Sherman, Mark E.

Abstract Frequent detection of serous tubal intraepithelial carcinoma (STIC) among BRCA1/2 mutation carriers undergoing risk-reducing surgery prompted the hypothesis that many adnexal high-grade serous carcinomas (HGSCs) arise from the fallopian tube, rather than the ovary, as supposed. The changing paradigm has important implications for HGSC prevention. Most data related to the frequency of STIC are derived from case series and estimates vary widely. Therefore, we analyzed population-based data from 10 523 surgeries including salpingectomy (Jan 2014–Dec 2016) that were examined using the “Sectioning and Extensively Examining the Fimbria” protocol, which optimizes STIC detection. Overall, STIC was detected in 40 (0.38%) specimens, including 32 diagnosed with concurrent gynecologic cancer. STIC was detected in 8 (<0.01%) of 9392 cases with benign diagnoses. We conclude that the relative rarity of STIC diagnoses in routine pathology practice has critical implications for research aiming to elucidate the pathogenesis of HGSC and developing prevention strategies.

Abstract Background: Pelvic inflammatory disease (PID) has been associated with ovarian cancer and Chlamydia trachomatis (chlamydia) is the leading cause of PID in the developed world. Primary infection with chlamydia is often asymptomatic and may persist for several months/years, thus ascertainment of PID in epidemiologic studies is challenging due to pervasive misclassification and under-reporting. To improve our understanding of the role of chlamydia and other infectious agents in developing ovarian cancer, we evaluated serologic markers of infection with incident ovarian cancer using a staged approach in two independent populations. Methods: Studies included: 1) a case-control study conducted in Poland of 278 ovarian cancer cases diagnosed from 2000 to 2003 and age- and study site-matched controls (n=556) and 2) a nested case-control study from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial including 160 women who eventually developed ovarian cancer and 159 matched controls. We calculated odds ratios (OR) and 95% confidence intervals (CI) for the association between serologic marker titers and ovarian cancer risk using logistic regression adjusted for matching factors and potential confounders. We evaluated laboratory cutpoints indicative of prior chlamydia infection and tested more stringent marker thresholds, identified in Poland and applied in PLCO, that could be relevant for a PID-like infection. Results: In the Polish study, positivity for the chlamydia plasmid-encoded Pgp3 protein was associated with increased ovarian cancer risk [OR (95% CI): 1.63 (1.20-2.22)]; when a positive result was redefined at a higher titer, ovarian cancer risk increased [cutpoint 2: 2.00 (1.38-2.89); cutpoint 3 (max OR): 2.19 (1.29-3.73)]. All other chlamydia markers measured were associated with increased risk at the more stringent positivity threshold in Poland. In the prospective PLCO study, Pgp3 was associated with elevated risk at the laboratory cutpoint [1.43 (0.78-2.63)]; using higher thresholds, Pgp3 was associated with increased ovarian cancer risk [cutpoint 2: 2.25 (1.07-4.71); cutpoint 3: 2.53 (0.63-10.08)]. In both studies, markers of other infections (i.e., Mycoplasma genitalium, herpes simplex virus-2, human papillomavirus, herpes simplex virus-1, polyomavirus, hepatitis B, hepatitis C, Epstein Barr virus, cytomegalovirus) measured were not associated with risk. Conclusions: In two independent populations, the gold standard serologic marker of prior/current chlamydia infection (Pgp3) was associated with a doubling in ovarian cancer risk, whereas markers of other infectious agents were unrelated. Although these findings need to be replicated, they suggest potential ovarian cancer risk reduction through targeted treatment of chlamydia infections. Citation Format: Britton Trabert, Tim Waterboer, Sally B. Coburn, Louise A. Brinton, Mark E. Sherman, Jolanta Lissowska, Beata Peplonska, Patricia Hartge, Michael Pawlita, Nicolas Wentzensen. Serologic markers of infectious agents and ovarian cancer: Markers of prior Chlamydia trachomatis infection associated with increased ovarian cancer risk in two independent populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4942.

