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Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data.
Lei, Haoyun; Gertz, E Michael; Schäffer, Alejandro A; Fu, Xuecong; Tao, Yifeng; Heselmeyer-Haddad, Kerstin; Torres, Irianna; Li, Guibo; Xu, Liqin; Hou, Yong; Wu, Kui; Shi, Xulian; Dean, Michael; Ried, Thomas; Schwartz, Russell.
Afiliación
  • Lei H; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
  • Gertz EM; Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
  • Schäffer AA; Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
  • Fu X; Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
  • Tao Y; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
  • Heselmeyer-Haddad K; Shenzhen Luohu People's Hospital, Shenzhen 518000, China.
  • Torres I; Shenzhen Luohu People's Hospital, Shenzhen 518000, China.
  • Li G; Genetics Branch, Cancer Genomics Section, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
  • Xu L; Department of Biology, University of Copenhagen, 1599 Copenhagen, Denmark.
  • Hou Y; Genetics Branch, Cancer Genomics Section, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
  • Wu K; Genetics Branch, Cancer Genomics Section, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
  • Shi X; Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
  • Dean M; Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
  • Ried T; Shenzhen Luohu People's Hospital, Shenzhen 518000, China.
  • Schwartz R; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Bioinformatics ; 37(24): 4704-4711, 2021 12 11.
Article en En | MEDLINE | ID: mdl-34289030
MOTIVATION: Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. RESULTS: In this work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Neoplasias Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Neoplasias Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido