Abstract
Background: Overexpression of dedicator of cytokinesis 1 (DOCK1) has been confirmed as an unfavorable prognostic marker in acute myeloid leukemia (AML). Purpose: This study is to explore the clinical implications of DOCK1 on AML patients underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT). Patients and methods: We analyzed 71 de novo AML patients treated with allo-HSCT and divided them into two groups (DOCK1high vs DOCK1low) by the median expression level of DOCK1. Results: High DOCK1 expression was associated with older age (P=0.019), wild-type CEBPA (P=0.002), IDH1/2 mutations (P=0.010) and RUNX1 mutation (P=0.005). Univariate analyses showed that DOCK1high and RUNX1 mutation were associated with shorter OS (P<0.001, P=0.024). Multivariate analysis confirmed the negative effect of high DOCK1 level on overall survival (P=0.010). Conclusion: Our results demonstrate that in AML patients who received allo-HSCT, high DOCK1 expression might have a persistent negative prognostic impact post-transplant.
Keywords: acute myeloid leukemia, allo-HSCT, DOCK1, prognosis
Background
Acute myeloid leukemia (AML) is a malignant disease of hemopoietic stem cells. It is characterized by clonal expansion of differentiated blasts of myeloid lineage.1 The current version of the WHO classification provides guidance on AML diagnosis, treatment and prognostication.2 The outcome of AML patients is heterogeneous, depending on both patient- and disease-related risk factors. Genomic abnormalities account for at least 60% of the variables in AML prognostication.3 For example, FLT3-ITD has a clear correlation with a poor outcome, while mutations in NPM1 bode a particularly better prognosis.4 The presence of biallelic mutations of CEBPA is an independent factor for favorable outcome in AML patients.5 A recent meta-analysis indicated that IDH1 mutations confer a poorer survival,6 so do genetic aberrations of TP537 or RUNX1.8 Genetic factors are incorporated to treatment design because they determine disease sensitivity to treatment as well as the tolerability of therapy.
Allogeneic hematopoietic stem cell transplantation (allo-HSCT) has a strong anti-leukemic effect and it is a commonly used option for AML post-remission consolidation therapy.9 It especially improves the outcomes of patients with poor-risk or intermediate-risk disease.10 Although allo-HSCT can decrease the frequency of relapse and prolong survival, many patients still relapse post-transplant.1 An increasing number of research focusing on post-transplant relapse have pointed out several factors including WBC count at diagnosis,11 cytogenetic risk status,12 initial induction response,13 and most importantly, minimal residual disease (MRD) status at the time of transplantation.14 In the MRD-negative patients who still experience relapse, genetic alterations might play an even bigger role in leukemogenesis and prognostication.15
The dedicator of cytokinesis 1 (DOCK) family is a class of the atypical Rho guanine nucleotide exchange factors (GEFs).16 As a major Rac GEF, DOCK proteins are involved in various cellular processes, such as cell adhesion, cell migration, actin cytoskeleton, and tumorigenesis.17,18 Sze-Hwei Lee, et al, have discovered that high expression of DOCK1 implied poor prognosis in AML patients.19 However, it is yet to be determined whether the expression level of DOCK1 has prognostic value in AML patients undergoing allo-HSCT. In this study, we will focus on the prognostic significance of DOCK1 in a cohort of AML patients undergoing allo-HSCT and discuss its clinical implications.
Patients and methods
Patients
A group of 71 de novo AML patients (41 males, 30 females) from The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov/) database, who had undergone allo-HSCT and had available DOCK1 expression data, were included in the study. TCGA database was originated from a single institution tissue banking protocol that was approved by the Washington University Human Studies Committee. All patients were between ages 18 and 88 and had previously untreated de novo AML. The samples were collected between November of 2001 and March of 2010. Clinical characteristics, as well as risk groups and the frequencies of known recurrently mutated genes, were highly representative of adult patients with de novo AML. The patients were treated in accordance with NCCN guidelines (www.nccn.org), with an emphasis on enrollment in therapeutic clinical trials whenever possible. Those patients with intermediate or unfavorable risk underwent allogeneic stem cell transplant if they were medically fit for transplantation, and if a suitably matched donor was available. Next-generation sequencing was utilized for detecting genetic mutations. Written informed consent was obtained from each patient approved by the Human Research Ethics Committee of Washington University. Clinical and molecular information at diagnosis were collected. All the data was publicly available on the website of the TCGA database.
