title: A prognostic model for lymph node-negative breast cancer patients based on the integration of proliferation and immunity.
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.
authors.affiliation: Interdisciplinary Program in Bioinformatics, College of Natural Science, Seoul National University, Seoul, Korea.
authors.forename: Ensel
authors.initials: E
authors.lastname: Oh
authors.forename: Yoon-La
authors.initials: YL
authors.lastname: Choi
authors.forename: Taesung
authors.initials: T
authors.lastname: Park
authors.forename: Seungyeoun
authors.initials: S
authors.lastname: Lee
authors.forename: Seok Jin
authors.initials: SJ
authors.lastname: Nam
authors.forename: Young Kee
authors.initials: YK
authors.lastname: Shin
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics / Q000276:immunology* / Q000401:mortality / Q000473:pathology* / Q000556:secondary / Q000628:therapy; D049109:Cell Proliferation; D016000:Cluster Analysis; D016002:Discriminant Analysis; D018572:Disease-Free Survival; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D053208:Kaplan-Meier Estimate; D016014:Linear Models; D008198:Lymph Nodes / Q000473:pathology*; D015233:Models, Statistical; D009361:Neoplasm Invasiveness; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome