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Gene Expression Profiles in Acute Myeloid Leukemia with Common Translocations Using SAGE

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Gene Expression Profiles in Acute Myeloid Leukemia with Common Translocations Using SAGE Gene expression profiles in acute myeloid leukemia with common translocations using SAGE Sanggyu Lee*†‡, Jianjun Chen*†, Guolin Zhou*†, Run Zhang Shi*, Gerard G. Bouffard§, Masha Kocherginsky¶, Xijin Geʈ, Miao Sun*, Nimanthi Jayathilaka*, Yeong Cheol Kimʈ, Neelmini Emmanuel*, Stefan K. Bohlander**, Mark Minden††, Justin Kline*, Ozden Ozer*, Richard A. Larson*, Michelle M. LeBeau*, Eric D. Green§, Jeffery Trent§‡‡, Theodore Karrison¶, Piu Paul Liu§, San Ming Wangʈ§§, and Janet D. Rowley*§§ Departments of *Medicine and ¶Health Studies, University of Chicago, Chicago, IL 60637; §National Human Genome Research Initiative, National Institutes of Health, Bethesda, MD 20892; ʈENH Research Institute, Northwestern University, Evanston, IL 60208; **Department of Medicine III, Ludwig Maximilians University and GSF-National Research Center for Environmental and Health, Clinical Cooperative Group ‘‘Leukemia,’’ 80539 Munich, Germany; and ††Department of Medical Oncology and Hematology, Princess Margaret Hospital, Toronto, ON, Canada M5G 2M9 Contributed by Janet D. Rowley, November 21, 2005 Identification of the specific cytogenetic abnormality is one of the sex, stage of disease, percentage of blasts in the sample, other critical steps for classification of acute myeloblastic leukemia (AML) chromosomal abnormalities, etc.) as well as for technical rea- which influences the selection of appropriate therapy and provides sons, such as the various platforms and algorithms used in the information about disease prognosis. However at present, the analysis. Moreover analysis of the same data set using different genetic complexity of AML is only partially understood. To obtain algorithms also yields different results (U. Kees, personal com- a comprehensive, unbiased, quantitative measure, we performed munication). However, this question of reproducibility has re- serial analysis of gene expression (SAGE) on CD15؉ myeloid pro- cently been reviewed by Sherlock (16), who concludes that when genitor cells from 22 AML patients who had four of the most very carefully controlled experiments are done in various labo- common translocations, namely t(8;21), t(15;17), t(9;11), and ratories, in general the results are comparable. However, when inv(16). The quantitative data provide clear evidence that the different materials and different platforms are used, the repro- major change in all these translocation-carrying leukemias is a ducibility is poor. decrease in expression of the majority of transcripts compared We used serial analysis of gene expression (SAGE) to obtain with normal CD15؉ cells. From a total of 1,247,535 SAGE tags, we quantitative, unbiased gene expression in bone marrow samples identified 2,604 transcripts whose expression was significantly from 22 patients with four subtypes of AML, namely de novo altered in these leukemias compared with normal myeloid pro- AMLM2 with t(8;21), AMLM3 or M3V with t(15;17), genitor cells. The gene ontology of the 1,110 transcripts that AMLM4Eo with inv(16), and AML with t(9;11) or treatment- matched known genes revealed that each translocation had a related t(9;11). The results of this analysis are presented here. uniquely altered profile in various functional categories including Results regulation of transcription, cell cycle, protein synthesis, and apo- ptosis. Our global analysis of gene expression of common trans- Characterization of the Leukemic Samples. We studied samples locations in AML can focus attention on the function of the genes obtained from diagnosis of 22 AML cases representing four de with altered expression for future biological studies as well as novo and one treatment-related subtypes: five each de novo highlight genes͞pathways for more specifically targeted therapy. t(8;21), t(15;17), inv(16), four de novo t(9;11), and three treat- ment-related t(9;11). All samples were verified by cytogenetic hematopoietic cell differention ͉ diagnostic microarray analysis showing the balanced abnormalities as the sole karyo- type change (except for no. 10) in Ͼ75% of the cells, and reverse-transcriptase PCR showing the presence of the expected he pathogenesis of acute myeloid leukemia (AML) in many fusion transcript (Tables 1 and 2, which are published as Tpatients is linked to oncogenic fusion proteins, generated as supporting information on the PNAS web site). a consequence of chromosome translocations or inversions (1). Many different translocations have been described in AML, the Distribution of the SAGE Tags and Match of SAGE Tags to Known most frequent being the t(9;11), t(15;17), t(8;21), and inv(16), Expressed Sequences. We collected a total of 1,247,535 SAGE