Vierkant, Robert A.

AbstractWe aimed to validate the prognostic association of p16 expression in ovarian high‐grade serous carcinomas (HGSC) and to explore it in other ovarian carcinoma histotypes. p16 protein expression was assessed by clinical‐grade immunohistochemistry in 6525 ovarian carcinomas including 4334 HGSC using tissue microarrays from 24 studies participating in the Ovarian Tumor Tissue Analysis consortium. p16 expression patterns were interpreted as abnormal (either overexpression referred to as block expression or absence) or normal (heterogeneous). CDKN2A (which encodes p16) mRNA expression was also analyzed in a subset (n = 2280) mostly representing HGSC (n = 2010). Association of p16 expression with overall survival (OS) was determined within histotypes as was CDKN2A expression for HGSC only. p16 block expression was most frequent in HGSC (56%) but neither protein nor mRNA expression was associated with OS. However, relative to heterogeneous expression, block expression was associated with shorter OS in endometriosis‐associated carcinomas, clear cell [hazard ratio (HR): 2.02, 95% confidence (CI) 1.47–2.77, p < 0.001] and endometrioid (HR: 1.88, 95% CI 1.30–2.75, p = 0.004), while absence was associated with shorter OS in low‐grade serous carcinomas (HR: 2.95, 95% CI 1.61–5.38, p = 0.001). Absence was most frequent in mucinous carcinoma (50%), and was not associated with OS in this histotype. The prognostic value of p16 expression is histotype‐specific and pattern dependent. We provide definitive evidence against an association of p16 expression with survival in ovarian HGSC as previously suggested. Block expression of p16 in clear cell and endometrioid carcinoma should be further validated as a prognostic marker, and absence in low‐grade serous carcinoma justifies CDK4 inhibition.

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 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

Abstract Background: The increased number of T-lymphocytes residing in the tumors of colorectal cancer (CRC) patients is consistently shown to predict better survival of CRC patients independent of stage. In addition to tumor itself, T-lymphocytes also infiltrate normal tissues adjacent to tumors; however, little is known about how the inter-individual T-cell variability in tumor-adjacent tissues affects CRC prognosis. Our goal was to characterize total T-cells in colorectal tumor and tumor-adjacent tissues and study their associations with all-cause and CRC-specific survival in the Iowa Women’s Health Study. Methods: We constructed tissue microarrays and quantified CD3 antibody staining (Clone F.7.238; Dako), an established marker for total T-cells, in paraffin-embedded tissue samples from 463 women diagnosed with incident CRC from 1986-2002 (mean age at diagnosis was 74 years). Up to 3 tumor and 3 tumor-adjacent cores were immunostained for each person. An experienced pathologist quantified CD3+ T cells in each area using 4 categories: non-detected, mild (1-10 cells per 0.28 mm2); moderate (11-29 cells per 0.28 mm2); and strong infiltration (≥30 cells per 0.28 mm2). The obtained scores were averaged over all quantified cores for each person. Cox regression was used to estimate the hazard ratio (HR) and 95% CI for all-cause and CRC mortality in relation to (1) tumor score; (2) tumor-adjacent score; and (3) the ratio of tumor to tumor-adjacent score, hereafter called Score ratio. All scores were categorized in quartiles. The final model adjusted for age at diagnosis, SEER stage, tumor grade, body mass and smoking history. Results: During follow-up for a maximum of 25 years, 67% of participants diagnosed with CRC died (~30% died from CRC). There was a weak but significant correlation between CD3 tumor and tumor-adjacent scores (Spearman coefficient: r=0.15, p=0.02). CD3 tumor score was inversely associated with stage and was higher in those with proximal colon cancer, and for MSI-high, CIMP-high and BRAF-mutated tumors. The HRs (95%CI) for the highest versus lowest category of CD3 tumor score were 0.61 (0.44-0.84) (p-trend=0.003) for all-cause mortality and 0.27 (0.15-0.48) (p-trend &lt;0.0001) for CRC-specific mortality. The tumor-adjacent score was not associated with any characteristics or death. The associations between the Score ratio and all-cause and CRC survival mirrored those for the tumor score but were weaker. The HRs (95%CI) for the highest versus lowest quartile of the Score ratio were 0.70 (0.45-1.09) (p-trend=0.09) for all-cause and 0.45 (0.23-0.87) (p-trend=0.003) for CRC-specific mortality. Conclusions. Based on data from prospectively identified CRC cases in the IWHS cohort, colorectal tumor T-cell infiltration is associated with improved survival. Further investigation is needed to determine the T-cell subtypes that are most predictive of CRC survival outcomes. Citation Format: Anna E. Prizment, Robert A. Vierkant, Thomas C. Smyrk, Lori S. Tillmans, Heather H. Nelson, Charles F. Lynch, Stephen N. Thibodeau, Timothy R. Church, James R. Cerhan, Kristin E. Anderson, Paul J. Limburg. Associations between total T-lymphocytes and colorectal cancer survival in a prospective cohort study of older women [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 2247. doi:10.1158/1538-7445.AM2017-2247

