Winham, Stacey J.

Background: Antidepressants are among the most prescribed medications in the United States. The aim of this study was to explore the prevalence of antidepressant prescriptions and investigate sex differences and age-sex interactions in adults enrolled in the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT) study. Materials and Methods: We conducted a retrospective analysis of the RIGHT study. Using electronic prescriptions, we assessed 12-month prevalence of antidepressant treatment. Sex differences and age-sex interactions were evaluated using multivariable logistic regression and flexible recursive smoothing splines. Results: The sample consisted of 11,087 participants (60% women). Antidepressant prescription prevalence was 22.24% (27.96% women, 13.58% men). After adjusting for age and enrollment year, women had significantly greater odds of antidepressant prescription (odds ratio = 2.29; 95% confidence interval = 2.07, 2.54). Furthermore, selective serotonin reuptake inhibitors (SSRIs) had a significant age-sex interaction. While SSRI prescriptions in men showed a sustained decrease with age, there was no such decline for women until after reaching ∼50 years of age. There are important limitations to consider in this study. Electronic prescription data were cross-sectional; information on treatment duration or adherence was not collected; this cohort is not nationally representative; and enrollment occurred over a broad period, introducing confounding by changes in temporal prescribing practices. Conclusions: Underscored by the significant interaction between age and sex on odds of SSRI prescription, our results warrant age to be incorporated as a mediator when investigating sex differences in mental illness, especially mood disorders and their treatment.

Abstract Objective We examined the long-term effects of premenopausal bilateral oophorectomy (PBO) with or without concurrent or preceding hysterectomy on physical and cognitive function and on odds of chronic conditions. Methods We enrolled 274 women with PBO with or without concurrent or preceding hysterectomy and 240 referents aged 55 years and older who were residents of Olmsted County, MN as of the PBO or index date. Chronic conditions were assessed via medical record abstraction. Cognitive diagnoses were based on neurocognitive testing. A physical function assessment included measures of strength and mobility. Multivariable regression models compared characteristics for women with PBO <46 years, PBO 46–49 years, and referent women with adjustments for age and other confounders. Results The clinical visits (median age, 67 years) were a median of 22 years after the PBO or index date. Of 274 women with PBO, 161 (59%) were <46 years at PBO and 113 (41%) were 46–49 years. Compared with referents, women with a history of PBO <46 years had increased odds of arthritis (odds ratio [OR], 1.64; 95% confidence interval [CI], 1.06–2.55), asthma (OR, 1.74; 95% CI, 1.03–2.93), obstructive sleep apnea (OR, 2.00; 95% CI, 1.23–3.26), and bone fractures (OR, 2.86; 95% CI, 1.17–6.98), and walked a shorter mean distance on a 6-minute walk test ( b = −18.43; P = 0.034). Compared with referents, women with a history of PBO at age 46–49 years had increased odds of arthritis (OR, 1.92; 95% CI, 1.16–3.18) and obstructive sleep apnea (OR, 2.21; 95% CI, 1.33–3.66). There were no significant differences in cognitive status in women with PBO compared with referents. Conclusions Women with a history of PBO with or without concurrent or preceding hysterectomy, especially at age <46 years, have more chronic conditions in late mid-life compared with referents.

Abstract Purpose: Germline pathogenic variants in CHEK2 confer moderately elevated breast cancer risk (odds ratio, OR ∼ 2.5), qualifying carriers for enhanced breast cancer screening. Besides pathogenic variants, dozens of missense CHEK2 variants of uncertain significance (VUS) have been identified, hampering the clinical utility of germline genetic testing (GGT). Experimental Design: We collected 460 CHEK2 missense VUS identified by the ENIGMA consortium in 15 countries. Their functional characterization was performed using CHEK2-complementation assays quantifying KAP1 phosphorylation and CHK2 autophosphorylation in human RPE1–CHEK2-knockout cells. Concordant results in both functional assays were used to categorize CHEK2 VUS from 12 ENIGMA case–control datasets, including 73,048 female patients with breast cancer and 88,658 ethnicity-matched controls. Results: A total of 430/460 VUS were successfully analyzed, of which 340 (79.1%) were concordant in both functional assays and categorized as functionally impaired (N = 102), functionally intermediate (N = 12), or functionally wild-type (WT)–like (N = 226). We then examined their association with breast cancer risk in the case–control analysis. The OR and 95% CI (confidence intervals) for carriers of functionally impaired, intermediate, and WT-like variants were 2.83 (95% CI, 2.35–3.41), 1.57 (95% CI, 1.41–1.75), and 1.19 (95% CI, 1.08–1.31), respectively. The meta-analysis of population-specific datasets showed similar results. Conclusions: We determined the functional consequences for the majority of CHEK2 missense VUS found in patients with breast cancer (3,660/4,436; 82.5%). Carriers of functionally impaired missense variants accounted for 0.5% of patients with breast cancer and were associated with a moderate risk similar to that of truncating CHEK2 variants. In contrast, 2.2% of all patients with breast cancer carried functionally wild-type/intermediate missense variants with no clinically relevant breast cancer risk in heterozygous carriers.

