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J Clin Oncol. 2010 May 10; 28(14): 2348–2355.
Published online 2010 April 5. doi:  10.1200/JCO.2009.27.3730
PMCID: PMC2881719

IDH1 and IDH2 Gene Mutations Identify Novel Molecular Subsets Within De Novo Cytogenetically Normal Acute Myeloid Leukemia: A Cancer and Leukemia Group B Study

Abstract

Purpose

To analyze the frequency and associations with prognostic markers and outcome of mutations in IDH genes encoding isocitrate dehydrogenases in adult de novo cytogenetically normal acute myeloid leukemia (CN-AML).

Patients and Methods

Diagnostic bone marrow or blood samples from 358 patients were analyzed for IDH1 and IDH2 mutations by DNA polymerase chain reaction amplification/sequencing. FLT3, NPM1, CEBPA, WT1, and MLL mutational analyses and gene- and microRNA-expression profiling were performed centrally.

Results

IDH mutations were found in 33% of the patients. IDH1 mutations were detected in 49 patients (14%; 47 with R132). IDH2 mutations, previously unreported in AML, were detected in 69 patients (19%; 13 with R172 and 56 with R140). R172 IDH2 mutations were mutually exclusive with all other prognostic mutations analyzed. Younger age (< 60 years), molecular low-risk (NPM1-mutated/FLT3-internal tandem duplication–negative) IDH1-mutated patients had shorter disease-free survival than molecular low-risk IDH1/IDH2-wild-type (wt) patients (P = .046). R172 IDH2-mutated patients had lower complete remission rates than IDH1/IDH2wt patients (P = .007). Distinctive microarray gene- and microRNA-expression profiles accurately predicted R172 IDH2 mutations. The highest expressed gene and microRNAs in R172 IDH2-mutated patients compared with the IDH1/IDH2wt patients were APP (previously associated with complex karyotype AML) and miR-1 and miR-133 (involved in embryonal stem-cell differentiation), respectively.

Conclusion

IDH1 and IDH2 mutations are recurrent in CN-AML and have an unfavorable impact on outcome. The R172 IDH2 mutations, previously unreported in AML, characterize a novel subset of CN-AML patients lacking other prognostic mutations and associate with unique gene- and microRNA-expression profiles that may lead to the discovery of novel, therapeutically targetable leukemogenic mechanisms.

INTRODUCTION

Despite progress in understanding mechanisms of leukemogenesis and improvement in treatment, only approximately 40% of younger (age < 60 years) and 10% of older (age ≥ 60 years) adults with acute myeloid leukemia (AML) achieve long-term survival.14 These results underscore the need for novel therapeutic strategies that would improve outcome. To this end, identification of subsets of patients with distinct clinical and biologic features that would help to stratify them to specific risk-adapted and/or molecularly targeted therapies is imperative.5,6

Cytogenetically normal AML (CN-AML) is the largest group among both younger and older AML patients and the best characterized molecularly.57 During the last 15 years, recurring mutations with prognostic significance in genes such as FLT3,8,9 NPM1,10,11 CEBPA,12,13 WT1,14,15 and MLL16,17 have been identified in de novo CN-AML. These markers are not mutually exclusive, and combinations of them may further refine prediction of the risk of adverse events. Thus, patients who carry an NPM1 mutation but not an FLT3 internal tandem duplication (ITD) are in the molecular low-risk group because they have a better outcome than patients who lack NPM1 mutations and/or carry an FLT3-ITD and therefore are in the molecular high-risk group.5,6 Among the latter, however, those who also harbor CEBPA mutations have outcomes similar to that of patients with mutated NPM1 and no FLT3-ITD.13 While the majority of CN-AML patients harbor one or more of the aforementioned mutations, in approximately 15% of the patients, no mutations have been detected,18 suggesting the existence of hitherto undiscovered genetic alterations contributing to leukemogenesis and defining molecular risk for these patients.

Recent studies revealed that mutations in IDH1 and IDH2, the genes encoding isoforms of the nicotinamide adenine dinucleotide phosphate–dependent isocitrate dehydrogenases, are recurrent in brain tumors, including WHO grade 4 gliomas and WHO grade 2 and 3 astrocytomas and oligodendrogliomas, and are associated with favorable outcome.19,20 Importantly, an IDH1 mutation was also discovered through massively parallel DNA sequencing analysis of the genome of a patient with CN-AML.21 In the same study,21 15 IDH1 mutations, but no IDH2 mutations, were also found in a validation set of 187 AML patients. An analysis of overall survival (OS) of this patient population (n = 188), which was heterogeneous with regard to AML type (de novo v secondary), age, and cytogenetics, showed no independent prognostic significance of IDH1 mutations. However, a subgroup analysis showed that IDH1 mutations were associated with CN-AML, being detected in 13 (16%) of 80 such patients, and that they conferred adverse prognosis in the absence of NPM1 mutations.21

To corroborate these preliminary data, we analyzed IDH1 and IDH2 mutations in a homogeneous cohort of 358 adults with de novo CN-AML treated with age-adapted intensive chemotherapy regimens on Cancer and Leukemia Group B (CALGB) first-line protocols and comprehensively characterized other gene mutations associated with outcome.

PATIENTS AND METHODS

Patients, Cytogenetic Analysis, and Treatment

We studied pretreatment bone marrow and blood samples with ≥ 20% blasts from 358 patients age 19 to 83 years with de novo CN-AML. Cytogenetic analyses at diagnosis were confirmed by central karyotype review.22 To establish CN-AML, ≥ 20 metaphase cells from diagnostic bone marrow had to be analyzed and the karyotype had to be normal.23 Institutional review board–approved informed consent for participation in the studies was obtained from all patients. Younger patients (age < 60 years; n = 159) were treated on CALGB 962124 and 1980825 protocols and older patients (age ≥ 60 years; n = 199) were enrolled on protocols 8525,26 8923,27 9420,28 9720,29 or 1020130 (for treatment details, see Appendix, online only). No patient included in our analysis received allogeneic transplantation in first complete remission (CR). The median follow-up for younger and older patients alive and included in this analysis was 7.0 and 3.8 years, respectively.

Molecular Analyses

For IDH1 and IDH2 mutational analyses, DNA fragments spanning exons 4 of IDH1 and IDH2, previously identified as “hot spots” for mutations in these genes,20 were amplified by polymerase chain reaction and directly sequenced as detailed in the Appendix. Other molecular markers—FLT3-ITD,9 FLT3 tyrosine kinase domain (FLT3-TKD) mutations,31 MLL partial tandem duplication (MLL-PTD),17,32 and mutations in the NPM1,33 CEBPA,13 and WT114 genes—were assessed centrally as previously reported.

Genome-Wide Expression Analyses

Gene- and microRNA-expression profiling were conducted using the Affymetrix U133 Plus 2.0 array (Affymetrix, Santa Clara, CA) and the Ohio State University custom microRNA array (OSU_CCC version 4.0), respectively, as reported previously,34,35 and described in the Appendix.

Statistical Analysis

Definitions of clinical end points—CR, disease-free survival (DFS), and OS—are provided in the Appendix. The differences among patients in baseline clinical and molecular features according to their IDH1 and IDH2 mutational status were tested using the Fisher's exact and Wilcoxon rank sum tests for categoric and continuous variables, respectively. Estimated probabilities of DFS and OS were calculated using the Kaplan-Meier method, and the log-rank test evaluated differences between survival distributions.

For expression profiling, summary measures of gene- and microRNA-expression levels were computed, normalized, and filtered (see Appendix). Expression signatures were derived by comparing gene- and microRNA-expression levels among patients with distinct types of IDH mutations and patients with wild-type IDH1 and IDH2 (IDH1/IDH2wt). Univariable significance levels of 0.001 for gene- and 0.005 for microRNA-expression profiling were used to determine the gene probe sets and microRNA probes comprising the signatures, respectively. Prediction of IDH mutation status using gene- and microRNA-expression profiles is described in the Appendix. All analyses were performed by the CALGB Statistical Center.

RESULTS

Frequency of IDH2 and IDH2 Mutations

Of the 358 AML patients analyzed, 118 (33%) harbored missense mutations in IDH genes. Forty-nine patients (14%) had IDH1 mutations, including R132 mutations detected in 46 patients, V71 in two patients, and concurrent R132 and V71 mutations in one patient (Table 1). Sixty-nine patients (19%) had IDH2 mutations: 56 were R140 and 13 were R172 mutations (Table 1). No patient had both IDH1 and IDH2 mutations.

Table 1.
Types of IDH1 and IDH2 Mutations in CN-AML

Associations of IDH Mutations With Pretreatment Characteristics

Comparisons of pretreatment clinical characteristics of IDH1- or IDH2-mutated patients with those of IDH1/IDH2wt patients are reported in Table 2. All types of IDH mutations were significantly associated with higher platelet counts, IDH2 mutations were associated with older age, and R172 IDH2 mutations were associated with low WBC and a low percentage of circulating blasts.

