2020
Cannabinoids Promote Progression of HPV-Positive Head and Neck Squamous Cell Carcinoma via p38 MAPK Activation
Liu C, Sadat S, Ebisumoto K, Sakai A, Panuganti B, Ren S, Goto Y, Haft S, Fukusumi T, Ando M, Saito Y, Guo T, Tamayo P, Yeerna H, Kim W, Hubbard J, Sharabi A, Gutkind J, Califano J. Cannabinoids Promote Progression of HPV-Positive Head and Neck Squamous Cell Carcinoma via p38 MAPK Activation. Clinical Cancer Research 2020, 26: 2693-2703. PMID: 31932491, PMCID: PMC7538010, DOI: 10.1158/1078-0432.ccr-18-3301.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsApoptosisCannabinoidsCell MovementCell ProliferationFemaleHead and Neck NeoplasmsHumansMiceMice, NudeP38 Mitogen-Activated Protein KinasesPapillomaviridaePapillomavirus InfectionsPrognosisReceptors, CannabinoidSquamous Cell Carcinoma of Head and NeckTumor Cells, CulturedXenograft Model Antitumor AssaysConceptsHead and neck squamous cell carcinomaHPV-positive head and neck squamous cell carcinomaHPV-positive HNSCC cell linesNeck squamous cell carcinomaHNSCC cell linesSingle-sample gene set enrichment analysisSquamous cell carcinomaP38 MAPK pathway activationHNSCC cohortCell carcinomaMAPK pathway activationHPV-negative head and neck squamous cell carcinomaHuman papillomavirus (HPV)-related headCell linesAnimal modelsCannabinoid receptor activationHPV- HNSCC patientsHead and neck squamous cell carcinomas dataMarijuana usePathway activationDaily marijuana useWhole-genome expression analysisCannabinoid exposureHNSCC patientsP38 MAPK activation
2016
DiSCoVERing Innovative Therapies for Rare Tumors: Combining Genetically Accurate Disease Models with In Silico Analysis to Identify Novel Therapeutic Targets
Hanaford A, Archer T, Price A, Kahlert U, Maciaczyk J, Nikkhah G, Kim J, Ehrenberger T, Clemons P, Dančík V, Seashore-Ludlow B, Viswanathan V, Stewart M, Rees M, Shamji A, Schreiber S, Fraenkel E, Pomeroy S, Mesirov J, Tamayo P, Eberhart C, Raabe E. DiSCoVERing Innovative Therapies for Rare Tumors: Combining Genetically Accurate Disease Models with In Silico Analysis to Identify Novel Therapeutic Targets. Clinical Cancer Research 2016, 22: 3903-3914. PMID: 27012813, PMCID: PMC5055054, DOI: 10.1158/1078-0432.ccr-15-3011.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsApoptosisBiomarkersCell Line, TumorCerebellar NeoplasmsComputational BiologyComputer SimulationCyclin-Dependent KinasesDisease Models, AnimalDrug DiscoveryGene Expression ProfilingGenetic Predisposition to DiseaseHumansMedulloblastomaMiceModels, BiologicalNeural Stem CellsPhosphorylationPiperazinesProto-Oncogene Proteins c-aktProto-Oncogene Proteins c-mycPyridinesTranscriptomeTumor Suppressor Protein p53Xenograft Model Antitumor AssaysConceptsGroup 3 medulloblastomaProgenitor cellsHuman neural stemCyclin-dependent kinasesRare tumorHuman neural stem cell modelNeural stemGenetically accurate modelsSurvival of miceDominant-negative p53Stem cell modelPotential effective treatmentConstitutively active AktAggressive medulloblastomaDrug sensitivity datasetsDrug sensitivity databaseNovel therapeutic targetsMedulloblastoma xenograftsAccurate disease modelsHuman stemInnovative therapiesIncreased apoptosisNeural stem cell modelIn silico analysisIn silico analysis methods
2015
KRAS Genomic Status Predicts the Sensitivity of Ovarian Cancer Cells to Decitabine
Stewart M, Tamayo P, Wilson A, Wang S, Chang Y, Kim J, Khabele D, Shamji A, Schreiber S. KRAS Genomic Status Predicts the Sensitivity of Ovarian Cancer Cells to Decitabine. Cancer Research 2015, 75: 2897-2906. PMID: 25968887, PMCID: PMC4506246, DOI: 10.1158/0008-5472.can-14-2860.Peer-Reviewed Original ResearchConceptsOvarian cancer cellsCancer cellsOvarian cancerHigh-grade serous ovarian cancer cellsGenomic statusBiomarkers of drug responseBcl-2 family inhibitorsAntitumor response rateSerous ovarian cancer cellsTreated with decitabineInhibit DNA methylationBreast cancer cellsDownregulation of DNMT1DNA methyltransferase inhibitionKRAS statusDNA methylationPredictive biomarkersSolid tumorsMEK inhibitorsMEK/ERK phosphorylationDecitabineBcl-2Drug responseXenograft modelLow-grade