Modulatory effect of neoadjuvant chemotherapy on biomarkers expression; assessment by digital image analysis and relationship to residual cancer burden in patients with invasive breast cancer.

The use of digital imaging techniques for biomarker assessment has gained recognition as a valid tool for clinical use. In this study, we used image analysis for evaluation of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor (HER2), Ki-67 index, and p53 in 172 patients with invasive breast cancer treated with neoadjuvant chemotherapy and compared it with an untreated group (100 cases). We also examined the relationship between biomarker expression and the extent of residual disease using the Web-based MD Anderson residual cancer burden (RCB) calculator. Residual disease was classified as RCB 0/I, II, and III corresponding to complete/near-complete response, moderate, and extensive residual disease, respectively. Overall change in ER, PR, and HER2 status in the treated group was seen in 9.02% (P = .0148), 18.4% (P = .011), and 12.0% (P = .0042), respectively. Change in HER2 status, positive to negative and negative to positive, occurred in 27.2% and 7.0%, respectively. The group with RCB 0/I was frequently younger (P = .0057) and showed higher ER(-) status (P = .0316), lower ER scores (P = .0103), higher Ki-67 index (P = .0008), and p53 (P = .0055) compared with those with RCB II and III. Pathologic tumor stage (P = .0072), lumpectomy versus mastectomy (P = .0048), and p53 expression (P = .0190) were independent predictors of recurrence-free survival. The RCB categories (P = .0003) and tumor grade (P = .0049) were independent predictors of overall survival. This is the first study to conduct a comprehensive analysis of biomarkers in neoadjuvant chemotherapy-treated patients versus an untreated group using the digital image analysis method. We have demonstrated for the first time the relationship between RCB, tumor biomarkers expression, and clinical outcome.

Bayesian inference of risk ratio of two proportions using a double sampling scheme.

We consider Bayesian point and interval estimation for a risk ratio of two proportion parameters using two independent samples of binary data subject to misclassification. In order to obtain model identifiability, we apply a double sampling scheme. For the identifiable model, we propose a Bayesian method for statistical inference for a two proportion risk ratio. Specifically, we derive an easy-to-implement closed-form sampling algorithm to draw from the posterior distribution of interest. We demonstrate the efficiency of our algorithm for Bayesian inference via Monte Carlo simulation studies and using a real data example.