Abstract Introduction: We used micro-computed tomography (microCT), a high-resolution imaging option, to detect and characterize microcalcifications (MCs) in three dimensions within pathology blocks of benign and malignant breast tissues. Methods: A set of 44 formalin-fixed, paraffin-embedded breast tissue blocks were assessed from n=22 women at two time points: benign breast biopsy and subsequent DCIS or DCIS with invasive disease breast cancer. Blocks were scanned using the Bruker Skyscan 1276 microCT at a resolution of 10 µm. SkyScan analysis CTAn software was utilized to prepare and analyze morphological parameters and multiple MCs contained within each block were averaged. Paired morphological parameters were compared between the benign and subsequent cancer samples within women using Wilcoxon signed-rank tests. Results: Median age of patients was 53 years (range 37-77 years) at initial benign biopsy and developed breast cancer at median of 5.6 years (range: 2.3-12.8 years) later. The initial benign biopsies were for proliferative disease without atypia in the majority (59%), while 23% had non-proliferative lesions and 18% atypical hyperplasia. The average structural model index was consistent with a near rod-like shape (median 2.99, range: 2.71-3.29). Paired comparisons between benign and cancer blocks showed that the average surface area of MCs was significantly higher in the cancer samples (p=0.0467), as was the average volume of MCs (p=0.0298) (Table 1). Conclusions: Preliminarily, we demonstrate that microCT of routinely prepared pathology blocks of breast tissues may be useful in characterizing the 3-D morphometry of MCs with suggestive differences in benign and cancer tissues. Given that MCs are a sentinel radiologic marker of DCIS but are common in benign breast disease, developing improved approaches for classifying MCs may have value in breast cancer screening and management. Table 1. Comparison of MC parameters in biopsy tissues (IQR = Interquartile Range). Benign sample from women who later developed breast cancer Breast cancer after prior benign sample Paired Differences (Cancer - Benign) Wilcoxon signed-rank p value (N=22) (N=22) (N=22) Number of Objects (Median (IQR)) 43.5 (14, 789) 69.5 (33, 289) -6 (-139, 67) 0.6830 Average Surface Area (mm2)(Median (IQR)) 0.0225 (0.0134, 0.0393) 0.0342 (0.0185, 0.0656) 0.0113 (-0.0027, 0.0322) 0.0467 Average Volume (mm3) (Median (IQR)) 0.0003 (0.0001, 0.0007) 0.0007 (0.0003, 0.0022) 0.0003 (0.000002, 0.0020) 0.0298 Average Surface Area/Volume (mm−1)(Median (IQR)) 214.92 (162.25, 328.39) 199.55 (151.89, 230.52) -20.85 (-86.05, 27.04) 0.1433 Average Structural Thickness (mm)(Median (IQR)) 0.0341(0.0196, 0.0446) 0.0395 (0.0336, 0.0505) 0.0074 (-0.0062, 0.0153) 0.1173 Average Structural Model Index (Median (IQR)) 2.9758 (2.8465, 3.1175) 3.0026 (2.9281, 3.0927) 0.0075 (-0.1073, 0.1381) 0.7656 Citation Format: Sarah E. Schrup, Thomas de Bel, Tanya Hoskin, Teresa Allers, Stacey Winham, Derek Radisky, Laura Pacheco-Spann, Lisa Seymour, Amy Degnim, Mark Sherman. High resolution microCT to analyze the 3D morphology of microcalcifications in benign breast disease and breast cancer biopsy tissues. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3587.

