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Characterization of epithelial-mesenchymal transition in acute myeloid leukemia

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Characterization of epithelial-mesenchymal transition in acute myeloid leukemia

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CHARACTERIZATION OF EPITHELIAL-MESENCHYMAL
TRANSITION IN ACUTE MYELOID LEUKEMIA

Yang Du
Master of Science
in Pharmaceutical Sciences

University of Southern California

May 2019

2
Contents

ACKNOWLEDGEMENTS .................................................................................................................................... 4
STATEMENT ........................................................................................................................................................ 5
ABSTRACT ............................................................................................................................................................ 6
CHAPTER 1 - INTRODUCTION ....................................................................................................................... 7
1. ACUTE MYELOID LEUKEMIA ........................................................................................................................................... 7
1.1 Epidemiology ..................................................................................................................................................... 7
1.2 Pathogenesis and genomics landscape .................................................................................................... 8
1.3 Diagnosis .......................................................................................................................................................... 10
1.4 Prognostic factors .......................................................................................................................................... 10
1.5 Treatment ......................................................................................................................................................... 12
2. EPITHELIAL-MESENCHYMAL TRANSITION .................................................................................................................. 14
2.1 EMT in solid cancer ....................................................................................................................................... 15
2.2 EMT in hematological malignancies ........................................................................................................ 16
2.3 EMT as a therapeutic target ....................................................................................................................... 16
3. VIMENTIN AND VIM ................................................................................................................................................... 18
3.1 Vimentin in solid cancer .............................................................................................................................. 19
3.2 Treatment targeting vimentin ................................................................................................................... 20
3.3 Vimentin in hematological malignancies ............................................................................................... 21
CHAPTER 2 - RESEARCH METHODS ........................................................................................................... 23
1. PATIENTS AND TREATMENT ........................................................................................................................................ 23
2. GENE EXPRESSION ANALYSES ..................................................................................................................................... 24
3. SOFTWARE AND STATISTICAL ANALYSES .................................................................................................................... 24
3
CHAPTER 3 - RESULTS .................................................................................................................................... 26
1. VIM EXPRESSION IN AML SAMPLES .......................................................................................................................... 26
2. ASSOCIATION BETWEEN VIM EXPRESSION AND PATIENT PRIMARY CHARACTERISTICS .......................................... 30
3. ASSOCIATION BETWEEN VIM EXPRESSION AND PATIENT MUTATIONAL STATUS .................................................... 36
4. PATIENTS WITH HIGH VIM EXPRESSION HAVE SHORTER OVERALL AND DISEASE-FREE SURVIVAL ......................... 39
5. VIM IS HYPOMETHYLATED IN PATIENTS WITH HIGH VIM EXPRESSION ................................................................... 55
CHAPTER 4 - DISCUSSION ............................................................................................................................ 57
CHAPTER 5 - CONCLUSION .......................................................................................................................... 61
REFERENCES ...................................................................................................................................................... 62

4
Acknowledgements
Firstly, I would like to express my sincere gratitude to my advisor Dr. Houda
Alachkar of School of Pharmacy at University of Southern California. Her guidance,
patience, motivation and continuous support help me accomplish this research.
I would also like to thank Dr. Okamoto and Dr. Duncan as my thesis committee,
for their insightful comments and encouragement helping me improve this thesis.
I would also like to thank my lab mates for their instruction on my experiments.
Finally, I would express my gratitude to my parents providing me the
opportunity to study here. Their unfailing support encourages me to accomplish
my research.

5
Statement
The results involved in this study were published in the paper “Upregulation
of the EMT marker vimentin is associated with poor clinical outcome in acute
myeloid leukemia.” in Journal of Translational Medicine on June 20
th
, 2018 (Wu
et al., 2018).

6
Abstract
Epithelial-mesenchymal transition (EMT) is a biological process in which
epithelial cells obtain the property of mesenchymal cells, identified by expression
of mesenchymal cells markers like vimentin. Vimentin (VIM) is a type III
intermediate filament that maintains cell integrity, and is involved in cell migration,
motility and adhesion. In solid cancers, EMT participates in tumor metastasis.
However, its role in hematological malignancies remains unclear. Acute myeloid
leukemia (AML) is a hematological malignancy characterized by invasion of
immature blood cells into bone marrow and peripheral blood. In this study, AML
gene expression datasets were analyzed for the expression of VIM in AML and its
association with patient’s clinical and molecular characteristics. The results
demonstrate that overexpression of the EMT marker vimentin is associated with
poor clinical outcomes in older patients with cytogenetically normal AML. Therefore,
investigating the role EMT may play in AML warrants further research.

7
Chapter 1 - Introduction
1. Acute myeloid leukemia
Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy
that affects the myeloid lineage where the poorly differentiated blood cells invade
the bone marrow and the peripheral blood with enhanced proliferating abilities, and
cause bone marrow failure.

1.1 Epidemiology
Based on the surveys between 2011 and 2015, the number of new cases
diagnosed as acute myeloid leukemia in the US was 4.3 per 100,000 per year and
the mortality rates were 2.8 per 100,000 per year. The incidence of AML was higher
in older adults at a median diagnosed age of 68. Older patients over 65 have higher
death rates accounting for 70.8% of deaths in AML, compared with younger
patients. A study based on Surveillance, Epidemiology and End Results (SEER)
data between 1997 and 2006 reported that older patients over 65 had a much
lower 5-year survival of less than 9.3%, compared to the total survival of over 21%
for all patients with AML (Thein et al., 2013).