Table 2.
Clinical Characteristics According to IDH Mutational Status in De Novo Cytogenetically Normal Acute Myeloid Leukemia

At diagnosis, patients with IDH1 mutations were less frequently FLT3-ITD–positive (P = .02), were more often categorized in the molecular low-risk group (NPM1-mutated/FLT3-ITD–negative; P = .003), and had a trend for lower frequencies of WT1 (P = .06) and CEBPA mutations (P = .08) compared with IDH1/IDH2wt patients (Table 3).

Table 3.
Molecular Characteristics According to IDH Mutational Status in De Novo Cytogenetically Normal Acute Myeloid Leukemia

Patients with R140 IDH2 mutations had a lower frequency of WT1 mutations (P = .007) than IDH1/IDH2wt patients. Strikingly, patients with R172 IDH2 mutations did not carry any other prognostic mutations, including FLT3-ITD, FLT3-TKD, MLL-PTD, or mutations in the NPM1, WT1, or CEBPA genes (Table 3).

Associations of IDH Mutations With Clinical Outcome

Considering all patients with IDH1 mutations, there was no difference in outcome compared with IDH1/IDH2wt patients (Appendix Table A1, online only). However, in an age group–stratified analysis, we observed a prognostic impact of IDH1 mutations on the subset of younger (age < 60 years) patients in the molecular low-risk group (NPM1-mutated/FLT3-ITD–negative). IDH1-mutated patients (all with R132 IDH1 mutation; see Appendix Table A2, online only for other clinical and molecular characteristics) had a significantly worse DFS (P = .046; 5-year DFS rates, 42% v 59%) and a trend for worse OS (P = .14; 5-year OS rates, 50% v 63%) compared with IDH1/IDH2wt patients (Figs 1A and and11B).

Fig 1.
Impact of distinct IDH mutation types on clinical outcome of patients with cytogenetically normal acute myeloid leukemia. (A) Disease-free survival and (B) overall survival of younger molecular low-risk patients according to IDH1 mutation status. (C) ...

With regard to IDH2 mutations, the outcome of patients with R140 IDH2 mutations did not differ significantly from the outcome of IDH1/IDH2wt patients (Appendix Table A1). However, R172 IDH2-mutated patients had a significantly lower CR rate than those with IDH1/IDH2wt (38% v 75%; P = .007; Fig 1C). When analysis was limited to older patients, who represented most patients with this mutation (77%), those with R172 IDH2 mutations had a lower CR rate (20% v 67%; P = .005) than IDH1/IDH2wt patients (Fig 1C). The estimated 3-year OS rates were 0% versus 17% for older patients with R172 IDH2 mutations compared with IDH1/IDH2wt patients, but the difference in OS duration for the two groups was not significant.

Since R172 IDH2 mutations were mutually exclusive with NPM1 mutations, to account for the potentially favorable clinical impact of NPM1 mutations on achievement of CR,33 we compared the outcome of patients with R172 IDH2 mutations with that of IDH1/IDH2wt patients without NPM1 mutations. We focused only on older patients because they represented the vast majority of patients with R172 IDH2 mutation. In this prognostically unfavorable subset, patients with R172 IDH2 mutations showed a trend for a lower CR rate (20% v 56%; P = .08).

Biologic Insights Concerning R172 IDH2 Mutations

To gain biologic insights into the potentially unfavorable prognostic significance of R172 IDH2 mutations, which are mutually exclusive with any other prognostically relevant mutations and therefore likely to identify a novel subset of CN-AML, we derived a gene-expression signature by comparing R172 IDH2-mutated patients with IDH1/IDH2wt patients. Because 77% of patients with R172 IDH2 mutations were older, to eliminate age-dependent bias, we analyzed only patients age ≥ 60 years. Of the 451 differentially expressed probe sets (P < .001), 365 were upregulated and 86 were downregulated in patients with R172 IDH2 mutations (Fig 2A; Appendix Table A3, online only).

Fig 2.
Genome-wide gene- and microRNA-expression profiles associated with R172 IDH2 mutations. (A) Gene-expression and (B) microRNA-expression signatures, derived from comparing older patients with R172 IDH2 mutations and those with the wild-type IDH1/IDH2 genes. ...

To assess the accuracy of this gene-expression signature to correctly identify R172 IDH2-mutated patients versus those with IDH1/IDH2wt, we conducted a leave-one-out cross-validated prediction analysis. The mutation status of 95.5% of patients (including five of six R172 IDH2-mutated patients) was correctly predicted (Table 4).

Table 4.
Accuracy of Prediction of the R172 IDH2 Mutational Status in Older CN-AML Using Leave-One-Out Cross-Validated Prediction Analysis From Gene- and MicroRNA-Expression Profiles

Among the most upregulated probe sets in R172 IDH2-mutated patients were those representing APP (nine-fold), CXCL12 (eight-fold), PAWR (eight-fold), CDC42BPA (eight-fold), and SPARC (seven-fold; Appendix Table A3). APP was previously reported to be upregulated in AML patients with complex karyotype.36 Polymorphism in CXCL12 (also known as SDF1) was associated with increased circulating blasts and extramedullary disease in AML.37 PAWR was found to regulate WT1 activity and to be overexpressed in myelodysplastic syndromes progressing to AML.38 In addition, CDC42BPA, although not directly associated with AML, seemingly participates in tumor cell invasion.39 In contrast, SPARC, encoding a matricellular glycoprotein with growth-inhibitory and antiangiogenic functions, was found to have lower expression in MLL-associated AML and tumor suppressor activity.40 Other genes of interest upregulated in the R172 IDH2-mutated patients were ID1 (four-fold), whose expression was recently correlated with poor outcome in AML41; ABCB1 (MDR1; five-fold) mediating chemoresistance42; and KRAS2 (2.6-fold), which is constitutively activated in several human cancers including AML.43

The downregulated genes we found include KYNU, which encodes a protein participating in the biosynthesis of NAD cofactors from tryptophan44; SUCLG2, involved in the Krebs cycle and mutated in Leigh-like syndrome45; CD93, involved in regulating phagocytosis of apoptotic cells and angiogenesis46; LY86 and LIST1, associated with immune response pathways47,48; and PTHR2, a receptor for the parathyroid hormone.49 To the best of our knowledge, none of these genes have previously been associated with AML.

Genome-wide profiling identifies aberrantly expressed microRNAs associated with distinct molecular subsets of CN-AML patients.50 Therefore, we derived a microRNA-expression signature associated with older R172 IDH2-mutated CN-AML. The signature comprised 24 differentially expressed (P < .005) probes, 13 of which were upregulated and 11 of which were downregulated in R172 IDH2-mutated patients (Fig 2B; Appendix Table A4, online only). In leave-one-out cross-validated prediction analysis, the mutation status of 93.2% of patients (including five of six R172 IDH2-mutated patients) was correctly predicted (Table 4).

Among the microRNAs most upregulated (> four-fold) in R172 IDH2-mutated patients were members of the miR-125 family (miR-125a-5p and miR-125b), miR-1, and miR-133. miR-125b has been shown to target the tumor suppressor gene TP53 and inhibit myeloid differentiation,51 whereas miR-1 and miR-133 have not been previously associated with human cancer, but they participate in cell fate decision mechanisms of pluripotent embryonic stem cells.52 Among the most downregulated probes were those representing mir-194-1, miR-526, miR-520a-3p, and mir-548b, none of which have previously been associated with normal hematopoiesis or AML.

DISCUSSION

Mutations in the IDH1 and IDH2 genes have been found in patients with glioma and predict favorable outcome.19 Using whole-genome sequencing and validation analyses, Mardis et al21 recently reported that IDH1 mutations can also be found in AML and are associated with normal karyotypes. Therefore, we analyzed a larger, more homogeneous cohort of de novo CN-AML (n = 358), comprising both younger and older patients treated with age-adapted chemotherapy regimens on first-line CALGB clinical trials.

The first salient finding of our study was that we not only confirmed the presence of IDH1 mutations but also found previously unreported IDH2 mutations in CN-AML.5 IDH1 mutations were found in 14% of our patients, which is similar to the findings of Mardis et al.21 In addition to the previously reported R132 IDH1 mutations, we identified three patients with a V71I IDH1 allele. Although this allele has been recently reported as a single nucleotide polymorphism (SNP), Bleeker et al53 did not find it in any of the 672 tumor samples and 84 cell lines they sequenced. This suggests that if V71I IDH1 is an SNP, it is rare and, therefore, the possibility that V71I IDH1 represents a novel IDH1 somatic or germline mutation associated with AML cannot be excluded.

Moreover, unlike Mardis et al,21 we also detected two different types of mutations in the IDH2 gene (ie, R140 and R172), which occurred with even greater frequency (19%) and, to the best of our knowledge, have not been previously reported in AML. Interestingly, while the R172 IDH2 mutation was previously found in gliomas, to the best of our knowledge, the R140 IDH2 mutation has not been previously reported in human cancer or normal tissue. Since changes in codon 140 detected in our patients led to the substitution of the arginine with three different amino acids (Table 1), it is likely that R140 IDH2 represents a somatic mutation associated with AML rather than a newly discovered SNP. Studies of normal tissues from R140 IDH2 AML patients are underway to confirm (or refute) the somatic nature of R140 IDH2. Both IDH2 mutations were associated with older age but, remarkably, only R172 IDH2 mutations were found in the absence of other recurrent mutations thereby identifying a novel subset of patients among those 15% of CN-AML patients for whom no prognostic gene mutation has been hitherto reported. When considered together, the frequency of mutations in genes encoding the isocitrate dehydrogenases is relatively high in CN-AML (33%), placing them among the most frequent mutations in CN-AML.

