Jensen, Matthew L.

Abstract Objectives: Mammographic microcalcifications (calcs) are a sentinel marker of in-situ and invasive breast cancer (BC) but most calcs reflect benign breast disease (BBD). BBD comprises a diverse set of lesions, variably associated with increased BC risk. While calc frequency data for the modern era are limited, historically, calcs were reported in 11% of BBD biopsies. Some studies suggest that calcs in BBD impact BC risks; however, the frequency and BC risks associated with calcs during the percutaneous biopsy era have not been clearly established. Accordingly, we assessed the association of calcs with specific BBD lesions and BC risk in a large contemporaneous BBD cohort including detailed pathology review. Methods: A cohort of 4,819 patients with percutaneously diagnosed BBD from 2002-2013 at Mayo Clinic, were identified. The cohort was followed from 6 months after biopsy until censoring, BC diagnosis, or December 2021. Mean follow-up was 11.3 (SD=5.1) years for BBD patients who remained cancer-free in follow-up compared with 7.4 (SD=4.3) years for cases. Histology of all biopsies were reviewed microscopically to record pathologic findings. A natural language processing algorithm (NLPA) was developed and utilized to identify the presence of calcs based on pathology reports. NLPA results were compared to 300 matched biopsy reports, taken as ground truth. We performed age-adjusted Cox proportional hazards regression to assess hazard ratios (HRs) with 95% confidence intervals (CIs) for specific BBD lesions, stratified by the presence (calc+) or absence of calcs (calcs-), with estimation of an interaction term to test for differences in risks by calc strata. Standardized incidence ratios (SIRs) estimated risks for calc+ and calc- BBD lesions versus matched strata for Iowa SEER population-based BC incidence rates. Results: The NLPA achieved sensitivity=0.993 and specificity=0.986. Overall, 42.8% of BBD biopsies were calc+, including 49.1% of biopsies preceding BC and 42.4% of biopsies not preceding BC (Chi-square p=0.02). Overall, risks of BC among women with BBD calc+ biopsies did not differ significantly from those of women with calc- BBD: HR=1.13 (95% CI: 0.90,1.41), p=0.290. SIRs were also similar for the two groups; women with BBD calc+ biopsies had an SIR =2.00 (95% CI: 1.72, 2.33) and BBD calc- biopsies yielded an SIR= 1.91 (95% CI: 1.64,2.22) (Table 1). HRs were similar for specific calc+ and calc- BBD lesions, with progressively higher risks for non-proliferative disease (NP), proliferative disease without atypia (PDWA) and atypical hyperplasia (AH) (pinteraction=0.55). Interaction terms were not significant for any specific lesion, apart from a marginally significant result for dilated ducts, wherein risks were marginally higher for calc- lesions (pInteraction =0.048), perhaps representing a chance finding. SIRs for calc+ versus calc- BBD lesions were also similar. Overall, 77.3% of AH was calc+; for AH calc+, SIR=3.76 (95%CI: 2.74-5.17) and for AH calc-, SIR=4.41 (95%CI: 2.56-7.59). Conclusion: In this recent, larger percutaneous BBD cohort, calcs were present in 42.8% of all specimens, representing a marked increase compared with historical BBD data, particularly those dominated by surgical biopsy for diagnosis and preceding implementation of full-field digital mammography. In this study, calcs in percutaneously diagnosed BBD were not associated with increased BC risk. Table 1: Comparison of standard incidence ratio of subsequent breast cancer with and without microcalcifications in benign breast disease by histologic impression (NP = non-proliferative disease, PDWA = proliferative disease without atypia, and AH = atypical hyperplasia). (1) compared to Iowa SEER population Citation Format: Sarah Schrup, Robert Vierkant, Stacey Winham, Celine Vachon, Derek Radisky, Jodi Carter, Laura Pacheco-Spann, Matthew Jensen, Amy Degnim, Mark Sherman. Benign breast disease and microcalcifications in percutaneous biopsies and breast cancer risk [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS07-02.

