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Journal of Clinical Oncology, Vol 24, No 24 (August 20), 2006: pp. 3904-3911 © 2006 American Society of Clinical Oncology. DOI: 10.1200/JCO.2006.06.9500 Adverse Prognostic Significance of KIT Mutations in Adult Acute Myeloid Leukemia With inv(16) and t(8;21): A Cancer and Leukemia Group B Study
From the Division of Hematology and Oncology, Department of Internal Medicine, and Division of Human Cancer Genetics, Department of Microbiology, Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH; The CALGB Statistical Center, Duke University Medical Center, Durham, NC; Department of Pathology, Department of Medicine and Cancer Research Center, University of Chicago, Chicago, IL; Department of Genetics, University of Alabama at Birmingham, Birmingham, AL; and the Department of Medicine, North Shore University Hospital, Manhasset, NY Address reprint requests to Guido Marcucci, MD, The Ohio State University, The Comprehensive Cancer Center, A433B Starling-Loving-Hall, 320 W 10th Ave, Columbus, OH 43210; e-mail: guido.marcucci{at}osumc.edu
PURPOSE: To analyze the prognostic impact of mutated KIT (mutKIT) in core-binding factor acute myeloid leukemia (AML) with inv(16)(p13q22) and t(8;21)(q22;q22). PATIENTS AND METHODS: Sixty-one adults with inv(16) and 49 adults with t(8;21), assigned to postremission therapy with repetitive cycles of higher dose cytarabine were analyzed for mutKIT in exon 17 (mutKIT17) and 8 (mutKIT8) by denaturing high-performance liquid chromatography and direct sequencing at diagnosis. The median follow-up was 5.3 years. RESULTS: Among patients with inv(16), 29.5% had mutKIT (16% with mutKIT17 and 13% with sole mutKIT8). Among patients with t(8;21), 22% had mutKIT (18% with mutKIT17 and 4% with sole mutKIT8). Complete remission rates of patients with mutKIT and wild-type KIT (wtKIT) were similar in both cytogenetic groups. In inv(16), the cumulative incidence of relapse (CIR) was higher for patients with mutKIT (P = .05; 5-year CIR, 56% v 29%) and those with mutKIT17 (P = .002; 5-year CIR, 80% v 29%) compared with wtKIT patients. Once data were adjusted for sex, mutKIT predicted worse overall survival (OS). In t(8;21), mutKIT predicted higher CIR (P = .017; 5-year CIR, 70% v 36%), but did not influence OS. CONCLUSION: We report for the first time that mutKIT, and particularly mutKIT17, confer higher relapse risk, and both mutKIT17 and mutKIT8 appear to adversely affect OS in AML with inv(16). We also confirm the adverse impact of mutKIT on relapse risk in t(8;21) AML. We suggest that patients with core-binding factor AML should be screened for mutKIT at diagnosis for both prognostic and therapeutic purposes, given that activated KIT potentially can be targeted with novel tyrosine kinase inhibitors.
