Jonathan Warrell
Associate Research ScientistCards
About
Research
Publications
2026
Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo
Fernandez Garcia M, Retallick-Townsley K, Pruitt A, Davidson E, Balafkan N, Warrell J, Huang T, Kibowen A, Chu Z, Dai Y, Fitzpatrick S, Meng R, Sen A, Cohen S, Livoti O, Khan S, Becker C, Luiz Teles e Silva A, Liu J, Dossou G, Cheung J, Liu S, Ghorbani S, Deans P, DeCiucis M, Emani P, Gao H, Shen H, Gerstein M, Wang Z, Huckins L, Hoffman E, Brennand K. Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo. Nature Neuroscience 2026, 29: 1079-1094. PMID: 42032432, PMCID: PMC13156037, DOI: 10.1038/s41593-026-02247-7.Peer-Reviewed Original ResearchConceptsPhenotypic convergenceRisk genesNeurodevelopmental disorder risk genesGlutamatergic neuronsNeurodevelopmental disorder genesLoss-of-function genesMature glutamatergic neuronsNeurodevelopmental disordersCo-expression patternsGenes in vitroChromatin biologyMitochondrial pathwayConvergent genesGene mutantsBiological annotationsNeural progenitor cellsCRISPR approachesSensory processing behaviorsPoint of convergenceGABAergic neuronsBiological pathwaysGene expressionGenesClinical associationsCo-expressionInterpretability and implicit model semantics in biomedicine and deep learning
Warrell J, Gancz M, Mohsen H, Emani P, Gerstein M. Interpretability and implicit model semantics in biomedicine and deep learning. Nature Machine Intelligence 2026, 8: 296-299. DOI: 10.1038/s42256-026-01177-0.Peer-Reviewed Original ResearchMetabolic characterization of tumor-immune interactions by multiplexed immunofluorescence reveals spatial mechanisms of immunotherapy response in non-small cell lung carcinoma (NSCLC)
Monkman J, Kilgallon A, Lawler C, Tubelleza R, Aung T, Warrell J, Vathiotis I, Trontzas I, Gavrielatou N, Nyein Chan N, Czertok R, Bookstein S, O’Byrne K, Markovits E, Rimm D, Kulasinghe A. Metabolic characterization of tumor-immune interactions by multiplexed immunofluorescence reveals spatial mechanisms of immunotherapy response in non-small cell lung carcinoma (NSCLC). Nature Communications 2026, 17: 837. PMID: 41634004, PMCID: PMC12868679, DOI: 10.1038/s41467-026-68633-8.Peer-Reviewed Original ResearchConceptsNon-small cell lung carcinomaImmune checkpoint inhibitorsProgression-free survivalTumor microenvironmentMultiplex immunofluorescenceAdvanced non-small cell lung carcinomaResponse to ICICell lung carcinomaMultivariate modelMetabolic characterizationTumor-immune interactionsCell-cell proximityCheckpoint inhibitorsICI treatmentICI responseImmunotherapy outcomesImmunotherapy responseLung carcinomaClinical outcomesImmune cellsPatientsMetabolic stateCellular phenotypesImmunofluorescenceTissue neighborhoods
2025
Spatial signatures for predicting immunotherapy outcomes using multi-omics in non-small cell lung cancer
Aung T, Monkman J, Warrell J, Vathiotis I, Bates K, Gavrielatou N, Trontzas I, Tan C, Fernandez A, Moutafi M, O’ Byrne K, Schalper K, Syrigos K, Herbst R, Kulasinghe A, Rimm D. Spatial signatures for predicting immunotherapy outcomes using multi-omics in non-small cell lung cancer. Nature Genetics 2025, 57: 2482-2493. PMID: 41073787, PMCID: PMC12513832, DOI: 10.1038/s41588-025-02351-7.Peer-Reviewed Original ResearchConceptsNon-small cell lung cancerTumor immune microenvironmentCell lung cancerLung cancerPredictive of poor outcomeResponse to immunotherapyCD4 T cellsProliferating tumor cellsResponse signatureImmunotherapy outcomesPrecision immunotherapyImmune microenvironmentT cellsPatient selectionNon-smallFavorable outcomeTumor cellsPoor outcomeImmunotherapyMulti-omics approachM1/M2 macrophagesBiomarkersMulti-OmicsCancerOutcomesPathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma
