Norman, Aaron D

Abstract Background Artificial intelligence (AI)–based, mammography breast cancer risk prediction models show improved discriminatory accuracy relative to clinical risk models. However, data on their calibration are limited. This study compared model performance of 3 clinical breast cancer risk models: Gail, Tyrer–Cuzick v8, and Breast Cancer Surveillance Consortium v3 with the MIRAI AI-risk model. Methods Digital mammograms were ascertained from a screening mammography cohort of 12 308 women within the Mayo Clinic Biobank with 250 incident breast cancers (176 invasive) within 5 years. We predicted 5-year breast cancer risk, estimated discriminatory accuracy (C-index), and calibration (observed-to-expected ratio) of both overall and invasive breast cancer and compared estimates using bootstrapping approaches. Results MIRAI demonstrated similar or improved discriminatory accuracy of overall breast cancer (C-index = 0.71, 95% confidence interval [CI] = 0.68 to 0.74) and invasive breast cancer (C-index = 0.71, 95% CI = 0.67 to 0.75) compared with clinical models (overall breast cancer: C-index = 0.59-0.68; invasive breast cancer: C-index = 0.60-0.68). MIRAI’s calibration for risk of overall breast cancer (observed-to-expected ratio = 0.96, 95% CI = 0.85 to 1.08) was improved compared with Gail (observed-to-expected ratio = 1.22, 95% CI = 1.07 to 1.38) and Breast Cancer Surveillance Consortium (observed-to-expected ratio = 1.38, 95% CI = 1.22 to 1.56) but similar to TC with volumetric percent density and polygenic risk score (observed-to-expected ratio = 0.99, 95% CI = 0.87 to 1.13). However, for low-risk women (approximately 50%), MIRAI overestimated risk of overall breast cancer. MIRAI also overestimated risk of invasive breast cancer across the risk spectrum (observed-to-expected ratio = 0.68, 95% CI = 0.58 to 0.78), whereas clinical models had good calibration (observed-to-expected ratio = 0.86–0.99). Conclusion MIRAI demonstrated stronger discriminatory accuracy than clinical models for 5-year overall and invasive breast cancer risk prediction but overestimated risk for both breast cancer endpoints. AI-based risk models should consider discriminatory accuracy and calibration for invasive cancer before implementation.

Background/Objectives: Family history is a known risk factor for multiple myeloma (MM) and its precursor condition, monoclonal gammopathy of undetermined significance (MGUS). Previous genome-wide association studies (GWASs) have identified 35 common loci associated with MM risk and 21 associated with MGUS. The objective of this study was to identify less common and rare genetic loci predisposing to MM/MGUS through whole-exome sequencing (WES)-based linkage analysis. Methods:Multipoint linkage analysis was conducted using the Multipoint Engine for Rapid Likelihood Inference (MERLIN) with the Lander–Green algorithm on germline WES data from 79 pedigrees with 2 or more affected relatives (120 MM, 86 MGUS, and 21 unaffected). Genome-wide linkage was evaluated using 12,946 independent single-nucleotide variants (linkage disequilibrium r2 < 0.05). Results: Significant linkage was observed at chromosome 6q22.33–q24.2 by the non-parametric model (logarithm-of-odds (LOD) = 3.3) and suggestive linkage by the dominant parametric model (heterogeneity LOD (HLOD) = 2.5). Fourteen rare variants within this region were prioritized using family-specific partial LOD scores and in silico functional prediction tools. Nine of these variants, REPS1, THEMIS, TAAR6, AHI1, VNN1, VNN3, MTFR2/FAM54A, LAMA2, and PHACTR2, overlapped immune-regulatory regions in blood cell lines and were not previously identified in GWASs. Conclusions: This study demonstrates the utility of applying a linkage analysis framework to familial WES data for identifying genomic regions and candidate genes that may contribute to MM/MGUS predisposition. These findings provide new insight into the inherited risk and etiology of familial MM and MGUS.

Radiomic parenchymal phenotypes on mammograms were used to predict breast cancer risk among both Black and White women, notably for false-negative findings and symptomatic interval cancer.