8
1.2 Pathogenesis and genomics landscape
Leukemia is the cancer of blood cells. The normal blood cells are derived from
the hematopoietic stem cells (HSCs) which reside in the bone marrow and give
rise into myeloid and lymphoid progenitors (Li et al., 2015). Lymphoid progenitors
are the origins of lymphocytes including T cells, B cells, and natural killer cells;
Myeloid stem cells differentiate into red blood cells, platelets and white blood cells
(Li et al., 2015). Acute myeloid leukemia results from genetic abnormalities such
as mutations and chromosomal translocations that affect the HSCs and the
myeloid progenitors, causing these cells to rapidly proliferate and fail to
differentiate into mature myeloid blood cells. The immature blood cells called blasts
invade into peripheral blood and accumulate in hematopoietic organs like bone
marrow resulting in bone marrow failure (Li et al., 2015).
Genomics sequencing, including whole genome and whole exome sequencing
of leukemia blasts obtained from patients with AML, have resulted in the
identification of the majority of genes affected by mutations and/or chromosomal
translocation and deletions in AML (Ley et al., 2013). Cytogenetic abnormalities
can be detected in approximately half of adult patients in AML. Frequent
chromosomal aberrations include t(8;21), t(15;17) and inv(16) (Kumar, 2011). The
genes to initiate AML cover FLT3, NPMI, CEBPA, IDH and TET2.
9
Approximately 30 percent of patients with AML carry mutations in the FLT3
gene, a receptor tyrosine kinase that is normally expressed in HSCs. The most
common FLT3 mutation is the internal tandem duplication (FLT3-ITD), which
results in a ligand-independent auto-phosphorylation and downstream signaling,
promoting cell survival, inhibition of apoptosis and increased proliferation (Grafone
et al., 2012). Patients with FLT3-ITD mutations have higher relapse rates and
shorter overall survival (Thomas and Campbell, 2019). FLT3 inhibitors have been
well investigated and applied in clinic (Thomas and Campbell, 2019). Another
frequently mutant gene is NPM1, accounting for 35% AML and less common in
young patients below 35 years (Verhaak et al., 2005). NPM1 mutation often co-
occurs with FLT3-ITD and is associated with higher white blood cells count
(Verhaak et al., 2005). However, in intermediate cytogenetic risk FLT3 ITD–
negative AML, NPM1 mutation shows better clinical outcomes (Verhaak et al.,
2005). The mutation of transcription factor CEBPA is also associated with
favorable outcomes in AML. CEBPA is a regulator of myeloid progenitors, playing
a role in the early stages of myeloid differentiation (Lagunas-Rangel et al., 2017).
Point mutations in IDH1 and IDH2 lead to amino acid changes, inducing DNA
hypermethylation and cell abnormal differentiation (Thomas and Campbell, 2019).
Some mutations have been found in preleukaemic hemopoietic stem cells like
mutant DNMT3A, TET2, and ASXL1, which are considered to precede
leukemogenic transformation (Short et al., 2018).
10
1.3 Diagnosis
The diagnosis of acute myeloid leukemia includes tests performed on blood
samples and bone marrow biopsies. Generally, AML is diagnosed when at least
20% of myeloid blasts are present in the bone marrow or peripheral blood (Short
et al., 2018). AML patients usually have lower red blood cells and platelets in the
peripheral blood, which is tested by complete blood count (CBC) and peripheral
blood smear. In specific subtypes of AML, the diagnose can also be confirmed by
the presence of certain chromosome changes, such as t(8;21), t(9;11), t(6;9),
t(1;22), fusion gene PML-RARA, BCR-ABL1 and mutant NPM1, RUNX1 (Estey,
2018). Chromosome and genetic tests are usually performed by fluorescent in situ
hybridization and polymerase chain reaction (Gulley et al., 2010).

1.4 Prognostic factors
AML is a heterogeneous disease with multiple molecular, cytogenetic, and
immunologic phenotypes that contribute to the clinical and prognostic evaluation
and risk classification.
Cytogenetics is important for risk stratification. Abnormal karyotypes have
been detected in approximately 60% patients with AML (Estey, 2018). Patients
with chromosome abnormalities including t(6;9), t(9;22), t(3;3), inv(3), deletion of
chromosome 5 or 7, abnormalities of chromosome 11 are associated with poor risk
11
in AML. t(8;21) in M2, t(15;17) in M3 and inv(16) are considered with favorable risk
(Estey, 2018). As for molecular genetics, several genes were found to be mutated
in patients with AML. FLT3-ITD occurs in approximately 30% of cytogenetic normal
AML and is significantly associated with adverse risk. Mutations in genes of
epigenetic modifiers like DNMT3A, ASXL1, IDH1/2, and TET2 are also linked with
worse clinical outcome (Papaemmanuil et al., 2016). Co-mutation of some genes
can confer different prognosis. Mutation of TP53, SXL1 and SRSF2 confers
intermediate risk individually. However, co-occurrence of them performs
particularly poor prognosis, which is probably due to gene-gene interaction
(Papaemmanuil et al., 2016). Furthermore, patients with mutant NPM1 or CEBPA
in the absent of FLT3-ITD have better clinical outcomes, but opposite results are
seen in FLT3-ITD positive patients (Grimwade, 2012).
In addition to molecular and cytogenetic, other clinical factors also contribute
to prognosis. For example, elderly patients over 60 usually have higher relapse
rates and inferior overall survival compared with younger patients, which is
probably due to drug resistance and the higher percentage of unfavorable
chromosome abnormalities observed in old patients (Leith et al., 1997). In addition,
specific markers like positive CD34 and P-glycoprotein are associated with worse
prognosis (Benderra et al., 2005). Patients with a blood infection and high white
blood cell count (>100,000/mm
3
) at diagnosis are regarded with worse outcomes
(Abramson and Melton, 2000). Invasion of leukemia cells in central nervous
12
system can be hard to be eliminated by chemotherapy drugs due to blood-brain
barrier (Vasilatou et al., 2014).

1.5 Treatment
Complete remission (CR) in AML requires normal values of absolute neutrophil
count (>1000/𝜇L) and platelet count (>100,000/𝜇L). No clusters or collections of
blast cells can be found by bone marrow biopsy and all hematocytes are shown
normal differentiation by bone marrow aspiration. Blast cells count should be below
5% in bone marrow and none of them exhibits leukemic phenotype (Cheson et al.,
2003).
Even with the significant advances in the last decade in understanding the
biology and genomics landscape of AML, the standard treatment for this disease
is mainly traditional chemotherapy followed in some patients by allogeneic stem
cell transplantation. In recent years, targeted therapy has entered the clinical care
of patients with AML. Targeted drugs like FLT3 inhibitors and IDH inhibitors have
been utilized for specific cytotoxicity to cancer cells. Anti-FLT3 drug Midostaurin is
the first targeted therapy approved for AML as a single agent and in combination
with chemotherapy (El Fakih et al., 2018). The phase 3 trial, which was completed
by 2017, confirmed the effect of Midostaurin in prolonging overall and event-free
survival among patients with AML and a FLT3 mutation (Thomas and Campbell,
13
2019). As for IDH inhibitors, Ivosidenib and Enasidenib have been approved to
treat AML induced by mutation of IDH1 and IDH2 respectively (Cerrano and
Itzykson, 2019). Other targeted drugs include Venetoclax (BCL-2 inhibitor) and
Glasdegib (targeting the Hedgehog pathway) (Cerrano and Itzykson, 2019)
(Chaudhry et al., 2017).
In addition, immunotherapies like anti-CD33 CAR-T cells are currently under
investigation and have shown promising results in AML (Kenderian et al., 2015).
These therapies consolidate cancer cells targeting and induction of immune
system for tumor elimination, avoiding the toxicity to normal cells, which is
expected to be applied for clinic in the future. Other strategies associated with
immunotherapies include anti-tumor monoclonal antibodies, peptide vaccines and
NK vaccines (Acheampong et al., 2018). Immunotherapies are expected as
potential alternative therapies to substitute chemotherapy with less adverse effect
in the future (Acheampong et al., 2018).