Aung T, Liu M, Su D, Shafi S, Boyaci C, Steen S, Tsiknakis N, Vidal J, Maher N, Micevic G, Tan S, Vesely M, Nourmohammadi S, Bai Y, Djureinovic D, Wong P, Bates K, Chan N, Gavirelatou N, He M, Burela S, Barna R, Bosic M, Bräutigam K, Illabochaca I, Chenhao Z, Gama J, Kreis B, Mohacsi R, Pillar N, Pinto J, Poulios C, Toli M, Tzoras E, Bracero Y, Bosisio F, Cserni G, Dema A, Fortarezza F, Gonzalez M, Gullo I, Gutiérrez F, Hacihasanoglu E, Jovic V, Lazar B, Olinca M, Neppl C, Oliveira R, Pezzuto F, Pinto D, Plotar V, Pop O, Rau T, Skok K, Sun W, Serbes E, Solass W, Stanowska O, Szasz M, Szymonski K, Thimm F, Vignati D, Vigdorovits A, Prieto V, Sinnberg T, Wilmott J, Cowper S, Warrell J, Saenger Y, Hartman J, Plummer J, Osman I, Rimm D, Acs B. Pathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma. JAMA Network Open 2025, 8: e2518906. PMID: 40608341, PMCID: PMC12232186, DOI: 10.1001/jamanetworkopen.2025.18906.Peer-Reviewed Original ResearchConceptsTumor-infiltrating lymphocytesIntraclass correlation coefficientHazard ratioPrognostic studyTIL scorePrognostic valuePrognostic associationRetrospective cohortTumor-infiltrating lymphocyte quantificationAssessment of tumor-infiltrating lymphocytesRetrospective cohort of patientsTumour-infiltrating lymphocyte assessmentMultivariate Cox regression analysisEosin-stained slidesCohort of patientsWhole tissue sectionsCox regression analysisTissue sectionsMelanoma tissue sectionsImmunotherapy outcomesMelanoma managementClinicopathological variablesRetrospective natureTest cohortInterobserver variabilityQuantum variational autoencoder utilizing regularized mixed-state latent representations
Wang G, Warrell J, Emani P, Gerstein M. Quantum variational autoencoder utilizing regularized mixed-state latent representations. Physical Review A 2025, 111: 042416. DOI: 10.1103/physreva.111.042416.Peer-Reviewed Original ResearchQuantum modelQuantum dataVariational autoencoderLatent representationQuantum computationNear-term quantum computersQuantum hardware resourcesOptimal latent representationEfficient data compressionLow-dimensional representationQuantum componentsDensity matrixLower-dimensional spaceQuantum frameworkMixed stateFederated LearningRepresentation learningLearned representationsLatent spaceData compressionHardware resourcesMachine learningSynthetic dataData generationAutoencoder
2024
1230 Design of enhanced TCR against cancer antigens using an AI system
Min M, Onoguchi K, Li T, Mori D, Warrell J, Machart P, Moesch A, Meiser A, Pait I, Okamura A, Muraoka D, Matsushita H, Bendjama K. 1230 Design of enhanced TCR against cancer antigens using an AI system. 2024, a1371-a1371. DOI: 10.1136/jitc-2024-sitc2024.1230.Peer-Reviewed Original ResearchA variational graph-partitioning approach to modeling protein liquid-liquid phase separation
Wang G, Warrell J, Zheng S, Gerstein M. A variational graph-partitioning approach to modeling protein liquid-liquid phase separation. Cell Reports Physical Science 2024, 5: 102292. PMID: 39866853, PMCID: PMC11760192, DOI: 10.1016/j.xcrp.2024.102292.Peer-Reviewed Original ResearchPredicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs
Song T, Cosatto E, Wang G, Kuang R, Gerstein M, Min M, Warrell J. Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs. Bioinformatics 2024, 40: ii111-ii119. PMID: 39230702, PMCID: PMC11373608, DOI: 10.1093/bioinformatics/btae383.Peer-Reviewed Original ResearchConceptsGene expressionSpatial gene expressionSpatial transcriptomics technologiesTissue histology imagesExpressed genesGene activationTranscriptomic technologiesMolecular underpinningsGraph neural networksState-of-the-artSpatial expressionGenesTissue architectureExpressionHistological imagesNeural networkSpatially informed gene signatures for response to immunotherapy in melanoma
Aung T, Warrell J, Martinez-Morilla S, Gavrielatou N, Vathiotis I, Yaghoobi V, Kluger H, Gerstein M, Rimm D. Spatially informed gene signatures for response to immunotherapy in melanoma. Clinical Cancer Research 2024, 30: 3520-3532. PMID: 38837895, PMCID: PMC11326985, DOI: 10.1158/1078-0432.ccr-23-3932.Peer-Reviewed Original ResearchGene signatureResistance to immunotherapyResponse to immunotherapyPrediction of treatment outcomeResistant to treatmentAccurate prediction of treatment outcomePredictive of responseImmunotherapy outcomesMelanoma patientsMelanoma specimensValidation cohortPatient stratificationDiscovery cohortTreatment outcomesImmunotherapyMelanomaTumorPatientsCohortS100BOutcomesGene expression dataGenesCD68+macrophagesExpression data