Statistical analysis
Descriptive statistics were used to summarize demographics, clinical and molecular information. Continuous variables were described in the form of the median with range. The Mann–Whitney U test was used for comparing two groups of numeric variables, and chi-square analysis was applied to the comparison of categorical variables. The clinical endpoint of this study was overall survival (OS), which was defined as the time from diagnosis to death or was censored at the last follow-up. The OS rate was calculated using the Kaplan–Meier method, and the survival curves were plotted. The log-rank test was used to analyze significant differences between survival distributions. A cox proportional hazard model was applied to univariable and multivariable analyses to assess possible prognostic factors. The relapse-free survival (RFS) rate, defined as the time from the date of diagnosis to relapse, was also analyzed. The level of statistical significance was set at P<0.05 for all analyses. SPSS software version 20.0 and GraphPad Prism software version 6.0 were used for all the statistical analyses.
Results
Association of DOCK1 expression level with demographic characteristics and prognostic factors
The total of 71 patients was divided into two groups (DOCK1high group and DOCK1low group) based on the median DOCK1 expression level. High DOCK1 expressers were associated with older age (≥60, P=0.019), with an average age of 55 compared with 51 in DOCK1low group (P=0.010). Patients with higher DOCK1 expression had a lower percentage of peripheral blood (PB) blasts (P=0.010) and fewer IDH1/2 mutations (P=0.010). FLT3-ITD appeared more frequently in DOCK1high patients (P=0.051). All the patients with CEBPA double mutation were in DOCK1low group while RUNX1 mutations were observed only in DOCK1high group (P=0.005). There were no differences in other clinical factors including WBC count, FAB subtypes and karyotypes, as well as molecular prognostic features like TP53, MLL-PTD and NPM1 mutations. As for the ratios of relapse, no differences were found between the two groups. There was no difference between the two groups on the transplant status. The summary of the association between DOCK1 expression level and the demographic and prognostic factors is displayed in Table 1.
Table 1.
Clinical and molecular characteristics of DOCK1high and DOCK1low patients
| Characteristics | DOCK1high (n=36) | DOCK1low (n=35) | U/χ2 | P-value |
|---|---|---|---|---|
| Age/years, median (range) | 55 (23–72) | 51 (18–69) | 854.0* | 0.010 |
| Age group/n (%) | 5.481§ | 0.019 | ||
| <60 years | 22 (61.1) | 30 (85.7) | ||
