Perturbation-response genes reveal signaling footprints in cancer gene expression

Aberrant cell signaling is known to cause cancer and many other diseases, as well as a focus of treatment. A common approach is to infer its activity on the level of pathways using gene expression. However, mapping gene expression to pathway components disregards the effect of post-translational modifications, and downstream signatures represent very specific experimental conditions. Here we present PROGENy, a method that overcomes both limitations by leveraging a large compendium of publicly available perturbation experiments to yield a common core of Pathway RespOnsive GENes. Unlike existing methods, PROGENy can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways for patient survival. Collectively, these results show that PROGENy more accurately infers pathway activity from gene expression than other methods.

[1]  B. Brüne,et al.  PI3K/Akt Is Required for Heat Shock Proteins to Protect Hypoxia-inducible Factor 1α from pVHL-independent Degradation* , 2004, Journal of Biological Chemistry.

[2]  E. Lander,et al.  Assessing the significance of chromosomal aberrations in cancer: Methodology and application to glioma , 2007, Proceedings of the National Academy of Sciences.

[3]  Helen E. Parkinson,et al.  ArrayExpress—a public database of microarray experiments and gene expression profiles , 2006, Nucleic Acids Res..

[4]  Benjamin M. Bolstad,et al.  affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..

[5]  T. Jacks,et al.  Mutant p53 Gain of Function in Two Mouse Models of Li-Fraumeni Syndrome , 2004, Cell.

[6]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of human colon and rectal cancer , 2012, Nature.

[7]  David Haussler,et al.  Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM , 2010, Bioinform..

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

[9]  Pooja Mittal,et al.  A novel signaling pathway impact analysis , 2009, Bioinform..

[10]  Lincoln Stein,et al.  Reactome: a database of reactions, pathways and biological processes , 2010, Nucleic Acids Res..

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

[12]  Michal Sheffer,et al.  Pathway-based personalized analysis of cancer , 2013, Proceedings of the National Academy of Sciences.

[13]  V. Tabor,et al.  Targeting PI3K/Akt represses Hypoxia inducible factor-1α activation and sensitizes Rhabdomyosarcoma and Ewing’s sarcoma cells for apoptosis , 2013, Cancer Cell International.

[14]  B. Neel,et al.  TNF-stimulated MAP kinase activation mediated by a Rho family GTPase signaling pathway. , 2011, Genes & development.

[15]  Cheryl H. Arrowsmith,et al.  Prevalent p53 mutants co-opt chromatin pathways to drive cancer growth , 2015, Nature.

[16]  F. Chibon,et al.  Cancer gene expression signatures - the rise and fall? , 2013, European journal of cancer.

[17]  Avi Ma'ayan,et al.  Expression2Kinases: mRNA profiling linked to multiple upstream regulatory layers , 2012, Bioinform..

[18]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[19]  Jeffrey T. Chang,et al.  Oncogenic pathway signatures in human cancers as a guide to targeted therapies , 2006, Nature.

[20]  Mariano J. Alvarez,et al.  Network-based inference of protein activity helps functionalize the genetic landscape of cancer , 2016, Nature Genetics.

[21]  Bertram Klinger,et al.  Discovering causal signaling pathways through gene-expression patterns , 2010, Nucleic Acids Res..

[22]  Gary D Bader,et al.  International network of cancer genome projects , 2010, Nature.

[23]  Jianmin Wu,et al.  Mutant p53 Drives Pancreatic Cancer Metastasis through Cell-Autonomous PDGF Receptor β Signaling , 2014, Cell.

[24]  Gordon K. Smyth,et al.  limma: Linear Models for Microarray Data , 2005 .

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

[26]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Gene Ontology Consortium The Gene Ontology (GO) database and informatics resource , 2003 .

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

[29]  Gary D Bader,et al.  Pathway and network analysis of cancer genomes , 2015, Nature Methods.

[30]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[31]  Michael L. Gatza,et al.  A pathway-based classification of human breast cancer , 2010, Proceedings of the National Academy of Sciences.

[32]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[33]  C. Wykoff,et al.  The tumour suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis , 1999, Nature.

[34]  A. Levine,et al.  Tumor-Associated Mutant p53 Drives the Warburg Effect , 2013, Nature Communications.

[35]  Wei Zhao,et al.  Role of PI3K/Akt and MEK/ERK in mediating hypoxia-induced expression of HIF-1alpha and VEGF in laser-induced rat choroidal neovascularization. , 2009, Investigative ophthalmology & visual science.

[36]  B Marshall,et al.  Gene Ontology Consortium: The Gene Ontology (GO) database and informatics resource , 2004, Nucleic Acids Res..