14
2. Epithelial-mesenchymal transition
Epithelial-mesenchymal transition (EMT) is a transformative process in which
epithelial cells obtain the properties of mesenchymal cells, losing their intercellular
adhesion and acquiring migratory and invasive capacities (Kalluri and Weinberg,
2009). Epithelial cells contain tight cell-cell interaction and form protective barrier
to isolate tissues and organs. Through EMT, the compact junction of cells become
loose and their migration capacities are induced (Shook and Keller, 2003).
Therefore, EMT is widely involved in the biological processes associated with cell
proliferation and migration (Kalluri and Weinberg, 2009).
There are three types of EMT participating in different physiological processes.
Type 1 EMT is associated with embryogenesis. This includes implantation of the
embryo and the initiation of placenta formation, where hypoblast cells undergo
EMT to form mesoderm and the endoderm (Kalluri and Weinberg, 2009). Type 2
EMT is involved in organ development, wound healing, tissue regeneration and
organ fibrosis (Kalluri and Weinberg, 2009). Type 3 EMT appears in cancer cells
and participates in tumor progression and metastasis. The occurrence of EMT is
defined by the changes of epithelial markers (E-cadherin and α- and β-catenin)
and mesenchymal markers (N-cadherin, vimentin, and fibronectin) (Chen et al.,
2018).

15
2.1 EMT in solid cancer
Due to enhanced mobility of mesenchymal cells after EMT, this process is
considered as facilitating cancer cells invasion and migration. Excessive epithelial
cell proliferation and angiogenesis are the markers for early primary epithelial
cancers (Kalluri and Weinberg, 2009). Activation of EMT is crucial for the
acquisition of malignant phenotypes for epithelial cells and tumorigenesis of
epithelial cancers. Studies on solid cancers demonstrate that some carcinoma
cells can express mesenchymal markers and acquire a mesenchymal phenotype
to break away from neighboring cells (Yang and Weinberg, 2008). The process of
EMT mediates the migration and dissemination of cancers cells from tumor tissues.
Downregulation of epithelial cell adhesion molecules like E-cadherin has been
studied in cancer, which is connected with the progression of EMT and acquisition
of an invasive phenotype (Kalluri and Weinberg, 2009).
In addition to the role it plays in promoting cells’ migration, EMT is also
associated with drug resistance in some cancers. For example, breast cancer cells
with high accumulation of mesenchymal markers have been shown higher
resistance to chemotherapy (Davis et al., 2014).

16
2.2 EMT in hematological malignancies
The participation of EMT in hematological malignancies has a different pattern
from that in epithelial cancers since leukemic cells do not undergo EMT. However,
an EMT-like process may play a role in blood cancers through the regulation of
EMT-transcription factors (EMT-TFs) (Gruszka et al., 2019) (Chen et al., 2018).
This EMT-like phenomenon decreases the adhesion of white blood cells and
facilitates the immature cells crossing the barrier of bone marrow, which
contributes to the early leukemogenesis (Gruszka et al., 2019). The major EMT-
TFs in myeloid malignancy including TWIST1, Snail 1/2 and ZEB have been shown
to play crucial roles in maintaining hematopoietic and leukemia stem cells (Chen
et al., 2018). In addition, expression of some EMT inducers is associated with anti-
apoptosis and drug resistance in leukemia (Chen et al., 2018).

2.3 EMT as a therapeutic target
The cancer therapy associated with EMT includes targeting EMT prototypical
markers and blocking EMT-inducing signals. Since EMT is a crucial process for
cancer cells acquiring induced migration ability, besides toxicity on cancer cells,
the treatment targeting EMT is expected to inhibit tumor metastasis as well.
The strategy of targeting EMT biomarkers is to eliminate the cells with
mesenchymal phenotype, which includes the expression of N-cadherin, vimentin,
17
and fibronectin. An anti-N-cadherin monoclonal antibody has shown to reduce
invasion of prostate cancer cells and induce apoptosis (Davis et al., 2014).
Vimentin inhibitors are also in study such as anti-vimentin drug Withaferin-A and
some microRNAs (Satelli and Li, 2011).
Another approach is to inhibit EMT-transcription factors or downstream
signaling at the early stage of cancer metastasis. For example, STAT3 inhibitors
like Stattic are being investigated to restrain the induction of EMT (Davis et al.,
2014). STAT3 is a signaling molecule required for EMT in some breast cancer cells
and ovarian cancer cells (Davis et al., 2014).

18
3. Vimentin and VIM
Vimentin (encoded by VIM) is a type III intermediate filament (IF) protein,
generally expressed in mesenchymal cells, leukocytes and predominately in
developing embryo or cells. It is the major component of cytoskeleton and plays a
key role in supporting the structure of cells by interacting with other structural
proteins like microtubules and microfilaments (Lowery et al., 2015). As a highly
conserved protein, vimentin maintains cellular integrity, holds the organelles in the
cytoplasm and provides resistance to cell stressors. As part of the cytoskeleton
network, it is also involved in regulating cell motility and mediating cellular
interactions (Battaglia et al., 2018).
Vimentin has been recognized as a canonical marker of EMT, highly
expressed at the end stage of EMT. In EMT, vimentin is associated with motility
and adhesion, as well as maintaining the shape of mesenchymal phenotype
(Mendez et al., 2010). It is crucial in inducing the shape changes of epithelial cells
to adopt mesenchymal shapes. Silencing vimentin decreases migration ability of
mesenchymal cells and induces an opposite shape changing similar to MET
(Mesenchymal-Epithelial Transition) (Mendez et al., 2010).

19
3.1 Vimentin in solid cancer
Given that vimentin is involved in cell migration and adhesion, it has been
regarded as a regulatory factor in cancer progression and invasion. At present,
upregulation of vimentin has been reported in various solid cancers, associated
with increased invasive capacity of cancer cells and worse outcomes, including
breast cancer, prostate cancer and malignant melanoma (Satelli and Li, 2011).
Through EMT, vimentin is expressed and associated with cytoskeletal
reorganization, from where the progression and invasion of tumor cells are
promoted. (Zeisberg and Neilson, 2009). Vimentin plays a key role in EMT of
breast cancers. Elevated vimentin expression was shown in several cell lines
associated with increased motility and invasiveness, and this effect was
downregulated by vimentin antisense oligonucleotides in MDA-MB-231 cells
(Satelli and Li, 2011). Overexpression of vimentin in prostate cancer was found in
the cell line PC-3M-1E8. Their invasiveness was modified by vimentin through
regulating E-cadherin/β-catenin complex via c-Src regulation (Satelli and Li, 2011).
In malignant melanoma, high expression of vimentin was significantly associated
with melanoma hematogenous metastasis, which is also regarded depending on
the EMT process (Li et al., 2010).
On the other hand, VIM has been shown to be highly methylated in advanced
colorectal cancers. 53-84% of colorectal carcinomas were detected with VIM gene
20
methylation (Shirahata and Hibi, 2014), which has a potential to be applied to the
detection of early and recurrent colorectal cancer. In addition, VIM is highly
methylated in well-differentiated gastric adenocarcinoma compared to poorly
differentiated type (Lee et al., 2014). These studies suggest that VIM is regulated
by epigenetic modification in some cancers.

