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. 2021 Jan 21;8:622393. doi: 10.3389/fcell.2020.622393

Multi-Omics Analysis of Acute Lymphoblastic Leukemia Identified the Methylation and Expression Differences Between BCP-ALL and T-ALL

Jin-Fan Li 1,†, Xiao-Jing Ma 1,†, Lin-Lin Ying 1, Ying-hui Tong 2,*, Xue-ping Xiang 1,*
PMCID: PMC7859262  PMID: 33553159

Abstract

Acute lymphoblastic leukemia (ALL) as a common cancer is a heterogeneous disease which is mainly divided into BCP-ALL and T-ALL, accounting for 80–85% and 15–20%, respectively. There are many differences between BCP-ALL and T-ALL, including prognosis, treatment, drug screening, gene research and so on. In this study, starting with methylation and gene expression data, we analyzed the molecular differences between BCP-ALL and T-ALL and identified the multi-omics signatures using Boruta and Monte Carlo feature selection methods. There were 7 expression signature genes (CD3D, VPREB3, HLA-DRA, PAX5, BLNK, GALNT6, SLC4A8) and 168 methylation sites corresponding to 175 methylation signature genes. The overall accuracy, accuracy of BCP-ALL, accuracy of T-ALL of the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) classifier using these signatures evaluated with 10-fold cross validation repeated 3 times were 0.973, 0.990, and 0.933, respectively. Two overlapped genes between 175 methylation signature genes and 7 expression signature genes were CD3D and VPREB3. The network analysis of the methylation and expression signature genes suggested that their common gene, CD3D, was not only different on both methylation and expression levels, but also played a key regulatory role as hub on the network. Our results provided insights of understanding the underlying molecular mechanisms of ALL and facilitated more precision diagnosis and treatment of ALL.

Keywords: acute lymphoblastic leukemia, Boruta, Monte Carlo feature selection, network analysis, hub, multi-omics, expression, methylation

Introduction

Acute lymphoblastic leukemia (ALL) as a common cancer is a heterogeneous disease that originates from lymphocyte progenitor cells of B-cells or T-cells. It is a childhood malignant tumor that comprises >25% of pediatric neoplasia in American (Jabbour et al., 2015; Pui et al., 2015). Among adults, the incidence of ALL is much lower, accounting for only 0.2% of all cancers. However, the prognosis of ALL remains worrying, with an estimated 5-year overall survival (OS) of between 20 and 40% (Sive et al., 2012; Wolach et al., 2017). According to the World Health Organization (WHO) classification, ALL can be divided into B-cell ALL (B-ALL) and T-cell ALL (T-ALL). B-cell precursor ALL (BCP-ALL) is one of the B-ALL (Herold et al., 2014; Jones et al., 2016). In children’s ALL, it is mainly divided into BCP-ALL and T-ALL, accounting for 80–85% and 15–20%, respectively (Graux, 2011). These different subtypes are characterized by structural chromosomal rearrangements and repeated copy number alterations, which with great clinical significance (Goldberg et al., 2003).

There are prognosis, treatment and genetics differences between BCP-ALL and T-ALL (Gutierrez et al., 2014; Pui et al., 2015): (1) The prognosis of T-ALL patients is always worse than BCP-ALL patients (Goldberg et al., 2003; Eckert et al., 2013); (2) Many targeted immunotherapies have been developed for BCP-ALL patients but not for T-ALL patients (Pui et al., 2015); (3) T-ALL is associated with a wide range of acquired genetic abnormalities, which leads to abnormal proliferation and development stagnation of malignant lymphoid progenitor cells (Van Vlierberghe et al., 2008; Teitell and Pandolfi, 2009). This poses a challenge to the development of targeted therapy with wide application value. In the studies of the gene expression profile of ALL, the high expression of CD45 in leukemia cells was not only related to the poor prognosis of BCP-ALL patients but also to the poor prognosis of T-ALL patients. However, the prognostic correlation of CD45 expression in T-ALL was much higher than that in BCP-ALL (Hermiston et al., 2003; Cario et al., 2014). Moreover, PR-104 has been shown to specifically target hypoxic regions of leukemia infiltration, and was effective in the treatment of T-ALL xenotransplantation, but not in the treatment of BCP-ALL xenograft (Benito et al., 2011).

