Abstract P2-03-11: Interaction between smoking history and gene expression levels impacts survival of breast carcinoma patients

Our investigations explore the association of cigarette smoking on breast cancer risk of recurrence and progression, in contrast to studies that focused on tobacco use and risk of breast cancer occurrence. The goal was to decipher the interaction between smoking history and expression levels of 22 gene candidates selected from microarray data obtained from laser capture microdissected carcinoma cells from 247 de-identified patient tissue biopsies on disease recurrence and overall patient survival of breast cancer patients. qRT-PCR was used to determine expression levels for NAT1, NAT2, COMT, SOD1, SOD2, BRCA1, BRCA2, APOC1, ARID1B, CTNNBL1, MSX1, UBE2F, IRF2, NCOA1, LECT2, THAP4, RIPK1, AGPAT1, C7orf23, CENPN, CETN1 and YTHDC2 selected from a previous study for 50 breast cancer patients with a history of cigarette smoking and 51 patients who had never smoked. For smokers and non-smokers separately, L1-penalized multivariable Cox regression models were fit to predict patient disease-free and overall survival, with 1000 splits of the data into training (70%) and test (30%) sets to determine predictive accuracy based on the C-index. The LASSO penalty was used to perform variable selection in each of the training sets, and a permutation procedure was used to determine a significance threshold for the number of times a variable was kept in the model. Multivariable analyses using the LASSO revealed CENPN, CETN1, CYP1A1, IRF2, LECT2, and NCOA1 to be significant predictors for both disease recurrence and mortality among smokers. Additionally, COMT was highly associated with recurrence, and NAT1 and RIPK1 were associated with mortality. In contrast, only IRF2, CETN1, and CYP1A1 were significant for disease recurrence and mortality among non-smokers, with NAT2 additionally significant for survival. Median, 25th percentile, and 75th percentile for the C-indexes based on the gene expression models are given in Table 1. Analysis of interaction between smoking status and gene expression values using the combined samples revealed significant interactions between smoking status and CYP1A1, LECT2, CETN1. Molecular signatures consisting of 7-8 genes were highly predictive for breast cancer recurrence and overall survival among smokers, with median C-index values of 0.8 and 0.73 for overall survival and recurrence, respectively. In contrast, the median C-index values for non-smokers was only 0.59. Hence, significant interactions between expression of crucial genes and cigarette smoking status appear to play a key role in predicting clinical outcomes of breast carcinoma patients. Supported in part by a grant from the Phi Beta Psi Charity Trust (TSK & JLW) and a Research of Women (ROW) grant to JLW from the EVP for Research and Innovation, University of Louisville. Citation Format: James L Wittliff, Sarah A Andres, Mohammad A Alatoum, Katie E Bickett, Theodore S Kalbfleisch, Guy N Brock. Interaction between smoking history and gene expression levels impacts survival of breast carcinoma patients [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-03-11.