Abstract P6-09-48: Development of a predictive model by gene expression profiles for pathological CR after neoadjuvant chemotherapy in ER-positive/HER2-negative breast cancer
Authors: Fujiki Y , Yamamoto Y , Yamamoto-Ibusuki M , Goto R , Tomiguchi M , Sueta A , Takeshita T , Iwase H 
Published: March 2017

Abstract
Background: Benefits of neoadjuvant chemotherapy (NAC) are tumor shrinkage, improvement of surgical outcome and monitoring of response to systemic therapy. Pathological complete response (pCR) is one of the indicators of the sensitivity to NAC and is associated with improved long-term outcomes. However, response to NAC is different by tumor subtype. It may be difficult to decide the indication to NAC in ER-positive/HER2-negative subtype because pCR rate and its prognostic value is lower than other subtype.In this study, we aimed to develop a predictive model by gene expression profiles for pCR after NAC in ER-positive/HER2-negative subtype.
Patients and Methods: From the previously published 10 microarray data base in Gene Expression Omnibus, we selected the 858 ER-positive/HER2-negative breast cancer patients who had received NAC. Each 10 data sets were analyzed by logistic regression analysis in terms of pCR. Each analyzed data were combined and the 5686 genes were selected as pCR-related genes in ER-positive/HER2-negative breast cancer. Furthermore, we selected top 20 genes related to chemo-sensitivity and top 20 genes related to chemo-resistance. Furthermore, expression levels of each 40 genes were examined by qRT-PCR using archival materials. Finally, predictive model were developed using most predictive genes chosen from these 40 genes. We used 85 formalin-fixed paraffin-embedded tissues from patients with ER-positive/HER2-negative breast cancer before NAC. Chemotherapy included anthracycline and/or taxane. These patients received subsequent surgery at Kumamoto University Hospital between 2004 and 2014. The overall pCR rate was 12.9% (9 patients).
Result: To develop a pCR prediction model, we selected top 8 pCR-related genes based on the expression levels of each genes by qRT-PCR from the 40 genes which were chosen previously published data base. We developed pCR prediction index (pPI) used by univariate and multivariate logistic regression analysis for the expression profile of the 8 genes by qRT-PCR and calculated based on odd's ration with multivariate analysis. pPI was distributed between 7.4 from 1.5E+11. The patients were divided into 2 groups (Low pPI group ; pPI &lt; 32 and High pPI group; pPI &gt; 32) according to pPI value. Each pCR rate were 0% (Low; 0/17) and 16.7% (High; 9/54), respectively (p=0.021). pPI was not associated with clinicopatholoical factors such as age, tumor size, nodal status, nuclear grade, ki67 labeling index. pPI (p=0.0198) was significantly related with pCR by multivariate analysis as well as tumor size (p=0.0136).
Conclusions: We developed pCR prediction model (pPI) based on gene expression levels of 8 genes for ER-positive/HER2-negative breast cancer. pPI may be useful tool for selecting NAC in ER-positive/HER2-negative breast cancer. We need to be validate the pCR predictive value of pPI with another data set.
Citation Format: Fujiki Y, Yamamoto Y, Yamamoto-Ibusuki M, Goto R, Tomiguchi M, Sueta A, Takeshita T, Iwase H. Development of a predictive model by gene expression profiles for pathological CR after neoadjuvant chemotherapy in ER-positive/HER2-negative breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-09-48.