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ISSN: 2155-6180
Journal of Biometrics & Biostatistics
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Comparison of MLL Fusion Genes Expression among the Cytogenetics Abnormalities of Acute Myeloid Leukemia and Their Clinical Effects

Senol Dogan1*, Amina Kurtovic-Kozaric2, Albenita Hajrovic1, Muhamed Lisic1 and Ercan Gokgoz3

1Department of Genetics and Bioengineering, Faculty of Engineering and Information Technologies, nternational Burch University, Sarajevo, Bosnia and Herzegovina

2Department of Clinical Pathology, University of Sarajevo, Sarajevo, Bosnia and Herzegovina

3Department of Information Technologies, Faculty of Engineering and Information Technologies, International Burch University, Sarajevo, Bosnia and Herzegovina

Corresponding Author:
Senol Dogan
Department of Genetics and Bioengineering
Faculty of Engineering and Information Technologies
International Burch University, Sarajevo
Bosnia and Herzegovina
Tel: +387 33 944 400
Fax: +387 33 944 500
E-mail: [email protected]

Received Date: March 21, 2016; Accepted Date: April 14, 2016; Published Date: April 21, 2016

Citation: Dogan S, Kurtovic-Kozaric A, Hajrovic A, Lisic M, Gokgoz E (2016) Comparison of MLL Fusion Genes Expression among the Cytogenetics Abnormalities of Acute Myeloid Leukemia and Their Clinical Effects. J Biom Biostat 7:312. doi: 10.4172/2155-6180.1000312

Copyright: ©2016 Dogan S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Mixed-lineage leukemia (MLL) is a subtype of acute myeloid leukemia with more aggressive prognosis than other subtypes. Translocations of MLL gene with other partner genes, forming the MLL-fusion proteins (MLL-FPs), are the main characteristics of MLL leukemia. Many studies have demonstrated that MLL-FPs such as: MLL-AF4, MLL-AF6, MLL-AF9, MLL-AF10, MLL-ENL, MLL-ELL, MLL-EPS15, as well as partial tandem duplication are the most common abnormalities that play significant role in MLL-rearranged leukemia. Gene expression profiles from 197 patients and 180 clinical data were downloaded from TCGA database. R statistical program has classified clinical and genomic data simultaneously according to cytogenetic abnormalities. As a result of this analysis, the most frequent 47 MLLFPs genes expression have been detected and compared with other cytogenetic abnormalities such as t(4;11), t(9;11), t(8;21), t(15;17), complex, inversion 16, trisomy 8 and cytogenetically normal AML. 35 out of 46 MLL-FPs genes presented with abnormal gene expression profile. This study showed that MLL-FPs are not just active and related with MLL, but also with other subtypes of AML.

Keywords

AML; MLL; Data mining; Cytogenetic abnormalities; Fusion protein; Gene expression

Introduction

Acute myeloid leukemia (AML) is a clonal hematopoietic disorder that may be derived from either a hematopoietic stem cell or a lineagespecific progenitor cell [1]. Hematopoietic tissues have a potential to produce various types of malignancies, such as acute lymphocytic leukemia (ALL), acute myelogenous leukemia (AML), chronic myelogenous leukemia (CML), chronic lymphocytic leukemia (CLL) [2]. Acute leukemia is one of the most common types of leukemia among adults and constitutes 97% of all childhood malignancies, which show clonal expansion and changing specific stages of normal myeloid and lymphoid hematopoiesis [3]. Although cytogenetic analysis has been used to identify the pathogenesis of acute myeloid leukemia (AML) for more than decades, clinically defined subtypes are very heterogeneous diseases and difficult to characterize in a same group [4,5]. The cytogenetic of the subtypes and chromosome translocation are the main key properties to distinguish the disease and prognostic factors of AML [1,6,7]. The AML and ALL cytogenetic reports have revealed that many non-random chromosome abnormalities included specific genes that implicated in the process of leukemogenesis [8].

Lastly, the new term mixed-lineage leukemia was added to the literature [9]. MLL gene plays a positive regulator of global gene expression in early embryonic development and hematopoiesis [10]. The gene encodes a very important epigenetic transcription factors, such as HOX genes [11]. In addition to that, MLL fusion proteins, main properties of MLL, are produced by chromosomal translocations, which affect the MLL gene at 11q23 [12]. If AML includes MLL chromosomal rearrangements and produces fusion proteins, it is assumed to be followed by poor prognosis and aggressive for infants [13-15]. The fusion proteins are observed in both hematological and solid tumors as breaking within genes on each chromosome [7]. The translocation genes involved in AML might be transcription regulators, which determine the cellular development and cell fate [16]. There are more than 80 different partner genes, but the most commonly observed MLL-FPs are MLL-AF4 in ALL, MLL-AF9 and MLL-AF10 in AML and MLL-ELL in MLL [17].

