  Two challenging problems in the clinical study of cancer are the
characterization of cancer subtypes and the classification of individual
patients according to those subtypes. Statistical approaches addressing these
problems are hampered by population heterogeneity and challenges inherent in
data integration across high-dimensional, diverse covariates. We have developed
a survival-supervised latent Dirichlet allocation (survLDA) modeling framework
to address these concerns. LDA models have proven extremely effective at
identifying themes common across large collections of text, but applications to
genomics have been limited. Our framework extends LDA to the genome by
considering each patient as a `document' with `text' constructed from clinical
and high-dimensional genomic measurements. We then further extend the framework
to allow for supervision by a time-to-event response. The model enables the
efficient identification of collections of clinical and genomic features that
co-occur within patient subgroups, and then characterizes each patient by those
features. An application of survLDA to The Cancer Genome Atlas (TCGA) ovarian
project identifies informative patient subgroups that are characterized by
different propensities for exhibiting abnormal mRNA expression and
methylations, corresponding to differential rates of survival from primary
therapy.
