Skip to main content
NCBI home page
Diagnostic Pathology logoLink to Diagnostic Pathology
. 2019 Jun 29;14:68. doi: 10.1186/s13000-019-0841-1

BCL2 overexpression: clinical implication and biological insights in acute myeloid leukemia

Jing-dong Zhou 1,2,3,#, Ting-juan Zhang 1,2,3,#, Zi-jun Xu 2,3,4,#, Yu Gu 1,2,3, Ji-chun Ma 2,3,4, Xi-xi Li 5, Hong Guo 2,3,4, Xiang-mei Wen 2,3,4, Wei Zhang 1,2,3, Lei Yang 1,2,3, Xing-hui Liu 6, Jiang Lin 2,3,4,✉, Jun Qian 1,2,3,✉
PMCID: PMC6599255  PMID: 31253168

Abstract

Background

BCL2 protein inhibitor venetoclax (ABT-199) has been authorized by Food and Drug Administration for relapsed/refractory chronic lymphoid leukemia with 17p deletion. Although venetoclax/ABT-199 also caused cell death in acute myeloid leukemia (AML), whether it could be applied to clinical treatment needs further studies. Here, we revealed clinical implication of BCL2 overexpression in de novo adult AML, and may provide theoretical basis for targeted therapy using venetoclax.

Methods

BCL2 expression was analyzed in adult AML patients from public datasets The Cancer Genome Atlas (TCGA) and confirmed by another independent cohort from our own data.

Results

BCL2 expression showed up-regulated in AML patients among TCGA data and confirmed by our own data. BCL2 overexpression was correlated with FAB-M0/M1, whereas BCL2 under-expression was related to FAB-M5. However, BCL2 expression has no effect on overall survival (OS) and leukemia-free survival (LFS) of AML patients (determined in BCL2low and BCL2high groups). Interestingly, in the BCL2low group, patients undergoing autologous or allogeneic hematopoietic stem cell transplantation (auto/allo-HSCT) had significantly better OS and LFS compared with patients only received chemotherapy, whereas, no significant difference was found in OS and LFS between chemotherapy and auto/allo-HSCT patients in the BCL2high group. BCL2 expression was found positively correlated with HOX family gene, and negatively correlated with tumor suppressor microRNA such as miR-195, miR-497, and miR-193b.

Conclusions

BCL2 overexpression identified specific FAB subtypes of AML, but it did not affect prognosis. Patients with BCL2 overexpression did not benefit from auto/allo-HSCT among whole-cohort-AML and cytogenetically normal AML.

Electronic supplementary material

The online version of this article (10.1186/s13000-019-0841-1) contains supplementary material, which is available to authorized users.

Keywords: BCL2, Expression, HSCT, ABT-199/venetoclax, AML

Background

Acute myeloid leukemia (AML) represents for a molecularly, biologically, clinically, and etiologically heterogeneous disorder with variable outcome [1]. Despite recent advances in treating leukemia including autologous or allogeneic hematopoietic stem cell transplantation (auto/allo-HSCT) and novel chemotherapy drugs, the overall prognosis for AML remains unsatisfactory [1, 2]. The improving sequencing methods have provided us a comprehensive understanding of the biology of AML, and could provide potential targeted therapies for the improvement of the clinical outcome of AML [3]. In the past thirty years, the only approved targeted drugs were all-trans retinoic acid and arsenic trioxide for acute promyelocytic leukemia (APL) [4], which comprises approximately 15% of AML patients [5]. Recently, Food and Drug Administration (FDA) has approved the midostaurin for AML with FLT3 mutations, which accounts for approximately 30% of AML patients [6]. Moreover, the approval of enasidenib, an IDH2 inhibitor, has also approved by FDA for IDH2-mutated AML as another breakthrough in AML therapy [7].

Located on chromosome 18q21.33, BCL2 gene is found in human B-cell lymphomas, which is first identified through cloning the breakpoint of a translocation of t(14;18) [8]. It has proven to be major negative regulator in apoptosis, playing key roles in neoplastic transformation and leukemogenesis [9]. BCL2 protein plays crucial role in inhibiting the influx of adenine nucleotides through the outer mitochondrial membrane, resulting in reducing ATP hydrolysis and inhibiting cytochrome-C release [10]. Based on its oncogenic role in cancer, a highly potent and selective inhibitor of BCL2, ABT-199, presents antitumor activity while sparing platelets [11]. In 2016, venetoclax (ABT-199) has been authorized by FDA for relapsed/refractory chronic lymphoid leukemia (CLL) with 17p deletion. Although ABT-199 also induced cell death in AML [12], whether it can be applied to clinical treatment needs further studies. Notably, the FDA granted accelerated approval to venetoclax in combination with hypomethylating agents azacitidine or decitabine or low-dose cytarabine for the treatment of newly-diagnosed AML in adults who are age 75 years or older, or who have comorbidities that preclude use of intensive induction chemotherapy [7]. Herein, we revealed clinical implication of BCL2 overexpression in de novo adult AML, and may provide theoretical basis for targeted therapy using BCL2 inhibitor venetoclax.

Patients and methods

Patients and ethics

A first cohort of 173 adult AML patients with BCL2 expression data from The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov/ and http://www.cbioportal.org/) were identified and included in this study [13]. A total of 73 patients accepted auto/allo-HSCT for consolidation treatment, and the remaining 100 patients only received chemotherapy. The main clinical and laboratory features of the AML patients were presented in Table 1. The study protocol was approved by the Washington University Human Studies Committee, and informed consents were obtained from all patients.

Table 1.