Abstract Purpose: To identify within demographic features and theoretical models of health behavior, the variables demonstrating differences in the behaviors, beliefs, attitudes, associations, and capacities of sporadic (non-adherent) and persistent (adherent) mammographic screeners for the purpose of guiding future interventions to improve mammographic screening adherence. Procedures: A survey was designed and mailed to all screening-age women of one Bemidji Area tribe. The survey incorporated demographic questions, and questions designed to inform about salient features of respondents' health literacy, social networks, social support, social norms, planned behavior, and health beliefs. Prior to mailing, each survey was assigned a unique identifier and sealed in an envelope. Surveys were mailed to 1,554 women in manner that ensured anonymity of the women's responses and no potential for investigators to discern women's identity via their mailing addresses or other information. Self-report data from returned surveys was used to determine women's mammographic screening status as adherent with local screening guidelines (annual mammogram beginning at age 40 for women at average risk) or non-adherent. Responses for all demographic and theoretical models of health behavior were appropriately categorized and analyzed utilizing global tests of hypotheses. Inherent multicollinearity issues required a two-step process of factor analysis followed by a multivariate analysis of variance. Where themes (e.g., demographics, health literacy, etc.) and factors within themes were significant, a univariate analysis was conducted to ascertain specific questions and responses that significantly differentiated the non-adherent from adherent screeners. Summary of data: We found no significant differences in non-adherent and adherent screeners' responses to questions related to social networks or social norms. We did find that responses from these two groups did differ on demographic features as well as on aspects of health literacy, social support, planned behavior and health beliefs. o Demographic differences involved biopsy histories and employment status. o Differentiating health literacy variables involved recognition of certain terms (e.g., annual) and understanding concepts such as ultrasound and biopsy. o Social support variables that distinguished non-adherent from adherent screeners involved sources of information support, characteristics of providers of emotional support, and sources of emotional support (e.g., solitary or communal). o Planned behavior variables illuminating differences between the screening groups involved (in additions to self-reported screening behaviors): attitudes toward the behavior of obtaining mammograms, intentions to engage in screening, and control beliefs. o Health Beliefs variables distinguishing the two sets of screeners comprised responses to questions about perceived barriers, self-efficacy, and cues to action. Conclusions: Study findings point to opportunities for tribal health and Indian Health Service (IHS) providers and staff to intervene in two broad areas. 1. Some findings associated with social support, planned behavior, and health beliefs can be used to design patient intake forms to alert tribal health and IHS providers to current and potentially non-adherent. 2. Using this information, providers can design interventions to move non-adherent and potentially non-adherent women toward persistent mammographic screening participation. An intervention study will be funded by the Minnesota Department of Health through its Eliminating Health Disparities Initiative. Citation Format: Wesley O. Petersen, Ann M. Nicometo, Robert A. Vierkant, Corinna Sabaque. Elements of theoretical models of health behavior: Planning for breast cancer screening interventions in an American Indian tribe. [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr B83.

2 Background: Women with atypical hyperplasia (AH) on breast biopsy have an aggregate increased risk of breast cancer (BC), but accurate personalized risk prediction is desirable to facilitate individual clinical management decisions. Currently used models provide poor BC risk prediction for women with AH. Our goal was to develop and validate an improved risk prediction model for women with AH. Methods: From a cohort of 13,538 women with benign breast disease from 1967-2001, pathology review confirmed 699 with AH. Clinical risk factors and histologic features of the tissue biopsy were recorded, and BC events were ascertained from study questionnaires, tumor registry, and review of medical records. Using a lasso approach, 23 variables were assessed for model inclusion. Lasso-identified features were then fit in a Cox regression model to estimate BC risk. Model discrimination was assessed with C-statistics in the model-building set and in a separate external validation set. Calibration was assessed by comparing observed to predicted breast cancer counts. Results: The model-building set comprised 699 women with 142 BC events (median follow-up 8.1 years), and the external validation set comprised 461 women with 114 BC events (median follow-up 11.4 years). The final model included three covariates: age at biopsy, age squared, and number of foci of AH. Model performance was good, with a C-statistic of 0.622 (SE = 0.027) in the model-building set and 0.594 (SE = 0.029) in the external validation set. The model is well-calibrated, with observed to expected numbers of BCs nearly equal across all post-biopsy follow-up years. Conclusions: We propose a new model for predicting BC risk in women with AH based on age at biopsy and number of foci of atypia. This model provides absolute risk estimates for women with AH, has good discriminatory ability, is well-calibrated, and validates in an external cohort.

18 Background: Benign breast disease (BBD) is diagnosed in 1-2 million women/year in the US, and while these patients are known to be at substantially increased risk for subsequent development of breast cancer, existing models for risk assessment perform poorly at the individual level. Here, we describe a DNA-microarray-based transcriptional model for breast cancer risk prediction for patients with sclerosing adenosis (SA), a lesion found in ¼ of all BBD patients that is characterized by epithelial proliferation, disordered acinar architecture, and stromal fibrosis. Methods: A training set was developed from 86 patients diagnosed with sclerosing adenosis (SA), of which 27 subsequently developed cancer within 10 years (cases) and 59 remained cancer-free at 10 years (controls). A diagonal linear discriminate analysis (DLDA)-prediction model for prediction of cancer within 10 years (SA TTC10) was generated from transcriptional profiles of formalin fixed, paraffin-embedded (FFPE) biopsy-derived RNA. This model was tested on a separate validation case-control set composed of 65 SA patients. Results: The SA TTC10 gene signature model, composed of 35 gene features, achieved a clear and significant separation between case and control with receiver operating characteristic area under the curve of 0.913 in the training set and 0.836 in the validation set. Conclusions: Our results provide the first demonstration that benign breast tissue contains transcriptional alterations that indicate risk of breast cancer development, and that essential precursor biomarkers of malignancy are present many years prior to cancer development. Furthermore, the SA TTC10 gene signature model, which can be assessed on FFPE biopsies, constitutes a novel prognostic biomarker for patients with SA.