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Investigating the effects of T cell mediated anti-leukemia activity in FLT3-ITD positive acute myeloid leukemia

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Investigating the effects of T cell mediated anti-leukemia activity in FLT3-ITD positive acute myeloid leukemia

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Content i
Investigating the Effects of T Cell Mediated Anti-Leukemia Activity in FLT3-ITD Positive
Acute Myeloid Leukemia

by

LUCAS GUTIERREZ

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
Clinical and Experimental Therapeutics

May 2021

Copyright 2021 Lucas Gutierrez
ii
Acknowledgements

First and foremost, I would like to thank my advisor Dr. Houda Alachkar for
providing guidance to me at every step of my PhD. She gave me the opportunity to learn
and better myself even when I was unsure of whether I could accomplish my project. I am
extremely grateful to you. I would like to thank my committee members, Dr. Stan Louie
and Dr. Ian Haworth for their time and advice regarding my research. I would also like to
thank Dr. Mojtaba Akthari for providing us with all the AML clinical samples and being a
wonderful doctor. I have never met a man more passionate about helping people and
hope that I can meet more people like him during my life. I would also like to thank Dr.
Eddie Loh, who helped me with my bioinformatics analysis and assisted me with the
completion of my project.
I was also very fortunate to have lab mates who constantly assisted me throughout
my academic journey. Tian Zhang, thank you teaching me the fundamentals of cloning
and being patient with me whenever I made mistakes. Sharon Wu for editing all of my
documents and providing advice regarding statistical tests. John Beckford for donating
blood for my project and teaching me new things regard sequencing every time I asked.
Pooja for assisting me with all my lab duties and giving me wise advice and counsel
whenever I asked. Each of you played a large role in my overall success and I will forever
be grateful to have friends like you all.
I am also thankful to my friends Joseph, Naren, Roshan, James, Kevin, Julio,
Shawheen, and Tristan for their antagonizing me when I needed to be antagonized and
supporting me when I needed to be supported. Most importantly, I am grateful to my
iii
parents, my girlfriend Evelyn for sticking with me through all the rough times and believing
that I could accomplish this degree.

iv
Table of Contents
Acknowledgements ........................................................................................................... ii
List of Tables ...................................................................................................................... v
List of Figures ................................................................................................................... vi
Abstract ........................................................................................................................... viii
Chapter 1: Background..................................................................................................... 1
1.1 Acute Myeloid Leukemia .....................................................................................................1
1.1.1 Definition and etiology ......................................................................................................................... 1
1.1.2. Incidence................................................................................................................................................. 2
1.1.3. Diagnosis ................................................................................................................................................ 3
1.1.4. Clinical Manifestation .......................................................................................................................... 5
1.1.5. Genomic Landscape ............................................................................................................................ 5
1.1.6. Risk Stratification and Prognosis .................................................................................................... 8
1.1.7. Treatment .............................................................................................................................................. 11
1.2. FMS-Like Tyrosine Kinase 3 Receptor ........................................................................... 15
1.2.1 FLT3-ITD Mutation ............................................................................................................................... 17
1.2.2 FLT3-ITD Targeted Therapy .............................................................................................................. 18
1.3 The role of adaptive immune cells in AML ..................................................................... 20
1.3.1 T Cell Development and V(D)J Recombination ........................................................................... 21
1.3.2. Antigen Presentation ......................................................................................................................... 24
1.3.3 T cell activation .................................................................................................................................... 27
1.3.4. Regulatory T cells............................................................................................................................... 29
1.3.5. FLT3-ITD as a neoantigen in AML .................................................................................................. 30
1.3.6. Immunotherapies to target the FLT3-ITD mutation in AML ..................................................... 31
Chapter 2: Development of a TCR based platform for the identification of a FLT3-
ITD reactive T cell ............................................................................................................ 33
2.1 Introduction ........................................................................................................................ 33
2.2. Materials and Methods ..................................................................................................... 35
2.3. Results ............................................................................................................................... 40
2.4. Discussion.......................................................................................................................... 65
Chapter 3: Midostaurin reduces Regulatory T cells markers in Acute Myeloid
Leukemia .......................................................................................................................... 70
3.1. Introduction ....................................................................................................................... 70
3.2. Materials and Methods ..................................................................................................... 71
3.3. Results ............................................................................................................................... 75
3.4. Discussion ......................................................................................................................... 87
Chapter 4: Summary and Future Directions ................................................................ 90
References ....................................................................................................................... 96

v
List of Tables

Table 1.1 AML FAB Classification (Riester) 15
Table 1.2 World Health Organization Classification of AML (Arber et al. 2016) 15
Table 1.3 Prognostic-risk Group Based on cytogenetic and molecular profile 17
Table 1.4. The Number of Human T Cell Receptor Gene Segments and the Sources of
T Cell Receptor Diversity (Charles A Janeway 2001) 35
Table 2.1. FLT3-ITD Peptides used for T cell Stimulation Experiments 49
Table 2.2. FLT3 WT Peptides used for T cell Stimulation Experiments 50
Table 2.3. Common CDR3 Sequences of FLT3-ITD Stimulated PBMCs found in 3
donors 60
Table 2.5. Donor 8 Top CDR3s for TCR β Subunit after Coculture with peptide-loaded
DCs 69
Table 2.6. Donor 8 Top CDR3s for TCR α Subunit after Coculture with peptide-loaded
DCs 70
Table 2.7. Donor 9 Top CDR3s for TCR β Subunit after Coculture with peptide-loaded
DCs 70
Table 2.8. Donor 9 Top CDR3s for TCR α Subunit after Coculture with peptide-loaded
DCs 70
Table 2.9. Donor 10 Top CDR3s for TCR β Subunit after Coculture with peptide-loaded
DCs 71
Table 2.10. Donor 10 Top CDR3s for TCR α Subunit after Coculture with peptide-loaded
DCs 71

vi

List of Figures

Figure 1.1 Normal hematopoiesis and Acute myeloid leukemia 13
Figure 1.2. Functional categories of mutations asociated with AML 20
Figure 1.3. Circos Plot showing comutations in patients with AML 20
Figure 1.4. Structure of FLT3 receptor 22
Figure 1.5. FLT3-mutated Signaling Pathway 23
Figure 1.6. Type I/II FLT3 Inhibitors 28
Figure 1.7. Human TCR gene rearrangement forming a functional gene via V(D)J
recombination 36
Figure 1.8. The basic MHC class 1 antigen presentation 38
Figure 1.9. Effector mechanisms of CD8+ T cells 40
Figure 1.10. Basic mechanisms used by Treg cells to promote peripheral tolerance 42
Figure 2.1. Healthy donor samples treated with 21 different FLT3-ITD or 5 different
FLT3-WT peptides 51
Figure 2.2. Summary Proportion of Top Clones for TCRβ Subunit. 54
Figure 2.3. Summary Proportion of Top Clones for TCRα Subunit 56
Figure 2.4. Summary Proportion of Top Clones for TCRb for separated T cell
populations. 58
Figure 2.5. Summary Proportion of Top Clones for TCRa for separated T cell
populations. 59
Figure 2.6. EXSAN anchor-and-grow process predicts peptide-CDR3 binding 61
Figure 2.7. Healthy patient CD8+ T cells were cocultured with DCs loaded with 21
different FLT3-ITD or 5 different FLT3-WT peptides. 63
vii
Figure 2.8. IFN-γ ELISpot screening of individual peptides after T cells were cocultured
with dendritic cells 64
Figure 2.9. Summary Proportion of Top Clones for TCRβ Subunit after Dendritic cell
Coculture 66
Figure 2.10. Summary Proportion of Top Clones for TCRα Subunit after Dendritic cell
Coculture 68
Figure 2.11 Chimera Binding of CDR3 regions and Peptides Corresponding with FLT3-
ITD Peptides 72
Figure 3.1. IC50 of four FLT3 Inhibitors in MV4-11, RPMI8402, and MOLT-4 78
Figure 3.2. Representative contour plots of T cell populations from Healthy donors
treated with inhibitors. 79
Figure 3.3. In vitro PBMCs were treated with 4 kinase inhibitors and normalized to IL-
2/IL-7 control population percentages 80
Figure 3.4. Combined quantification of multiple samples of PBMCs treated with 0.5, 1
and 2uM of midostaurin and normalized to IL-2/IL-7 control population percentages. 80
Figure 3.5. Midostaurin reduces CD4 + CD25 + FOXP3+ population. 81
Figure 3.6. Midostaurin Reduces FOXP3 Expression and T cell cytokines. 83
Figure 3.7. Midostaurin reduces Tregs in AML cells. 85
Figure 3.8. Midostaurin does not affect TFGβ levels. 86
Figure 3.9. Midostaurin alters CD4 + CD25 + T cell population and T cell gene
expression markers in patients with AML. 87
Figure 3.10. Treatment of T cells with midostaurin decreases leukemia engraftment.
Leukemia engraftment determined by flow cytometry 88
viii

Abstract
Acute Myeloid Leukemia (AML) is a hematologic malignancy characterized by
great heterogeneity in the molecular genetic aberrations and clinical outcome.
Unfortunately, with current induction and consolidation chemotherapy, the 5-year survival
rate is only about 30%. Currently, the only treatment that has a curative potential in AML
is allogeneic hematopoietic stem cell transplant (HSCT), in which stem cells are collected
from a donor and transplanted into a patient to restore the normal hematopoietic function.
One of the major benefit of HSCT is the emergence of graft vs leukemia (GvL) effect,
where the T cells from the donor marrow can remove residual malignant cells thus, curing
the patient of AML. T cells identify specific neoantigens associated with AML due to the
specificity of their T cell receptors (TCR).
Because T cells play an important role in the GvL effect, better understanding of
the T cell repertoire and function in AML is necessary to enhance this phenomenon. FLT3-
ITD, a common mutation in AML (30% normal karyotype patients with AML), is associated
with poor outcome and thus presents a potential neoantigen that can be targeted by T
cells. In fact, due to its prevalence in patients with AML, many tyrosine kinase inhibitors
(TKIs) have been developed to target this mutation. Of these TKIs, midostaurin is the first
in its class to receive FDA approval for treatment of AML in combination with standard
chemotherapy in the pre-transplant setting. Although this TKIs can be effective, AML can
develop resistance, thereby limiting the efficacy of this treatment. However, because
multiple studies have demonstrated the effect of TKIs on T cell signaling pathways, it is
possible that they can positively affect outcomes of immunotherapy. Therefore, it is
ix
possible that TKI treatment of patients with AML could affect T cells in a way that may
lead to a synergistic response against AML blasts.
Therefore, to study potential immunotherapeutic strategies against AML, this study
has two main objectives: 1) to identify a FLT3-ITD reactive T cell receptor and 2)
determine if TKIs will enhance the effect of GvL in cytotoxic T cells. To achieve these
objectives, I propose the following aims: aim 1: develop a TCR based prediction platform
that enables the identification of TCR clones that are specific to the FLT3-ITD neoantigen
and aim 2: assess the effect of TKIs on T cell populations and function.
In order to develop a platform to identify TCR clones against FLT3-ITD
neoantigens, we selected FLT3-ITD sequences previously found in patients with AML.
Using Immune Epitope Database and Analysis Resource, T cell epitope prediction and T
cell epitopes-MHC binding prediction tools we generated peptides with high affinity to the
top three most common HLA alleles. FLT3-ITD and FLT3-WT peptides were synthesized.
Peripheral blood mononuclear cells (PBMC) from healthy donors were pulsed with the
peptides pool in the presence of IL-2 and IL-7 and cultured for one week. Cells were
analyzed by flow cytometry to assess changes in T cell populations. Two of the samples
were magnetically enriched for CD3 positive cells (T cells) and then further sorted into the
following T cell populations: CD8+, CD4+ and CD4+ CD25+. RNA was extracted from the
collected cells, and 5’Race RNA based NGS was performed for TCRA and TCRB
repertoire. Sequence reads were analyzed using MiXCR and “tcR” software. We
compared the TCRB diversity and the V and J segment utilization difference between
cells pulsed with FLT3-ITD and FLT3-WT peptides using the Inverse Simpson index (IS)
and Jensen Shannon Divergence index (JSD), respectively. We also compared the TCRA
x
and TCRB clonal expansion between FLT3-WT and FLT3-ITD peptide stimulated cells.
In a second series of experiments, I also co-cultured T cells with isolate dendritic cells
from the same healthy donors preloaded with either FLT3-WT or FLT3-ITD peptides. the
co-culture and T cell stimulation was followed with measurement of T cell activation via
detection of interferon-γ (IFN-γ) ELISpot assay. TCR-seq analysis was performed also on
expanded T cells and top 10 CDR3s were tested in silico to detect binding between TCRs
and FLT3-ITD peptides. It was estimated that resulting CDR3s provided no obvious steric
clashes with processed peptides and would likely allow for T cell activation.
In order to assess the effect of TKIs on T cells phenotypes and functions, T cells
were obtained from healthy donors, and stimulated with four different TKIs (sorafenib,
tandutinib, midostaurin, and quizartenib). Treatment with midostaurin but not the other
three TKIs resulted in a significant decrease in CD4 + CD25 + FOXP3+ T cell
population and FOXP3 mRNA expression in healthy and AML PBMCs. Similarly,
samples collected from patients with AML treated with midostaurin showed a reduction
in Tregs markers. IFN-γ, tumor necrosis factor-α (TNF-α), and IL-10 levels were also
reduced following midostaurin treatment. My findings provide evidence that midostaurin
may enhance the GvL effect via modulating the T cell population, repertoire, and
function. Overall, this work explores the development of a platform that can incorporate
information regarding the structure of the TCR and peptide/major histocompatibility
complex to optimize cancer immunotherapy as well as discuss the possibilities of TKI
effects on T cells to enhance the GvL effect.

