Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data

Haoyun Lei, E. Michael Gertz, Alejandro A. Schäffer, Xuecong Fu, Yifeng Tao, Kerstin Heselmeyer-Haddad, Irianna Torres, Guibo Li, Liqin Xu, Yong Hou, Kui Wu, Xulian Shi, Michael Dean, Thomas Ried, Russell Schwartz*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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. 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. Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution.
Original languageEnglish
JournalBioinformatics
ISSN1367-4811
DOIs
Publication statusAccepted/In press - 2021

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