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

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 the PLINK 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.

Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status

“…After adjustment for planar mammographic percent density (PD), each feature attenuated only slightly and retained statistical significance; however, simultaneous inclusion of these features in a model with PD did not significantly improve the ability to predict breast cancer [8]. Other studies have shown that differences in texture and density features are related to predisposing mutations and tumor type including BRCA1/BRCA2 mutation carriers [12–14] and estrogen receptor (ER) status [15–17]. Thus, the density patterns of the parenchymal tissue have attracted clinical attention because of their potential to offer additional information about subtype and cancer biology.…”

Detecting the “gist” of breast cancer in mammograms three years before localized signs of cancer are visible

“…For example, a Bayesian Artificial Neural Network algorithm, can distinguish the appearance of the parenchyma in patients with or without BRCA1/2-related breast cancer. 35,36 Potentially, the gist signal that humans detect could be a marker for one or more genetic subtypes. This is a question that our current data cannot address but would be worthy of further investigation.…”

Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

“…Furthermore, the literature lacks large-scale longitudinal studies monitoring longitudinal changes in automated parenchymal texture descriptors over successive mammograms, which could elucidate the mechanisms of breast cancer development [ 11 ] and the causal relations between the texture risk scoring and breast cancer [ 102 , 103 ]. Finally, crucial questions to be addressed in such rich datasets are the causes of inter-woman variation in mammographic parenchymal patterns [ 104 , 105 ] and in the relation of texture risk markers to the subsequent location and grading of tumors, disease mortality, and treatment effects [ 20 , 21 ].…”