Frost, Marlene H.

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 Background Prior epidemiologic studies have shown an inverse relationship of aspirin and NSAID use with breast cancer (BC) risk in the general population. Women with benign breast disease (BBD) are at an increased BC risk compared with women in the general population, and we have shown that BBD biopsies contain increased numbers of immune cells compared with normal breast tissues donated for research in the Komen Tissue Bank. We hypothesized that NSAID use among women with BBD is associated with reduced risk of BC. Methods We evaluated BC risk in relation to aspirin and NSAID use in a single institution retrospective cohort of 4,498 women who received a biopsy diagnosis of BBD between 1/1/1992 and 12/31/2001. Information on usual medication use, including average number of days used per month (0, 1-7, 8-28 and >28), was collected via questionnaires mailed after identification of participants. NSAIDs were categorized into three classes: 1) Aspirin, 2) Ibuprofen (plus other prostaglandin synthesis inhibitors), and 3) COX inhibitors. BC events were captured via medical record, questionnaires, and Tumor Registry records. Medication use was evaluated for associations with clinical and pathology features. Hazard ratios (HR) and 95% confidence intervals (CI) examining associations between medication use and BC risk were estimated using Cox regression analyses, with adjustment for age and BMI at biopsy, years between biopsy and questionnaire, and pathologic features related to BC risk (severity of BBD and degree of involution in background lobules). Tests for trend examining frequency of use were calculated from ordered values (lowest to highest) in Cox models with a one degree-of-freedom linear term. We assessed BC associations with any type of NSAID use, as well as the three subclasses. Results Information on NSAID use was available for 3,089 (69%) of the participants in the cohort; these women had a median age of 49 and did not differ in BC risk from women who did not provide data on NSAID use (p=0.46). Of the 3,089 respondents, 313 (10.1%) subsequently developed BC with median follow-up of 17 years. Age was significantly associated with use of all types of NSAIDs, with increasing use in older women. BMI at the time of biopsy, severity of BBD findings, and involution in background lobules were also associated with NSAID use; thus these factors and age were adjusted for in risk association analyses. Ever users of NSAIDs (any type) had a significantly lower risk of BC than never users (HR 0.76, CI 0.59-0.98, p=0.03), and increasing frequency of NSAID use was significantly associated with lower BC risk (HRs 0.81, 0.79, and 0.70 for regular use 1-7, 8-28, and >28 days/month), trend p=0.02. Analyses of drug classes revealed that ever use of ibuprofen (HR 0.61, CI 0.42-0.89, p=0.01) and ever use of COX inhibitors (HR 0.59, CI 0.39-0.89, p=0.01) were associated with decreased BC risk. COX inhibitors in particular showed a strong dose response effect with increasing use (HRs 0.97, 0.52, and 0.47 for regular use 1-7, 8-28, and >28 days/month), trend p<0.01. Use of aspirin was not associated with BC risk (HR 0.88, CI 0.64-1.21, p=0.44). Conclusions In this exploratory analysis, we found use of ibuprofen and other NSAIDs to be associated with decreased BC risk among women who had undergone a BBD biopsy. These findings suggest that anti-inflammatory approaches may have value in chemoprevention among women with BBD. Citation Format: Amy Degnim, Robert Vierkant, Stacey Winham, Marlene Frost, Daniel Visscher, Jodi Carter, Suneetha Kaggal, Julie Cunningham, Teresa Allers, Tanya Hoskin, Ethan Heinzen, Celine Vachon, Keith Knutson, Keith Knutson, Derek Radisky, Mark Sherman. NSAID use and breast cancer risk in a retrospective cohort of women with benign breast disease [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-08-06.

Abstract Background: Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples. Results: As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit ("QA"), and QIAGEN GeneRead DNA FFPE Kit ("QGR"). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different DNA extraction methods. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92 % of the uniquely called FFPE variants were of low allelic frequency range (<5%), and collectively shared a “C>T|G>A” mutational signature known to be representative of FFPE artifacts resulting from cytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates. Conclusions: Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

Abstract Background Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are often sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples. Results As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit ("QA"), and QIAGEN GeneRead DNA FFPE Kit ("QGR"). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different library preparations. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92% of the uniquely called FFPE variants were of low allelic frequency range (<5%), and collectively shared a “C>T|G>A” mutational signature known to be representative of FFPE artifacts resulting fromcytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates. Conclusions Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

