Gene Screening for Prognosis of Non-Muscle-Invasive Bladder Carcinoma under Competing Risks Endpoints

SIMPLE SUMMARY: A vital task in contemporary cancer research is to discover clinically useful molecular markers for diagnosis and prognosis from microarray or sequencing data. However, reliable and efficient statistical tools are lacking in terms of marker screening and selection for high-throughput data with complicated survival endpoints, such as competing risks. Motivated by a study on progression of non-muscle invasive bladder carcinoma for 300 subjects with competing risk endpoints, this paper proposed a controlled screening procedure to fast eliminate most of irrelevant markers, before more precise selection can be further pursued. Combining screening with a boosting procedure, a significant six-gene signature for progression was identified subsequently, showing improved prediction performance over existing alternatives at a lower computational cost. The proposed method is readily applicable to other types of high-throughput cancer data with competing risk events, providing a desired addition to a biomedical researcher’s toolbox. ABSTRACT: Background: Discovering clinically useful molecular markers for predicting the survival of patients diagnosed with non–muscle-invasive bladder cancer can provide insights into cancer dynamics and improve treatment outcomes. However, the presence of competing risks (CR) endpoints complicates the estimation and inferential framework. There is also a lack of statistical analysis tools and software for coping with the high-throughput nature of these data, in terms of marker screening and selection. Aims: To propose a gene screening procedure for proportional subdistribution hazards regression under a CR framework, and illustrate its application in using molecular profiling to predict survival for non-muscle invasive bladder carcinoma. Methods: Tumors from 300 patients diagnosed with bladder cancer were analyzed for genomic abnormalities while controlling for clinically important covariates. Genes with expression patterns that were associated with survival were identified through a screening procedure based on proportional subdistribution hazards regression. A molecular predictor of risk was constructed and examined for prediction accuracy. Results: A six-gene signature was found to be a significant predictor associated with survival of non–muscle-invasive bladder cancer, subject to competing risks after adjusting for age, gender, reevaluated WHO grade, stage and BCG/MMC treatment (p-value < 0.001). Conclusion: The proposed gene screening procedure can be used to discover molecular determinants of survival for non–muscle-invasive bladder cancer and in general facilitate high-throughput competing risks data analysis with easy implementation.