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Updated: Jun 27, 2026

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Multi-gene expression assays in breast cancer: a literature review
Tsung-Yen Hsieh1, Chi-Cheng Huang1,2, Ling-Ming Tseng1,3
1Division of Breast Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei.
Background And Objective:
Breast cancer is the most common malignancy among women worldwide and is characterized by marked inter- and intratumoral heterogeneity, which poses challenges for accurate prognosis and optimal treatment selection when relying solely on traditional clinicopathological factors. Multi-gene expression assays, such as Oncotype DX, MammaPrint, and Prosigna, have emerged as valuable tools that integrate molecular profiling into clinical decision-making, offering refined prognostic and predictive insights. This review aims to summarize their development, validation, clinical utility, limitations, and future directions.
Methods:
A comprehensive narrative review was conducted through PubMed, Embase, and Web of Science [2000-2024] to identify original studies, validation trials, meta-analyses, and guidelines relevant to major multi-gene assays in early-stage breast cancer. Eligible literature focused on assay development, prognostic accuracy, impact on adjuvant therapy decisions, cost-effectiveness, and technical considerations.
Key Content And Findings:
Oncotype DX, MammaPrint, and Prosigna have been validated in large prospective trials to improve risk stratification and guide chemotherapy use, thereby reducing overtreatment in low-risk patients. Their clinical impact is supported by integration into National Comprehensive Cancer Network (NCCN) guidelines; however, barriers include high cost, inconsistent reimbursement, technical variability, limited applicability in certain subtypes, and underrepresentation of non-Western populations in validation studies.
Conclusions:
Multi-gene expression assays have reshaped precision oncology in breast cancer by enabling more individualized therapy and supporting de-escalation strategies. Future integration with multi-omics data, development of novel predictive assays, and expansion of validation in diverse populations are essential to maximize their global clinical utility.
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