Abstract Background Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are often sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples. Results As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit ("QA"), and QIAGEN GeneRead DNA FFPE Kit ("QGR"). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different library preparations. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92% of the uniquely called FFPE variants were of low allelic frequency range (<5%), and collectively shared a “C>T|G>A” mutational signature known to be representative of FFPE artifacts resulting fromcytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates. Conclusions Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

Background More than 1.5 million women per year have a benign breast biopsy resulting in concern about their future breast cancer (BC) risk. This study examined the performance of 2 BC risk models that integrate clinical and histologic findings in this population. Methods The BC risk at 5 and 10 years was estimated with the Breast Cancer Surveillance Consortium (BCSC) and Benign Breast Disease to Breast Cancer (BBD‐BC) models for women diagnosed with benign breast disease (BBD) at the Mayo Clinic from 1997 to 2001. Women with BBD were eligible for the BBD‐BC model, but the BCSC model also required a screening mammogram. Calibration and discrimination were assessed. Results Fifty‐six cases of BC were diagnosed among the 2142 women with BBD (median age, 50 years) within 5 years (118 were diagnosed within 10 years). The BBD‐BC model had slightly better calibration at 5 years (0.89; 95% confidence interval [CI], 0.71‐1.21) versus 10 years (0.81; 95% CI, 0.70‐1.00) but similar discrimination in the 2 time periods: 0.68 (95% CI, 0.60‐0.75) and 0.66 (95% CI, 0.60‐0.71), respectively. In contrast, among the 1089 women with screening mammograms (98 cases of BC within 10 years), the BCSC model had better calibration (0.94; 95% CI, 0.85‐1.43) and discrimination (0.63; 95% CI, 0.56‐0.71) at 10 years versus 5 years (calibration, 1.31; 95% CI, 0.94‐2.25; discrimination, 0.59; 95% CI, 0.46‐0.71) where discrimination was not different from chance. Conclusions The BCSC and BBD‐BC models were validated in the Mayo BBD cohort, although their performance differed by 5‐year risk versus 10‐year risk. Further enhancement of these models is needed to provide accurate BC risk estimates for women with BBD.

Purpose Women with atypical hyperplasia (AH) on breast biopsy have an aggregate increased risk of breast cancer (BC), but existing risk prediction models do not provide accurate individualized estimates of risk in this subset of high-risk women. Here, we used the Mayo benign breast disease cohort to develop and validate a model of BC risk prediction that is specifically for women with AH, which we have designated as AH-BC. Patients and Methods Retrospective cohorts of women age 18 to 85 years with pathologically confirmed benign AH from Rochester, MN, and Nashville, TN, were used for model development and external validation, respectively. Clinical risk factors and histologic features of the tissue biopsy were selected using L1-penalized Cox proportional hazards regression. Identified features were included in a Fine and Gray regression model to estimate BC risk, with death as a competing risk. Model discrimination and calibration were assessed in the model-building set and an external validation set. Results The model-building set consisted of 699 women with AH, 142 of whom developed BC (median follow-up, 8.1 years), and the external validation set consisted of 461 women with 114 later BC events (median follow-up, 11.4 years). The final AH-BC model included three covariates: age at biopsy, age at biopsy squared, and number of foci of AH. At 10 years, the AH-BC model demonstrated good discrimination (0.63 [95% CI, 0.57 to 0.70]) and calibration (0.87 [95% CI, 0.66 to 1.24]). In the external validation set, the model showed acceptable discrimination (0.59 [95% CI, 0.51 to 0.67]) and calibration (0.91 [95% CI, 0.65 to 1.42]). Conclusion We have created a new model with which to refine BC risk prediction for women with AH. The AH-BC model demonstrates good discrimination and calibration, and it validates in an external data set.

