2024
Stochastic modeling of a gene regulatory network driving B cell development in germinal centers
Koshkin A, Herbach U, Martínez M, Gandrillon O, Crauste F. Stochastic modeling of a gene regulatory network driving B cell development in germinal centers. PLOS ONE 2024, 19: e0301022. PMID: 38547073, PMCID: PMC10977792, DOI: 10.1371/journal.pone.0301022.Peer-Reviewed Original ResearchConceptsGene regulatory network structureGene regulatory networksGene expression dataExpression dataB cell differentiationSingle-cellAssociated with cell developmentGC B cell differentiationStages of B-cell differentiationB cell developmentSelection of B cellsGene regulationRegulatory networksTranscriptome dataSystems biologyHigh-affinity antibodiesRegulatory mechanismsCell developmentGenesAdaptive immune systemMRNA distributionPlasmablast stageGerminal centersDifferentiationImmune system
2023
Proteomic-based stratification of intermediate-risk prostate cancer patients
Zhong Q, Sun R, Aref A, Noor Z, Anees A, Zhu Y, Lucas N, Poulos R, Lyu M, Zhu T, Chen G, Wang Y, Ding X, Rutishauser D, Rupp N, Rueschoff J, Poyet C, Hermanns T, Fankhauser C, Martínez M, Shao W, Buljan M, Neumann J, Beyer A, Hains P, Reddel R, Robinson P, Aebersold R, Guo T, Wild P. Proteomic-based stratification of intermediate-risk prostate cancer patients. Life Science Alliance 2023, 7: e202302146. PMID: 38052461, PMCID: PMC10698198, DOI: 10.26508/lsa.202302146.Peer-Reviewed Original ResearchConceptsGleason grade groupBiochemical recurrenceRisk of biochemical recurrenceIntermediate-risk patientsNon-aggressive diseaseProstate cancer managementProstate cancer patientsMultivariate Cox regressionHigh-risk groupPatient treatment decisionsProstatic adenocarcinomaGleason gradePrognostic indicatorMatched tumorCancer managementCox regressionCancer patientsGrade groupTreatment decisionsPatientsSurvival analysisRisk scoreClinical applicationGleasonProstateConvergent evolution and B-cell recirculation in germinal centers in a human lymph node
Pelissier A, Stratigopoulou M, Donner N, Dimitriadis E, Bende R, Guikema J, Martinez M, van Noesel C. Convergent evolution and B-cell recirculation in germinal centers in a human lymph node. Life Science Alliance 2023, 6: e202301959. PMID: 37640448, PMCID: PMC10462906, DOI: 10.26508/lsa.202301959.Peer-Reviewed Original ResearchConceptsGerminal centersLymph nodesHuman lymph nodesGC responseHuman LNImmune responseDevelopment of autoimmune diseasesConvergent evolutionB cell clonesB cell recirculationEffective immune responseExpanded clonesLaser capture microdissectionPhylogenetic tree analysisIndividual germinal centersB cellsAutoimmune diseasesAntigen responseMouse modelModel antigenAntigenClonal diversity
2022
PCfun: a hybrid computational framework for systematic characterization of protein complex function
Sharma V, Fossati A, Ciuffa R, Buljan M, Williams E, Chen Z, Shao W, Pedrioli P, Purcell A, Martínez M, Song J, Manica M, Aebersold R, Li C. PCfun: a hybrid computational framework for systematic characterization of protein complex function. Briefings In Bioinformatics 2022, 23: bbac239. PMID: 35724564, PMCID: PMC9310514, DOI: 10.1093/bib/bbac239.Peer-Reviewed Original ResearchConceptsProtein complex functionGO termsProtein complex databasesGene Ontology (GONatural language processing techniquesRandom forestLanguage processing techniquesNearest neighborsProtein complexesSystematic annotationCellular statesBiological functionsBiological processesQuery vectorComplex queriesWord vectorsUnsupervised approachSupervised approachChild termsProteinMolecular biologySubunit compositionComplex databasesComplex functionsComputational framework