3.2 Treatment targeting vimentin
As vimentin is a marker of mesenchymal cells and has shown overexpression
in different cancer types, drugs targeting vimentin may exhibit efficacy by inducing
apoptosis of cancer cells, especially the invasive phenotypes. In addition, targeting
vimentin may induce a MET effect to decrease the migration of cancer cells.
The anticancer drug Silibinin demonstrated inhibition of invasion, motility, and
migration of prostate cancer cells via downregulation of vimentin (Satelli and Li,
2011). An Anti-vimentin drug Withaferin-A also decreased the migration and
invasion of human breast and lung cancer cell lines (Davis et al., 2014).
Furthermore, anti-vimentin microRNA-17-3p has shown suppression of prostate
tumors (Zhang et al., 2009). Several anti-EMT microRNAs can induce a
mesenchymal-epithelial transition by regulating the expression of EMT markers. In
thyroid carcinomas, microRNA-200 treatment resulted in a decrease of vimentin
and accumulation of E-cadherin in tumor cells, inducing those cells acquiring the
21
epithelial phenotype with inhibited invasive abilities (Braun et al., 2010). In addition,
vimentin-specific antibodies or aptamers may show potential to be utilized in the
future for migratory cancers (Satelli and Li, 2011).

3.3 Vimentin in hematological malignancies
Vimentin is widely expressed in blood cells and shows different structural
characteristics in different phenotypes. In monocytes, vimentin forms rich
filamentous networks in cytoplasm, but in granulocytic cells, it only appears as a
single bundle of filaments limited to the nucleus (Ferrari et al., 1986). Study
conducted using mouse myeloid leukemia (M1) showed that the synthesis of
vimentin was higher when the cells were induced to differentiate into macrophages
and acquire the locomotive activities. Thick bundles of vimentin occupied large
parts of cytoplasm in cells after differentiation, but not in M1 cells (Tsuru et al.,
1990). When the promyelocytic leukemia cells NB4 were induced to differentiate
into neutrophil lineage or monocytic phenotype respectively, the expression of
vimentin was shown to decrease in mature neutrophils, but increase in monocytic
cells (Bruel et al., 2001). This suggests vimentin may be involved in tumorigenesis
of specific myeloid leukemia. Furthermore, in human follicular lymphoma, vimentin
can be recognized and bind to malignant cells’ B-cell receptors in tumor
22
microenvironment, which suggests vimentin may also play a role in the
pathogenesis of some B cell malignancies (Cha et al., 2013).
In addition, vimentin can be induced by IFs through a partial EMT. Since blood
cells do not undergo EMT, some other mechanism may be involved in regulating
the expression of vimentin like DNA methylation or its connection to increased β-
catenin accumulation.
Based on these speculations, we hypothesize vimentin also play a role in acute
myeloid leukemia. As vimentin is encoded by the gene VIM, we conduct analyses
comparing the association between VIM expression, clinical characteristics and
clinical outcomes in patients with AML.

23
Chapter 2 - Research Methods
1. Patients and treatment
VIM Z-score and mRNA expression (RNA Seq V2 RSEM) data of AML patients
from the cancer genome atlas (TCGA) dataset were included in this study (Ley et
al., 2013). 173 patients were involved (median age 58 years; range 18–88) in the
dataset with their clinical data. Except for the groups with intermediate and poor
cytogenetic risk, these patients were diagnosed and received treatment according
to National Comprehensive Cancer Network (NCCN) guidelines between
November 2001 and March 2010 (Ley et al., 2013). The patients with intermediate
and poor cytogenetic risk received clinical trials and allogeneic stem cell
transplants when there were applicable matched donors. The stratification of the
risk groups and their diagnosis were done under the standard of NCCN guideline.
The AML subtype classifications were stratified according to the French-American-
British (FAB) classification. The gene expression data for VIM were collected and
reported in 2013. The expression data of other frequently mutant genes in AML of
patients were included such as FLT3, TP53, NPM1, etc. The data of gene
expression, gene methylation as well as patients clinical and survival data on
TCGA database were accessed on March 10, 2018 via cBioportal (Cerami et al.,
2012) (Ley et al., 2013).

24
2. Gene expression analyses
Gene expression data (RNA sequencing Z-score) were downloaded from
TCGA database. The patients were categorized into two groups for analysis based
on high expression (Z-score ≥ 1) and low expression (Z-score < 1) of the gene VIM.
Additional division was conducted by Z-score ≥ 2 and Z-score < 2.

3. Software and statistical analyses
Mann–Whitney U’s non-parametric and Fisher’s exact test were utilized for the
continuous and categorical variables analysis of associations between VIM
expression and patients clinical and molecular characteristics, which were reached
using STATA 12.0 SE. Overall survival (OS) is defined as the length when a patient
was diagnosed or received treatment for a disease until his death and the
treatment ends. Disease free survival (DFS) is the length of complete remission
after a particular treatment for a patient until his relapse or death. Kaplan–Meier
survival analysis were used to compare the overall survival and disease-free
survival between patients with high or low VIM expression. The figures were
generated with GraphPad Prism software package (ver. 5.0; GraphPad Software
Inc., La Jolla, CA, USA). Cox Proportional Hazards Model was set up through
STATA 12.0 SE for multivariate analysis to assess the association between VIM
expression and OS as well as DFS after adjusting for other clinical factors. Survival
25
analysis comparing the patients of younger (age < 60) and older (age ≥ 60) were
conducted except for patients with t(15;17).

26
Chapter 3 - Results
1. VIM expression in AML samples
VIM mRNA expression data were log2 transformed and the distribution was
shown by Histograms (Fig. 1a). Dotplot depicted frequency of VIM mRNA
expression Z-score (Fig. 1b). Data of VIM log2-transformed mRNA expression
against VIM Z-scores were shown through a scatterplot (Fig. 1c: adjusted-R
2
:
0.775, p ≤ 0.001). Based on the distribution data, the patients were divided by VIM
Z-score cut off 1 and 2 for the subsequent analysis.

27

Figure 1. VIM Z-score and mRNA expression distribution. a Histogram of VIM
(log2 transformed) mRNA expression; b Dotplot of VIM mRNA expression Z-score;
c Scatterplot of VIM log2 mRNA expression vs VIM Z-scores.
c.
a. b.
28
Then the participation of VIM in primitive progenitor cells were examined.
CD34+/CD38− and CD34+/CD38+ cells are recognized as leukemia stem cells
and progenitors respectively. AML-GSE30377 data is a dataset containing gene
expression data of 23 AML patients which were sorted into stem cells and
progenitors according to CD34 and CD38 markers. VIM expression was analyzed
and found to be significantly lower in the CD34−/CD38− cells compared to
unsorted cells. However, there was no significant difference in the sorted
population enriched for the stem cells and the progenitors compared to the other
populations (Fig. 2).

