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

Authors

  • Li Hui,

    Corresponding author
    1. Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637
    • Author to whom correspondence should be addressed. Electronic mail: huili@uchicago.edu; Telephone: 773-834-5099; Fax: 773-702-0371.

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  • Giger Maryellen L.,

    1. Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637
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  • Sun Chang,

    1. Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637
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    • a)

      H. Li and C. Sun contributed equally to this work.

    • c)

      Currently at Laboratory for Conservation and Utilization of Bio-Resources, and Key Laboratory of Microbial Diversity in Southwest China, Ministry of Education, Yunnan University, Kunming, Peopleˈs Republic of China.

  • Ponsukcharoen Umnouy,

    1. Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637
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  • Huo Dezheng,

    1. Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637
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  • Lan Li,

    1. Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637
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  • Olopade Olufunmilayo I.,

    1. Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637
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  • Jamieson Andrew R.,

    1. Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637
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  • Brown Jeremy Bancroft,

    1. Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637
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  • Di Rienzo Anna

    1. Departments of Radiology, Human Genetics, Health Studies, and Medicine, The University of Chicago, Chicago, Illinois 60637
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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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