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Two challenging problems in the clinical study of cancer are thecharacterization of cancer subtypes and the classification of individualpatients according to those subtypes. Statistical approaches addressing theseproblems are hampered by population heterogeneity and challenges inherent indata integration across high-dimensional, diverse covariates. We have developeda survival-supervised latent Dirichlet allocation (survLDA) modeling frameworkto address these concerns. LDA models have proven extremely effective atidentifying themes common across large collections of text, but applications togenomics have been limited. Our framework extends LDA to the genome byconsidering each patient as a `document' with `text' constructed from clinicaland high-dimensional genomic measurements. We then further extend the frameworkto allow for supervision by a time-to-event response. The model enables theefficient identification of collections of clinical and genomic features thatco-occur within patient subgroups, and then characterizes each patient by thosefeatures. An application of survLDA to The Cancer Genome Atlas (TCGA) ovarianproject identifies informative patient subgroups that are characterized bydifferent propensities for exhibiting abnormal mRNA expression andmethylations, corresponding to differential rates of survival from primarytherapy.

Survival-supervised latent Dirichlet allocation models for genomic analysis of time-to-event outcomes