Abstract In breast adipose tissue, macrophages that encircle damaged adipocytes form “crown-like structures of breast” (CLS-B). Although CLS-B have been associated with breast cancer, their role in benign breast disease (BBD) and early carcinogenesis is not understood. We evaluated breast biopsies from three age-matched groups (n = 86 each, mean age 55 years), including normal tissue donors of the Susan G. Komen for the Cure Tissue Bank (KTB), and subjects in the Mayo Clinic Benign Breast Disease Cohort who developed cancer (BBD cases) or did not develop cancer (BBD controls, median follow-up 14 years). Biopsies were classified into histologic categories, and CD68-immunostained tissue sections were evaluated for the frequency and density of CLS-B. Our data demonstrate that CLS-B are associated with BBD: CLS-B–positive samples were significantly less frequent among KTB biopsies (3/86, 3.5%) than BBD controls (16/86 = 18.6%, P = 0.01) and BBD cases (21/86 = 24%, P = 0.002). CLS-B were strongly associated with body mass index (BMI); BMI &lt; 25: 7% CLS-B positive, BMI 25–29: 13%, and BMI ≥ 30: 29% (P = 0.0005). Among BBD biopsies, a high CLS-B count [&gt;5 CLS-B/sample: 10.5% (BBD cases) vs 4.7% (BBD controls), P = 0.007] conferred a breast cancer OR of 6.8 (95% CI, 1.4–32.4), P = 0.02, after adjusting for adipose tissue area (cm2), histologic impression, and BMI. As high CLS-B densities are independently associated with an increased breast cancer risk, they may be a promising histologic marker of breast cancer risk in BBD. Cancer Prev Res; 11(2); 113–9. ©2017 AACR.

Abstract Background: The Breast Cancer Surveillance Consortium (BCSC) model predicts invasive breast cancer risk in women with Benign Breast Disease (BBD), and recently incorporated BBD histology into the model. The BCSC has been validated in the Mayo Mammography Health Study, but has yet to be examined in Mayo Clinic’s BBD cohort. The Benign Breast Disease to Breast Cancer (BBD-BC) model predicts risk of both invasive and in situ breast cancer. Here we compare the performance of the BCSC and BBD-BC in Mayo Clinic’s BBD cohort. Methods: Eligible women underwent a breast biopsy with benign findings at the Mayo Clinic between years 1997-2001 and had a 4-view screening mammogram within six months of biopsy. Clinical BI-RADS density assessments using the 4th edition American College of Radiology were available on all mammograms, and were coded as almost entirely fat, scattered fibroglandular densities, heterogeneously dense, and extremely dense. Risk at 5 and 10 years of invasive cancer only (BCSC model) or both invasive and in situ cancer (BBD-BC model) were estimated. In situ cancers were censored at time of diagnosis for both models. Concordance statistics, i.e. model discrimination (higher is better), for each model were calculated using a Cox proportional hazards model with predicted risk as the sole predictor, and were compared using permutation tests. The ratio of total predicted (sum of predicted risk) to observed number of invasive breast cancers was used to assess model calibration. Calibration was not formally compared across models due to differences in how DCIS was treated in the development of each (BCSC censored; BBD-BC event). Results: 999 women met inclusion criteria. BI-RADS density was categorized as fatty in 46 (4.6%), scattered densities in 372 (37.2%), heterogeneously dense in 423 (42.3%), and extremely dense in 158 (15.8%). 62 invasive cancers occurred over a median 13.3yrs of follow-up, with 16 (25.8%) and 48 (77.4%) of the cancers occurring with-in 5 and 10 years. The concordance of the BCSC at 5 years was 0.550 (95% CI 0.409—0.691), compared to 0.719 (95% CI 0.578—0.860) for the BBD-BC (p-value=0.005). At 10 years the BCSC concordance was not significantly different from the BBD-BC, at 0.624 (95% CI 0.542—0.706) and 0.662 (95% CI 0.580—0.744), respectively (p-value=0.306). The BCSC over predicted the number invasive cancers in the BBD cohort at 5 years (predicted-to-observed=1.54; 95% CI 1.01—2.70), but was well calibrated at 10 years (1.06; 95% CI 0.83, 1.47). Conclusions: The BCSC model performed reasonably well in the BBD Cohort 10 years post-biopsy, but overestimated risk at 5 years. Additional study is needed to improve models for prediction of breast cancer risk among women with BBD. Citation Format: Ryan D. Frank, Celine M. Vachon, Stacey J. Winham, Robert A. Vierkant, Marlene H. Frost, Derek C. Radisky, Daniel W. Visscher, Amy C. Degnim. Validation of the BCSC model within the Mayo Benign Breast Disease Cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5299. doi:10.1158/1538-7445.AM2017-5299