Identifying gene expression changes in breast cancer that distinguish early and late relapse among uncured patients.
BACKGROUND
In recent years, microarray technology has revealed many tumor-expressed genes prognostic of clinical outcomes in early-stage breast cancer patients. However, in the presence of cured patients, evaluating gene effect on time to relapse is quite complex since it may affect either the probability of never experiencing a relapse (cure effect) or the time to relapse among the uncured patients (disease progression effect) or both. In this context, we propose a simple and an efficient method for identifying gene expression changes that characterize early and late recurrence for uncured patients.


RESULTS
Simulation results show the good performance of the proposed statistic for detecting a disease progression effect. In a study of early-stage breast cancer, our results show that the proposed statistic provides a more powerful basis for gene selection than the classical Cox model-based statistic. From a biological perspective, many of the genes identified here as associated with the speed of disease recurrence have known roles in tumorigenesis.
Broët
Philippe
P
Faculté de Médecine-Université Paris-XI, IFR69, 16 Avenue Paul Vaillant Couturier 94807 Villejuif Cedex, France. broet@vjf.inserm.fr
Kuznetsov
Vladimir A
VA
Bergh
Jonas
J
Liu
Edison T
ET
Miller
Lance D
LD
D014408:Biomarkers, Tumor; D001943:Breast Neoplasms / Q000235:genetics* / Q000628:therapy*; D003198:Computer Simulation; D018450:Disease Progression; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D016016:Proportional Hazards Models; D012008:Recurrence; D013997:Time Factors; D016896:Treatment Outcome
D014408:Biomarkers, Tumor