1
Chapter 1: Background

1.1 Acute Myeloid Leukemia
1.1.1 Definition and etiology

Acute myeloid leukemia (AML) is a blood cancer characterized by the abnormal
differentiation/growth of hematopoietic stem cells (HSCs), in which myeloblasts
accumulate throughout the body (Döhner, Weisdorf and Bloomfield 2015a). Leukemia
arises as a result of HSCs accumulating somatic mutations and cytogenetic aberrations
that prevent them from differentiating properly into mature myeloid cells; this in turn leads
to a build-up of undifferentiated cells known as leukemic blasts (Fig. 1.1). The expansion
of abnormal myeloid cells occurs at the expense of the normal and functional circulating
cells. AML usually has a rapid onset of symptoms and the clinical presentation of AML at
diagnosis can vary dramatically (Estey and Döhner 2006, Estey 2018).
In most individuals diagnosed with AML, no predisposing risk factor can be
identified, but the risk of developing AML is increased by exposure to DNA-damaging
agents. (Shimizu, Schull and Kato 1990). Additionally, certain alkylating agents class and
topoisomerase II inhibitors have been demonstrated to contribute to the development of
therapy-related AML (Bueso-Ramos et al. 2015). AML susceptibility may have a genetic
component, as individuals who are relatives of patients with various hematological
malignancies have been shown to have an increased risk of developing a similar
malignancy (Landgren et al. 2008). In addition, certain inherited disorders such as Down’s
syndrome, Fanconi’s anemia, Bloom syndrome, ataxia-telangiectasia, Diamond–

2
Blackfan anemia, Schwachman–Diamond syndrome, and severe congenital neutropenia
all carry an increased risk for AML development (Seif 2011).

Figure 1.1 Hierarchical model of Normal Hematopoiesis and Human Acute Myeloid
Leukemia (Thomas and Majeti et al. 2017)
1.1.2. Incidence

The incidence of AML has increased over time in many Western countries such as
the United Kingdom, which saw a 29% increase from 1993-1995 to 2015-2017 (Shallis et
al. 2019). In addition, according to the National Institutes of Health’s surveillance,
epidemiology, and end results program, it is estimated that 19940 new cases will be
identified in 2020. AML most commonly occurs in elderly people, as the median age of
diagnosis is 67 years (Döhner et al. 2015a). However, children can also be affected by
this disease, and the incidence of AML in children is significantly less than that of adult

3
(0–14 years of age) is 7.7 per million in the United States (Puumala et al. 2013). In the
United States, AML is more common among non-Hispanic whites

than among Hispanic whites, blacks, Asians and Pacific Islanders (Dores et al. 2012).
1.1.3. Diagnosis

Typically, a patient is diagnosed with AML if approximately 20% or more of the
cells in the bone marrow and blood are comprised of blasts (Dores et al. 2012).
Additionally, methods such as immunophenotyping and cytogenetic and molecular
characterization of myeloblasts are used to distinguish AML from other types of leukemias
and to define AML subtypes. AML can be confirmed if two of the following markers are
identified on myeloblasts: myeloperoxidase, CD13, CD33, CDw65 or CD117 (Bene et al.
1995, Webber, Cushing and Li 2008). Studies have also shown that lymphoid antigens
are detectable in ~25% of individuals with AML: the T cell antigen CD7 has been reported
in 10–30% of patients or the B cell antigen CD19 that is expressed in ~3% of patients
(Khwaja et al. 2016). Acute leukemia is characterized by expression of antigens of the
myeloid and lymphoid lineage. These malignant cells may arise from an immature
leukemic stem cell, which is pluripotent for both myeloid and lymphatic lineage (Bene et
al. 1995).
Immunophenotypic characterization of whole-blood and bone marrow aspirates
from patients are utilized by physicians for the identification and classification of AML to
better understand the proper treatment and disease course (Percival et al. 2017, Heuser
et al. 2020). Cytogenetic analysis is also required for diagnosis, determining the
appropriate treatment, understanding disease risk and predicting clinical outcome.

4
Initially, the French-American-British classification system was used to
differentiate subsets of AML based on cellular morphology and cytochemistry (Bennett et
al. 1976) (Table 1.1). However, currently the World Health Organization (WHO) has
defined seven main subtypes of AML by incorporating genetic criteria along with
morphological and cytochemical characteristics of the disease (Kovrigina 2018) (Table
1.2). This is because certain genetic subtypes can provide researchers with important
prognostic information and are used to guide important management decisions such as
whether to carry out allogeneic HSC transplantation (HSCT) in first remission.

Table 1.1 AML FAB Classification (Arber et al. 2016a)
FAB Subtype Name
M0 Undifferentiated acute myeloblastic leukemia
M1 Acute myeloblastic leukemia with minimal maturation
M2 Acute myeloblastic leukemia with maturation
M3 Acute promyelocytic leukemia
M4 Acute myelomonocytic leukemia
M5 Acute myelomonocytic leukemia with eosinophilia
M6 Acute monocytic leukemia
M7 Acute erythroid leukemia
M8 Acute megakaryoblastic leukemia

Table 1.2 World Health Organization Classification of AML (Arber et al. 2016b)
AML with Recurrent Genetic Abnormalities
● AML with a translocation between chromosomes 8 and 21 [t(8;21)]
● AML with a translocation or inversion in chromosome 16 [t(16;16) or inv(16)]
● APL with the PML-RARA fusion gene
● AML with a translocation between chromosomes 9 and 11 [t(9;11)]
● AML with a translocation between chromosomes 6 and 9 [t(6:9)]
● AML with a translocation or inversion in chromosome 3 [t(3;3) or inv(3)]
● AML (megakaryoblastic) with a translocation between chromosomes 1 and
22 [t(1:22)]
● AML with the BCR-ABL1 (BCR-ABL) fusion gene
● AML with mutated NPM1 gene
● AML with biallelic mutations of the CEBPA gene
● AML with mutated RUNX1 gene (Provisional entity

5
AML with myelodysplasia-related changes
AML related to previous chemotherapy or radiation
AML not otherwise specified
Myeloid sarcoma (also known as granulocytic sarcoma or chloroma)
Myeloid proliferations related to Down syndrome
Undifferentiated and biphenotypic acute leukemias

1.1.4. Clinical Manifestation

The clinical manifestation of AML results from the accumulation of immature and
undifferentiated myeloblasts found in both the bone marrow and peripheral blood.
Patients with AML experience symptoms such as lethargy, loss of appetite, fatigue,
anemia, neutropenia, and an increased tendency to bruise and bleed due to
thrombocytopenia (Röllig and Ehninger 2015). In addition, there have been reported
cases of AML with high leukemia burden resulting in spontaneous cell lysis, and ultimately
leading to conditions such as hyperkalemia, hypocalcemia, hyperphosphatasemia,
hyperuricemia, and increased plasma levels of lactose dehydrogenase (Ejaz et al. 2015).
1.1.5. Genomic Landscape
AML cells are associated with various genetic and epigenetic changes that cause
abnormal cellular proliferation, survival and differentiation (Grimwade and Mrózek 2011)
(Fig. 1.2). In fact, experiments studying leukemogenesis in mouse models have shown
that multiple cooperating mutations are required for the development of AML (Schessl et
al. 2005, Grisolano et al. 2003). These coinciding mutations are categorized into various
functional groups. These groups are mutations in transcription factors that play a role in
cell differentiation and self-renewal, mutations in signaling pathways that function in cell
proliferation and survival, epigenetic mutations that regulate the expression of certain
genes, and mutations in the spliceosome and cohesion complexes (Ley et al. 2013). The

6
cooperation of these mutations in specific groups are which are typically acquired
sequentially in a multiple step procedure and affect cellular functions that finally lead to
the AML condition.
A large research effort supported by the National Institute of Health leveraged
whole-genome or exome sequencing of 200 peripheral blood samples of adult patients
with AML, resulted in the identification of approximately 2,000 different mutated genes. In
this study, it was identified that the FMS-related tyrosine kinase 3 (FLT3), nucleophosmin
(NPM1), and DNA methyltransferase 3A (DNMT3A) genes were
mutated in more than 25% of patients (Ley et al. 2013). Typically, the co-occurrence of
mutations in AML is highly variable in every patient, however certain mutations co-
occurred at a relatively higher frequencies than others. One example of this phenomenon
is the co-occurrence of acquired mutations in the CCAAT/enhancer-binding protein-α
(CEBPA) and the GATA-binding protein 2 (GATA2), which are only found in combination
with one another (Greif et al. 2012). The mutational profile of

7

Figure 1.2. Functional Categories of Genes that are Commonly Mutated in Acute Myeloid
Leukemia (Döhner et al. 2015a).

individuals can be highly complex and variable in the context of different genes. (Kottaridis
et al. 2001, Marcucci et al. 2010, Weissmann et al. 2012) (Fig 1.3). This genomic
heterogeneity can create major obstacles in the effective treatment of patients with AML.
Genomic studies of AML samples have identified common somatic mutations in
epigenetic regulation of gene expression. These include genes that are involved in
processes such as DNA methylation and post-translational histone modifications, such
as DNMT3A, TET2, WT1, and IDH1/ IDH2. DNMT3A mutations are observed in
approximately 20–25% of patients with de novo AML and play a crucial role in putting

8
limits on self-renewal of stem/progenitor cells, as well as regulating myeloid lineage
differentiation (Challen et al. 2011). The TET enzymes regulate the conversion of DNA
base 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC), which is the first step
of DNA demethylation. Patients with AML with mutations in TET2 display site-specific
alterations in 5mC and 5hmC, that likely contribute to leukemic transformation (Figueroa
et al. 2010). Approximately 10% of patients with AML present with WT1, which plays a
role in cell growth and has been shown to be associated with a poor overall survival rate
(Becker et al. 2010, Virappane et al. 2008). Studies involving the IDH enzymes studies
have demonstrated that mutations in these proteins contribute to unregulated production
of 2-hydroxyglutarate, a metabolite excessively created in certain tumors (Ward et al.
2010).

Fig 1.3 Mutation spectrum of AML patients (Tsui et al. 2020)
1.1.6. Risk Stratification and Prognosis
Accurate assessment of prognostic factors is central to the management of AML
and by stratifying patients according to their risk of treatment resistance or treatment-

9
related mortality, physicians can decide the appropriate course of action to take for each
individual patient. Among clinical factors, increased age is associated with lower rates of
complete remission and decreased overall survival (Shah et al. 2013). Other variables
such as platelet count, serum creatinine, and albumin may also play a role in treatment
related mortality (Walter et al. 2011). While clinical factors serve as important indicators
in therapeutic decisions, cytogenetic mutations have been shown to be the single greatest
prognostic factor for complete remission and overall survival in AML. In fact, cases can
be stratified into favorable, intermediate or poor prognostic risk groups based on this
criteria (Table 1.3).
Gene mutations have helped further refine risk stratification based on cytogenetic
changes alone. For example, patients with t(8;21) and the presence of a c-KIT mutation
have been shown to be at increased risk of relapse and decreased rates of overall survival
when compared to patients with intermediate-risk AML (Patel et al. 2012, Qin et al. 2014).
Another example for this phenomenon is a study which provided evidence that patients
with normal cytogenetics over the age of 60 have a decreased overall survival and
relapse-free survival showed when diagnosed with the FLT3-ITD mutation (Port et al.
2014). This has resulted in the classification of cytogenetically normal AML with FLT3-
ITD in the poor risk group.

10
Table 1.3 Prognostic-risk Group Based on cytogenetic and molecular profile (De
Kouchkovsky et al. 2016)
Prognostic Risk Group Cytogenetic profile Cytogenetic Profile and
molecular abnomalities
Favorable t(8:21)(q22;q22)
inv(16)(p13;1q22)
t(15;17)(q22;q12)

t(8:21)(q22;q22) with no c-
KIT mutation
inv(16)(p13;1q22)

t(15;17)(q22;q12)

Mutated NPM1 without
FLT3-ITD (CN-AML)
Mutated biallelic CEBPA
(CN-AML)

Intermediate CN-AML
t(9;11)(p22;q23)

t(8:21)(q22;q22) with
mutated c-KIT
CN-AML other than those
included in the favorable
or adverse prognostic
group
t(9;11)(p22;q23)

Adverse inv(3)(q21q26.2)

t(6;9)(p23;q34)

11q abnormalities other
than t(9;11)
− 5 or del(5q) − 7
Complex karyotype

TP53 mutation, regardless
of cytogenetic profile

CN with FLT3-ITD
CN with DNMT3A

CN with KMT2A-PTD
inv(3)(q21q26.2)

t(6;9)(p23;q34)
11q abnormalities other
than t(9;11)

11
− 5 or del(5q) −7
Complex karyotype
1.1.7. Treatment
The therapeutic strategy to treat patients with AML has largely remained
unchanged over the last 3 decades (Döhner et al. 2015b).

