Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers

Hui Li, Maryellen L. Giger, Chang Sun, Umnouy Ponsukcharoen, Dezheng Huo, Li Lan, Olufunmilayo I. Olopade, Andrew R. Jamieson, Jeremy Bancroft Brown, Anna Di Rienzo

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Purpose: 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.

Original languageEnglish (US)
Article number031917
JournalMedical physics
Volume41
Issue number3
DOIs
StatePublished - Mar 2014
Externally publishedYes

Fingerprint

Single Nucleotide Polymorphism
Biomarkers
Breast Neoplasms
Phenotype
Genes
Precision Medicine
Nipples
Genetic Association Studies
Multigene Family
Gene Frequency
Breast
Software
Genotype
DNA
Breast Density

Keywords

  • association study
  • CAD
  • genomic biomarkers
  • image-based phenotypes
  • mammographic parenchymal patterns
  • quantitative image analysis

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

Li, H., Giger, M. L., Sun, C., Ponsukcharoen, U., Huo, D., Lan, L., ... Rienzo, A. D. (2014). Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. Medical physics, 41(3), [031917]. https://doi.org/10.1118/1.4865811

Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. / Li, Hui; Giger, Maryellen L.; Sun, Chang; Ponsukcharoen, Umnouy; Huo, Dezheng; Lan, Li; Olopade, Olufunmilayo I.; Jamieson, Andrew R.; Brown, Jeremy Bancroft; Rienzo, Anna Di.

In: Medical physics, Vol. 41, No. 3, 031917, 03.2014.

Research output: Contribution to journalArticle

Li, H, Giger, ML, Sun, C, Ponsukcharoen, U, Huo, D, Lan, L, Olopade, OI, Jamieson, AR, Brown, JB & Rienzo, AD 2014, 'Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers', Medical physics, vol. 41, no. 3, 031917. https://doi.org/10.1118/1.4865811
Li, Hui ; Giger, Maryellen L. ; Sun, Chang ; Ponsukcharoen, Umnouy ; Huo, Dezheng ; Lan, Li ; Olopade, Olufunmilayo I. ; Jamieson, Andrew R. ; Brown, Jeremy Bancroft ; Rienzo, Anna Di. / Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. In: Medical physics. 2014 ; Vol. 41, No. 3.
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abstract = "Purpose: 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.",
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