The second important finding relates to the prognostic significance of IDH mutations in specific age and molecular subsets of CN-AML. We showed that although IDH1 mutations did not affect outcome in the whole cohort of CN-AML patients, they conferred worse prognosis in younger patients with molecular low-risk CN-AML. These results differ from two previous studies reporting that IDH1 mutations conferred adverse outcome in NPM1wt patients with CN-AML21 or various karyotypes.54 Differences in sizes of patient cohorts analyzed, varying inclusion criteria (eg, we studied only de novo AML patients whereas Schnittger et al54 also analyzed secondary AML), age, and treatment administered might contribute to these discrepancies among studies, which require further investigation for resolution.

Most patients with R172 IDH2 mutations failed to achieve a CR following intensive cytarabine/anthracycline-based induction chemotherapy. Because NPM1 mutations are a strong, favorable prognosticator in older CN-AML patients,33 we also separately analyzed older patients without NPM1 mutations; even then, the CR rate of patients with R172 IDH2 mutations tended to be lower than that of IDH1/IDH2wt patients. These results suggest that it is the presence of the R172 IDH2 mutation itself rather than the absence of NPM1 mutations that decreases the odds of achieving CR. However, given the relatively small number of R172 IDH2-mutated patients in our cohort, larger studies should corroborate our results. Notably, in contrast with our data in CN-AML, R172 IDH2 mutations were reported to predict a favorable outcome in patients with gliomas,20 thereby supporting the notion that the prognostic significance of molecular markers may vary according to distinct biologic and/or therapeutic contexts in which they are evaluated. Furthermore, in contrast with R172 IDH2 mutations, the outcome of patients with R140 IDH2 mutations was not different from the outcome of patients with wt IDH1 and IDH2 genes, thereby suggesting different contributions to leukemogenesis from these two mutation types.

Finally, we showed that R172 IDH2 mutations in CN-AML are associated with unique gene- and microRNA-expression signatures. Although the signature did not include previously reported unfavorable prognosticators in CN-AML (ie, BAALC, ERG, and MN1),5561 it comprised other upregulated genes associated with adverse karyotypes (APP),36 unfavorable outcome (ID1),41 increased rate of extramedullary disease (CXCL12),37 or increased chemoresistance (ABCB1) in AML,42 supporting the negative prognostic significance of this mutation type. Furthermore, among microRNAs differentially expressed in R172 IDH2-mutated patients, we noted upregulation of miR-125b, previously found to block myeloid differentiation,51 and miR-1 and miR-133, not reported previously in AML but involved in embryonal stem-cell differentiation.52 Importantly, both gene- and microRNA-expression signatures appeared to predict the R172 IDH2 mutational status with high accuracy, thus supporting the view that patients with R172 IDH2 mutations profoundly differ biologically and clinically from patients with wt IDH1 and IDH2 alleles.

The mechanisms through which IDH1 and IDH2 mutations contribute to malignant transformation are under investigation. Thompson62 postulated that IDH1 and IDH2 mutations result in gain rather than loss of function, given the high frequency of somatic mutations affecting a single codon and the absence of other mutations causing gene inactivation. Indeed, Dang et al63 showed that the R132 IDH1 mutation causes the encoded enzyme to acquire the ability to convert α-ketoglutarate to 2-hydroxy-glutarate, which accumulates in the affected cells. This likely contributes to malignant transformation since inborn errors of 2-hydroxy-glutarate metabolism have been associated with an increased risk of brain tumors.63 While similar mechanisms might be operative in patients harboring IDH2 mutations, to the best of our knowledge, no functional study of the mutant proteins has been reported.

In summary, we report here that IDH1 mutations predict shorter DFS in younger molecular low-risk CN-AML patients, R172 IDH2 mutations are mutually exclusive with other known prognostic mutations and denote a novel subset of older CN-AML patients characterized by resistance to induction chemotherapy, and R140 IDH2 mutations do not appear to confer prognostic significance. By deriving gene- and microRNA-expression signatures, we uncovered intriguing features in R172 IDH2-mutated patients that may lead to better understanding of the biologic role of this mutation and to the design of novel therapies targeting aberrant isocitrate dehydrogenase–driven activation of metabolic pathways.

Acknowledgment

We thank Professor Albert de la Chapelle for the helpful discussion.

Appendix

The following Cancer and Leukemia Group B institutions, principal investigators, and cytogeneticists participated in this study: 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, Diane Minka, and Nyla A. Heerema (Grant No. CA77658); North Shore–Long Island Jewish Health System, Manhasset, NY: Daniel R. Budman and Prasad R.K. Koduru (Grant No. CA35279); University of Iowa Hospitals, Iowa City, IA: Daniel A. Vaena and Shivanand R. Patil (Grant No. CA47642); Roswell Park Cancer Institute, Buffalo, NY: Ellis G. Levine and AnneMarie W. Block (Grant No. CA02599); Duke University Medical Center, Durham, NC: Jeffrey Crawford, Mazin B. Qumsiyeh, John Eyre, and Barbara K. Goodman (Grant No. CA47577); University of Chicago Medical Center, Chicago, IL: Hedy L. Kindler, Diane Roulston, Katrin M. Carlson, Yanming Zhang, and Michelle M. Le Beau (Grant No. CA41287); Washington University School of Medicine, St. Louis, MO: Nancy L. Bartlett, Michael S. Watson, Eric C. Crawford, Peining Li, and Jaime Garcia-Heras (Grant No. CA77440); University of North Carolina, Chapel Hill, NC: Thomas C. Shea and Kathleen W. Rao (Grant No. CA47559); University of Massachusetts Medical Center, Worcester, MA: William V. Walsh, Vikram Jaswaney, Michael J. Mitchell, and Patricia Miron (Grant No. CA37135); Dartmouth Medical School, Lebanon, NH: Konstantin Dragnev, Doris H. Wurster-Hill, and Thuluvancheri K. Mohandas (Grant No. CA04326); Dana-Farber Cancer Institute, Boston, MA: Harold J. Burstein, Ramana Tantravahi, Leonard L. Atkins, Paola Dal Cin, and Cynthia C. Morton (Grant No. CA32291); Vermont Cancer Center, Burlington, VT: Steven M. Grunberg, Elizabeth F. Allen, and Mary Tang (Grant No. CA77406); Ft. Wayne Medical Oncology/Hematology, Ft. Wayne, IN: Sreenivasa Nattam and Patricia I. Bader; Eastern Maine Medical Center, Bangor, ME: Harvey M. Segal and Laurent J. Beauregard (Grant No. CA35406); Weill Medical College of Cornell University, New York, NY: John Leonard, Ram S. Verma, Prasad R.K. Koduru, Andrew J. Carroll, and Susan Mathew (Grant No. CA07968); Mount Sinai School of Medicine, New York, NY: Lewis R. Silverman and Vesna Najfeld (Grant No. CA04457); University of Puerto Rico School of Medicine, San Juan, PR: Eileen I. Pacheco, Paola Dal Cin, Leonard L. Atkins, and Cynthia C. Morton; Rhode Island Hospital, Providence, RI: William Sikov, Teresita Padre-Mendoza, Hon Fong L. Mark, Shelly L. Kerman, and Aurelia Meloni-Ehrig (Grant No. CA08025); State University of New York Upstate Medical University, Syracuse, NY: Stephen L. Graziano and Constance K. Stein (Grant No. CA21060); Minneapolis Veterans Administration Medical Center, Minneapolis, MN: Vicki A. Morrison and Sugandhi A. Tharapel (Grant No. CA47555); University of California at San Diego, San Diego, CA: Barbara A. Parker, Renée Bernstein, and Marie L. Dell'Aquila (Grant No. CA11789); Christiana Care Health Services, Newark, DE: Stephen S. Grubbs, Jeanne M. Meck, and Digamber S. Borgaonkar (Grant No. CA45418); Long Island Jewish Medical Center Community Clinical Oncology Program, Lake Success, NY: Kanti R. Rai and Prasad R.K. Koduru (Grant No. CA11028); University of Illinois at Chicago, Chicago, IL: David J. Peace, Maureen M. McCorquodale, and Kathleen E. Richkind (Grant No. CA74811); Western Pennsylvania Hospital, Pittsburgh, PA: John Lister and Gerard R. Diggans; University of Minnesota, Minneapolis, MN: Bruce A. Peterson, Diane C. Arthur, and Betsy A. Hirsch (Grant No. CA16450); University of Missouri/Ellis Fischel Cancer Center, Columbia, MO: Michael C. Perry and Tim H. Huang (Grant No. CA12046); University of Maryland Cancer Center, Baltimore, MD: Martin J. Edelman, Joseph R. Testa, Maimon M. Cohen, and Yi Ning (Grant No. CA31983); Walter Reed Army Medical Center, Washington, DC: Brendan M. Weiss, Rawatmal B. Surana, and Digamber S. Borgaonkar (Grant No. CA26806); Georgetown University Medical Center, Washington, DC: Minnetta C. Liu and Jeanne M. Meck (Grant No. CA77597); McGill Department of Oncology, Montreal, Quebec, Canada: J.L. Hutchison and Jacqueline Emond (Grant No. CA31809); Medical University of South Carolina, Charleston, SC: Mark R. Green, G. Shashidhar Pai, and Daynna J. Wolff (Grant No. CA03927); University of Nebraska Medical Center, Omaha, NE: Anne Kessinger and Warren G. Sanger (Grant No. CA77298); University of Alabama at Birmingham, Birmingham, AL: Robert Diasio and Andrew J. Carroll (Grant No. CA47545); University of Cincinnati Medical Center, Cincinnati, OH: Orlando J. Martelo and Ashok K. Srivastava (Grant No. CA47515); Columbia-Presbyterian Medical Center, New York, NY: Rose R. Ellison and Dorothy Warburton (Grant No. CA12011); Massachusetts General Hospital, Boston, MA: Jeffrey W. Clark, Paola Dal Cin, and Cynthia C. Morton (Grant No. CA 12449); Virginia Commonwealth University Minority-Based Community Clinical Oncology Program, Richmond, VA: John D. Roberts and Colleen Jackson-Cook (Grant No. CA52784); State University of New York Maimonides Medical Center, Brooklyn, NY: Sameer Rafla and Ram S. Verma (Grant No. CA25119); Southern Nevada Cancer Research Foundation Community Clinical Oncology Program, Las Vegas, NV: John A. Ellerton and Marie L. Dell'Aquila (Grant No. CA35421); University of California at San Francisco, San Francisco, CA: Charles J. Ryan and Kathleen E. Richkind (Grant No. CA60138).

