Citations

Abstract Background: Biopsy diagnosis of benign breast disease (BBD) based on the most severe lesion in a sample predicts future breast cancer risk and has implications for screening and management. Lobules are the functional unit of the breast and the structures from which BBD arises. We developed and preliminarily validated an automated computational pathology algorithm to discriminate normal from BBD breast lobules as a step toward automated comprehensive characterization of benign biopsies. Methods: 152 BBD biopsies (27 training, 125 validation) from the Mayo Clinic were examined using scanned digital images of H&E stained sections. For each image, a pathologist annotated up to 10 representative lobules as normal or BBD using standard pathology criteria. A deep learning algorithm to quantify lobular features was developed using 129 lobules from 27 subjects. Nine features were identified that discriminate normal vs BBD lobules, expressing lobule size, acini size and number, acinar lumen size, proportion of lobular stroma, and capillaries. Here, we validate their performance to discriminate normal vs BBD lobules in a set of 1250 lobules from 125 subjects using area under the ROC curve (AUC) analysis. Random forest analysis was used for multivariable modeling; model performance was assessed with a tenfold cross-validation approach. Results: Median subject age was 52 years. Among the 125 validation subjects, BBD findings were nonproliferative in 39%, proliferative in 45%, and atypical hyperplasia in 16%. Sections included 552 (44%) normal lobules and 698 BBD (56%) lobules, with representation of both lobule types on each section. In univariate analyses, four individual features showed good discrimination between normal vs. BBD lobules, yielding the following AUCs: lobule size= 0.74, mean acini size= 0.75, epithelial area= 0.75, and number of acini with large lumens= 0.76. Lobule size and epithelial area were highly correlated (Spearman rank correlation r = 0.95), but both of these features showed lower correlation with mean acini size and number of acini with large lumens (each r < 0.5). With random forest modeling, number of acini with large lumens was the strongest discriminating feature, followed by mean acini size, lobule size, and epithelial area. With ten-fold cross validation of the multivariable random forest model, the overall AUC was 0.82 (95% CI: 0.79-0.85). Conclusion: Our validation showed that automated quantitative computational pathology assessment of breast lobules can discriminate normal versus BBD on a per lobule basis. This finding supports the feasibility of developing automated algorithms to classify every lesion in breast biopsies, expanding beyond visual assessment. Further studies using deep learning may reveal novel pathology features for classifying BBD biopsies with the potential to strengthen estimation of breast cancer risk. Citation Format: Amy C. Degnim, Thomas de Bel, Mark E. Sherman, Derek C. Radisky, Stacey J. Winham, Tanya L. Hoskin, Melody L. Stallings Mann, Marlene Frost, Robert A. Vierkant, Brendan T. Broderick, Ethan P. Heinzen, Rushin Brahmbhatt, Muhammad Arshad, Celine M. Vachon, Jodi M. Carter, Lori A. Denison, Daniel W. Visscher, Jeroen van der Laak. Discrimination of benign breast disease from normal lobules using an automated computational pathology algorithm [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2113.

1061 Background: Benign breast disease (BBD) is a significant risk factor for breast cancer (BC); however little is known about the aggressiveness of the BCs these patients develop. Shared features between select atypias and low-grade DCIS have led some to speculate that a history of BBD portends the development of well-differentiated BCs. Methods: The Mayo BBD cohort includes 9,376 women who had benign breast biopsies from 1967–1991. Data on BC events were obtained from medical records and questionnaires. Tissue from the BCs was reviewed by a single breast pathologist (DWV). Results: Our cohort included 6,244 patients with nonproliferative disease (NP), 2,801 women with proliferative disease without atypia (PDWA), and 331 with atypical hyperplasias (AH). With a median of 18 years of follow-up, 799 patients with BBD have developed breast cancer, 416 initially had NP, 313 had PDWA, and 70 AH. BC tissue was available for 703 of the women. The cancers were invasive ductal in 76.1% (n = 535), invasive lobular in 9.4% (n = 66), and DCIS alone in 14.5% (n = 102) patients. Grade is currently available for 500 women, and is well differentiated in 29.4% (n = 150), moderately differentiated in 42.7% (n = 218) and poorly differentiated in 27.8% (n = 142). In 537 cases the malignant tissue had a concurrent benign component consisting of NP in 24% (n = 129), PDWA in 25.1% (n = 135), and AH in 50.8% (n = 273). In regards to tumor size, 69.6% had T1 tumors, 24.9% T2 tumors, and 5.5% T3 disease. Metastasis to lymph nodes occurred in 25% of patients. Median time to BC was 12.3 years and did not differ across the different benign entities. Breast cancer developed within 5 years from the initial BBD in 163 women (20%). In this group, tumor size was greater (p = 0.02), and there were more poorly differentiated tumors (p = 0.006) than in those who later developed BC. Conclusions: In the BCs that have developed in our BBD cohort, we do not see a preponderance of well-differentiated, good-risk BCs. Patients diagnosed within 5 years of BBD appear to have BCs with more aggressive features. No significant financial relationships to disclose.

