Abstract

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 new targets for drug development. Significance: TNBC is characterized by higher rates of relapse, greater metastatic potential, and shorter overall survival compared with other major breast cancer subtypes. The identification of biomarkers that can help guide treatment decisions in TNBC remains a clinically unmet need. Understanding the mechanisms that drive resistance is key to the design of novel therapeutic strategies to help prevent the development of metastatic disease and, ultimately, to improve survival in this patient population.

Keywords

Breast cancerSelection (genetic algorithm)Triple-negative breast cancerCancerComputational biologyTriple negativeMedicineBioinformaticsOncologyBiologyInternal medicineComputer scienceArtificial intelligence

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2020 The Lancet 1658 citations

Publication Info

Year
2019
Type
review
Volume
9
Issue
2
Pages
176-198
Citations
1321
Access
Closed

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Ana C. Garrido-Castro, Nancy U. Lin, Kornélia Polyák (2019). Insights into Molecular Classifications of Triple-Negative Breast Cancer: Improving Patient Selection for Treatment. Cancer Discovery , 9 (2) , 176-198. https://doi.org/10.1158/2159-8290.cd-18-1177

Identifiers

DOI
10.1158/2159-8290.cd-18-1177