AML treatment begins with
intensive induction chemotherapy, which is the continuous infusion of cytarabine and
anthracycline. If complete remission is achieved after intensive therapy, then appropriate
post-remission consolidation therapy is essential. A complete response is typically
achieved in 60 to 85% of patients who are younger than the age of 60 and 40-60% of
patients that are over 60 (Döhner et al. 2015a).
Induction chemotherapy has been shown to result in remission in a majority of
newly diagnosed AML patients, however, the rate of relapse is still relatively common and
can be highly variable according to age as well as the underlying cytogenetic or molecular
profile of the malignant cells (Koreth et al. 2009). As a result, allogeneic hematopoietic
stem cell transplant (aHSCT) is generally applied as treatment in patients with
intermediate-risk and poor-risk AML in first complete remission but not indicated for
favorable-risk AML (Cornelissen et al. 2012). The patient’s hematopoietic stem cells and
immune cells are reconstituted after transplantation from that of a healthy HLA-matched
donor.
A crucial aspect of this process is the alloreactivity of donor T cells against the
residual leukemia, which is termed the “graft-versus-leukemia” (GvL) effect: a major
mechanism for the curative effect of aHSCT. Unfortunately, the GvL effect is countered

12
by alloreactivity of the donor immune cells directed against the patient’s normal tissues,
which is termed “graft-versus-host disease” (GvHD). GvHD is a multi-system transplant
associated toxicity that typically affects organs such as the skin, gastrointestinal tract,
liver, and lungs in the initial stages but ultimately affects almost
any organ (Rowe 2008).
After aHSCT, the T cells from the healthy donor provide various beneficial
functions, such as enhancement of engraftment and, in the context of AML, eradication
of the residual leukemia clones. In fact, it has been demonstrated that a major benefit of
aHSCT is the GvL effect, which is the result of donor T cells capable of targeting residual
AML cells, resulting in dramatically reduced risk of relapse (Zilberberg, Feinman and
Korngold 2015). Evidence for the GvL effect is provided by the increased risk of relapse
after syngeneic transplantation as well as the utility of donor leukocyte infusions for the
treatment of relapsed patients post-aHSCT in which some patients respond, often with
durable remissions (Mawad, Lionberger and Pagel 2013). Therefore, the idea that the
graft can exert anti-leukemia effects has been well established in the field and is a crucial
aspect of the mechanism for how aHSCT can potentially cure patients AML.
Despite the acceptance of the concept of GvL, there is no consensus on the cells
responsible for this effect other than T cells are clearly involved or of the target structures
recognized on the tumor cells. In fact, it should also be noted that these same T cells can
also result in a phenomenon known as graft-versus-host disease (GvHD), whereby the
grafted T cells can target normal host tissues. Unfortunately, these effects can range from
a mild skin rash to life-threatening complications (Rowe 2008). Due to GvHD, aHSCT is

13
limited in its potential utility as an effective therapy for the treatment of certain AML
patients can be limited. Because of complexity of the system at hand which includes
factors such as the heterogeneity of AML as a disease, the large variety of major
histocompatibility complex genes, and different immune effectors involved in these
processes, strategies aiming to enhance the GvL while harnessing the GvHD remain a
big challenge in the field.
1.1.7.1 AML Targeted Therapy
As progress has been made in understanding the genomic and molecular
mechanisms involved in the development of AML, certain therapies have been developed
to target recurring mutations ad aberrated pathways. One common mutation that has
been exploited by pharmaceutical companies is the FLT3 gene mutation, which signals
via a tyrosine kinase domain. Therefore, TKI agents have been developed to target
various points of the ATP binding site in the intracellular tyrosine kinase domain of the
FLT3 receptor. Type 1 inhibitors, such as sunitinib, lestaurtinib, midostaurin, crenolanib,
and gilteritinib function by binding to the ATP-binding site of the tyrosine kinase in both
the active and inactive conformation. On the other hand, type 2 inhibitors such as
sorafenib, quizartinib and ponatinib, function by binding to the hydrophobic region of the
ATP-binding domain during its inactive state
to prevent receptor signaling (Yu et al., 2020) (Fig 1.5). FLT3 inhibitors that are currently
used in the clinic to treat patients with AML include midostaurin (PKC-412) and giltertinib,
which have both demonstrated significant responses in patients with AML (Wu, Li and
Zhu 2018, Levis and Perl 2020).

14
Another targeted therapy for the treatment of AML is the inhibition of isocitrate
dehydrogenase 1 (IDH1) and IDH2 proteins. These proteins are commonly mutated in
AML patients that are cytogenetically normal (IDH1 6–16%, IDH2 8–19%), and play a
crucial role in cellular metabolism by converting alpha-ketoglutarate to 2 hydroxyglutarate,
ultimately promoting proliferation and inhibiting cellular differentiation (Marcucci et al.
2010). Currently, ivosidenib and enasidenib are FDA-approved drugs used to treat AML
by inhibiting the proteins IDH1 and IDH2, respectively. The result of this inhibition is that
leukemic cells undergo normal differentiation and maturation, ultimately reducing
immature blast counts. Some of the safety concerns associated with IDH inhibitors
include differentiation syndrome as well as cytokine storm (Yu et al. 2020).
Another protein target that has been demonstrated to be efficacious in combating
AML is BCL-2, which is a protein that functions to inhibit apoptosis by stabilizing the
mitochondria and preventing the activation of certain pro-apoptotic proteins. In the context
of AML, BCL-2 overexpression has been associated with worse responses to therapy as
well as therapeutic resistance (Tóthová et al. 2002, Banker et al. 1998). In addition, BCL-
2 is over-expressed in leukemia stem cells (LSC), and in vitro, targeting BCL-2 allows for
the specific eradication of this population (Lagadinou et al. 2013). The only currently FDA
approved BCL-2 inhibitor for AML is Venetoclax, used with chemotherapy in people with
newly diagnosed AML who are 75 years or older (Birkinshaw et al. 2019).
The Hedgehog signaling pathway has also been shown to be activated in AML,
and researchers have utilized this knowledge by targeting Smoothened (SMO), a key
transmembrane protein in this pathway. Glasdegib, a SMO antagonist and oral inhibitor

15
of the Hedgehog signaling pathway, has been FDA approved to treat patients with AML
in combination with standard chemotherapy. (Norsworthy et al., 2019) In fact, it is
currently undergoing clinical development for use in other malignancies such as
myelodysplastic syndrome.
1.2. FMS-Like Tyrosine Kinase 3 Receptor
The FLT3 gene is located on chromosome 13q12 and contains 24 exons encoding
a 993-amino acid protein. The FLT3 signaling protein is a type III receptor tyrosine kinase
that is a similar to KIT, FMS, and PDGFR that is normally expressed by hematopoietic
stem and progenitor cells (HSPCs). There is a large body of work has shown that the
FLT3 receptor plays an important role in survival, proliferation and differentiation of
hematopoietic progenitor/stem cells (Lyman and Jacobsen 1998). However, after these
cells undergo differentiation into other cell types, FLT3 expression is typically lost
(Stirewalt and Radich 2003). The FLT3 receptor consists of five immunoglobulin ‐like
domains in the extracellular region, a juxtamembrane (JM) domain, a tyrosine kinase (TK)
domain separated by a kinase insert domain and a C ‐terminal domain on the intracellular
region (Rosnet et al. 1993) (Fig 1.4). Amino acid residues 572–603 and 604-958
represent the juxtamembrane and tyrosine kinase domains, respectively (Griffith et al.
2004).

16

Figure 1.4. Structure of FLT3 receptor
(Elyamany et al. 2014)
FLT3 signaling is initiated by the binding
of FLT3 ligand (FL) to its extracellular domain
which leads to dimerization. This ligand-induced
dimerization of receptors is believed to expose
tyrosine autophosphorylation sites as well as
stabilize the active conformational state to
enhance the activation of the receptors. Trans-
phophorylation of the tyrosine residues in the
activation-loop induces multiple intracellular signaling proteins such as PI-3-kinase/AKT,
RAS/MAPK, and STAT5 (Fig. 1.5). This ultimately leads to hematopoietic cell survival,
proliferation and differentiation (Hannum et al. 1994).
FLT3 has been shown to be overexpressed at both an RNA and protein level in
most B cell lineage leukemias and AML (Carow et al. 1996). Some studies have also
provided evidence that it is overexpressed in a subset of T-acute lymphocytic leukemia
(ALL) and chronic myeloid leukemia (CML). These studies have also shown that the
leukemic cells of B cell lineage ALL and AML can frequently co-express FLT3 ligand(FL),
hereby creating an autocrine or paracrine signaling loop that results in the

17

Figure 1.5. FLT3-mutated Signaling Pathway (Kavanagh et al. 2017)

constitutive activation of FLT3 (Zheng et al. 2004). Also, the addition of FL to cell culture
media stimulates proliferation of many leukemia-derived cell lines as well as primary AML
samples in vitro.

1.2.1 FLT3-ITD Mutation
The identification of FLT3 mutations in leukemias were a pivotal discovery that
confirmed the importance of FLT3 signaling in these malignancies (Daver et al.
2019). FLT3 mutations are one of the most frequent mutations in AML and occur in
approximately third of the patients. FLT3 mutations consist of two groups, the internal
tandem duplication (ITD) and point mutations in the kinase domain mutations. The FLT3-
ITD is an in-frame mutation that is highly individual regarding size and length and ranges
from 3-400 bp in the juxtamembrane region of the FLT3 receptor (Nakao et al. 1996).

18
Studies have shown that clones harboring FLT3-ITDs of different lengths have been
observed in AML cells of the same patient. Because ITD mutations interfere
with the regulatory capabilities of the juxtamembrane region and KD point mutations most
frequently involve the activation loop, both types of mutations result in constitutive
activation of the receptor’s tyrosine kinase activity in the absence of ligand (Griffith et al.
2004). Studies in patients with AML have provided evidence that FLT3-ITD is a strong
and independent predictor of poor clinical outcome, as patients with a FLT3-ITD mutation
have an increased risk of relapse and have a lower overall survival rate compared to
patients lacking this mutation (Kottaridis et al. 2001).
FLT3 mutations are an important molecular target and therefore, many companies
continue to develop therapeutics to target it for the treatment of AML. FLT3 inhibitors have
been used as a monotherapy in clinical trials and have shown limited clinical responses
(Levis et al. 2011, Fiedler et al. 2015). One explanation for this is that the resulting
genomic instability can lead to multiple pathways of escape from dependence on FLT3
signaling for continued survival. The ultimate goal is to combine FLT3 TKIs with other
molecularly targeted agents affecting the other pathways that, together with FLT3
mutations, cooperate to fully transform hematopoietic stem/progenitor cells. Successful
achievement of this goal may improve the outcome for FLT3-mutant leukemia patients by
preventing relapse and reducing the toxicities (Daver et al. 2019).
1.2.2 FLT3-ITD Targeted Therapy
Due to the high frequency of FLT3 mutations in patients with AML, a number of
TKIs have been developed to disrupt this oncogenic signaling. Overall, the use of FLT3

19
inhibitors has provided evidence of substantial clinical benefit in patients with
relapsed/refractory AML and has offered promising treatment strategies for those with
few available options. Currently, the FLT3 inhibitors available to patients with

AML include midostaurin and gilteritinib.
Midostaurin is a first generation TKI; it was initially investigated in combination
with standard 7 + 3 chemotherapy in patients aged 18-59 years with newly diagnosed
AML and FLT3 mutations. This combination of treatment demonstrated a significant
improvement in both event-free survival and overall survival when compared with 7 + 3
chemotherapy alone (Stone et al. 2017). Due to the results of this pivotal phase 3 trial,
midostaurin combined with induction and consolidation therapy was approved by the US
Food and Drug Administration in 2017 for the treatment of adults with AML that have
been identified to have a mutated form of FLT3 protein. However, it should be noted
that in patients undergoing aHSCT, midostaurin must be discontinued prior to the
procedure, as is currently being investigated in patients in a post-transplant setting
(Maziarz et al., 2020).
Gilteritinib is a next-generation TKI that was initially used in a phase I/II dose
escalation trial in patients with relapsed or refractory AML aged 18 and older. In total
252 patients were enrolled in the trial, and treatment with gilteritinib resulted in a 40%
overall response rate. Higher doses of gilteritinib resulted in higher response rates, as
patients receiving more than 80 mg/day of gilteritinib achieved an overall response rate
of 52%. In addition, response rates were considerably lower in patients with wild type
FLT3 (12%), when compared to patients with FLT3 mutations (49%) (Smith et al. 2012).