Abstract Over one million women in the United States receive biopsy diagnoses of benign breast disease (BBD) each year, which confer a 1.5–4.0-fold increase in breast cancer risk. Studies in the general population suggest that nonsteroidal anti-inflammatory agents (NSAID) lower breast cancer risk; however, associations among women with BBD are unknown. We assessed whether NSAID use among women diagnosed with BBD is associated with lower breast cancer risk. Participants included 3,080 women (mean age = 50.3 ± 13.5 years) in the Mayo BBD surgical biopsy cohort diagnosed between January 1, 1992 and December 31, 2001 who completed breast cancer risk factor questionnaires that assessed NSAID use, and whose biopsies underwent detailed pathology review, masked to outcome. Women were followed from date of BBD biopsy to breast cancer diagnosis (main outcome) or censoring (death, prophylactic mastectomy, reduction mammoplasty, lobular carcinoma in situ or last contact). Median follow-up time was 16.4 ± 6.0 years. Incident breast cancer was diagnosed among 312 women over a median follow-up of 9.9 years. Regular non-aspirin NSAID use was associated with lower breast cancer risk [HR = 0.63; 95% confidence interval (CI) = 0.46–0.85; P = 0.002] with trends of lower risk (highest tertiles of use vs. nonuse) for greater number of years used [HR = 0.55; 95% CI = 0.31–0.97; Ptrend = 0.003), days used per month (HR = 0.51; 95% CI = 0.33–0.80; Ptrend = 0.001) and lifetime number of doses taken (HR = 0.53; 95% CI = 0.31–0.89; Ptrend = 0.003). We conclude that nonaspirin NSAID use is associated with statistically significant lower breast cancer risk after a BBD biopsy, including a dose–response effect, suggesting a potential role for NSAIDs in breast cancer prevention among patients with BBD.

Abstract So far, 23 germline susceptibility loci have been associated with multiple myeloma (MM) risk. It is unclear whether the genetic variation associated with MM susceptibility also predisposes to its precursor, monoclonal gammopathy of undetermined significance (MGUS). Leveraging 2434 MM cases, 754 MGUS cases, and 2 independent sets of controls (2567/879), we investigated potential shared genetic susceptibility of MM and MGUS by (1) performing MM and MGUS genome-wide association studies (GWAS); (2) validating the association of a polygenic risk score (PRS) based on 23 established MM loci (MM-PRS) with risk of MM, and for the first time with MGUS; and (3) examining genetic correlation of MM and MGUS. Heritability and genetic estimates yielded 17% (standard error [SE] ±0.04) and 15% (SE ±0.11) for MM and MGUS risk, respectively, and a 55% (SE ±0.30) genetic correlation. The MM-PRS was associated with risk of MM when assessed continuously (odds ratio [OR], 1.17 per SD; 95% confidence interval [CI], 1.13-1.21) or categorically (OR, 1.70; 95% CI, 1.38-2.09 for highest; OR, 0.71; 95% CI, 0.55-0.90 for lowest compared with middle quintile). The MM-PRS was similarly associated with MGUS (OR, 1.19 per SD; 95% CI, 1.14-1.26 as a continuous measure, OR, 1.77, 95%CI: 1.29-2.43 for highest and OR, 0.70, 95%CI: 0.50-0.98 for lowest compared with middle quintile). MM and MGUS associations did not differ by age, sex, or MM immunoglobulin isotype. We validated a 23-SNP MM-PRS in an independent series of MM cases and provide evidence for its association with MGUS. Our results suggest shared common genetic susceptibility to MM and MGUS.

Abstract This is a protocol for a co‐registered Cochrane and Campbell Review (Methodology). The objectives are as follows: To identify, describe and assess methods for: when to replicate a systematic review; how to replicate a systematic review.

Abstract We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.