Abstract Artificial intelligence (AI) models applied to mammography have been shown to improve breast cancer (BC) detection and risk estimation. The Transpara AI detection system has been associated with short and longer-term risk of invasive BC. Whether Transpara can identify changes in the breast associated with tamoxifen (TAM) and aromatase inhibitor (AIs) treatment is unknown. We compare changes in the Transpara malignancy score in the unaffected breast of BC cases using TAM or AIs to those of untreated women without cancer. Cases and controls were sampled from the Mayo Clinic Rochester Breast Screening Practice. Eligible cases were women with breast cancer treated with TAM and/or AIs for at least 8 months who had full field digital (FFDM) mammograms within two years prior to start of treatment (pretreatment mammogram). Controls did not have BC, were not using TAM, AIs, or postmenopausal hormones, and had at least two FFDM screening mammograms. Transpara scores (1-10) were calculated for the contralateral breast in cases and a randomly selected side for controls. Two controls were matched to each case on menopausal status at both mammograms, time between mammograms (< 6 months) and baseline Transpara score (±2 units). Conditional logistic regression was used to assess the association of treatment (case (TAM/AIs) vs. control) with change in Transpara score between the pretreatment mammogram and the mammogram taken closest to 13 months later. Change was classified as having a decrease in Transpara score between the two mammograms vs. no change or an increase. Models were adjusted for age and Transpara score at pretreatment mammogram, and time between the two mammograms. We performed analyses on all women combined and stratified by time on treatment, menopausal status, and treatment type (TAM vs. AIs). A total of 134 cases were identified, with 53 on TAM (21 premenopausal and 32 postmenopausal) and 81 on AIs (postmenopausal); 268 controls were matched to the cases. Characteristics of the study population are summarized (Table). Most case mammograms were obtained within six months prior to treatment start [median 2.63 months (interquartile range (IQR), 1.62, 5.72)] and median time on TAM/AIs was 10.9 months (IQR, 8.03, 16.6) at time of second mammogram. The median absolute change in Transpara score between the two mammograms was similar in cases [0 (IQR, -2, 1)] vs. controls [0 (IQR, -1.25, 1)]; 40.3% of cases and 39.2% of controls had any decrease. Conditional logistic models found no evidence for an association between TAM/AIs and decrease in Transpara score [odds ratio (OR) = 1.07; 95% confidence interval (CI), 0.65, 1.78]. When stratified by median time on treatment (10.9 months), there was suggestion of a stronger association for those on treatment for a longer duration (OR = 1.21; 95% CI, 0.61, 2.40 for >10.9 months vs. OR = 0.96; 95% CI, 0.43, 2.13 for < 10.9 months) but these results were not statistically significant. Similarly, odds ratios were stronger in postmenopausal (OR = 1.18; 95% CI, 0.67, 2.07) compared to premenopausal (OR = 0.91; 95% CI, 0.24, 3.45) women, and for AIs (OR = 1.21, 95% CI, 0.64, 2.29) vs. TAM (OR = 0.93; 95% CI, 0.39, 2.22) but none of these results reached statistical significance. In summary, we found no association between treatment with TAM/AIs and change in Transpara score. However, our study had limited power. Also, longer time on treatment as well as investigation of AI-based scores trained directly to assess BC risk may be necessary to identify relevant changes. Citation Format: Yvonne Wang, Christopher Scott, Matthew Jensen, Dan Hursh, Kathleen Brandt, Aaron Norman, Fang Fang Wu, Stacey Winham, Karla Kerlikowske, Celine Vachon. Changes in an artificial intelligence model score for breast cancer detection (Transpara) with tamoxifen and aromatase inhibitor treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-09-04.