Correlation of BCL2 expression with clinic-pathologic characteristics in AML among TCGA cohort

Patient’s parameters BCL2 expression
Low (n = 87) High (n = 86) P
Sex, male/female 49/38 43/43 0.448
Median age, years (range) 61 (22–82) 56 (18–88) 0.106
Median WBC, ×109/L (range) 17.9 (0.6–223.8) 15.25 (0.4–297.4) 0.041
Median PB blasts, % (range) 24 (0–94) 46 (0–98) 0.033
Median BM blasts, % (range) 73 (30–98) 72 (30–100) 0.893
FAB classifications 0.000
 M0 4 12 0.038
 M1 15 29 0.015
 M2 21 17 NS
 M3 5 11 NS
 M4 22 12 NS
 M5 16 2 0.001
 M6 1 1 NS
 M7 1 2 NS
 No data 2 0 NS
Cytogenetics 0.239
 Normal 44 32 NS
 t(15;17) 5 10 NS
 t(8;21) 6 1 NS
 inv.(16) 3 7 NS
  + 8 3 5 NS
 del(5) 0 1 NS
 -7/del(7) 4 4 NS
 11q23 2 1 NS
 Others 10 9 NS
 Complex 9 15 NS
 No data 1 1 NS
Gene mutation
 FLT3 (+/−) 23/64 26/60 0.616
 NPM1 (+/−) 28/59 20/66 0.235
 DNMT3A (+/−) 23/64 19/67 0.595
 IDH2 (+/−) 9/78 8/78 1.000
 IDH1 (+/−) 5/82 11/75 0.124
 TET2 (+/−) 9/78 6/80 0.590
 RUNX1 (+/−) 5/82 10/76 0.188
 TP53 (+/−) 6/81 8/78 0.590
 NRAS (+/−) 5/82 7/79 0.566
 CEBPA (+/−) 7/80 6/80 1.000
 WT1 (+/−) 2/85 8/78 0.057
 PTPN11 (+/−) 3/84 5/81 0.496
 KIT (+/−) 5/82 2/84 0.443
 U2AF1 (+/−) 4/83 3/83 1.000
 KRAS (+/−) 4/83 3/83 1.000
 SMC1A (+/−) 5/82 2/84 0.443
 SMC3 (+/−) 4/83 3/83 1.000
 PHF6 (+/−) 1/86 4/82 0.211
 STAG2 (+/−) 2/85 3/83 0.682
 RAD21 (+/−) 4/83 0/86 0.121
Open in a new tab

AML acute myeloid leukemia, WBC white blood cells, PB peripheral blood, BM bone marrow, FAB French-American-British classification, NS no significant

A second cohort of 154 AML patients and 35 healthy donors was also enrolled in the study. The main clinical and laboratory features of the AML patients were presented in Additional file 1. All participants provided informed consents, and the study was approved by the Institutional Review Board of the Affiliated People’s Hospital of Jiangsu University.

Samples preparation, RNA isolation, and reverse transcription

Bone marrow (BM) aspirate specimens were collected from 35 controls, 154 AML patients at diagnosis time, 48 AML patients at complete remission (CR) time, and 23 AML patients at relapse time. BM mononuclear cells (BMMNCs) were separated using Lymphocyte Separation Medium (Beijing Solarbio Science & Technology Co., Ltd., Beijing, China). Total RNA was extracted form BMMNCs using Trizol reagent (Invitrogen, Carlsbad, CA). Reverse transcription was performed to synthesize cDNA using random primers as our previous reports [14–17].

RT-qPCR

Real-time quantitative PCR (RT-qPCR) was performed to examine BCL2 mRNA using AceQ qPCR SYBR Green Master Mix (Vazyme Biotech Co., Piscataway, NJ). The primers used for BCL2 expression were 5′-CCCTGGTGGACAACATCG-3′ (forward) and 5′-CAGGAGAAATCAAACAGAGGC-3′ (reverse). Housekeeping gene ABL1 was detected by RT-qPCR using 2 × SYBR Green PCR Mix (Multisciences, Hangzhou, China) [14–17]. Relative BCL2 mRNA levels were calculated using 2-∆∆CT method.

Bioinformatics analyses

The comparison of BCL2 expression in AML from TCGA data and controls was performed by GEPIA (http://gepia.cancer-pku.cn/detail.php) [18]. Differential gene expression analysis for RNA/microRNA sequencing data was calculated using the raw read counts with the R/Bioconductor package “edgeR”, all analyses were controlled for the false discovery rate (FDR) by the Benjamini-Hochberg procedure. Functional and signaling pathway enrichment was conducted using online website of STRING (http://string-db.org). The microRNA which could target BCL2 was identified by TargetScan (http://www.targetscan.org/vert_72/), mirDIP (http://ophid.utoronto.ca/mirDIP/), miRWalk (http://mirwalk.umm.uni-heidelberg.de/), and miRDB (http://mirdb.org/miRDB/). All basic statistical analyses were performed using the base functions in R version 3.4 (https://www.r-project.org).

Statistical analyses

SPSS 22.0 and GraphPad Prism 5.0 were used for statistical analyses and figures creation. Mann-Whitney’s U test was used for the comparison of continuous variables, whereas Pearson Chi-square analysis or Fisher exact test was applied for the comparison of categorical variables. The prognostic effect of BCL2 expression on leukemia-free survival (LFS) and overall survival (OS) was analyzed though Kaplan-Meier analysis using Log-rank test. Univariate and multivariate proportional hazard regression analysis was performed using Cox regression. The P value (two-tailed) equal or less than 0.05 in all statistical analyses was defined as statistically significant.

