Banding Together: A Systematic Comparison of The Cancer Genome Atlas and the Mitelman Databases

Denomy, Connor; Germain, Samuel; Haave, Bjorn; Vizeacoumar, Frederick S.; Freywald, Andrew; Weaver, Beth A.

doi:10.1158/0008-5472.can-19-0585

Cited by 7 publications

(10 citation statements)

References 23 publications

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“…The notebooks compare the frequencies of copy number changes in the Mitelman DB with those calculated from TCGA data for three well‐known deletions: breast cancer (Chromosome 1), kidney adenocarcinoma (Chromosome 3), and acute myeloid leukemia (Chromosome 5). In agreement with Denomy et al 14 we found similar patterns of gains and losses in the two data sets (Figure 5). Moreover, Table S1 (Supplementary material) shows the Pearson correlation of frequencies computed from Mitelman DB and TCGA‐BRCA for each chromosome.…”

Section: Resultssupporting

confidence: 93%

“…Moreover, Table S1 (Supplementary material) shows the Pearson correlation of frequencies computed from Mitelman DB and TCGA‐BRCA for each chromosome. According to the analysis, most of the significant correlations of Table S1 are also significant in the results of Denomy et al 14 with few exceptions, likely due to different resolution levels.…”

Section: Resultsmentioning

confidence: 80%

“…Several methods to parse cytogenetic nomenclature 14–16 have been recently developed. ISCN‐SNAKE 14 has been used to compare a select number of malignant neoplasms in the Mitelman DB with sequence data from The Cancer Genome Atlas (TCGA) project and was found to have a very good correspondence as regards chromosome gains, amplifications, and heterozygous deletions, indicating that cytogenetically identified aberrations, in spite of their lower resolution level, are in fact on par with genomic analyses. CytoGPS 16 parses ISCN 17 karyotypes into a machine‐readable format and converts it into a binary Loss‐Gain‐Fusion (LGF) model which allows researchers to process automatically thousands of karyotypes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A cloud‐based resource for genome coordinate‐based exploration and large‐scale analysis of chromosome aberrations and gene fusions in cancer

Wang

Zheng

Lee

et al. 2023

Genes Chromosomes & Cancer

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Cytogenetic analysis provides important information on the genetic mechanisms of cancer. The Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer (Mitelman DB) is the largest catalog of acquired chromosome aberrations, presently comprising >70 000 cases across multiple cancer types. Although this resource has enabled the identification of chromosome abnormalities leading to specific cancers and cancer mechanisms, a large-scale, systematic analysis of these aberrations and their downstream implications has been difficult due to the lack of a standard, automated mapping from aberrations to genomic coordinates. We previously introduced CytoConverter as a tool that automates such conversions. Cyto-Converter has now been updated with improved interpretation of karyotypes and has been integrated with the Mitelman DB, providing a comprehensive mapping of the 70 000+ cases to genomic coordinates, as well as visualization of the frequencies of chromosomal gains and losses. Importantly, all CytoConverter-generated genomic coordinates are publicly available in Google BigQuery, a cloud-based data warehouse, facilitating data exploration and integration with other datasets hosted by the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC) Resource. We demonstrate the use of BigQuery for integrative analysis of Mitelman DB with other cancer datasets, including a comparison of the frequency of imbalances identified in Mitelman DB cases with those found in The Cancer Genome Atlas (TCGA) copy number datasets. This solution provides opportunities to leverage the power of cloud computing for low-cost, scalable, and integrated analysis of chromosome aberrations and gene fusions in cancer.

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Section: Resultssupporting

confidence: 93%

Section: Resultsmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

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A cloud‐based resource for genome coordinate‐based exploration and large‐scale analysis of chromosome aberrations and gene fusions in cancer

Wang

Zheng

Lee

et al. 2023

Genes Chromosomes & Cancer

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“…The rearrangement produces a fused tyrosine kinase signaling protein that directly affects the cell cycle, frequently causing chronic myelocytic leukemia and, less frequently, acute lymphoblastic leukemia. Chromosome rearrangements and genome instability are now considered as hallmarks of cell transformation, with hundreds of signature rearrangements identified in human cancers (49). Recently, genomic techniques have been used to study cancer genomes on a large scale (50), confirming and dramatically extending earlier findings.…”

Section: Chromosomal Rearrangements That Affect Phenotypementioning

confidence: 59%

Vertebrate Chromosome Evolution

Damas

Corbo

Lewin

2021

Annu. Rev. Anim. Biosci.

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The study of chromosome evolution is undergoing a resurgence of interest owing to advances in DNA sequencing technology that facilitate the production of chromosome-scale whole-genome assemblies de novo. This review focuses on the history, methods, discoveries, and current challenges facing the field, with an emphasis on vertebrate genomes. A detailed examination of the literature on the biology of chromosome rearrangements is presented, specifically the relationship between chromosome rearrangements and phenotypic evolution, adaptation, and speciation. A critical review of the methods for identifying, characterizing, and visualizing chromosome rearrangements and computationally reconstructing ancestral karyotypes is presented. We conclude by looking to the future, identifying the enormous technical and scientific challenges presented by the accumulation of hundreds and eventually thousands of chromosome-scale assemblies. Expected final online publication date for the Annual Review of Animal Biosciences, Volume 9 is February 16, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…The database is searchable by a wide variety of fields, such as patient age, publication authors, gene, tumor histology, tissue type, mutation recurrence, associated clinical features, and cancer types. 255 …”

Section: Fusion Oncogene Databasesmentioning

confidence: 99%

Clinically relevant fusion oncogenes: detection and practical implications

et al. 2022

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Mechanistically, chimeric genes result from DNA rearrangements and include parts of preexisting normal genes combined at the genomic junction site. Some rearranged genes encode pathological proteins with altered molecular functions. Those which can aberrantly promote carcinogenesis are called fusion oncogenes. Their formation is not a rare event in human cancers, and many of them were documented in numerous study reports and in specific databases. They may have various molecular peculiarities like increased stability of an oncogenic part, self-activation of tyrosine kinase receptor moiety, and altered transcriptional regulation activities. Currently, tens of low molecular mass inhibitors are approved in cancers as the drugs targeting receptor tyrosine kinase (RTK) oncogenic fusion proteins, that is, including ALK, ABL, EGFR, FGFR1-3, NTRK1-3, MET, RET, ROS1 moieties. Therein, the presence of the respective RTK fusion in the cancer genome is the diagnostic biomarker for drug prescription. However, identification of such fusion oncogenes is challenging as the breakpoint may arise in multiple sites within the gene, and the exact fusion partner is generally unknown. There is no gold standard method for RTK fusion detection, and many alternative experimental techniques are employed nowadays to solve this issue. Among them, RNA-seq-based methods offer an advantage of unbiased high-throughput analysis of only transcribed RTK fusion genes, and of simultaneous finding both fusion partners in a single RNA-seq read. Here we focus on current knowledge of biology and clinical aspects of RTK fusion genes, related databases, and laboratory detection methods.

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Banding Together: A Systematic Comparison of The Cancer Genome Atlas and the Mitelman Databases

Cited by 7 publications

References 23 publications

A cloud‐based resource for genome coordinate‐based exploration and large‐scale analysis of chromosome aberrations and gene fusions in cancer

A cloud‐based resource for genome coordinate‐based exploration and large‐scale analysis of chromosome aberrations and gene fusions in cancer

Vertebrate Chromosome Evolution

Clinically relevant fusion oncogenes: detection and practical implications

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