2024
Identifying high‐risk multiple myeloma patients: A novel approach using a clonal gene signature
Li J, Wang C, Cheng C. Identifying high‐risk multiple myeloma patients: A novel approach using a clonal gene signature. International Journal Of Cancer 2024 PMID: 38874435, DOI: 10.1002/ijc.35057.Peer-Reviewed Original ResearchInternational Staging SystemHigh-risk patientsMultiple myelomaGene signaturePrognostic stratificationHigh-risk multiple myeloma patientsRevised ISSMultiple myeloma patientsCopy number alterationsLow-risk groupPrognostic gene signatureResponse to dexamethasoneHighest-risk patientsCytogenetic abnormalitiesR-ISSMyeloma patientsPrognostic valueStaging systemPoor prognosisHeterogeneous diseaseGene expression dataPrognosis predictionPatientsTreatment managementPrognosisSex-Based Differences in Melanoma Survival Improvement from 2004 to 2018
Shaw V, Hudock A, Zhang B, Amos C, Cheng C. Sex-Based Differences in Melanoma Survival Improvement from 2004 to 2018. Cancers 2024, 16: 1308. PMID: 38610986, PMCID: PMC11011041, DOI: 10.3390/cancers16071308.Peer-Reviewed Original ResearchCarcinoma in situCancer-specific survivalMale patientsSurvival improvementPatient demographic groupsImproved cancer-specific survivalDemographic groupsSurvival analysisTime to progressionCohort of patientsMultivariate Cox regressionSurvival disparitiesCharacterize disparitiesSocioeconomic statusIncreased ORAsian patientsSex-based differencesFemale patientsMelanoma survivalAnalysis of lesionsEnd ResultsPatient subgroupsCox regressionAge groupsMelanomaImmune cell pair ratio captured by imaging mass cytometry has superior predictive value for prognosis of non‐small cell lung cancer than cell fraction and density
Li J, Cheng C. Immune cell pair ratio captured by imaging mass cytometry has superior predictive value for prognosis of non‐small cell lung cancer than cell fraction and density. Cancer Communications 2024, 44: 589-592. PMID: 38532538, PMCID: PMC11110949, DOI: 10.1002/cac2.12540.Peer-Reviewed Original ResearchRacial and socioeconomic disparities in survival improvement of eight cancers
Shaw V, Zhang B, Tang M, Peng W, Amos C, Cheng C. Racial and socioeconomic disparities in survival improvement of eight cancers. BJC Reports 2024, 2: 21. DOI: 10.1038/s44276-024-00044-y.Peer-Reviewed Original ResearchCancer-specific survivalPatients' cancer-specific survivalBlack patientsPopulation-based cohort studyCancer-related disparitiesRacial disparity gapSurvival improvementCancer sitesDisparity gapSocioeconomic disparitiesPancreatic cancerCancer outcomesHispanic patientsCohort studyRate of improvementCancer-free survivalWhite patientsRacial differencesBackgroundMany studiesEnd ResultsIdentified factorsLongitudinal dataProstate cancerDisparitiesBreastEnhancing prognostic power in multiple myeloma using a plasma cell signature derived from single-cell RNA sequencing
Li J, Arsang-Jang S, Cheng Y, Sun F, D’Souza A, Dhakal B, Hari P, Huang Q, Auer P, Li Y, Urrutia R, Zhan F, Shaughnessy J, Janz S, Dong J, Cheng C. Enhancing prognostic power in multiple myeloma using a plasma cell signature derived from single-cell RNA sequencing. Blood Cancer Journal 2024, 14: 38. PMID: 38443358, PMCID: PMC10915134, DOI: 10.1038/s41408-024-01024-8.Peer-Reviewed Original ResearchConceptsInternational Staging SystemPlasma cell malignancyMultiple myelomaCell malignancySpectrum of plasma cell dyscrasiasPlasma cell gene signaturePresence of cytogenetic abnormalitiesRevised ISSHeterogeneous plasma cell malignancyTumor immune microenvironmentPlasma cell dyscrasiaRefining risk stratificationPlasma cell signatureTherapeutic approach to MMShorter overall survivalCytogenetic abnormalitiesOverall survivalR-ISSTP53 mutationsImmune microenvironmentPrognostic effectClinical outcomesStaging systemRisk stratificationPrognostic powerDeepCG: A cell graph model for predicting prognosis in lung adenocarcinoma
