Hub genes in a pan-cancer co-expression network show potential for predicting drug responses

Background: The topological analysis of networks extracted from different types of “omics” data is a useful strategy for characterizing biologically meaningful properties of the complex systems underlying these networks. In particular, the biological significance of highly connected genes in diverse molecular networks has been previously determined using data from several model organisms and phenotypes. Despite such insights, the predictive potential of candidate hubs in gene co-expression networks in the specific context of cancer-related drug experiments remains to be deeply investigated. The examination of such associations may offer opportunities for the accurate prediction of anticancer drug responses. Methods: Here, we address this problem by: a) analyzing a co-expression network obtained from thousands of cancer cell lines, b) detecting significant network hubs, and c) assessing their capacity to predict drug sensitivity using data from thousands of drug experiments. We investigated the prediction capability of those genes using a multiple linear regression model, independent datasets, comparisons with other models and our own in vitro experiments. Results: These analyses led to the identification of 47 hub genes, which are implicated in a diverse range of cancer-relevant processes and pathways. Overall, encouraging agreements between predicted and observed drug sensitivities were observed in public datasets, as well as in our in vitro validations for four glioblastoma cell lines and four drugs. To facilitate further research, we share our hub-based drug sensitivity prediction model as an online tool. Conclusions: Our research shows that co-expression network hubs are biologically interesting and exhibit potential for predicting drug responses in vitro. These findings motivate further investigations about the relevance and application of our unbiased discovery approach in pre-clinical, translationally-oriented research.

[1]  Benjamin Haibe-Kains,et al.  Research and applications: Comparison and validation of genomic predictors for anticancer drug sensitivity , 2013, J. Am. Medical Informatics Assoc..

[2]  Rafael A. Irizarry,et al.  A framework for oligonucleotide microarray preprocessing , 2010, Bioinform..

[3]  A. Goldenberg,et al.  Revisiting inconsistency in large pharmacogenomic studies. , 2017, F1000Research.

[4]  Michael P. Morrissey,et al.  Pharmacogenomic agreement between two cancer cell line data sets , 2015, Nature.

[5]  Mark Gerstein,et al.  The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics , 2007, PLoS Comput. Biol..

[6]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[7]  Petr Smirnov,et al.  Gene isoforms as expression-based biomarkers predictive of drug response in vitro , 2017, Nature Communications.

[8]  Laura M. Heiser,et al.  A community effort to assess and improve drug sensitivity prediction algorithms , 2014, Nature Biotechnology.

[9]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[10]  Olaf Wolkenhauer,et al.  Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks , 2016, Front. Physiol..

[11]  Sridhar Ramaswamy,et al.  Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells , 2012, Nucleic Acids Res..

[12]  Hedi Peterson,et al.  g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments , 2007, Nucleic Acids Res..

[13]  Xiaoshan Wu,et al.  Transcriptome analysis of coding and long non-coding RNAs highlights the regulatory network of cascade initiation of permanent molars in miniature pigs , 2017, BMC Genomics.

[14]  J. Barnholtz-Sloan,et al.  Computational identification of multi-omic correlates of anticancer therapeutic response , 2014, BMC Genomics.

[15]  Julio Saez-Rodriguez,et al.  Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties , 2012, PloS one.

[16]  Joshua A. Bittker,et al.  Correlating chemical sensitivity and basal gene expression reveals mechanism of action , 2015, Nature chemical biology.

[17]  Galina V. Glazko,et al.  Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data , 2012, Front. Gene..

[18]  Alex H. Wagner,et al.  DGIdb 3.0: a redesign and expansion of the drug–gene interaction database , 2017, bioRxiv.

[19]  Zhuowen Tu,et al.  Similarity network fusion for aggregating data types on a genomic scale , 2014, Nature Methods.

[20]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[21]  G. Dittmar,et al.  RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples , 2017, BMC Genomics.

[22]  M. S. Mukhtar,et al.  Network biology discovers pathogen contact points in host protein-protein interactomes , 2018, Nature Communications.

[23]  Zhaleh Safikhani,et al.  PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies , 2017, bioRxiv.

[24]  Emanuel J. V. Gonçalves,et al.  A Landscape of Pharmacogenomic Interactions in Cancer , 2016, Cell.

[25]  Francisco Azuaje,et al.  Computational models for predicting drug responses in cancer research , 2016, Briefings Bioinform..

[26]  Reinhard Laubenbacher,et al.  Comparison of Reverse‐Engineering Methods Using an in Silico Network , 2007, Annals of the New York Academy of Sciences.

[27]  Gajendra P. S. Raghava,et al.  Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine , 2016, Scientific Reports.

[28]  S. Ramaswamy,et al.  Systematic identification of genomic markers of drug sensitivity in cancer cells , 2012, Nature.

[29]  Chun Xing Li,et al.  Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection , 2015, BMC Cancer.

[30]  S. Niclou,et al.  Comprehensive Analysis of Glycolytic Enzymes as Therapeutic Targets in the Treatment of Glioblastoma , 2015, PloS one.

[31]  Scott E. Martin,et al.  Reproducible pharmacogenomic profiling of cancer cell line panels , 2016, Nature.

[32]  Francisco Azuaje,et al.  Analysis of the dynamic co-expression network of heart regeneration in the zebrafish , 2016, Scientific Reports.

[33]  Nci Dream Community.  A community effort to assess and improve drug sensitivity prediction algorithms , 2014 .

[34]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[35]  Zalmiyah Zakaria,et al.  A review on the computational approaches for gene regulatory network construction , 2014, Comput. Biol. Medicine.

[36]  Dianne P. O'Leary,et al.  Why Do Hubs in the Yeast Protein Interaction Network Tend To Be Essential: Reexamining the Connection between the Network Topology and Essentiality , 2008, PLoS Comput. Biol..

[37]  K. Kohn,et al.  CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set. , 2012, Cancer research.

[38]  P. Lichter,et al.  Differentiation Therapy Exerts Antitumor Effects on Stem-like Glioma Cells , 2010, Clinical Cancer Research.

[39]  Lee A. D. Cooper,et al.  The OncoPPi network of cancer-focused protein–protein interactions to inform biological insights and therapeutic strategies , 2017, Nature Communications.

[40]  M. Gerstein,et al.  Genomic analysis of essentiality within protein networks. , 2004, Trends in genetics : TIG.

[41]  Jun Yu,et al.  Mucosal microbiome dysbiosis in gastric carcinogenesis , 2017, Gut.

[42]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[43]  Matko Bosnjak,et al.  REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms , 2011, PloS one.

[44]  Justin Guinney,et al.  Systematic Assessment of Analytical Methods for Drug Sensitivity Prediction from Cancer Cell Line Data , 2013, Pacific Symposium on Biocomputing.

[45]  Leng Han,et al.  Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types , 2014, Nature Communications.

[46]  Andrew H. Beck,et al.  Revisiting inconsistency in large pharmacogenomic studies , 2015, bioRxiv.

[47]  F. Azuaje.  Selecting biologically informative genes in co-expression networks with a centrality score , 2014, Biology Direct.

[48]  Michael Mitzenmacher,et al.  Detecting Novel Associations in Large Data Sets , 2011, Science.