In this study, starting with methylation and gene expression data, we analyzed the molecular differences between BCP-ALL and T-ALL, screened out the molecular characteristics, and explored the relationship between these characteristics and the two subtypes of ALL.

Materials and Methods

The Multi-Omics Dataset of ALL

We downloaded the methylation and expression data of 69 BCP-ALL and 30 T-ALL patients from GEO (Gene Expression Omnibus) under accession number of GSE49031 and GSE47051 (Nordlund et al., 2013, 2015; Borssen et al., 2018), respectively. It was a large study performed by Uppsala University. There were originally 945 methylation samples and 108 expression samples. But the overlapped sample size between methylation data and expression data was 99 and within the 99 samples, there were 69 BCP-ALL and 30 T-ALL patients. Our goal was to systematically investigate the molecular differences between BCP-ALL and T-ALL and try to use these molecular differences to explain the clinical differences.

The methylation data were generated with Illumina HumanMethylation450 BeadChip and there were 485,577 methylation probes. Since there were missing values, we filtered the probes with missing values in at least 20% samples and kept 485,096 probes. Since the probes out of gene ranges were hard to explain, we kept the 317,845 probes that can be annotated onto genes and imputed the missing values using KNN (K = 10) method. Meanwhile, the expression data were generated with Affymetrix Human Genome U133 Plus 2.0 Array. The expression values of probes corresponding to the same gene were averaged. At last, the dataset was the expression levels of 15,888 genes and methylation levels of 317,845 probes in 69 BCP-ALL and 30 T-ALL patients.

Filter the Irrelevant Features Using Boruta

As we mentioned before, there were 15,888+317,845 = 333,733 features for each ALL sample. The number of features was much larger than the sample size. If we directly analyze all these 333,733 features, there will be too much noise and too many random feature combinations that can classify the samples. Therefore, we filtered the irrelevant features using Boruta method (Kursa and Rudnicki, 2010). The Boruta method can find out the relevant features and significantly reduce the number of features based on ensemble learning of random forest classifiers. Boruta is a widely used method and has been proven to be an effective method to find all relevant features (Pan et al., 2020; Yuan et al., 2020; Zhang et al., 2020).

Identify the Important Features Using Monte Carlo Feature Selection

Although Boruta method can filter irrelevant features and keep the relevant features, usually the number of features was still too large and the importance of features were still unknown. We need more sophisticated feature selection method to calculate the importance of features and rank the features. In this study, we applied MCFS (Monte Carlo Feature Selection) (Draminski et al., 2008). The MCFS has been widely used for feature selection (Chen et al., 2018, 2019; Pan et al., 2018, 2019a,b; Li et al., 2020). It divided the whole dataset into many small subsets. The subsets had much less features and the data structure of these subsets were relatively simple. Decision trees can be easily constructed. Based on all the trees on all the subsets, the importance of each feature can be calculated. The basic idea was that if a feature appeared in many trees, it was important and if a feature can classify many samples correctly, it was important. Based on these two rules, the importance of each feature was calculated. What’s more, the data was shuffled to generate random importance of each feature, the significance of each feature can be estimated by comparing the random importance and actual importance. At last, the significant features with importance much greater than permutated importance can be selected. Meanwhile, the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) rules within the trees can be cross-validated and their accuracy can be estimated.

Results and Discussion

The Relevant Features Identified by Boruta

As we mentioned there were 333,733 features (15,888 expression feature and 317,845 methylation features) for each ALL sample. The number of features were much larger than the sample size (99 in this study). Most of the features were not relevant to ALL. Keeping such features in the dataset will introduce noise and make the analysis inaccurate. Therefore, we adopted Boruta method (Kursa and Rudnicki, 2010) to remove irrelevant features. After running Boruta, 1,398 features were kept. Within these 1,398 features, there were 1,374 methylation features and 24 expression features.

The Important Features Identified by MCFS

The number of features filtered by Boruta (1,398) was still too large to be biomarkers. Therefore, we further reduced the number of features with MCFS method and finally identified 175 significant features. Within the 175 features, there were 168 methylation features (probe IDs starting with “cg”) and 7 expression features (CD3D, VPREB3, HLA-DRA, PAX5, BLNK, GALNT6, SLC4A8). These 175 features were given in Table 1. The annotations of the 168 methylation probes of in Supplementary Table 1.