tags Ϸ which, taken together with their variants, account for 20–30% from the 22 AML libraries. From these SAGE tags, we identified of AML cases (2, 3), although a recent analysis by Mitelman et 209,486 unique SAGE tags. Matching these SAGE tags to the al. (4) suggests that the proportion may be closer to 10%. These reference database shows that 136,010 SAGE tags matched to recurring translocations are now the basis for classification of known gene transcripts, and 73,476 had no match representing some patients with AML. Despite genetic heterogeneity, there potentially novel transcripts (Table 2). The number of SAGE is increasing evidence for some common molecular and biolog- tags per library ranged from 23,176 to 84,249. Therefore, the ical mechanisms in the genesis of AML. In particular, one of the libraries were normalized to Ϸ50,000 tags per library for com- components of each fusion protein is almost invariably a tran- parison, as described in Methods. The number of unique tran- scription factor, frequently involved in the regulation of myeloid scripts in each translocation varied substantially; however, the cell differentiation (5). As a consequence, AML-associated fusion proteins function as aberrant transcriptional regulators with the potential to interfere with the normal processes of Conflict of interest statement: No conflicts declared. myeloid cell differentiation. Abbreviations: AML, acute myeloid leukemia; SAGE, serial analysis of gene expression. Genome-wide gene expression profiling is becoming useful for †S.L., J.C., and G.Z. contributed equally to this work. the classification of many types of cancer (6, 7), including AML ‡Present address: School of Life Sciences and Biotechnology, Kyungpook National Univer- and acute lymphoblastic leukemia (8–15). Although AML sub- sity, Daegu 702-701, Korea. types can be distinguished by oligonucleotide microarrays, the ‡‡Present address: TGen, Phoenix, AZ 85004. results of analysis of different translocations between laborato- §§To whom correspondence may be addressed. E-mail: [email protected] or ries are not always similar. This lack of consistency has probably [email protected]. resulted from the heterogeneous nature of clinical samples (age, © 2006 by The National Academy of Sciences of the USA 1030–1035 ͉ PNAS ͉ January 24, 2006 ͉ vol. 103 ͉ no. 4 www.pnas.org͞cgi͞doi͞10.1073͞pnas.0509878103 Downloaded by guest on September 30, 2021 regulated transcripts in each translocation is summarized in Fig. 2b. The expression pattern was relatively uniform between patients with the same translocation compared with other trans- locations, as illustrated in Table 3, which is published as sup- porting information on the PNAS web site, showing data from 1,110 SAGE tags for all four translocations. The importance of quantitative SAGE data is illustrated in Table 3, which allows for direct comparison of expression levels with no manipulation of the primary information except for normalization of all leukemia samples to Ϸ50,000 tags. We also identified the transcripts whose expression pattern, either increased or decreased, was common in all four translocations (Table 4, which is published as sup- porting information on the PNAS web site). t(8;21) had the largest number of transcripts that showed a statistically signifi- cant difference from the normal CD15ϩ cells and had the highest expression level, whereas inv(16) and t(9;11) had the smallest number of transcripts with altered expression (Fig. 1). As a consequence, a smaller number of transcripts were specific discriminators for t(9;11), inv(16), and t(15;17) than for t(8;21). Abnormally Expressed Genes in Each Type of Translocation Related to Cellular Function. To determine the nature of the highly expressed genes in each translocation, we selected the top 20 genes that were significantly highly expressed in each translocation (Table 5, which is published as supporting information on the PNAS web site). Interestingly, nine of the 20 genes highly expressed in t(8;21) were related to ribosomal proteins. This finding is not unexpected, because these are cells involved in very active protein synthesis. However, it is perplexing that only some of MEDICAL SCIENCES them are overexpressed and they are overexpressed only in the t(8;21). The list of those SAGE tags that are underexpressed also includes ribosomal proteins, most of which (eight of 20) are in the t(8;21), but two and three are in the inv(16) and t(15;17), Fig. 1. The expression level of the 2,604 SAGE tags whose expression was respectively. Note that hemoglobin alpha was the top gene and statistically significantly different from CD15ϩ control cells has been con- hemoglobin gamma 2 was the third nonribosomal protein gene verted to a ‘‘heat’’ map with red representing overexpression and green in the list. Expression of hemoglobin genes was not expected. underexpression of
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