2025
Current cutting-edge omics techniques on musculoskeletal tissues and diseases
Li X, Fang L, Zhou R, Yao L, Clayton S, Muscat S, Kamm D, Wang C, Liu C, Qin L, Tower R, Karner C, Guilak F, Tang S, Loiselle A, Meyer G, Shen J. Current cutting-edge omics techniques on musculoskeletal tissues and diseases. Bone Research 2025, 13: 59. PMID: 40484858, PMCID: PMC12146411, DOI: 10.1038/s41413-025-00442-z.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsGenomicsHumansMetabolomicsMusculoskeletal DiseasesMusculoskeletal SystemProteomicsConceptsDisease-associated alterationsImpact quality of lifeIntervertebral disc degenerationMusculoskeletal tissuesMolecular landscapeDisc degenerationQuality of lifeBone fracturesRheumatoid arthritisBulk transcriptomesTherapeutic targetTissue microenvironmentClinical applicationPathophysiological processesDisease mechanismsEconomic burdenMusculoskeletal disordersImpact qualityOmics technologiesMulti-omics integrationDiseaseSingle-cellTissueSpatial organization of cellsCellular heterogeneityPrognostic and Predictive Insights From Genomic Assays for Breast Cancer in Diverse Populations
Abdou Y, Kantor O, Racz J, Newman L, Pierce L, Winer E. Prognostic and Predictive Insights From Genomic Assays for Breast Cancer in Diverse Populations. JAMA Oncology 2025, 11: 655-663. PMID: 40178824, DOI: 10.1001/jamaoncol.2025.0178.Peer-Reviewed Original ResearchConceptsEthnic disparitiesBreast cancer mortality ratesBreast cancerGenomic assaysCancer mortality ratesBiological characteristics of tumorsPredictive valueDiverse populationsGenomic assay resultsBreast cancer outcomesPersonalized clinical decision-makingBasal-like subtypeAggressive tumor phenotypeBreast cancer recurrenceCharacteristics of tumorsRandomized clinical trialsEthnic groupsSocial determinantsClinical decision-makingMultigene assaysLuminal BCancer outcomesDistribution of risk estimatesSurvival outcomesMolecular subtypesscMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links
Wang G, Zhao J, Lin Y, Liu T, Zhao Y, Zhao H. scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links. Nature Communications 2025, 16: 4994. PMID: 40442129, PMCID: PMC12122792, DOI: 10.1038/s41467-025-60333-z.Peer-Reviewed Original ResearchConceptsDeep learning frameworkSingle-cell multi-omics researchSingle-cell multi-omics dataLearning frameworkMulti-omics dataGenerative adversarial networkSingle-cell technologiesData alignmentSingle-cell resolutionMulti-omics researchDownstream analysisCellular statesOmics datasetsAdversarial networkNeural networkProteomic profilingCorrelated featuresBiological informationOmics perspectiveDiverse datasetsFeature topologyDisease mechanismsCell embeddingData resourcesRelationship inferenceDynamic clustering of genomics cohorts beyond race, ethnicity—and ancestry
Mohsen H, Blenman K, Emani P, Morris Q, Carrot-Zhang J, Pusztai L. Dynamic clustering of genomics cohorts beyond race, ethnicity—and ancestry. BMC Medical Genomics 2025, 18: 87. PMID: 40375077, PMCID: PMC12082885, DOI: 10.1186/s12920-025-02154-z.Peer-Reviewed Original ResearchConceptsGenomic variationGenomic cohortsStudy of human genomic variationWhole exome sequencing datasetsTrait-specific lociHuman genomic variationCancer-relevant genesGenomic patternsGenomic signalsGenomic studiesSequencing datasetsCancer typesGermline variantsDisease predispositionBiological processesFunctional analysisGeographic scalesPhenotypic continuumClustering patternsPotential driversDiverse data collectionsRace categoriesLociGenesComplete portraitFactors Affecting Genomic Testing in Prostate Cancer: Results From the Decision-Making, Experience, and Confidence In Determining Genomic Evaluation (DECIDE) Survey
