Dataset Information

Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint.

ABSTRACT: GenomePaint (https://genomepaint.stjude.cloud/) is an interactive visualization platform for whole-genome, whole-exome, transcriptome, and epigenomic data of tumor samples. Its design captures the inter-relatedness between DNA variations and RNA expression, supporting in-depth exploration of both individual cancer genomes and full cohorts. Regulatory non-coding variants can be inspected and analyzed along with coding variants, and their functional impact further explored by examining 3D genome data from cancer cell lines. Further, GenomePaint correlates mutation and expression patterns with patient outcomes, and supports custom data upload. We used GenomePaint to unveil aberrant splicing that disrupts the RING domain of CREBBP, discover cis activation of the MYC oncogene by duplication of the NOTCH1-MYC enhancer in B-lineage acute lymphoblastic leukemia, and explore the inter- and intra-tumor heterogeneity at EGFR in adult glioblastomas. These examples demonstrate that deep multi-omics exploration of individual cancer genomes enabled by GenomePaint can lead to biological insights for follow-up validation.

SUBMITTER: Zhou X

PROVIDER: S-EPMC7884056 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint.

Zhou Xin X Wang Jian J Patel Jaimin J Valentine Marc M Shao Ying Y Newman Scott S Sioson Edgar E Tian Liqing L Liu Yu Y Brady Samuel W SW Flasch Diane D Ma Xiaotu X Liu Yanling Y Paul Robin R Edmonson Michael N MN Rusch Michael C MC Li Chunliang C Baker Suzanne J SJ Easton John J Zhang Jinghui J

Cancer cell 20210101 1

GenomePaint (https://genomepaint.stjude.cloud/) is an interactive visualization platform for whole-genome, whole-exome, transcriptome, and epigenomic data of tumor samples. Its design captures the inter-relatedness between DNA variations and RNA expression, supporting in-depth exploration of both individual cancer genomes and full cohorts. Regulatory non-coding variants can be inspected and analyzed along with coding variants, and their functional impact further explored by examining 3D genome d ...[more]

PMID: 33434514

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Project description:Background: Improved healthcare and recent breakthroughs in technology have substantially reduced cancer mortality rates worldwide. Recent advancements in next-generation sequencing (NGS) have allowed genomic analysis of the human transcriptome. Now, using NGS we can further look into small non-coding regions of RNAs (sncRNAs) such as microRNAs (miRNAs), Piwi-interacting-RNAs (piRNAs), long non-coding RNAs (lncRNAs), and small nuclear/nucleolar RNAs (sn/snoRNAs) among others. Recent studies looking at sncRNAs indicate their role in important biological processes such as cancer progression and predict their role as biomarkers for disease diagnosis, prognosis, and therapy. Results: In the present study, we data mined publically available small RNA sequencing data from colorectal tissue samples of eight matched patients (benign, tumor, and metastasis) and remapped the data for various small RNA annotations. We identified aberrant expression of 13 miRNAs in tumor and metastasis specimens [tumor vs benign group (19 miRNAs) and metastasis vs benign group (38 miRNAs)] of which five were upregulated, and eight were downregulated, during disease progression. Pathway analysis of aberrantly expressed miRNAs showed that the majority of miRNAs involved in colon cancer were also involved in other cancers. Analysis of piRNAs revealed six to be over-expressed in the tumor vs benign cohort and 24 in the metastasis vs benign group. Only two piRNAs were shared between the two cohorts. Examining other types of small RNAs [sn/snoRNAs, mt_rRNA, miscRNA, nonsense mediated decay (NMD), and rRNAs] identified 15 sncRNAs in the tumor vs benign group and 104 in the metastasis vs benign group, with only four others being commonly expressed. Conclusion: In summary, our comprehensive analysis on publicly available small RNA-seq data identified multiple differentially expressed sncRNAs during colorectal cancer progression at different stages compared to normal colon tissue. We speculate that deciphering and validating the roles of sncRNAs may prove useful in colorectal cancer prognosis, diagnosis, and therapy.

Dataset Information

Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint.

Publications

Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint.

Similar Datasets

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