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Breast Cancer Research and Treatment

, Volume 171, Issue 1, pp 33–41 | Cite as

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

  • Ryo Tsunashima
  • Yasuto Naoi
  • Kenzo Shimazu
  • Naofumi Kagara
  • Masashi Shimoda
  • Tomonori Tanei
  • Tomohiro Miyake
  • Seung Jin Kim
  • Shinzaburo Noguchi
Preclinical study

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.

Keywords

ER-positive breast cancer Late recurrence prediction Extended hormonal therapy Multi-gene assay Microarray 

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

Notes

Acknowledgements

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

Compliance with ethical standards

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.

Ethical approval

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

Supplementary material

10549_2018_4812_MOESM1_ESM.pptx (69 kb)
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)
10549_2018_4812_MOESM2_ESM.doc (87 kb)
Supplementary material 2 (DOC 87 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Breast and Endocrine SurgeryOsaka University Graduate School of MedicineOsakaJapan

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