Abstract Over one million biopsies are performed annually in the U.S. to evaluate suspicious breast lesions. Most biopsies lead to a diagnosis of benign breast disease (BBD) and these women have a 1.5- to fourfold increase in subsequent breast cancer (BC) risk compared with the general population. A polygenic risk score (PRS) including 313 common variants was developed to predict BC risk in the general population and has improved performance when added to existing BC prediction models. However, the value of PRS in risk prediction models among women with BBD has not been evaluated. To evaluate the role of the established PRS, BBD, and BC risk, we pooled data from 15,194 white women (6,706 BC cases and 8,488 controls) participating in five Breast Cancer Association Consortium case-control studies. BC risk associations were evaluated by self-reported BBD history and combinations of PRS expressed in tertiles using odds ratios (OR), considering the lowest category of PRS among women without BBD as the referent. Associations were also assessed in an independent case-cohort study (831 BC cases and 1,845 controls) using hazard ratios (HR). Among the case-control studies, the PRS was strongly associated with BC risk (OR=2.50 per standard deviation increase), and associations were similar in women with (OR=2.60) and without a history of BBD (OR=2.44, p-interaction=0.23). Women with BBD have a higher risk of BC than those without BBD within each level of PRS. Compared to women without BBD and in the lowest PRS category, those with BBD and PRS in the highest tertile had 5-fold increased odds of BC (OR = 5.38; 95% CI 4.73-6.13). Associations were similar in an independent case-cohort study. These findings indicate that BBD history and PRS are independent contributors to BC risk, and consideration of both simultaneously could improve breast cancer risk prediction. Future studies are needed to integrate PRS with other risk measures to refine absolute BC risk prediction among BBD patients. Associations of BC risk with combinations of PRS (tertiles) and history of BBD in five case-control studies and a case-cohort study from the Breast Cancer Association Consortium. Case-control studies Case-cohort study PRS score range History of BBD n BC cases n controls OR (95% CI)1 n BC cases n controls HR (95% CI)2 Tertile 1 (≤ -0.57) No 1009 2692 1.00 (Ref) 117 584 1.00 (Ref) Yes 508 902 1.54 (1.35-1.76) 69 163 2.05 (1.45-2.89) Tertile 2 (-0.58, -0.03) No 1469 2001 1.97 (1.78-2.17) 181 475 1.93 (1.48-2.51) Yes 760 798 2.65 (2.34-3.01) 108 170 2.99 (2.18-4.09) Tertile 3 (≥ -0.02) No 1910 1551 3.32 (3.01-3.67) 214 336 3.28 (2.52-4.26) Yes 1050 544 5.38 (4.73-6.13) 142 117 4.83 4.25-8.02) 1.Logistic regression analysis, adjusted for study site/genomic platform and age at enrollment. OR, odds ratio; CI, confidence interval.2.Cox proportional hazards regression analysis, accounting for the case-cohort study design and adjusted for age at enrollment. HR, hazard ratio; CI, confidence interval. Citation Format: Stacey J. Winham, Mark Sherman, Robert Vierkant, Bryan McCauley, Christopher Scott, Mia Gaudet, Melissa Troester, Sandhya Pruthi, Derek Radisky, Amy Degnim, Kristan Aronson, Rachel Murphy, Pascal Guenel, Therese Truong, Jenny Chang-Claude, Heiko Becher, Montserrat Garcia-Closas, Stephen Chanock, Thomas Ahearn, Xiaohong Yang, Doug Easton, Manjeet Bolla, Celine Vachon, Breast Cancer Association Consortium. Association of polygenic risk scores with breast cancer risk among women with benign breast disease. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5241.

