Mining genome sequencing data to identify the genomic features linked to breast cancer histopathology

Zheng Ping; Gene P Siegal; Jonas S Almeida; Stuart J Schnitt; Dejun Shen

doi:10.4103/2153-3539.126147

ORIGINAL ARTICLE

Year : 2014 | Volume : 5 | Issue : 1 | Page : 3

Mining genome sequencing data to identify the genomic features linked to breast cancer histopathology

Zheng Ping¹, Gene P Siegal¹, Jonas S Almeida², Stuart J Schnitt³, Dejun Shen¹
¹ Department of Pathology, Division of Anatomic Pathology, Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, Alabama, USA
² Department of Pathology, Division of Informatics, Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, Alabama, USA
³ Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA

Correspondence Address:
Dejun Shen
Department of Pathology, Division of Anatomic Pathology, Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, Alabama
USA

Source of Support: None, Conflict of Interest: None

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DOI: 10.4103/2153-3539.126147

Background: Genetics and genomics have radically altered our understanding of breast cancer progression. However, the genomic basis of various histopathologic features of breast cancer is not yet well-defined. Materials and Methods: The Cancer Genome Atlas (TCGA) is an international database containing a large collection of human cancer genome sequencing data. cBioPortal is a web tool developed for mining these sequencing data. We performed mining of TCGA sequencing data in an attempt to characterize the genomic features correlated with breast cancer histopathology. We first assessed the quality of the TCGA data using a group of genes with known alterations in various cancers. Both genome-wide gene mutation and copy number changes as well as a group of genes with a high frequency of genetic changes were then correlated with various histopathologic features of invasive breast cancer. Results: Validation of TCGA data using a group of genes with known alterations in breast cancer suggests that the TCGA has accurately documented the genomic abnormalities of multiple malignancies. Further analysis of TCGA breast cancer sequencing data shows that accumulation of specific genomic defects is associated with higher tumor grade, larger tumor size and receptor negativity. Distinct groups of genomic changes were found to be associated with the different grades of invasive ductal carcinoma. The mutator role of the TP53 gene was validated by genomic sequencing data of invasive breast cancer and TP53 mutation was found to play a critical role in defining high tumor grade. Conclusions: Data mining of the TCGA genome sequencing data is an innovative and reliable method to help characterize the genomic abnormalities associated with histopathologic features of invasive breast cancer.

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