29

Figure 2. VIM mRNA expression in sorted AML cells according to their leukemia
stem cell markers expression. VIM gene expression data obtained from the
GSE30377 dataset, in which leukemia blasts obtained from AML patients (n = 23)
were sorted into CD34+CD38-, CD34+CD38+, CD34-CD38-, and CD34-CD38+
populations, VIM mRNA levels compared between the different sorted cell
population and unsorted cells. * p < 0.05.
C D 3 4 + /C D 3 8 -
C D 3 4 + /C D 3 8 +
C D 3 4 -/C D 3 8 +
C D 3 4 -/C D 3 8 -
U n s o rte d
-1 .0
-0 .5
0 .0
0 .5
1 .0
A M L - G S E 3 0 3 7 7
V IM E x p re s s io n (L o g 2 )
* *
30
2. Association between VIM expression and patient primary characteristics
Log 2-transformed VIM mRNA expression in AML were analyzed associating
to patients’ cytogenetic risk and cytogenetic status. Cytogenetically normal (CN-
AML) patients had significantly higher median VIM mRNA expression than
cytogenetically abnormal (CA-AML) patients (Fig. 3a: 15.4 vs 14.8; p = 0.0007). In
addition, compared to those with intermediate and favorable risk, patients with poor
cytogenetic risk had significantly lower median VIM mRNA expression (Fig. 3b: p
= 0.0011). Log 2-transformed VIM mRNA expression was also analyzed based on
AML FAB subtypes stratified into M0 to M7 classifications. M3, M4 and M5 FAB
classes had significantly higher median VIM mRNA expression compared to M0
and M2 FAB classes (Fig. 3c: p ≤ 0.0001).

31

Figure 3. VIM mRNA expression in patients with AML according to cytogenetic,
risk and FAB classifications. VIM log2 mRNA expression categorized by a
cytogenetic status, b risk status and c FAB classification: asterisk compared to M0,
hash symbol compared to M2, plus sign compared to M3 and cap symbol
compared to M5. *p < 0.05; ***p < 0.001.
a. b.
c.
32
In order to assess the associations between VIM expression and patients’
clinical characteristics, 173 AML patients were dichotomized according to VIM Z-
score into high group (Z-score ≥ 1) and low group (Z-score < 1) (Table 1). Patients
with high VIM expression (Z ≥ 1) accounts for 16% (n = 28). Higher WBC counts
(median: 69.2 vs 12.6, p < 0.001) and higher bone marrow blasts (median %: 82.5
vs 71, p = 0.017) were shown in patients with high VIM compared to patients with
low expression. High VIM expression was also significantly associated with age
(median: 63.5 vs 57; p = 0.048), normal cytogenetic (%: 71.4 vs 41.4, p = 0.003)
and transplant status (%: 21.4 vs 46.2; p = 0.021). There was no significant
association between high VIM expression (Z ≥ 1) and FAB subtype, sex, or median
peripheral blood blast percentage.

33
Table 1 Clinical characteristics of 173 AML patients according to VIM expression
Z-score ≥ 1
Characteristic
Z-Score (<1)
(n=145)
Z-Score (≥1)
(n=28)
p-value
Age, median (years) 57 63.5 0.048
Young (<60 years) 81 (55.9%) 10 (64.3%) 0.063
Old (≥60 years) 64 (44.1%) 18 (35.7%)

Sex
Female (n, %) 68 (46.9%) 13 (46.4%) >0.999
Male (n, %) 77 (53.1%) 15 (53.6%)

FAB
M0 (n, %) 15 (10.3%) 1 (3.57%) 0.476
M1 (n, %) 39 (26.9%) 5 (17.9%) 0.353
M2 (n, %) 34 (23.5%) 4 (14.3%) 0.329
M3 (n, %) 11 (7.59%) 5 (17.9%) 0.146
M4 (n, %) 27 (18.6%) 7 (25.00%) 0.446
M5 (n, %) 12 (8.28%) 6 (21.4%) 0.083
M6 (n, %) 2 (1.38%) 0 (0.00%) >0.999
M7 (n, %) 3 (2.07%) 0 (0.00%) >0.999
WB Count, median 12.6 69.2 <0.001
ln (WB count), mean 2.446 3.749 <0.001
% BM Blast, median 71 82.5 0.017
% PB Blast, median 34 44.5 0.253
Risk Status
Poor (n, %) 42 (29.0%) 3 (10.7%) 0.057
Intermediate (n, %) 73 (50.3%) 19 (67.9%) 0.091
Good (n, %) 28 (19.3%) 5 (17.9%) >0.999
Cytogenetic Status
Normal (n, %) 60 (41.4%) 20 (71.4%) 0.003
Abnormal (n, %) 83 (57.2%) 7 (25.0%)

Transplant Status
No (n, %) 78 (53.8%) 22 (78.6%) 0.021
Yes (n, %) 67 (46.2%) 6 (21.4%)

34
Furthermore, associations between VIM expression and patients primary
characteristics according to VIM Z-score Z ≥ 2 and Z < 2 were performed. High
VIM was associated with advanced age (median: 69 vs 57, p = 0.036), median
white blood cell count (median: 69.2 vs 15.1; p = 0.016) and increased peripheral
blast counts (median %: 65 vs 33, p = 0.022) (Table 2).

35
Table 2 Clinical characteristics of 173 AML patients according to VIM expression
Z-score ≥ 2
Characteristic
Z-Score (<2)
(n=165)
Z-Score (≥2)
(n=8)
p-value
Age, median (years) 57 69 0.036
Young (<60 years) 90 (54.55%) 1 (12.5%) 0.028
Old (≥60 years) 75 (45.45%) 7 (87.5%)

Sex >0.999
Female (n, %) 77 (46.67%) 4 (50.00%)

Male (n, %) 88 (53.33%) 4 (50.00%)

FAB
M0 (n, %) 16 (9.70%) 0 (0.00%) >0.999
M1 (n, %) 42(25.45%) 2 (25.00%) >0.999
M2 (n, %) 36 (21.82%) 2 (25.00%) >0.999
M3 (n, %) 14 (8.48%) 2 (25.00%) 0.165
M4 (n, %) 32 (19.39%) 2 (25.00%) 0.659
M5 (n, %) 18 (10.91%) 0 (0.00%) >0.999
M6 (n, %) 2 (1.21%) 0 (0.00%) >0.999
M7 (n, %) 3 (1.81%) 0 (0.00%) >0.999
WB Count, median 15.1 69.2 0.016
ln (WB Count), mean 2.591 4.0292 0.012
% BM Blast, median 72 85.5 0.061
% PB Blast, median 33 65 0.022
Risk Status
Poor (n, %) 44 (26.7%) 1 (12.5%) 0.677
Intermediate (n, %) 88 (53.3%) 4 (50.0%) >0.999
Good (n, %) 31 (18.9%) 2 (25.00%) 0.622
Cytogenetic Status 0.256
Normal (n, %) 75 (45.45%) 5 (62.50%)

Abnormal (n, %) 88 (53.33%) 2 (25.00%)

Transplant Status 0.141
No (n, %) 93 (56.36%) 7 (87.5%)

Yes (n, %) 72 (43.64%) 1 (12.5%)

36
3. Association between VIM expression and patient mutational status
Additionally, VIM expression according to patient’s gene mutational status was
analyzed and VIM high expression (Z ≥ 1) was associated with NPM1 mutation
only (%: 46.4% vs 24.1%; p value: 0.021, Table 3). However, when considering Z-
score ≥ 2, this association was lost (%: 37.5 vs 27.3%; p value: 0.687, Table 4).