Results

BCL2 overexpression in AML

A cohort of 173 de novo adult AML patients with BCL2 expression data from public TCGA datasets was used for differential expression analysis. By using the GEPIA (http://gepia.cancer-pku.cn/detail.php), we found BCL2 expression in AML patients was significantly increased compared with GTEx normal BM samples (P < 0.001, Fig. 1a). In order to confirm the results, we further analyzed BCL2 expression in the second cohort of 154 AML patients from our hospital. Similarly, BCL2 expression was markedly up-regulated in newly diagnosed AML compared with controls and AML patients achieved CR (P < 0.001 and = 0.041, Fig. 1b). Moreover, BCL2 transcript level was significantly increased in AML at relapse time compared with those at CR time (P = 0.024, Fig. 1b).

Fig. 1.

Fig. 1

Open in a new tab

BCL2 overexpression in AML. a: BCL2 expression in controls and AML patients from TCGA datasets using the GEPIA (http://gepia.cancer-pku.cn/detail.php). b: BCL2 expression in controls, newly diagnosed AML, AML achieved complete remission, and relapsed AML in another cohort from our hospital

BCL2 expression identified specific FAB subtypes of AML

In order to explore the clinical implication of BCL2 expression in AML, we further divided these cases into two groups (BCL2high and BCL2low) based on median level of BCL2 transcript. The comparison of clinical/laboratory characteristics of the AML patients between two groups were summarized in Table 1. There were no significant differences between BCL2high and BCL2low groups in sex, age, BM blasts, and the distributions of cytogenetics (P > 0.05). However, BCL2high cases had significantly lower white blood cells (WBC) and higher peripheral blood (PB) blasts compared with BCL2low cases (P = 0.041 and 0.033). Additionally, significant differences in the distributions of FAB classifications and cytogenetics were found between two groups (P = 0.000). BCL2 overexpression was markedly correlated with FAB-M0/M1 (P = 0.038 and 0.015), whereas BCL2 under-expression was associated with FAB-M5 (P = 0.001). Among gene mutations, no significant differences were found, besides BCL2high tended to be associated with WT1 mutations (P = 0.057).

BCL2 expression did not affect prognosis in AML

Among the tested AML patients, a total of 73 cases received auto/allo-HSCT for consolidation treatment (after induction chemotherapy), whereas the other 100 cases only received chemotherapy. In both chemotherapy and auto/allo-HSCT groups, BCL2high patients showed similar OS (median 26.3 vs 15.8 months) and LFS (median 11.1 vs 9.3 months) time compared with BCL2low patients (Fig. 2a and c). Among cytogenetically normal AML (CN-AML), there was also no significant difference in OS (median 24.6 vs 18.1 months) and LFS (median 9.6 vs 11.6 months) time between BCL2high and BCL2low groups (Fig. 2b and d). Moreover, no matter in either chemotherapy or auto/allo-HSCT groups, no significant differences were found in OS and LFS time between BCL2low and BCL2high groups among whole-cohort-AML (Chemotherapy group: OS median 8.1 vs 8.0 months and LFS median 8.0 vs 5.9 months; auto/allo-HSCT group: OS median 30.0 vs 56.3 months and LFS median 14.6 vs 13.8 months) and CN-AML (Chemotherapy group: OS median 15.5 vs 8.2 months and LFS median 12.0 vs 8.2 months; auto/allo-HSCT group: OS median 24.6 vs 56.3 months and LFS median 8.6 vs 13.8 months) (Fig. 2e-l). Moreover, Cox regression analysis also confirmed that BCL2 did not independently affect the OS and LFS in whole-cohort-AML (Table 2).

Fig. 2.

Fig. 2

Open in a new tab

The impact of BCL2 expression on survival of AML patients from TCGA cohort. a-d: Kaplan–Meier survival curves of OS and LFS in both chemotherapy and HSCT groups. e-h: Kaplan–Meier survival curves of OS and LFS in chemotherapy group. i-l: Kaplan–Meier survival curves of OS and LFS in HSCT groups

Table 2.

Cox regression analyses of variables for OS and LFS in whole-cohort-AML among TCGA cohort

Variables OS LFS
Univariate analysis Multivariate analysis Univariate analysis Multivariate analysis
HR (95% CI) P HR (95% CI) P HR (95% CI) P HR (95% CI) P
BCL2 expression 1.000 (1.000–1.000) 0.185 1.000 (1.000–1.000) 0.761 1.000 (1.000–1.000) 0.356
Age 1.040 (1.027–1.054) 0.000 1.027 (1.011–1.042) 0.001 1.035 (1.022–1.048) 0.000 1.027 (1.013–1.041) 0.000
WBC 1.003 (0.999–1.006) 0.119 1.007 (1.003–1.012) 0.001 1.003 (1.000–1.006) 0.091 1.003 (1.000–1.006) 0.040
Karyotype risk 1.854 (1.465–2.346) 0.000 2.208 (1.591–3.063) 0.000 1.829 (1.448–2.311) 0.000 2.065 (1.593–2.676) 0.000
Treatment regimens 0.551 (0.389–0.780) 0.001 0.441 (0.284–0.687) 0.000 0.615 (0.434–0.871) 0.006 0.546 (0.366–0.815) 0.003
FLT3 mutations 1.269 (0.869–1.852) 0.217 1.254 (0.859–1.829) 0.241
NPM1 mutations 1.220 (0.837–1.778) 0.301 1.268 (0.869–1.848) 0.218
CEBPA mutations 0.913 (0.464–1.796) 0.792 1.053 (0.535–2.073) 0.881
DNMT3A mutations 1.615 (1.104–2.362) 0.014 1.472 (0.951–2.279) 0.083 1.511 (1.035–2.206) 0.033 1.302 (0.860–1.973) 0.212
IDH1 mutations 0.843 (0.466–1.527) 0.574 0.890 (0.492–1.611) 0.700
IDH2 mutations 1.113 (0.649–1.910) 0.697 0.987 (0.576–1.691) 0.963
TET2 mutations 0.953 (0.514–1.767) 0.879 0.945 (0.510–1.751) 0.857
RUNX1 mutations 1.853 (1.077–3.186) 0.026 1.692 (1.137–2.518) 0.009 1.644 (0.959–2.817) 0.071 1.322 (0.912–1.916) 0.141
TP53 mutations 3.687 (2.144–6.339) 0.000 2.379 (1.211–4.673) 0.012 3.257 (1.912–5.549) 0.000 1.642 (0.904–2.984) 0.103
Open in a new tab