| ≥60 years | 14 (38.9) | 5 (14.3) | ||
| Gender/n (%) | 0.739§ | 0.390 | ||
| Male | 19 (52.8) | 22 (62.9) | ||
| Female | 17 (47.2) | 13 (37.1) | ||
| WBC count/×109/L, median (range) | 23.35 (0.6–102.5) | 32.4 (1.2–223.8) | 506.0* | 0.154 |
| BM blasts/%, median (range) | 70 (30–100) | 71 (34–99) | 585.5* | 0.609 |
| PB blasts/%, median (range) | 41 (0–91) | 62.5 (5–96) | 393.0* | 0.010 |
| FAB subtypes/n (%) | 9.396§ | 0.225 | ||
| M0 | 7 (19.4) | 2 (5.9) | 3.022§ | 0.151 |
| M1 | 9 (25.0) | 14 (41.2) | 1.823§ | 0.177 |
| M2 | 7 (19.4) | 11 (32.4) | 1.347§ | 0.246 |
| M3 | 0 (0.0) | 1 (2.9) | 1.043§ | 0.493 |
| M4 | 8 (22.2) | 5( 14.7) | 0.747§ | 0.387 |
| M5 | 3 (8.3) | 1 (2.9) | 1.001§ | 0.614 |
| M6 | 1 (2.8) | 0 (0.0) | 0.986§ | 1.000 |
| M7 | 1 (2.8) | 0 (0.0) | 0.986§ | 1.000 |
| Karyotype/n (%) | 8.636§ | 0.472 | ||
| Normal | 16 (44.4) | 17 (48.6) | 0.122§ | 0.727 |
| Complex | 7 (19.4) | 4 (11.4) | 0.871§ | 0.351 |
| 8 Trisomy | 4 (11.1) | 2 (5.7) | 0.668§ | 0.674 |
| inv(16)/CBFβ-MYH11 | 4 (11.1) | 1 (2.9) | 1.847§ | 0.357 |
| 11q23/MLL | 2 (5.6) | 1 (2.9) | 0.319§ | 1.000 |
| −7/7q- | 1 (2.8) | 2( 5.7) | 0.378§ | 0.614 |
| t(15;17)/PML-RARA | 0 (0.0) | 1 (2.9) | 1.043§ | 0.493 |
| t(9;22)/BCR-ABL1 | 1 (2.8) | 1 (2.9) | 0.000§ | 1.000 |
| t(8;21)/RUNX1-RUNX1T1 | 0 (0.0) | 1 (2.9) | 1.043§ | 0.493 |
| Others | 1 (2.8) | 5 (14.3) | 3.038§ | 0.107 |
| Risk/n (%) | 1.574§ | 0.455 | ||
| Good | 4 (11.1) | 3 (8.8) | 0.102§ | 1.000 |
| Intermediate | 18 (50.0) | 22 (64.7) | 1.544§ | 0.214 |
| Poor | 14 (38.9) | 9 (26.5) | 1.222§ | 0.269 |
| FLT3-ITD | 5.937§ | 0.015 | ||
| Presence | 13 (36.1) | 4 (11.4) | ||
| Absence | 23 (63.9) | 28 (88.6) | ||
| NPM1 | 0.378§ | 0.539 | ||
| Mutation | 8 (22.2) | 10 (28.6) | ||
| Wild type | 28 (77.8) | 25 (71.4) | ||
| CEBPA | 9.273§ | 0.010 | ||
| Single mutation | 0 (0.0) | 5 (14.3) | ||
| Double mutation | 0 (0.0) | 3 (8.6) | 3.222§ | 0.115 |
| Wild type | 36 (100.0) | 27 (77.1) | 9.273§ | 0.002 |
| DNMT3A | 2.124§ | 0.145 | ||
| Mutation | 6 (16.7) | 11 (31.4) | ||
| Wild type | 30 (83.3) | 24 (68.6) | ||
| IDH1/2 | 6.601§ | 0.010 | ||
| Mutation | 4 (11.1) | 13 (37.1) | ||
| Wild type | 32 (88.9) | 27 (62.9) | ||
| WT1 | 2.129§ | 0.260 | ||
| Mutation | 6 (16.7) | 2 (5.7) | ||
| Wild type | 30 (83.3) | 33 (94.3) | ||
| RUNX1 | 8.765§ | 0.005 | ||
| Mutation | 8 (22.2) | 0 (0.0) | ||
| Wild type | 28 (77.8) | 35 (100.0) | ||
| MLL-PTD | 1.001§ | 0.614 | ||
| Presence | 3 (8.3) | 1 (2.9) | ||
| Absence | 33 (91.7) | 34 (97.1) | ||
| NRAS/KRAS | 1.522§ | 0.260 | ||
| Mutation | 2 (5.6) | 5 (14.3) | ||
| Wild type | 34 (94.4) | 30 (85.7) | ||