20
In addition to TKIs, T cells have been used to target cells with FLT3-ITD
mutations in a preclinical setting. For example, chimeric antigen receptor (CAR) T cells
targeting the FLT3-ITD mutation have demonstrated promise. In one study, researchers
used a human FLT3-ITD+ AML xenograft mouse model, to show that FLT3L CAR-T
cells could significantly prolong the survival of mice. In addition,
treatment with FLT3L CAR-T cells demonstrated no negative effects on the colony
formation of stem cells derived from normal human umbilical cord blood (Wang et al.
2018).
1.3 The role of adaptive immune cells in AML
As mentioned previously, aHSCT is currently the only curative therapy available
for patients with AML largely due to the GvL effect. Although the specific mechanism for
this effect is not entirely determined, it has been concluded that the donor-derived
immune system plays a crucial role in the recovery of patients with AML. More specifically,
this response is largely due to the engrafted T cells that can identify the residual AML
cells for elimination. This highly specific response is due to the tremendous diversity of T
cell receptors, which allows the adaptive immune system to generate a response against
foreign pathogens. The antigens of these pathogens are presented to and recognized by
the antigen-specific T cells, which leads to cell priming, activation, and differentiation
(Obst 2015).
Peripheral T cells are composed of different functional subsets, naive T cells,
memory T cells, and regulatory T (Treg) cells (Golubovskaya and Wu 2016). T cell
responses commence when naive T cells recognize and bind to their corresponding
antigenic peptides presented on major histocompatibility complex (MHC) molecules by
professional antigen-presenting cells through a TCR that typically consists of an alpha (α)

21
and a beta (β) protein chain (Petrova, Ferrante and Gorski 2012). Antigen recognition
requires interaction of the appropriate peptide-major histocompatibility complex (pMHC)
with the corresponding TCR. Once this occurs, phosphorylation of intracellular domains
and activation of enzymes in the T cell, result in the differentiation from a naïve T cell to
an effector phenotype (Restifo and Gattinoni 2013). These effectors can subsequently
identify infected cells and induce apoptosis to prevent the spread of the pathogen to other
tissues (Martínez-Lostao, Anel and Pardo 2015). Most of the effector cells are short-lived,
however, a proportion survive as memory T cells which can further differentiate into
heterogeneous populations based on properties such as tissue localization and self-
renewal capacities. These memory T cells maintain long-term immunity in vertebrates,
however, further research is required to determine their origin and lineage (Kumar,
Connors and Farber 2018).
1.3.1 T Cell Development and V(D)J Recombination

The human TCR repertoire or the range of different TCRs expressed, plays a vital
role in host defense, as the immense diversity allows for identification of a wide range of
antigens. This large diversity of TCRs is due to the unique development of selection
processes that T cells must undergo. More specifically, T lymphocyte development begins
in the bone marrow, where common lymphoid progenitors migrate to the thymus—the
primary site for T cell development. During this stage, a process called V(D)J
recombination begins, with the β chain consisting of variable (V), diversity (D), and joining
(J) segments and the α chain composed of only V and J segments (Shortman and Wu
1996). This process provides TCR diversity through multiple mechanisms such

22
combinatorial diversity, which is defined as the sheer number of each type of gene
segments within the two chains that allow for large combinatorial possibility in
rearrangement (Table 1.4) (Cabaniols et al. 2001).
This process begins with proteins called recombination activating gene 1 (RAG1)
and RAG2, which recognize recombination signal sequences (RSS) that are found near
every V, D, and J segment and consist of three distinct elements: a heptamer and a
nonamer sequence, separated by a spacer element that is either 12 or 23 base pairs
long. This process begins with the beta subunit, in which a D gene is paired with a J gene
initially, followed by a V gene and finally the 1 constant gene. A similar process occurs
with the alpha subunit, with the exception of the D gene, which is only present in the Beta
subunit. The RAG proteins make a double-stranded break in the RSS of the TCR genes
and rejoins them rapidly (Eastman, Villey and Schatz 1999). However, additional diversity
is introduced through a phenomenon termed “junctional diversity”, whereby the junctions
between rearranged gene segments contain small nucleotide insertions and deletions
due to an enzyme called terminal deoxynucleotidyl transferase (Candéias, Muegge and
Durum 1996). This process occurs for both the α and β subunit, whereby the final
assembly of the TCR heterodimer via VDJ recombination can be seen in Figure 1.6.
In both the β and α chain of the TCR, the variable regions contain highly diverse
loops that are called the complementarity ‐determining regions (CDR)1, CDR2, and
CDR3, that make direct contact with pMHC (Cole et al. 2014). It is currently understood
that CDR1 and CDR2 loops are encoded by the V gene segment, while the CDR3 loops
are encoded by the genes spanning the V, D, and J segments for the β subunit or the V
and J segments for the α subunit (Hughes et al. 2003). Therefore, when comparing the

23
different regions, the CDR3 loops are significantly more diverse due to the of the addition
and loss of nucleotides via junctional diversity during the V(D)J recombination process.
This addition or subtraction of nucleotides leads to variability in both the amino acid
composition as well as diversity of CDR3 loop size (Rock et al. 1994).
Table 1.4. The Number of Human T Cell Receptor Gene Segments and the Sources of T
Cell Receptor Diversity (Charles A Janeway 2001)
Element TCR β Subunit TCR α Subunit
Number of Variable (V)
Gene Segments
52 ~70
Number of Diversity (D)
Gene Segments
2 0
Number of Joining (J)
Gene Segments
13 61
Number of Variable (V)
Gene Pairs
5.8x10
6

Junctional Diversity ~2x10
11

Total Diversity ~10
18

24

Figure 1.6. Human TCR gene rearrangement forming a functional gene via V(D)J
recombination (Wang et al. 2016)

1.3.2. Antigen Presentation

Major histocompatibility class I and MHC class II molecules both function to
present processed peptides at the surface of the cell to CD8+ and CD4+ T cells,

25
respectively. The peptides presented on either of these proteins originate from different
sources. MHC class I molecules are expressed on all nucleated cells and
typically present intracellular self-antigens as well as pathogen antigens to effector T cells
that affect cellular immunity. On the other hand, MHC class II molecules are only
expressed on antigen-presenting cells such as macrophages, B cells, and dendritic cells
to present extracellular pathogen or non-self antigens to activate T cells (Wieczorek et
al., 2017, Neefjes et al., 2011). The presentation of peptides on these respective proteins
is important in coordinating the CD8+ and CD4+ T cell responses, which are both
necessary for proper immune function and cancer eradication (Yossef et al. 2018).
However, it should be noted that a phenomenon termed “cross-presentation” occurs,
whereby exogenous antigens are processed and presented by MHC class I (Gutiérrez-
Martínez et al. 2015). In addition, endogenous proteins generated through autophagy
have been demonstrated to be presented by MHC class II molecules (Crotzer and Blum
2009).
The first step of intracellular antigen presentation of viral or tumor proteins is
ubiquitination, which leads to proteolytic degradation by proteasomes. These resulting
peptide fragments are then transported to the endoplasmic reticulum by the transporter
associated with antigen processing (TAP) protein as summarized in Figure 1.7 (Lauvau
et al. 1999). Next, the N-terminus of the degraded protein fragment is further cleaved by
aminopeptidases to a length of approximately eight to nine amino acids. After that, a
portion of the peptide is non-covalently associated with a newly synthesized MHC
molecule to form the pMHC complex (Garstka et al. 2015). Finally, the pMHC is

26
transported from the Golgi apparatus to the outer cell membrane where it can interact
with CD8+

cytotoxic T cells (Kedzierska and Koutsakos 2020) (Fig. 1.7).
Dendritic cells (DCs) are specialized, bone marrow-derived leukocytes that are
critical to initiating adaptive or acquired antigen-specific T cell responses. In fact, they are
recognized as the most potent antigen-presenting cells (APCs) with the ability to activate
naive T cells and to initiate adaptive immune responses (Rossi and Young 2005). DCs
possess several mechanisms such as macropinocytosis,

Figure 1.7. The basic MHC class 1 antigen presentation (Neefjes et al. 2011)

phagocytosis, and receptor-mediated adsorptive that allow them to capture and present
antigens (Brossart et al. 2001). To begin a CD8+ T-cell immune response, TCRs must

27
interact with APCs presenting pMHCs, CD8 co-receptors, and CD28 with CD80/CD86 on
DCs. Furthermore, T cells must receive additional cytokine signals such as IL-2, IL-15,
and interferon- γ (IFN- γ) which can ultimately result in the activation of naive T cells
(Gascoigne et al. 2011). However, it should be noted that if antigen
presentation to a naіve T cell via HLA/peptide complexes occurs without the appropriate
interaction of costimulatory molecules, T cell anergy can be initiated, and ultimately lead
to apoptosis (Chen and Flies 2013).
1.3.3 T cell activation

Priming or activation of T cells by DCs (or other APCs) takes place in the
secondary lymphoid organs, which function to allow DCs to migrate from the periphery
after antigen-uptake (Hughes et al. 2016). Initially, the amount of naive CD8+

or CD4+ T
cells for a specific epitope is low. However, once the appropriate interaction of TCR and
respective antigen occurs, the T cell becomes activated and differentiates into an effector
cell via interaction with the APC as stated previously (Kedzierska and Koutsakos 2020).
Once the TCR binds to the pMHC, crosslinking of the TCR/CD3 complex causes
phosphorylation of tyrosine residues within the cytoplasmic tail of CD3. Once this occurs,
activation of intracellular signal transduction pathways leads to the secretion of cytokines
for T cell proliferation (Smith-Garvin, Koretzky and Jordan 2009). This primary immune
response ultimately leads to the clonal expansion of antigen-specific T cells with distinct
phenotypical characteristics that correlate with functionally discrete CD8+

T cell subsets
(Kedzierska and Koutsakos 2020).
The two major subtypes of effector cells are CD4+ helper T cells (Th) that function
via the secretion of cytokines and CD8+ cytotoxic T cells/lymphocytes(CTLs) with their

28
targeted cell lysis capabilities (CTLs) (caspases within the target cell to ultimately cause
apoptosis (Chowdhury and Lieberman 2008). Another mechanism of CTL-mediated
killing is binding of Fas ligand (FasL) on the CTL to Fas on the target cell, which induces
apoptosis (Pennock et al. 2013). CTL-mediated targeted cytotoxicity is dictated by various
mechanisms including the delivery of cytotoxic
granule proteins granzyme B and perforin, the upregulation of Fas ligand on the T cell
surface, and secretion of cytokines. Perforin is a pore-forming protein that undergoes
polymerization in the lipid bilayer of the target cell membrane to form an aqueous channel
to allow the entrance of granzymes (Kawasaki et al. 2000). The most important granzyme
is Granzyme B, a serine protease that cleaves and activates the proteins termed
caspases, that ultimately lead to apoptosis (Hassin et al. 2011). Finally, cytokines
produced by CD8+

T cells upon antigen encounter include IFN-γ
and TNF-α, function to activate macrophages and induce inflammation (Kuwano,
Kawashima and Arai 1993) (Fig. 1.8).

Figure 1.8. Effector mechanisms of CD8+ T cells (Perdomo-Celis, Taborda and Rugeles
et al. 2019)

29

1.3.4. Regulatory T cells

Regulatory T cells (Tregs) are a subset of T cells that function in the maintenance
of immune self-tolerance via the suppression of aberrant or excessive immune responses
that can be harmful (Wing and Sakaguchi 2012). Tregs
suppressive mechanisms include suppression by inhibitory cytokines, direct cytolysis,
metabolic disruption, and by modulation of DC maturation or function. Indeed, Tregs cells
have a pivotal role in preventing both autoimmune and chronic inflammatory diseases,
such as type 1 diabetes and inflammatory bowel disease respectively (Vignali, Collison
and Workman 2008)(Fig. 1.9). However, they also been shown to block beneficial
immune responses by preventing the clearance of certain pathogens as well as limiting
anti-tumor immunity. In the context of AML, Tregs have been shown to play a complicated
role in the treatment and pathology of the disease. For example, one study demonstrated
that the co-transfer of CD4+CD25+ Tregs and CD4+CD25− effector T cells into MHC-
mismatched mice with leukemia prevented GvHD while preserving the beneficial GvL
effect (Edinger et al. 2003). In addition, there is evidence that patients with AML receiving
peripheral blood stem cell grafts with higher proportions of Tregs had a better 3-year
survival rate compared with those receiving grafts with lower proportions of Treg
populations (Danby et al. 2016). Conversely, there is evidence Tregs inhibit cytotoxic T
lymphocytes activity by creating of an immunosuppressive microenvironment that
prevents the eradication of malignant hematopoietic cells (Buggins et al. 2001).
Additionally, AML cells can influence the conversion of CD4+ CD25− cells into Tregs via
tryptophan catabolism (Curti et al. 2007). Tregs have been shown to suppress the T cell-
mediated immune response against the leukemia cells by secretion of cytokines such as

30
transforming growth factor β(TGFβ) or IL-10, and inhibiting dendritic cell maturation (Jin
et al. 2014).

Figure 1.9. Basic mechanisms used by Treg cells to promote peripheral tolerance (Vignali
et al. 2008)

1.3.5. FLT3-ITD as a neoantigen in AML

As mentioned previously, the GvL effect associated with aHSCT is based on the
premise that T cells have the capacity to specifically kill tumor cells and persist to
eliminate residual leukemia cells. Adoptive T cell therapy uses the same principles of
aHSCT, however, instead of obtaining stem cells from a related donor, T cells are
harvested from the peripheral blood, tumor specific TCRs are identified and expanded,
and reinfused into the cancer patient (Perica et al. 2015). Unfortunately, these methods

31
may be unsuccessful in AML due to a variety of factors. One such factor is the AML
microenvironment that includes suppressive immune cells such as Tregs and myeloid
derived suppressor cells that inhibit cytotoxic T cell function. In addition, other hurdles
include the downregulation of neoantigen expression, upregulation of proteins that
function to protect against apoptosis, as well as causing T cell exhaustion (Austin et al.,
2016).
However, one method that is gaining popularity is transduction of TCR genes that
recognizes the complex of cancer-specific epitopes on a particular MHC class I molecule.
The efficacy of this procedure has been demonstrated in cancers such as melanoma.
Previous reports have provided evidence that T cells against leukemia-specific antigens
can be expanded from the blood of patients as well as healthy donors, as they have been
demonstrated to have a more diversified T cell repertoire with superior proliferation
behavior when compared with cancer patients. In fact, studies have shown naіve/memory
CD8+

T cell responses in healthy individuals against leukemia associated antigens such
as WT1 and PRAME (Rezvani et al. 2007, Weber et al. 2009, Griffioen et al. 2006,
Quintarelli et al. 2008, Grube et al. 2007).