Patients and Methods

Treatment.

Patients enrolled on Cancer and Leukemia Group B (CALGB) 19808 were randomly assigned to receive induction chemotherapy with cytarabine, daunorubicin, and etoposide with or without PSC 833 (valspodar), a multidrug resistance protein inhibitor.24 On achievement of complete response (CR), patients were assigned to intensification with high-dose cytarabine and etoposide for stem-cell mobilization followed by myeloablative treatment with busulfan and etoposide supported by autologous peripheral blood stem-cell transplantation. Patients enrolled on CALGB 9621 were treated similarly to those on CALGB 19808, as previously reported.25

Older patients were all treated with cytarabine/daunorubicin-based induction therapy followed by cytarabine-based consolidation therapy. Patients on CALGB 8525 were treated with induction chemotherapy consisting of cytarabine in combination with daunorubicin and were randomly assigned to consolidation with different doses of cytarabine followed by maintenance treatment.26 Patients on CALGB 8923 were treated with induction chemotherapy consisting of cytarabine in combination with daunorubicin and were randomly assigned to receive postremission therapy with cytarabine alone or in combination with mitoxantrone.27 Patients on CALGB 9420 and 9720 received induction chemotherapy consisting of cytarabine in combination with daunorubicin and etoposide, with (CALGB 9420) or with/without (CALGB 9720) the multidrug resistance protein modulator valspodar.28,29 Patients on CALGB 9420 received postremission therapy with cytarabine (2 g/m2/d) alone, and patients on CALGB 9720 received a single cytarabine/daunorubicin consolidation course identical to the induction regimen and were then randomly assigned to low-dose recombinant interleukin-2 maintenance therapy or none.28 Patients on CALGB 10201 received induction chemotherapy consisting of cytarabine and daunorubicin, with or without the BCL2 antisense oblimersen sodium. The consolidation regimen included two cycles of cytarabine (2 g/m2/d) with or without oblimersen (Marcucci G: J Clin Oncol 25:360s, 2007 [suppl; abstr 7012]).

Sample preparation.

Patients enrolled on the treatment protocols were also enrolled on the companion protocols CALGB 9665 (leukemia tissue bank), CALGB 8461 (cytogenetic studies), and CALGB 20202 (molecular studies in AML) and consented to pretreatment bone marrow (BM) and peripheral blood collection. Samples were subjected to Ficoll-Hypaque gradient separation, and mononuclear cells were cryopreserved until use. Genomic DNA extraction and quality control of the extracted nucleic acids were performed as reported elsewhere.14

The primers used for polymerase chain reaction (PCR) amplification were IDH1F: AGCTCTATATGCCATCACTGC, IDH1R: AACATGCAAAATCACATTATTGCC, IDH2F: AATTTTAGGACCCCCGTCTG, and IDH2R: CTGCAGAGACAAGAGGATGG.

PCR conditions for IDH1 and IDH2 amplifications were identical, using 20 to 50 ng genomic DNA and HotStar Taq DNA polymerase kit (Qiagen, Valencia, CA) in a 50-μL reaction with the following amplification program: denaturing at 95°C for 1 minute, annealing at 57°C for 1 minute, and extension at 72°C for 1 minute, for 35 cycles. The reactions were run in a DNA Engine Dyad Peltier Thermal Cycler (Bio-Rad, Hercules, CA). The PCR products were then sequenced as previously reported.20

Definition of clinical end points.

CR required absolute neutrophil counts ≥ 1,500/μL, platelet counts ≥ 100,000/μL, no leukemic blasts in the blood, BM cellularity greater than 20% with maturation of all cell lines, no Auer rods, less than 5% BM blast cells, and no evidence of extramedullary leukemia, all of which had persisted for at least 1 month. Relapse was defined by ≥ 5% BM blasts, circulating leukemic blasts, or the development of extramedullary leukemia. Overall survival was measured from the date the patient was enrolled onto the study until the date of death, and patients alive at last follow-up were censored. Disease-free survival was measured from the date of CR until the date of relapse or death; patients alive and relapse-free at last follow-up were censored (Cheson BD: J Clin Oncol 8:813-819, 1990).

Genome-wide gene- and microRNA-expression analyses.

For gene-expression microarrays, summary measures of gene expression were computed for each probe set using the robust multichip average method, which incorporates quantile normalization of arrays. Expression values were logged (base 2) before analysis. A filtering step was performed to remove probe sets that did not display significant variation in expression across arrays. In this procedure, a χ2 test was used to test whether the observed variance in expression of a probe set was significantly larger than the median observed variance in expression for all probe sets using α = .01 as the significance level. A total of 24,437 probe sets passed the filtering criterion. Comparisons of gene expression were made between R172 IDH2-mutated and IDH1/IDH2-wild-type (wt) patients (R172 IDH2-mutated, n = 6; IDH1/IDH2wt, n = 83) using a univariable significance level of 0.001.

For microRNA microarrays, signal intensities were calculated for each spot making an adjustment for local background. Intensities were log-transformed and log-intensities from replicate spots were averaged. Quantile normalization was performed on arrays using all human and mouse microRNA probes represented on the array. For each microRNA probe, an adjustment was made for batch effects (ie, differences in expression related to the batch in which arrays were hybridized). Further analysis was limited to the 895 unique human probes represented on the array. Comparisons of microRNA expression were made between R172 IDH2-mutated and IDH1/IDH2wt patients (R172 IDH2-mutated, n = 6; IDH1/IDH2wt, n = 82) using a univariable significance level of 0.005. Analyses were performed using BRB-ArrayTools Version 3.8.0 Beta_2 Release developed by Richard Simon, DSc, and Amy Peng Lam.

Prediction of IDH2 mutation status from expression profiles.

We implemented compound covariate prediction using leave-one-out cross-validation to predict R172 IDH2-mutated versus IDH1/IDH2wt status of patients from gene- and microRNA-expression profiles (Radmacher MD: J Comput Biol 9:505-511, 2002).56 For gene-expression arrays, each patient, one at a time, was removed from analysis and the expression profiles of the remaining R172 IDH2-mutated versus IDH1/IDH2wt patients were compared to derive a gene- or microRNA-expression signature. A compound covariate was then computed for each patient on the basis of this signature: the value of the compound covariate for patient i was ci = Σ wj xij, where xij is the log-transformed expression value for probe set j in patient i and wj is the weight assigned to probe set j (in this case, wj was set equal to the two-sample t statistic for the comparison of the R172 IDH2-mutated and IDH1/IDH2wt groups for probe set j). The sum is over all j probe sets included in the signature. A classification threshold was computed to be the midpoint of the means of the compound covariate values for the R172 IDH2-mutated and IDH1/IDH2wt groups. The compound covariate was then calculated for the left-out patient, and its IDH2 status was predicted by comparing its value to the classification threshold. This entire process was repeated until every patient had been left out one time and mutation status had been predicted. The overall accuracy of the prediction is indicated, as are the sensitivity and specificity for prediction of R172 IDH2 mutations.

Table A1.