Abstract Introduction: Defining the histologic correlates of mammographic breast density (MD) may provide insights into why elevated MD is related to increased breast cancer risk. Studies suggest that reduced terminal ductal lobular (TDLU) involution is associated with elevated MD, and both are independent breast cancer risk factors among women who have undergone a biopsy for benign breast disease (BBD). Prior digital histologic analyses of normal breast tissues revealed epithelial nuclear density (END) and TDLU involution are correlated. Accordingly, we examined associations of END with TDLU involution and MD in clinical biopsies. Methods: We analyzed 262 image-guided breast biopsies diagnosed as BBD from 224 women. TDLU involution was visually assessed as TDLU count/mm2 and TDLU span (inversely related to level of involution) in background normal tissue, evaluated using digitized images. The Genie Classifier (Aperio) was applied to images to estimate nuclei count per unit epithelial area, (END). Single X-ray Absorptiometry of pre-biopsy craniocaudal digital mammograms was applied to measure global MD (percent fibroglandular volume (%FGV)). Analysis of covariance, adjusted for age and body mass index, examined mean END differences across tertiles of TDLU/MD measures. Analyses were conducted at the biopsy level using SAS PROC GENMOD to account for within-woman correlations. All tests were two-tailed. Results: Overall, 67% of BBD biopsies were proliferative. Higher END was observed among proliferative than non-proliferative BBD (median END: 10,187 vs. 9,953 respectively; p=0.04). Among all women, END significantly increased with increasing tertiles of TDLU measures (p-trends: TDLU count/100mm2=0.0001, TDLU span=0.046). Whereas TDLU metrics were positively associated with %FGV, no relationship was observed between END and %FGV. In analyses stratified by BBD severity, however, END and %FGV were positively associated among women with non-proliferative disease (p-trend=0.04), findings not observed with proliferative disease. Conclusions: Automated END and visually assessed TDLU involution metrics were positively associated with each other and with MD. However, associations were diluted for proliferative lesions, suggesting that applying automated digital pathology tools to unsegmented digital images of whole sections of BBD biopsies does not demonstrate the same associations with MD as visual assessment of TDLU involution. Citation Format: Maeve Mullooly, Samantha Puvanesarajah, Shaoqi Fan, Ruth M. Pfeiffer, Linnea Olsson, Manila Hada, Erin L. Kirk, Pamela M. Vacek, Donald L. Weaver, John A. Shepherd, Amir P. Mahmoudzadeh, Jeff Wang, Stephen M. Hewitt, Sally D. Herschorn, Mark E. Sherman, Melissa A. Troester, Gretchen L. Gierach. Utilizing digital pathology to understand breast epithelial characteristics of benign breast disease among women undergoing diagnostic image-guided breast biopsy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3260.

Abstract Background The immunohistochemical (IHC) 4 + clinical treatment score (C) i.e. IHC4+C assay combines IHC assessment of four routinely measured markers in the clinical setting, including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and KI67, with other clinical parameters for aiding decision-making on adjuvant chemotherapy in hormone positive (HR+) breast cancer. It is low cost, widely available and has been shown to provide similar prognostic information to the 21-gene (Oncotype DX) test. Clinical use of IHC4+C is limited by the need for standardized scoring of the IHC markers. Here, we examine the prognostic value of a digital image analysis (DIA) based IHC4+C assay in two study populations and compare this with pathologists' visual assay in a subset of patients. We also evaluated its potential clinical relevance in relation to the Nottingham prognostic index [NPI; i.e. low (&lt;3.4), intermediate (3.4-5.4) and high (&gt;5.4)] and IHC defined breast cancer subtypes (i.e. luminal A-like and B-like). Methods Using DIA data on ER, PR, HER2 and KI67, in addition to clinicopathological characteristics of tumors, we computed DIA based IHC4+C (i.e. DIAIHC4+C) assay for 2,034 women from study populations in Poland (PBCS) and the United Kingdom (SEARCH). A total of 246 deaths occurred over a median follow-up period of 6.7 years. A relevant threshold was determined using quartile analysis and the relationship between dichotomous categories (&gt;75th vs ≤ 75th percentile) of DIAIHC4+C and 10-year breast cancer specific survival (BCSS) was investigated using Kaplan-Meier survival curves and in Cox proportional hazard regression models, overall and stratified by study, NPI and subtype. Results DIAIHC4+C score was associated with worse 10-year BCSS overall [HR (95% CI) &gt;75th vs ≤ 75th percentile = 2.92 (1.77, 4.82); p-value &lt;0.0001] and in both the PBCS [3.48 (1.40, 8.65); p-value = 0.007] and SEARCH [3.87 (2.24, 6.68); p-value &lt;0.0001] study populations. DIAIHC4+C was associated with significantly worse survival outcomes in women with intermediate NPI [1.94 (1.31, 2.88); p-value = 0.001] and luminal A-like subtype of HR+ tumors [5.37 (3.50, 8.26); p-value &lt;0.0001]. DIA and visual IHC4+C results were highly correlated (r = 0.95) and survival associations and model fit parameters were similar for DIA [3.82 (2.08, 7.00); p-value &lt; 0.0001; AIC = 472.3] and visual [4.73 (2.55, 8.76); p-value &lt; 0.0001; AIC = 466.1] IHC4+C scores amongst 444 patients with data from both sources. Conclusion These findings are in support of the prognostic value of DIAIHC4+C assay in HR+ breast cancer and demonstrate its potential clinical relevance as an adjunct to the NPI. Given the advantages of being high-throughput and highly reproducible, DIA based methods could find use in the standardization of the analytical processes for the IHC4+C assay. Citation Format: Mustapha Abubakar, Paul D. Pharoah, Mark E. Sherman, Montserrat Garcia-Closas, Mitch Dowsett. Digital image analysis based IHC4+C assay and prognosis in hormone receptor-positive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2971.