1.3.6. Immunotherapies to target the FLT3-ITD mutation in AML

The use of TKIs in the clinic have greatly improved patient survival and
prolonged disease remission. However, these inhibitors are not entirely FLT3-ITD -
specific and have previously displayed off-target effects (Zhang and Loughran 2011).
They have been shown to cause various effects on immune cells such as T cells,
natural killer (NK) cells, and B cells due to their various mechanisms of action

32
(Steegmann et al. 2012). One example of this is Dasatinib, a TKI that has been shown
to interfere with the activity of several Src family kinases that are important for immune
response, such as Lck and Fyn in T cell signaling (Giansanti et al. 2014). In addition, it
has been demonstrated to inhibit proliferation and activation of T cells as well as
suppress the cytotoxic activity of natural killer cells (Damele et al. 2018,
Marinelli Busilacchi et al. 2018). The immunostimulatory effects of TKIs also extend to
suppressor cells, as the frequency of Tregs lower in patients with leukemia on imatinib
therapy (Hayashi et al. 2012). Another good example of TKIs affecting T cell function
would be studies in hepatocellular carcinoma, in which patients treated with sorafenib
demonstrated increased secretion of IFN-gamma by CD8+ T cells and was associated
with improved overall survival and progression-free survival (Kalathil et al., 2019).
Another study using hepatocellular carcinoma mouse models demonstrated that CAR T
cells targeting extracellular GPC3 had increased efficacy in sorafenib-treated groups
when compared to controls (Wu et al., 2019). Therefore, in this study, the effects of the
TKIs on T cell populations will be further elucidated to determine the potential for
synergistic or inhibitory effects against AML. We hypothesize that TKIs enhance the T
cell-mediated anti-leukemia activity against the FLT3-ITD neoantigen in AML. Our first
specific aim is to identify T cell clones responsible for the GvL effect against FLT3-ITD
via the EXSAN algorithm. Our second specific aim is to determine the effect of FLT3
inhibitors on T cells.

33
Chapter 2: Development of a TCR based platform for the identification of a FLT3-
ITD reactive T cell

2.1 Introduction

Despite some improvement in clinical outcome that can be attributed to the
development of FLT3 inhibitors, these agents demonstrated minimal efficacy when used
as single agents. Still, the only current curative treatment for FLT3 mutant AML patients
remains allogeneic HSCT (Short et al. 2019, Medinger, Lengerke and Passweg 2016).
This is largely due to the GvL effect, whereby the immune cells from a healthy donor are
grafted onto an AML patient, and target the residual blasts after chemotherapy treatment
for apoptosis. Recent studies have revealed that T cells recognizing cancer-specific
antigens or neoantigens, may have significant impact on the clinical response (Jiang et
al. 2019, Kato et al. 2018, Waldman, Fritz and Lenardo 2020). Higher numbers of somatic
mutations may increase the chance of generating highly immunogenic neoantigens that
induce/activate T cells (Efremova et al. 2017). Therefore, one option that can be used to
enhance a patient’s T cell-mediated antitumor immune responses is via peptide
vaccinations based on the neoantigens presented by the patient’s AML blasts. In fact,
these peptide vaccines targeting cancer-specific neoantigens have been demonstrated
to activate or induce antigen-specific T cells in cancer patients (Han et al. 2020,
Hollingsworth and Jansen 2019). These same principles can therefore be applied to
target the FLT3-ITD mutation.
Unfortunately, most attempts to target neoantigens with peptide vaccines have
achieved limited success (Melero et al. 2014). This is likely because neoantigens are
structurally similar to normal host proteins and are therefore, TCRs capable of identifying

34
them are subject to both tolerance mechanisms during T cell development (Coulie et al.
2014). In addition, because most high-affinity TCRs for neoantigens are deleted, the
affinities of the remaining TCRs for these neoantigens is lower than TCR affinities against
foreign antigens (Aleksic et al. 2012). Since the cytotoxicity of T cells has been
demonstrated to be correlated with TCR binding affinity, it is likely that immune responses
against neoantigens will be less successful than those against foreign antigens (Tian et
al. 2007). The identification of TCRs capable of identifying and targeting these
neoantigens is the first step to more effective immunotherapies against cancer. Therefore,
we began by attempting to identify TCRs capable of binding to FLT3-ITD peptide/MHC
complexes.
After this, we utilized an algorithm called “Explicit Solvent Anchored Fragment-
Based Docking” or EXSAN. This algorithm functions by predicting the conformation of a
peptide bound to an MHC with a known structure. It also applies explicit water molecules
to the peptide-protein interface and is capable of calculating the interactions that would
not be possible without the presence of a water network. Because no prior data regarding
binding is required, EXSAN can predict the docking of any peptide to any protein interface.
Although EXSAN was originally developed for MHC class II molecules, it can also be
applied to predict which peptides will bind to MHC class I molecules. In addition, it can be
used to predict which peptide-MHC (pMHC) combinations can engage the TCR for
immune response. Therefore, by applying the EXSAN algorithm to TCR sequences after
peptide stimulation, it is possible to identify which clonally expanded TCRs can elicit an
immune response against specific FLT3-ITD peptides. This will lead to the development
of a platform that can take both the MHC/FLT3-ITD neoantigen complex as well as the
TCR into account to determine which combination will lead to the appropriate immune

35
response. Ultimately, this platform can be applied to other neoantigens in the context of
other cancers.
Given that the adoptive transfer of patient-derived neoantigen-specific T cells has
already shown positive clinical results, the use of neoantigen vaccines might be an
auspicious opportunity to induce or expand mutation specific T cells in cancer patients
(Matsuda et al. 2018, Perumal et al. 2020). Therefore, we hypothesize that FLT3-ITD
presents a viable neoantigen that will influence the TCR repertoire and the expansion of
particular T cell clones. Here, we test this hypothesis and develop a platform to identify
TCR clones against FLT3-ITD neoantigens.

2.2. Materials and Methods
Isolation of Peripheral Blood Mononuclear Cells (PBMCs)

PBMCs from healthy donors were drawn and collected in sterile EDTA tubes (BD
Vacutainer, Franklin Lakes, NJ). PBMCs were isolated by centrifugation over Ficoll-
Paque PLUS density gradients (GE Healthcare, Uppsala, Sweden). Blood was diluted 1:2
in PBS, overlaid on Ficoll lymphocyte separation medium, and centrifuged at 400 x g for
30 minutes at room temperature. PBMCs were collected and washed twice with
phosphate buffered saline (Sigma, St. Louis, MO). The final pellet was resuspended in
20% FBS RPMI media and used in the proceeding experiments.

Identification of T cell Epitopes
The Immune Epitope Database and Analysis server was utilized to predict the MHC-I
and MHC-II binding epitopes for both the FLT3-ITD and FLT3-WT protein. A set of

36
reference HLA alleles was used for predicting the MHC-I and MHC-II T-cell epitope.
These epitopes with a binding affinity towards MHC-I and MHC-II alleles were selected
to activate both CD8+ cytotoxic T-cell and CD4+ T cell mediated immune response.

Peptide Stimulation

Antigenic peptides derived from the FLT3-ITD and FLT3-WT proteins of 8– and 14–amino
acid length were produced using the “T cell epitopes-MHC binding prediction” in IEDB
analysis resource, and purchased from Biomatik (Wilmington, Delaware). A total of 20
peptides were estimated for the FLT3-ITD protein and 5 peptides were predicted for the
FLT3-WT protein. Peptides (5 mg) were dissolved with the mix of acetonitrile and distilled
water and aliquoted to 5 tubes and then lyophilized to make each 1mg peptides. These
peptides were stored at -80 °C. Prior to experiments, the 1mg peptide stocks were diluted
in 100µl DMSO to make a concentration of 10µg/µl. This was further diluted to 2 µg/µl
with RPMI supplemented with 20% fetal bovine serum and filtered before applying to
PBMCs. After this, a total of 200 µg of peptides were used to treat the PBMCs for both
the FLT3-ITD and FLT3-WT proteins. More specifically, 10 µg of each FLT3-ITD peptide
(20 total) and 40 µg of each FLT3-WT peptide (5 total) were used to stimulate the
respective PBMC group combined with IL-2 (5ng/ml) and IL-7 (10 ng/ml). PBMCs were
allowed to incubate at 5% CO2, 37°C for two weeks. 3x10
6
of the PBMCs were used for
FACS Analysis and 5x10
6
of the PBMCs were used for RNA extraction by using the
Qiagen (Valencia, CA) RNeasy Mini kit for sequencing.

Explicit Solvent Anchoring and Docking (EXSAN) prediction algorithm for Peptide and
TCR docking

37
EXSAN is an algorithm that was constructed to be a universal protein-peptide docking
program that requires no knowledge-based implementation of a specific protein complex.
All of the parameters the algorithm uses to discern the binding of a peptide
sequence are universal properties such as hydrogen bonding, hydrophobic interaction,
electrostatic interactions, water displacement, salt bridges, hydrophilic interactions,
amide interactions, etc. This was initially developed for MHC class II proteins, however,
it can also be applied to MHC class I. Rotatable bonds are given an energy score, which
is used to subtract from the overall number of unique fragments. After the full length
peptide has been created, a representative output is designated as a “consensus”
peptide. This consensus is produced via the 100 peptide conformations with the best
score. The peptide sequences initially generated by the IEDB were utilized on EXSAN
and docked in the MHC/TCR complex. Peptide sequences bound to the MHC class I from
the wild-type FLT3 receptor and the mutant FLT3-ITD were predicted.

Dendritic Cell Culture and Peptide Stimulation
Monocyte-derived, immature DCs were generated from PBMCs using the plastic
adherence method, as previously described (Nair, Archer and Tedder 2012). Briefly,
apheresis samples were collected and PBMCs were isolated and washed with PBS
before resuspended in 20% FBS RPMI and then incubated at approximately 1 × 10
6

cells/cm
2

in an appropriately sized tissue culture flask and incubated at 37°C, 5% CO2.
After 90 minutes, nonadherent cells were collected, and the flasks were vigorously
washed with PBS, and then incubated with GM-CSF (20ng/ml) and IL-4(20ng/ml) 20%
RPMI media for 48 hours. This process was repeated once more, and on day 4, the

38
immature dendritic cells were given IFN- γ(10ng/ml) for 24 hours before being loaded with
experimental peptides. These peptide-loaded DCs were co-cultured with T cells for 48
hours before collection for ELISpot analysis, flow cytometry, and TCR sequencing.

Fluorescence Activated Cell Sorting (FACS) Analysis
The T cell population from PBMCs were analyzed via FACS after two weeks of treatment
with tyrosine kinase inhibitors and IL-2/IL-7. T cell populations were identified through
PerCP-labelled anti-CD3 (eBioscience, San Diego, CA), and specific T cell
subpopulations were identified with PE-labelled anti-CD4 (eBioscience, San Diego, CA),
PE-Cyanine7-labelled anti-CD8, and APC-labelled anti-CD25 (eBioscience, San Diego,
CA). Data was acquired using the BD LSRII Flow Cytometer (BD Biosciences, San Jose,
CA), and analyzed using FACS Diva software (BD Biosciences, San Jose, CA). Cells
were gated for CD3+, then CD4+, and finally, the percentage of CD4+CD25+ populations
were quantified.

T Cell Receptor Sequencing
We extracted total RNA from either FACS-sorted T cells or expanded T cells after peptide
stimulation. cDNAs with common 5ʹ-RACE adapter were synthesized by utilizing SMART
library construction kit using up to 100ng of total peripheral blood RNA or in vitro T-Cell
RNA as input (Clontech, Mountain View, CA). The fusion PCR was performed to amplify
TCR α and TCR β cDNAs using a forward primer corresponding to the SMART adapter
sequence and reverse primers corresponding to the constant region of each of TCR α and
TCR β. After adding the Illumina index sequences with barcode using the Nextera Index

39
kit (Illumina, San Diego, CA), the final library product was purified using Agencourt
Ampure XP beads (Beckman Coulter Life Sciences, Indianapolis, IN) according to the
SMARTer Human TCR Profiling kit instructions. Final libraries were assessed for quality
and quantified using Agilent High Sensitivity D1000 ScreenTape as well as High
Sensitivity ScreenTape Reagents on the 4200 TapeStation (Agilent Technologies, Santa
Clara, CA). The prepared libraries were pooled in equimolar concentrations using 12
samples based on the values obtained from TapeStation. Samples were sequenced by
300-bp paired-end reads on the MiSeq (Illumina). Obtained sequence reads were
analyzed using MixCR software and “tcR” software.

IFN- γ ELISpot Analysis
PVDT plates (Millipore, MAIPS4510) coated with 50 μl of 10 μg/ml IFN-γ capture
antibody and incubated overnight at 4°C. Prior to coculture, the plates were washed 3
times with PBS, followed by blocking with 20%FBS RPMI media for 2 hours at room
temperature. After 24 hours of coculture, cells were harvested from ELISPOT plates,
washed 5 times with 0.05% Tween 20 PBS, and incubated for 2 hours at room
temperature with 100 μl/well of a 0.22-μm-filtered 1 μg/ml biotinylated anti–human IFN-γ
detection antibody solution (Mabtech, clone 7-B6-1, diluted in 1× PBS supplemented with
0.5% FBS). The plate was then washed 3 times with PBS-T, followed by a 1-hour
incubation with 100 μl/well of streptavidin-ALP (Mabtech, diluted 1:3,000 with above
diluent). The plate was then washed 5 times with PBS followed by development with 100
μl per well of 0.45-μm-filtered BCIP/NBT substrate solution (KPL, Inc.). The reaction was
stopped by rinsing thoroughly with deionized water. ELISPOT plates were scanned and

40
counted using an ImmunoSpot plate reader and associated software (Cellular
Technologies Limited, Ltd).
2.3. Results
Peripheral Blood Mononuclear Cell Stimulation with FLT3 Peptides
We selected ten FLT3-ITD sequences that were previously found in patients with
AML(Scholl et al., 2006). Using the Immune Epitope Database and Analysis Resource, T
cell epitope prediction and T cell epitopes-MHC binding prediction tools, we generated
peptides with high affinity to the top three common HLA alleles. FLT3-ITD (N=21) (Table
2.1) and FLT3-WT (N=5) (Table 2.2) peptides were synthesized and used for T cell
stimulation. Peripheral blood mononuclear cells (PBMC) from healthy donors (N=3) were
pulsed with the peptides pool in the presence of IL-2 and IL-7 and cultured for two weeks.
Cells were analyzed by flow cytometry to assess changes in T cell populations (Fig. 2.1A).
No changes were observed between the T cells stimulated with either the ITD or the WT
peptides (Fig. 2.1B).

