It is of interest to identify heterogeneous effects of genetic biomarkers on patients' survival and conduct proper statistical inference. We propose a novel fused procedure to draw inference on all predictors within the framework of censored quantile regression. The proposed estimator combines a sequence of low dimensional model fitting based on multi-sample splitting and variable selection. We show that, under some regularity conditions, the estimator is consistent and converges weakly to a Gaussian process indexed by the quantile level. Simulation studies indicate that our procedure properly quantifies the uncertainty of effect estimates in high dimensional settings. We apply our method to analyze the heterogeneous effects of SNPs residing in the lung cancer pathways on patients' survival, using the Boston Lung Cancer Survivor Cohort, a cancer epidemiology study investigating the molecular mechanism of lung cancer.