title: Enhancing cancer clonality analysis with integrative genomics.
abstract: BACKGROUND
  It is understood that cancer is a clonal disease initiated by a single cell, and that metastasis, which is the spread of cancer from the primary site, is also initiated by a single cell. The seemingly natural capability of cancer to adapt dynamically in a Darwinian manner is a primary reason for therapeutic failures. Survival advantages may be induced by cancer therapies and also occur as a result of inherent cell and microenvironmental factors. The selected "more fit" clones outmatch their competition and then become dominant in the tumor via propagation of progeny. This clonal expansion leads to relapse, therapeutic resistance and eventually death. The goal of this study is to develop and demonstrate a more detailed clonality approach by utilizing integrative genomics.
  
  
  METHODS
  Patient tumor samples were profiled by Whole Exome Sequencing (WES) and RNA-seq on an Illumina HiSeq 2500 and methylation profiling was performed on the Illumina Infinium 450K array. STAR and the Haplotype Caller were used for RNA-seq processing. Custom approaches were used for the integration of the multi-omic datasets.
  
  
  RESULTS
  Reported are major enhancements to CloneViz, which now provides capabilities enabling a formal tumor multi-dimensional clonality analysis by integrating: i) DNA mutations, ii) RNA expressed mutations, and iii) DNA methylation data. RNA and DNA methylation integration were not previously possible, by CloneViz (previous version) or any other clonality method to date. This new approach, named iCloneViz (integrated CloneViz) employs visualization and quantitative methods, revealing an integrative genomic mutational dissection and traceability (DNA, RNA, epigenetics) thru the different layers of molecular structures.
  
  
  CONCLUSIONS
  The iCloneViz approach can be used for analysis of clonal evolution and mutational dynamics of multi-omic data sets. Revealing tumor clonal complexity in an integrative and quantitative manner facilitates improved mutational characterization, understanding, and therapeutic assignments.
authors.forename: Erich A
authors.initials: EA
authors.lastname: Peterson
authors.forename: Michael A
authors.initials: MA
authors.lastname: Bauer
authors.forename: Shweta S
authors.initials: SS
authors.lastname: Chavan
authors.forename: Cody
authors.initials: C
authors.lastname: Ashby
authors.forename: Niels
authors.initials: N
authors.lastname: Weinhold
authors.forename: Christoph J
authors.initials: CJ
authors.lastname: Heuck
authors.forename: Gareth J
authors.initials: GJ
authors.lastname: Morgan
authors.forename: Donald J
authors.initials: DJ
authors.lastname: Johann
mesh_terms: D060965:Clonal Evolution / Q000235:genetics*; D044127:Epigenesis, Genetic; D057890:Epigenomics / Q000379:methods*; D023281:Genomics / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000235:genetics*