TABLE 1.

The 175 important features identified by MCFS.

Rank Feature Rank Feature Rank Feature Rank Feature Rank Feature
1 cg26547698 36 cg19365697 71 cg00262446 106 cg06786219 141 cg20278269
2 CD3D 37 cg11086982 72 cg11071448 107 cg23387468 142 cg27627006
3 cg04690998 38 cg23275914 73 cg15188623 108 cg27280688 143 cg09203501
4 VPREB3 39 cg01391022 74 cg07255197 109 cg02368508 144 cg24690709
5 cg26437842 40 cg01686739 75 cg25468516 110 cg04715649 145 cg02673417
6 cg09740560 41 cg10746778 76 cg01582937 111 cg19610383 146 cg27263049
7 cg18085400 42 cg06560887 77 cg11139102 112 cg22056218 147 cg18245281
8 cg02891579 43 cg06876053 78 cg04473078 113 cg01290568 148 cg01467417
9 cg24710886 44 cg22051146 79 cg17355865 114 cg10253457 149 cg12971694
10 cg05998426 45 cg06571407 80 cg13948857 115 cg26833538 150 cg13804478
11 HLA-DRA 46 cg19785066 81 cg02655351 116 cg09976369 151 cg17984638
12 cg09773499 47 cg04346861 82 cg08874645 117 cg26795340 152 cg19844326
13 cg24999105 48 cg23379806 83 cg19843939 118 cg11963912 153 cg24864097
14 cg26607748 49 cg00004667 84 cg03364781 119 cg20464143 154 cg22628286
15 cg09983897 50 cg02334109 85 cg05524458 120 cg02297801 155 cg11321459
16 cg25620356 51 cg27021986 86 cg24937136 121 cg07003587 156 cg14989202
17 cg01731685 52 cg00231528 87 cg02574101 122 cg22905350 157 cg13094252
18 cg00661777 53 cg08894788 88 cg19006008 123 cg05115424 158 cg11348106
19 cg13031167 54 cg09897604 89 cg22232207 124 cg13482010 159 cg12960305
20 cg08146609 55 cg09864245 90 cg13767306 125 cg15662251 160 cg19339902
21 cg26121730 56 cg10156042 91 cg14788673 126 cg15897310 161 cg06560379
22 cg22881247 57 cg26574610 92 PAX5 127 cg14251777 162 cg00739471
23 cg14913610 58 cg22964469 93 cg02022181 128 cg03145274 163 SLC4A8
24 cg00120948 59 cg20934596 94 cg20117103 129 cg07151443 164 cg26262049
25 cg09285418 60 cg05533539 95 cg19750657 130 GALNT6 165 cg05276137
26 cg01937819 61 cg10142436 96 cg07545925 131 cg01278291 166 cg17398227
27 cg20907136 62 cg19140262 97 cg12577411 132 cg08995609 167 cg16324306
28 cg14499058 63 cg10789956 98 cg20090290 133 cg22996440 168 cg03437770
29 cg08347042 64 cg14590369 99 cg23616139 134 cg19921353 169 cg08854008
30 cg04926556 65 cg00310940 100 cg08187585 135 cg01591579 170 cg14154784
31 cg07217499 66 cg01595717 101 BLNK 136 cg09578155 171 cg01456517
32 cg18696027 67 cg27531366 102 cg16824282 137 cg12763828 172 cg02709032
33 cg03100639 68 cg07786657 103 cg02625929 138 cg01176028 173 cg03802696
34 cg09989037 69 cg04370174 104 cg10591771 139 cg27036638 174 cg06164961
35 cg06132620 70 cg10131232 105 cg02056653 140 cg26396492 175 cg12024826
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As we mentioned in section “Methods,” the MCFS method can also extract the classification rules. The confusion matrix of these RIPPER classification rules evaluated with 10-fold cross validation repeated 3 times was given in Table 2. The overall accuracy, accuracy of BCP-ALL, accuracy of T-ALL were 0.973, 0.990, and 0.933, respectively. These results meant that these features can classify the BCP-ALL and T-ALL very well.

