ArticleJournal of pathology informatics2014
Mining genome sequencing data to identify the genomic features linked to breast cancer histopathology.
Article in Journal of pathology informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 29 citations in OpenAlex.
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- Identification and evaluation of a risk model predicting the prognosis of breast cancer based on characteristic signatures.Translational cancer research · 2023Article
- Loss of Brca1 and Trp53 in adult mouse mammary ductal epithelium results in development of hormone receptor-positive or hormone receptor-negative tumors, depending on inactivation of Rb family proteins.Breast cancer research : BCR · 2022Article
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- Recovery of T-cell receptor V(D)J recombination reads from lower grade glioma exome files correlates with reduced survival and advanced cancer grade.Journal of neuro-oncology · 2018Article
- Recovery of Immunoglobulin VJ Recombinations from Pancreatic Cancer Exome Files Strongly Correlates with Reduced Survival.Cancer microenvironment : official journal of the International Cancer Microenvironment Society · 2018Article
- Identification of specific feed-forward apoptosis mechanisms and associated higher survival rates for low grade glioma and lung squamous cell carcinoma.Journal of cancer research and clinical oncology · 2018Article
- The Landscape of Whole-genome Alterations and Pathologic Features in Genitourinary Malignancies: An Analysis of the Cancer Genome Atlas.European urology focus · 2017Article
- Copy Number Profiling of MammaPrint™ Genes Reveals Association with the Prognosis of Breast Cancer Patients.Journal of breast cancer · 2017Article
- Dramatic response of metaplastic breast cancer to chemo-immunotherapy.NPJ breast cancer · 2017Article
- ERBB2 mutation is associated with a worse prognosis in patients with CDH1 altered invasive lobular cancer of the breast.Oncotarget · 2016Article
- A microscopic landscape of the invasive breast cancer genome.Scientific reports · 2016Article
- Integration of Multi-Modal Biomedical Data to Predict Cancer Grade and Patient Survival.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2016Article
- A novel gammaretroviral shuttle vector insertional mutagenesis screen identifies SHARPIN as a breast cancer metastasis gene and prognostic biomarker.Oncotarget · 2015Article
- Genome and transcriptome delineation of two major oncogenic pathways governing invasive ductal breast cancer development.Oncotarget · 2015Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenetics 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
methodsThe 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.
resultsValidation 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.
conclusionsData 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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