Abstract Background: Endometrial cancer (EC) is the most common gynecologic malignancy in developed countries and the incidence and mortality of EC continue to rise in the US. When detected early, EC is highly curable, suggesting that early detection strategies could reduce EC mortality. Women who develop EC often have a prolonged history of abnormal bleeding, offering a window for identifying many at-risk women prior to the development of advanced cancer. To evaluate risk prediction, novel sampling and early detection strategies in women at increased risk of EC, we established a prospective cohort of women with abnormal bleeding that is followed for endometrial hyperplasia and cancer endpoints. Methods: We consecutively enrolled women ≥45 years old presenting to the Mayo Clinic for evaluation of abnormal uterine bleeding (AUB), postmenopausal bleeding (PMB), thickened endometrial stripe (ES) on imaging, or Lynch Syndrome (LS). From all women, an intravaginal tampon and an endometrial brushing via Tao brush were collected before endometrial biopsy. Epidemiological EC risk factor data, clinical data, and endometrial biopsy histology results were abstracted from medical records. Absolute risk of endometrial hyperplasia and EC were calculated overall and in strata based on clinical symptoms and risk factors. Results: Among the first 1,000 women enrolled into the study, 712 (71%) were overweight or obese, 534 (53%) were postmenopausal, and 214 (22%) were current hormone replacement therapy (HRT) users. Endometrial biopsy and, if indicated, subsequent hysterectomy, detected 39 ECs, 16 atypical hyperplasias, and 28 hyperplasias without atypia at baseline evaluation. Among 534 postmenopausal women, 453 (85%) reported any type of bleeding, with spotting (39%) and light bleeding (27%) being more common than heavy bleeding (14%). Among perimenopausal women with AUB (n=452), the absolute risk of EC at baseline was 0.9% and among women with PMB, the absolute risk of EC at baseline was 7.5%. Among women with PMB, EC risk was modified by HRT use (risk of 2.6% in women on HRT and 8.2% in women not on HRT, respectively), obesity (risk of 5.3%, 8.1% and 9.5% for women with normal, overweight, and obese BMI, respectively), and type of bleeding (risk of 9.7%, 4.9%, and 8.2% in women with spotting, light or heavy bleeding, respectively). Discussion: While risk of endometrial cancer in perimenopausal women with AUB is low, approximately 8% of women presenting for clinical work up of PMB have underlying EC. Clinical presentation and risk factors modify the absolute risk of EC and may inform clinical management. Additional recruitment and prospective follow-up is underway to improve estimates of short- and long-term EC risk in women with PMB. With its collection of tampon and Tao brush samples, the study will provide a unique resource to evaluate molecular markers of EC and EC risk in elevated-risk women. Citation Format: Nicolas A. Wentzensen, Megan Clarke, Viji Shridhar, Maureen Lemens, Matthew Hopkins, Lisa Ahlberg, Shannon Laughlin-Tommaso, Karl Podratz, Mark Sherman, Jamie Bakkum-Gamez. A prospective study of endometrial cancer detection in women presenting for evaluation of abnormal peri- and postmenopausal bleeding [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2203.

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.

