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A prognostic model for lymph node-negative breast cancer patients based on the integration of proliferation and immunity

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Abstract

A model for a more precise prognosis of the risk of relapse is needed to avoid overtreatment of lymph node-negative breast cancer patients. A large derivation data set (n = 684) was generated by pooling three independent breast cancer expression microarray data sets. Two major prognostic factors, proliferation and immune response, were identified among genes showing significant differential expression levels between the good outcome and poor outcome groups. For each factor, four proliferation-related genes (p-genes) and four immunity-related genes (i-genes) were selected as prognostic genes, and a prognostic model for lymph node-negative breast cancer patients was developed using a parametric survival analysis based on the lognormal distribution. The p-genes showed a predominantly negative correlation (coefficient: −0.603) with survival time, while the i-genes showed a positive correlation (coefficient: 0.243), reflecting the beneficial effect of the immune response against deleterious proliferative activity. The prognostic model shows that approximately 54% of lymph node-negative breast cancer patients were predicted to be distant metastasis-free for more than 5 years with at least 85% survival probability. The prognostic model showed a robust and high prognostic performance (HR 2.85–3.45) through three external validation data sets. Based on the integration of proliferation and immunity, the new prognostic model is expected to improve clinical decision making by providing easily interpretable survival probabilities at any time point and functional causality of the predicted prognosis with respect to proliferation and immune response.

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Acknowledgments

This study was supported by a Grant from the Korea Science and Engineering Foundation (KOSEF) (20090083312) and the Global Frontier Project grant (NRF-M1AXA002-2010-0029795) of National Research Foundation funded by the Ministry of Education, Science and Technology of Korea. We are very grateful Dr. Yun-Chul Hong (Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea) for thoughtful suggestions and critical comments.

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Authors and Affiliations

  1. Interdisciplinary Program in Bioinformatics, College of Natural Science, Seoul National University, Seoul, Korea

    Ensel Oh, Taesung Park & Young Kee Shin

  2. Laboratory of Cancer Genomics and Molecular Pathology, Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

    Yoon-La Choi

  3. Department of Statistics, College of Natural Science, Seoul National University, Seoul, Korea

    Taesung Park

  4. Department of Applied Statistics, College of Natural Science, Sejoung University, Seoul, Korea

    Seungyeoun Lee

  5. Division of Breast and Endocrine Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

    Seok Jin Nam

  6. Research Institute of Pharmaceutical Science, Department of Pharmacy, Seoul National University College of Pharmacy, Seoul, Korea

    Young Kee Shin

  7. Laboratory of Molecular Pathology and Cancer Genomics, Department of Pharmacy, College of Pharmacy, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Korea

    Young Kee Shin

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  1. Ensel Oh
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  2. Yoon-La Choi
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Correspondence to Young Kee Shin.

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Oh, E., Choi, YL., Park, T. et al. A prognostic model for lymph node-negative breast cancer patients based on the integration of proliferation and immunity. Breast Cancer Res Treat 132, 499–509 (2012). https://doi.org/10.1007/s10549-011-1626-8

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  • DOI: https://doi.org/10.1007/s10549-011-1626-8

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