Chen, Wei

Abstract Introduction: Atypical hyperplasia (also known as proliferative disease with atypia), while categorized as a benign finding, has been consistently associated with an increased risk of subsequent breast cancer that persists for decades after initial diagnosis. There are various other lesions that are classified as benign breast disease (BBD) that are routinely identified by pathologists that may also increase risk and be of use to inform breast cancer risk models. We sought to identify which BBD lesions were associated with increased risk of breast cancer in an African American (AA) cohort. Methods: Benign breast biopsies from 3,895 AA women diagnosed with BBD between 1997 and 2010 in metropolitan Detroit were reviewed for 12 benign features including columnar alterations, ductal ectasia, ductal hyperplasia, fibrosis, apocrine metaplasia, lobular hyperplasia, calcifications, cysts, intraductal papilloma, radial scar, sclerosing adenosis, and fibroadenomas. Women were followed for subsequent breast cancer using the Metropolitan Detroit Cancer Surveillance System, part of the Surveillance, Epidemiology, and End Results (SEER) cancer registry. Associations between BBD features and subsequent breast cancer were examined using chi-square tests. Features that had a significant chi-square test result were also combined into scores describing the overall number of BBD features identified, termed busy breast score. Multivariable log-binomial regression with backward selection based on BIC criteria was performed to assess risk ratio and 95% confidence intervals of breast cancer on 12 candidate features. Multivariable log-binomial regression was performed for busy breast score as well. All regression models were adjusted for age at biopsy and overall impression for presence of proliferative disease with or without atypia using non-proliferative disease as the reference. Models were checked for multicollinearity using a variable inflation factor (VIF). Results: Of the 3,895 AA women in the BBD cohort, 210 developed a subsequent breast cancer. The median age at biopsy among those without a subsequent cancer (controls) was 47 years of age, while cases were 53 years of age (p-value<0.001). In univariate analyses, ductal hyperplasia (p-value=0.001), lobular hyperplasia (p-value<0.001), calcifications (p-value<0.004), cysts (p-value=0.003), intra-ductal papilloma (p-value=0.005), sclerosing adenosis (p-value=0.002), columnar alterations (p-value<0.001) and radial scar (p-value=0.001) were associated with subsequent breast cancer. Fibroadenomas were inversely associated with subsequent breast cancer (p-value<0.001). In multivariable model after backwards selection, presence of columnar alterations was associated with increased risk of breast cancer, (RR=1.57, 95% CI: 1.16, 2.13). Combining the 9 BBD features associated with subsequent breast cancer in univariate analysis, those with a busy breast score of 5+ was associated with 2.5 times increased risk compared to those with none of these features (RR=2.52, 95% CI: 1.41, 4.49). As a continuous variable for busy breast score, each additional feature conferred a 20% increase in risk of breast cancer (RR=1.20, 95% CI: 1.10, 1.31). Conclusions: Columnar alterations confer increased risk of breast cancer beyond the risks associated with atypical hyperplasia, as does the presence of multiple types of BBD lesions in a single biopsy. These estimates may help improve the current risk assessment models for African American women and highlight the need for additional research regarding the utility of closer surveillance and potentially chemoprevention for reduction of breast cancer in these women. Citation Format: Michele L. Cote, Wei Chen, Julie J. Ruterbusch, Eman Abdulfatah, Visakha Pardeshi, Asra N. Shaik, Marcel T. Ghanim, MHD Fayez Daaboul, Daniel W. Visscher, Sudeshna Bandyopadhyay, Rouba Ali-Fehmi. Benign breast disease and subsequent breast cancer risk: The Detroit cohort. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A25.

Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.

Purpose Although it is well-established that fluorouracil- (FU-) based adjuvant therapy improves survival for patients with resected high-risk colon cancer, the magnitude of adjuvant therapy benefit across specific subgroups and for individual patients has been uncertain. Patients and Methods Using a pooled data set of 3,302 patients with stage II and III colon cancer from seven randomized trials comparing FU + leucovorin or FU + levamisole to surgery alone, we performed an analysis based on a Cox proportional hazards regression model. Treatment, age, sex, tumor location, T stage, nodal status, and grade were tested for both prognostic and predictive significance. Model derived estimates of 5-year disease-free survival and overall survival (OS) for surgery alone and surgery plus FU-based therapy were calculated for a range of patient subsets. Results Nodal status, T stage, and grade were the only prognostic factors independently significant for both disease-free survival and OS. Age was significant only for OS. In a multivariate analysis, adjuvant therapy showed a beneficial treatment effect across all subsets. Treatment benefits were consistent across sex, location, age, T-stage, and grade. A significant stage by treatment interaction was present, with treatment benefiting stage III patients to a greater degree than stage II patients. Conclusion Patients with high-risk resected colon cancer obtain benefit from FU-based therapy across subsets of age, sex, location, T stage, nodal status, and grade. Model estimates of survival stratified by T stage, nodal status, grade, and age are available at http://www.mayoclinic.com/calcs . This information may improve patients' and physicians' understanding of the potential benefits of adjuvant therapy.