Bitting R, McNair C, Wyatt A, Vandekerkhove G, Choi T, Leader A, Blanding-Godbolt J, Gross L, Hamade K, Halabi S, Giri V. Factors Affecting Genomic Testing in Prostate Cancer: Results From the Decision-Making, Experience, and Confidence In Determining Genomic Evaluation (DECIDE) Survey. JCO Precision Oncology 2025, 9: e2400821. PMID: 40373262, DOI: 10.1200/po-24-00821.Peer-Reviewed Original ResearchConceptsGenomic testingTraining of health care professionalsHereditary cancer assessmentSomatic genomic testingHealth care providersHealth care professionalsAcademic medical centerLack of educationSelf-confidenceProvider confidenceCare providersCare professionalsCommon barriersSurvey domainsProstate cancerPractical barriersMultiple-response questionsTest utilizationMedical oncologistsMedical CenterEnhanced implementationDecision-making factorsProvidersDecision-makingPercentage of responsesDeciphering the longitudinal trajectories of glioblastoma ecosystems by integrative single-cell genomics
Spitzer A, Johnson K, Nomura M, Garofano L, Nehar-belaid D, Darnell N, Greenwald A, Bussema L, Oh Y, Varn F, D’Angelo F, Gritsch S, Anderson K, Migliozzi S, Gonzalez Castro L, Chowdhury T, Robine N, Reeves C, Park J, Lipsa A, Hertel F, Golebiewska A, Niclou S, Nusrat L, Kellet S, Das S, Moon H, Paek S, Bielle F, Laurenge A, Di Stefano A, Mathon B, Picca A, Sanson M, Tanaka S, Saito N, Ashley D, Keir S, Ligon K, Huse J, Yung W, Lasorella A, Iavarone A, Verhaak R, Tirosh I, Suvà M. Deciphering the longitudinal trajectories of glioblastoma ecosystems by integrative single-cell genomics. Nature Genetics 2025, 57: 1168-1178. PMID: 40346362, PMCID: PMC12081298, DOI: 10.1038/s41588-025-02168-4.Peer-Reviewed Original ResearchConceptsSingle-cell genomicsSingle-nucleus RNA sequencingMalignant cell statesDNA sequencesNeuronal cell typesTumor microenvironmentRNA sequencingMalignant cell fractionCell statesCell typesCell fractionIsocitrate dehydrogenase (IDH)-wildtype glioblastomaStandard-of-care therapyCo-evolutionMolecular heterogeneityTumor microenvironment modifierEcosystemTumor microenvironment compositionSubsets of patientsSequenceRecurrent glioblastomaGenomic landscape and homologous recombination repair deficiency signature in stage I-III and de novo stage IV primary breast cancers
Jeon J, Chen K, Madison R, Schrock A, Sokol E, Levy M, Rozenblit M, Huang R, Pusztai L. Genomic landscape and homologous recombination repair deficiency signature in stage I-III and de novo stage IV primary breast cancers. The Oncologist 2025, 30: oyaf089. PMID: 40421962, PMCID: PMC12107548, DOI: 10.1093/oncolo/oyaf089.Peer-Reviewed Original ResearchConceptsHomologous recombination deficiencyDe novo stage IV breast cancerStage IV breast cancerPrimary breast cancerIV breast cancerStage I-III cancerStage I-IIIBreast cancerGenomic alterationsFrequency of genomic alterationsHER2+ cancersI-IIITargetable genomic alterationsGenomic landscapePlatinum-based treatmentWild-type cancersHR repairCancer-related genesER+/HER2- cancersLate relapsePrimary tumorFoundation MedicinePIK3CA mutationsMutation statusNo significant differenceDecoding human brain evolution: Insights from genomics
