A model-based statistic for detecting molecular markers associated with complex survival patterns in early-stage cancer

Philippe Broët,Thierry Moreau

doi:10.1186/2043-9113-2-14

Abstract

BackgroundIn 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.MethodWe 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.ResultsSimulation 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.ConclusionWith 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.

Highlights

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
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
A different approach, which defines the cumulative hazard as a bounded increasing positive function and relies on a mechanistic model of cancer, has been introduced by Yakovlev et al [2,3,4]. This cure rate model defines the improper survival distribution whereby each individual is exposed to recurrences that arise from unobservable tumor clonogens surviving the primary treatment

Summary

Introduction

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. A different approach, which defines the cumulative hazard as a bounded increasing positive function and relies on a mechanistic model of cancer, has been introduced by Yakovlev et al [2,3,4] This cure rate model (sometimes referred as promotion time cure model [5]) defines the improper survival distribution whereby each individual is exposed to recurrences that arise from unobservable tumor clonogens surviving the primary treatment. If the probability of being cured (no clonogen) after the primary treatment is identical across all patients, it necessarily implies a same distribution for the number of surviving clonogens among uncured patients In this context and from a Bayesian perspective, Yin et al [9] have proposed a family of transformation cure models that gives more flexibility for modelling survival curves and includes the two-component mixture model and the Poisson cure model as special cases [9,10]. This family does not provide an easy biological interpretation regarding changes in the cure fraction, the distribution of surviving clonogens and the tumor progression

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Discussion

Conclusion