| TET2 | 0.001§ | 1.000 | ||
| Mutation | 2 (5.6) | 2 (5.7) | ||
| Wild type | 34 (94.4) | 33 (94.3) | ||
| TP53 | 4.121§ | 0.115 | ||
| Mutation | 4 (11.1) | 0( 0.0) | ||
| Wild type | 32 (88.9) | 35 (100.0) | ||
| KIT | 3.045§ | 0.239 | ||
| Mutation | 3 (8.3) | 0 (0.0) | ||
| Wild type | 33 (91.7) | 3 5(100.0) | ||
| PTPN11 | 2.029§ | 0.199 | ||
| Mutation | 1 (2.8) | 4 (11.4) | ||
| Wild type | 35 (97.2) | 31 (88.6) | ||
| PHF6 | 1.001§ | 0.614 | ||
| Mutation | 3 (8.3) | 1 (2.9) | ||
| Wild type | 33 (91.7) | 34 (97.1) | ||
| Relapse | 0.029§ | 0.864 | ||
| Yes | 24 (66.7) | 24 (68.6) | ||
| No | 12 (33.3) | 11 (31.4) | ||
| HSCT | 0.145§ | 0.930 | ||
| Haplo | 1 (2.8) | 1 (2.8) | 0.000§ | 1.000 |
| Sib allo | 19 (52.8) | 20 (57.1) | 0.144§ | 0.705 |
| MUD | 16 (44.4) | 14 (40.0) | 0.137§ | 0.712 |
| Transplant status/n (%) | 1.735§ | 0.420 | ||
| CR1 | 19 (52.8) | 21 (60.0) | 0.376§ | 0.540 |
| CR2 | 5 (13.9) | 7 (20.0) | 0.472§ | 0.492 |
| Others | 12 (33.3) | 7 (20.0) | 1.610§ | 0.205 |
Note: *Mann–Whitney U test; §chi-square test.
Abbreviations: WBC, white blood cell; BM, bone marrow; PB, peripheral blood; FAB, French American British; HSCT, hematopoietic stem cell transplantation; Haplo, haploidentical; Allo, allogeneic; MUD, matched unrelated donor; CR, complete remission.
Higher expression level of DOCK1 indicated shorter OS in AML patients undergoing allo-HSCT
Univariate and multivariate analysis using cox proportional hazard models synthesized possible prognostic elements including DOCK1 expression level (high vs low), age (≥60 vs <60 years), WBC (≥30 vs <30×109/L), risk stratification (poor vs non-poor) and genetic mutation like FLT3-ITD, NPM1, DNMT3A, CEBPA double mutation, IDH1 and RUNX1. The results are shown in Tables 2 and 3.
Table 2.
Univariate analysis for OS
| Variables | OS | |
|---|---|---|
| HR (95% CI) | P-value | |
| DOCK1 (high vs low) | 2.940 (1.655–5.211) | <0.001 |
| Age (≥60 vs <60 years) | 1.406 (0.769–2.571) | 0.268 |
| WBC (≥30 vs <30×109/L) | 0.986 (0.571–1.702) | 0.959 |
| Risk(poor vs non-poor) | 1.290 (0.719–2.313) | 0.393 |
| FLT3-ITD (negative vs positive) | 0.600 (0.319–1.131) | 0.114 |
| NPM1 (wild vs mutated) | 1.243 (0.651–2.372) | 0.510 |
| DNMT3A (wild vs mutated) | 0.794 (0.421–1.498) | 0.477 |
| CEBPA double mutation | 0.656 (0.159–2.705) | 0.559 |
| IDH1/2 (wild vs mutated) | 1.275 (0655–2.483) | 0.475 |
| RUNX1 (wild vs mutated) | 0.410 (0.190–0.887) | 0.024 |
| Transplant in CR1/2 (yes vs no) | 0.615 (0.337–1.124) | 0.114 |
Abbreviations: OS, overall survival; WBC, white blood cell; CR, complete remission.
Table 3.