OS overall survival, LFS leukemia-free survival, HR hazard ratio, CI confidence interval, WBC white blood cells. Variables in multivariate analysis including BCL2 expression, age, WBC, karyotype (favorable vs. intermediate vs. poor), treatment regimens (without/with HSCT) and gene mutations (mutant vs. wild-type)

High expression of BCL2 in AML patients did not benefit from transplantation

To investigate whether AML patients with high expression of BCL2 could benefit from auto/allo-HSCT, survival in patients with auto/allo-HSCT were compared among both BCL2high and BCL2low groups. In the BCL2low group, the patients undergoing auto/allo-HSCT had significantly better OS and LFS compared with patients only received chemotherapy among both total AML (OS median 56.3 vs 8.0 months and LFS median 13.8 vs 5.9 months) and CN-AML (OS median 56.3 vs 8.2 months and LFS median 13.8 vs 8.2 months) (Fig. 3a-d). In the BCL2high group, no significant differences in OS and LFS were found between auto/allo-HSCT and chemotherapy groups among both total AML (OS median 30.0 vs 8.1 months and LFS median 14.6 vs 8.0 months) and CN-AML (OS median 24.6 vs 15.5 months and LFS median 12.0 vs 8.6 months) (Fig. 3e-h).

Fig. 3.

Fig. 3

Open in a new tab

The effect of HSCT on survival of AML patients among different BCL2 expression groups from TCGA cohort. a-d: Kaplan–Meier survival curves of OS and LFS in low BCL2 expression group. e-h: Kaplan–Meier survival curves of OS and LFS in high BCL2 expression group

Molecular signatures associated with BCL2 in AML

To gain insights into the biological function of BCL2, we first compared the transcriptomes of BCL2high and BCL2low groups. This comparison yielded 1533 differentially expressed genes (FDR < 0.05, |log2 FC| > 1; Fig. 4a and b; Additional file 2), in which 569 genes were positively correlated with BCL2 expression, and 964 were negatively correlated. Several genes such as PAX2, HOXC6, HOXC10, HOXC9, SOX11, HOXD13, HOXC8, WT1, SALL4, HOXC11, HOXC4, HOXC12, HOXC5, and HOXD12 reported with proto-leukemia effects were identified within this signature positively correlated with BCL2 expression. Among the negatively associated genes, BCL2 expression related to the anti-leukemia-associated genes such as CDKN2B, LGALS3, CDH6, THBS1, ITGB2, ROBO1, DOK2, DKK2, DKK1, and LEP. Furthermore, the Gene Ontology analysis revealed that these genes involved in biologic processes, including system development, signaling, cell communication, and cell adhesion (Fig. 4c).

Fig. 4.

Fig. 4

Open in a new tab

Molecular signatures associated with BCL2 in AML from TCGA cohort. a: Expression heatmap of differentially expressed genes between BCL2low and BCL2high AML patients among TCGA datasets (FDR < 0.05, P < 0.05 and |log2 FC| > 1). b: Volcano plot of differentially expressed genes between BCL2low and BCL2high AML patients. c: Gene Ontology analysis of DEGs conducted using online website of STRING (http://string-db.org). d: Expression heatmap of differentially expressed microRNAs between BCL2low and BCL2high AML patients among TCGA datasets (FDR < 0.05, P < 0.05 and |log2 FC| > 1). e: Venn results of microRNAs which could target BCL2 predicted by TargetScan (http://www.targetscan.org/vert_72/), mirDIP (http://ophid.utoronto.ca/mirDIP/), miRWalk (http://mirwalk.umm.uni-heidelberg.de/), and miRDB (http://mirdb.org/miRDB/)

Next, we also derived microRNA expression signatures associated with BCL2 expression. A total of 19 microRNAswas significantly correlated including 11 positive and 8 negative (FDR < 0.05, |log2 FC| > 1; Fig. 4d; Additional file 3). Negatively correlated microRNAs included miR-195, miR-497, miR-135a, miR-196a, miR-193b, miR-455, miR-375, and miR-205, which have been found to have anti-leukemia effects in previous studies. Of these microRNAs, miR-195 and miR-497 was identified as predicted microRNAs that could direct target BCL2 (Fig. 4e, Additional file 4).

Discussion

In this study, we found and verified that BCL2 expression was significantly up-regulated in newly diagnosed AML especially in relapsed AML among two independent cohorts in consistent with previous studies [19–28]. Previously, BCL2 overexpression showed heterogenous expression in the range of 34 to 87% [19]. Although BCL2 overexpression in AML cells correlates with CD34 and CD117 positivity by other investigators [19, 20], we did not found the association of BCL2 expression with BM blasts, despite the fact that BCL2high patients showed higher percentage of PB blasts. Among FAB subtypes, BCL2 overexpression was significantly correlated with FAB-M0/M1, whereas BCL2 under-expression was associated with FAB-M5, which was in consistent with previous reports [19]. Interestingly, although previous studies revealed that BCL2 overexpression correlated with poor response to chemotherapy [19–22], we did not found the negative effect of BCL2 overexpression on clinical outcome of AML. Similarly, several investigators also did not show the significant association of BCL2 overexpression with prognosis [23, 24]. In addition, increasing studies attempted to show the transcript ratio of FLT3 + KIT/BCL2, FLT3/BCL2, and BAX/BCL2 (or combined with WT1 or MDR1) may affect prognosis in AML [25–28]. Thus, we deduced that BCL2 expression was not a valuable single factor that affecting prognosis in AML.