TABLE 2.

The confusion matrix of the RIPPER rules evaluated with 10-fold cross validation repeated 3 times.

Predicted BCP-ALL Predicted T-ALL
Actual BCP-ALL 205 2
Actual T-ALL 6 84
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The Enrichment Analysis of the Selected Genes

Based on the annotations in Supplementary Table 1, we mapped the 168 methylation probes onto 175 genes. There were two overlapped genes (CD3D and VPREB3) between the 175 methylation signature genes and the 7 expression signature genes. We combined the 175 methylation signature genes and the 7 expression signature genes. Since there were two overlapped genes between them, there were 180 selected genes. We enriched the 180 selected genes onto KEGG pathways using WebGestalt1 (Wang et al., 2017). The KEGG enrichment results were shown in Figure 1. The x axis was log2 of enrichment ratio while the y axis was the -Log10 of FDR. The pathways on the top right corner were the significantly enrich pathways. It can be seen that hsa04640 Hematopoietic cell lineage was the enriched KEGG pathway. The were 11 selected genes on hsa04640 Hematopoietic cell lineage pathway: CD3D, CD3E, CD3G, CD59, FCER2, GP9, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DRA and IL1B. The enrichment p value and FDR were 3.28e-9 and 5.35e-7, respectively. Its enrichment ratio was 11. As CD3D was dysfunctional on both methylation and gene expression levels, HLA-DRA was dysfunctional on gene expression levels and other genes were dysfunctional on methylation levels, the hsa04640 Hematopoietic cell lineage pathway was dysfunctional on both methylation and gene expression levels.

FIGURE 1.

FIGURE 1

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The enrichment results of the 180 selected genes using WebGestalt. The x axis was log2 of enrichment ratio while the y axis was the -Log10 of FDR. The pathways on the top right corner were the significantly enrich pathways. It can be seen that hsa04640 Hematopoietic cell lineage was the enriched KEGG pathway.

The Network of Methylation and Expression Signature Genes

We searched the methylation and expression signature genes in STRING database2 (Szklarczyk et al., 2019) and their network with highest confidence (confidence score >0.900) was shown in Figure 2. The confidence score integrated the information from multiple sources including text mining, experiments, databases, co-expression, neighborhood, gene fusion and co-occurrence. It ranged from 0 to 1. The higher the confidence score was, the more reliable the interaction was. The cutoff of confidence score was set to be 0.900 since 0.900 was considered to be highest confidence in the STRING database. It can be seen that CD3D was the hub of the whole network. CD3D and another neighbor gene on the network, HLA-DRA, both belonged to hsa04640 Hematopoietic cell lineage pathway. The protein encoded by CD3D is part of the T cell receptor / CD3 complex (TCR/CD3 complex) and is involved in T cell development and signal transduction (Shi et al., 2019). CD3D has been shown to work with PKRCQ as a model to distinguish between B-ALL and T-ALL (Ma et al., 2016).

FIGURE 2.

FIGURE 2

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The network of methylation and expression signature genes. The methylation and expression signature genes were colored in gray and pink respectively. The red node, CD3D, was both methylation signature gene and expression signature gene. CD3D was the hub node of the network.

The Functional Analysis of the Selected Genes

Within the 7 expression signature genes, beside CD3D which was discussed above, VPREB3 and HLA-DRA also looked promising.

VPREB3 is the B-cell receptor component and its overexpression can activate the pro-survival PI3K pathway (Soldini et al., 2014). It has been reported as a biomarker for B-cell lymphoma by many studies (Heerema-McKenney et al., 2010; Rodig et al., 2010; Soldini et al., 2014).

HLA-DRA is related to the antigen presentation steps of the immune system (Hotchkiss et al., 2013). In the study of Morrison et al. (2010), women and children with multiple sclerosis (MS) had a fourfold increased risk of developing ALL. And, there was a certain correlation between MS and HLA-DRA single nucleotide polymorphism (SNP) (Morrison et al., 2010). Moreover, HLA genes are candidate genetic susceptibility loci for childhood ALL, HLA-DP1 was significantly correlated with ALL in children (Urayama et al., 2012). According to Ross et al. (2019), the ablation of POZ domain of ZBTB17 (Miz-1) interferes with its interaction with c-MYC and delays the occurrence of T-ALL and B-ALL.