Multivariate analysis for OS
| Variables | OS | |
|---|---|---|
| HR (95% CI) | P-value | |
| DOCK1 (high vs low) | 3.027 (1.304–7.025) | 0.010 |
| Age (≥60 vs <60 years) | 0.977 (0.492–1.942) | 0.947 |
| WBC (≥30 vs <30×109/L) | 0.615 (0.291–1.300) | 0.203 |
| Risk (poor vs non-poor) | 1.282 (0.605–2.717) | 0.517 |
| FLT3-ITD (negative vs positive) | 0.435 (0.183–1.036) | 0.060 |
| NPM1 (wild vs mutated) | 1.285 (0.483–3.422) | 0.616 |
| DNMT3A (wild vs mutated) | 0.479 (0.216–1.064) | 0.071 |
| CEBPA double mutation | 2.519 (0.456–13.908) | 0.289 |
| IDH1/2 (wild vs mutated) | 0.921 (0.351–2.416) | 0.867 |
| RUNX1 (wild vs mutated) | 0.725 (0.257–2.045) | 0.543 |
| Transplant in CR1/2 (yes vs no) | 0.459 (0.216–0.976) | 0.043 |
Abbreviations: OS, overall survival; WBC, white blood cell; CR, complete remission.
Univariate analysis and Kaplan–Meier survival curve both revealed that patients undergoing allo-HSCT in DOCK1high group had a shorter OS (Table 2, Figure 1, both P<0.001). Mutations in RUNX1 also had an adverse effect on OS (Table 2, P=0.024). The result of multivariate analysis further demonstrated the unfavorable effect of high DOCK1 expression (Table 3, P=0.010). Multivariate analysis also showed that patients who received a transplant in CR1/2 had longer OS (Table 3, P=0.043). RUNX1, FLT3-ITD and NPM1 were not correlated with the OS in AML patients undergoing allo-HSCT in our cohort. The results of univariate and multivariate analysis of RFS, as well as the Kaplan-Meier curve, were provided in Tables S1 and S2 and Figure S1 in the Supplementary Appendix.
Figure 1.
Kaplan–Meier curves of overall survival (OS). Patients in DOCK1high group have shorter OS than those in DOCK1low group.
Table S1.
Univariate analysis for RFS
| Variables | RFS | |
|---|---|---|
| HR (95% CI) | P-value | |
| DOCK1 (high vs low) | 1.264 (0.716-2.233) | 0.419 |
Abbreviations: RFS, relapse-free survival; CR, complete remission.
Table S2.
Multivariate analysis for RFS
| Variables | RFS | |
|---|---|---|
| HR (95% CI) | P-value | |
| DOCK1 (high vs low) | 1.242 (0.585–2.634) | 0.573 |
| Age (≥60 vs <60 years) | 0.620 (0.276–1.392) | 0.247 |
| WBC (≥30 vs <30×109/L) | 1.832 (0.884–3.799) | 0.104 |
| Risk (poor vs non-poor) | 0.771 (0.353-1.686) | 0.515 |
| FLT3-ITD (negative vs positive) | 0.502 (0.237–1.061) | 0.071 |
| NPM1 (wild vs mutated) | 1.910 (0.807–4.520) | 0.141 |
| DNMT3A (wild vs mutated) | 0.981 (0.448–2.146) | 0.961 |
| CEBPA double mutation | 0.474 (0.092–2.450) | 0.373 |
| IDH1/2 (wild vs mutated) | 1.002 (0.372–2.700) | 0.996 |
| RUNX1 (wild vs mutated) | 1.139 (0.319–4.061) | 0.841 |
| Transplant in CR1/2 (yes vs no) | 0.866 (0.409–1.832) | 0.707 |
Abbreviations: RFS, relapse-free survival; WBC, white blood cell; CR, complete remission.
Discussion
In our study, overexpression of DOCK1 can shorten OS in AML patients undergoing allo-HSCT, implying that allo-HSCT cannot eliminate the negative effect of DOCK1 overexpression. As an important prognostic marker, DOCK1 might be a potential therapeutic target for AML treatment.
Sze-Hwei Lee, et al19 showed that higher DOCK1 expression was associated with many other prognostic factors including intermediate-risk cytogenetics. As a curative-intent method, allo-HSCT has a well-documented anti-tumor effect especially on high-risk and intermediate-risk AML patients.20 The prognostic influence of some prognostic mutations can be ameliorated by allo-HSCT.21 For example, Ma Y, et al concluded that allo-HSCT could efficiently reduce relapse and boost survival for patients with FLT3-ITD mutation.22 Yang Xu, et al have shown that allo-HSCT could prolong survival in cytogenetically normal AML.23 Our study came to similar conclusions that allo-HSCT could overcome the adverse effect of some prognostic factors, such as FLT3-ITD, DNMT3A and RUNX1. However, the inferior outcome associated with DOCK1 overexpression could not be completely overcome by allo-HSCT. Though transplant status in CR1/2 prolongs survival to some extent, the statistical significance still brings the level of DOCK1 expression to our attention.
