A polymorphism associated with STAT3 expression and response of chronic myeloid leukemia to interferon {alpha}

Interferon ${alpha}$ (IFN) induces variable responses in chronic myeloidleukemia (CML), with 8–30% of early chronic phase casesachieving a complete cytogenetic response. We hypothesized thatpolymorphic differences in genes encoding IFN signal transductioncomponents might account for different patient responses. Westudied 174 IFN-treated patients, of whom 79 achieved less than35% Philadelphia-chromosome (Ph) positive metaphases (responders)and 95 failed to show any cytogenetic response (more than 95%Ph-positive metaphases; non-responders). We compared 17 singlenucleotide polymorphisms (SNPs) at IFNAR1, IFNAR2, JAK1, TYK2,STAT1, STAT3 and STAT5a/b between the two groups and found asignificant difference for rs6503691, a SNP tightly linked toSTAT5a, STAT5b and STAT3 (minor allele frequency 0.16 for non-responders;0.06 for responders, P=0.007). Levels of STAT3 mRNA correlatedwith rs6503691 genotype (P<0.001) as assessed by real timequantitative PCR and therefore we conclude that rs6503691 isassociated with the STAT3 expression levels and response ofCML patients to IFN.

Key words: STAT3, chronic myeloid leukemia, interferon.

Introduction

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Introduction

Interferon ${alpha}$ (IFN) induces heterogeneous responses in chronicmyeloid leukemia (CML), with up to 80% of early chronic phasepatients achieving hematologic remission but only 8–30%achieving complete cytogenetic remission.^1–6 Althoughresponse correlates with Hasford and Sokal risk scores^7,8 andmay be influenced by other factors such as the presence or absenceof deletions at the reciprocal ABL/BCR junction on the 9q+ chromosome,⁹ the molecular basis for heterogeneous responses, and indeedmore broadly the mechanism of response to IFN, remains poorlyunderstood.

The type 1 IFN receptor is heterodimeric in structure, withthe two subunits encoded by the genes IFNAR1 and IFNAR2. Bindingof IFN to the receptor induces activation of the JAK1 and TYK2non-receptor tyrosine kinases which then phosphorylate STATproteins.¹⁰ Phosphorylated STAT dimers migrate to the nucleuswhere they activate the transcription of target genes. Inheritedsingle nucleotide polymorphisms (SNPs) in genes encoding componentsof the IFN signal transduction cascade have been associatedwith diseases such as systemic lupus erythematosus, athsma andCrohn’s disease.^11–13 We hypothesized that polymorphicdifference in this cascade might account for the different responsesof CML patients to IFN.

Design and Methods

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Design and Methods

Patient samples
Initially we studied 174 pre-treatment genomic DNA (gDNA) samplesfrom BCR-ABL-positive CML patients receiving IFN as part ofthe German CML studies I-III.^3,14,15 Two patient groups wereselected on the basis of availability of DNA and maximal responseto therapy: responders (n=79) achieved a major ( $≤$ 35% Philadelphiachromosome-positive metaphases) or complete cytogenetic response(0% Philadelphia chromosome-positive metaphases) whereas thenon-responders (n=95) failed to show any cytogenetic response(>95% Philadelphia chromosome-positive metaphases) aftera median of 38 and 22 months treatment, respectively, afterinitiation of treatment. Samples from an additional 245 pre-treatmentCML cases for whom both DNA and cDNA were available were usedto compare STAT expression levels with genotype. The study wasapproved by the Internal Review Boards from participating institutionsand informed consent was provided according to the Declarationof Helsinki.

SNP genotyping by pyrosequencing
We studied 17 single nucleotide polymorphisms (SNPs) that werewithin or close to the genes encoding IFNAR1, IFNAR2, JAK1,TYK2, STAT1, STAT3 and STAT5a/b. SNPs were selected on the basisof published data indicating positive associations with oneor more human diseases, or as tagged SNPs with minor allelefrequencies (maf) >0.2 from the International HapMap Project(release 21; www.hapmap.org). We did not include STAT2 and STAT4in the analysis as there have not, to our knowledge, been anyreports implicating these proteins in the pathogenesis of myeloiddisorders. Furthermore, because of the limited number of casesavailable for analysis we deliberately did not attempt to captureall genetic variation at these loci due to the loss of statisticalpower this would entail. Pyrosequencing was performed as described¹⁶ using primers and dispensation orders as shown in OnlineSupplementary Tables S1A and B. Markers were quantified usingthe Allele Frequency Quantification function in the SNP Software(Biotage AB, Uppsala, Sweden) and called as homozygous whenone allele gave a reading of >90% and heterozygous when bothalleles were called as 40–60%.

