Robust analysis of cancer heterogeneity for high-dimensional data

Cancer heterogeneity plays an important role in the understanding of tumor etiology, progression, and response to treatment. To accommodate heterogeneity, cancer subgroup analysis has been extensively conducted. However, most of the existing studies share the limitation that they cannot accommodate heavy-tailed or contaminated outcomes and also high dimensional covariates, both of which are not uncommon in biomedical research. In this study, we propose a robust subgroup identification approach based on M-estimators together with concave and pairwise fusion penalties, which advances from existing studies by effectively accommodating high-dimensional data containing some outliers. The penalties are applied on both latent heterogeneity factors and covariates, where the estimation is expected to achieve subgroup identification and variable selection simultaneously, with the number of subgroups being apriori unknown. We innovatively develop an algorithm based on parallel computing strategy, with a significant advantage of capable of processing large-scale data. The convergence property of the proposed algorithm, oracle property of the penalized M-estimators, and selection consistency of the proposed BIC criterion are carefully established. Simulation and analysis of TCGA breast cancer data demonstrate that the proposed approach is promising to efficiently identify underlying subgroups in high-dimensional data.