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      Whole-exome sequencing and clinical interpretation of FFPE tumor samples to guide precision cancer medicine

      research-article
      Author(s): 1 , 2 , 1 , 2 , 2 , 2 , 2 , 1 , 2 , 1 , 2 , 2 , 2 , 2 , 2 , 3 , 2 , 2 , 2 , 2 , 2 , 2 , 2 , 1 , 2 , 2 , 2 , 1 , 1 , 2 , 1 , 1 , 4 , 1 , 1 , 1 , 5 , 1 , 5 , 1 , 1 , 2 , 2 , # , 2 , 6 , # , 1 , 2
      Publication date (Electronic):
      Journal: Nature medicine

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          Abstract

          Translating whole exome sequencing (WES) for prospective clinical use may impact the care of cancer patients; however, multiple innovations are necessary for clinical implementation. These include: (1) rapid and robust WES from formalin-fixed paraffin embedded (FFPE) tumor tissue, (2) analytical output similar to data from frozen samples, and (3) clinical interpretation of WES data for prospective use. Here, we describe a prospective clinical WES platform for archival FFPE tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a “long tail” of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15/16 patients. In one patient, previously undetected findings guided clinical trial enrollment leading to an objective clinical response. Overall, this methodology may inform the widespread implementation of precision cancer medicine.

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          Is Open Access

          NCBI Reference Sequences: current status, policy and new initiatives

          Kim D. Pruitt, Tatiana Tatusova, William Klimke … (2009)
          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.
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            High-throughput oncogene mutation profiling in human cancer.

            Roman Thomas, Alissa Baker, Ralph M Debiasi … (2007)
            Systematic efforts are underway to decipher the genetic changes associated with tumor initiation and progression. However, widespread clinical application of this information is hampered by an inability to identify critical genetic events across the spectrum of human tumors with adequate sensitivity and scalability. Here, we have adapted high-throughput genotyping to query 238 known oncogene mutations across 1,000 human tumor samples. This approach established robust mutation distributions spanning 17 cancer types. Of 17 oncogenes analyzed, we found 14 to be mutated at least once, and 298 (30%) samples carried at least one mutation. Moreover, we identified previously unrecognized oncogene mutations in several tumor types and observed an unexpectedly high number of co-occurring mutations. These results offer a new dimension in tumor genetics, where mutations involving multiple cancer genes may be interrogated simultaneously and in 'real time' to guide cancer classification and rational therapeutic intervention.
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              High-resolution mapping of copy-number alterations with massively parallel sequencing.

              Derek Y Chiang, Gad Getz, David B. Jaffe … (2009)
              Cancer results from somatic alterations in key genes, including point mutations, copy-number alterations and structural rearrangements. A powerful way to discover cancer-causing genes is to identify genomic regions that show recurrent copy-number alterations (gains and losses) in tumor genomes. Recent advances in sequencing technologies suggest that massively parallel sequencing may provide a feasible alternative to DNA microarrays for detecting copy-number alterations. Here we present: (i) a statistical analysis of the power to detect copy-number alterations of a given size; (ii) SegSeq, an algorithm to segment equal copy numbers from massively parallel sequence data; and (iii) analysis of experimental data from three matched pairs of tumor and normal cell lines. We show that a collection of approximately 14 million aligned sequence reads from human cell lines has comparable power to detect events as the current generation of DNA microarrays and has over twofold better precision for localizing breakpoints (typically, to within approximately 1 kilobase).
              Article 0 comments Cited 198 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 9502015
                Journal ID (pubmed-jr-id): 8791
                Journal ID (nlm-ta): Nat Med
                Journal ID (iso-abbrev): Nat. Med.
                Title: Nature medicine
                ISSN (Print): 1078-8956
                ISSN (Electronic): 1546-170X
                Publication date Nihms-submitted: 16 December 2013
                Publication date (Electronic): 18 May 2014
                Publication date (Print): June 2014
                Publication date PMC-release: 01 December 2014
                Volume: 20
                Issue: 6
                Pages: 682-688
                Affiliations
                [1 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, Massachusetts 02115, USA
                [2 ]Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA
                [3 ]Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
                [4 ]Children’s Hospital Boston, Boston, MA 02115
                [5 ]Brigham and Women’s Hospital, Boston, MA 02115
                [6 ]Massachusetts General Hospital Cancer Center and Department of Pathology, Boston, MA 02114
                Author notes
                [# ] Corresponding Authors: Levi A. Garraway, M.D., Ph.D., Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, D1542, Boston, MA 02115, USA, Phone: 617-632-6689, Fax: 617- 582-7880, levi_garraway@ 123456dfci.harvard.edu , Gad Getz, Ph.D, Cancer Genome Analysis, Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA, Phone: 617-714-7471, Fax: 617-800-1152, gadgetz@ 123456broadinstitute.org
                [*]

                These authors contributed equally to this work

                [**]

                These authors contributed equally to this work

                Article
                Manuscript ID: NIHMS531666
                DOI: 10.1038/nm.3559
                PMC ID: 4048335
                PubMed ID: 24836576
                SO-VID: 79f4fb73-3e6b-4b8c-bf9d-eaf1e45fe9ff
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                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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