Nonrandom cytogenetic abnormalities are identified in the diagnostic bone marrow (BM) of approximately 55% of adult patients with de novo acute myeloid leukemia (AML)1 and constitute important independent prognostic factors for clinical outcome.1-6 Relatively common cytogenetic abnormalities [ie, inv(16)(p13q22) or t(16;16)(p13;q22), hereafter abbreviated inv(16), and t(8;21)(q22;q22)] disrupt genes encoding subunits of core-binding factor (CBF),7,8 a heterodimeric transcription factor involved in regulation of hematopoiesis.9 Therefore, the AML subgroups with inv(16) and t(8;21) are collectively referred to as CBF AML. When compared with other cytogenetic groups, patients with CBF AML have a relatively favorable outcome, particularly when treated with consolidation regimens containing repetitive cycles of high-dose cytarabine.10-12 Nevertheless, only approximately 50% of patients carrying these cytogenetic aberrations are alive at 5 years.13 This suggests that some patients have more aggressive leukemic phenotypes and indicates the need for treatment optimization with novel and/or more aggressive therapies. However, to implement risk-adapted treatments, it is necessary to identify markers that could predict clinical outcome at diagnosis, aid in patients’ prognostic stratification, and potentially serve as targets for novel agents. The mutated KIT gene (mutKIT) is a good candidate to fulfill these criteria. The KIT gene, located at chromosome band 4q11-12,14 encodes a 145-kD transmembrane glycoprotein, which is a member of the type III receptor tyrosine kinase family.15,16 The structure of type III receptor tyrosine kinases consists of five immunoglobulin-like domains in the extracellular portion of the receptor, a transmembrane and juxtamembrane domain, and an intracellular kinase domain.17 Following ligand binding, the receptor dimerizes, becomes phosphorylated, and activates downstream signaling pathways involved in proliferation, differentiation, and survival.18 Ligand-independent activation of KIT can be caused by gain-of-function mutations that have been reported in CBF AML19-27 and in other human malignancies (eg, GI stromal tumors,28-30 mastocytosis,31-33 and germ cell tumors34-36). In CBF AML, KIT mutations cluster most frequently within exon 17 (KIT17), which encodes the KIT activation loop (A-loop) in the kinase domain, and in exon 8 (KIT8), which encodes an evolutionarily highly conserved region in the extracellular portion of the KIT receptor, believed to play a role in receptor dimerization.17,19,20,22-24,26,27,37 Although it has been reported recently that A-loop mutations in KIT exon 17 (mutKIT17) predict a significantly worse outcome in both adult20,21 and pediatric22 AML with t(8;21), the prognostic impact of KIT mutations in AML with inv(16) remains controversial.20,24 Care et al24 reported that mutations in KIT exon 8 (mutKIT8) adversely affected the relapse rate, whereas Cairoli et al20 did not show any prognostic relevance of mutKIT in AML with inv(16). Lack of multivariable analyses for evaluation of mutKIT in the context of other prognostic factors,20,24 potential differences in the treatments received,24 and a small number of patients20 might have contributed to these discrepancies. In this study, we analyze the prognostic impact of KIT mutations in a relatively large group of uniformly treated adults with CBF AML assigned to postremission therapy with repetitive cycles of higher dose cytarabine.
Patients and Treatment Protocols We analyzed 110 patients with de novo CBF AML, 61 with inv(16) and 49 with t(8;21), who were enrolled on Cancer and Leukemia Group B (CALGB) treatment protocols 8525, 9222, 9621, and 19808, and provided written, institutional review board–approved, protocol-specific informed consent. The diagnosis of CBF AML was based on detection of inv(16) or t(8;21) by standard cytogenetic analyses carried out in CALGB-designated institutional laboratories, interpreted according to the International System for Human Cytogenetic Nomenclature,38 and confirmed by central karyotype review. All patients were enrolled on the prospective cytogenetics companion study CALGB 8461.2 Details of the therapeutic protocols have been reported previously.39-43 All patients received so-called 7 + 3 induction with cytarabine 100 to 200 mg/m2/d for 7 days in combination with daunorubicin alone or with daunorubicin and etoposide with or without the multidrug resistance modulator PSC-833. Patients achieving complete remission (CR) were assigned to receive postremission therapy containing repetitive cycles of higher dose cytarabine according to one of the following schedules: three or four courses of cytarabine 3 g/m2 every 12 hours on days 1, 3, and 5 administered during 3 hours; or four courses of cytarabine 400 mg/m2 as a continuous 24-hour infusion for 5 days. No significant differences in outcome between the consolidation schedules were observed in a previous CALGB study.13
Criteria for Response, Relapse, and Definition of Clinical End Points Overall survival (OS) was measured from the date enrolled onto the study until death or date last known alive. Cumulative incidence of relapse (CIR) was measured, only in patients who achieved a CR, from the CR date to date of relapse, death, or date last known alive, for which death in CR was considered a competing risk.