37
Table 3 Expression of VIM (Z-score ≥ 1) according to the top mutations present in
AML (N=173 patients)
Genes
Z-Score (<1)
(n=145)
Z-Score (≥1)
(n=28)
p-value
FLT3 (n, %) 41 (28.3%) 8 (28.6%) >0.999
TP53 (n, %) 14 (9.66%) 0 (0.00%) 0.130
NPM1 (n, %) 35 (24.1%) 13 (46.4%) 0.021
NRAS (n, %) 10 (6.90%) 2 (7.14%) >0.999
TET2 (n, %) 14 (9.66%) 1 (3.57%) 0.470
RUNX1 (n, %) 14 (9.66%) 1 (3.57%) 0.470
CEBPA (n, %) 12 (8.28%) 1 (3.57%) 0.696
WT1 (n, %) 10 (6.90%) 0 (0.00%) 0.369
DNMT3A (n, %) 34 (23.4%) 8 (28.6%) 0.631
IDH1 (n, %) 13 (8.97%) 3 (10.7%) 0.726
IDH2 (n, %) 13 (8.97%) 4 (14.3%) 0.484

38
Table 4 Expression of VIM (Z-score ≥ 2) according to the top mutations present in
AML (N=173 patients)
Genes
Z-Score (<2)
(n=165)
Z-Score (≥2)
(n=8)
p-value
FLT3 (n, %) 47 (28.5%) 2 (25.0%) >0.999
TP53 (n, %) 14 (8.48%) 0 (0.00%) >0.999
NPM1 (n, %) 45 (27.3%) 3 (37.5%) 0.687
NRAS (n, %) 12 (7.27%) 0 (0.00%) >0.999
TET2 (n, %) 15 (9.09%) 0 (0.00%) >0.999
RUNX1 (n, %) 15 (9.09%) 0 (0.00%) >0.999
CEBPA (n, %) 13 (7.88%) 0 (0.00%) >0.999
WT1 (n, %) 10 (6.06%) 0 (0.00%) >0.999
DNMT3A (n, %) 41 (24.8%) 1 (12.5%) 0.681
IDH1 (n, %) 15 (9.09%) 1 (12.5%) 0.548
IDH2 (n, %) 15 (9.09%) 2 (25.0%) 0.179

39
4. Patients with high VIM expression have shorter overall and disease-free
survival
Overall survival and disease-free survival (OS and DFS, respectively) between
patients with high (Z ≥ 1) and low (Z < 1) VIM expression were compared. Patients
with high VIM expression (Z ≥ 1) were shown significantly shorter median OS
compared with patients with low VIM expression (median: 7.95 months vs 19.2
months; p = 0.029) (Fig. 4a). Similarly, high VIM expression patients had
significantly shorter DFS than low VIM expression patients as well (median: 5.65
months vs 10.8 months; p = 0.0138) (Fig. 4b). Then the patients were stratified
according to age for further overall survival analysis. After stratification, high VIM
expression (Z ≥ 1) was still significantly associated with worse overall survival in
older AML patients (Fig. 5a: age ≥ 60—median 5.4 vs 9.9 months: p = 0.0257) but
not in younger patients (Fig. 5b: age < 60). Importantly, when these patients were
stratified by cytogenetic status, cytogenetically normal patients showed higher VIM
expression (Z ≥ 1) was significantly associated with shorter overall survival (Fig.
6a, b: 7.95 vs 24.6 months: p = 0.0102), but the cytogenetically abnormal patients
did not. Stratification of patients according to transplant status showed a similar
trend of an association between high VIM expression and shorter overall survival,
whereas, it did not reach statistical significance (no transplant— Fig. 7a: 6.9 vs
40
10.2 months: p = 0.1607; received transplant— Fig. 7b: 24.8 vs 30.6 months: p =
0.5265).

41
a.

b.

Figure 4. Survival analysis of AML patients associated with VIM expression. a
Overall survival of 173 AML patients with VIM Z-score ≥ 1 and VIM Z-score < 1. b
Disease-free survival of 171 AML patients with VIM Z-score ≥ 1 and VIM Z-score
< 1.
0 50 100 150
0
50
100
Months
Overall Survival
Z<1
Z>1
p-value: 0.029
n=145
n=28
0 50 100 150
0
50
100
Months
Disease-Free Survival
Z < 1
Z > 1
p-value: 0.0138
n=143
n=28
42
a.

b.

Figure 5. Survival analysis of AML patients associated with VIM expression after
age-stratification. a Overall survival of AML patients with VIM Z-score ≥ 1 and VIM
Z-score < 1 in older patients (age ≥ 60). b Overall survival of AML patients with
VIM Z-score ≥ 1 and VIM Z-score < 1 in younger patients (age < 60).

0 20 40 60 80 100
0
50
100
Overall Survival (Months)
Percent survival
Z score<1
Z score>1
p=0.0257
n=18
n=64
0 50 100 150
0
50
100
Overall Survival (Months)
Percent survival
Z Score<1
Z score>1
p=0.8802
n=81
n=10
43
a.

b.

Figure 6. Survival analysis of AML patients associated with VIM expression after
stratification according to cytogenetic status. a Overall survival of AML patients
with VIM expression VIM Z-score ≥ 1 and VIM Z-score < 1 in cytogenetically normal
patients. b Overall survival of AML patients with VIM expression VIM Z-score ≥ 1
and VIM Z-score < 1 in cytogenetically abnormal patients.
0 50 100 150
0
50
100
Overall Survival (Months)
Percent survival
Z<1
Z>1
p=0.0102
n=60
n=20
0 50 100 150
0
50
100
Overall Survival (Months)
Percent survival
Z<1
Z>1
p=0.9941
n=83
n=7
44
a.

b.