Within the 175 methylation signature genes, there were many great candidates, such as HDAC4, HDAC9, LMO2, MEF2D, CD40, PAX5, BLNK and TLE1.

HDAC4 and HDAC9 are Histone deacetylases (HDACs) which may be a potential target for cancer treatment, including hematological malignancies. Moreno et al. (2010) detected the expression profile of HDAC gene in ALL samples by PCR. It was found that HDAC1 and HDAC4 were highly expressed in T-ALL and HDAC5 was highly expressed in B-ALL. Moreover, the expression of HDAC9 was correlated with B-ALL patients (Moreno et al., 2010).

LMO2 plays an essential role during early hematopoiesis and is frequently activated in T-ALL patients (Morishima et al., 2019). Wu et al. have deeply studied the mechanism of LMO2 in T-ALL and found that LMO2 can induce the transcriptional inhibition of ZEB1, while ZEB1 plays an important role in promoting T cell differentiation and may play an anti-cancer role in T-ALL (Wu et al., 2018). Several studies have also confirmed that LMO2 plays an important role in T-ALL (Curtis and McCormack, 2010; Homminga et al., 2012; Rahman et al., 2017).

MEF2D has been reported as a biomarker for a B-ALL subtype with a low survival rate. According to Zhang M et al., MEF2D-SS18 fusion gene blocks the differentiation of B cells, which plays an important role in the pathogenesis and prognosis of B-ALL (Zhang et al., 2018). Besides, Suzuki et al. (2016) confirmed that MEF2D-BCL9 fusion gene is associated with juvenile acute BCP-ALL.

CD40 is the member of the tumor necrosis factor receptor (TNFR) family, are critical regulators of lymphocyte growth and differentiation. Troeger et al. (2008) confirmed that the high expression of CD40 in BCP-ALL cells is an independent prognostic indicator, which indicates a better recurrence-free survival.

PAX5 is a haplotype tumor suppressor gene in human B-All, which is involved in a variety of chromosome translocation (Jamrog et al., 2018). In the investigation and analysis of Bastian et al. (2019), it was found that the army of patients with BCP-ALL subgroup carried PAX5 mutation.

BLNK is an adapter molecule essential to the development of normal B cells and is associated with increased pro-B/pre-B-cell expansion in mice. It was reported that BLNK deficiency was one of the main causes of B-ALL (Imai et al., 2004). The results of Nakayama et al. suggested that somatic loss of BLNK and concomitant mutations leading to constitutive activation of Jak/STAT5 pathway result in the generation of BCP-ALL (Nakayama et al., 2009).

TLE1 can be used as an indicator of poor prognosis of T-ALL (Brassesco et al., 2018) and the expression of ATP10A was up-regulated in BCP-ALL (Olsson et al., 2014).

Conclusion

Although there have been studies on the clinical differences between BCP-ALL and T-ALL, there has been no in-depth study of their underlying mechanism. In our study, the multi-omics profiles in BCP-ALL and T-ALL were analyzed. The discovered epigenetic changes of ALL and their possible effects on gene expression can help us understand the molecular mechanisms of the development, progression and recurrence of ALL. In ALL, those molecular characteristics have the function of differential diagnosis, targeted therapy and so on. At the same time, our research not only provides new information about the methylation and gene expression pattern of ALL, but also provides a selective reference for the study of ALL genes and methylation sites.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

Author Contributions

J-FL, X-pX, and X-JM contributed to the study design. L-LY conducted the literature search. Y-hT, J-FL, and X-JM acquired the data. J-FL and X-pX wrote the article. X-JM performed data analysis. J-FL and L-LY revised the article and gave the final approval of the version to be submitted. All authors read and approved the final manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding. This research was supported by the National Natural Science Foundation of China under Grant No. 81803549 and Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ18H310002.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2020.622393/full#supplementary-material

Supplementary Table 1

The annotations of the 168 methylation features.

Click here for additional data file. (23.9KB, XLSX)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1

The annotations of the 168 methylation features.

Click here for additional data file. (23.9KB, XLSX)

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

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