A model-based statistic for detecting molecular markers associated with complex survival patterns in early-stage cancer
1 Assistance Publique-Hôpitaux de Paris, Hôpital Paul Brousse
2 Faculty of Medicine, University Paris-Sud, Paris, France
3 , INSERM, UMR-669, Villejuif, France
4 INSERM, U 1018, Biostatistics Team, 94807 Villejuif, France
5 , Université Paris-Sud, UMR-S 1018, 94807 Villejuif, France
Journal of Clinical Bioinformatics 2012, 2:14 doi:10.1186/2043-9113-2-14Published: 6 August 2012
In early-stage of cancer, primary treatment can be considered as effective at eliminating the tumor for a non-negligible proportion of patients whereas for the others it leads to a lower tumor burden and thereby potentially prolonged survival. In this mixed population of patients, it is of great interest to detect complex differences in survival distributions associated with molecular markers that potentially activate latent downstream pathways implicated in tumor progression.
We propose a novel model-based score test designed for identifying molecular markers with complex effects on survival in early-stage cancer. From a biological point of view, the proposed score test allows to detect complex changes in the survival distributions linked to either the tumor burden or its dynamic growth.
Simulation results show that the proposed statistic is powerful at identifying departure from the null hypothesis of no survival difference. The practical use of the proposed statistic is exemplified by analyzing the prognostic impact of Kras mutation in early-stage of lung adenocarcinomas. This analysis leads to the conclusion that Kras mutation has a significant negative prognostic impact on survival. Moreover, it emphasizes that the complex role of Kras mutation on survival would have been overlooked by considering results from the classical logrank test.
With the growing number of biological markers to be tested in early-stage cancer, the proposed score test statistic is a powerful tool for detecting molecular markers associated with complex survival patterns.