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Integrated gene expression profile predicts prognosis of breast cancer patients

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Abstract

Gene expression data has in recent years demonstrated the superior capacity to predict the prognosis of breast cancer patients unreceiving adjuvant chemotherapy comparing to the information available from traditional clinical and pathological sources. Meanwhile, adjuvant chemotherapy can significantly improve survival of breast cancer. It would be inappropriate to ignore its effect on prognosis. We hypothesized that an integrated gene expression profile can predict the prognosis of breast cancer patients receiving chemotherapy. Therefore, we screened the specific gene markers and constructed an integrated 24-gene signature by low-density microarray including the “poor signature” and genes related to resistance to chemotherapy. The gene signature stratified correctly patients into good prognosis group and poor prognosis group. In addition, the Kaplan–Meier analyses for disease-free survival as a function of the 24-gene signature showed highly significant differences between the two groups (Log Rank test P < 0.0001 = Univariate and multivariate Cox’s proportional-hazards regression analyses indicated that the signature represents the strongest independent prognostic factor for breast cancer patients. When compared with single signature, such as Oncotype DX™ and 70 poor signature, the integrated signature showed more predominant power of predication in breast cancer patients receiving chemotherapy. Such integrated signature will critically aid clinical decision making at the level of individualization for most breast cancer patients receiving chemotherapy.

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Acknowledgement

This research was supported in part by the grants from the National Basic Research Program of China (2006CB910501), National Natural Science Foundation of China (30371580, 30572109); Shanghai Science and Technology Committee (03J14019, 06DJ14004, 06DZ19504).

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Authors and Affiliations

  1. Department of Oncology, Breast Cancer Institute, Cancer Hospital/Cancer Institute, Shanghai Medical College, Institutes of Biomedical Science, Fudan University, 270 Dong’an Road, Shanghai, 200032, P.R. China

    Lian-Fang Li, Zhe-Bing Liu, Zhen-Zhou Shen & Zhi-Ming Shao

  2. National Engineering Center for Biochip at Shanghai, Shanghai, 201203, P.R. China

    Xiao-Jing Xu, Ying Zhao & Wei-Rong Jin

Authors
  1. Lian-Fang Li
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  2. Xiao-Jing Xu
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  3. Ying Zhao
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  4. Zhe-Bing Liu
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  5. Zhen-Zhou Shen
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  6. Wei-Rong Jin
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  7. Zhi-Ming Shao
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Corresponding author

Correspondence to Zhi-Ming Shao.

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Lian-Fang Li and Xiaojing Xu have contributed equally to this work.

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Li, LF., Xu, XJ., Zhao, Y. et al. Integrated gene expression profile predicts prognosis of breast cancer patients. Breast Cancer Res Treat 113, 231–237 (2009). https://doi.org/10.1007/s10549-008-9925-4

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  • DOI: https://doi.org/10.1007/s10549-008-9925-4

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