title: Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer.
abstract: De novo inference of clinically relevant gene function relationships from tumor RNA-seq remains a challenging task. Current methods typically either partition patient samples into a few subtypes or rely upon analysis of pairwise gene correlations that will miss some groups in noisy data. Leveraging higher dimensional information can be expected to increase the power to discern targetable pathways, but this is commonly thought to be an intractable computational problem.
  
  
  
  In this work we adapt a recently developed machine learning algorithm for sensitive detection of complex gene relationships. The algorithm, CorEx, efficiently optimizes over multivariate mutual information and can be iteratively applied to generate a hierarchy of relatively independent latent factors. The learned latent factors are used to stratify patients for survival analysis with respect to both single factors and combinations. These analyses are performed and interpreted in the context of biological function annotations and protein network interactions that might be utilized to match patients to multiple therapies.
  
  
  
  Analysis of ovarian tumor RNA-seq samples demonstrates the algorithm's power to infer well over one hundred biologically interpretable gene cohorts, several times more than standard methods such as hierarchical clustering and k-means. The CorEx factor hierarchy is also informative, with related but distinct gene clusters grouped by upper nodes. Some latent factors correlate with patient survival, including one for a pathway connected with the epithelial-mesenchymal transition in breast cancer that is regulated by a microRNA that modulates epigenetics. Further, combinations of factors lead to a synergistic survival advantage in some cases.
  
  
  
  In contrast to studies that attempt to partition patients into a small number of subtypes (typically 4 or fewer) for treatment purposes, our approach utilizes subgroup information for combinatoric transcriptional phenotyping. Considering only the 66 gene expression groups that are found to both have significant Gene Ontology enrichment and are small enough to indicate specific drug targets implies a computational phenotype for ovarian cancer that allows for 366possible patient profiles, enabling truly personalized treatment. The findings here demonstrate a new technique that sheds light on the complexity of gene expression dependencies in tumors and could eventually enable the use of patient RNA-seq profiles for selection of personalized and effective cancer treatments.
authors.affiliation: Lyrid LLC, South Pasadena, USA. spepke@lyridllc.com.
authors.forename: Shirley
authors.identifier: http://orcid.org/0000-0003-3516-7209
authors.initials: S
authors.lastname: Pepke
authors.affiliation: Information Sciences Institute, University of Southern California, Marina Del Rey, USA.
authors.forename: Greg
authors.initials: G
authors.lastname: Ver Steeg
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D058750:Epithelial-Mesenchymal Transition / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D058977:Molecular Sequence Annotation; D009362:Neoplasm Metastasis; D014411:Neoplastic Stem Cells / Q000473:pathology; D010051:Ovarian Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology / Q000628:therapy*; D025941:Protein Interaction Mapping; D017423:Sequence Analysis, RNA
keywords: Information optimization; Latent factors; Machine learning; Ovarian cancer; Personalized medicine; RNA-seq; Tumor transcriptome