qualities, strand bias, and tailed distance features), and validated somatic mutations to make predictions. Given paired normal/tumour bam files, mutationSeq will output the probability of each candidate site being somatic.
View all literature mentionsSoftware providing de novo, parallel, paired-end sequence assembler that is designed for short reads. ABySS 1.0 originally showed that assembling human genome using short 50 bp sequencing reads was possible by aggregating half terabyte of compute memory needed over several computers using standardized message passing system. ABySS 2.0 is Resource Efficient Assembly of Large Genomes using Bloom Filter. ABySS 2.0 departs from MPI and instead implements algorithms that employ Bloom filter, probabilistic data structure, to represent de Bruijn graph and reduce memory requirements.
View all literature mentionsA software pipeline for analyzing ABySS-assembled contigs from shotgun transcriptome data.
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