乳腺癌
选择(遗传算法)
三阴性乳腺癌
癌症
计算生物学
三重阴性
医学
生物信息学
肿瘤科
生物
内科学
计算机科学
人工智能
作者
Ana C. Garrido-Castro,Nancy U. Lin,Kornélia Polyák
出处
期刊:Cancer Discovery
[American Association for Cancer Research]
日期:2019-01-24
卷期号:9 (2): 176-198
被引量:1321
标识
DOI:10.1158/2159-8290.cd-18-1177
摘要
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.
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