22
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Multiregion ultra‐deep sequencing reveals early intermixing and variable levels of intratumoral heterogeneity in colorectal cancer

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Intratumor heterogeneity ( ITH) contributes to cancer progression and chemoresistance. We sought to comprehensively describe ITH of somatic mutations, copy number, and transcriptomic alterations involving clinically and biologically relevant gene pathways in colorectal cancer ( CRC). We performed multiregion, high‐depth (384× on average) sequencing of 799 cancer‐associated genes in 24 spatially separated primary tumor and nonmalignant tissues from four treatment‐naïve CRC patients. We then used ultra‐deep sequencing (17 075× on average) to accurately verify the presence or absence of identified somatic mutations in each sector. We also digitally measured gene expression and copy number alterations using NanoString assays. We identified the subclonal point mutations and determined the mutational timing and phylogenetic relationships among spatially separated sectors of each tumor. Truncal mutations, those shared by all sectors in the tumor, affected the well‐described driver genes such as APC, TP53, and  KRAS . With sequencing at 17 075×, we found that mutations first detected at a sequencing depth of 384× were in fact more widely shared among sectors than originally assessed. Interestingly, ultra‐deep sequencing also revealed some mutations that were present in all spatially dispersed sectors, but at subclonal levels. Ultra‐high‐depth validation sequencing, copy number analysis, and gene expression profiling provided a comprehensive and accurate genomic landscape of spatial heterogeneity in CRC. Ultra‐deep sequencing allowed more sensitive detection of somatic mutations and a more accurate assessment of ITH. By detecting the subclonal mutations with ultra‐deep sequencing, we traced the genomic histories of each tumor and the relative timing of mutational events. We found evidence of early mixing, in which the subclonal ancestral mutations intermixed across the sectors before the acquisition of subsequent nontruncal mutations. Our findings also indicate that different CRC patients display markedly variable ITH, suggesting that each patient's tumor possesses a unique genomic history and spatial organization.

          Related collections

          Most cited references18

          • Record: found
          • Abstract: found
          • Article: not found

          The genomic landscapes of human breast and colorectal cancers.

          Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            NCBI Reference Sequences: current status, policy and new initiatives

            NCBI's Reference Sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/) is a curated non-redundant collection of sequences representing genomes, transcripts and proteins. RefSeq records integrate information from multiple sources and represent a current description of the sequence, the gene and sequence features. The database includes over 5300 organisms spanning prokaryotes, eukaryotes and viruses, with records for more than 5.5 × 106 proteins (RefSeq release 30). Feature annotation is applied by a combination of curation, collaboration, propagation from other sources and computation. We report here on the recent growth of the database, recent changes to feature annotations and record types for eukaryotic (primarily vertebrate) species and policies regarding species inclusion and genome annotation. In addition, we introduce RefSeqGene, a new initiative to support reporting variation data on a stable genomic coordinate system.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the human and mouse genomes.

              Effective use of the human and mouse genomes requires reliable identification of genes and their products. Although multiple public resources provide annotation, different methods are used that can result in similar but not identical representation of genes, transcripts, and proteins. The collaborative consensus coding sequence (CCDS) project tracks identical protein annotations on the reference mouse and human genomes with a stable identifier (CCDS ID), and ensures that they are consistently represented on the NCBI, Ensembl, and UCSC Genome Browsers. Importantly, the project coordinates on manually reviewing inconsistent protein annotations between sites, as well as annotations for which new evidence suggests a revision is needed, to progressively converge on a complete protein-coding set for the human and mouse reference genomes, while maintaining a high standard of reliability and biological accuracy. To date, the project has identified 20,159 human and 17,707 mouse consensus coding regions from 17,052 human and 16,893 mouse genes. Three evaluation methods indicate that the entries in the CCDS set are highly likely to represent real proteins, more so than annotations from contributing groups not included in CCDS. The CCDS database thus centralizes the function of identifying well-supported, identically-annotated, protein-coding regions.
                Bookmark

                Author and article information

                Contributors
                steve.rozen@duke.nus.edu.sg
                iain.tan.b.h@singhealth.com.sg
                Journal
                Mol Oncol
                Mol Oncol
                10.1002/(ISSN)1878-0261
                MOL2
                Molecular Oncology
                John Wiley and Sons Inc. (Hoboken )
                1574-7891
                1878-0261
                20 October 2016
                February 2017
                : 11
                : 2 ( doiID: 10.1002/mol2.2017.11.issue-2 )
                : 124-139
                Affiliations
                [ 1 ] Centre for Computational Biology Duke‐NUS Medical School Singapore Singapore
                [ 2 ] Program in Cancer and Stem Cell Biology Duke‐NUS Medical School Singapore Singapore
                [ 3 ] Institute of Cellular and Molecular Biology Singapore Singapore
                [ 4 ] Department of Medical Oncology National Cancer Centre Singapore Singapore
                [ 5 ] Department of Pathology Singapore General Hospital Singapore
                [ 6 ] Genome Institute of Singapore, A*STAR Singapore
                [ 7 ] Department of Colorectal Surgery Singapore General Hospital Singapore
                [ 8 ] Colorectal Practice Mount Elizabeth Medical Centre Singapore Singapore
                [ 9 ] Cancer Science Institute National University of Singapore Singapore
                Author notes
                [*] [* ] Correspondence

                S. G. Rozen, Centre for Computational Biology, Duke‐NUS Medical School, 8 College Road, Singapore 169857, Singapore

                E‐mail: steve.rozen@ 123456duke.nus.edu.sg

                and

                I. B. Tan, National Cancer Centre Singapore, 11 Hospital Drive, Singapore 169610, Singapore

                E‐mail: iain.tan.b.h@ 123456singhealth.com.sg

                Article
                MOL212012
                10.1002/1878-0261.12012
                5527459
                28145097
                32b7069c-d41d-493d-8390-71e475f323ea
                © 2016 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 August 2016
                : 07 September 2016
                Page count
                Figures: 5, Tables: 3, Pages: 16, Words: 8000
                Funding
                Funded by: Singapore Ministry of Health and Agency for Science Technology and Research
                Funded by: National Medical Research Council
                Award ID: NMRC/CIRG/1369/2013
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                mol212012
                February 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.1.4 mode:remove_FC converted:25.07.2017

                Oncology & Radiotherapy
                colorectal cancer,copy number variation,gene expression,genetic heterogeneity

                Comments

                Comment on this article