Samples, RNA Extraction, and Reverse Transcriptase Polymerase Chain Reaction Total RNA was extracted using Trizol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. For each sample, 2 µg of RNA were reverse transcribed into cDNA using random hexamer primers as described previously.45 cDNAs were amplified in a 50-µL polymerase chain reaction (PCR) using AmpliTaq Gold (Applied Biosystems, Foster City, CA). PCR fragments of 180 base pairs spanning the entire KIT8 (forward primer, ATTCTGACGTCAATGCTGCC; reverse primer, GGCAGTACAGAAGCAGAGCAT) and of 270 base pairs spanning the entire KIT17 (primer sequences reported previously46) were amplified. Negative controls were included in every step of the assays. PCR products of appropriate size were identified on agarose gels for all analyzed samples.
Mutational Screening by Denaturing High-Performance Liquid Chromatography
Mutation Confirmation by Direct Sequencing and Cloning
Statistical Analysis
To determine if mutational KIT status was associated with outcome once we adjust for other important variables, proportional hazards models were constructed for OS, and multivariable models using Gray’s method were constructed for CIR,48 using a forward-selection procedure. Variables entered into the model were significant at All analyses were performed by the CALGB Statistical Center.
Incidence and Type of KIT Mutations at Diagnosis Mutations in the KIT gene were found in 18 (29.5%) of the 61 patients with inv(16) and in 11 (22%) of the 49 patients with t(8;21) (Table 1).
All mutations found in KIT17 clustered within the A-loop. MutKIT17s were found in 10 (16%) patients with inv(16) and resulted in replacement of amino acid D816. Nine (18%) patients with t(8;21) had mutKIT17, which resulted in replacement of amino acids D816 or N822 in most patients. One patient with t(8;21) had a double mutation in KIT17 (N822Y and S821G), and another had a novel insertion of the amino acid triplet SLL between L813 and A814. As previously reported,24,27 all mutKIT8s were either small deletions or deletions and insertions involving (in all but one patient) codon D419, causing its loss or replacement. A sole mutKIT8 was detected in eight (13%) and two (4%) patients with inv(16) and t(8;21), respectively. Five patients—three with inv(16) and two with t(8;21)—had a mutKIT8 in addition to mutKIT17. However, the concurrent mutKIT8 was detectable at a considerably lower level compared with the mutKIT17 in all five patients. Thus, for our study, these patients were analyzed together with patients with mutKIT17.
Clinical Characteristics at Diagnosis
Patients with inv(16) and mutKIT were older (P = .001), more frequently male (P = .05), and had a higher percentage of PB blasts (P = .008) compared with those with wtKIT. In subset analyses, in addition to the aforementioned characteristics, patients with mutKIT17 had a higher percentage of BM blasts (P = .04) than those with wtKIT. The only difference between patients with sole mutKIT8 and those with wtKIT was that the former were older (P = .04). Among patients with t(8;21), there were no significant differences in pretreatment characteristics between mutKIT and wtKIT patients. The same was true when we compared mutKIT17 and wtKIT patients (data not shown). Because only two patients with t(8;21) had sole mutKIT8, a comparison with the wtKIT group could not be performed.
Prognostic Impact of KIT Mutations in Patients With inv(16) The outcome data with respect to the mutational status of KIT are summarized in Table 2. The CR rates of mutKIT and wtKIT patients were not significantly different. This remained true when both mutKIT17 and sole mutKIT8 were compared separately with the wtKIT group. Patients with mutKIT had a worse CIR than patients with wtKIT (P = .05). The estimated 5-year CIR rates were 56% for mutKIT and 29% for wtKIT patients (Fig 1A). The adverse effect of mutKIT on CIR was due mainly to the presence of mutKIT17 (Fig 1B), where the estimated 5-year CIR rate was 80% (Table 2). Although there was a significant difference in CIR between mutKIT17 and wtKIT (P = .002), no significant difference in CIR between sole mutKIT8 and wtKIT patients was observed. Once adjusted for sex, patients with mutKIT17 had more than six times the risk of relapse (P < .001) than wtKIT patients (Table 4).
