reviewCancer DiscoveryJan 24, 2019GREEN OA

Insights into Molecular Classifications of Triple-Negative Breast Cancer: Improving Patient Selection for Treatment

Harvard University · Dana-Farber Cancer Institute

PubMed
Indexed incrossrefpubmed

Abstract

Triple-negative breast cancer (TNBC) remains the most challenging breast cancer subtype to treat. To date, therapies directed to specific molecular targets have rarely achieved clinically meaningful improvements in outcomes of patients with TNBC, and chemotherapy remains the standard of care. Here, we seek to review the most recent efforts to classify TNBC based on the comprehensive profiling of tumors for cellular composition and molecular features. Technologic advances allow for tumor characterization at ever-increasing depth, generating data that, if integrated with clinical-pathologic features, may help improve risk stratification of patients, guide treatment decisions and surveillance, and help identify…

Citation impact

1,427
total citations
FWCI
46.13
Percentile
100%
References
162
Citations per year

Authors

3

Topics & keywords

Keywords
  • Breast cancer
  • Selection (genetic algorithm)
  • Triple-negative breast cancer
  • Cancer
  • Computational biology
  • Triple negative
  • Medicine
  • Bioinformatics
UN Sustainable Development Goals
  • Good health and well-being
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Funding