41

Table 2.1 FLT3-ITD Peptides used for T cell Stimulation Experiments
Peptide Number FLT3-ITD
1 REYGSSDNEYFYV
2 NEYFYVDFRV
3 RVYGSSDNEYFYV
4 FPRENLEFFYV
5 NLDNEYFYV
6 YVDFRVTGSSDNEY
7 YEYDLKWEFDFREY
8 REYGSSDNEYF
9 WEFDFREYEYDL
10 REQMVQVTGSSD
11 RENLDNEYFYV
12 YEYDLKEYEYDL
13 FPREYLEFDFRVYV
14 WEFPRENLDNEYF
15 RVYGSSDNEYF
16 YFYVDFREQMVQV
17 FPRENLEFREF
18 YVDFREYGSSDNEY
19 YVDFRVYGSSDNEY
20 YFYVDFREQM
21 EYFYVDFRV

42

Table 2.2 FLT3 WT Peptides used for T cell Stimulation Experiments
Peptide Number FLT3 WT
22 VQVTGSSDNEYFYV
23 EYFYVDFREY
24 YEYDLKWEFPRENL
25 REYEYDLKWEF
26 YFYVDFREY

43

Figure 2.1. Healthy donor samples treated with 21 different FLT3-ITD or 5 different
FLT3-WT peptides as well as with cytokines IL-2(5ng/ml) and IL-7(10ng/ml) for 2 weeks.
A) Representative contour plots of T cell populations from healthy patients treated with
different. B) In vitro PBMCs were treated with different peptides and normalized to IL-
2/IL-7 control population percentages (N=3).

FSC
SSC
CD3
FSC
CD4
CD3
CD25
CD4
CD3
CD8
FLT3-ITD FLT3-WT IL-2/IL-7
69.8 87.3 22.6 72.8
3.42
57.6 85.2 72.1 4.02
22.7
59.6 86.9 72.5 3.88 21.8
A)
B)
Negative Ctrl
FLT3-ITD
FLT3-WT
0.0
0.5
1.0
1.5
Peptide Treatment
Fold Change
Live Lymphocytes
CD3
CD3CD8
CD3CD4
CD3CD4CD25
Negative Ctrl
FLT3-ITD
FLT3-WT
0.0
0.5
1.0
1.5
Peptide Treatment
Fold Change
Live Lymphocytes
CD3
CD3CD8
CD3CD4
CD3CD4CD25

44
Sequencing of TCR  repertoire reveals no major differences between T cells stimulated
with FLT3-ITD peptides and FLT3-WT peptides
RNA was extracted from the collected cells, and 5’Race RNA based NGS was
performed for TCR  (n=3) and TCR  (n=5) repertoire. The average number of different
TCR  clones we identified was 32320 ±12789. Sequence reads were processed using
MiXCR, an advanced alignment algorithm that processes tens of millions of reads in
minutes and achieves high V and J gene assignment accuracy (Bolotin et al. 2015). After
proper alignment of the raw data, the results were further analyzed using “tcR” software,
an R package for the analysis of TCR repertoires that brings together widely used
methods for individual repertoire analyses and TCR repertoires comparison. The useful
features that can be utilized include gene usage comparison, customizable search for
clonotypes shared among repertoires, spectratyping, various repertoire diversity
measures and other commonly used approaches to the repertoire analysis (Nazarov et
al. 2015).
We analyzed T cell diversity, which has been used as quantifiable measure of the
TCR repertoire that is associated with various immune states (Speranza et al. 2018, Attaf,
Huseby and Sewell 2015). Therefore, in the context of our peptide stimulations, it is
believed that diversity would decrease overall because of the T cells capable of binding
to the FLT3 peptides would clonally expand, thereby occupying a larger portion of the
overall clonotypes. Top clonal proportions were analyzed for the TCR  subunits for 5
donors, demonstrating that T cells treated with the ITD peptides had less clonally diverse
populations compared to the WT stimulated groups for 4 patients, however, the inverse
was true for donor 1(Fig. 2.2A). We compared the TCR  diversity of the V and J segment

45
utilization difference between cells pulsed with FLT3-ITD and FLT3-WT peptides. We also
determined TCR  diversity using the Inverse Simpson index (IS) (Fig 2.2B) and Jensen
Shannon Divergence index (JSD) (Fig 2.2C), respectively. The majority of V and J
segment utilization JSD between FLT3-ITD and FLT3-WT values were less than 0.001,
and only two samples exhibited a V segment utilization JSD higher than 0.01. Among the
five samples, two exhibited 50% decreased TCR diversity in the FLT3-ITD stimulated
cells compared with FLT3-WT. We also compared the gene usage for the TRBV (Fig
2.2D) and TRBJ (Fig 2.2E) genes for the TCR  subunit, however, there was no
discernable difference between T cells treated with the ITD and WT peptides.

46

Figure 2.2 Top Clones for TCR  Subunit A) Top clonal proportions of TCR  subunits. B)
Inverse Simpson indices comparing TCR  subunits after treatment with FLT3-ITD or
FLT3-WT peptides. C) Jensen Shannon indices for V and J genes for TCR subunits. D)
V gene usage for TCR  subunits. E) J gene usage for TCR  subunits.

TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
0
2000
4000
6000
8000
10000
Combined Patients 1,3,4 Beta J Segment
FLT3-WT
FLT3-ITD
E)
Frequency
J Genes
TRBV10-1
TRBV10-2
TRBV10-3
TRBV11-1
TRBV11-2
TRBV11-3
TRBV12-3
TRBV12-4
TRBV12-5
TRBV13
TRBV14
TRBV15
TRBV16
TRBV18
TRBV19
TRBV2
TRBV20-1
TRBV21-1
TRBV23-1
TRBV24-1
TRBV25-1
TRBV27
TRBV28
TRBV29-1
TRBV3-1
TRBV30
TRBV4-1
TRBV4-2
TRBV4-3
TRBV5-1
TRBV5-4
TRBV5-5
TRBV5-6
TRBV5-8
TRBV6-1
TRBV6-2
TRBV6-3
TRBV6-4
TRBV6-5
TRBV6-6
TRBV6-7
TRBV7-1
TRBV7-2
TRBV7-3
TRBV7-4
TRBV7-6
TRBV7-7
TRBV7-8
TRBV7-9
TRBV9
0
1000
2000
3000
4000
5000
Combined Patients 1,3,4 Beta V Segment
FLT3-WT
FLT3-ITD
TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
0
2000
4000
6000
8000
10000
Combined Patients 1,3,4 Beta J Segment
FLT3-WT
FLT3-ITD
D)
Frequency
V Genes
TRBV10-1
TRBV10-2
TRBV10-3
TRBV11-1
TRBV11-2
TRBV11-3
TRBV12-3
TRBV12-4
TRBV12-5
TRBV13
TRBV14
TRBV15
TRBV16
TRBV18
TRBV19
TRBV2
TRBV20-1
TRBV21-1
TRBV23-1
TRBV24-1
TRBV25-1
TRBV27
TRBV28
TRBV29-1
TRBV3-1
TRBV30
TRBV4-1
TRBV4-2
TRBV4-3
TRBV5-1
TRBV5-4
TRBV5-5
TRBV5-6
TRBV5-8
TRBV6-1
TRBV6-2
TRBV6-3
TRBV6-4
TRBV6-5
TRBV6-6
TRBV6-7
TRBV7-1
TRBV7-2
TRBV7-3
TRBV7-4
TRBV7-6
TRBV7-7
TRBV7-8
TRBV7-9
TRBV9
0
1000
2000
3000
4000
5000
Combined Patients 1,3,4 Beta V Segment
FLT3-WT
FLT3-ITD
TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
0
2000
4000
6000
8000
10000
Combined Patients 1,3,4 Beta J Segment
FLT3-WT
FLT3-ITD
D)
Frequency
V Genes
B)
FLT3-WT FLT3-ITD
0
500
1000
1500
Other Beta Subunits Inverse Simpson
Donor 1
Donor 2
Donor 3
Donor 4
Donor 5
FLT3-WT FLT3-ITD
0
500
1000
1500
Other Beta Subunits Inverse Simpson
Donor 1
Donor 2
Donor 3
Donor 4
Donor 5
A)
WT ITD
Donor 1
WT ITD
Donor 2
WT ITD
Donor 3
WT ITD
Donor 4
WT ITD
Donor 5
Clonal Proportion
1.00
0.75
0.50
0.25
0.00
P B M C s # 1
P B M C s # 2
P B M C s # 3
P B M C s # 4
P B M C s # 5
-15
-10
-5
0
Jensen Shannon Index
Beta Jensen Shannon
J Genes
V Genes
C)
D o n o r 1
D o n o r 2
D o n o r 3
D o n o r 4
D o n o r 5
-15
-10
-5
0
Jensen Shannon Index
Beta Subunits Jensen Shannon
J Genes
V Genes
P B M C s # 1
P B M C s # 2
P B M C s # 3
P B M C s # 4
P B M C s # 5
-15
-10
-5
0
Jensen Shannon Index
Beta Jensen Shannon
J Genes
V Genes C)
D o n o r 1
D o n o r 2
D o n o r 3
D o n o r 4
D o n o r 5
-15
-10
-5
0
Jensen Shannon Index
Beta Subunits Jensen Shannon
J Genes
V Genes

47
Sequencing of TCR  repertoire reveals no major differences between T cells stimulated
with FLT3-ITD peptides and FLT3-WT peptides
The average number of different TCR  clones we identified was 47396 ±12257.
Top clonal proportions were analyzed for the TCR  subunits for three donors,
demonstrating that T cells treated with the ITD peptides had less clonally diverse
populations compared to the WT stimulated groups for two donors, however, the inverse
was true for donor 1(Fig. 2.3A). Then, we compared the TCR  diversity of the V and J
segment utilization difference between cells pulsed with FLT3-ITD and FLT3-WT
peptides. We also determined TCR  diversity using the IS (Fig 2.3B) and JSD (Fig 2.3C),
respectively. Donor #2 demonstrated a V and J segment utilization JSD value of less than
0.001 between FLT3-ITD and FLT3-WT stimulated groups. However, donors 1 and 3
exhibited a V and J segment utilization JSD higher than 0.001. Among the 3 samples, two
exhibited 50% decreased TCR diversity in the FLT3-ITD stimulated cells compared with
FLT3-WT. We also compared the gene usage for the TRAV (Fig 2.3D) and TRAJ (Fig
2.3E) genes for the TCR  subunit, however, found no difference between the groups.