MutKIT also adversely influenced survival (P = .009; Fig 2; Table 4), once the model was adjusted for sex. Results were similar in subset analyses for both patients with mutKIT17 and those with sole mutKIT8 (Table 4).
Prognostic Impact of KIT Mutations in Patients With t(8;21) Forty-three patients (88%) with t(8;21) achieved a CR. With a median follow-up of 5.3 years (range, 1.3 to 10.5 years), the 5-year CIR and OS rates were 44% and 46%, respectively. The outcome data with respect to the mutational status of KIT are listed in Table 3. There was no difference in CR rates between the mutKIT and the wtKIT groups. Patients with mutKIT had a higher CIR than patients with wtKIT (P = .017). The estimated 5-year CIR was 70% for mutKIT patients compared with 36% for wtKIT patients (Fig 3). When adjusted for percentage of PB blasts, mutKIT (P = .004) remained an independent predictor for relapse. Patients with mutKIT had an estimated risk of relapse five times higher than wtKIT patients (Table 5). No difference in OS was observed between the two groups (Table 3). The same was true when we compared mutKIT17 with wtKIT (data not shown).
Only two patients with t(8;21) had sole mutKIT8. Both achieved a CR. One patient experienced a relapse and subsequently died after 1.4 years, whereas the other is still alive and continuously disease free at 2.1 years.
The prognostic significance of KIT mutations in patients with inv(16) has been analyzed previously,20,24 but the results have been inconsistent. Care et al24 found that mutKIT8 adversely affected the relapse rate but not OS, whereas Cairoli et al,20 in a smaller study, were unable to show a prognostic impact of KIT mutations. We report here, in a relatively large cohort of homogeneously treated AML patients with inv(16), that the presence of KIT mutations conferred a higher risk of relapse. Notably, after subset analyses, it has become clear for the first time that the difference in CIR between patients with and without mutKIT could be attributed to the presence of mutKIT17. The mutKIT17 patients had more than six times the risk of relapse compared with those without KIT mutations. We also observed a negative impact of KIT mutations, both mutKIT17 and mutKIT8, on survival after adjusting for sex. Although males survived longer than the females on average, neither males nor females with KIT mutations fared as well as their counterparts without KIT mutations (data not shown). Future studies including a larger number of adequately treated patients are necessary to confirm our results. We also found that mutKIT predicted a higher relapse risk in the group of AML patients with t(8;21), in which all but two patients with mutKIT had mutKIT17. Similarly, two groups have recently reported the adverse impact of KIT mutations in adult t(8;21) AML.20,21 Schnittger et al,21 who evaluated exclusively patients with KIT17 mutations at codon D816, showed that these mutations adversely affected both the event-free survival and OS. Cairoli et al20 analyzed a group of patients with mutKIT located within exons 17, 8, and 11. As a group, they had a higher risk of relapse and shorter survival than patients with wtKIT. However, in subset analyses, only mutKIT17 were shown to have prognostic relevance.20 In contrast to the aforementioned studies, we did not observe an adverse effect of mutKIT on survival. This discrepancy among studies might be related to differences in treatment regimens, including salvage therapy, and the relatively small numbers of patients analyzed, but other potential contributing clinical and molecular factors remain to be elucidated. Detection of KIT mutations in CBF AML is important not only for outcome prediction, but also has therapeutic implications. It has been shown that, akin to other mutations of tyrosine kinase (TK) genes in leukemia, such as BCR-ABL or FLT3 internal tandem duplication, gain-of-function KIT mutations may serve as a target for TK inhibitors. Consistent with previous studies, we observed that mutKIT17 in