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Construction of a novel multi-gene assay (42-gene classifier) for prediction of late recurrence in ER-positive breast cancer patients

  • Preclinical study
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Purpose

Prediction models for late (> 5 years) recurrence in ER-positive breast cancer need to be developed for the accurate selection of patients for extended hormonal therapy. We attempted to develop such a prediction model focusing on the differences in gene expression between breast cancers with early and late recurrence.

Methods

For the training set, 779 ER-positive breast cancers treated with tamoxifen alone for 5 years were selected from the databases (GSE6532, GSE12093, GSE17705, and GSE26971). For the validation set, 221 ER-positive breast cancers treated with adjuvant hormonal therapy for 5 years with or without chemotherapy at our hospital were included. Gene expression was assayed by DNA microarray analysis (Affymetrix U133 plus 2.0).

Results

With the 42 genes differentially expressed in early and late recurrence breast cancers in the training set, a prediction model (42GC) for late recurrence was constructed. The patients classified by 42GC into the late recurrence-like group showed a significantly (P = 0.006) higher late recurrence rate as expected but a significantly (P = 1.62 × E−13) lower rate for early recurrence than non-late recurrence-like group. These observations were confirmed for the validation set, i.e., P = 0.020 for late recurrence and P = 5.70 × E−5 for early recurrence.

Conclusion

We developed a unique prediction model (42GC) for late recurrence by focusing on the biological differences between breast cancers with early and late recurrence. Interestingly, patients in the late recurrence-like group by 42GC were at low risk for early recurrence.

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Abbreviations

ER:

Estrogen receptor

PR:

Progesterone receptor

HER2:

Human epidermal growth factor receptor 2

DMFS:

Distant metastasis-free survival

IHC:

Immunohistochemistry

FISH:

Fluorescence in situ hybridization

HG:

Histological grade

LR:

Late recurrence-like

NLR:

Non-late recurrence-like

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Acknowledgements

This study was supported, in part, by the Knowledge Cluster Initiative of the Ministry of Education, Culture, Sports, Science and Technology.

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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

    Ryo Tsunashima, Yasuto Naoi, Kenzo Shimazu, Naofumi Kagara, Masashi Shimoda, Tomonori Tanei, Tomohiro Miyake, Seung Jin Kim & Shinzaburo Noguchi

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  1. Ryo Tsunashima
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  2. Yasuto Naoi
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  4. Naofumi Kagara
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  9. Shinzaburo Noguchi
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Corresponding author

Correspondence to Yasuto Naoi.

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Conflict of interest

Professor Noguchi has been an advisor for Taiho, AstraZeneca and Novartis, and has received research funding for other studies from Sysmex, AstraZeneca, Novartis, Chugai, Daiichi-Sankyo, Kyowa-Kirin, Takeda, Pfizer, Ono, Taiho, and Eisai, and honoraria from AstraZeneca, Novartis, Pfizer, Chugai, Takeda, Sysmex, Nippon Kayaku, Ono. Dr. Kim has received honoraria from AstraZeneca. Dr. Shimazu has received honoraria from AstraZeneca and Chugai. Dr. Naoi has received research funding from Sysmex and AstraZeneca for other studies.

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This study complies with the current relevant laws of and guidelines for Japan.

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10549_2018_4812_MOESM1_ESM.pptx

Supplementary Fig. 1. Prediction of prognosis by 42GC for recurrent breast cancer patients. Distant metastasis-free survival (DMFS) rates were compared between the late recurrence-like (LR) and the non-late recurrence-like (NLR) groups in the training set (a) and the validation set (b). Supplementary material 1 (PPTX 69 KB)

Supplementary material 2 (DOC 87 KB)

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Tsunashima, R., Naoi, Y., Shimazu, K. et al. Construction of a novel multi-gene assay (42-gene classifier) for prediction of late recurrence in ER-positive breast cancer patients. Breast Cancer Res Treat 171, 33–41 (2018). https://doi.org/10.1007/s10549-018-4812-0

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  • DOI: https://doi.org/10.1007/s10549-018-4812-0

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