A discrimination index for selecting markers of tumor growth dynamic across multiple cancer studies with a cure fraction

• We propose a novel index of discrimination for cross-study analysis in oncogenomics.
• We derive the index under a cure rate survival model.
• The index performs better than classical indices based on simulations.
• The index allows to identify genomic markers with a consistent effect on tumor growth dynamics.

To identify genomic markers with consistent effect on tumor dynamics across multiple cancer series, discrimination indices based on proportional hazards models can be used since they do not depend heavily on the sample size. However, the underlying assumption of proportionality of the hazards does not always hold, especially when the studied population is a mixture of cured and uncured patients, like in early-stage cancers.We propose a novel index that quantifies the capability of a genomic marker to separate uncured patients, according to their time-to-event outcomes. It allows to identify genomic markers characterizing tumor growth dynamic across multiple studies.Simulation results show that our index performs better than classical indices based on the Cox model. It is neither affected by the sample size nor the cure rate fraction.In a cross-study of early-stage breast cancers, the index allows to select genomic markers with a potential consistent effect on tumor growth dynamics.