Clinical Outcome of Patients With IDH1 or Patients With R140 IDH2 Mutation

Outcome EndpointIDH1-Mutated (n = 49)
R140 IDH2-Mutated (n = 56)
IDH1/IDH2wt (n = 240)
P (IDH1-Mutated v IDH1/IDH2-wt)P (R140 IDH2 Mutated v IDH1/IDH2wt)
%95% CI%95% CI%95% CI
Complete remission737075.86.40
Overall survival.33.58
    Median, years1.31.41.4
    Alive at 3 years2917 to 413926 to 523327 to 39
Disease-free survival.30.82
    Median, years1.11.31.1
    Disease-free at 3 years2814 to 432815 to 433225 to 39

Abbreviation: wt, wild-type.

Table A2.

Clinical and Molecular Characteristics According to IDH1 Mutational Status

CharacteristicIDH1-Mutated* (n = 14)
IDH1/IDH2wt (n = 38)
P
No.%No.%
Age, years.48
    Median4249
    Range21-5719-59
Male sex4292155.12
Race.65
    White12863489
    Nonwhite214411
Hemoglobin, g/dL.45
    Median9.69.2
    Range7.1-12.46.4-12.3
Platelet count, ×109/L.07
    Median14761
    Range11-38012-445
WBC count, ×109/L.40
    Median31.422.1
    Range1.7-127.71.6-146.0
Percentage of PB blasts.003
    Median8240
    Range10-890-90
Percentage of BM blasts.04
    Median7764
    Range42-9210-91
Extramedullary involvement4331950.34
    FLT3-TKD.41
        Present17821
        Absent13933079
    WT11.0
        Mutated1738
        Wild-type13933592
    CEBPA1.0
        Mutated0013
        Wild-type141003797
    MLL-PTD1.0
        Present0025
        Absent141003695

NOTE. Patients were younger than age 60 years and were molecular low-risk (ie, NMP1-mutated and FLT3-ITD–negative) with de novo cytogenetically normal acute myeloid leukemia.

Abbreviations: wt, wild-type; PB, peripheral blood; BM, bone marrow; ITD, internal tandem duplication; TKD, tyrosine kinase domain; PTD, partial tandem duplication.

*All patients harbored the R132IDH1 mutation.

Table A3.

Differentially Expressed (P < .001) Probe Sets Between R172 IDH2-Mutated (n = 6) and IDH1/IDH2wt (n = 83) Older Patients

