Haoyun Lei1, E Michael Gertz2, Alejandro A Schäffer2, Xuecong Fu3, Yifeng Tao1, Kerstin Heselmeyer-Haddad4, Irianna Torres4, Guibo Li5, Liqin Xu6, Yong Hou5, Kui Wu5, Xulian Shi3, Michael Dean7, Thomas Ried4, Russell Schwartz1,8. 1. Computational Biology Dept, Carnegie Mellon University, Pittsburgh, PA, 15213, USA. 2. Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA. 3. Shenzhen Luohu People's Hospital, Shenzhen, 518000, China. 4. Genetics Branch, Cancer Genomics Section, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA. 5. Department of Biology, University of Copenhagen, Copenhagen, 1599, Denmark. 6. Department of Biotechnology and Biomedicine, Technical University of Denmark, Soltofts Plads, 2800 Kongens Lyngby, Denmark. 7. Laboratory of Translational Genomics, Division of Cancer Epidemiology & Genetics, National Cancer Institute, U.S. National Institutes of Health, Gaithersburg, MD, 20814, USA. 8. Dept. of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Abstract
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 (SV) 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 the present work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization (miFISH) 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 (ILP) 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: Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution.
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 (SV) 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 the present work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization (miFISH) 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 (ILP) 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: Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution.
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Authors: Salim Akhter Chowdhury; E Michael Gertz; Darawalee Wangsa; Kerstin Heselmeyer-Haddad; Thomas Ried; Alejandro A Schäffer; Russell Schwartz Journal: Bioinformatics Date: 2015-06-15 Impact factor: 6.937
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