In this work, we introduce a novel extension of the Weibull mixture cure model that incorporates a frailty component, employed to model an underlying latent population heterogeneity with respect to the outcome risk. Additionally, high-dimensional covariates are integrated into both the cure rate and survival part of the model, providing a comprehensive approach to employ the model in the context of high-dimensional omics data.
We perform variable selection via an adaptive elastic-net penalization, and propose a novel approach to inference using the expectation–maximization (EM) algorithm.
We apply the novel approach to analyze RNAseq gene expression data from bulk breast cancer patients included in The Cancer Genome Atlas (TCGA) database.
A set of prognostic biomarkers is then derived from selected genes, and subsequently validated via functional enrichment analysis.
A prognostic risk score index based on the identified biomarkers is proposed and validated by exploring the patients' survival.
Kızılaslan, Fatih, Swanson, David M., and Vitelli, Valeria (2024). "A Weibull Mixture Cure Frailty Model for High-dimensional Covariates". arXiv DOI: 10.48550/arXiv.2401.06575
It includes all the source code necessary to run the introduced and utilized methods in Kızılaslan, Swanson and Vitelli (2024).
application.R includes the code for applying the methods to the TCGA-BRCA RNA-seq data.
EM_penMCFM.R includes EM algorithm codes based on the methods employed in the study.
functions.R includes the related functions in the algorithms.
GMIFS_MCM.R includes the related functions for the GMIFS algorithm for the MCM based on the Fu et al. (2022).
GMIFS_penCox_penMCFM.R includes the related functions for the GMIFS and penCox algorithms for the penMCFM.
plots_sim.R draw the figures based on the simulation results. Results can be loaded from data frames df_bp_R1.RData, df_betap_R1.RData and df.penCox.1se.train.Cstat.RData in Simulation>Data.
sim_penMCFM.R is a function designed for running simulations for the MCFM, utilizing various methods.
Data includes data frames obtained from simulation results, enabling the generation of plots
Figures includes all the plots from the simulation section of the study.
We present all the files in the Supplementary Materials of the study.
Data includes prior knowledge of BRCA genes from CoxReg study of Li and Liu (2021).
Figures includes all the plots relevant to the application section of the study.
Results contains all the selected genes and comprehensive results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses in excel files.
Fu, H., Nicolet, D., Mrózek, K., Stone, R.M., Eisfeld, A.K., Byrd, J.C., Archer, K.J. (2022). Controlled variable selection in Weibull mixture cure models for high‐dimensional data. Statistics in Medicine, 41 (22), 4340-4366. DOI: 10.1002/sim.9513
Li, L., Liu, ZP. (2021). Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models. Journal of Translational Medicine, 19, 514 (2021). DOI: 10.1186/s12967-021-03180-y