Although it is well known that the fusion proteins activity are specific reasons for The previous studies show that statistical analysis of genomic data and clinical research should have been done together to understand better of new type of leukemia [18-21]. Development of MLL, the research has been done to show the potential correlation between the proteins and other subtypes. Finding the expression activity of the fusion genes is the main target of the research. The main idea of the research is to see the genes expression profile of MLL-FPs in other subtypes of AML and potential relation between them.

Materials and Methods

The cancer genome atlas, R, HCE 3.5, Genevestigator

Gene expression and clinical data of acute myeloid leukemia are the main material for the research, which are downloaded from TCGA database, https://tcga-data.nci.nih.gov/tcga/. The data have been mined by different computational programs and web tools (Figure 1).

biometrics-biostatistics-data-management

Figure 1: Flowchart of the data management

The statistical analyses and data comparison have been done by R statistical program (https://www.r-project.org), which is created for this particular research to analyze the data. The program categorizes genes which represent different expression profiles in the subtype of AML. HCE 3.5 (http://www.cs.umd.edu/hcil/hce/) has been used to cluster gene expressions. According to the clinical data, the different subtypes have been found and abnormally expressed MLL fusion genes were detected. Synchronization of the clinical and expression data is the key part of the method.

Clinical data

AML clinical data have been used to find subtypes which are made of distinct cytogenetic abnormalities. Patients have different chromosomal abnormalities which are detected, identified and separated into different types: t(4;11), t(9;11), t(8;21), t(15;17), complex, inversion 16, trisomy 8 and AML. TCGA ID numbers of each patient is very useful for matching with their gene expression value. Therefore, same IDs have been used to find the correct gene expression value in the subtype of AML. The clinical data were prepared to find their gene expression values.

Gene expression data according to AML subtypes

197 AML patients’ expression values and 19798 genes were compared within each subtype. According to cytogenetic abnormalities which were derived from the clinical data, patients have been separated and categorized into subgroups of AML (Figure 1).

The IDs of each subgroup member is used to find their gene expression value. Then the abnormally high and low expressed genes of the subtypes have been compared among each other and average value (Table 1).

Abnormality LEG Expression HEG Expression Average
t(4;11) MLLT7 229.2 MLLT2 20.248 3.536
t(9;11) ARHGEF12 208.2 MLLT2 30.241 4.271
t(8;21) ArgBP2 242.7 MLLT2 16.497 3.975
t(15;17) LASP1 64.0 ARHGEF12 74.948 3.276
Complex ArgBP2 307.6 MLLT2 25.618 4.393
Inv16 ARHGEF12 222.3 MLLT2 23.813 4.406
Trisomy ARHGEF12 115.5 MLLT2 36.988 4.424
AML ArgBP2 269.1 MLLT2 23.846 4.372

Table 1: Selected high and low expressed genes in the subgroups. LEG:
Low Expressed Genes, HEG: High Expressed Genes. Average shows
the gene expression values of all patients.

Although there are more than 80 MLL fusion encoded genes [15], the 46 most frequently found have been analyzed. The R program categorized and selected the genes depending on their expression abnormalities of AML cytogenetic disorders.

Results

Clinical data analysis

69 cytogenetic abnormalities patients have been extracted from 197 AML as subtypes which include t(8;21) (10%), t(15;17) (22%), complex (38%), MLL, inversion 16 (13%), trisomy 8 (14%) and MLL (3%). The following step was to find the MLL-FP genes expression profile of the subtypes and compare them among each other (Figure 2).

biometrics-biostatistics-chromosomal-abnormalities

Figure 2: Percentage of patients with chromosomal abnormalities according to clinical TCGA data.

Clinical outcome of the subtypes

The patients of the subtypes show different survival rate (Figure 3). While t(15,17) and inv16 have the highest survival rate, t(9:11) and t(4:11) have the lowest survival rate. It is obvious that patients with the subtypes involving MLL translocation on 11q23, have the worst prognosis.

biometrics-biostatistics-chromosomal-aberrations

Figure 3: The survival rate of chromosomal aberrations found in 69 patients.

Gene expression result among subtypes

The comparison of 46 mostly common MLL-FPs gene expression shows different profile and abnormal deviations (Table 2).