Liu Y, Li M, Segal A, Zhang M, Sestan N. Decoding human brain evolution: Insights from genomics. Current Opinion In Neurobiology 2025, 92: 103033. PMID: 40334295, DOI: 10.1016/j.conb.2025.103033.Peer-Reviewed Original ResearchConceptsHuman brain evolutionNonhuman primatesBrain evolutionHigh-throughput functional screeningSingle-cell resolutionAdvanced cognitive abilitiesGenetic basisCognitive abilitiesFunctional screeningGenetic changesGenetic underpinningsBrain featuresLiving relativesHuman-specific featuresComprehensive atlasGenomic profilingHuman brainFunctional specializationMolecular levelTissue-based gene expression testing in localized prostate cancer
Sivanesan N, Diaz G, Sprenkle P. Tissue-based gene expression testing in localized prostate cancer. Current Opinion In Urology 2025, 35: 432-438. PMID: 40314067, DOI: 10.1097/mou.0000000000001289.Peer-Reviewed Original ResearchConceptsLocalized prostate cancerTissue-based genomic testingProstate cancerRisk stratificationGenomic testingManagement of localized prostate cancerProstate cancer risk stratificationIdentified high-risk patientsClinically significant cancerProstate cancer outcomesImpact of genomic testingHigh-risk patientsAssess tumor aggressivenessLow-risk casesGenomic assaysGene expression testsTreatment decision-makingPSA levelsGleason scoreSignificant cancerOncotype DXTumor aggressivenessTherapeutic choiceReduce overtreatmentProspective studyEarly Release - Large-Scale Genomic Analysis of SARS-CoV-2 Omicron BA.5 Emergence, United States - Volume 31, Supplement—May 2025 - Emerging Infectious Diseases journal - CDC
Pham K, Chaguza C, Lopes R, Cohen T, Taylor-Salmon E, Wilkinson M, Katebi V, Grubaugh N, Hill V. Early Release - Large-Scale Genomic Analysis of SARS-CoV-2 Omicron BA.5 Emergence, United States - Volume 31, Supplement—May 2025 - Emerging Infectious Diseases journal - CDC. Emerging Infectious Diseases 2025, 31: s45-s56. PMID: 40359081, PMCID: PMC12078544, DOI: 10.3201/eid3113.240981.Peer-Reviewed Original ResearchAdvancing translational exposomics: bridging genome, exposome and personalized medicine
Sarigiannis D, Karakitsios S, Anesti O, Stem A, Valvi D, Sumner S, Chatzi L, Snyder M, Thompson D, Vasiliou V. Advancing translational exposomics: bridging genome, exposome and personalized medicine. Human Genomics 2025, 19: 48. PMID: 40307849, PMCID: PMC12044731, DOI: 10.1186/s40246-025-00761-6.Peer-Reviewed Original ResearchConceptsExposome-wide association studyBridge genomicsLifestyle exposuresEnhancing causal inferencePublic health decision-makingEnvironmental health researchHealth decision-makingMulti-omics technologiesGenomic variationGenomic dataAssociation studiesHealth outcomesBioinformatics approachHealth researchPrecision preventionGenetic variabilityExposome dataExposure-response relationshipMulti-omicsGenomeInternal exposomeVulnerable populationsComplex diseasesDisease phenotypePublic healthA genetically informed brain atlas for enhancing brain imaging genomics
Bao J, Wen J, Chang C, Mu S, Chen J, Shivakumar M, Cui Y, Erus G, Yang Z, Yang S, Wen Z, Zhao Y, Kim D, Duong-Tran D, Saykin A, Zhao B, Davatzikos C, Long Q, Shen L. A genetically informed brain atlas for enhancing brain imaging genomics. Nature Communications 2025, 16: 3524. PMID: 40229250, PMCID: PMC11997130, DOI: 10.1038/s41467-025-57636-6.Peer-Reviewed Original ResearchConceptsBrain imaging genomicsImaging genomicsComplex traits/diseasesSNP heritabilityFunctional annotationGenetic architecturePolygenic risk scoresGenomic investigationsGenetic ancestryDiscovery powerBrain atlasesHuman brain structureGenetic determinantsNeuroanatomical heterogeneityGenomeNeuroanatomical variationImaging endophenotypesBrain structuresMolecular levelBrain voxelsHeritabilityPhenotypic correlationsGiant regionGeneticsBrain conditionsPrecision medicine in the pediatric and neonatal intensive care units through genomics
