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Turk J Hematol 2016;33:286-292Ghasemi M, et al: Identification of Individual Genes for BortezomibTable 1. List of genes involved in specific pathways. The genes presented in this table are outcomes of Pearson correlation analysis done by using bortezomibchemosensitivity data and gene expression data for the multiple myeloma cell lines.Pathway MappedIDAngiogenesisCadherin signaling pathwayGonadotropin releasing hormone receptor pathwayGene Name/Gene Symbol PANTHER Family/Subfamily PANTHER Protein Class SpeciesFGF2 FGF2; ortholog Fibroblast growth factor 2(PTHR11486:SF68)Growth factor Homo sapiensKRAS GTPase KRas; KRAS; ortholog GTPase KRAS (PTHR24070:SF186) Small GTPase Homo sapiensCDHS Cadherin 5; CDH5; ortholog Cadherin-5 (PTHR24027:SF89) Cell junction protein; cadherin Homo sapiensCSNK2A2 Casein kinase II subunit al pha’; CSNK2A2; ort Casein kinase II subunit alpha’(PTHR24054:SF3)SMAD4 Mothers against decapentaplegic homolog Mothers against decapentaplegichomolog 4Homo sapiensTranscription factor Homo sapiensGNAO1 Guanine nudeotide-binding protein G(o) subunit Guanine nucleotide-binding protein Heterotrimeric G- protein Homo sapiensIflammation mediated by chemokine and cytokine signaling pathwayIntegrin signalling pathwayUbiquitinproteasome pathwayWnt signaling pathwayFGF2: Fibroblast growth factor 2.ARRB2 Beta-arrestin-2; ARRB2; ortholog Beta-arrestin-2 (PTHR11792:SF20) Enzyme modulator Homo sapiensRGS4 Regulator of G protein signaling 4 Regulator of G protein signaling 4(PTHR10845:SF40)G protein modulator Homo sapiensGNAO1 Guanine nucleotide binding protein G (0) subunit Guanine nucleotide binding protein Heterotrimeric G protein Homo sapiensKRAS GTPase KRas; KRAS; ortholog GTPase KRAS (PTHR24070:SF186) Small GTPase Homo sapiensCOL1A2 Collagen alpha-2(I) chain; COL1A2; ortholog Collagen alpha- 2(I) chain(PTHR24023:SF441)Transporter; surfactant; receptor;extracellular matrixHomo sapiensKRAS GTPase KRas; KRAS; ortholog GTPase KRAS (PTHR24070:SF186) Small GTPase Homo sapiensPSMD8 26S proteasome non- ATPase regulatory subunit 26S Proteasome non-atpase regulatory Enzyme modulator Homo sapiensARRB2 Beta-arrestin-2; ARRB2; ortholog Beta-arrestin-2 (PTHR11792:SF20) Enzyme modulator Homo sapiensCDHS Cadherin 5; CDH5; ortholog Cadherin-5 (PTHR24027:SF89) Cell junctionprotein; cadherinSMAD4 Mothers against decapentaplegic omolog Mothers against decapentaplegichomolog 4CSNK2A2 Casein kinase II subunit al pha’; CSNK2A2; ort Casein kinase II subunit alpha’(PTHR24054:SF3)Homo sapiensTranscription factor Homo sapiensHomo sapiens289
Ghasemi M, et al: Identification of Individual Genes for BortezomibTurk J Hematol 2016;33:286-292DiscussionIn this in silico study, the hierarchical clustering of myeloma celllines according to bortezomib hemosensitivity biomarker geneshas been described. The heat map represented the clustering of 8myeloma cell lines based on 73 genes disclosing either bortezomibresistance or less sensitive myeloma cells. Likewise, the concordancesbetween gene expression and IC50 values of 8 myeloma cell linesare shown for 73 genes. Furthermore, the relevant biologicalpathway analyses of the genes whose expressions are concordantwith bortezomib cytotoxicity were explored via the molecularfunctional analyses of the 73 genes (Figures 2, 3, and 4). The genesinvolved in specific pathways regarding the proteasome inhibitors,and particularly bortezomib, are related to the critical pathologicalevents of MM such as tumor angiogenesis and neoplastic signalingpathways (cadherin, integrin, Wnt, GnRH, ubiquitin), as well aschemokine-mediated inflammation (Table 1). Those pathwaysare essentially important in the biology of myeloma, such as theubiquitin proteasome system, which plays a role in the regulationof most cellular pathways, and its deregulation in MM represents atarget for proteasome inhibition via bortezomib [20]. Proliferationand apoptosis pathways are pathologically regulated by theubiquitin-proteasome system, resulting in cellular neoplastictransformation in MM [21]. Targeting pathological angiogenesisin MM via bortezomib may delay tumor growth and reducecytokine paracrine loops mediated by angiogenic factors [22].Meanwhile, the signal transducers and activators of transcriptionproteins represent a family of cytoplasmic transcription factorsthat regulate a pleiotropic range of biological processes in MM[23]. Cell-cell interactions and cancer-initiating cells furthercomplicate