Probe SetGene SymbolDescriptionFold-Change: R172/wt
Upregulated gene probes
    200602_atAPPAmyloid beta (A4) precursor protein9.50
    209687_atCXCL12Chemokine (C-X-C motif) ligand 12 (stromal cell-derived factor 1)8.01
    204004_atPAWRPRKC, apoptosis, WT1, regulator7.91
    214464_atCDC42BPACDC42 binding protein kinase alpha (DMPK-like)7.73
    203666_atCXCL12Chemokine (C-X-C motif) ligand 12 (stromal cell-derived factor 1)7.48
    226192_at7.39
    200665_s_atSPARCSecreted protein, acidic, cysteine-rich (osteonectin)7.30
    218901_atPLSCR4Phospholipid scramblase 47.28
    214953_s_atAPPAmyloid beta (A4) precursor protein7.10
    204005_s_atPAWRPRKC, apoptosis, WT1, regulator6.79
    215116_s_atDNM1Dynamin 16.77
    230896_atBEND4BEN domain containing 46.66
    221530_s_atBHLHE41Basic helix-loop-helix family, member e416.60
    213506_atF2RL1Coagulation factor II (thrombin) receptor-like 16.50
    236635_atZNF667Zinc finger protein 6676.23
    1554182_atTRIM74Tripartite motif-containing 746.18
    218086_atNPDC1Neural proliferation, differentiation and control, 15.98
    236793_at5.90
    235759_at5.85
    205839_s_atBZRAP1Benzodiazapine receptor (peripheral) associated protein 15.77
    201069_atMMP2Matrix metallopeptidase 2 (gelatinase A, 72 kda gelatinase, 72 kda type IV collagenase)5.73
    222862_s_atAK5Adenylate kinase 55.51
    226223_at5.35
    223885_atCALN1Calneuron 15.09
    239082_at5.07
    225342_atAK3L1Adenylate kinase 3-like 15.06
    209993_atABCB1ATP-binding cassette, subfamily B (MDR/TAP), member 14.96
    223125_s_atC1orf21Chromosome 1 open reading frame 214.92
    1570412_at4.90
    202018_s_atLTFLactotransferrin4.87
    230266_atRAB7BRAB7B, member RAS oncogene family4.82
    204348_s_atAK3L1Adenylate kinase 3-like 14.60
    37005_atNBL1Neuroblastoma, suppression of tumorigenicity 14.44
    208937_s_atID1Inhibitor of DNA binding 1, dominant negative helix-loop-helix protein4.23
    240671_at4.18
    204352_atTRAF5TNF receptor-associated factor 54.16
    219569_s_atTMEM22Transmembrane protein 224.14
    244741_s_atMGC9913Hypothetical protein MGC99134.14
    222281_s_at4.04
    226587_atSNRPNSmall nuclear ribonucleoprotein polypeptide N4.02
    230698_atCALN1Calneuron 13.97
    237591_atFLJ42957FLJ42957 protein3.89
    242457_at3.85
    227415_atDGKHDiacylglycerol kinase, eta3.81
    229715_at3.74
    209487_atRBPMSRNA binding protein with multiple splicing3.73
    202073_atOPTNOptineurin3.72
    226125_at3.67
    209994_s_atABCB1ATP-binding cassette, subfamily B (MDR/TAP), member 13.67
    230224_atZCCHC18Zinc finger, CCHC domain containing 183.56
    227108_atSTARD9Star-related lipid transfer (START) domain containing 93.56
    223126_s_atC1orf21Chromosome 1 open reading frame 213.54
    226591_atSNRPNSmall nuclear ribonucleoprotein polypeptide N3.53
    209982_s_atNRXN2Neurexin 23.53
    1555912_atST7OT1ST7 overlapping transcript 1 (non-protein coding)3.51
    218966_atMYO5CMyosin VC3.48
    215811_at3.43
    1558103_a_at3.40
    230175_s_at3.40
    221272_s_atC1orf21Chromosome 1 open reading frame 213.36
    201621_atNBL1Neuroblastoma, suppression of tumorigenicity 13.34
    238127_atFLJ41484Hypothetical LOC6506693.33
    207120_atZNF667Zinc finger protein 6673.26
    211110_s_atARAndrogen receptor3.23
    241916_at3.23
    207550_atMPLMyeloproliferative leukemia virus oncogene3.23
    212667_atSPARCSecreted protein, acidic, cysteine-rich (osteonectin)3.22
    204347_atAK3L1Adenylate kinase 3-like 13.20
    214373_at3.19
    221974_atIPWImprinted in Prader-Willi syndrome (non-protein coding)3.18
    213484_at3.18
    212607_atAKT3V-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma)3.14
    1557961_s_atLOC100127983Hypothetical protein LOC1001279833.04
    1558102_at3.02
    243364_atAUTS2Autism susceptibility candidate 22.97
    232511_at2.95
    232653_at2.93
    209291_atID4Inhibitor of DNA binding 4, dominant negative helix-loop-helix protein2.89
    212463_atCD59CD59 molecule, complement regulatory protein2.84
    219228_atZNF331Zinc finger protein 3312.83
    235831_at2.83
    212842_x_atRGPD5RANBP2-like and GRIP domain containing 52.82
    219308_s_atAK5Adenylate kinase 52.81
    238861_at2.80
    237571_at2.80
    243880_atGOSR2Golgi SNAP receptor complex member 22.77
    1554183_s_atTRIM74Tripartite motif-containing 742.76
    235421_atMAP3K8Mitogen-activated protein kinase kinase kinase 82.76
    238097_atLOC100128430Hypothetical protein LOC1001284302.73
    242406_at2.71
    240182_at2.70
    1559494_at2.68
    1555168_a_atCALN1Calneuron 12.67
    244726_at2.66
    233379_atFLJ14213Protor-22.65
    226922_atRANBP2RAN binding protein 22.63
    1563369_atFLJ42957FLJ42957 protein2.63
    1553204_atC20orf200Chromosome 20 open reading frame 2002.61
    218094_s_atDBNDD2Dysbindin (dystrobrevin binding protein 1) domain containing 22.61
    227845_s_atSHDSrc homology 2 domain containing transforming protein D2.60
    218898_atFAM57AFamily with sequence similarity 57, member A2.60
    235094_at2.58
    235408_x_atZNF117Zinc finger protein 1172.58
    224998_atCMTM4CKLF-like MARVEL transmembrane domain containing 42.58
    1559203_s_atKRASV-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog2.58
    240090_at2.57
    235099_atCMTM8CKLF-like MARVEL transmembrane domain containing 82.57
    229876_atPHKA1Phosphorylase kinase, alpha 1 (muscle)2.57
    222786_atCHST12Carbohydrate (chondroitin 4) sulfotransferase 122.55
    243952_atpsiTPTE22TPTE pseudogene2.55
    233029_atOBSCNObscurin, cytoskeletal calmodulin and titin-interacting rhogef2.55
    1555898_atLOC400986Protein immunoreactive with anti-PTH polyclonal antibodies2.54
    228580_atHTRA3Htra serine peptidase 32.53
    238643_at2.52
    236610_at2.51
    220450_at2.50
    1556474_a_atFLJ38379Hypothetical FLJ383792.48
    240903_at2.46
    1562245_a_atZNF578Zinc finger protein 5782.45
    235697_at2.44
    225305_atSLC25A29Solute carrier family 25, member 292.44
    225306_s_atSLC25A29Solute carrier family 25, member 292.43
    233055_at2.42
    225354_s_atSH3BGRL2SH3 domain binding glutamic acid-rich protein like 22.40
    244740_atMGC9913Hypothetical protein MGC99132.39
    219355_atCXorf57Chromosome X open reading frame 572.37
    221207_s_atNBEANeurobeachin2.36
    1553982_a_atRAB7BRAB7B, member RAS oncogene family2.34
    1555960_atHINT1Histidine triad nucleotide binding protein 12.34
    208498_s_atAMY1AAmylase, alpha 1A (salivary)2.33
    223377_x_atCISHCytokine inducible SH2-containing protein2.33
    1556008_a_at2.33
    1555833_a_atIRGQImmunity-related gtpase family, Q2.32
    241897_at2.32
    226197_at2.31
    230861_atDKFZP434L187Hypothetical LOC260822.31
    206693_atIL7Interleukin 72.30
    240539_at2.29
    209068_atHNRPDLHeterogeneous nuclear ribonucleoprotein D-like2.26
    227524_at2.26
    221877_atIRGQImmunity-related gtpase family, Q2.25
    232114_atMED12LMediator complex subunit 12-like2.25
    1557557_atLOC100129196Similar to hcg20332982.24
    218735_s_atZNF544Zinc finger protein 5442.24
    226503_atRIF1RAP1 interacting factor homolog (yeast)2.23
    231947_atMYCT1Myc target 12.23
    241840_at2.23
    221832_s_atLUZP1Leucine zipper protein 12.23
    203410_atAP3M2Adaptor-related protein complex 3, mu 2 subunit2.23
    229005_at2.22
    221223_x_atCISHCytokine inducible SH2-containing protein2.22
    216247_atRPS20Ribosomal protein S202.22
    230630_atAK3L1Adenylate kinase 3-like 12.21
    239734_at2.21
    222857_s_atKCNMB4Potassium large conductance calcium-activated channel, subfamily M, beta member 42.20
    239567_at2.20
    223593_atAADATAminoadipate aminotransferase2.18
    228569_atPAPOLAPoly(A) polymerase alpha2.18
    64488_atIRGQImmunity-related gtpase family, Q2.17
    244753_at2.17
    209525_atHDGFRP3Hepatoma-derived growth factor, related protein 32.16
    239252_at2.16
    203794_atCDC42BPACDC42 binding protein kinase alpha (DMPK-like)2.15
    212851_atDCUN1D4DCN1, defective in cullin neddylation 1, domain containing 4 (S. cerevisiae)2.15
    236474_at2.15
    226541_atFBXO30F-box protein 302.15
    205450_atPHKA1Phosphorylase kinase, alpha 1 (muscle)2.15
    230002_atCLCC1Chloride channel CLIC-like 12.15
    239067_s_atPANX2Pannexin 22.14
    204269_atPIM2Pim-2 oncogene2.14
    232280_atSLC25A29Solute carrier family 25, member 292.14
    205240_atGPSM2G-protein signaling modulator 2 (AGS3-like, C. elegans)2.12
    212179_atSFRS18Splicing factor, arginine/serine-rich 182.12
    1556735_at2.12
    242494_at2.12
    229631_atDNHD1Dynein heavy chain domain 12.11
    218224_atPNMA1Paraneoplastic antigen MA12.11
    47560_atLPHN1Latrophilin 12.11
    211163_s_atTNFRSF10CTumor necrosis factor receptor superfamily, member 10c, decoy without an intracellular domain2.11
    211621_atARAndrogen receptor2.11
    238038_at2.10
    212609_s_atAKT3V-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma)2.09
    204453_atZNF84Zinc finger protein 842.08
    233479_at2.08
    230680_at2.07
    223249_atCLDN12Claudin 122.06
    236202_at2.05
    201999_s_atDYNLT1Dynein, light chain, Tctex-type 12.05
    227412_atPPP1R3EProtein phosphatase 1, regulatory (inhibitor) subunit 3E2.04
    206222_atTNFRSF10CTumor necrosis factor receptor superfamily, member 10c, decoy without an intracellular domain2.04
    207623_atABCF2ATP-binding cassette, subfamily F (GCN20), member 22.04
    219623_atACTR5ARP5 actin-related protein 5 homolog (yeast)2.03
    227093_atUSP36Ubiquitin specific peptidase 362.02
    219236_atPAQR6Progestin and adipoq receptor family member VI2.02
    241788_x_at2.02
    235680_at2.01
    235779_atLOC284408Hypothetical protein LOC2844082.01
    221480_atHNRNPDHeterogeneous nuclear ribonucleoprotein D (AU-rich element RNA binding protein 1, 37 kda)2.01
    218162_atOLFML3Olfactomedin-like 32.01
    236916_at2.01
    243365_s_atAUTS2Autism susceptibility candidate 22.00
    227963_at2.00
    213124_atZNF473Zinc finger protein 4732.00
    214461_atLBPLipopolysaccharide binding protein1.99
    244647_at1.99
    227234_atLOC100132815Hypothetical protein LOC1001328151.98
    235533_atCOX19COX19 cytochrome c oxidase assembly homolog (S. cerevisiae)1.98
    214996_at1.97
    212427_atKIAA0368Kiaa03681.97
    227573_s_atOBSL1Obscurin-like 11.96
    223600_s_atKIAA1683Kiaa16831.96
    1556121_at1.96
    226586_atANKS6Ankyrin repeat and sterile alpha motif domain containing 61.96
    228259_s_atEPB41L4AErythrocyte membrane protein band 4.1 like 4A1.96
    220117_atZNF385DZinc finger protein 385D1.95
    1556694_a_at1.94
    232614_at1.94
    224719_s_atC12orf57Chromosome 12 open reading frame 571.93
    244534_at1.93
    37408_atMRC2Mannose receptor, C type 21.93
    1562244_atZNF578Zinc finger protein 5781.93
    1558604_a_at1.93
    228002_atIDI2Isopentenyl-diphosphate delta isomerase 21.92
    1560562_a_atZNF677Zinc finger protein 6771.92
    228192_atC6orf125Chromosome 6 open reading frame 1251.92
    1552381_atSRrp35Serine-arginine repressor protein (35 kda)1.91
    34726_atCACNB3Calcium channel, voltage-dependent, beta 3 subunit1.91
    227206_at1.91
    241912_atZNF814Zinc finger protein 8141.91
    243480_at1.91
    219448_atTMEM70Transmembrane protein 701.90
    1552423_atETV3Ets variant 31.90
    203488_atLPHN1Latrophilin 11.90
    236815_at1.90
    1552347_atCRYZL1Crystallin, zeta (quinone reductase)-like 11.90
    227333_at1.89
    243951_atABCB1ATP-binding cassette, subfamily B (MDR/TAP), member 11.88
    236543_at1.88
    230757_at1.87
