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Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer

S. Pepke, G. Ver Steeg
doi: https://doi.org/10.1101/043257
S. Pepke
1, Lyrid LLC, South Pasadena, CA
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  • For correspondence: spepke@lyridllc.com
G. Ver Steeg
2, Information Sciences Institute, University of Southern California
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  • For correspondence: gregv@isi.edu
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Abstract

Background 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 (co-expression) 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.

Methods In this work we adapt a recently developed machine learning algorithm, CorEx, that efficiently optimizes over multivariate mutual information for sensitive detection of complex gene relationships. The algorithm can be iteratively applied to generate a hierarchy of latent factors. Patients are stratified relative to each factor and combinatoric survival 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.

Results 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 potentially druggable microRNA. Further, combinations of factors lead to a synergistic survival advantage in some cases.

Conclusions 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 both found to have significant Gene Ontology enrichment and are small enough to indicate specific drug targets implies a computational phenotype for ovarian cancer that allows for 366 possible 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.

  • Abbreviations:
    CorEx
    Correlation Explanation
    DNA
    Deoxyribonucleic acid
    EMT
    Epithelial-mesenchymal transition
    FDR
    False discovery rate
    GO
    Gene Ontology
    IC
    Information content
    GTEx
    Genotype Tissue Expression
    KEGG
    Kyoto Encyclopedia of Genes and Genomes
    MI
    Mutual information
    mRNA
    Messenger ribonucleic acid
    NCBI
    National Center for Biotechnology Information
    PARP
    Poly ADP ribos polymerase
    PCA
    Principal component analysis
    PPI
    Protein-protein interaction
    RAM
    Random access memory
    RNA
    Ribonucleic acid
    RPKM
    Reads per kilobase of transcript per million mapped reads
    TC
    Total correlation
    TCGA
    The Cancer Genome Atlas
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted September 19, 2016.
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    Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer
    S. Pepke, G. Ver Steeg
    bioRxiv 043257; doi: https://doi.org/10.1101/043257
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    Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer
    S. Pepke, G. Ver Steeg
    bioRxiv 043257; doi: https://doi.org/10.1101/043257

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