Apoptosis plays crucial roles in the command of tissue homeostasis, and is important in the clearance of infected, unwanted, or otherwise damaged cells [29]. Meanwhile, deregulation of apoptosis may give rise to neoplastic transformation [9]. It has been well demonstrated that BCL2 acted as a negative regulator on cellular apoptosis and is a druggable target [9, 30–32]. In hematologic malignancies, the impairment of apoptosis process is often caused by BCL2 overexpression [32]. Taking these into account, targeting BCL2 proteins to cause apoptosis is considered as a potential therapeutic approach in hematological malignancies [33–36]. Early efforts in BCL2 inhibitor including ABT-737 and ABT-263/navitoclax were encountered with disappointment in clinic because of dose-dependent thrombocytopenia [31]. In 2013, Souers et al. recently reported the re-engineering of ABT-263/navitoclax to create ABT-199/venetoclax, which was a highly potent and selective inhibitor of BCL2 [11]. By clinical studies, venetoclax presented high rate of treatment response as a single drugs in refractory/relapsed CLL [37]. Of note, ABT-199/venetoclax has been authorized by FDA for relapsed or refractory CLL with 17p deletion in 2016. In addition to CLL, ABT-199 also powerfully kills a various array of non-Hodgkin lymphoma and AML cell lines [12], suggesting that the drug has the potential to be efficacious in multiple hematologic malignancies. From our study, we observed that AML patients with BCL2 under-expression could benefit from auto/allo-HSCT, whereas patients with BCL2 overexpression did not benefit from auto/allo-HSCT.

Herein, we further determined the molecular signatures associated with BCL2 in AML to further get better understanding of AML biology. We found that BCL2 dysregulation was significantly associated with HOX gene family, which was reported highly correlated with hematopoiesis and leukemogenesis [38, 39]. Moreover, for microRNAs, we found BCL2 expression was negatively correlated with several microRNAs such as miR-195, miR-497, miR-135a, miR-196a, miR-193b, miR-455, miR-375, and miR-205, which were found to be associated with AML pathogenesis or patients prognosis by previous studies [40–44]. Of these microRNAs, miR-195 and miR-497 was identified as predicted microRNAs that could direct target BCL2. Obviously, further studies are needed to confirm the direct connections of BCL2 with microRNAs by luciferase assay.

Conclusion

BCL2 overexpression identified specific FAB subtypes of AML, but it did not affect prognosis. Patients with BCL2 overexpression did not benefit from auto/allo-HSCT among whole-cohort-AML and CN-AML.

Additional files

Additional file 1: (19.1KB, docx)

Clinic-pathologic characteristics in AML from our cohort. (DOCX 19 kb)

Additional file 2: (50.8KB, xlsx)

Different expressed genes of microRNA for BCL2high and BCL2low. (XLSX 50 kb)

Additional file 3: (1.6MB, xlsx)

Different expressed genes of RNA for BCL2high and BCL2low. (XLSX 1682 kb)

Additional file 4: (37.7KB, txt)

Venn results of microRNAs targeting BCL2. (TXT 37 kb)

Acknowledgements

None.

Abbreviations

AML

Acute myeloid leukemia

APL

Acute promyelocytic leukemia

BM

Bone marrow

BMMNCs

BM mononuclear cells

CLL

Chronic lymphoid leukemia

CN-AML

Cytogenetically normal AML

CR

Complete remission

FDA

Food and Drug Administration

HSCT

Hematopoietic stem cell transplantation

LFS

Leukemia-free survival

OS

Overall survival

RT-qPCR

Real-time quantitative PCR

TCGA

The Cancer Genome Atlas

Authors’ contributions

J-dZ and JQ conceived and designed the experiments; J-dZ and T-jZ performed the experiments; J-dZ and Z-jX analyzed the data; WZ and LY collected the clinical data; JL, J-cM, HG, X-mW, X-hL offered technique support; J-dZ wrote the paper. All authors read and approved the final manuscript.

Funding

The work was supported by National Natural Science foundation of China (81270630), Medical Innovation Team of Jiangsu Province (CXTDB2017002), Six Talent Peaks Project in Jiangsu Province (2015-WSN-115), Natural Science Foundation of Jiangsu Province for Youths (BK20180280), Scientific Research Foundation of Affiliated People’s Hospital of Jiangsu University for Ph.D. (KFB201603), Zhenjiang Clinical Research Center of Hematology (SS2018009), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_1821, KYCX18_2281), Social Development Foundation of Zhenjiang (SH2016045, SH2017040, SH2018044), Clinical Medical Science Development Foundation of Jiangsu University (JLY20160011), The Key Disciplines Group Construction Project of Pudong Health Bureau of Shanghai (PWZxq2017–15).

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The present study approved by the Ethics Committee and Institutional Review Board of the Affiliated People’s Hospital of Jiangsu University.

Consent for publication

Written informed consents were obtained from all enrolled individuals prior to their participation.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jing-dong Zhou, Ting-juan Zhang and Zi-jun Xu contributed equally to this work.

Contributor Information

Jiang Lin, Email: 2651329493@qq.com.

Jun Qian, Email: qianjun0007@hotmail.com.