2021
TITAN: T-cell receptor specificity prediction with bimodal attention networks
Weber A, Born J, Martínez M. TITAN: T-cell receptor specificity prediction with bimodal attention networks. Bioinformatics 2021, 37: i237-i244. PMID: 34252922, PMCID: PMC8275323, DOI: 10.1093/bioinformatics/btab294.Peer-Reviewed Original ResearchConceptsK-nearest neighborAttention networkLeverage transfer learningState-of-the-artK-nearest-neighbor (KNN) classifierInput data spaceK-NN classifierBimodal neural networkSMILES sequencesTransfer learningData augmentationAttention heatmapsCompetitive performanceNeural networkData spaceT cell receptorBoost performanceT-cell receptor sequencingClassifierNetworkImproved performanceT cellsPrediction of specificityPerformanceSequencing of T-cell receptorOn the feasibility of deep learning applications using raw mass spectrometry data
Cadow J, Manica M, Mathis R, Reddel R, Robinson P, Wild P, Hains P, Lucas N, Zhong Q, Guo T, Aebersold R, Martínez M. On the feasibility of deep learning applications using raw mass spectrometry data. Bioinformatics 2021, 37: i245-i253. PMID: 34252933, PMCID: PMC8275322, DOI: 10.1093/bioinformatics/btab311.Peer-Reviewed Original ResearchConceptsRaw mass spectrometry dataDeep learning modelsRaw MS dataMass spectrometry dataClassification performanceDeep learningMS dataMass spectrometryLearning modelsSpectrometry dataApplication of deep learningMS imagesNatural image classificationDeep learning applicationsPrivacy of individualsTransfer learning techniqueData-independent-acquisitionMS2 spectraClassification taskData processing pipelinesClassification labelsImage classificationFeature vectorTransfer learningSample sparsityMultiscale Modeling of Germinal Center Recapitulates the Temporal Transition From Memory B Cells to Plasma Cells Differentiation as Regulated by Antigen Affinity-Based Tfh Cell Help
Tejero E, Lashgari D, García-Valiente R, Gao X, Crauste F, Robert P, Meyer-Hermann M, Martínez M, van Ham S, Guikema J, Hoefsloot H, van Kampen A. Multiscale Modeling of Germinal Center Recapitulates the Temporal Transition From Memory B Cells to Plasma Cells Differentiation as Regulated by Antigen Affinity-Based Tfh Cell Help. Frontiers In Immunology 2021, 11: 620716. PMID: 33613551, PMCID: PMC7892951, DOI: 10.3389/fimmu.2020.620716.Peer-Reviewed Original ResearchMeSH KeywordsAsymmetric Cell DivisionB-LymphocytesCD40 AntigensCell LineageComputer SimulationGene Regulatory NetworksGerminal CenterHumansImmunologic MemoryInterferon Regulatory FactorsLymphopoiesisModels, ImmunologicalPlasma CellsPositive Regulatory Domain I-Binding Factor 1Proto-Oncogene Proteins c-bcl-6Signal TransductionT Follicular Helper CellsTime FactorsConceptsB cell to plasma cell differentiationAsymmetric divisionRegulatory interactions of transcription factorsPlasma cell differentiationInteraction of transcription factorsCore gene regulatory networkGene regulatory networksCell differentiationCell-fate decisionsTemporal switchB cell receptor affinityGerminal center reactionB cellsCD40 signaling pathwayRegulatory networksRegulatory interactionsTranscription factorsEffective immune protectionCenter reactionSignaling pathwayAdaptive immune systemT follicular helper cellsPlasma cell generationMemory B cellsMolecular modules
2020
Convergent network effects along the axis of gene expression during prostate cancer progression
