Analysis of breast cancer related gene expression using natural splines and the Cox proportional hazard model to identify prognostic associations

Bas Kreike (1) , Guus Hart (1) , Harry Bartelink (1) , Marc J. Vijver (2)

1 Divisions of Radiation Oncology and Experimental Therapy
2 Department of Pathology

Many studies correlating gene expression data to clinical parameters assume a linear increase or decrease of the clinical parameter under investigation with the expression of a gene. We have studied genes encoding important breast cancer-related proteins using a model for survival-type data that is based on natural splines and the Cox proportional hazard model, thereby removing the linearity assumption. Expression data of 16 genes were studied in relation to metastasis-free probability in a cohort of 295 consecutive breast cancer patients treated at The Netherlands Cancer Institute. The independent predictive power for disease outcome of the 16 individual genes was tested in a multivariable model with known clinical and pathological risk factors. There is a linear relationship between increasing expression and a higher or lower hazard for distant metastasis for , , , , , and ; for , , , , , , , , and there is no clear increase or decrease; and for there seems to be a non-linear relation. Multivariable analysis showed that the 70-gene prognosis profile outperforms all the other variables in the model (hazard-rate 5.4, 95% CI 2.5–11.7;  = 0.000018). -expression seems to have a non-linear relation with disease outcome, indicating that lower but also higher expression of are associated with worse outcome compared to intermediate expression levels; the other genes show no or a linear relation.

Mots clés: Breast cancer; Microarray gene expression data; Natural spline analysis; Cox proportional hazard analysis; Distant metastasis-free probability

Bas Kreike, Guus Hart, Harry Bartelink, Marc J. Vijver. Analysis of breast cancer related gene expression using natural splines and the Cox proportional hazard model to identify prognostic associations. Breast Cancer Research and Treatment, 2009, 122 (3), pp.711-720. ⟨10.1007/s10549-009-0588-6⟩. ⟨hal-00535405⟩