Rationale and Objectives
The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors.
Materials and Methods
Our institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and to participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature, including demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to Breast Imaging Reporting and Data System (BI-RADS). We developed predictive models using logistic regression to determine the predictive ability of (1) demographic variables, (2) 10 selected genetic variants, or (3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross-validation, used this risk estimate to construct Receiver Operator Characteristic Curve (ROC) curves, and compared the area under the ROC curve (AUC) of each using the DeLong method.
Results
The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants ( P = 0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; P < 0.001) and the genetic model (AUC = .601; P < 0.001).
Conclusions
BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy.
Introduction
Over the last several decades, predictive variables have been discovered and incorporated into risk prediction models with the goal of personalizing breast cancer screening and diagnosis. One highly predictive source of information is abnormality level feature descriptors observed on mammography as described in the Breast Imaging Reporting and Data System (BI-RADS) . Other emerging sources are the ever-growing genome-wide association studies (GWAS) that identify genetic variants (single nucleotide polymorphisms [SNPs]). The SNPs discovered via recent GWAS are distinct from mutations in the BRCA1 and BRCA2 tumor suppressor genes . Although both are germline genetic risk factors (inherited from parental lineage), SNPs discovered in recent GWAS are single base pair DNA sequence variations conferring modest risk (low penetrance) but occurring commonly (high frequency) within the human population. Expansion of genetic risk prediction may depend on polygenic risk stratification, that is, weighing many high-frequency, low-penetrance SNPs at once . Early attempts to use such SNPs to predict breast cancer risk have demonstrated only modest improvements over conventional demographic risk factors, like those in the Gail model .
Breast cancer risk is determined by a combination of genetic and environmental factors. Intermediate phenotypes like imaging can capture and convey these interactions of these risk factors and provide biomarkers that can augment comprehensive risk prediction. Because demographic risk factors, genetic variants, and imaging features will all likely have some level of predictive value, determining which variables provide the best predictive power in any given setting becomes extremely important. Investing limited resources in collection of the best predictive variables will provide the most benefit. Prior literature evaluated risk prediction with genetics and breast density and one paper added BI-RADS assessment category . Despite the proven predictive ability of abnormality-level features described in the BI-RADS lexicon (e.g. mass and calcification descriptors as well as associated findings like architectural distortion), comparison to demographic or genetic risk has been limited. To estimate breast cancer risk in women recommended for breast biopsy, we compare the performance of predictive models using distinct data elements: demographic risk factors, germline genetic variants, or mammography abnormality features.
Materials and Methods
Subjects
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Epidemiological Risk Factors
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Genetic Variants
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Table 1
Common Genetic Variants Associated with Breast Cancer
SNPs Chromosome Gene High-risk Allele Low-risk Allele RS1045485 2q CASP8 G C RS13281615 8q Unknown G A RS13387042 2q Unknown A G RS2981582 10q FGFR2 T C RS3803662 16q TOX3 T C RS3817198 11p LSP1 C T RS889312 5q MAP3K1 C A RS10941679 5p Unknown G A RS999737 14q RAD51L1 C T RS11249433 1p Unknown C T
SNP, single nucleotide polymorphism.
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Mammography Features
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Statistical Analysis
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Table 2
Demographic Variables, Genetic Factors, and Mammographic Features in the Predictive Models \*
Variable Cases
( N = 373) Controls
( N = 395) OR 95% CI ¥P Value ¥ Age at menarche ≥14 105 (28.2%) 84 (21.2%) Referent 12–13 189 (50.7%) 224 (56.6%) 0.63 (0.33,1.21) 0.15 7–11 79 (21.1%) 88 (22.2%) 0.73 (0.29,1.83) 0.46 Number of biopsies 0 303 (81.2%) 337 (85.6%) Referent 1 60 (16.0%) 52 (13.1%) 1.35 (0.84,2.16) 0.21 ≥2 10 (2.6%) 6 (1.5%) 2.89 (0.95,8.75) 0.061 Number of pregnancies ≥6 52 (14.0%) 47 (12.0%) Referent 3–5 164 (43.9%) 175 (44.4%) 1.09 (0.63,1.89) 0.76 1–2 126 (33.8%) 129 (32.7%) 1.18 (0.66,2.13) 0.57 0 31 (8.3%) 43 (10.9%) 0.72 (0.34,1.51) 0.38 Number of first-degree relatives with breast cancer 0 268 (71.8%) 325 (82.2%) Referent 1 91 (24.4%) 57 (14.4%)2.05(1.34,3.15)0.0010 ≥2 14 (3.7%) 13 (3.2%) 1.68 (0.70,4.04) 0.24 Number of risk-conferring variant alleles 0–6 26 (6.9%) 58 (14.6%) Referent 7 or 8 97 (26.0%) 126 (31.9%)1.92(1.04,3.55)0.037 9 or 10 135 (36.1%) 126 (31.9%)2.69(1.46,4.95)0.0016 11 or 12 90 (24.1%) 73 (18.4%)2.74(1.41,5.30)0.0029 ≥13 25 (6.7%) 12 (3.0%)4.95(1.89,12.96)0.0012 Breast density Fatty 24 (6.5%) 26 (6.5%) Referent Scattered 30 (8.1%) 58 (14.6%) 0.46 (0.12,1.80) 0.24 Heterogeneous 293 (78.6%) 298 (75.4%) 0.99 (0.25,3.92) 0.98 Extremely dense 25 (6.8%) 14 (3.4%) 2.47 (0.27,22.27) 0.37 Mass margin Circumscribed 16 (4.2%) 36 (9.1%)0.42(0.21,0.85)0.016 Obscured 8 (2.1%) 14 (3.5%) 0.59 (0.22,1.58) 0.29 Ill defined 47 (12.6%) 48 (12.1%) 1.42 (0.87,2.31) 0.16 Spiculated 82 (21.9%) 4 (1.0%)27.56(9.64,78.78)<0.001 Suspicious microcalcification shape 63 (16.8%) 79 (20.0%) 1.49 (0.82,2.72) 0.19 Suspicious microcalcification distribution 79 (21.1%) 117 (29.6%)0.56(0.33,0.94)0.029 Architectural distortion 50 (13.4%) 21 (5.3%)2.32(1.25,4.28)0.0073
¥ Bold signifies statistically significant variables.
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Assessment of Model Performance
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Institutional Review Board (IRB) Approvals
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Results
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Table 3
Frequency of Microcalcification Descriptors in Cases and Controls
Control Case Total Microcalcification shape Benign descriptor 33 (53%) 29 (47%) 62 Amorphous 10 (83%) 2 (17%) 12 Pleomorphic 70 (54%) 59 (46%) 129 Fine linear 0 (0%) 5 (100%) 5 Total 113 95 208 Microcalcification distribution Benign descriptor 11 (52%) 10 (48%) 21 Grouped 115 (62%) 71 (38%) 186 Segmental 0 (0%) 6 (100%) 6 Linear 18 (46%) 21 (54%) 39 Total 144 108 252
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Model Performance
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Discussion
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Funding
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Acknowledgments
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Appendix
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Table A1
Performance of MAMMOGRAPHIC Models Using Progressively More Recent Subsets of the Data
Time Frame_n_ AUC 1989–2010 768 .693 1993–2010 743 .701 1998–2010 662 .711
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