Integrated landscape of copy number variation and RNA expression associated with nodal metastasis in invasive ductal breast carcinoma
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Michael Behring1,2, Sadeep Shrestha1, Upender Manne2,3, Xiangqin Cui4, Agustin Gonzalez-Reymundez5,6, Alexander Grueneberg6 and Ana I. Vazquez5,6
1Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
2Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294, USA
3Department of Pathology and Surgery, University of Alabama at Birmingham, Birmingham, AL 35294, USA
4Biostatistics Department, University of Alabama at Birmingham, Birmingham, AL 35294, USA
5Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA
6Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
Michael Behring, email: [email protected]
Ana I. Vazquez, email: [email protected]
Keywords: breast cancer; lymph node metastasis; integrated genomics; copy number variation; differential expression
Received: February 14, 2018 Accepted: October 31, 2018 Published: December 07, 2018
Background: Lymph node metastasis (NM) in breast cancer is a clinical predictor of patient outcomes, but how its genetic underpinnings contribute to aggressive phenotypes is unclear. Our objective was to create the first landscape analysis of CNV-associated NM in ductal breast cancer. To assess the role of copy number variations (CNVs) in NM, we compared CNVs and/or associated mRNA expression in primary tumors of patients with NM to those without metastasis.
Results: We found CNV loss in chromosomes 1, 3, 9, 18, and 19 and gains in chromosomes 5, 8, 12, 14, 16-17, and 20 that were associated with NM and replicated in both databases. In primary tumors, per-gene CNVs associated with NM were ten times more frequent than mRNA expression; however, there were few CNV-driven changes in mRNA expression that differed by nodal status. Overlapping regions of CNV changes and mRNA expression were evident for the CTAGE5 gene. In 8q12, 11q13-14, 20q1, and 17q14-24 regions, there were gene-specific gains in CNV-driven mRNA expression associated with NM.
Methods: Data on CNV and mRNA expression from the TCGA and the METABRIC consortium of breast ductal carcinoma were utilized to identify CNV-based features associated with NM. Within each dataset, associations were compared across omic platforms to identify CNV-driven variations in gene expression. Only replications across both datasets were considered as determinants of NM.
Conclusions: Gains in CTAGE5, NDUFC2, EIF4EBP1, and PSCA genes and their expression may aid in early diagnosis of metastatic breast carcinoma and have potential as therapeutic targets.
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