Charmpi K, Guo T, Zhong Q, Wagner U, Sun R, Toussaint N, Fritz C, Yuan C, Chen H, Rupp N, Christiansen A, Rutishauser D, Rüschoff J, Fankhauser C, Saba K, Poyet C, Hermanns T, Oehl K, Moore A, Beisel C, Calzone L, Martignetti L, Zhang Q, Zhu Y, Martínez M, Manica M, Haffner M, Aebersold R, Wild P, Beyer A. Convergent network effects along the axis of gene expression during prostate cancer progression. Genome Biology 2020, 21: 302. PMID: 33317623, PMCID: PMC7737297, DOI: 10.1186/s13059-020-02188-9.Peer-Reviewed Original ResearchConceptsHigh-throughput genomic measurementsProstate cancer progressionGene expressionMolecular networksCopy number alterationsCancer progressionComplex genomic alterationsTumor phenotypePrediction of recurrence-free survivalGenomic measurementsRecurrence-free survivalProstate cancer patientsProteomic alterationsGenomic aberrationsAggressive tumor phenotypeGenomic alterationsDownstream proteinsGenomic effectsNetwork-based approachProstate samplesTumor siteBiochemical stateMalignant tumorsProtein levelsTumor tissuesFPGA Accelerated Analysis of Boolean Gene Regulatory Networks
Manica M, Polig R, Purandare M, Mathis R, Hagleitner C, Martínez M. FPGA Accelerated Analysis of Boolean Gene Regulatory Networks. IEEE/ACM Transactions On Computational Biology And Bioinformatics 2020, 17: 2141-2147. PMID: 31494553, DOI: 10.1109/tcbb.2019.2936836.Peer-Reviewed Original ResearchConceptsQualitative models of gene regulatory networksModels of gene regulatory networksAdvanced high-throughput technologiesGene regulatory networksHigh-throughput technologiesComplex molecular networkBoolean modelRegulatory networksBiological insightsT-cell large granular lymphocytic leukemiaMolecular networksAttractor detectionField Programmable Gate ArrayLarge granular lymphocytic leukemiaSoftware simulation toolGranular lymphocytic leukemiaSimulation toolPerformance improvementReconfigurable integrated circuitsInferring clonal composition from multiple tumor biopsies
Manica M, Kim H, Mathis R, Chouvarine P, Rutishauser D, De Vargas Roditi L, Szalai B, Wagner U, Oehl K, Saba K, Pati A, Saez-Rodriguez J, Roy A, Parsons D, Wild P, Martínez M, Sumazin P. Inferring clonal composition from multiple tumor biopsies. Npj Systems Biology And Applications 2020, 6: 27. PMID: 32843649, PMCID: PMC7447821, DOI: 10.1038/s41540-020-00147-5.Peer-Reviewed Original ResearchConceptsCopy number alterationsEffects of copy number alterationsGenetic instabilityClonal evolutionClonal compositionDrug-resistant prostate cancerAcquisition of mutationsDrug-resistant subclonesPhylogeny reconstructionTumor phylogeniesRelative abundanceTumor biopsiesWilms tumorProstate cancerGenetic alterationsGenetic profileMultiple biopsiesTumor profilingHepatocellular carcinomaPhylogenyTumorSubclonesAnalysis of simulated dataMutationsBiopsyComputational Model Reveals a Stochastic Mechanism behind Germinal Center Clonal Bursts
Pélissier A, Akrout Y, Jahn K, Kuipers J, Klein U, Beerenwinkel N, Martínez M. Computational Model Reveals a Stochastic Mechanism behind Germinal Center Clonal Bursts. Cells 2020, 9: 1448. PMID: 32532145, PMCID: PMC7349200, DOI: 10.3390/cells9061448.Peer-Reviewed Original ResearchConceptsClonal diversityB cell receptorSequence of nucleotidesEvolutionary phylogenetic treesGC dynamicsIn silico vaccine designPhylogenetic treeClonal dominanceAffinity to antigenB cell differentiationSpecialized compartmentsAntibody genesGenetic eventsInterclonal competitionClonal burstEvolutionary processB cell maturationIntracellular componentsB-cell variantsClonal dynamicsSomatic hypermutationB cellsIncreased affinityAffinity maturationGerminal centersPan-cancer analysis of somatic mutations and epigenetic alterations in insulated neighbourhood boundaries