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 Background: Ovarian endometrioid carcinoma (OEC) is an uncommon, understudied histotype that is distinguished from other histotypes by unique genomic features and high hormone receptor expression. There is growing interest in the biological relevance of subtyping using immunohistochemistry (IHC). Methods: Women with primary invasive OEC diagnosed from 1989 to 2010 were retrospectively identified from the population-based Alberta Cancer Registry, Canada. Pathology slide review and IHC staining for Napsin A, TP53, and WT1 were used to reduce histologic misclassification, resulting in N=166 participants (low-stage N=138, high-stage N=28), excluding those who received neo-adjuvant chemotherapy. The following markers were stained and scored on tissue microarrays: TP53 (normal/abnormal), PMS2/MSH6 (mismatch repair proficient [MMRp]/deficient [MMRd]), PGR (loss/retained), and CTNNB1 (nuclear/cytoplasmic). Using a hierarchical decision tree by stage group (low/high), we categorized tumors as TP53 abnormal, MMRd, PGR loss, nuclear CTNNB1, and no specific immunohistochemical profile (NSIP). Distributions and five-year disease specific survival (DSS) was estimated for each group; hazard ratios (HRs) adjusted for age were estimated using Cox regression with NSIP as referent group. Results: Among low-stage cases, five-year survival was as follows: 80% for TP53 abnormal (N=10, 7%), 87% for MMRd (N=15, 11%), 80% for PGR loss (N=10, 7%), 98% for nuclear CTNNB1 (N=63, 46%), and 95% for NSIP group (N=40, 29%). Overall, IHC group was associated with DSS among low stage cases (LRT p-value=0.006), and the group with nuclear CTNNB1 had a small but statistically significant DSS advantage (HR=0.089, 95% CI 0.011-0.742, p=0.025). TP53 and PGR loss suggested shorter DSS than the referent NSIP group, but not statistically significantly so (HR=1.26 and 3.08, respectively). Among high-stage cases, five-year DSS was as follows: 57% for TP53 abnormal (N=7, 25%), 75% for MMRd (N=8, 29%), 33% for nuclear CTNNB1 (N=3, 11%), and 17% for NSIP (N=10, 36%). There were no high-stage cases in the PGR-loss group. Nuclear CTNNB1 group had a shorter DSS among high-stage cases, but this was not statistically significantly so (HR=1.16, p=0.85). Conclusion: Pending replication in additional datasets, results suggest that OEC cases in nuclear CTNNB1 or NSIP IHC groups may have a favorable prognosis in which de-escalation of therapy can be considered. In addition, abnormal TP53 and loss of PGR may identify a high-risk subset of low-stage OEC. Citation Format: Brooke Jorgensen, Stacey Winham, Julie Cunningham, Sebastian Armasu, Chen Wang, Hunter Atkinson, Bryan McCauley, Linda Kelemen, Martin Koebel, Ellen Goode. Towards a combined immunohistochemistry-based subtyping of ovarian endometrioid carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6499.

Abstract Purpose: Benign breast disease (BBD) is viewed as a non-obligate precursor stage in the development of breast cancer (BC). Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks has been routinely used in BC research. However, RNA-seq of heterogeneous bulk samples represents an averaged expression profile across all cell mixtures. Downstream analyses, such as biomarker discovery, are likely confounded by differences in cell proportions. There is a lack of research in optimizing strategies for cell type deconvolution especially for FFPE samples. We evaluated and optimized strategies for cell type deconvolution using existing single cell RNA-seq (scRNA-seq) and single nucleus RNA-seq (snRNA-seq) breast tissue references, and applied these to a cohort of BBD FFPE samples to assess risk of subsequent BC. Methods: 7 scRNA-seq and 3 snRNA-seq public samples from women’s breast tissue were normalized using SCTransform, then integrated using Harmony to form a complete reference set. Bioinformatics evaluations examined the performance of existing statistical and deep learning-based deconvolution methods. Computational simulations were conducted to evaluate those methods on three scenarios: 1) baseline; 2) incomplete reference set; 3) FFPE artifacts. FFPE artifacts were simulated based on a generalized additive model using 7 in-house paired FFPE-fresh frozen samples. Finally, we applied the best performing method to our BBD cohort of 62 samples. Differential expression analysis adjusting for cell proportions was performed to identify transcriptomic aberrations in BBD cases. Results: snRNA-seq was more sensitive in capturing adipocytes, while scRNA-seq was more sensitive in detecting lymphocytes in breast tissue. The median number of detectable genes per cell was higher for scRNA-seq than snRNA-seq before integration (1264 vs 558). Marker gene-based regression methods (e.g. CIBERSORT) displayed higher variance and lower bias compared to methods directly using scRNA-seq data (e.g. SCDC). Incomplete reference data led to increased bias and little effect on variance. FFPE artifacts had a higher impact on performance compared to incomplete reference data. Overall, Scaden, a deep learning-based method, was the top performing method across all simulation scenarios and was especially robust to FFPE artifacts. 59 differential expressed genes (DEGs) were identified with fold change &gt; 1.5 and p value &lt; 0.01. DEGs involved in BC, e.g. S100A8, WNT4 and HIST1H3H, would be left out if cell proportions were not properly adjusted. Conclusions: We constructed the first complete single cell reference data for women’s breast tissue and benchmarked existing cell type deconvolution methods. Compared to analyses not adjusting for cell proportions, we observed substantial differences in perturbed genes and pathways associated with BC risk for BBD patients. Citation Format: Yuanhang Liu, Chen Wang, Stacey J. Winham, Aditya Bhagwate, Robert A. Vierkant, Julie M. Cunningham, Bryan M. McCauley, William A. Jons, Derek C. Radisky, Amy C. Degnim. Deconvolution of cell type composition in FFPE tissue: Application to benign breast disease. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4291.