References

  • 1.Döhner H, Weisdorf DJ, Bloomfield CD. Acute Myeloid Leukemia. N Engl J Med. 2015;373(12):1136–1152. doi: 10.1056/NEJMra1406184. [DOI] [PubMed] [Google Scholar]
  • 2.Döhner H, Estey E, Grimwade D, Amadori S, Appelbaum FR, Büchner T, Dombret H, Ebert BL, Fenaux P, Larson RA, Levine RL, Lo-Coco F, Naoe T, Niederwieser D, Ossenkoppele GJ, Sanz M, Sierra J, Tallman MS, Tien HF, Wei AH, Löwenberg B, Bloomfield CD. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129(4):424–447. doi: 10.1182/blood-2016-08-733196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Coombs CC, Tallman MS, Levine RL. Molecular therapy for acute myeloid leukaemia. Nat Rev Clin Oncol. 2016;13(5):305–318. doi: 10.1038/nrclinonc.2015.210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Antman KH. Introduction: the history of arsenic trioxide in cancer therapy. Oncologist. 2001;6(Suppl 2):1–2. doi: 10.1634/theoncologist.6-suppl_2-1. [DOI] [PubMed] [Google Scholar]
  • 5.Yamamoto JF, Goodman MT. Patterns of leukemia incidence in the United States by subtype and demographic characteristics, 1997-2002. Cancer Causes Control. 2008;19(4):379–390. doi: 10.1007/s10552-007-9097-2. [DOI] [PubMed] [Google Scholar]
  • 6.Levis M. Midostaurin approved for FLT3-mutated AML. Blood. 2017;129(26):3403–3406. doi: 10.1182/blood-2017-05-782292. [DOI] [PubMed] [Google Scholar]
  • 7.Bohl SR, Bullinger L, Rücker FG. New targeted agents in acute myeloid leukemia: new Hope on the rise. Int J Mol Sci. 2019;20(8):1983. doi: 10.3390/ijms20081983. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Tsujimoto Y, Finger LR, Yunis J, Nowell PC, Croce CM. Cloning of the chromosome breakpoint of neoplastic B cells with the t(14;18) chromosome translocation. Science. 1984;226(4678):1097–1099. doi: 10.1126/science.6093263. [DOI] [PubMed] [Google Scholar]
  • 9.Testa U, Riccioni R. Deregulation of apoptosis in acute myeloid leukemia. Haematologica. 2007;92(1):81–94. doi: 10.3324/haematol.10279. [DOI] [PubMed] [Google Scholar]
  • 10.Tsujimoto Y. Role of Bcl-2 family proteins in apoptosis: apoptosomes or mitochondria? Genes Cells. 1998;3(11):697–707. doi: 10.1046/j.1365-2443.1998.00223.x. [DOI] [PubMed] [Google Scholar]
  • 11.Souers AJ, Leverson JD, Boghaert ER, Ackler SL, Catron ND, Chen J, Dayton BD, Ding H, Enschede SH, Fairbrother WJ, Huang DC, Hymowitz SG, Jin S, Khaw SL, Kovar PJ, Lam LT, Lee J, Maecker HL, Marsh KC, Mason KD, Mitten MJ, Nimmer PM, Oleksijew A, Park CH, Park CM, Phillips DC, Roberts AW, Sampath D, Seymour JF, Smith ML, Sullivan GM, Tahir SK, Tse C, Wendt MD, Xiao Y, Xue JC, Zhang H, Humerickhouse RA, Rosenberg SH, Elmore SW. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat Med. 2013;19(12):202–208. doi: 10.1038/nm.3048. [DOI] [PubMed] [Google Scholar]
  • 12.Pan R, Hogdal LJ, Benito JM, Bucci D, Han L, Borthakur G, Cortes J, DeAngelo DJ, Debose L, Mu H, Döhner H, Gaidzik VI, Galinsky I, Golfman LS, Haferlach T, Harutyunyan KG, Hu J, Leverson JD, Marcucci G, Müschen M, Newman R, Park E, Ruvolo PP, Ruvolo V, Ryan J, Schindela S, Zweidler-McKay P, Stone RM, Kantarjian H, Andreeff M, Konopleva M, Letai AG. Selective BCL-2 inhibition by ABT-199 causes on-target cell death in acute myeloid leukemia. Cancer Discov. 2014;4(3):362–375. doi: 10.1158/2159-8290.CD-13-0609. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Cancer Genome Atlas Research Network. Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A, Hoadley K, Triche TJ, Jr, Laird PW, Baty JD, Fulton LL, Fulton R, Heath SE, Kalicki-Veizer J, Kandoth C, Klco JM, Koboldt DC, Kanchi KL, Kulkarni S, Lamprecht TL, Larson DE, Lin L, Lu C, McLellan MD, McMichael JF, Payton J, Schmidt H, Spencer DH, Tomasson MH, Wallis JW, Wartman LD, Watson MA, Welch J, Wendl MC, Ally A, Balasundaram M, Birol I, Butterfield Y, Chiu R, Chu A, Chuah E, Chun HJ, Corbett R, Dhalla N, Guin R, He A, Hirst C, Hirst M, Holt RA, Jones S, Karsan A, Lee D, Li HI, Marra MA, Mayo M, Moore RA, Mungall K, Parker J, Pleasance E, Plettner P, Schein J, Stoll D, Swanson L, Tam A, Thiessen N, Varhol R, Wye N, Zhao Y, Gabriel S, Getz G, Sougnez C, Zou L, Leiserson MD, Vandin F, Wu HT, Applebaum F, Baylin SB, Akbani R, Broom BM, Chen K, Motter TC, Nguyen K, Weinstein JN, Zhang N, Ferguson ML, Adams C, Black A, Bowen J, Gastier-Foster J, Grossman T, Lichtenberg T, Wise L, Davidsen T, Demchok JA, Shaw KR, Sheth M, Sofia HJ, Yang L, Downing JR, Eley G. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368(22):2059–2074. doi: 10.1056/NEJMoa1301689. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Zhang TJ, Zhou JD, Zhang W, Lin J, Ma JC, Wen XM, Yuan Q, Li XX, Xu ZJ, Qian J. H19 overexpression promotes leukemogenesis and predicts unfavorable prognosis in acute myeloid leukemia. Clin Epigenetics. 2018;10:47. doi: 10.1186/s13148-018-0486-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Zhou JD, Wang YX, Zhang TJ, Li XX, Gu Y, Zhang W, Ma JC, Lin J, Qian J. Identification and validation of SRY-box containing gene family member SOX30 methylation as a prognostic and predictive biomarker in myeloid malignancies. Clin Epigenetics. 2018;10:92. doi: 10.1186/s13148-018-0523-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Zhou JD, Zhang TJ, Li XX, Ma JC, Guo H, Wen XM, Zhang W, Yang L, Yan Y, Lin J, Qian J. Epigenetic dysregulation of ID4 predicts disease progression and treatment outcome in myeloid malignancies. J Cell Mol Med. 2017;21(8):1468–1481. doi: 10.1111/jcmm.13073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Lin J, Ma JC, Yang J, Yin JY, Chen XX, Guo H, Wen XM, Zhang TJ, Qian W, Qian J, Deng ZQ. Arresting of miR-186 and releasing of H19 by DDX43 facilitate tumorigenesis and CML progression. Oncogene. 2018;37(18):2432–2443. doi: 10.1038/s41388-018-0146-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98–W102. doi: 10.1093/nar/gkx247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Mehta SV, Shukla SN, Vora HH. Overexpression of Bcl2 protein predicts chemoresistance in acute myeloid leukemia: its correlation with FLT3. Neoplasma. 2013;60(6):666–675. doi: 10.4149/neo_2013_085. [DOI] [PubMed] [Google Scholar]
  • 20.Lauria F, Raspadori D, Rondelli D, Ventura MA, Fiacchini M, Visani G, Forconi F, Tura S. High bcl-2 expression in acute myeloid leukemia cells correlates with CD34 positivity and complete remission rate. Leukemia. 1997;11(12):2075–2078. doi: 10.1038/sj.leu.2400854. [DOI] [PubMed] [Google Scholar]
  • 21.Kornblau SM, Thall PF, Estrov Z, Walterscheid M, Patel S, Theriault A, Keating MJ, Kantarjian H, Estey E, Andreeff M. The prognostic impact of BCL2 protein expression in acute myelogenous leukemia varies with cytogenetics. Clin Cancer Res. 1999;5(7):1758–1766. [PubMed] [Google Scholar]
  • 22.Irvine AE, McMullin MF, Ong YL. Bcl-2 family members as prognostic indicators in AML. Hematology. 2002;7(1):21–31. doi: 10.1080/10245330290011838. [DOI] [PubMed] [Google Scholar]
  • 23.El-Shakankiry NH, El-Sayed GM, El-Maghraby S, Moneer MM. Bcl-2 protein expression in egyptian acute myeloid leukemia. J Egypt Natl Canc Inst. 2009;21(1):71–76. [PubMed] [Google Scholar]
  • 24.Naumovski L, Martinovsky G, Wong C, Chang M, Ravendranath Y, Weinstein H, Dahl G. BCL-2 expression does not not correlate with patient outcome in pediatric acute myelogenous leukemia. Leuk Res. 1998;22(1):81–87. doi: 10.1016/S0145-2126(97)00127-6. [DOI] [PubMed] [Google Scholar]
  • 25.Del Poeta G, Venditti A, Del Principe MI, Maurillo L, Buccisano F, Tamburini A, Cox MC, Franchi A, Bruno A, Mazzone C, Panetta P, Suppo G, Masi M, Amadori S. Amount of spontaneous apoptosis detected by Bax/Bcl-2 ratio predicts outcome in acute myeloid leukemia (AML) Blood. 2003;101(6):2125–2131. doi: 10.1182/blood-2002-06-1714. [DOI] [PubMed] [Google Scholar]
  • 26.Sharawat SK, Bakhshi R, Vishnubhatla S, Bakhshi S. High receptor tyrosine kinase (FLT3, KIT) transcript versus anti-apoptotic (BCL2) transcript ratio independently predicts inferior outcome in pediatric acute myeloid leukemia. Blood Cells Mol Dis. 2015;54(1):56–64. doi: 10.1016/j.bcmd.2014.07.019. [DOI] [PubMed] [Google Scholar]
  • 27.Venditti A, Del Poeta G, Maurillo L, Buccisano F, Del Principe MI, Mazzone C, Tamburini A, Cox C, Panetta P, Neri B, Ottaviani L, Amadori S. Combined analysis of bcl-2 and MDR1 proteins in 256 cases of acute myeloid leukemia. Haematologica. 2004;89(8):934–939. [PubMed] [Google Scholar]
  • 28.Karakas T, Miething CC, Maurer U, Weidmann E, Ackermann H, Hoelzer D, Bergmann L. The coexpression of the apoptosis-related genes bcl-2 and wt1 in predicting survival in adult acute myeloid leukemia. Leukemia. 2002;16(5):846–854. doi: 10.1038/sj.leu.2402434. [DOI] [PubMed] [Google Scholar]
  • 29.Hotchkiss RS, Strasser A, McDunn JE, Swanson PE. Cell death. N Engl J Med. 2009;361(16):1570–1583. doi: 10.1056/NEJMra0901217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Cang S, Iragavarapu C, Savooji J, Song Y, Liu D. ABT-199 (venetoclax) and BCL-2 inhibitors in clinical development. J Hematol Oncol. 2015;8:129. doi: 10.1186/s13045-015-0224-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Davids MS, Letai A. ABT-199: taking dead aim at BCL-2. Cancer Cell. 2013;23(2):139–141. doi: 10.1016/j.ccr.2013.01.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Anderson MA, Huang D, Roberts A. Targeting BCL2 for the treatment of lymphoid malignancies. Semin Hematol. 2014;51(3):219–227. doi: 10.1053/j.seminhematol.2014.05.008. [DOI] [PubMed] [Google Scholar]