Consistent with the previous study,19 our study showed that patients with DOCK1 overexpression were older and had higher PB blast percentage at diagnosis. But there was no distinct relationship between DOCK1 expression and karyotype or cytogenetics, probably due to the implementation of allo-HSCT. We found a negative correlation between DOCK1 expression and frequencies of FLT3-ITD, RUNX1 mutations in our cohort. Since these were well-defined poor prognostic factors, the negative correlation could further imply that high DOCK1 expression could be an independent risk factor for AML outcome post-transplant.
As a GEF, DOCK1 plays multiple roles in physiological and pathological conditions. Researches have shown that DOCK1 regulates cell motility and tumor invasion in glioma cell by binding with engulfment and cell motility protein 1 (ELMO1).24,25 Laurin, et al, reported that DOCK1 was a critical regulator in HER2-mediated breast cancer metastasis.18 In human colorectal cancer, DOCK1 was simulated by cortactin, which could promote cell migration and invasion.26 Downregulation of DOCK1 may prevent epithelial–mesenchymal transition in bladder cancer.16 Furthermore, in vivo researches by Tajiri, et al. have demonstrated the effectiveness of targeting DOCK1 in the ras-driven cancer cell.27 Hence, targeted therapy against DOCK1 deserves attention for future anti-cancer drug design. As demonstrated by the current study, the unfavorable effect of DOCK1 could not be eliminated by allo-HSCT, it would be worth investigating DOCK1-targeted therapy in AML patients for post-transplant maintenance.
Our study is limited by sample size and its retrospective nature. A larger cohort study is in need to confirm our findings. Further bench work exploring the role of DOCK1 in leukemogenesis are also imperative.
In conclusion, higher DOCK1 expression could predict a worse outcome in AML patients and its effect could not be overcome by allo-HSCT. DOCK1 is a potential anti-leukemia target for AML treatment.
Acknowledgments
We thank all of the members who participated in providing and collating data. We also would like to thank Yifan Pang for editing the article. This study was supported by grants from the National Natural Science Foundation of China (nos 81500118 and 61501519), the China Postdoctoral Science Foundation Funded Project (project no. 2016M600443), and PLAGH Project of Medical Big Data (project no. 2016MBD-025).
Ethics approval and consent to participate
The study was approved by the Human Research Ethics Committee of Washington University. Informed consent was obtained from all the participants.
Availability of data and materials
All the data in this study were generated by TCGA database.
Author contributions
Lin Fu and Xiaoyan Ke designed the study and substantially contributed to the overall analysis of the study. Gaoqi Zhang wrote the manuscript. Jinlong Shi organized the data. Xinrui Yang, Xinpei Zhang, Jilei Zhang, Siyuan Yang and Jing Wang performed statistical analyses and analyzed the data. Jilei Zhang provided guidance on modification. All authors contributed to data analysis, drafting and revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.
Disclosure
The authors report no conflicts of interest in this work.
Supplementary materials
We analyze the relapse-free survival (RFS) rate using the univariate and multivariate analysis. The log-rank test was used to analyze significant differences between survival distributions. A cox proportional hazard model was applied to univariable and multivariable analyses to assess possible prognostic factors. DOCK1 expression did not show any significant effect on RFS outcome in uni- and multivariate analysis (Tables S1 and S2). The Kaplan–Meier curve of RFS showed no significance either (Figure S1).
Kaplan–Meier curves of relapse-free survival (RFS). There is no difference between DOCK1high group and DOCK1low group in the length of RFS.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Kaplan–Meier curves of relapse-free survival (RFS). There is no difference between DOCK1high group and DOCK1low group in the length of RFS.
Data Availability Statement
All the data in this study were generated by TCGA database.