patients with inv(16) occurred exclusively at codon D816,20,21,24 whereas in patients with t(8;21), mutKIT17 occurred mainly at codons D816 or N822.23,26 In one patient with t(8;21), we describe a novel mutation in KIT exon 17 that led to an insertion of the amino acid triplet serine-leucine-leucine in the A-loop. Although we observed several different mutKIT8s, either small deletions or deletions and insertions, all but one affected the same codon D419, a finding consistent with previous studies.24,27 Given the aforementioned heterogeneity of KIT mutations, it is crucial to determine the exact type of KIT mutation in each case because specific TK inhibitors are active against particular KIT mutations. In vitro experiments showed that cells carrying exon 17 mutations involving the N822 in the A-loop23,49 or variants of mutKIT850-52 are sensitive to the TK inhibitor imatinib. Indeed, variable responses to imatinib given as a single agent or in combination with other chemotherapeutics have been reported anecdotally in CBF AML with mutKIT8, whereas patients with D816 mutations did not show any response.53,54 Nevertheless, in vitro data have demonstrated that D816 mutations in the A-loop can be targeted successfully with other TK inhibitor compounds such as PKC41221,55,56 or dasatinib.57 Importantly, clinical activity of PKC412 has been reported in a patient with mast cell leukemia harboring a mutation occurring at codon D816,58 and a phase II trial with PKC412 for patients with aggressive systemic mastocytosis and mast cell leukemia (www.ClinicalTrials.gov, identifier NCT00233454) has been initiated recently. In conclusion, we show that KIT mutations, especially those occurring within sequences of exon 17 encoding the A-loop, represent the first molecular prognostic marker in AML with inv(16) and t(8;21). On the basis of our results and those of others,20,21,24 we propose that screening for KIT mutations be performed in all CBF AML patients with inv(16) and t(8;21) for risk stratification and, potentially, therapeutic purposes. Because particular TK inhibitors are active against specific KIT mutations, it is essential to determine the exact type of KIT mutation to decide which TK inhibitor might be active in a given patient with a KIT mutation. We believe that our results and the literature data20,21,24,49-58 provide a rationale for designing a future clinical trial that will investigate the use of TK inhibitors as part of therapy administered to CBF AML patients with KIT mutations.
The following Cancer and Leukemia Group B institutions, principal investigators, and cytogeneticists participated in this study: North Shore University Hospital, Manhasset, NY: Daniel R. Budman, Natalie B. Kardon and Prasad R.K. Koduru (grant no. CA35279); Wake Forest University School of Medicine, Winston-Salem, NC: David D. Hurd, P. Nagesh Rao, Wendy L. Flejter, and Mark J. Pettenati (Grant No. CA03927); The Ohio State University Medical Center, Columbus, OH: Clara D. Bloomfield, Karl S. Theil, and Nyla Heerema (Grant No. CA77658); Dana-Farber Cancer Institute, Boston, MA: George P. Canellos, Ramana Tantravahi, Cynthia C. Morton, Leonard L. Atkins, and Paola Dal Cin (Grant No. CA32291); Washington University School of Medicine, St Louis, MO: Nancy L. Bartlett, Jaime Garcia-Heras, and Michael S. Watson (Grant No. CA77440); University of Alabama at Birmingham: Robert Diasio and Andrew J. Carroll (Grant No. CA47545); Parkview Hospital, Ft Wayne, IN: Sreenivasa Nattam and Patricia I. Bader; Dartmouth Medical School, Lebanon, NH: Marc S. Ernstoff, Doris H. Wurster-Hill, and Thuluvancheri K. Mohandas (Grant No. CA04326); University of Maryland Cancer Center, Baltimore, MD: Martin Edelman, Maimon M. Cohen, and Judith Stamberg (Grant No. CA31983); Mount Sinai School of Medicine, New York, NY: Lewis R. Silverman and Vesna Najfeld (Grant No. CA04457); Weill Medical College of Cornell University, New York, NY: Scott Wadler, Ram S. Verma (deceased), and Prasad R.K. Koduru (Grant No. CA07968); Duke University