Duy P, Dylik B, Deniz E. Precision medicine in the pediatric and neonatal intensive care units through genomics. Current Opinion In Pediatrics 2025, 37: 211-215. PMID: 40298123, PMCID: PMC12055474, DOI: 10.1097/mop.0000000000001471.Peer-Reviewed Original ResearchConceptsNeonatal intensive care unitGenome-wide sequencing technologiesSingle nucleotide resolutionWhole-genome sequencingIntensive care unitGene therapyPrecision medicineNucleotide resolutionSequencing technologiesGenetic perturbationsGenomic medicineGenomic technologiesCare unitFDA-approved gene therapyGenetic associationGenetic diagnosisHuman disordersCritically ill childrenOmics technologiesMolecular diagnosisGenetic conditionsDisease biologyClinically actionable findingsPathological workupDiagnostic adjunctComparative Genomic Characterization of Small Cell Carcinoma of the Bladder Compared With Urothelial Carcinoma and Small Cell Lung Carcinoma
Jaime-Casas S, Chawla N, Salgia N, Mercier B, Govindarajan A, Li X, Castro D, Ebrahimi H, Barragan-Carrillo R, Zang P, LeVee A, Zugman M, Dizman N, Hsu J, Meza L, Zengin Z, Chehrazi-Raffle A, Dorff T, Pal S, Tripathi A. Comparative Genomic Characterization of Small Cell Carcinoma of the Bladder Compared With Urothelial Carcinoma and Small Cell Lung Carcinoma. JCO Precision Oncology 2025, 9: e2400947. PMID: 40209138, DOI: 10.1200/po-24-00947.Peer-Reviewed Original ResearchConceptsSmall cell bladder cancerSmall cell lung cancerUrothelial carcinomaGenomic alterationsBladder cancerSmall cell lung cancer patientsSmall cell lung carcinomaSmall cell carcinomaRare histologic variantCell lung carcinomaAggressive disease courseCell lung cancerPathogenic genomic alterationsImproving clinical outcomesTreated with approachesHistological variantsCell carcinomaHistological groupsLung carcinomaClinical outcomesPoor outcomeRetrospective natureDisease courseLung cancerGenomic profilingComprehensive genomic and evolutionary analysis of biofilm matrix clusters and proteins in the Vibrio genus
Yang Y, Yan J, Olson R, Jiang X. Comprehensive genomic and evolutionary analysis of biofilm matrix clusters and proteins in the Vibrio genus. MSystems 2025, 10: e00060-25. PMID: 40207939, PMCID: PMC12090793, DOI: 10.1128/msystems.00060-25.Peer-Reviewed Original ResearchConceptsGene clusterVibrio speciesEvolutionary analysisBiofilm dispersalBiofilm matrix proteinsVibrio cholerae</i>Genetic modificationTail proteinsVibrio genusCholera pathogenIdentified genesGene groupsVibrio cholerae</i>.Host functionsEvolutionary patternsBiofilm diversityBiofilm formationBiofilm proteinsBiofilm matrixVibrioBiofilm developmentEngineered biofilmsGenusStructural domainsAcute diarrheal diseaseTranslational genomics of osteoarthritis in 1,962,069 individuals
Hatzikotoulas K, Southam L, Stefansdottir L, Boer C, McDonald M, Pett J, Park Y, Tuerlings M, Mulders R, Barysenka A, Arruda A, Tragante V, Rocco A, Bittner N, Chen S, Horn S, Srinivasasainagendra V, To K, Katsoula G, Kreitmaier P, Tenghe A, Gilly A, Arbeeva L, Chen L, de Pins A, Dochtermann D, Henkel C, Höijer J, Ito S, Lind P, Lukusa-Sawalena B, Minn A, Mola-Caminal M, Narita A, Nguyen C, Reimann E, Silberstein M, Skogholt A, Tiwari H, Yau M, Yue M, Zhao W, Zhou J, Alexiadis G, Banasik K, Brunak S, Campbell A, Cheung J, Dowsett J, Faquih T, Faul J, Fei L, Fenstad A, Funayama T, Gabrielsen M, Gocho C, Gromov K, Hansen T, Hudjashov