Recurrent copy number alterations in young women with breast cancer
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Chen Chi1,2, Leigh C. Murphy1,3 and Pingzhao Hu1,2,4
1Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
2The George and Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, Manitoba, Canada
3Research Institute of Oncology and Hematology, Cancer Care Manitoba, Winnipeg, Manitoba, Canada
4Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
Pingzhao Hu, email: [email protected]
Keywords: recurrent copy number alterations; breast cancer; young women; risk genes; graph algorithm
Received: September 07, 2017 Accepted: January 24, 2018 Published: January 29, 2018
Breast cancer diagnosis in young women has emerged as an independent prognostic factor with higher recurrence risk and death than their older counterparts. We aim to find recurrent somatic copy number alteration (CNA) regions identified from breast cancer microarray data and associate the CNA status of the genes harbored in the regions to the survival of young women with breast cancer.
By using the interval graph-based algorithm we developed, and the CNA data consisting of a Discovery set with 130 young women and a Validation set with 125 young women, we identified 38 validated recurrent CNAs containing 39 protein encoding genes. CNA gain regions encompassing genes CAPN2, CDC73 and ASB13 are the top 3 with the highest occurring frequencies in both the Discovery and Validation dataset, while gene SGCZ ranked top for the recurrent CNA loss regions. The mutation status of 9 of the 39 genes shows significant associations with breast cancer specific survival. Interestingly, the expression level of 2 of the 9 genes, ASB13 and SGCZ, shows significant association with survival outcome. Patients with CNA mutations in both of these genes had a worse survival outcome when compared to patients without the gene mutations. The mutated CNA status in gene ASB13 was associated with a higher gene expression, which predicted patient survival outcome. Together, identification of the CNA events with prognostic significance in young women with breast cancer may be used in genomic-guided treatment.
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