  AVG AML t(4;11) t(9;11) t(8;21) t(15;17) Complex Inversion 16 Trisomy 8
MLLT2 21945.8 23846 20248.8 30241.4 16497.8 212.2 25618.8 23813.7 36988
LASP1 13034.2 17052.2 14414.8 13910.3 16251.6 64 16301.3 17757.3 12539.8
FOXO3A 8659.5 11759 12371.2 5682.6 11351.2 127 12697.9 9441.6 8945.2
PICALM 10142.1 14368.3 9036.1 11011.2 11205.8 1238.5 13839.2 13086.2 11577.9
CREBBP 8020.4 9493.4 7986.9 9701.6 9430.9 1139.3 9945.3 9956.9 7982.1
MIFL 7627.6 8627.3 6424.6 7559.8 8206.8 1423.6 9061.8 8358.5 12358.3
GMPS 4416.2 5379.6 5605.7 6211.5 4718.9 247.1 4647.9 6337.5 3144.6
CASC5 2301.1 2548.7 3861.9 2660 2015.7 87.4 2456.3 2839.2 2187
EP300 3166.5 3312.2 3189.5 4507.7 3076.4 536.6 3723.9 2877 4254.9
MAML2 1424.2 1680.4 2960.4 496.3 1549.7 205.2 2286 2249 222.4
LPP 3290.6 4443.1 2728.7 5231.2 3063.8 291.3 3969.8 4503 3246
SMAP1 3634.3 5139.7 2673.7 4660 4466 243.4 4703.9 5112.1 3581.3
MLLT6 3385.8 3898.9 2271.4 3983.2 3189.3 156.2 4269.8 3708.4 6122.3
GAS7 4971 7917.8 2235.9 7763.8 7880.8 774.1 5250.9 6934.7 3956.6
AFF3 3981 3905.3 2087.3 6345.4 3564.9 504.1 3271.5 5386.7 6706.8
ARHGAP26 2363 2117.7 1651.8 2908.6 1126 4982.7 1451.3 2580.4 1839.8
MLLT11 4439.1 4338.1 1472.1 1766.3 7518.2 497.2 5269.1 5832.7 8718.2
PBX1 698 902.7 1471.5 587.3 332.5 344.6 1338.4 326.3 485.5
EEN 1592.3 1989.1 1401.1 2037.2 1699.3 178 2245.8 2108.2 1476.7
RARA 1428 1770.4 1252.5 1324.1 2258.1 94.5 1854.4 1911.1 1301.5
MLLT3 1031.3 1473.1 1247.1 1317.9 861 173.4 2453.2 991.3 175.5
SEPT9 1443.1 1702.4 1142.1 1347.2 1551.4 291.5 1600.5 1782.9 2386
MPFYVE 1248.8 1245.7 1135.6 1889.8 1136 312.5 1210.1 1228.8 1829.2
NCKIPSD 1649.6 1504.1 1089.2 1874.2 2418.1 398.6 1751.5 1891.8 2123.8
ELL 773 745.1 992.2 580.1 912.8 461.3 752.8 904.4 807.2
TIRAP 1526.9 941.6 954.7 1052.6 956.8 5210.1 912.1 925.3 676.8
DAB2IP 416.9 437.5 549.1 382.9 503.6 81.4 556.7 421.3 423.5
TET1 1308 690.1 469.6 1034.8 1811.7 3251.6 981 946.7 660.4
ArgBP2 656.6 269.1 398.4 233.8 242.7 2972.6 307.6 199.2 241.7
ARHGEF12 10942.5 277.9 384.6 208.2 303.7 74948.2 414.8 222.3 115.5
MLLT4 307.21 239.84 229.60 146.4 345.25 481.82 477.25 239.25 230.85
MLLT7 670.1 439.6 229.2 339.2 523.4 2403.7 418.5 303.9 472.6
MLLT10 7718.3 7720.9 6137.4 9266.8 8128.1 6755.4 8030.5 7558.9 8151.2
MYOF1 2595.5 3397.7 5361.5 4787.5 935.9 1699 1894 3363.2 127.1
ACACA 3846.4 2989.4 2590.1 2932.4 4305.8 4407 3097.3 3382.2 6209.8
FNBP1 1195.6 1814 1405.3 774.5 922 1009.6 1918.8 2123.4 215.6
EPS15 1658.8 1418.5 1192.8 1293.9 1843.2 3034 1592.6 1447.8 1207.4

Table 2: MLL fusion genes expression in different subtypes of AML.