Abstract Breast parenchymal patterns on radiologic images are associated with breast cancer risk. Radiomic features have been proposed as quantitative measures of parenchymal patterns. We defined intrinsic imaging phenotypes of breast parenchymal patterns based on radiomic features extracted from full field digital mammography (FFDM) in breast screening populations and assessed whether these phenotypes are associated with breast cancer risk and masking. We selected 30,000 women with 4-view FFDM exams from Hologic machines from three institutions (Hospital of the University of Pennsylvania, Mayo Clinic, and San Francisco Mammography Registry), randomly split into a training (20,000 women) and test set (10,000 women). In total, 390 radiomic features were automatically extracted from each image using a validated software pipeline, standardized, and adjusted for site differences using ComBat. We used two methods, hierarchical clustering and Principal Components (PCs) analysis, to classify significant variation among the features in the training set and replicate among the test set. Next, we applied the replicated clusters and PCs to an independent nested case-control set [1082 invasive breast cancer (BC) cases (of which 151 were Black and 893 White women, 38 other race) matched to 2837 controls (411 Black and 2345 White women, 81 other race) on age, race, timing of images, and site]. We examined associations of the clusters and PCs with invasive breast cancer risk, as well as masking [defined as a false-negative (FN) screen (124 cases and 319 matched controls) and additionally the subset with symptomatic interval cancer (IC) within 12 months of negative screen (88 cases and 223 matched controls)] using conditional logistic regression. We evaluated their association with breast cancer alone, and with adjustment for age, body mass index (BMI) and breast density assessed by Breast Imaging Reporting and Data System (BI-RADS) using likelihood ratio tests. We estimated discrimination using area under the curve (AUC) and compared AUCs for models that included the radiomic clusters and PCs with the model that included only age, BMI and density. We also stratified analyses by race (Black/White). From hierarchical clustering, we defined six statistically significant phenotype clusters (each of at least 1000 women) in the training set which were replicated in the test set. For PC Analysis, we identified six PCs in the training set, explaining 85% of the variation in texture features and reproduced these in the test set. The six radiomic phenotype clusters (P< 0.001) and six PCs (P< 0.001) were both associated with invasive BC, including after adjusting for age, BMI, and density (cluster P=0.004; PCs P< 0.001). Improvements in discrimination of invasive BC with inclusion of PCs or clusters were more pronounced among Black women (Table). Further, the PCs (P< 0.001) and clusters (P< 0.001) were significantly associated with FN overall and for symptomatic IC (PCs P< 0.001; clusters P=0.001), but only PCs remained significant after adjusting for age, BMI and density (PCs P=0.004 for FN; PCs P=0.007 for symptomatic IC). Discrimination of masking also improved with inclusion of both clusters and PCs (Table). We identified reproducible radiomic phenotypes that are associated with invasive BC risk, above and beyond breast density with the strongest associations for invasive BC among Black women and symptomatic interval cancers. Citation Format: Stacey Winham, Anne Marie McCarthy, Aimilia Gastounioti, Christopher Scott, Aaron Norman, Walter C. Mankowski, Lauren Pantalone, Matthew Jensen, Eric A. Cohen, Hannah Horng, Kathleen Brandt, Emily F. Conant, Karla Kerlikowske, Despina Kontos, Celine Vachon. Radiomic phenotypes of breast texture and association with breast cancer risk and masking [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr GS4-06.