    212923_s_atC6orf145Chromosome 6 open reading frame 1451.87
    222147_s_atACTR5ARP5 actin-related protein 5 homolog (yeast)1.87
    1553247_a_atZNF709Zinc finger protein 7091.87
    238191_at1.86
    238376_at1.86
    231116_at1.86
    225629_s_atZBTB4Zinc finger and BTB domain containing 41.86
    229328_atZNF540Zinc finger protein 5401.85
    227992_s_atNCRNA00085Non-protein coding RNA 851.85
    228826_at1.85
    211929_atHNRNPA3Heterogeneous nuclear ribonucleoprotein A31.84
    220459_atMCM3APASMCM3AP antisense RNA (non-protein coding)1.84
    239329_at1.83
    239295_atSRrp35Serine-arginine repressor protein (35 kda)1.83
    226567_atUSP14Ubiquitin specific peptidase 14 (trna-guanine transglycosylase)1.83
    212772_s_atABCA2ATP-binding cassette, subfamily A (ABC1), member 21.83
    212855_atDCUN1D4DCN1, defective in cullin neddylation 1, domain containing 4 (S. cerevisiae)1.82
    239829_at1.82
    225888_atC12orf30Chromosome 12 open reading frame 301.82
    229654_atZNF44Zinc finger protein 441.82
    238484_s_at1.82
    229234_atZC3H12BZinc finger CCCH-type containing 12B1.82
    201067_atPSMC2Proteasome (prosome, macropain) 26S subunit, atpase, 21.81
    202743_atPIK3R3Phosphoinositide-3-kinase, regulatory subunit 3 (gamma)1.80
    221735_atWDR48WD repeat domain 481.80
    202259_s_atN4BP2L2NEDD4 binding protein 2-like 21.80
    205145_s_atMYL5Myosin, light chain 5, regulatory1.80
    244665_at1.80
    215137_at1.79
    231978_atTPCN2Two pore segment channel 21.79
    223185_s_atBHLHE41Basic helix-loop-helix family, member e411.79
    227308_x_atLTBP3Latent transforming growth factor beta binding protein 31.79
    209020_atC20orf111Chromosome 20 open reading frame 1111.79
    236106_at1.78
    230616_atLAMB2LLaminin, beta 2-like1.78
    225710_atGNB4Guanine nucleotide binding protein (G protein), beta polypeptide 41.78
    221976_s_atHDGFRP3Hepatoma-derived growth factor, related protein 31.78
    243178_at1.78
    227272_atC15orf52Chromosome 15 open reading frame 521.77
    227314_atITGA2Integrin, alpha 2 (CD49B, alpha 2 subunit of VLA-2 receptor)1.77
    238454_atZNF540Zinc finger protein 5401.77
    229854_atOBSCNObscurin, cytoskeletal calmodulin and titin-interacting rhogef1.76
    204376_atVPRBPVpr (HIV-1) binding protein1.76
    212206_s_atH2AFVH2A histone family, member V1.76
    242109_atSYTL3Synaptotagmin-like 31.75
    238700_at1.75
    229720_atBAG1BCL2-associated athanogene1.75
    243924_atLOC100127980Hypothetical protein LOC1001279801.74
    227865_atC9orf103Chromosome 9 open reading frame 1031.74
    209983_s_atNRXN2Neurexin 21.74
    200690_atHSPA9Heat shock 70 kda protein 9 (mortalin)1.74
    223321_s_atFGFRL1Fibroblast growth factor receptor-like 11.74
    232866_atZNF135Zinc finger protein 1351.74
    231902_atZNF827Zinc finger protein 8271.73
    210484_s_atMGC31957Hypothetical protein MGC319571.73
    219266_atZNF350Zinc finger protein 3501.73
    225120_atPURBPurine-rich element binding protein B1.73
    244597_atLOC26010Viral DNA polymerase-transactivated protein 61.73
    221831_atLUZP1Leucine zipper protein 11.72
    1569713_at1.72
    235564_atZNF117Zinc finger protein 1171.72
    220328_atPHC3Polyhomeotic homolog 3 (Drosophila)1.72
    220032_atC7orf58Chromosome 7 open reading frame 581.72
    235406_x_at1.72
    1554076_s_atTMEM136Transmembrane protein 1361.71
    237856_atRAP1GDS1RAP1, GTP-GDP dissociation stimulator 11.71
    44696_atTBC1D13TBC1 domain family, member 131.70
    237389_at1.70
    234998_at1.70
    226944_atHTRA3Htra serine peptidase 31.70
    223849_s_atMOV10Mov10, Moloney leukemia virus 10, homolog (mouse)1.70
    209838_atCOPS2COP9 constitutive photomorphogenic homolog subunit 2 (Arabidopsis)1.70
    228709_atTPRTranslocated promoter region (to activated MET oncogene)1.69
    238483_at1.69
    212293_atHIPK1Homeodomain interacting protein kinase 11.68
    236127_atZBTB17Zinc finger and BTB domain containing 171.68
    211277_x_atAPPAmyloid beta (A4) precursor protein1.68
    1558459_s_atLOC401320Hypothetical LOC4013201.68
    225487_atTMEM18Transmembrane protein 181.67
    207693_atCACNB4Calcium channel, voltage-dependent, beta 4 subunit1.67
    205079_s_atMPDZMultiple PDZ domain protein1.67
    232553_atPCYT1BPhosphate cytidylyltransferase 1, choline, beta1.67
    214961_atKIAA0774Kiaa07741.67
    207561_s_atACCN3Amiloride-sensitive cation channel 31.67
    1557248_atZNF814Zinc finger protein 8141.67
    219944_atCLIP4CAP-GLY domain containing linker protein family, member 41.66
    209733_at1.65
    219145_atLPHN1Latrophilin 11.65
    238131_atPHC2Polyhomeotic homolog 2 (Drosophila)1.65
    1570329_at1.65
    209530_atCACNB3Calcium channel, voltage-dependent, beta 3 subunit1.64
    227505_at1.64
    232716_at1.64
    218596_atTBC1D13TBC1 domain family, member 131.64
    206142_atZNF135Zinc finger protein 1351.63
    231941_s_atMUC20Mucin 20, cell surface associated1.63
    65591_atWDR48WD repeat domain 481.62
    235530_at1.62
    1552986_atLOC142937Hypothetical protein BC0081311.61
    232758_s_at1.60
    219287_atKCNMB4Potassium large conductance calcium-activated channel, subfamily M, beta member 41.60
    228012_atMATR3Matrin 31.60
    243398_at1.60
    214289_atPSMB1Proteasome (prosome, macropain) subunit, beta type, 11.59
    237561_x_at1.58
    1558164_s_atPEX13Peroxisomal biogenesis factor 131.58
    242307_atZNF789Zinc finger protein 7891.58
    235814_at1.57
    213367_atLOC791120Hypothetical LOC7911201.57
    241318_at1.56
    1552643_atZNF626Zinc finger protein 6261.55
    213444_atZNF862Zinc finger protein 8621.55
    205964_atZNF426Zinc finger protein 4261.54
    213306_atMPDZMultiple PDZ domain protein1.53
    215370_at1.52
    232919_atAFG3L2AFG3 atpase family gene 3-like 2 (yeast)1.52
    1558969_a_atRPL32P3Ribosomal protein L32 pseudogene 31.50
    234034_at1.49
    227504_s_at1.49
    215296_atCDC42BPACDC42 binding protein kinase alpha (DMPK-like)1.48
    231260_atBC036928Hypothetical protein BC0369281.48
    211947_s_atBAT2D1BAT2 domain containing 11.48
    218655_s_atCCDC49Coiled-coil domain containing 491.47
    232199_at1.45
    218002_s_atCXCL14Chemokine (C-X-C motif) ligand 141.44
    1558819_atLOC100131819Similar to hcg17788141.35
Downregulated gene probes
    217840_atDDX41DEAD (Asp-Glu-Ala-Asp) box polypeptide 410.66
    200618_atLASP1LIM and SH3 protein 10.66
    219074_atTMEM184CTransmembrane protein 184C0.65
    230363_s_atINPP5FInositol polyphosphate-5-phosphatase F0.64
    209188_x_atDR1Down-regulator of transcription 1, TBP-binding (negative cofactor 2)0.63
    223329_x_atSUGT1SGT1, suppressor of G2 allele of SKP1 (S. cerevisiae)0.63
    215774_s_atSUCLG2Succinate-coa ligase, GDP-forming, beta subunit0.63
    228578_atRBM45RNA binding motif protein 450.63
    218327_s_atSNAP29Synaptosomal-associated protein, 29 kda0.62
    223299_atSEC11CSEC11 homolog C (S. cerevisiae)0.62
    207654_x_atDR1Down-regulator of transcription 1, TBP-binding (negative cofactor 2)0.61
    225389_atBTBD6BTB (POZ) domain containing 60.61
    224309_s_atSUGT1SGT1, suppressor of G2 allele of SKP1 (S. cerevisiae)0.61
    203732_atTRIP4Thyroid hormone receptor interactor 40.61
    221688_s_atIMP3IMP3, U3 small nucleolar ribonucleoprotein, homolog (yeast)0.60
    222503_s_atWDR41WD repeat domain 410.60
    215075_s_atGRB2Growth factor receptor-bound protein 20.60
    225890_atC20orf72Chromosome 20 open reading frame 720.59
    222537_s_atCDC42SE1CDC42 small effector 10.59
    224874_atPOLR1DPolymerase (RNA) I polypeptide D, 16 kda0.58
    202579_x_atHMGN4High mobility group nucleosomal binding domain 40.58
    209787_s_atHMGN4High mobility group nucleosomal binding domain 40.58
    216652_s_atDR1Down-regulator of transcription 1, TBP-binding (negative cofactor 2)0.57
    221492_s_atATG3ATG3 autophagy related 3 homolog (S. cerevisiae)0.57
    230592_atNSL1NSL1, MIND kinetochore complex component, homolog (S. cCerevisiae)0.57
    224511_s_atTXNDC17Thioredoxin domain containing 170.57
    217909_s_atMLXMAX-like protein X0.56
    209786_atHMGN4High mobility group nucleosomal binding domain 40.56
    211725_s_atBIDBH3 interacting domain death agonist0.56
    229742_atC15orf61Chromosome 15 open reading frame 610.55
    225917_at0.55
    211936_atHSPA5Heat shock 70 kda protein 5 (glucose-regulated protein, 78 kda)0.55
    224643_atPRRC1Proline-rich coiled-coil 10.55
    203846_atTRIM32Tripartite motif-containing 320.55
    205055_atITGAEIntegrin, alpha E (antigen CD103, human mucosal lymphocyte antigen 1; alpha polypeptide)0.55
    225850_atSFT2D1SFT2 domain containing 10.54
    226242_atC1orf131Chromosome 1 open reading frame 1310.54
    209748_atSPASTSpastin0.54
    220140_s_atSNX11Sorting nexin 110.54
    236846_atLOC284757Hypothetical protein LOC2847570.54
    239342_atDGKZDiacylglycerol kinase, zeta 104 kda0.53
    227413_atUBLCP1Ubiquitin-like domain containing CTD phosphatase 10.53
    202101_s_atRALBV-ral simian leukemia viral oncogene homolog B (ras related; GTP binding protein)0.53
    218987_atATF7IPActivating transcription factor 7 interacting protein0.52
    1562648_atCCDC88ACoiled-coil domain containing 88A0.51
    229120_s_atC1orf56Chromosome 1 open reading frame 560.50
    1568742_at0.49
    218157_x_atCDC42SE1CDC42 small effector 10.49
    226276_atTMEM167ATransmembrane protein 167A0.48
    204862_s_atNME3Nonmetastatic cells 3, protein expressed in0.48
    227624_atTET2Tet oncogene family member 20.48
    207724_s_atSPASTSpastin0.46
    204608_atASLArgininosuccinate lyase0.46
    204810_s_atCKMCreatine kinase, muscle0.46
    1554077_a_atTMEM53Transmembrane protein 530.45
    206907_atTNFSF9Tumor necrosis factor (ligand) superfamily, member 90.44
    238063_atTMEM154Transmembrane protein 1540.44
    204880_atMGMTO-6-methylguanine-DNA methyltransferase0.43
    204385_atKYNUKynureninase (L-kynurenine hydrolase)0.41
    207131_x_atGGT1Gamma-glutamyltransferase 10.37
    222752_s_atTMEM206Transmembrane protein 2060.37
    204994_atMX2Myxovirus (influenza virus) resistance 2 (mouse)0.36
    1558770_a_atPIK3R6Phosphoinositide-3-kinase, regulatory subunit 60.36
    1554628_atZNF57Zinc finger protein 570.34
    210629_x_atLST1Leukocyte specific transcript 10.34
    205147_x_atNCF4Neutrophil cytosolic factor 4, 40 kda0.34
    207677_s_atNCF4Neutrophil cytosolic factor 4, 40 kda0.34
    223059_s_atFAM107BFamily with sequence similarity 107, member B0.33
    219033_atPARP8Poly (ADP-ribose) polymerase family, member 80.32
    214574_x_atLST1Leukocyte specific transcript 10.31
    241464_s_at0.31
    223058_atFAM107BFamily with sequence similarity 107, member B0.31
    211581_x_atLST1Leukocyte specific transcript 10.31
    219506_atC1orf54Chromosome 1 open reading frame 540.31
    210663_s_atKYNUKynureninase (L-kynurenine hydrolase)0.30
    214181_x_atLST1Leukocyte specific transcript 10.29
    211582_x_atLST1Leukocyte specific transcript 10.29
    1553311_atC20orf197Chromosome 20 open reading frame 1970.24
    212459_x_atSUCLG2Succinate-coa ligase, GDP-forming, beta subunit0.24
    215772_x_atSUCLG2Succinate-coa ligase, GDP-forming, beta subunit0.22
    214835_s_atSUCLG2Succinate-coa ligase, GDP-forming, beta subunit0.20
    206772_atPTH2RParathyroid hormone 2 receptor0.19
    202878_s_atCD93CD93 molecule0.15
    200923_atLGALS3BPLectin, galactoside-binding, soluble, 3 binding protein0.14
    205859_atLY86Lymphocyte antigen 860.14
    217388_s_atKYNUKynureninase (L-kynurenine hydrolase)0.09