The black and gray cells represent the high expressed and low expressed values of the genes, respectively. The comparison is done between average of the gene expression and the subtypes.

MLLT2 is up-regulated in all translocations except in t(15;17). MLLT4 and MLLT7 are down-regulated in t(4;11) having a significant deviation when compared to average. For instance, ARHGEF12 is down-regulated in t(9;11), inv16, trisomy 8 and AML, but it is upregulated in t(15;17). ArgBP2 is a quite good example of downregulated gene expression in t(8;21), complex and AML. LASP1 expression fluctuates, it is down-regulated in t(15;17), but up-regulated in t(8;21). AF1Q and MLLT10 expression is high, but MYOF1 and GPHN expressions are low in int(8;21) and t(9;11) subtypes, respectively. t(15;17) has the highest gene expression diversity in both high and low directions if compared with other subtypes. t(15:17) and trisomy 8 have more diverse genes which represent up and downregulated in the subgroups. For instance, EPS15, ArgBP2, AFX, ARHGAP26, CXXC6, ARHGEF12, TIRAP are highly expressed genes and MAPRE1, EP300, LASP1, MLLT6, RARA, SEPT9, ELL, EEN, CCDC94, MPFYVE, AF15q14, CREBBP, GAS7, CIP29, CBL, PICALM, MAML2, ABI1, AF9-MLLT3, AF9q34, FOXO3A, SMAP1, AFF3, NCKIPSD, EEFSEC, GMPS, LPP, AF4P12, MIFL, AF4, AF1Q are low expressed in t(15:17). Trisomy 8 as a subtype includes AF1Q, AF4p12, MIFL, AF4, GPHN, ACACA, MLLT6 as highly expressed genes and MYOF1, ARHGEF 12, TIRAP, MAML2, FNBP1 as low expressed ones. Interestingly, inv16 has no significant MLL-FPs genes expression variations. The results support our theory that MLL-FPs genes are not just active in MLL, but also in other subtypes.

MLL-FP genes and clinical outcome

According to the AML subtypes, clinical outcome shows high heterogeneity. While t(15,17) and inv16 have the highest survival rate and relatively good prognosis, t(9:11) is lethal and leads to very bad prognosis (Figure 3).

Hierarchical clustering of MLL protein genes

MLL-FP genes are correlated with each other depending on their expression and clustered to determine their relation. Some of the genes show strong correlation with each other in different subtypes (Figure 4). The first group genes are ACACA, FNBP1 and EPS15 and strongly correlated to all the subtypes. MLLT7, MLLT10, MYOF1 are the second group, ArgBP2, ARGHEF12 and MLLT4 are the third highly correlated genes. TET1 is distinct and has a unique expression profile in all the groups. MPFYVE, NCKIPSD, ELL, TIRAP, DAB2IP, PBX1, EEN, ARHGAP26, AFF3 and GAS7 are the fourth and the maximum correlated genes in all the subtypes. The fifth group of genes demonstrate diverse expression value and are slightly correlated with each other (Figure 4).

biometrics-biostatistics-protein-genes

Figure 4: Hierarchical clustering of MLL protein genes.

Discussion

However, it is well known that the genes responsible for MLL-FPs are important key factor for MLL leukemogenesis, the research has been done to investigate the expression profile in other subtypes of AML. 46 most commonly found MLL-FPs genes have been compared among the subtypes that the gene expression profile is presented as high and low (Table 2). Analysis of variations in gene expression in different cytogenetic abnormalities showed that significant number of genes is down-regulated in t(15;17). Furthermore, there are some genes which have opposite direction expressions. Therefore, MLL-FPs show diverse expression profiles in different subtypes of AML.

Despite of the fact that MLL is a new subtype of AML leukemia, there is no significant and efficient individual therapy so far. MLL is a result of crucial change in genetic conformation by the translocation and MLL protein complex that can lead to leukemogenesis. The analysis of MLL-FPs might be prospective target to find common properties and patterns between MLL type and other subtypes of AML. Moreover, we can find the correlation between abnormal expression of the genes and clinical outcome among all the subtypes.

Conclusion

MLL is very aggressive type and there is no strong therapy. The bad prognosis and clinical outcome may be correlated with the fusion proteins activity. That`s why the gene expression profile might be used to find therapeutic target genes and help us to see the relation between the subtypes of AML and MLL-FPs. The major contribution of the research is to find the reason of different clinical outcome and survival rate among the subtypes of AML and MLL-FP activity.

Acknowledgements

We are thankful for Bosna Sema Education Institutions for their kind help and support.

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