48

Figure 2.2. Summary Proportion of Top Clones for TCR  Subunit A) Top clonal
proportions of TCR  subunits. B) Inverse Simpson indices comparing TCR  subunits
after treatment with FLT3-ITD or FLT3-WT peptides. C) Jensen Shannon indices for V
and J genes for TCR  subunits. D) V gene usage for TCR  subunits. E) J gene usage
for TCR  subunits.
V Genes
Frequency
TRAV1-1
TRAV1-2
TRAV10
TRAV11
TRAV12-1
TRAV12-2
TRAV12-3
TRAV13-1
TRAV13-2
TRAV14/DV4
TRAV16
TRAV17
TRAV18
TRAV19
TRAV2
TRAV20
TRAV21
TRAV22
TRAV23/DV6
TRAV24
TRAV25
TRAV26-1
TRAV26-2
TRAV27
TRAV29/DV5
TRAV3
TRAV30
TRAV34
TRAV35
TRAV36/DV7
TRAV38-1
TRAV38-2/DV8
TRAV39
TRAV4
TRAV40
TRAV41
TRAV5
TRAV6
TRAV7
TRAV8-1
TRAV8-2
TRAV8-3
TRAV8-4
TRAV8-6
TRAV8-7
TRAV9-1
TRAV9-2
TRAJ7
TRAJ8
TRAJ9
0
1000
2000
3000
4000
5000
V Genes
Frequency
Combined Alpha V Gene Usage
FLT3-WT
FLT3-ITD
TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
0
2000
4000
6000
8000
10000
Combined Patients 1,3,4 Beta J Segment
FLT3-WT
FLT3-ITD
D)
J Genes
Frequency
TRAJ10
TRAJ11
TRAJ12
TRAJ13
TRAJ14
TRAJ15
TRAJ16
TRAJ17
TRAJ18
TRAJ20
TRAJ21
TRAJ22
TRAJ23
TRAJ24
TRAJ26
TRAJ27
TRAJ28
TRAJ29
TRAJ3
TRAJ30
TRAJ31
TRAJ32
TRAJ33
TRAJ34
TRAJ36
TRAJ37
TRAJ38
TRAJ39
TRAJ4
TRAJ40
TRAJ41
TRAJ42
TRAJ43
TRAJ44
TRAJ45
TRAJ46
TRAJ47
TRAJ48
TRAJ49
TRAJ5
TRAJ50
TRAJ52
TRAJ53
TRAJ54
TRAJ56
TRAJ57
TRAJ6
TRAJ7
TRAJ8
TRAJ9
0
500
1000
1500
2000
2500
V Genes
Frequency
Compiled Alpha J Segment
FLT3-WT
FLT3-ITD
TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
0
2000
4000
6000
8000
10000
Combined Patients 1,3,4 Beta J Segment
FLT3-WT
FLT3-ITD
E)
J Genes
Frequency
TRAJ10
TRAJ11
TRAJ12
TRAJ13
TRAJ14
TRAJ15
TRAJ16
TRAJ17
TRAJ18
TRAJ20
TRAJ21
TRAJ22
TRAJ23
TRAJ24
TRAJ26
TRAJ27
TRAJ28
TRAJ29
TRAJ3
TRAJ30
TRAJ31
TRAJ32
TRAJ33
TRAJ34
TRAJ36
TRAJ37
TRAJ38
TRAJ39
TRAJ4
TRAJ40
TRAJ41
TRAJ42
TRAJ43
TRAJ44
TRAJ45
TRAJ46
TRAJ47
TRAJ48
TRAJ49
TRAJ5
TRAJ50
TRAJ52
TRAJ53
TRAJ54
TRAJ56
TRAJ57
TRAJ6
TRAJ7
TRAJ8
TRAJ9
0
500
1000
1500
2000
2500
V Genes
Frequency
Compiled Alpha J Segment
FLT3-WT
FLT3-ITD
TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
0
2000
4000
6000
8000
10000
Combined Patients 1,3,4 Beta J Segment
FLT3-WT
FLT3-ITD
E)
J Genes
Frequency
TRAJ10
TRAJ11
TRAJ12
TRAJ13
TRAJ14
TRAJ15
TRAJ16
TRAJ17
TRAJ18
TRAJ20
TRAJ21
TRAJ22
TRAJ23
TRAJ24
TRAJ26
TRAJ27
TRAJ28
TRAJ29
TRAJ3
TRAJ30
TRAJ31
TRAJ32
TRAJ33
TRAJ34
TRAJ36
TRAJ37
TRAJ38
TRAJ39
TRAJ4
TRAJ40
TRAJ41
TRAJ42
TRAJ43
TRAJ44
TRAJ45
TRAJ46
TRAJ47
TRAJ48
TRAJ49
TRAJ5
TRAJ50
TRAJ52
TRAJ53
TRAJ54
TRAJ56
TRAJ57
TRAJ6
TRAJ7
TRAJ8
TRAJ9
0
500
1000
1500
2000
2500
V Genes
Frequency
Compiled Alpha J Segment
FLT3-WT
FLT3-ITD
TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
0
2000
4000
6000
8000
10000
Combined Patients 1,3,4 Beta J Segment
FLT3-WT
FLT3-ITD
E)
A)
WT ITD
Donor 1
WT ITD
Donor 2
WT ITD
Donor 3
Clonal Proportion
1.00
0.75
0.50
0.25
0.00
WT
ITD
0
500
1000
1500
2000
2500
TCR Alpha Inverse Simpson Indices
Patient 1
Patient 2
Patient 3
B)
WT
IT D
0
500
1000
1500
2000
2500
TCR Alpha Inverse Simpson Indices
Patient 1
Patient 2
Patient 3
Donor 1
Donor 2
Donor 3
P B M C s # 1
P B M C s # 2
P B M C s # 3
-4
-3
-2
-1
0
Jensen Shannon Index
Alpha Jensen Shannon
J Genes
V Genes
C)
P B M C s # 1
P B M C s # 2
P B M C s # 3
-4
-3
-2
-1
0
Jensen Shannon Index
Alpha Jensen Shannon
J Genes
V Genes
Donor 1
Donor 2
Donor 3

49
Sequencing of TCR  and TCR  repertoire of sorted T cell populations reveals no major
differences between T cells stimulated with FLT3-ITD peptides and FLT3-WT peptides
Two of the samples were magnetically enriched for CD3 positive cells (T cells) and
then further sorted into the following T cell populations: CD8+, CD4+ and CD4+ CD25+.
After sequencing the TCR  subunit, no major difference in top clonal proportions was
detected between the FLT3-ITD or WT peptide stimulated groups for either the
CD3+CD4+ or CD3+CD8+ sorted T cells (Fig. 2.4A-C). The same was true for the TCR 
subunit (Fig. 2.5 A-C). We also repeated the diversity measurements done previously.
First, via the IS metric, donor 7 demonstrated a slight increase in diversity for FLT3-ITD
peptide stimulated T cells compared to WT peptides in CD3+CD4+ T cells, however, there
was decrease in diversity for the CD3+CD8+ and the CD3+CD4+CD25+ sorted groups
regarding the TCR  subunit (Fig 2.4A-C). Donor 6 showed no difference in IS for the
TCR  subunit in the CD3+CD4+ T cell group and an increase in diversity for both the
CD3+CD8+ and CD3+CD4+CD25+ sorted T cell groups (Fig 2.4A-C). For the IS of the
TCR  subunit, donor 6 demonstrated an increase in diversity across all three sorted
groups in the FLT3-ITD stimulated group compared to the WT stimulated group (Fig 2.4A-
C). Next, donor 7 had JSD measurements below 0.001 for the TCR  subunits regarding
the V and J gene segments in all the sorted T cell populations after peptide stimulation
(Fig 2.4A-C). Donor 6 had JSD measurements that were slightly above 0.001 in both the
TCR  and TCR  subunits for the V and J gene segments (Fig 2.5A-C). These IS and
JSD values indicate no real difference in diversity between the FLT3-ITD and WT
stimulated groups.

50

Figure 2.3. Summary Proportion of Top Clones for TCR  for separated T cell
populations. Top clonal populations, Inverse Simpson Index, and Jensen Shannon
Index for Beta subunits of A) CD3+CD4+, B) CD3+CD8+, and C) CD3+CD4+CD25+
populations.
WT
IT D
0
200
400
600
800
CD3CD4 Beta Inverse Simpson
Patient 6
Patient 7
WT
IT D
0
500
1000
1500
CD3CD8 Beta Inverse Simpson
Patient 6
Patient 7
WT
IT D
0
500
1000
1500
CD3CD4CD25 Beta Inverse Simpson
Patient 6
Patient 7
P B M C s # 6
P B M C s # 7
-15
-10
-5
0
Jensen Shannon Index
CD3CD8 Beta Jensen Shannon
J Genes
V Genes
P B M C s # 6
P B M C s # 7
-15
-10
-5
0
Jensen Shannon Index
CD3CD4CD25 Beta Jensen Shannon
J Genes
V Genes
A) B) C)
WT ITD
Donor 6
WT ITD
Donor 7
WT ITD
Donor 6
WT ITD
Donor 7
WT ITD
Donor 6
WT ITD
Donor 7
CD3+CD4+ CD3+CD8+ CD3+CD4+CD25+
TCR β Subunit
P B M C s # 6
P B M C s # 7
-15
-10
-5
0
Jensen Shannon Index
CD3CD4 Beta Jensen Shannon
J Genes
V genes
Donor 6
Donor 7
Donor 6
Donor 7
Donor 6
Donor 7

51

Figure 2.5. Summary Proportion of Top Clones for TCR  subunits for separated T cell
populations. Top clonal populations, Inverse Simpson Index, and Jensen Shannon
Index for Alpha subunits of A) CD3+CD4+, B) CD3+CD8+, and C) CD3+CD4+CD25+
populations.
WT
IT D
0
200
400
600
800
1000
CD3CD4 Alpha Inverse Simpson
Patient 6
WT
IT D
0
200
400
600
800
1000
CD3CD8 Alpha Inverse Simpson
Patient 6
WT
IT D
900
1000
1100
1200
1300
1400
CD3CD4CD25 Inverse Simpson
Patient 6
P B M C s # 6
-15
-10
-5
0
Jensen Shannon Index
CD3CD4CD25 Jensen Shannon
J Genes
V Genes
P B M C s # 6
-15
-10
-5
0
Jensen Shannon Index
CD3CD4 Alpha Jensen Shannon
J Genes
V Genes
P B M C s # 6
-10
-5
0
Jensen Shannon Index
CD3CD8 ALpha Jensen Shannon
V Genes
J Genes
B) C)
WT ITD
Donor 6
WT ITD
Donor 6
WT ITD
Donor 6
CD3+CD4+ CD3+CD8+ CD3+CD4+CD25+
Donor 6
Donor 6
Donor 6
WT
IT D
0
500
1000
1500
2000
2500
TCR Alpha Inverse Simpson Indices
Patient 1
Patient 2
Patient 3
Donor 6
Donor 7
A)
TCR! Subunit

52
Isolation of TCRs in FLT3-ITD stimulated T cells and utilizing the EXSAN algorithm to
determine CDR3s that may bind to mutated peptides
We also identified seven CDR3 clones from the TCR  that were present in FLT3-
ITD peptide stimulated but not FLT3-WT stimulated T-cells from three healthy PBMC
samples (Table 2.3). However, we were unable to identify CDR3 clones from the TCR 
that were present in the FLT3-ITD peptide stimulated but not FLT3-WT stimulated T cells
from the same donors. The seven CDR3s in conjunction with the peptide sequences
modeled from the sequence-based prediction program were then computationally docked
in the MHC/TCR complex using EXSAN anchor-and-grow process. Some peptides were
found to be non-binders to all TCR clones, but others were predicted to be a binder to at
least one TCR and non-binders for other TCRs indicating the influence of the CDR3
region. The FLT3-ITD peptide sequences, RENLDNEYFYV, was able to bind to one
CDR3 sequence, but failed to bind to other CDR3 sequences (Fig 2.6). These data
suggest that the presence of the CDR3 in the model significantly influences the peptide
binding.

Table 2.3. Common CDR3 Sequences of FLT3-ITD Stimulated PBMCs found in 3
donors
Patient 1 Clone
Count
Patient 2 Clone
Count
Patient 3 Clone
Count
CDR3 Amino Acid
Sequence
4 1 2 CAASSNTGNQFYF
1 1 3 CAASSQGGSEKLVF
1 1 3 CAARNSGNTPLVF
1 1 6 CALSGQGAQKLVF
1 2 2 CALRNNNARLMF
1 1 4 CALSDQGAQKLVF
1 1 164 CAYRSASGTYKYIF

53

Figure 2.6. EXSAN anchor-and-grow process predicts peptide-CDR3 binding

Peptide Stimulation of T cells via Cultured Dendritic cells
Because there was no expansion of specific T cell populations that were exclusive
to the FLT3-ITD stimulated groups, we utilized a different method to assess differential
clonal expansion of T cell populations with FLT3-ITD stimulation. To this aim, we used
the plastic adherence method to develop dendritic cells from monocyte cultures. After
developing these dendritic cells in vitro, they were loaded with the respective peptides
and cocultured with CD8+ T cells that were isolated from PBMCs. Once the CD8+ T cells
had been cocultured with the dendritic cells for 24 hours, a portion of the cells were
analyzed for flow cytometry once more to assess changes in T cell populations (Fig. 2.7),
FYVDFREY
Non-binder peptide FLT3-
WT)
REYGSSDNEYF
non-binder peptide (FLT3-
ITD)
FPRENLEFFYV
binder peptide (FLT3-
ITD)
CDR3 sequence:
CAASSNTGNQFYF
CDR3 sequence: CAASSNTGNQFYF
binder
RENLDNEYFYV
(FLT3-ITD)
CDR3 sequence: CAARNSGNTPLVF
non-binder

54
however, no changes were observed between the T cells stimulated with either the ITD
or the WT peptides. Following the incubation coculture, we transferred the remaining cells
from the 6 well plates to a 96-well ELISpot plate. The ELISpot assay allows
characterization of single-cell immune responses through detection of secreted analytes
and allows for the handling of viable T cells after the assay is concluded (Möbs and
Schmidt 2016). In addition, we selected IFN-γ as the specific analyte because it is
associated with antigen-specific T cell responses and has been demonstrated as a
favorable response in the context of cancer therapy (Ni and Lu 2018, Badovinac and
Harty 2000). We developed the IFN-γ ELISPOT assay plates we stained the cells from
coculture wells that measured the levels of IFN-γ secretion against certain peptides for
the different donors, inferring that each donor’s PBMCs responded to different peptides
(Fig. 2.8).

55

Figure 2.7. Healthy patient CD8+ T cells were cocultured with DCs loaded with 21
different FLT3-ITD or 5 different FLT3-WT peptides as well as with cytokines IL-
2(5ng/ml) and IL-7(10ng/ml) for 2 weeks. A) Representative contour plots of T cell
populations from healthy patients treated with different. B) In vitro PBMCs were treated
with different peptides and normalized to IL-2/IL-7 control population percentages (N=3).

FSC
SSC
CD3
FSC
CD4
CD3
CD25
CD4
CD3
CD8
FLT3-ITD FLT3-WT IL-2/IL-7
A)
38.9 97.0
24.7 6.13 63.4
38.8 97.8
24.6 9.23 63.6
39.1 97.5 62.5 28.2 5.06
FLT3-WT
FLT3-ITD
CTRL
0.0
0.5
1.0
1.5
Compiled Flow Data
Treatment
Fold Change
FSC/SSC
FSCCD3
+
CD3
+
CD4
+
CD3
+
CD4
+
CD25
+
CD3
+
CD8
+
F L T 3 -W T
F L T 3 -IT D
C T R L
0.0
0.5
1.0
1.5
Compiled Flow Data
Treatment
Fold Change
FSC/SSC
FSCCD3
+
CD3
+
CD4
+
CD3
+
CD4
+
CD25
+
CD3
+
CD8
+
B)

56

Figure 2.8. IFN-γ ELISpot screening of individual peptides after T cells were cocultured
with dendritic cells in A) Patient 8, B) Patient 9, C) Patient 10.

