title: Detecting copy number status and uncovering subclonal markers in heterogeneous tumor biopsies.
abstract: BACKGROUND
  Genomic aberrations can be used to determine cancer diagnosis and prognosis. Clinically relevant novel aberrations can be discovered using high-throughput assays such as Single Nucleotide Polymorphism (SNP) arrays and next-generation sequencing, which typically provide aggregate signals of many cells at once. However, heterogeneity of tumor subclones dramatically complicates the task of detecting aberrations.
  
  
  RESULTS
  The aggregate signal of a population of subclones can be described as a linear system of equations. We employed a measure of allelic imbalance and total amount of DNA to characterize each locus by the copy number status (gain, loss or neither) of the strongest subclonal component. We designed simulated data to compare our measure to existing approaches and we analyzed SNP-arrays from 30 melanoma samples and transcriptome sequencing (RNA-Seq) from one melanoma sample.We showed that any system describing aggregate subclonal signals is underdetermined, leading to non-unique solutions for the exact copy number profile of subclones. For this reason, our illustrative measure was more robust than existing Hidden Markov Model (HMM) based tools in inferring the aberration status, as indicated by tests on simulated data. This higher robustness contributed in identifying numerous aberrations in several loci of melanoma samples. We validated the heterogeneity and aberration status within single biopsies by fluorescent in situ hybridization of four affected and transcriptionally up-regulated genes E2F8, ETV4, EZH2 and FAM84B in 11 melanoma cell lines. Heterogeneity was further demonstrated in the analysis of allelic imbalance changes along single exons from melanoma RNA-Seq.
  
  
  CONCLUSIONS
  These studies demonstrate how subclonal heterogeneity, prevalent in tumor samples, is reflected in aggregate signals measured by high-throughput techniques. Our proposed approach yields high robustness in detecting copy number alterations using high-throughput technologies and has the potential to identify specific subclonal markers from next-generation sequencing data.
authors.affiliation: Department of Pathology and Yale Cancer Center, Yale University School of Medicine, New Haven, Connecticut, USA.
authors.forename: Fabio
authors.initials: F
authors.lastname: Parisi
authors.forename: Stephan
authors.initials: S
authors.lastname: Ariyan
authors.forename: Deepak
authors.initials: D
authors.lastname: Narayan
authors.forename: Antonella
authors.initials: A
authors.lastname: Bacchiocchi
authors.forename: Kathleen
authors.initials: K
authors.lastname: Hoyt
authors.forename: Elaine
authors.initials: E
authors.lastname: Cheng
authors.forename: Fang
authors.initials: F
authors.lastname: Xu
authors.forename: Peining
authors.initials: P
authors.lastname: Li
authors.forename: Ruth
authors.initials: R
authors.lastname: Halaban
authors.forename: Yuval
authors.initials: Y
authors.lastname: Kluger
mesh_terms: D000483:Alleles; D014408:Biomarkers, Tumor / Q000235:genetics*; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D056915:DNA Copy Number Variations / Q000235:genetics; D018628:Gene Dosage / Q000235:genetics*; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology*; D015203:Reproducibility of Results
chemical_list: D014408:Biomarkers, Tumor