University of Wisconsin Department of Biostatistics and Medical Informatics University of Wisconsin Department of Biostatistics and Medical Informatics

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. 1. Introduction. Technological advances continue to increase both the ease and accuracy with which measurements of the genome and phenome can be obtained and, consequently, genomic-based studies of disease often involve highly diverse types of data collected on large groups of patients. The primary goals of such studies involve identifying genomic features useful for characterizing patient subgroups as well as predicting patient-specific disease course and/or likelihood of response to treatment. Doing so requires statistical methods that handle complex interactions, accommodate population heterogeneity, and allow for data integration across multiple sources. A number of statistical methods are available for survival-related feature identification and prediction (for a review, see Witten and Tibshirani (2009); Li and Li (2004); Wei and Li (2007)). Most often, classical models for a survival response are coupled with some dimension-reduction method, providing a concise representation of the genomic features affecting patient outcome. Although useful, the majority of these methods identify a set of covariates common to all patients and as a result may " distort what is observed " in the presence of population heterogeneity (Aalen, 1988). Survival-supervised clustering approaches naturally accommodate heterogeneity, providing for efficient and effective identification of patient subgroups (Dettling and Buhlmann, 2002; Li and Gui, 2004). However, these approaches do not identify salient features associated with subgroups; …