PURPOSE Chronic lymphocytic leukemia (CLL)–phenotype monoclonal B-cell lymphocytosis (MBL) is a premalignant condition that is roughly 500-fold more common than CLL. It is unknown whether the two-fold increased risk of developing melanoma associated with CLL extends to individuals with MBL. METHODS Using the Mayo Clinic Biobank, we identified participants who were 40 years or older with no previous hematological malignancies, who resided in the 27 counties around Mayo Clinic, and who had available biospecimens for screening. Eight-color flow cytometry was used to screen for MBL. Individuals with MBL were classified as low-count MBL (LC-MBL) or high-count MBL on the basis of clonal B-cell percent. Incident melanomas were identified using International Classification of Diseases codes and confirmed via medical records review. Cox regression models were used to estimate hazard ratios (HRs) and 95% CI. RESULTS Of the 7,334 participants screened, 1,151 were identified with a CD5-positive MBL, of whom 1,098 had LC-MBL. After a median follow-up of 3.2 years (range, 0-13.5), 131 participants developed melanoma, of whom 36 individuals were positive for MBL. The estimated 5-year cumulative incidence of melanoma was 3.4% and 2.0% among those with and without MBL, respectively. After adjusting for age, sex, and history of previous melanoma, individuals with MBL exhibited a 1.86-fold (95% CI, 1.25 to 2.78) risk of melanoma. This elevated risk persisted when analysis was restricted to those without a history of melanoma (HR, 2.05 [95% CI, 1.30 to 3.23]). Individuals with LC-MBL had a 1.92-fold (95% CI, 1.29 to 2.87) increased risk of developing melanoma overall and a 2.74-fold increased risk (95% CI, 1.50 to 5.03) of melanoma in situ compared with those without MBL. CONCLUSION LC-MBL is associated with an approximately two-fold increased risk of melanoma overall and a 2.74-fold increased risk of melanoma in situ.

PURPOSE Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown. METHODS We identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance. RESULTS On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures ( P LRT values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065, P = .01) but did not reach statistical significance for interval cancer. CONCLUSION AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.

Abstract High alcohol intake and breast density increase breast cancer (BC) risk, but their interrelationship is unknown. We examined whether volumetric density modifies and/or mediates the alcohol-BC association. BC cases (n = 2233) diagnosed from 2006 to 2013 in the San Francisco Bay area had screening mammograms 6 or more months before diagnosis; controls (n = 4562) were matched on age, mammogram date, race or ethnicity, facility, and mammography machine. Logistic regression was used to estimate alcohol-BC associations adjusted for age, body mass index, and menopause; interaction terms assessed modification. Percent mediation was quantified as the ratio of log (odds ratios [ORs]) from models with and without density measures. Alcohol consumption was associated with increased BC risk (2-sided Ptrend = .004), as were volumetric percent density (OR = 1.45 per SD, 95% confidence interval [CI] = 1.36 to 1.56) and dense volume (OR = 1.30, 95% CI = 1.24 to 1.37). Breast density did not modify the alcohol-BC association (2-sided P &gt; .10 for all). Dense volume mediated 25.0% (95% CI = 5.5% to 44.4%) of the alcohol-BC association (2-sided P = .01), suggesting alcohol may partially increase BC risk by increasing fibroglandular tissue.

Abstract Background: Our genome-wide association study (GWAS) of chronic lymphocytic leukemia (CLL) identified 4 highly correlated intronic variants within the IRF8 gene that were associated with CLL. These results were further supported by a recent meta-analysis of our GWAS with two other GWAS of CLL, supporting the IRF8 gene as a strong candidate for CLL risk. Methods: To refine the genetic association of CLL risk, we conducted Sanger sequencing of IRF8 in 94 CLL cases and 96 controls. We then conducted fine mapping by genotyping 39 variants (of which 10 were identified from sequencing) in 745 CLL cases and 1,521 controls. We also assessed these associations with risk of other non-Hodgkin lymphoma (NHL) subtypes. Results: The strongest association with CLL risk was observed with a common single-nucleotide polymorphism (SNP) located within the 3′ untranslated region (UTR) of IRF8 (rs1044873, log additive OR = 0.7, P = 1.81 × 10−6). This SNP was not associated with the other NHL subtypes (all P &gt; 0.05). Conclusions: We provide evidence that rs1044873 in the IRF8 gene accounts for the initial GWAS signal for CLL risk. This association appears to be unique to CLL with little support for association with other common NHL subtypes. Future work is needed to assess functional role of IRF8 in CLL etiology. Impact: These data provide support that a functional variant within the 3′UTR of IRF8 may be driving the GWAS signal seen on 16q24.1 for CLL risk. Cancer Epidemiol Biomarkers Prev; 22(3); 461–6. ©2013 AACR.