Medical Center, Durham, NC: Jeffrey Crawford, Mazin B. Qumsiyeh, and Barbara K. Goodman (Grant No. CA47577); Roswell Park Cancer Institute, Buffalo, NY: Ellis G. Levine and Anne Marie W. Block (Grant No. CA02599); University of California, San Diego, CA: Joanne E. Mortimer, E. Robert Wassman, Renée Bernstein (deceased), and Marie L. Dell’Aquila (Grant No. CA11789); University of Iowa Hospitals, Iowa City, IA: Gerald H. Clamon and Shivanand R. Patil (Grant No. CA47642); University of Missouri/Ellis Fischel Cancer Center, Columbia, MO: Michael C. Perry, Judith H. Miles, and Tim H. Huang (Grant No. CA12046); University of Puerto Rico, San Juan, PR: Enrique Velez-Garcia; University of Vermont, Burlington, VT: Hyman B. Muss, Elizabeth F. Allen, and Mary Tang (Grant No. CA77406); University of Chicago Medical Center, Chicago, IL: Gini Fleming, Katrin M. Carlson, and Michelle M. Le Beau (Grant No. CA41287); University of Illinois MB CCOP, Chicago, IL: Lawrence E. Feldman, Maureen M. McCorquodale, and Valerie Lindgren (Grant No. CA74811); University of North Carolina, Chapel Hill, NC: Thomas Shea and Kathleen W. Rao (Grant No. CA47559); Walter Reed Army Medical Center, Washington, DC: Thomas Reid and Digamber S. Borgaonkar (Grant No. CA26806); Christiana Care Health Services Inc, Newark, DE: Stephen S. Grubbs and Jeanne M. Meck (Grant No. CA45418); Columbia-Presbyterian Medical Center, New York, NY: Rose R. Ellison and Dorothy Warburton (Grant No. CA12011); Eastern Maine Medical Center, Bangor, ME: Philip L. Brooks and Laurent J. Beauregard (Grant No. CA35406); Georgetown University Medical Center, Washington, DC: Edward P. Gelmann and Jeanne M. Meck (Grant No. CA77597); Long Island Jewish Medical Center CCOP, Lake Success, NY: Kanti R. Rai and Prasad R.K. Koduru (Grant No. CA11028); SUNY Upstate Medical University, Syracuse, NY: Stephen L. Graziano and Constance K. Stein (Grant No. CA21060); University of Massachusetts Medical School, Worcester, MA: William W. Walsh and Vikram Jaswaney (Grant No. CA37135); University of Nebraska Medical Center, Omaha, NE: Anne Kessinger and Warren G. Sanger (Grant No. CA77298); University of Tennessee Cancer Center, Memphis, TN: Harvey B. Niell and Sugandhi A. Tharapel (Grant No. CA47555); Western Pennsylvania Hospital, Pittsburgh, PA: Richard K. Shadduck and Gerard R. Diggans; Rhode Island Hospital, Providence, RI: William Sikov and Shelly L. Kerman (Grant No. CA08025).
The authors indicated no potential conflicts of interest.
Supported in part by National Cancer Institute (Bethesda, MD) Grants No. CA77658, CA101140, CA31946, CA16058, CA102031, CA41287, CA095512, and K08-CA90469, and The Coleman Leukemia Research Foundation. Presented at the Plenary Session of the 42nd Annual Meeting of the American Society of Clinical Oncology, Atlanta, GA, June 2-6, 2006. Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.
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Copyright © 2006 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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ABSTRACT
INTRODUCTION
1,500/µL and platelets more than 100,000/µL in blood (peripheral blood [PB]) was mandatory, as well as no evidence for circulating blasts and/or extramedullary leukemia. Relapse was defined as reoccurrence of more than 5% of leukemic blasts in BM, reappearance of circulating blasts, or the development of extramedullary leukemia.
2% and 
= .05, unless found to be confounded with mutational KIT status, defined by a change in the estimated coefficient by at least 20%. Variables, other than KIT status, considered for model inclusion were hemoglobin, platelet and WBC counts, percentage of blasts in the PB and BM, age, race, sex, and secondary cytogenetic aberrations. Estimates of hazard ratios and corresponding 95% CIs were obtained for each variable in the final models, and the proportional hazards assumption was checked individually for each variable in the OS models. Adjusted survival curves were generated using average covariate values. 
