2024
Enhancing 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 powerAssessment 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
Prognostic 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
Is 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