Molecular insights into the classification of luminal breast cancers: The genomic heterogeneity of progesterone-negative tumors

Research output: Contribution to journalArticle

Abstract

Estrogen receptor (ER)-positive progesterone receptor (PR)-negative breast cancers are infrequent but clinically challenging. Despite the volume of genomic data available on these tumors, their biology remains poorly understood. Here, we aimed to identify clinically relevant subclasses of ER+/PR− breast cancers based on their mutational landscape. The Cancer Genomics Data Server was interrogated for mutational and clinical data of all ER+ breast cancers with information on PR status from The Cancer Genome Atlas (TCGA), Memorial Sloan Kettering (MSK), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) projects. Clustering analysis was performed using gplots, ggplot2, and ComplexHeatmap packages. Comparisons between groups were performed using the Student’s t-test and the test of Equal or Given Proportions. Survival curves were built according to the Kaplan–Meier method; differences in survival were assessed with the log-rank test. A total of 3570 ER+ breast cancers (PR− n = 959, 27%; PR+ n = 2611, 73%) were analyzed. Mutations in well-known cancer genes such as TP53, GATA3, CDH1, HER2, CDH1, and BRAF were private to or enriched for in PR− tumors. Mutual exclusivity analysis revealed the presence of four molecular clusters with significantly different prognosis on the basis of PIK3CA and TP53 status. ER+/PR− breast cancers are genetically heterogeneous and encompass a variety of distinct entities in terms of prognostic and predictive information.

Original languageEnglish
Article number510
JournalInternational Journal of Molecular Sciences
Volume20
Issue number3
DOIs
Publication statusPublished - Feb 1 2019

Keywords

  • Breast cancer
  • Mutational profiling
  • PI3K pathway
  • Progesterone receptor negative
  • TP53

ASJC Scopus subject areas

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry

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