Zhang B, Li C, Wu J, Zhang J, Cheng C. DeepCG: A cell graph model for predicting prognosis in lung adenocarcinoma. International Journal Of Cancer 2024, 154: 2151-2161. PMID: 38429627, PMCID: PMC11015971, DOI: 10.1002/ijc.34901.Peer-Reviewed Original ResearchClonal gene signatures predict prognosis in mesothelioma and lung adenocarcinoma
Lin Y, Burt B, Lee H, Nguyen T, Jang H, Lee C, Hong W, Ripley R, Amos C, Cheng C. Clonal gene signatures predict prognosis in mesothelioma and lung adenocarcinoma. Npj Precision Oncology 2024, 8: 47. PMID: 38396241, PMCID: PMC10891127, DOI: 10.1038/s41698-024-00531-y.Peer-Reviewed Original ResearchMalignant pleural mesotheliomaMalignant pleural mesothelioma patientsIntratumoral heterogeneityTranscriptome dataPrognostic stratificationPrognostic valueLung adenocarcinomaRNA-seq dataRNA-seq datasetsGene signatureCopy number variationsCancer typesAssociated with poor prognosisLow intratumoral heterogeneityGenomic regionsNumber variationsPleural mesotheliomaPoor prognosisClinical factorsTumor samplesInferior regionsGenesPrognostic genesTumorCancerTissue-resident memory T cell signatures from single-cell analysis associated with better melanoma prognosis
Jiang C, Chao C, Li J, Ge X, Shen A, Jucaud V, Cheng C, Shen X. Tissue-resident memory T cell signatures from single-cell analysis associated with better melanoma prognosis. IScience 2024, 27: 109277. PMID: 38455971, PMCID: PMC10918229, DOI: 10.1016/j.isci.2024.109277.Peer-Reviewed Original ResearchTumor immune microenvironmentTissue-resident memory TMemory TOverall survivalT cellsAssociated with longer overall survivalT cell-mediated responsesLonger overall survivalMemory B cellsT cell populationsAbundance of T cellsCell-mediated responsesHigh-risk categoryMelanoma patientsNK cellsImmune microenvironmentPatient survivalMelanoma prognosisImmune activationB cellsM1 macrophagesPeripheral tissuesRisk scoreCell populationsPatientsINTEGRAL‐ILCCO cohort data analysis revealed the association of clonal haematopoiesis with an increased risk of lung cancer
Cheng C, Hong W, Amos C. INTEGRAL‐ILCCO cohort data analysis revealed the association of clonal haematopoiesis with an increased risk of lung cancer. Clinical And Translational Discovery 2024, 4 DOI: 10.1002/ctd2.258.Peer-Reviewed Original ResearchRisk of lung cancerIncreased risk of lung cancerLung cancer riskVariant allele frequencyClonal haematopoiesisCancer riskCH mutationsInternational Lung Cancer ConsortiumLung cancerIncreased riskRisk factorsLung cancer etiologyHighest variant allele frequencyLung cancer casesLung cancer histological subtypesCohort data analysisCancer histological subtypesCancer ConsortiumCancer casesLow variant allele frequencyCancer etiologyDose-response relationshipHigher VAFHistological subtypesLow-VAFAssessment of human leukocyte antigen-based neoantigen presentation to determine pan-cancer response to immunotherapy
