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Multi-gene classifiers for prediction of recurrence in breast cancer patients

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  • The way to the next generation molecular diagnostics
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Abstract

Accurate prediction of recurrence risk is of vital importance for tailoring adjuvant chemotherapy for individual breast cancer patients. Although recurrence risk has been assessed by means of examination of histological data and biomarkers (ER, PR, HER2, Ki67), such conventional examinations are not accurate enough to select subsets of patients who are at sufficiently low risk of recurrence to be spared adjuvant chemotherapy without comprising the prognosis. In the past two decades or so, comprehensive gene expression analysis technology has rapidly developed and made it possible to construct recurrence prediction models for breast cancer based on multi-gene expression in tumor tissues. These models include MammaPrint, Oncotype DX, PAM50 ROR, GGI, EndoPredict, BCI, and Curebest 95GC. In clinical practice, these multi-gene classifiers are mostly used for ER-positive and node-negative breast cancer patients for whom deciding the indication of adjuvant chemotherapy based on conventional histological examination findings alone is often difficult. This article briefly reviews these multi-gene expression-based classifiers with special emphasis on Curebest™ 95GC, which was developed by us for ER-positive and node-negative breast cancer patients.

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Abbreviations

ER:

Estrogen receptor

PR:

Progesterone receptor

HER2:

Human epidermal growth factor receptor 2

DRFS:

Distant recurrence-free survival

TAM:

Tamoxifen

pCR:

Pathological complete response

GC:

Gene classifier

FFPE:

Formalin fixed and paraffin embedded

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Acknowledgments

This study was supported in part by the Knowledge Cluster Initiative of the Ministry of Education, Culture, Sports, Science and Technology, Japan. SN received honoraria and research funding from Sysmex Corporation, and SN and YN hold a patent on Curebest™.

Ethical standard

The experiments comply with the current laws of the country in which they were performed.

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Authors and Affiliations

  1. Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, 2-2-E10 Yamadaoka, Suita-shi, Osaka, 565-0871, Japan

    Yasuto Naoi & Shinzaburo Noguchi

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  1. Yasuto Naoi
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  2. Shinzaburo Noguchi
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Correspondence to Yasuto Naoi.

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Naoi, Y., Noguchi, S. Multi-gene classifiers for prediction of recurrence in breast cancer patients. Breast Cancer 23, 12–18 (2016). https://doi.org/10.1007/s12282-015-0596-9

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  • DOI: https://doi.org/10.1007/s12282-015-0596-9

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