Pinoli P, Stamoulakatou E, Nguyen A, Martínez M, Ceri S. Pan-cancer analysis of somatic mutations and epigenetic alterations in insulated neighbourhood boundaries. PLOS ONE 2020, 15: e0227180. PMID: 31945090, PMCID: PMC6964824, DOI: 10.1371/journal.pone.0227180.Peer-Reviewed Original ResearchMeSH KeywordsAmino Acid MotifsBinding SitesCCCTC-Binding FactorChromosomes, Human, Pair 11DNA Copy Number VariationsDNA MethylationDNA Mutational AnalysisEpigenesis, GeneticExonsFemaleGene Expression Regulation, NeoplasticGenome, HumanHumansInsulator ElementsMutation RateNeoplasmsPoint MutationPromoter Regions, GeneticConceptsCTCF motifsCopy number alterationsSomatic mutationsAbnormal methylationCTCF binding sitesCopy number alteration eventsAnalysis of somatic mutationsMatched normal samplesCancer typesCTCF bindingOncogene dysregulationMutation enrichmentPan-cancer analysisPositive selectionEpigenetic alterationsIn-boundaryGenomic alterationsMotifMutational signaturesBinding sitesMutationsCTCFPan-cancerCopyNormal samples
2019
Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer
Oskooei A, Manica M, Mathis R, Martínez M. Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer. Scientific Reports 2019, 9: 15918. PMID: 31685861, PMCID: PMC6828742, DOI: 10.1038/s41598-019-52093-w.Peer-Reviewed Original ResearchConceptsMembrane receptor pathwayDrug sensitivity predictionProtein-protein interaction networkDrug sensitivityGenomics of Drug SensitivityDrug targetsGene expression dataIGFR signaling pathwaysAssignment of high weightsBiomarker identificationExpression dataInteraction networkSensitivity predictionSignaling pathwaySignaling pathway inhibitorsReceptor pathwayTree ensemblesPathway inhibitorPathwayGenomeGenesGDSCNeighborhoods of influenceIdentificationSynthetic datasetsToward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders
Manica M, Oskooei A, Born J, Subramanian V, Sáez-Rodríguez J, Martínez M. Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders. Molecular Pharmaceutics 2019, 16: 4797-4806. PMID: 31618586, DOI: 10.1021/acs.molpharmaceut.9b00520.Peer-Reviewed Original ResearchConceptsConvolutional encoderReceptor tyrosine kinasesProtein-protein interaction networkAttention-based encoderStructural similarity indexSelection of encodingDrug designDrug sensitivity predictionGene expression profilesIn silico predictionSensitivity predictionAttention weightsLeukemia cell linesSMILES sequencesInformative genesGene expression profiles of tumorsApoptotic processInteraction networkExpression profiles of tumorsBaseline modelIntracellular interactionsEncodingTyrosine kinaseDevelopment of personalized therapiesGenesA Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer
Wagner J, Rapsomaniki M, Chevrier S, Anzeneder T, Langwieder C, Dykgers A, Rees M, Ramaswamy A, Muenst S, Soysal S, Jacobs A, Windhager J, Silina K, van den Broek M, Dedes K, Martínez M, Weber W, Bodenmiller B. A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer. Cell 2019, 177: 1330-1345.e18. PMID: 30982598, PMCID: PMC6526772, DOI: 10.1016/j.cell.2019.03.005.Peer-Reviewed Original ResearchConceptsBreast cancerBreast cancer ecosystemsTumor-associated macrophagesResponse to therapyHuman breast tumorsTumor cell compositionHuman breast cancerNon-tumor tissue samplesPrecision medicine approachT cellsBreast tumorsImmune cellsPoor prognosisAntibody panelTumor cellsTumor ecosystemHeterogeneous diseaseClinical dataDisease progressionTumorSingle-cell atlasMass cytometryPhenotypic abnormalitiesMedicine approachImmune ecosystemA Probabilistic Model of the Germinal Center Reaction
