title: Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer.
abstract: Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than "upstream" nodes that are potentially causal of "downstream" changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2-phosphatidylinositide 3-kinase-AKT-MAPK growth pathway andATR-p53-BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF-WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR-IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.
authors: Co-first authors Oncology Translational Medicine, Novartis, Cambridge, MA, USA savina.jaeger@novartis.com jeremy.jenkins@novartis.com.
authors: Savina
authors: S
authors: Jaeger
authors: Co-first authors ONC Target Discovery, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA.
authors: Junxia
authors: J
authors: Min
authors: Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Basel, Switzerland.
authors: Florian
authors: F
authors: Nigsch
authors: Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA.
authors: Miguel
authors: M
authors: Camargo
authors: Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA Pfizer, Cambridge, MA, USA.
authors: Janna
authors: J
authors: Hutz
authors: Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA.
authors: Allen
authors: A
authors: Cornett
authors: NIBR IT, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA.
authors: Stephen
authors: S
authors: Cleaver
authors: Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA.
authors: Alan
authors: A
authors: Buckler
authors: Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA.
authors: Jeremy L
authors: JL
authors: Jenkins
mesh_terms: D055808:Drug Discovery; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000188:drug therapy* / Q000378:metabolism / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D012333:RNA, Messenger / Q000378:metabolism; D012372:ROC Curve; D015398:Signal Transduction; D064726:Triple Negative Breast Neoplasms / Q000188:drug therapy* / Q000378:metabolism
chemical_list: D012333:RNA, Messenger
keywords: Causal modeling; networks; triple-negative breast cancer