NOTE. False discovery rate = 0.054; global test P = .007). Ordered by fold changes.

Abbreviation: wt, wild-type.

Table A4.

Differentially Expressed (P < .005) Probes Between R172 IDH2-Mutated (n = 6) and IDH1/IDH2wt (n = 82) Patients

Target MicroRNASequence (unique ID)Fold-Change: R172/wt
Upregulated microRNA probes
    hsa-miR-1AATGCTATGGAATGTAAAGAAGTATGTATTTTTGGTAGGC10.35
    hsa-miR-1AATGCTATGGAATGTAAAGAAGTATGTATTTTTGGTAGGC7.11
    hsa-miR-133aTTGGTCCCCTTCAACCAGCTGTAGCTGTGCATTGATGGCG7.00
    hsa-miR-125bTCCCTGAGACCCTAACTTGTGATGTTTACCGTTTAAATCC4.99
    hsa-miR-125a-5pTCTAGGTCCCTGAGACCCTTTAACCTGTGAGGACATCCAG4.96
    hsa-miR-133aCCTCTTCAATGGATTTGGTCCCCTTCAACCAGCTGTAGCT4.24
    hsa-miR-1TGGACCTGCTAAGCTATGGAATGTAAAGAAGTATGTATCT4.08
    hsa-miR-125bACCAGACTTTTCCTAGTCCCTGAGACCCTAACTTGTGAGG3.91
    hsa-miR-421AATGAATCATCAACAGACATTAATTGGGCGCCTGCTCTGT1.94
    hsa-miR-374aACATCGGCCATTATAATACAACCTGATAAGTGTTATAGCA1.84
    hsa-miR-361-5pGGA TTT GGG AGC TTA TCA GAA TCT CCA GGG GTA CTT TAT A1.65
    hsa-miR-26aGTGGCCTCGTTCAAGTAATCCAGGATAGGCTGTGCAGGTC1.61
    hsa-miR-30dTTGTAAACATCCCCGACTGGAAGCTGTAAGACACAGCTAA1.61
Downregulated microRNA probes
    hsa-miR-7GGACCGGCTGGCCCCATCTGGAAGACTAGTGATTTTGTTG0.76
    hsa-mir-345CTGAACGAGGGGTCTGGAGGCCTGGGTTTGAATATCGACA0.72
    hsa-miR-129-5pTGGATCTTTTTGCGGTCTGGGCTTGCTGTTCCTCTCAACA0.69
    hsa-mir-632TCCTACCGCAGTGCTTGACGGGAGGCGGAGCGGGGAACGA0.69
    hsa-miR-615-5pCTCGGGAGGGGCGGGAGGGGGGTCCCCGGTGCTCGGATCT0.65
    hsa-miR-1301CAGGGGCTGGGCCTGCAGCTGCCTGGGCAGAGCGGCTCCT0.60
    hsa-mir-639ACGGGGCGCGCGCGGCCTGGAGGGGCGGGGCGGACGCAGA0.57
    hsa-mir-548bCAGACTATATATTTAGGTTGGCGCAAAAGTAATTGTGGTT0.46
    hsa-miR-520a-3pGAG AGA AAA GAA AGT GCT TCC CTT TGG ACT GTT TCG GTT T0.41
    hsa-miR-526aCTC AGG CTG TGA CCC TCT AGA GGG AAG CAC TTT CTG TTG C0.33
    hsa-mir-194-1CCAATTTCCAGTGGAGATGCTGTTACTTTTGATGGTTACC0.32

NOTE. False discovery rate = 0.166; global P = .03. Ordered by fold-change.

Abbreviations: wt, wild-type; ID, identification.

Footnotes

See accompanying article on page 2356

Supported in part by National Cancer Institute Grants No. CA101140, CA114725, CA31946, CA33601, CA16058, CA77658, CA129657, and CA140158, by The Coleman Leukemia Research Foundation, and by the Deutsche Krebshilfe Dr Mildred Scheel Cancer Foundation (H.B.).

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

The author(s) indicated no potential conflicts of interest.

AUTHOR CONTRIBUTIONS

Conception and design: Guido Marcucci, Clara D. Bloomfield

Financial support: Guido Marcucci, Clara D. Bloomfield

Administrative support: Michael A. Caligiuri, Clara D. Bloomfield

Provision of study materials or patients: Guido Marcucci, Bayard L. Powell, Thomas H. Carter, Jonathan E. Kolitz, Meir Wetzler, Andrew J. Carroll, Maria R. Baer, Michael A. Caligiuri, Richard A. Larson, Clara D. Bloomfield

Collection and assembly of data: Guido Marcucci, Kati Maharry, Yue-Zhong Wu, Michael D. Radmacher, Susan P. Whitman, Heiko Becker, Sebastian Schwind, Klaus H. Metzeler, Andrew J. Carroll, Clara D. Bloomfield

Data analysis and interpretation: Guido Marcucci, Kati Maharry, Yue-Zhong Wu, Michael D. Radmacher, Krzysztof Mrózek, Dean Margeson, Kelsi B. Holland, Clara D. Bloomfield

Manuscript writing: Guido Marcucci, Kati Maharry, Michael D. Radmacher, Krzysztof Mrózek, Susan P. Whitman, Heiko Becker, Sebastian Schwind, Klaus H. Metzeler, Clara D. Bloomfield

Final approval of manuscript: Guido Marcucci, Kati Maharry, Yue-Zhong Wu, Michael D. Radmacher, Krzysztof Mrózek, Dean Margeson, Kelsi B. Holland, Susan P. Whitman, Heiko Becker, Sebastian Schwind, Klaus H. Metzeler, Bayard L. Powell, Thomas H. Carter, Jonathan E. Kolitz, Meir Wetzler, Andrew J. Carroll, Maria R. Baer, Michael A. Caligiuri, Richard A. Larson, Clara D. Bloomfield

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