Breast Cancer Research and Treatment

, Volume 132, Issue 2, pp 499–509 | Cite as

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

  • Ensel Oh
  • Yoon-La Choi
  • Taesung Park
  • Seungyeoun Lee
  • Seok Jin Nam
  • Young Kee Shin
Preclinical Study

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.

Keywords

Lymph node-negative breast cancer Prognostic genes Proliferation Immune response Parametric model Gene signature 

Supplementary material

10549_2011_1626_MOESM1_ESM.xlsx (1.1 mb)
Supplementary material 1 (XLSX 1118 kb)
10549_2011_1626_MOESM2_ESM.pptx (672 kb)
Supplementary material 2 (PPTX 672 kb)
10549_2011_1626_MOESM3_ESM.doc (128 kb)
Supplementary material 3 (DOC 129 kb)
10549_2011_1626_MOESM4_ESM.docx (25 kb)
Supplementary material 4 (DOCX 25 kb)

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Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  • Ensel Oh
    • 1
  • Yoon-La Choi
    • 2
  • Taesung Park
    • 1
    • 3
  • Seungyeoun Lee
    • 4
  • Seok Jin Nam
    • 5
  • Young Kee Shin
    • 1
    • 6
    • 7
  1. 1.Interdisciplinary Program in Bioinformatics, College of Natural ScienceSeoul National UniversitySeoulKorea
  2. 2.Laboratory of Cancer Genomics and Molecular Pathology, Department of Pathology, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulKorea
  3. 3.Department of Statistics, College of Natural ScienceSeoul National UniversitySeoulKorea
  4. 4.Department of Applied Statistics, College of Natural ScienceSejoung UniversitySeoulKorea
  5. 5.Division of Breast and Endocrine Surgery, Department of Surgery, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulKorea
  6. 6.Research Institute of Pharmaceutical Science, Department of PharmacySeoul National University College of PharmacySeoulKorea
  7. 7.Laboratory of Molecular Pathology and Cancer Genomics, Department of Pharmacy, College of PharmacySeoul National UniversitySeoulKorea

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