Abstract Background:Breast terminal duct lobular units (TDLUs), the source of most breast cancer (BC) precursors, are shaped by age-related involution, a gradual process, and postpartum involution (PPI), a dramatic inflammatory process that restores baseline microanatomy after weaning. Dysregulated PPI is implicated in the pathogenesis of postpartum BCs. We propose that assessment of TDLUs in the postpartum period may have value in risk estimation, but characteristics of these tissues in relation to epidemiological factors are incompletely described.Methods: Using validated Artificial Intelligence and morphometric methods, we analyzed digitized images of tissue sections of normal breast tissues stained with hematoxylin and eosin from donors <45 years from the Komen Tissue Bank (180 parous and 545 nulliparous). Metrics assessed by AI, included: TDLU count; adipose tissue fraction; mean acini count/TDLU; mean dilated acini; mean average acini area; mean “capillary” area; mean epithelial area; mean ratio of epithelial area versus intralobular stroma; mean mononuclear cell count (surrogate of immune cells); mean fat area proximate to TDLUs and TDLU area. We compared epidemiologic characteristics collected via questionnaire by parity status and race, using a Wilcoxon rank sum test or Fisher’s exact test. Histologic features were compared between nulliparous and parous women (overall and by time between last birth and donation [ recent birth: ≤5 years versus remote birth: >5 years]) using multivariable regression models.Results:Normal breast tissues of parous women contained significantly higher TDLU counts and acini counts, more frequent dilated acini, higher mononuclear cell counts in TDLUs and smaller acini area per TDLU than nulliparas (all multivariable analyses p<0.001). Differences in TDLU counts and average acini size persisted for >5 years postpartum whereas increases in immune cells were most marked <5 years of a birth. Relationships were suggestively modified by several other factors, including demographic and reproductive characteristics, ethanol consumption and breastfeeding duration. Conclusions:Our study identified sustained expansion of TDLU numbers and reduced average acini area among parous versus nulliparous women and notable increases in immune responses within five years following childbirth. Further, we show that quantitative characteristics of normal breast samples vary with demographic features and BC risk factors.

Abstract Background: High mammographic breast density is a strong, well-established breast cancer risk factor. A recent study found increased expression of stem cell markers in the areas of high breast density, but, whether stem cells may explain high breast cancer risk due to high breast density is unknown. We investigated the association between breast density and the risk of breast cancer by the status of stem cell markers CD44, CD24, and ALDH1A1 in the tumor. Methods: We included 223 postmenopausal women with primary invasive or in-situ breast cancer and 399 age-matched controls from Mayo Clinic Mammography Study. Percent breast density, absolute dense area and non-dense area were assessed using computer-assisted thresholding technique. Immunohistochemical analysis of the markers was performed on tumor tissue microarrays according to a standard protocol with commercial antibodies. Cases with positive staining (≥1% positively stained cells) on one or more cores were considered marker-positive. We used polychotomous logistic regression to quantify the associations of breast density measures with breast cancer risk by the status of each of the markers. Heterogeneity tests were conducted to compare the differences in associations by marker-defined tumor subtypes. Results: Of the 223 cancers in the study, 182 (82%) were positive for CD44, 83 (37%) for CD24 and 52 (23%) for ALDH1A1. Associations of percent density were not significantly different across any of the marker-defined subtypes (density 51%+ vs. 11-25%: OR=2.83, 95% CI 1.49-5.37 for CD44+ vs. OR=1.87, 95% CI 0.47-7.51 for CD44-, p-heterogeneity=0.66; OR=2.80, 95% CI 1.27-6.18 for CD24+ vs. OR=2.44, 95% CI 1.14-5.22 for CD24-, p-heterogeneity=0.61; OR=3.04, 95% CI 1.14-8.10 for ALDH1A1+ vs. OR=2.57. 95% CI 1.30-5.08 for ALDH1A1-, p-heterogeneity=0.94). Positive associations of absolute dense area and inverse associations of non-dense area with breast cancer risk were similar across marker-defined subtypes. Similar associations of breast density measures with breast cancer were seen when cancer subtypes were defined using combined marker expression status (none, one positive marker, or two and more positive markers). Conclusions: We found no evidence of differential associations of breast density measures with breast cancer risk by the status of stem cell markers. However, our power was limited and further studies in larger study populations are warranted to confirm these associations in a larger sample. Citation Format: Lusine Yaghjyan, Ashwini Esnakula, Christopher Scott, Akemi Wijayabahu, Matthew Jensen, Celine Vachon. Associations of mammographic breast density with breast stem cell marker-defined breast cancer subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 592.