Multivariate transcriptome analysis identifies networks and key drivers of chronic lymphocytic leukemia relapse risk and patient survival.

Ti'ara L Griffen, Eric B Dammer, Courtney D Dill, Kaylin M Carey, Corey D Young, Sha'Kayla K Nunez, Adaugo Q Ohandjo, Steven M Kornblau, James W Lillard
Author Information
  1. Ti'ara L Griffen: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, 720 Westview Dr SW, HG 341B, Atlanta, GA, 30310, USA.
  2. Eric B Dammer: Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, 30322, USA.
  3. Courtney D Dill: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, 720 Westview Dr SW, HG 341B, Atlanta, GA, 30310, USA.
  4. Kaylin M Carey: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, 720 Westview Dr SW, HG 341B, Atlanta, GA, 30310, USA.
  5. Corey D Young: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, 720 Westview Dr SW, HG 341B, Atlanta, GA, 30310, USA.
  6. Sha'Kayla K Nunez: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, 720 Westview Dr SW, HG 341B, Atlanta, GA, 30310, USA.
  7. Adaugo Q Ohandjo: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, 720 Westview Dr SW, HG 341B, Atlanta, GA, 30310, USA.
  8. Steven M Kornblau: Department of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
  9. James W Lillard: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, 720 Westview Dr SW, HG 341B, Atlanta, GA, 30310, USA. jlillard@msm.edu.

Abstract

BACKGROUND: Chronic lymphocytic leukemia (CLL) is an indolent heme malignancy characterized by the accumulation of CD5 CD19 B cells and episodes of relapse. The biological signaling that influence episodes of relapse in CLL are not fully described. Here, we identify gene networks associated with CLL relapse and survival risk.
METHODS: Networks were investigated by using a novel weighted gene network co-expression analysis method and examining overrepresentation of upstream regulators and signaling pathways within co-expressed transcriptome modules across clinically annotated transcriptomes from CLL patients (N���=���203). Gene Ontology analysis was used to identify biological functions overrepresented in each module. Differential Expression of modules and individual genes was assessed using an ANOVA (Binet Stage A and B relapsed patients) or T-test (SF3B1 mutations). The clinical relevance of biomarker candidates was evaluated using log-rank Kaplan Meier (survival and relapse interval) and ROC tests.
RESULTS: Eight distinct modules (M2, M3, M4, M7, M9, M10, M11, M13) were significantly correlated with relapse and differentially expressed between relapsed and non-relapsed Binet Stage A CLL patients. The biological functions of modules positively correlated with relapse were carbohydrate and mRNA metabolism, whereas negatively correlated modules to relapse were protein translation associated. Additionally, M1, M3, M7, and M13 modules negatively correlated with overall survival. CLL biomarkers BTK, BCL2, and TP53 were co-expressed, while unmutated IGHV biomarker ZAP70 and cell survival-associated NOTCH1 were co-expressed in modules positively correlated with relapse and negatively correlated with survival days.
CONCLUSIONS: This study provides novel insights into CLL relapse biology and pathways associated with known and novel biomarkers for relapse and overall survival. The modules associated with relapse and overall survival represented both known and novel pathways associated with CLL pathogenesis and can be a resource for the CLL research community. The hub genes of these modules, e.g., ARHGAP27P2, C1S, CASC2, CLEC3B, CRY1, CXCR5, FUT5, MID1IP1, and URAHP, can be studied further as new therapeutic targets or clinical markers to predict CLL patient outcomes.

Keywords

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Grants

  1. R25 GM058268/NIGMS NIH HHS
  2. R25GM058268/NIGMS NIH HHS
  3. R25CA056452/NCI NIH HHS
  4. U54CA118638/NCI NIH HHS

MeSH Term

Humans
Leukemia, Lymphocytic, Chronic, B-Cell
Gene Expression Profiling
Gene Regulatory Networks
Male
Female
Recurrence
Multivariate Analysis
Biomarkers, Tumor
Middle Aged
Transcriptome
Aged
Neoplasm Recurrence, Local
Prognosis

Chemicals

Biomarkers, Tumor

Word Cloud

Created with Highcharts 10.0.0relapseCLLmodulessurvivalcorrelatedassociatednovelbiologicalusinganalysispathwaysco-expressedpatientsnegativelyoveralllymphocyticleukemiaBepisodessignalingidentifygenenetworksriskNetworkstranscriptomefunctionsgenesBinetStagerelapsedclinicalbiomarkerM3M7M13positivelybiomarkersknowncanARHGAP27P2C1SCASC2CLEC3BCRY1CXCR5FUT5MID1IP1URAHPpatientBACKGROUND:ChronicindolenthememalignancycharacterizedaccumulationCD5CD19cellsinfluencefullydescribedMETHODS:investigatedweightednetworkco-expressionmethodexaminingoverrepresentationupstreamregulatorswithinacrossclinicallyannotatedtranscriptomesN���=���203GeneOntologyusedoverrepresentedmoduleDifferentialExpressionindividualassessedANOVAT-testSF3B1mutationsrelevancecandidatesevaluatedlog-rankKaplanMeierintervalROCtestsRESULTS:EightdistinctM2M4M9M10M11significantlydifferentiallyexpressednon-relapsedcarbohydratemRNAmetabolismwhereasproteintranslationAdditionallyM1BTKBCL2TP53unmutatedIGHVZAP70cellsurvival-associatedNOTCH1daysCONCLUSIONS:studyprovidesinsightsbiologyrepresentedpathogenesisresourceresearchcommunityhubegstudiednewtherapeutictargetsmarkerspredictoutcomesMultivariateidentifieskeydriverschronicRelapseSurvivalWGCNA

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