title: Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology.
abstract: BACKGROUND
  Comparative genomic hybridization (CGH) arrays are increasingly used in personalized medicine programs to identify gene copy number aberrations (CNAs) that may be used to guide clinical decisions made during molecular tumor boards. However, analytical processes such as the centralization step may profoundly affect CGH array results and therefore may adversely affect outcomes in the precision medicine context.
  
  
  METHODS
  The effect of three different centralization methods: median, maximum peak, alternative peak, were evaluated on three datasets: (i) the NCI60 cell lines panel, (ii) the Cancer Cell Line Encyclopedia (CCLE) panel, and (iii) the patients enrolled in prospective molecular screening trials (SAFIR-01 n = 283, MOSCATO-01 n = 309), and compared with karyotyping, drug sensitivity, and patient-drug matching, respectively.
  
  
  RESULTS
  Using the NCI60 cell lines panel, the profiles generated by the alternative peak method were significantly closer to the cell karyotypes than those generated by the other centralization strategies (P < 0.05). Using the CCLE dataset, selected genes (ERBB2, EGFR) were better or equally correlated to the IC50 of their companion drug (lapatinib, erlotinib), when applying the alternative centralization. Finally, focusing on 24 actionable genes, we observed as many as 7.1% (SAFIR-01) and 6.8% (MOSCATO-01) of patients originally not oriented to a specific treatment, but who could have been proposed a treatment based on the alternative peak centralization method.
  
  
  CONCLUSIONS
  The centralization method substantially affects the call detection of CGH profiles and may thus impact precision medicine approaches. Among the three methods described, the alternative peak method addresses limitations associated with existing approaches.
authors.affiliation: Sage Bionetworks, Seattle, USA INSERM U981, Gustave Roussy, University Paris XI, Villejuif.
authors.forename: F
authors.initials: F
authors.lastname: Commo
authors.affiliation: Sage Bionetworks, Seattle, USA INSERM U981, Gustave Roussy, University Paris XI, Villejuif Department of Medical Oncology, Gustave Roussy, Villejuif, France.
authors.forename: C
authors.initials: C
authors.lastname: Ferté
authors.affiliation: INSERM U981, Gustave Roussy, University Paris XI, Villejuif Department of Medical Oncology, Gustave Roussy, Villejuif, France.
authors.forename: J C
authors.initials: JC
authors.lastname: Soria
authors.affiliation: Sage Bionetworks, Seattle, USA.
authors.forename: S H
authors.initials: SH
authors.lastname: Friend
authors.affiliation: INSERM U981, Gustave Roussy, University Paris XI, Villejuif Department of Medical Oncology, Gustave Roussy, Villejuif, France.
authors.forename: F
authors.initials: F
authors.lastname: André
authors.affiliation: Sage Bionetworks, Seattle, USA justin.guinney@sagebase.org.
authors.forename: J
authors.initials: J
authors.lastname: Guinney
mesh_terms: D015331:Cohort Studies; D055028:Comparative Genomic Hybridization / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics; D006801:Humans; D057285:Precision Medicine / Q000379:methods*
keywords: aCGH; comparative genomic hybridization; precision medicine; targeted therapy