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

Author:

Wang Janet1ORCID,Zheng Jeanne2,Lee Elaine E.3,Aguilar Boris3ORCID,Phan John4,Abdilleh Kawther5,Taylor Ronald C.6ORCID,Longabaugh William3ORCID,Johansson Bertil7,Mertens Fredrik7,Mitelman Felix7,Pot David4ORCID,LaFramboise Thomas1

Affiliation:

1. Department of Genetics and Genome Sciences Case Western Reserve University Cleveland Ohio USA

2. School of Public Health Brown University Providence Rhode Island USA

3. Institute for Systems Biology Seattle Washington USA

4. General Dynamics Information Technology Rockville Maryland USA

5. Pancreatic Cancer Action Network Manhattan Beach California USA

6. Division of Cancer Treatment and Diagnosis, National Cancer Institute Rockville Maryland USA

7. Division of Clinical Genetics, Department of Laboratory Medicine Lund University Lund Sweden

Abstract

AbstractCytogenetic 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. CytoConverter 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.

Funder

National Institutes of Health

Cancerfonden

Barncancerfonden

Publisher

Wiley

Subject

Cancer Research,Genetics

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