Abstract P2-11-20: Endocrine resistance in breast cancer: A dynamic gene expression analysis approach reveals potential new responsible genes
Authors: Asghari Arvand , Brauchle Natascha , Gill Michael , Maroufy Vahed , Umetani Michihisa , Wu Hulin 
Published: March 2020

Abstract
While ER-targeted endocrine therapies are the most successful therapies for ER-positive breast cancer, tumor recurrence and endocrine-resistance happens in many of the cases. Despite efforts, the underlying genes and mechanisms of this acquired resistance remain largely unknown. Here, we utilized a recently developed pipeline for dynamic gene regulatory network analysis1to re-analyze the publically available datasets related to endocrine resistance in breast cancer in order to find new target genes related to endocrine resistance. The pipeline exploits the time-resolved expression pattern of genes to identify dynamic responsive genes (DRG) and group them into multiple modules based on the similarity in their dynamic activities, which leads us to identify more significant genes compared with previous gene expression analysis models that solely focus on expression value of the genes. Here, we analyzed a dataset of time-resolved gene expression during MCF-7 cell transition to endocrine-resistant cells by long-term deprivation of estrogen, which is an accepted model of endocrine resistance2. The dataset is publically available as GES20361 in the Gene Expression Omnibus (GEO) database. Based on their dynamic expression pattern over time, our method of analysis grouped genes into three main modules. Interestingly, the majority of well-studied genes in the literature related to endocrine resistance are part of one of these modules, while there are many genes such as GABPA, TFDP1, MCM7, MDC1, and FOXC1 that are potential novel targets for treatments against endocrine resistance. Module One included genes related to endocrine resistance, mainly those related to cell cycle, such as CCNA1, CCNA2, CCND1, and CDK family, and DNA replication and repair, such as MDC1 and PARP1, as well as EGFR. Consistent with the previous literature on endocrine resistant breast cancer cells, these genes all showed similar up-regulated expression patterns over time. Furthermore, our model confirmed the identified genes shown in the previous paper2 (POLD1, TERF1, RAF1, and E2F1) as part of Module One and as important genes in endocrine resistance regulation. Module Two was enriched for genes related to PI3K/AKT/mTOR signaling pathway, while Module Three mostly contained tyrosine kinase related genes, which not only confirms the previous findings related to the importance of these genes in acquiring endocrine resistance, but also indicates the difference between the expression patterns of these genes compare to cell cycle genes and those in Module One. Our analysis revealed that the genes in Module One continued their up-regulating trend beyond day 90, when the marked transition toward resistance happens and resistance phenotype starts to arise, while genes in Module Two got up-regulated before the 90 days and down-regulated afterward, suggesting different roles from the genes in Module One. Lastly, we studied the gene regulatory networks, transcription factors, and miRNAs upstream of the identified genes, and discovered several interesting targets, such as MiR-193b and MiR-16, which have integral parts in the regulation of our identified genes and thus can be of great importance toward endocrine resistance.
References:
1. Carey, Michelle, Juan Camilo Ramírez, Shuang Wu, and Hulin Wu. "A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection." Statistical methods in medical research 27, no. 7 (2018): 1930-1955. 2.Aguilar, H., X. Sole, N. Bonifaci, J. Serra-Musach, A. Islam, N. Lopez-Bigas, M. Mendez-Pertuz et al. "Biological reprogramming in acquired resistance to endocrine therapy of breast cancer." Oncogene 29, no. 45 (2010): 6071.
Citation Format: Arvand Asghari, Natascha Brauchle, Michael Gill, Vahed Maroufy, Michihisa Umetani, Hulin Wu. Endocrine resistance in breast cancer: A dynamic gene expression analysis approach reveals potential new responsible genes [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-11-20.