  • 33.Scarfò L, Ghia P. Reprogramming cell death: BCL2 family inhibition in hematological malignancies. Immunol Lett. 2013;155(1–2):36–39. doi: 10.1016/j.imlet.2013.09.015. [DOI] [PubMed] [Google Scholar]
  • 34.Kang MH, Reynolds CP. Bcl-2 inhibitors: targeting mitochondrial apoptoticpathways in cancer therapy. Clin Cancer Res. 2009;15(4):1126–1132. doi: 10.1158/1078-0432.CCR-08-0144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Davids MS, Letai A. Targeting the B-cell lymphoma/leukemia 2 family in cancer. J Clin Oncol. 2012;30(25):3127–3135. doi: 10.1200/JCO.2011.37.0981. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Vogler M, Dinsdale D, Dyer MJ, Cohen GM. Bcl-2 inhibitors: small moleculeswith a big impact on cancer therapy. Cell Death Differ. 2009;16(3):360–367. doi: 10.1038/cdd.2008.137. [DOI] [PubMed] [Google Scholar]
  • 37.Roberts AW, Seymour JF, Brown JR, Wierda WG, Kipps TJ, Khaw SL, Carney DA, He SZ, Huang DC, Xiong H, Cui Y, Busman TA, McKeegan EM, Krivoshik AP, Enschede SH, Humerickhouse R. Substantial susceptibility of chronic lymphocytic leukemia to BCL2 inhibition: results of a phase I study of navitoclax in patients with relapsed or refractory disease. J Clin Oncol. 2012;30(5):488–496. doi: 10.1200/JCO.2011.34.7898. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Alharbi RA, Pettengell R, Pandha HS, Morgan R. The role of HOX genes in normal hematopoiesis and acute leukemia. Leukemia. 2013;27(5):1000–1008. doi: 10.1038/leu.2012.356. [DOI] [PubMed] [Google Scholar]
  • 39.De Braekeleer E, Douet-Guilbert N, Basinko A, Le Bris MJ, Morel F, De Braekeleer M. Hox gene dysregulation in acute myeloid leukemia. Future Oncol. 2014;10(3):475–495. doi: 10.2217/fon.13.195. [DOI] [PubMed] [Google Scholar]
  • 40.Hong Z, Zhang R, Qi H. Diagnostic and prognostic relevance of serum miR-195 in pediatric acute myeloid leukemia. Cancer Biomark. 2018;21(2):269–275. doi: 10.3233/CBM-170327. [DOI] [PubMed] [Google Scholar]
  • 41.Díaz-Beyá M, Brunet S, Nomdedéu J, Tejero R, Díaz T, Pratcorona M, Tormo M, Ribera JM, Escoda L, Duarte R, Gallardo D, Heras I, Queipo de Llano MP, Bargay J, Monzo M, Sierra J, Navarro A, Esteve J. Cooperative AML group CETLAM (Grupo Cooperativo Para el Estudio y Tratamiento de las Leucemias Agudas y Mielodisplasias). MicroRNA expression at diagnosis adds relevant prognostic information to molecular categorization in patients with intermediate-risk cytogenetic acute myeloid leukemia. Leukemia. 2014;28(4):804–812. doi: 10.1038/leu.2013.281. [DOI] [PubMed] [Google Scholar]
  • 42.Coskun E, von der Heide EK, Schlee C, Kühnl A, Gökbuget N, Hoelzer D, Hofmann WK, Thiel E, Baldus CD. The role of microRNA-196a and microRNA-196b as ERG regulators in acute myeloid leukemia and acute T-lymphoblastic leukemia. Leuk Res. 2011;35(2):208–213. doi: 10.1016/j.leukres.2010.05.007. [DOI] [PubMed] [Google Scholar]
  • 43.Bhayadia R, Krowiorz K, Haetscher N, Jammal R, Emmrich S, Obulkasim A, Fiedler J, Schwarzer A, Rouhi A, Heuser M, Wingert S, Bothur S, Döhner K, Mätzig T, Ng M, Reinhardt D, Döhner H, Zwaan CM, van den Heuvel Eibrink M, Heckl D, Fornerod M, Thum T, Humphries RK, Rieger MA, Kuchenbauer F, Klusmann JH. Endogenous tumor suppressor microRNA-193b: therapeutic and prognostic value in acute myeloid leukemia. J Clin Oncol. 2018;36(10):1007–1016. doi: 10.1200/JCO.2017.75.2204. [DOI] [PubMed] [Google Scholar]
  • 44.Bi L, Zhou B, Li H, He L, Wang C, Wang Z, Zhu L, Chen M, Gao S. A novel miR-375-HOXB3-CDCA3/DNMT3B regulatory circuitry contributes to leukemogenesis in acute myeloid leukemia. BMC Cancer. 2018;18(1):182. doi: 10.1186/s12885-018-4097-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Additional file 1: (19.1KB, docx)

Clinic-pathologic characteristics in AML from our cohort. (DOCX 19 kb)

Additional file 2: (50.8KB, xlsx)

Different expressed genes of microRNA for BCL2high and BCL2low. (XLSX 50 kb)

Additional file 3: (1.6MB, xlsx)

Different expressed genes of RNA for BCL2high and BCL2low. (XLSX 1682 kb)

Additional file 4: (37.7KB, txt)

Venn results of microRNAs targeting BCL2. (TXT 37 kb)

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Articles from Diagnostic Pathology are provided here courtesy of BMC

RESOURCES