Abstract Background: Monoclonal gammopathy of undetermined significance (MGUS) is a common pre-malignant plasma cell disorder associated with a 1% per year risk of progression to multiple myeloma or related malignancy. Since the risk factors for MGUS are poorly defined, the goal of the present study was to determine if the risk of MGUS is increased in first degree relatives of patients with known multiple myeloma (MM) or MGUS. Methods: MGUS probands (index cases) were recruited from a population-based prevalence study of MGUS in Olmsted County, MN while MM probands were recruited through the Mayo Clinic practice. Consenting probands were asked to provide contact information on all first-degree relatives ages 40 years and older. Serum samples were then collected from first-degree relatives with informed consent and subjected to agaraose-gel electrophoresis and immunofixation. The prevalence of MGUS in first-degree relatives of MM and MGUS probands was compared to population-based rates from Olmsted County using risk ratios (RR). For comparisons to the reference population, only cases detected by protein electrophoresis and confirmed by immunofixation were included so that the diagnostic strategy was identical in the two groups being compared. Results: Serum samples were obtained from 911 relatives of 329 unique families, including 493 siblings, 324 children and 94 parents of patients with MGUS or MM. Using protein electrophoresis, monoclonal protein was detected in the serum of 55 (6%) while immunofixation identified 28 additional relatives (3%), for an age- and sex-adjusted prevalence rate of 8.1% (95%CI: 6.3, 9.8). The age-specific prevalence of MGUS in first-degree relatives increased with age (1.9%, 6.9%, 11.6%, 14.6%, and 21.0% for ages 40–49, 50–59, 60–69, 70–79 and ≥80, respectively; P<0.001). Based on similar MGUS detection methodology as the reference population, there was a significantly higher risk of MGUS in first-degree relatives (age-adjusted RR, 2.6, 95%CI: 1.9, 3.4) compared to the reference population. The increased risk (P<0.001) was seen both in relatives of MM probands (RR, 2.0, 95%CI: 1.4, 2.8), as well as MGUS probands (RR, 3.3, 95%CI: 2.1, 4.8). This association was similar across age of proband, age and gender of relative, and relationship of the first-degree relative. When examining whether the increased risk of MGUS in relatives was specific to probands with a large monoclonal protein concentration or specific monoclonal immunoglobulin isotype (i.e. high-risk MGUS phenotype), there was suggestion of a greater risk for relatives of probands with a high (3 1.5 g/dL) M-protein (RR, 2.8, 95%CI: 2.0,3.8) compared to lower M-protein levels (RR, 1.8, 95%CI: 1.1,2.8) although the difference did not reach statistical significance (P=0.12). The risk of MGUS in relatives did not differ by proband’s isotype (IgG vs. other). Conclusions: First-degree relatives of patients with MM or MGUS have a greater than two-fold risk of MGUS compared to the general population, implying underlying genetic predisposition for these diseases and providing rationale for identifying genetic determinants of MGUS. This study also provides important baseline rates of MGUS in first-degree relatives that impact the clinical care of these patients.

Abstract Next‐generation sequencing identified about 60 genes recurrently mutated in chronic lymphocytic leukemia (CLL). We examined the additive prognostic value of the total number of recurrently mutated CLL genes (i.e., tumor mutational load [TML]) or the individually mutated genes beyond the CLL international prognostic index (CLL‐IPI) in newly diagnosed CLL and high‐count monoclonal B‐cell lymphocytosis (HC MBL). We sequenced 59 genes among 557 individuals (112 HC MBL/445 CLL) in a multi‐stage design, to estimate hazard ratios (HR) and 95% confidence intervals (CI) for time‐to‐first treatment (TTT), adjusted for CLL‐IPI and sex. TML was associated with shorter TTT in the discovery and validation cohorts, with a combined estimate of continuous HR = 1.27 (CI:1.17‐1.39, P = 2.6 × 10 −8 ; c ‐statistic = 0.76). When stratified by CLL‐IPI, the association of TML with TTT was stronger and validated within low/intermediate risk (combined HR = 1.54, CI:1.37‐1.72, P = 7.0 × 10 −14 ). Overall, 80% of low/intermediate CLL‐IPI cases with two or more mutated genes progressed to require therapy within 5 years, compared to 24% among those without mutations. TML was also associated with shorter TTT in the HC MBL cohort (HR = 1.53, CI:1.12‐2.07, P = .007; c ‐statistic = 0.71). TML is a strong prognostic factor for TTT independent of CLL‐IPI, especially among low/intermediate CLL‐IPI risk, and a better predictor than any single gene. Mutational screening at early stages may improve risk stratification and better predict TTT.