G, Ingvarsson T, Johnson J, Jonsson H, Kakehi S, Karjalainen J, Kasbohm E, Lemmelä S, Lin K, Liu X, Loef M, Mangino M, McCartney D, Millwood I, Richman J, Roberts M, Ryan K, Samartzis D, Shivakumar M, Skou S, Sugimoto S, Suzuki K, Takuwa H, Teder-Laving M, Thomas L, Tomizuka K, Turman C, Weiss S, Wu T, Zengini E, Zhang Y, Ferreira M, Babis G, Baras A, Barker T, Carey D, Cheah K, Chen Z, Cheung J, Daly M, de Mutsert R, Eaton C, Erikstrup C, Furnes O, Golightly Y, Gudbjartsson D, Hailer N, Hayward C, Hochberg M, Homuth G, Huckins L, Hveem K, Ikegawa S, Ishijima M, Isomura M, Jones M, Kang J, Kardia S, Kloppenburg M, Kraft P, Kumahashi N, Kuwata S, Lee M, Lee P, Lerner R, Li L, Lietman S, Lotta L, Lupton M, Mägi R, Martin N, McAlindon T, Medland S, Michaëlsson K, Mitchell B, Mook-Kanamori D, Morris A, Nabika T, Nagami F, Nelson A, Ostrowski S, Palotie A, Pedersen O, Rosendaal F, Sakurai-Yageta M, Schmidt C, Sham P, Singh J, Smelser D, Smith J, Song Y, Sørensen E, Tamiya G, Tamura Y, Terao C, Thorleifsson G, Troelsen A, Tsezou A, Uchio Y, Uitterlinden A, Ullum H, Valdes A, van Heel D, Walters R, Weir D, Wilkinson J, Winsvold B, Yamamoto M, Zwart J, Stefansson K, Meulenbelt I, Teichmann S, van Meurs J, Styrkarsdottir U, Zeggini E. Translational genomics of osteoarthritis in 1,962,069 individuals. Nature 2025, 641: 1217-1224. PMID: 40205036, PMCID: PMC12119359, DOI: 10.1038/s41586-025-08771-z.Peer-Reviewed Original ResearchConceptsEffector genesGenome-wide association study meta-analysesTargets of approved drugsVariant associationsTranslational genomicsEpigenomic profilingStudy meta-analysesCircadian clockBiological processesLines of evidenceConditions associated with disabilityRepurposing opportunitiesSignal enrichmentGenesEffectorPathwayIndependent associationsMeta-analysesEffect sizeAccelerated translationEpigenomeTranscriptomeProteomicsDisease-modifying treatmentsOsteoarthritisPsychiatric genetics in the diverse landscape of Latin American populations
Bruxel E, Rovaris D, Belangero S, Chavarría-Soley G, Cuellar-Barboza A, Martínez-Magaña J, Nagamatsu S, Nievergelt C, Núñez-Ríos D, Ota V, Peterson R, Sloofman L, Adams A, Albino E, Alvarado A, Andrade-Brito D, Arguello-Pascualli P, Bandeira C, Bau C, Bulik C, Buxbaum J, Cappi C, Corral-Frias N, Corrales A, Corsi-Zuelli F, Crowley J, Cupertino R, da Silva B, De Almeida S, De la Hoz J, Forero D, Fries G, Gelernter J, González-Giraldo Y, Grevet E, Grice D, Hernández-Garayua A, Hettema J, Ibáñez A, Ionita-Laza I, Lattig M, Lima Y, Lin Y, López-León S, Loureiro C, Martínez-Cerdeño V, Martínez-Levy G, Melin K, Moreno-De-Luca D, Muniz Carvalho C, Olivares A, Oliveira V, Ormond R, Palmer A, Panzenhagen A, Passos-Bueno M, Peng Q, Pérez-Palma E, Prieto M, Roussos P, Sanchez-Roige S, Santamaría-García H, Shansis F, Sharp R, Storch E, Tavares M, Tietz G, Torres-Hernández B, Tovo-Rodrigues L, Trelles P, Trujillo-ChiVacuan E, Velásquez M, Vera-Urbina F, Voloudakis G, Wegman-Ostrosky T, Zhen-Duan J, Zhou H, Santoro M, Nicolini H, Atkinson E, Giusti-Rodríguez P, Montalvo-Ortiz J. Psychiatric genetics in the diverse landscape of Latin American populations. Nature Genetics 2025, 57: 1074-1088. PMID: 40175716, PMCID: PMC12133068, DOI: 10.1038/s41588-025-02127-z.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesPsychiatric genomicsPsychiatric genome-wide association studiesLarge-scale genome-wide association studiesGenetic risk lociNon-European populationsGenetic diversityRisk lociGenetic admixtureBurden of psychiatric disordersAssociation studiesPsychiatric disordersEuropean ancestryPsychiatric geneticsGenomeHealthcare disparitiesConsortium effortLatin American populationsPromote equityEnvironmental factorsDiversityAmerican populationDiverse landscapeLociAncestryGenomic analysis of 11,555 probands identifies 60 dominant congenital heart disease genes