Figure 7. Survival analysis of AML patients associated with VIM expression after
stratification of transplant status. a Overall survival of AML patients with VIM
expression VIM Z-score ≥ 1 and VIM Z-score < 1 in patients who did not receive
transplant. b Overall survival of AML patients with VIM expression VIM Z-score ≥
1 and VIM Z-score < 1 in patients who received transplant.
0 50 100 150
0
50
100
Overall Survival (Months)
Percent survival
Z<1
Z>1
n=78
n=22
p=0.1607
0 50 100 150
0
50
100
Overall Survival (Months)
Percent survival
Z<1
Z>1
p=0.5265
n=67
n=6
45
Then three additional datasets were chosen to assess the association between
VIM expression and patients’ clinical outcomes. The patients were separated
according to median VIM expression into high expression (over medium) and low
expression (below medium). Two Metzeler datasets recorded cytogenetically
normal patients only (Metzeler et al., 2008). Through the first one, patients with
high VIM expression had significantly shorter overall survival than the patients with
low expression (Fig. 8a: n = 163: 263 vs 657 days; p value: 0.0396). The second
dataset of that showed a non-significant but similar trend that High VIM expression
was associated with worse overall survival (Fig. 8b: n = 79: 392 vs 624 days; p
value: 0.1002). However, the Bullinger dataset which included both CN-AML and
CA-AML patients showed no significant association between VIM expression and
patients clinical outcomes (Fig. 8c) (Bullinger et al., 2004).

46

Figure 8. Survival analysis of patients with AML associated with VIM expression
in three different datasets. Overall survival of patients with AML in various datasets
after dichotomization of VIM mRNA expression into high and low according to the
log-2 median-centered expression: a, b CN-AML (Metzeler, Blood, 2008) and c
CN-AML and CA-AML (Bullinger, NEJM, 2004)
c.
a. b.
47
Multivariate analyses were performed according to patients clinical outcomes.
After adjustment for other risk factors, patients with AML still showed a significant
association between high VIM (Z ≥ 1) expression and worse disease free survival
in total (HR: 2.42 95% CI 1.26–4.66; p = 0.008, Table 5), but the association of
that to over survival did not reach statistical significance (HR: 1.42 95% CI 0.85–
2.38; p = 0.178, Table 6). However, higher VIM expression (Z ≥ 2) in AML was
significantly associated with shorter multivariate analyses (HR: 3.99 95% CI 1.65–
9.66; p = 0.002, Table 7).

48
Table 5 Multivariate analysis of disease-free survival of patients with AML for VIM
expression Z-score ≥ 1 (n=166)
Variables Hazard Ratio 95% CI p-value
lnWBC 1.05 0.87 1.27 0.633
PB Blast 1.01 1.001 1.02 0.028
Cytogenetic
Abnormality
1.82 0.83 4.00 0.134
Cytogenetic Risk
Intermediate 4.76 1.73 13.1 0.002
Poor 3.85 1.49 9.90 0.005
Transplant Status 0.98 0.58 1.65 0.926
FLT3 2.03 1.21 3.40 0.007
VIM 2.42 1.26 4.66 0.008

49
Table 6 Multivariate analysis of overall Survival of AML patients associated with
VIM expression Z-score ≥ 1 (n=169)
Variables Hazard Ratio 95% CI p-value
Age 1.02 0.99 1.03 0.054
Cytogenetic Risk
Intermediate 2.73 1.35 5.52 0.005
Poor 5.66 2.46 13.0 <0.001
Transplant Status 0.43 0.27 0.71 0.001
DNMT3A 1.54 0.97 2.45 0.067
RUNX1 2.04 1.05 3.98 0.035
TP53 2.13 1.04 4.38 0.039
VIM 1.42 0.85 2.38 0.178

50
Table 7 Multivariate analysis of overall survival of AML patients associated with
VIM expression Z-score ≥ 2 (n=169)
Variables Hazard Ratio 95% CI p-value
Age 1.01 1.00 1.03 0.110
Cytogenetic Risk
Intermediate 2.91 1.44 5.88 0.003
Poor 6.13 2.65 14.2 <0.001
Transplant Status 0.40 0.25 0.65 <0.001
DNMT3A 1.55 0.97 2.46 0.066
RUNX1 2.25 1.15 4.40 0.018
TP53 2.20 1.07 4.51 0.031
VIM 3.99 1.65 9.66 0.002

51
Stratification according to age remained significant association between high
VIM expression (Z ≥ 1) and worse overall survival even after adjustment for other
risk factors in older patients (HR: 1.99 95% CI 1.04–3.81; p = 0.038, Table 8), but
not showed in younger patients (Table 9). Higher VIM expression (Z ≥ 2) had a
similar trend in older patients as well (HR: 4.27 95% CI 1.62–11.3; p = 0.003, Table
10), even excluding patients with t(15;17) (HR: 4.30 95% CI 1.60– 11.6; p = 0.004).

52
Table 8 Multivariate analysis of overall survival of AML patients associated with
VIM expression Z-score ≥ 1 in older patients (age ≥ 60; n=80)
Variables Hazard Ratio 95% CI p-value
Age 1.02 0.97 1.06 0.451
Cytogenetic Risk
Intermediate 0.90 0.35 2.32 0.828
Poor 3.60 1.18 11.0 0.024
Transplant Status 0.274 0.12 0.62 0.002
DNMT3A 1.77 0.94 3.32 0.077
RUNX1 1.67 0.72 3.86 0.230
TP53 1.31 0.57 3.01 0.517
VIM 1.99 1.04 3.81 0.038

53
Table 9 Multivariate analysis of overall survival of AML patients associated with
VIM expression Z-score ≥ 1 in young patients (Age < 60; n=89)
Variables Hazard Ratio 95% CI p-value
Age 0.99 0.967 1.02 0.690
Cytogenetic Risk
Intermediate 3.57 1.22 10.4 0.020
Poor 4.72 1.31 17.0 0.017
Transplant Status 0.73 0.34 1.58 0.419
DNMT3A 1.99 0.93 4.30 0.078
RUNX1 5.32 1.66 17.1 0.005
TP53 5.37 0.62 46.5 0.127
VIM 1.02 0.38 2.72 0.969

54
Table 10 Multivariate analysis of overall survival of AML patients associated with
VIM expression Z-score ≥ 2 in old patients (Age ≥ 60; n=80)
Variables Hazard Ratio 95% CI p-value
Age 1.01 0.96 1.05 0.727
Cytogenetic Risk
Intermediate 1.01 0.40 2.58 0.976
Poor 4.05 1.31 12.5 0.015
Transplant Status 0.23 0.10 0.53 <0.001
DNMT3A 1.76 0.93 3.30 0.081
RUNX1 1.99 0.85 4.67 0.114
TP53 1.31 0.57 2.99 0.521
VIM 4.27 1.62 11.3 0.003

55
5. VIM is hypomethylated in patients with high VIM expression
Due to no consistent AML-specific mutation associated with VIM upregulation,
other particular mechanisms might be involved in vimentin signaling. Therefore,
VIM methylation β values were compared between VIM high expression (Z ≥ 1)
and low expression (Z < 1) for the participation of epigenetic changes. High VIM
had significantly lower methylation than low VIM expression (Fig. 9; median
methylation β value: 0.1 vs 0.2, p = 0.0052).