Han J, Dong Y, Zhu X, Reuben A, Zhang J, Xu J, Bai H, Duan J, Wan R, Zhao J, Bai J, Xia X, Yi X, Cheng C, Wang J, Wang Z. Assessment of human leukocyte antigen-based neoantigen presentation to determine pan-cancer response to immunotherapy. Nature Communications 2024, 15: 1199. PMID: 38331912, PMCID: PMC10853168, DOI: 10.1038/s41467-024-45361-5.Peer-Reviewed Original ResearchConceptsImmune checkpoint inhibitorsHLA-INeoantigen presentationPresentation capacityPatients treated with immune checkpoint inhibitorsHuman leukocyte antigen class IICI responseImmune checkpoint inhibitor treatmentResponse to immunotherapyAntigen presentation capacityAntigen presentation pathwayCheckpoint inhibitorsSurvival benefitNeoantigen productionTumor microenvironmentCancer patientsPresentation pathwayClinical utilityClass IPatientsTumorPresentationPresentation scoreScoresImmunotherapyLung cancer in ever- and never-smokers: findings from multi-population GWAS studies
Li Y, Xiao X, Li J, Han Y, Cheng C, Fernandes G, Slewitzke S, Rosenberg S, Zhu M, Byun J, Bossé Y, McKay J, Albanes D, Lam S, Tardon A, Chen C, Bojesen S, Landi M, Johansson M, Risch A, Bickeböller H, Wichmann H, Christiani D, Rennert G, Arnold S, Goodman G, Field J, Davies M, Shete S, Le Marchand L, Liu G, Hung R, Andrew A, Kiemeney L, Sun R, Zienolddiny S, Grankvist K, Johansson M, Caporaso N, Cox A, Hong Y, Lazarus P, Schabath M, Aldrich M, Schwartz A, Gorlov I, Purrington K, Yang P, Liu Y, Bailey-Wilson J, Pinney S, Mandal D, Willey J, Gaba C, Brennan P, Xia J, Shen H, Amos C. Lung cancer in ever- and never-smokers: findings from multi-population GWAS studies. Cancer Epidemiology Biomarkers & Prevention 2024, 33: 389-399. PMID: 38180474, PMCID: PMC10905670, DOI: 10.1158/1055-9965.epi-23-0613.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociLung cancer riskAssociation studiesCis-regulation of gene expressionNever-smokersCancer riskNever-smoker lung cancerQuantitative trait lociComplicated genetic architectureFunctional analysisExcessive DNA damageRisk of variantsLung cancerGenetic epidemiological studiesIndependent lociCis-regulationGenetic architectureGWA studiesTrait lociMultiple lines of evidenceGenetic heterogeneityNever-smoker groupGene expressionLines of evidenceEver-smokers
2023
Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images
Gan J, Wang H, Yu H, He Z, Zhang W, Ma K, Zhu L, Bai Y, Zhou Z, Yullie A, Bai X, Wang M, Yang D, Chen Y, Chen G, Lasenby J, Cheng C, Wu J, Zhang J, Wang X, Chen Y, Wang G, Xia T. Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images. IScience 2023, 26: 107243. PMID: 37767002, PMCID: PMC10520807, DOI: 10.1016/j.isci.2023.107243.Peer-Reviewed Original ResearchT-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy
Chen Z, Min M, Guo H, Cheng C, Clancy T, Ning X. T-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy. Lecture Notes In Computer Science 2023, 13976: 174-191. DOI: 10.1007/978-3-031-29119-7_11.Peer-Reviewed Original ResearchT cell receptorReinforcement learningBaseline methodsT cellsT cell receptor recognitionProximal policy optimizationPrecision immunotherapyMutated T-cell receptorImmune responseOptimal T-cell receptorSurface of T cellsDeep autoencoderPolicy optimizationReward functionDevelopment of personalized treatmentsScoring functionVirus-infected cellsHealth status of cellsTCR sequencesMutation policyPersonalized treatmentMotif discoveryStatus of cellsProtein complexesImmunotherapyPrognostic landscape of mitochondrial genome in myelodysplastic syndrome after stem-cell transplantation