Sierant M, Jin S, Bilguvar K, Morton S, Dong W, Jiang W, Lu Z, Li B, López-Giráldez F, Tikhonova I, Zeng X, Lu Q, Choi J, Zhang J, Nelson-Williams C, Knight J, Zhao H, Cao J, Mane S, Sedore S, Gruber P, Lek M, Goldmuntz E, Deanfield J, Giardini A, Mital S, Russell M, Gaynor J, King E, Wagner M, Srivastava D, Shen Y, Bernstein D, Porter G, Newburger J, Seidman J, Roberts A, Yandell M, Yost H, Tristani-Firouzi M, Kim R, Chung W, Gelb B, Seidman C, Brueckner M, Lifton R. Genomic analysis of 11,555 probands identifies 60 dominant congenital heart disease genes. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2420343122. PMID: 40127276, PMCID: PMC12002227, DOI: 10.1073/pnas.2420343122.Peer-Reviewed Original ResearchConceptsCongenital heart disease genesCongenital heart diseaseDamaging variantsMissense variantsAnalyzing de novo mutationsCHD probandsEpidermal growth factor (EGF)-like domainsNeurodevelopmental delayLoss of function variantsParent-offspring triosSyndromic congenital heart diseaseHeart disease genesDisease genesGenomic analysisCongenital heart disease subtypesAssociated with neurodevelopmental delayTetralogy of FallotFunctional variantsIncomplete penetranceCHD phenotypesGenesAssociated with developmentGenetic testingMolecular diagnosticsExtracardiac abnormalitiesClustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data
Li Z, Windels S, Malod-Dognin N, Weinberg S, Marazita M, Walsh S, Shriver M, Fardo D, Claes P, Pržulj N, Van Steen K. Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data. Bioinformatics 2025, 41: btaf122. PMID: 40119919, PMCID: PMC11978392, DOI: 10.1093/bioinformatics/btaf122.Peer-Reviewed Original ResearchConceptsNonnegative matrix tri-factorizationMulti-view clustering methodsClustering methodMulti-view clusteringLow-rank embeddingDrivers of clusteringMatrix tri-factorizationBiologically meaningful interpretationSingle-view approachImage dataAdjusted Rand IndexEmbedding vectorsEmbedding frameworkFacial annotationsOmics dataSynthetic datasetsTri-factorizationRelevant embeddingsRand indexClusters of individualsOmicsComprehensive clusteringEmbeddingCluster individualsExternal qualityGenomic Landscape of Late-Stage Gastric Cancer: Analysis From KEYNOTE-059, KEYNOTE-061, and KEYNOTE-062 Studies
Janjigian Y, Cecchini M, Shitara K, Enzinger P, Wainberg Z, Chau I, Satoh T, Lee J, Nebozhyn M, Loboda A, Kobie J, Vajdi A, Shih C, Cristescu R, Cao Z. Genomic Landscape of Late-Stage Gastric Cancer: Analysis From KEYNOTE-059, KEYNOTE-061, and KEYNOTE-062 Studies. JCO Precision Oncology 2025, 9: e2400456. PMID: 40117530, PMCID: PMC11949223, DOI: 10.1200/po-24-00456.Peer-Reviewed Original ResearchConceptsTumor mutational burdenHomologous recombination deficiencyMicrosatellite instability-highGenomic characteristicsChromosomal instabilityKEYNOTE-062Late-stage GCKEYNOTE-059KEYNOTE-061Molecular subtypesAsian originWhole-exome sequencingGastric cancerSubtype prevalenceEpstein-Barr virus-positiveHomologous recombination deficiency scoresPretreatment tumor samplesWES dataGene expression signaturesCancer Genome AtlasMicrosatellite instability-high subtypeRNA sequencingGenomic landscapeEarly-stage GCGenomic alterations
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