title: Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers.
abstract: OBJECTIVE
  In this pilot study, the authors examined associations between image-based phenotypes and genomic biomarkers. The potential genetic contribution of UGT2B genes to interindividual variation in breast density and mammographic parenchymal patterns is demonstrated by performing an association study between image-based phenotypes and genomic biomarkers [single-nucleotide polymorphism (SNP) genotypes].
  
  
  METHODS
  This candidate-gene approach study included 179 subjects for whom both mammograms and blood DNA samples had been obtained. The full-field digital mammograms were acquired using a GE Senographe 2000D FFDM system (12-bit; 0.1 mm-pixel size). Regions-of-interest, 256 × 256 pixels in size, selected from the central breast region behind the nipple underwent computerized image analysis to yield image-based phenotypes of mammographic density and parenchymal texture patterns. SNP genotyping was performed using a Sequenom MassArray System. One hundred twenty three SNPs with minor allele frequency above 5% were genotyped for the UGT2B gene clusters, and used in the study. The association between the image-based phenotypes and genomic biomarkers was assessed with the Pearson correlation coefficient via thePLINK software, and included permutation and correction for multiple SNP comparisons.
  
  
  RESULTS
  From the phenotype-genotype association analysis, a parenchyma texture coarseness feature was found to be correlated with SNP rs451632 after multiple test correction for the multiple SNPs (p = 0.022). The power law β, which is used to characterize the frequency component of texture patterns, was found to be correlated with SNP rs4148298 (p = 0.035).
  
  
  CONCLUSIONS
  The authors' results indicate that UGT2B gene variation may contribute to interindividual variation in mammographic parenchymal patterns and breast density. Understanding the relationship between image-based phenotypes and genomic biomarkers may help understand the biologic mechanism for image-based biomarkers and yield a future role in personalized medicine.
authors.affiliation: Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637.
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.affiliation: Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637.
authors.forename: Maryellen L
authors.initials: ML
authors.lastname: Giger
authors.affiliation: Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637.
authors.forename: Chang
authors.initials: C
authors.lastname: Sun
authors.affiliation: Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637.
authors.forename: Umnouy
authors.initials: U
authors.lastname: Ponsukcharoen
authors.affiliation: Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637.
authors.forename: Dezheng
authors.initials: D
authors.lastname: Huo
authors.affiliation: Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637.
authors.forename: Li
authors.initials: L
authors.lastname: Lan
authors.affiliation: Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637.
authors.forename: Olufunmilayo I
authors.initials: OI
authors.lastname: Olopade
authors.affiliation: Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637.
authors.forename: Andrew R
authors.initials: AR
authors.lastname: Jamieson
authors.affiliation: Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637.
authors.forename: Jeremy Bancroft
authors.initials: JB
authors.lastname: Brown
authors.affiliation: Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637.
authors.forename: Anna
authors.initials: A
authors.lastname: Di Rienzo
mesh_terms: D000465:Algorithms; D000483:Alleles; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism* / Q000473:pathology*; D016208:Databases, Factual; D005260:Female; D005583:Fourier Analysis; D005787:Gene Frequency; D056726:Genetic Association Studies; D005838:Genotype; D014453:Glucuronosyltransferase / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D008327:Mammography / Q000379:methods*; D005810:Multigene Family; D010641:Phenotype; D010865:Pilot Projects; D020641:Polymorphism, Single Nucleotide; D012984:Software
chemical_list: D014453:Glucuronosyltransferase