Deciphering the combinatorial interaction landscape

From cellular activation to drug combinations, the control of biological systems involves multiple stimuli that can elicit complex nonlinear interactions. To elucidate the functions and logic of stimulus interactions, we developed SAIL (Synergistic/Antagonistic Interaction Learner). SAIL uses a machine learning classifier trained to categorize interactions across a complete taxonomy of possible combinatorial effects. The strategy resolves the most informative interactions, and helps infer their functions and regulatory mechanisms. SAIL-predicted interaction mechanisms controlling key immune functions were experimentally validated. SAIL can integrate results from multiple datasets to derive general properties of how cells respond to multiple stimuli. Using public immunological datasets, we assembled a fine-grained landscape of ∼30000 interactions. Analysis of the landscape shows the context-dependent functions of individual modulators, and reveals a probabilistic algebra that links the separate and combined stimulus effects. SAIL is available through a user friendly interface to resolve the effect of stimulus and drug combinations.

[1]  Charity W. Law,et al.  voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.

[2]  S. M. Toor,et al.  Immune checkpoint inhibitors: recent progress and potential biomarkers , 2018, Experimental & Molecular Medicine.

[3]  B. Eynde,et al.  Tryptophan-Degrading Enzymes in Tumoral Immune Resistance , 2015, Front. Immunol..

[4]  G. Deepe,et al.  Metallothioneins: Emerging Modulators in Immunity and Infection , 2017, International journal of molecular sciences.

[5]  Karen A. Ryall,et al.  Systems biology approaches for advancing the discovery of effective drug combinations , 2015, Journal of Cheminformatics.

[6]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[7]  Rationalizing combination therapies , 2017, Nature Medicine.

[8]  Amir K. Foroushani,et al.  Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen , 2019, Nature Communications.

[9]  S. Olthof,et al.  The combination of valproic acid and lithium delays hematopoietic stem/progenitor cell differentiation. , 2012, Blood.

[10]  B. Gilbert,et al.  Synergistic TLR2/6 and TLR9 Activation Protects Mice against Lethal Influenza Pneumonia , 2012, PloS one.

[11]  Andrew D. Rouillard,et al.  Enrichr: a comprehensive gene set enrichment analysis web server 2016 update , 2016, Nucleic Acids Res..

[12]  Zhijin Wu,et al.  Preprocessing of oligonucleotide array data , 2004, Nature Biotechnology.

[13]  Charles Schmidt.  The benefits of immunotherapy combinations , 2017, Nature.

[14]  Andreas Bender,et al.  DeepSynergy: predicting anti-cancer drug synergy with Deep Learning , 2017, Bioinform..

[15]  O. Beretta,et al.  Synergism between Curdlan and GM-CSF Confers a Strong Inflammatory Signature to Dendritic Cells , 2012, The Journal of Immunology.

[16]  Emmanuel Barillot,et al.  Combinatorial code governing cellular responses to complex stimuli , 2015, Nature Communications.

[17]  Tsippi Iny Stein,et al.  The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses , 2016, Current protocols in bioinformatics.

[18]  S. Sealfon,et al.  Pandemic H1N1 influenza A viruses suppress immunogenic RIPK3-driven dendritic cell death , 2017, Nature Communications.

[19]  Q. Wang,et al.  Progress of immune checkpoint therapy in the clinic (Review). , 2018, Oncology reports.

[20]  Boris M. Hartmann,et al.  Combinatorial Cytokine Code Generates Anti-Viral State in Dendritic Cells , 2014, Front. Immunol..

[21]  Stephen T. C. Wong,et al.  Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction , 2019, Bioinform..

[22]  Sean R. Davis,et al.  GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor , 2007, Bioinform..

[23]  George D. Kalliolias,et al.  TNF biology, pathogenic mechanisms and emerging therapeutic strategies , 2016, Nature Reviews Rheumatology.

[24]  S. Bonini,et al.  Who benefits from immunotherapy? , 1997, Allergy.

[25]  Bin Chen,et al.  Characteristics of Drug Combination Therapy in Oncology by Analyzing Clinical Trial Data on Clinicaltrials.Gov , 2014, Pacific Symposium on Biocomputing.

[26]  M. David,et al.  Immunomodulatory functions of type I interferons , 2012, Nature Reviews Immunology.

[27]  L. Turka,et al.  Impaired T cell function in RANTES-deficient mice. , 2002, Clinical immunology.

[28]  T. L. Deem,et al.  VCAM-1 Activation of Endothelial Cell Protein Tyrosine Phosphatase 1B1 , 2007, The Journal of Immunology.

[29]  Jeffrey A. Wiser,et al.  ImmPort: disseminating data to the public for the future of immunology , 2014, Immunologic Research.