the biology of MM [24]. A previous study, in accordancewith our present results, examined gene ontogeny related tobortezomib and suggested involvement in cellular developmentand carcinogenesis [25].In the present study, by performing in silico correlation analysis,we determined genes whose expressions are correlated withbortezomib chemosensitivity in MM cell lines. Among 73genes that are highly correlated with drug-resistant response(absolute Pearson r-value of >0.80), 20 genes showed a reversecorrelation with chemosensitivity to bortezomib. This means thatoverexpression of these genes makes cancer cells more sensitiveto bortezomib and the expressions of these genes are associatedwith good prognosis. Conversely, 53 genes are positively correlatedto bortezomib response and make cells more resistant to drugtreatment, and overexpression of these genes is associated withpoor prognosis (supplementary data). We also tried to determinethe pathways in which these genes are involved and figure outthe relation between outcome and MM drug resistance profile byusing another classification system. The PANTHER classificationsystem was designed to classify proteins and their genes in order tofacilitate high-throughput analysis. Proteins have been classifiedaccording to family and subfamily, molecular function, biologicalprocess, and pathway. Further in vitro and clinical validation studiesare needed to determine and validate the exact role of each geneor panel of genes that are suggested in the present study as genebiomarkers for bortezomib-resistant response in MM cancer.In 2007 Mulligan et al. assessed the feasibility of prospectivepharmacogenomics research in multicenter international clinicaltrials of bortezomib in MM [26]. They tried to highlight those geneswhose expressions are related to drug response and survival usingbone marrow clinical samples by performing gene set enrichmentanalysis, analysis of clinical response, and overall survival analysis.The present study has two main differences from that study interms of genomic approach and databases used. Our databasecame from established MM cell lines and the genomic approachwas analysis of correlations between gene expression and drugresponse. Despite the two different approaches, we can see thatmany genes in this study and in that of Mulligan et al. overlap. Onthe other hand, our analysis shows some other genes that are ableto predict response to bortezomib.Cancer cell lines have a notable role in cancer drug discovery.Jaeger et al. found that drug sensitivity in cancer cell lines is nottissue-specific and recommended that, to get the most trustableresults using cell lines, it will be necessary to include those celllines’ molecular characteristics [27]. Similarly, in this experiment wedid integrate those data into biological analysis, such as pathwayanalysis and hierarchical clustering.The overall results of the present data mining study reveal thecomplicated nature of MM [28] and locate the drug bortezomib atthe critical crossroads of the pathobiology of the disease, drivingthe clinical course of MM. For instance, the LP-1 cell line wasfound to be resistant to bortezomib in our present study (Figure3). A previous study suggested that the expression of Apaf-1 mightbe predictive of the response to proteasome inhibition [29]. Basedon our present results, patients with MM mimicking the molecularprofile/behavior of LP-1 at any clinical evaluation point during thelong-term clinical course of MM will be candidates for therapeuticregimens other than bortezomib. Ideally, those multiresistantMM patients should be single- or multiple-transplanted basedon individual clinical responses [14]. We intend to test thesehypotheses in future experiments designed to examine genomicprofiles of the biological samples obtained from our MM patientcohort.The results of our present study represent the rational basisfor future molecular studies dealing with biological myelomasamples (peripheral blood and/or bone marrow) obtained from‘real-life’ patients with MM. This issue is not just academicallyimportant since the proper selection of anti-myeloma drugsin everyday clinical practice during the long-term incurableadvanced clinical forms of MM is challenging even to themost skilled clinicians. Randomized clinical trials (RCTs) usuallycompare drugs but do not decide on treatment strategies andproper selection of drug combinations [30], particularly for290
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