Abstract Purpose: Young women’s breast cancers (YBCs), defined herein as breast cancer (BC) diagnosed at age ≤ 40 years, are etiologically and biologically distinct from postmenopausal BCs, demonstrating a higher proportion of estrogen receptor (ER) negative subtypes and a worse prognosis. Studies have not assessed whether risk factors such as recent parity and body mass index (BMI) are linked to protein expression profiles in YBCs. Accordingly, we examined protein expression assessed with GeoMx® Digital Spatial Profiling (DSP) in 640 YBCs prepared as tissue microarrays (TMAs) in the Young Women’s BC Study (YWBCS). Methods: The YWBCS is a multi-institutional cohort of 1,297 women with incident BC diagnosed at age &lt;40 years identified through medical record review, with risk factor data collected via questionnaires. TMAs including 1,505 cores of 640 YBCs that were collected prior to therapy and were suitable for molecular analysis were evaluated for 72 markers with NanoString GeoMx DSP. Associations of log-transformed biomarker expression with BMI and time from last live birth to BC diagnosis were carried out using linear mixed modeling approaches, accounting for multiple tumor tissue cores per individual. Results: Mean age at BC was 35.5 years (range 17-40). Tumor subtypes based on clinical immunostaining were the following: luminal B 46%; luminal A 36%; triple negative 12% and HER2+ 6%. Of 72 biomarkers tested, 53 (74%) were evaluable after QC and normalization. GeoMx ER and PR protein levels were higher in luminal A and luminal B YBCs than in HER2+ and TNBC (p=2x10-94 and 2x10-52, respectively), supporting the accuracy of the GeoMx assay. Compared to nulliparas or those with most recent birth &gt; 3 years before BC, those who gave birth ≤ 3 years prior to BC had tumors with higher expression of PR (8x10-6) and ER (p=0.006), and lower expression of BCL-2 (p=0.006). In contrast, women with last birth ≥ 6 years prior to BC had higher expression of CD8 (p=0.006) and PD-1 (p=0.001) than nulliparas and those with more recent births. Compared to women with BMI between 18.5 and 29.9, those with higher or lower BMIs had higher expression of CD34 (p=0.0003), CD4 (p=0.0009) and CD14 (p=0.001). BCL-2 expression was higher among women with BMI &lt; 18.5 compared to those with higher values (p=0.005). Women with BMI of 30+ had higher β2-microglobulin levels than those with lower BMIs (p=0.003). Inverse dose-response associations (with higher BMI related to lower levels) were observed for expression of ER (p=0.01), pan cytokeratin (p=0.004) and four proteins in the p13K/AKT signaling pathway (p ≤ 0.01 for each). Conclusions: Preliminarily, we identify differences in hormonal and immune markers in YBCs in relation to timing of births and BMI, suggesting that these factors may contribute to etiologic heterogeneity. Multivariate modeling is ongoing to incorporate molecular subtype and other risk factors. Citation Format: Robert A. Vierkant, Laura M. Pacheco-Spann, Stacey J. Winham, Jennifer M. Kachergus, Ji Shi, Craig Snow, Fergus J. Couch, Janet E. Olson, Chen Wang, Derek C. Radisky, Amy C. Degnim, E. Aubrey Thompson, Laura C. Collins, Kathryn J. Ruddy, Ann H. Partridge, Mark E. Sherman. Associations of parity and body mass index with NanoString digital spatial protein profiling in early onset breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1000.