Thomas M, Klein U, Lygeros J, Martínez M. A Probabilistic Model of the Germinal Center Reaction. Frontiers In Immunology 2019, 10: 689. PMID: 31001283, PMCID: PMC6456718, DOI: 10.3389/fimmu.2019.00689.Peer-Reviewed Original ResearchConceptsB cell differentiationMemory B cell differentiationSpecialized compartmentsAntibody genesPlasma cell differentiationFate selectionB cellsIndividual cell propertiesCell differentiationB cell maturationMolecular eventsEfficient stochastic simulationExtracellular dynamicsQuantitative stochastic modelMemory B cell formationAntigen affinityB cell formationGillespie algorithmDifferentiationCell propertiesCell productionMemory B cells
2018
The number of titrated microRNA species dictates ceRNA regulation
Chiu H, Martínez M, Komissarova E, Llobet-Navas D, Bansal M, Paull E, Silva J, Yang X, Sumazin P, Califano A. The number of titrated microRNA species dictates ceRNA regulation. Nucleic Acids Research 2018, 46: gky286-. PMID: 29684207, PMCID: PMC5961349, DOI: 10.1093/nar/gky286.Peer-Reviewed Original ResearchConceptsCo-regulationPrediction of gene expressionMiRNA speciesTumor suppressor PTENCeRNA regulationGene expressionCeRNA interactionsMicroRNA speciesBiochemical assaysMultiple miRNAsSpeciesOncogene CCND1Non-tumour contextsRegulating microRNAsMiRNAsInteraction kineticsEndogenous RNAIndependent tumorsCeRNA networkHIF1AInteractomeCeRNAGenesRNAOncogeneCellCycleTRACER accounts for cell cycle and volume in mass cytometry data
Rapsomaniki M, Lun X, Woerner S, Laumanns M, Bodenmiller B, Martínez M. CellCycleTRACER accounts for cell cycle and volume in mass cytometry data. Nature Communications 2018, 9: 632. PMID: 29434325, PMCID: PMC5809393, DOI: 10.1038/s41467-018-03005-5.Peer-Reviewed Original Research
2017
High-throughput validation of ceRNA regulatory networks
Chiu H, Martínez M, Bansal M, Subramanian A, Golub T, Yang X, Sumazin P, Califano A. High-throughput validation of ceRNA regulatory networks. BMC Genomics 2017, 18: 418. PMID: 28558729, PMCID: PMC5450082, DOI: 10.1186/s12864-017-3790-7.Peer-Reviewed Original ResearchConceptsCeRNA interactionsMiRNA targetsCancer genesCompetitive endogenous RNAHigh-throughput validationIndirect regulatory influencesCellular contextMiRNA activityTumor contextMCF7 cellsTarget abundanceGenesPhysiological relevanceExperimental assaysCompetitive endogenous RNA networkAbundanceConclusionsOur resultsPredicted targetsTCGA profileMiRNAsRegulatory influenceEndogenous RNAResultsToRNABackgroundMicroRNAs
2016
Depletion of FOXM1 via MET Targeting Underlies Establishment of a DNA Damage–Induced Senescence Program in Gastric Cancer
Francica P, Nisa L, Aebersold D, Langer R, Bladt F, Blaukat A, Stroka D, Martínez M, Zimmer Y, Medová M. Depletion of FOXM1 via MET Targeting Underlies Establishment of a DNA Damage–Induced Senescence Program in Gastric Cancer. Clinical Cancer Research 2016, 22: 5322-5336. PMID: 27185371, DOI: 10.1158/1078-0432.ccr-15-2987.Peer-Reviewed Original ResearchConceptsDNA damage-induced senescenceMET inhibitionMET targetingGastric tumorsNegative regulator of senescenceHuman gastric cancer cell linesGastric cancer cell linesInfliction of DNA damageAberrant MET expressionEctopic expression of FoxM1Tumor tissue microarraysCopy number alterationsRegulation of senescenceMET receptor tyrosine kinaseDepletion of FOXM1Receptor tyrosine kinasesGastric tumor cellsExpression of FoxM1Cancer cell linesCancer Genome AtlasDownregulation of FoxM1Deregulated signalingDownstream effectorsNegative regulatorTumor radiosensitivity