Expression analysis
Reverse transcriptase real-time PCR (RQ-PCR) was performed toquantify STAT3, STAT5a and STAT5b expression relative to GUSBexpression and as an internal control for cDNA quality and quantity.Complementary DNA synthesis was performed by standard proceduresand GUSB quantification was performed.¹⁷ STAT3 expression wasdetermined by using the custom designed PerfectProbe Gene DetectionKit (PrimerDesign, Southampton, UK) (sense primer: 5'-GAAGGAGGCGTCACTTTCAC-3';antisense primer: 5'-CTGCTGCTTTGTGTATGGTTC-3'; probe 5'FAM-CTCTTACCGCTGATGTCCTTCTCCACCCAGGTAAGAG-DABCYL3').STAT5a and STAT5b expression was determined using the inventoriedTaqMan^® Gene Expression Assay by Applied Biosystems (FosterCity, CA, USA). PCRs were performed on the Corbett Rotor-Gene6000 (Corbett Life Science, Cambridge, UK). After demonstratingequal amplification efficiencies for each target, samples weretested in triplicate and mean STAT levels were normalized toGUSB and compared using the 2^–Ct method.¹⁸

Statistical analysis
To investigate the distribution of baseline values between groups,univariate tests were performed by using the Mann-Whitney, Fisher’sexact or ${chi}$ ² tests, as appropriate. The possible independent influenceof rs6503691 was assessed by multiple Cox regression analysisusing SAS version 9.1.3 (SAS Institute Inc., Cary, NC, USA).Real time PCR results were compared to genotype by Kruskal-Wallisanalysis.

Results and Discussion

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Results and Discussion

Initially we genotyped 12 SNPs and compared the allele frequenciesbetween responders and non-responders. As shown in Table 1A,only one SNP (rs6503691) in exon 1 of STAT5b showed a significantdifference with a maf of 0.16 for non-responders versus 0.06for responders (P=0.0066, odds ratio 0.36, 95% confidence intervals0.17–0.76). Typing of an additional 5 SNPs in the samegenomic region (rs6503695, rs16967611, rs9900213, rs17500235,rs17591972) failed to reveal any other significant associations(Table 1A). It is notable that this SNP has been recently reportedto be associated with the risk of developing breast cancer.¹⁹We evaluated the impact of rs6503691 in more detail by takingother prognostic factors into account. On univariate analysis,the leukocyte count, percentage blasts, spleen size, Sokal scoreand rs6503691 genotype were all significantly associated withresponse (Table 1B). On multivariate analysis, however, rs6503691genotype fell marginally below the level of significance (P=0.056;Table 1C).

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Table 1A. Summary of genotyping analysis.

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Table 1B. Clinical characteristics and univariate analysis of responders and non-responders.

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Table 1C. Multiple logistic regression analysis.

Inspection of the HapMap data shows that rs6503691 falls ina region of strong linkage disequilibrium at 17q21 that includesthe entire STAT5A gene as well as the 5' end of STAT5B and the3' end of STAT3 (Figure 1A). Potentially then, this SNP couldbe linked to other variants that might influence the expressionof any of these three genes. We therefore compared rs6503691genotype with STAT5A, STAT5B and STAT3 mRNA levels in 245 pre-treatmentCML cases. As shown in Figure 1B, STAT3 expression was stronglyrelated to rs6503691 genotype (P<0.0001) but no differenceswere seen for STAT5A or STAT5B. Strikingly, a nearby polymorphismhas recently been reported to be linked to both STAT3 mRNA levelsand the response of metastatic renal cell carcinoma to IFN.²⁰BCR-ABL is known to activate STAT3²¹ and elevated expressionof SOCS3, a known STAT3 target, confers IFN resistance to CMLcells.²² Taken together, these results indicate that polymorphicdifferences in STAT3 expression levels may be a determinantof response to IFN in CML, and that the marginal lack of significanceon multivariate analysis may have been due, at least in part,to limited sample numbers.

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Figure 1. (A) HapMap data showing the position of rs6503961 in relation to STAT5A, STAT5B and STAT3, plus linkage disequilibrium in this region. (B) STAT3 but not STAT5A or STAT5B expression normalized to GUSB is significantly related to rs6503691 genotype in 245 patients with CML (P=<0.0001; Kruskall-Wallis test). n.s = not significant.

In the past decade, treatment of patients with CML has beentransformed by the introduction of imatinib and other secondgeneration tyrosine kinase inhibitors (TKIs). Nevertheless,IFN still remains relevant and its use as part of combinationtherapy with TKIs has attracted considerable interest, supportedby favorable early clinical results and the recent demonstrationthat IFN activates dormant hemopoietic stem cells in vivo.²³Furthermore, discontinuation of imatinib in cases that haveachieved complete molecular remission (CMR) does not alwayslead to relapse, and it has been suggested that sustained CMRmay be influenced by prior treatment with IFN.²⁴ We suggestthat the impact of rs6503691 should be evaluated in these novelsettings.

Acknowledgments

we are grateful to all those who contributed to the sample anddata collection at the CML trial office in Mannheim, Germany.

Footnotes

Funding: the study was supported by Deutsche Krebshilfe, LeukaemiaResearch (UK), the Wessex Cancer Trust, the Lady Tata MemorialTrust, the Competence Network ‘Acute and chronic leukemias’,sponsored by the German Bundesministerium für Bildung undForschung (Projektträger Gesundheitsforschung; DLR e.V.-01 GI9980/6), the German José-Carreras-Leukämiestiftung(H03/01) and the European LeukemiaNet within the 6th EuropeanCommunity Framework Programme for Research and TechnologicalDevelopment.

The online version of this article has a supplementary appendix.

Authorship and Disclosures

SK, AH and NC designed the study. SK, KW, TE, AC, HW designedand performed the laboratory analysis. RH, AR and AH providedsamples and clinical data. SK and NC analyzed the data. SK andNC wrote the paper, and all authors contributed to the finalversion.

The authors reported no potential conflicts of interest.

Received for publication May 16, 2009. Revision received June 23, 2009. Accepted for publication June 26, 2009.

References

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