Limited epidemiological evidence suggests that the etiology of hormone receptor positive (HR+) breast cancer may differ by levels of histologic grade and proliferation. We pooled risk factor and pathology data on 5,905 HR+ breast cancer cases and 26,281 controls from 11 epidemiological studies. Proliferation was determined by centralized automated measures of KI67 in tissue microarrays. Odds ratios (OR), 95% confidence intervals (CI) and p‐values for case–case and case–control comparisons for risk factors in relation to levels of grade and quartiles (Q1–Q4) of KI67 were estimated using polytomous logistic regression models. Case–case comparisons showed associations between nulliparity and high KI67 [OR (95% CI) for Q4 vs. Q1 = 1.54 (1.22, 1.95)]; obesity and high grade [grade 3 vs. 1 = 1.68 (1.31, 2.16)] and current use of combined hormone therapy (HT) and low grade [grade 3 vs. 1 = 0.27 (0.16, 0.44)] tumors. In case–control comparisons, nulliparity was associated with elevated risk of tumors with high but not low levels of proliferation [1.43 (1.14, 1.81) for KI67 Q4 vs. 0.83 (0.60, 1.14) for KI67 Q1]; obesity among women ≥50 years with high but not low grade tumors [1.55 (1.17, 2.06) for grade 3 vs. 0.88 (0.66, 1.16) for grade 1] and HT with low but not high grade tumors [3.07 (2.22, 4.23) for grade 1 vs. 0.85 (0.55, 1.30) for grade 3]. Menarcheal age and family history were similarly associated with HR+ tumors of different grade or KI67 levels. These findings provide insights into the etiologic heterogeneity of HR+ tumors.

Abstract Background: High percent mammographic density (MD), which reflects the relative fibroglandular tissue content of the breast, is one of the strongest breast cancer risk factors; however, the pathologic mediators of this risk are unknown. We hypothesize that analysis of breast tissue sections using deep learning approaches may characterize histologic features that underpin risk associated with high MD. Methods: Non-targeted H&E stained breast tissue sections of diagnostic image-guided breast biopsies were evaluated among 588 women enrolled following an abnormal mammogram in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project (2007-2010). Overall volumetric percent MD for the biopsied breast and localized volumetric percent MD surrounding the biopsy site were determined for each participant. A deep convolutional neural network (CNN) model was trained to identify and quantitatively assess breast epithelial, stroma and fat tissue and their organizational and spatial arrangements. Least absolute shrinkage and selection operator (Lasso) regression was used to determine relationships between MD measures and pathological features. To ensure reliability of the model, a cross-validation strategy was employed to build and assess the performance of the fitted model. Finally, Spearman correlation coefficients were estimated to test the association between the predicted density values by each model (predicting overall or localized MD) and the actual MD measurements. We report the average and standard deviation (SD) of the correlation coefficients. Results: In an independent validation set, the CNN model was 95.5% accurate in classifying epithelial, stromal and fat tissue. The mean (SD) correlations between the predicted model and the actual measurements for overall and localized MD were 0.70 (0.06) and 0.65 (0.06) respectively. The amount of stroma identified (normalized to tissue area) had the highest selection probability (P-value) by the Lasso model and thus the strongest positive relationship with MD (P-value &gt;0.9 for each MD measurement). In contrast, the amount of normalized epithelial tissue was not related to MD (P-value=0.01 for each MD measurement). No association was observed for the total normalized fat area with MD (P-value &lt;0.31 for each MD measurement). In addition, the number of distributed epithelial regions was positively associated, whereas the distance between epithelial regions was inversely associated with overall MD (P-value &lt;0.87 and 0.62, respectively). Conclusions: These results show that greater stromal tissue amount and spatial distribution patterns of epithelial regions, rather than total epithelial amounts, had the strongest relationships with elevated MD. Future work will determine the relationship of these MD features with biopsy diagnosis. Citation Format: Maeve Mullooly, Babak Ehteshami Bejnordi, Maya Palakal, Pamela M. Vacek, Donald L. Weaver, John A. Shepherd, Bo Fan, Amir Pasha Mahmoudzadeh, Jeff Wang, Jason M. Johnson, Sally D. Herschorn, Brian L. Sprague, Ruth M. Pfeiffer, Louise A. Brinton, Mark E. Sherman, Andrew Beck, Gretchen L. Gierach. Application of convolutional neural networks to breast biopsies to uncover tissue correlates of mammographic breast density [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 4235. doi:10.1158/1538-7445.AM2017-4235