56

Figure 9. Associations between methylation and VIM expression. VIM methylation
β value comparison between patients according to VIM expression (Z-score ≥ 1
and < 1). A non-parametric Mann–Whitney U test was used to compare the median
of methylation β value between the groups (p value: 0.0052).

Z < 1 Z > 1
0.0
0.2
0.4
0.6
0.8
1.0
VIM Methylation (%)
VIM Methylation
**
57
Chapter 4 - Discussion
Epithelial-mesenchymal transition (EMT) is a biological process in which the
epithelial cells obtain the property of mesenchymal cells, identified by loss of E-
cadherin and expression of mesenchymal cells markers like vimentin. EMT has
been shown to enhance tumor migration and invasion. It also plays a role in
tumorigenesis and induces therapy resistance. Importantly, EMT participates not
only in cancers of epithelial origin, but also in other common cancers like breast
cancer and liver cancer. In hematological malignancies, EMT can be induced by
EMT transcription factors like TWIST1 and TWIST2 and is involved in leukemic
stem cells maintenance (Kahlert et al., 2017).
Vimentin is recognized as an EMT marker and the expression of VIM is linked
to the completion of EMT. Vimentin is a type 3 intermediate filament protein
involved in the formation of cytoskeleton. Vimentin is encoded by VIM and
upregulation of VIM has been found to be associated with worse clinical outcomes
in several solid cancers like breast, gastrointestinal, and prostate cancers. As a
reporter of EMT, overexpression of VIM is associated with enhanced migration and
invasion abilities of cells. VIM is involved in regulating the cell adhesion, cellular
signaling and EMT signaling pathways (Herrmann et al., 1989).
As leukemia cells do not undergo EMT, the mechanism of EMT and EMT
markers in hematological malignancy remains unclear. In this study, the
58
association of VIM in acute myeloid leukemia was analyzed. The results
demonstrate that higher expression of VIM is associated with worse clinical
outcomes in older patients with cytogenetically normal AML. In recent years, many
novel and potent therapies for AML have been developed. Yet, the clinical outcome
for older patients who are unable to receive intensive chemotherapy is still
discouraging. The median survival of those patients over 60 is less than 10 months
(Döhner et al., 2015). Our results demonstrated the potential for vimentin to be
utilized as a therapeutic target for the treatment of AML particularly in older patients,
possibly in the form of targeted therapy or immunotherapy. Further investigation is
required for the future preclinical and clinical investigation of anti-vimentin therapy
in leukemia.
Based on our results, VIM is not associated with any gene mutations in these
patients. This suggests that other mechanisms like epigenetic modification may be
involved in regulating VIM expression. Higher VIM hypermethylation has been
reported in advanced colorectal cancers and well-differentiated gastric
adenocarcinoma (Lee et al., 2014). Also, the methylation inhibitor 5-Aza-
Deoxycytidine was shown to increase folds of vimentin mRNA expression in
different colon cancer cell lines (Satelli and Li, 2011). However, in AML we found
high VIM is significantly associated with lower VIM methylation.
Vimentin is an important component of intermediate filaments and involved in
the formation of cytoskeleton. The intracellular cytoskeleton maintains the shape
59
of cells and provides mechanical resistance to deformation (Fletcher and Mullins,
2010). As a highly dynamic protein, vimentin can attach to the nucleus, altering
nuclear architecture and chromatin distribution (Keeling et al., 2017). It is crucial
for ensuring the integrity of cytoplasm and holds the structure of cells (Lowery et
al., 2015). In hematological malignancies, it may play a similar role in stabilizing
the malignant cells to increase their resistance to extracellular stress.
On the other hand, as a marker of EMT, higher expression of vimentin has
been found to be associated with increased mobility in many cell types including
leukocytes. Cell migration is dependent on actin filaments. Vimentin can interact
with actin filaments and restrict flow of actin. Meanwhile, vimentin can induce
contact-dependent cell migration in breast cancers (Battaglia et al., 2018). In
hematological malignancies, vimentin may enhance tissue infiltration or
engraftment of leukemia cells in similar mechanisms.
In addition, EMT can be induced in different cancer types by EMT transcription
factors, accompanied with expression of vimentin. In myeloid malignancies, high
expression of certain EMT-TFs has been found in leukemia stem cells, involved in
maintaining LSCs, promoting leukemia cells growth and inducing drug resistance
(Chen et al., 2018). However, the association of such EMT-TFs in regulating VIM
expression remains unknown in AML.
Treatment targeting EMT has shown efficacy in reducing tumor migration and
been applied to treatment for some solid cancers. Vimentin is a typical marker for
60
immunotherapy targeting invasive tumor, by inhibiting the cells experiencing EMT.
As vimentin is associated with a worse clinical outcome in acute myeloid leukemia,
vimentin can be a potential therapeutic target for further research, applied in
hematological malignancies.

61
Chapter 5 - Conclusion
In conclusion, upregulation of the EMT marker VIM is associated with poor
clinical outcomes in older patients with cytogenetically normal AML. VIM and EMT
are valuable prognostic markers for future targeted therapy and further research
on function and mechanisms in AML.

62
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Abstract (if available)

Abstract Epithelial-mesenchymal transition (EMT) is a biological process in which epithelial cells obtain the property of mesenchymal cells, identified by expression of mesenchymal cells markers like vimentin. Vimentin (VIM) is a type III intermediate filament that maintains cell integrity, and is involved in cell migration, motility and adhesion. In solid cancers, EMT participates in tumor metastasis. However, its role in hematological malignancies remains unclear. Acute myeloid leukemia (AML) is a hematological malignancy characterized by invasion of immature blood cells into bone marrow and peripheral blood. In this study, AML gene expression datasets were analyzed for the expression of VIM in AML and its association with patient’s clinical and molecular characteristics. The results demonstrate that overexpression of the EMT marker vimentin is associated with poor clinical outcomes in older patients with cytogenetically normal AML. Therefore, investigating the role EMT may play in AML warrants further research.

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Asset Metadata

Creator Du, Yang (author)

Core Title Characterization of epithelial-mesenchymal transition in acute myeloid leukemia

School School of Pharmacy

Degree Master of Science

Degree Program Pharmaceutical Sciences

Publication Date 05/08/2019

Defense Date 05/01/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag acute myeloid leukemia,epithelial-mesenchymal transition,OAI-PMH Harvest,vimentin

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Alachkar, Houda (committee chair), Duncan, Roger F. (committee member), Okamoto, Curtis T. (committee member)

Creator Email duyang125@126.com,duyang125@hotmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-169009

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Legacy Identifier etd-DuYang-7422.pdf

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Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

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