Dong J, Buradagunta C, Zhang T, Spellman S, Bolon Y, DeZern A, Gadalla S, Deeg H, Nazha A, Cutler C, Cheng C, Urrutia R, Auer P, Saber W. Prognostic landscape of mitochondrial genome in myelodysplastic syndrome after stem-cell transplantation. Journal Of Hematology & Oncology 2023, 16: 21. PMID: 36899395, PMCID: PMC9999628, DOI: 10.1186/s13045-023-01418-4.Peer-Reviewed Original ResearchConceptsWhole-genome sequencingTransplant-related mortalityRelapse-free survivalMtDNA mutationsAllo-HCTImpact of mtDNA mutationsWhole-genome sequencing effortsOverall survivalTransplant outcomesCenter for International Blood and Marrow Transplant ResearchPrognostic performance of modelsAllogeneic hematopoietic cell transplantationPotential pathogenic variantsAllo-HCT outcomesInternational Prognostic ScorePredictors of OSStandard clinical parametersStem-cell transplantationHematopoietic cell transplantationMarrow Transplant ResearchAssociated with inferior transplant outcomesMitochondrial genomeMtDNA genesMtDNA variantsInferior transplant outcomesBinding peptide generation for MHC Class I proteins with deep reinforcement learning
Chen Z, Zhang B, Guo H, Emani P, Clancy T, Jiang C, Gerstein M, Ning X, Cheng C, Min M. Binding peptide generation for MHC Class I proteins with deep reinforcement learning. Bioinformatics 2023, 39: btad055. PMID: 36692135, PMCID: PMC9907221, DOI: 10.1093/bioinformatics/btad055.Peer-Reviewed Original Research
2022
Impaired Plasma B Cell Markers in CD138+ Cells Predict Prognosis in Multiple Myeloma
Dong J, Li J, Buradagunta C, Janz S, Urrutia R, Cheng C. Impaired Plasma B Cell Markers in CD138+ Cells Predict Prognosis in Multiple Myeloma. Blood 2022, 140: 10054-10055. DOI: 10.1182/blood-2022-158718.Peer-Reviewed Original ResearchHigh-throughput proteomics: a methodological mini-review.
Cui M, Cheng C, Zhang L. High-throughput proteomics: a methodological mini-review. Laboratory Investigation 2022, 102: 1170-1181. PMID: 36775443, DOI: 10.1038/s41374-022-00830-7.Peer-Reviewed Original ResearchConceptsMining of proteomic dataHigh-throughput proteomic approachPost-genomic eraProtein Pathway ArrayHigh-throughput proteomicsMolecular mechanisms of pathogenesisSingle-cell proteomicsSingle-molecule proteomicsSignaling networksProteomic dataMechanisms of pathogenesisProteomic approachNext-generation tissue microarrayPathway arrayProteomic methodologiesProteomicsMolecular mechanismsPrognostic oncologyPrecision medicineOlink ProteomicsTissue microarrayDrug discoveryBiomedical researchClinical practiceComputational methodsIs the Product Method More Efficient Than the Difference Method for Assessing Mediation?
Cheng C, Spiegelman D, Li F. Is the Product Method More Efficient Than the Difference Method for Assessing Mediation? American Journal Of Epidemiology 2022, 192: 84-92. PMID: 35921210, PMCID: PMC10144745, DOI: 10.1093/aje/kwac144.Peer-Reviewed Original ResearchAddressing Extreme Propensity Scores in Estimating Counterfactual Survival Functions via the Overlap Weights
Cheng C, Li F, Thomas LE, Li F. Addressing Extreme Propensity Scores in Estimating Counterfactual Survival Functions via the Overlap Weights. American Journal Of Epidemiology 2022, 191: 1140-1151. PMID: 35238335, DOI: 10.1093/aje/kwac043.Peer-Reviewed Original Research