P e p tid e 1
P e p tid e 2
P e p tid e 3
P e p tid e 4
P e p tid e 5
P e p tid e 6
P e p tid e 7
P e p tid e 8
P e p tid e 9
Pep tide 10
Pep tide 11
Pep tide 12
Pep tide 13
Pep tide 14
Pep tide 15
Pep tide 16
Pep tide 17
Pep tide 18
Pep tide 19
Pep tide 20
Pep tide 21
Pep tide 22
Pep tide 23
Pep tide 24
Pep tide 25
Pep tide 26
C o n tro l
IT D P o o l
W T P o o l
0
50
100
150
Peptide Treatment
Spots
Donor CR
Number of Spots
FLT3 Peptide
A)
P e p tid e 1
P e p tid e 2
P e p tid e 3
P e p tid e 4
P e p tid e 5
P e p tid e 6
P e p tid e 7
P e p tid e 8
P e p tid e 9
Pep tide 10
Pep tide 11
Pep tide 12
Pep tide 13
Pep tide 14
Pep tide 15
Pep tide 16
Pep tide 17
Pep tide 18
Pep tide 19
Pep tide 20
Pep tide 21
Pep tide 22
Pep tide 23
Pep tide 24
Pep tide 25
Pep tide 26
C o n tro l
IT D P o o l
W T P o o l
0
10
20
30
Peptide Treatment
Spots
Donor LG
Number of Spots
FLT3 Peptide
C)
P e p tid e 1
P e p tid e 2
P e p tid e 3
P e p tid e 4
P e p tid e 5
P e p tid e 6
P e p tid e 7
P e p tid e 8
P e p tid e 9
Pep tide 10
Pep tide 11
Pep tide 12
Pep tide 13
Pep tide 14
Pep tide 15
Pep tide 16
Pep tide 17
Pep tide 18
Pep tide 19
Pep tide 20
Pep tide 21
Pep tide 22
Pep tide 23
Pep tide 24
Pep tide 25
Pep tide 26
C o n tro l
IT D P o o l
W T P o o l
0
10
20
30
40
Peptide Treatment
Spots
Donor JB
Number of Spots
FLT3 Peptide
B)

57
Sequencing of TCR  repertoire after dendritic cell coculture reveals no major differences
between T cells stimulated with FLT3-ITD peptides and FLT3-WT peptides
RNA was extracted from the collected cells after ELISpot analysis, and 5’Race
RNA based NGS was performed for both the TCR  (n=3) and TCR  (n=3) repertoire. The
average number of different TCR  clones we identified was 22511 ±1252. Sequence
reads were once again analyzed using MiXCR and “tcR” software. Top clonal proportions
were analyzed for the TCR  subunits for the 3 donors, demonstrating varying results, as
T cells treated with the ITD peptides had less clonally diverse populations compared to
the WT stimulated and CTRL groups for patient 10, however, the inverse was true for
patient 8, and patient 9 showed no overall trend (Fig. 2.9A). Next, we compared the TCR 
diversity of the V and J segment utilization difference between cells pulsed with FLT3-ITD
and FLT3-WT peptides as well as the control group. Once again, we determined TCR 
diversity using the IS (Fig 2.9B) and (JSD) (Fig 2.9C), respectively. All of the V and J
segment utilization JSD between FLT3-ITD and FLT3-WT values were less than 0.001.
Among the five samples, one exhibited 50% decreased TCR diversity in the FLT3-ITD
stimulated cells compared with the control group while the other 2 showed an increase in
diversity compared to both controls and FLT3-WT stimulation. We also compared the
gene usage for the TCRV (Fig 2.9D) and TCRJ (Fig 2.9E) genes for the TCR  subunit,
however, found no difference between the groups.

58

Figure 2.9. Summary Proportion of Top Clones for TCR  Subunit after Dendritic cell
Coculture A) Top clonal proportions of TCR  subunits. B) Inverse Simpson indices
comparing TCR  subunits after treatment with FLT3-ITD or FLT3-WT peptides. C)
Jensen Shannon indices for V and J genes for TCR subunits. D) V gene usage for
TCR  subunits. E) J gene usage for TCR  subunits.

TRBV10-1
TRBV10-2
TRBV10-3
TRBV11-1
TRBV11-2
TRBV11-3
TRBV12-3
TRBV12-4
TRBV12-5
TRBV13
TRBV14
TRBV15
TRBV16
TRBV18
TRBV19
TRBV2
TRBV20-1
TRBV21-1
TRBV23-1
TRBV24-1
TRBV25-1
TRBV27
TRBV28
TRBV29-1
TRBV3-1
TRBV30
TRBV4-1
TRBV4-2
TRBV4-3
TRBV5-1
TRBV5-4
TRBV5-5
TRBV5-6
TRBV5-8
TRBV6-1
TRBV6-2
TRBV6-3
TRBV6-4
TRBV6-5
TRBV6-6
TRBV6-7
TRBV7-1
TRBV7-2
TRBV7-3
TRBV7-4
TRBV7-6
TRBV7-7
TRBV7-8
TRBV7-9
0
500
1000
1500
2000
2500
Dendritic Cells Stimulation Combined Beta V Genes
FLT3-WT
FLT3-ITD
CTRL
V Genes
Frequency
D)
TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
0
1000
2000
3000
4000
5000
Dendritic Cells Stimulation Combined Beta J Genes
FLT3-WT
FLT3-ITD
CTRL
Frequency
J Genes
E)
TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
0
1000
2000
3000
4000
5000
Dendritic Cells Stimulation Combined Beta J Genes
FLT3-WT
FLT3-ITD
CTRL
Frequency
J Genes
E)
TRBJ1-1
TRBJ1-2
TRBJ1-3
TRBJ1-4
TRBJ1-5
TRBJ1-6
TRBJ2-1
TRBJ2-2
TRBJ2-3
TRBJ2-4
TRBJ2-5
TRBJ2-6
TRBJ2-7
0
1000
2000
3000
4000
5000
Dendritic Cells Stimulation Combined Beta J Genes
FLT3-WT
FLT3-ITD
CTRL
Frequency
J Genes
E)
Donor JB
Donor LG
Donor CR
0.0000
0.0002
0.0004
0.0006
Jensen Shannon Index
Alpha WT vs CTRL
V Genes
J Genes
C)
Pa tient 8
Pa tient 9
Pa tient 10
0.0000
0.0002
0.0004
0.0006
0.0008
0.0010
Jensen Shannon Index
Beta ITD vs WT
V Genes
J Genes
WT
ITD
Donor 8
Clonal Proportion
1.00
0.75
0.50
0.25
0.00
CTRL WT ITD
Donor 9
CTRL WT ITD
Donor 10
CTRL WT ITD
Beta TCR
A)
B)
Beta Subunit
C T R L
FLT3-ITD
FLT3-WT
0
500
1000
1500
Beta Inverse Shannon Index
Donor 8
Donor 9
Donor 10
Donor JB
Donor LG
Donor CR
0.0000
0.0002
0.0004
0.0006
Jensen Shannon Index
Alpha WT vs CTRL
V Genes
J Genes
C)
D o n o r 8
D o n o r 9
D o n o r 1 0
0.0000
0.0002
0.0004
0.0006
0.0008
0.0010
Jensen Shannon Index
Beta ITD vs WT
V Genes
J Genes

59

Sequencing of TCR  repertoire after dendritic cell coculture reveals no major differences
between T cells stimulated with FLT3-ITD peptides and FLT3-WT peptides
Next, the average number of different TCR  clones we identified was 41119
±2614. Top clonal proportions were analyzed for the TCR  subunits for 3 donors, and
similarly to the TCR  subunit, the T cells stimulated with FLT3-ITD peptides in donor 10
demonstrated less clonally diverse populations compared to the WT and control groups,
while the inverse was true for donor 8, and donor 9 showed no overall trend (Fig. 2.10A).
Then, we compared the TCR  diversity of the V and J segment utilization difference
between cells pulsed with FLT3-ITD and FLT3-WT peptides. We also determined TCR 
diversity using the IS (Fig 2.10B) and JSD (Fig 2.10C), respectively. Donor 8 showed an
increase in diversity for the T cells treated with FLT3-ITD peptides compared to the other
groups, while the other donors showed no real change between groups. In addition, none
of the donors demonstrated a V and J segment utilization JSD value of more than 0.001
between FLT3-ITD and FLT3-WT stimulated groups. We also compared the gene usage
for the TCRV (Fig 2.10D) and TCRJ (Fig 2.10E) genes for the TCR  subunit, however,
there was no discernable difference between T cells treated with the ITD and WT
peptides.

60

Figure 2.10. Summary Proportion of Top Clones for TCR  Subunit after Dendritic cell
Coculture A) Top clonal proportions of TCR  subunits. B) Inverse Simpson indices
comparing TCR  subunits after treatment with FLT3-ITD or FLT3-WT peptides. C)
Jensen Shannon indices for V and J genes for TCR subunits. D) V gene usage for
TCR  subunits. E) J gene usage for TCR  subunits.
TRAV1-1
TRAV1-2
TRAV10
TRAV11
TRAV12-1
TRAV12-2
TRAV12-3
TRAV13-1
TRAV13-2
TRAV14/DV4
TRAV16
TRAV17
TRAV18
TRAV19
TRAV2
TRAV20
TRAV21
TRAV22
TRAV23/DV6
TRAV24
TRAV25
TRAV26-1
TRAV26-2
TRAV27
TRAV29/DV5
TRAV3
TRAV30
TRAV34
TRAV35
TRAV36/DV7
TRAV38-1
TRAV38-2/DV8
TRAV39
TRAV4
TRAV40
TRAV41
TRAV5
TRAV6
TRAV7
TRAV8-1
TRAV8-2
TRAV8-3
TRAV8-4
TRAV8-6
TRAV8-7
TRAV9-1
TRAV9-2
0
1000
2000
3000
4000
Dendritic Cells Stimulation Combined Alpha V Genes
FLT3-WT
FLT3-ITD
CTRL
Frequency
V Genes
D)
TRAJ10
TRAJ11
TRAJ12
TRAJ13
TRAJ14
TRAJ15
TRAJ16
TRAJ17
TRAJ18
TRAJ20
TRAJ21
TRAJ22
TRAJ23
TRAJ24
TRAJ26
TRAJ27
TRAJ28
TRAJ29
TRAJ3
TRAJ30
TRAJ31
TRAJ32
TRAJ33
TRAJ34
TRAJ36
TRAJ37
TRAJ38
TRAJ39
TRAJ4
TRAJ40
TRAJ41
TRAJ42
TRAJ43
TRAJ44
TRAJ45
TRAJ46
TRAJ47
TRAJ48
TRAJ49
TRAJ5
TRAJ50
TRAJ52
TRAJ53
TRAJ54
TRAJ56
TRAJ57
TRAJ6
TRAJ7
TRAJ8
TRAJ9
0
500
1000
1500
2000
Dendritic Cells Stimulation Combined Alpha J Genes
FLT3-WT
FLT3-ITD
CTRL
Frequency
J Genes
E)
TRAJ10
TRAJ11
TRAJ12
TRAJ13
TRAJ14
TRAJ15
TRAJ16
TRAJ17
TRAJ18
TRAJ20
TRAJ21
TRAJ22
TRAJ23
TRAJ24
TRAJ26
TRAJ27
TRAJ28
TRAJ29
TRAJ3
TRAJ30
TRAJ31
TRAJ32
TRAJ33
TRAJ34
TRAJ36
TRAJ37
TRAJ38
TRAJ39
TRAJ4
TRAJ40
TRAJ41
TRAJ42
TRAJ43
TRAJ44
TRAJ45
TRAJ46
TRAJ47
TRAJ48
TRAJ49
TRAJ5
TRAJ50
TRAJ52
TRAJ53
TRAJ54
TRAJ56
TRAJ57
TRAJ6
TRAJ7
TRAJ8
TRAJ9
0
500
1000
1500
2000
Dendritic Cells Stimulation Combined Alpha J Genes
FLT3-WT
FLT3-ITD
CTRL
Frequency
J Genes
E)
TRAJ10
TRAJ11
TRAJ12
TRAJ13
TRAJ14
TRAJ15
TRAJ16
TRAJ17
TRAJ18
TRAJ20
TRAJ21
TRAJ22
TRAJ23
TRAJ24
TRAJ26
TRAJ27
TRAJ28
TRAJ29
TRAJ3
TRAJ30
TRAJ31
TRAJ32
TRAJ33
TRAJ34
TRAJ36
TRAJ37
TRAJ38
TRAJ39
TRAJ4
TRAJ40
TRAJ41
TRAJ42
TRAJ43
TRAJ44
TRAJ45
TRAJ46
TRAJ47
TRAJ48
TRAJ49
TRAJ5
TRAJ50
TRAJ52
TRAJ53
TRAJ54
TRAJ56
TRAJ57
TRAJ6
TRAJ7
TRAJ8
TRAJ9
0
500
1000
1500
2000
Dendritic Cells Stimulation Combined Alpha J Genes
FLT3-WT
FLT3-ITD
CTRL
Frequency
J Genes
E)
Donor JB
Donor LG
Donor CR
0.0000
0.0002
0.0004
0.0006
Jensen Shannon Index
Alpha WT vs CTRL
V Genes
J Genes
C)
Pa tient 8
Pa tient 9
Pa tient 10
0.0000
0.0002
0.0004
0.0006
0.0008
0.0010
Jensen Shannon Index
Beta ITD vs WT
V Genes
J Genes
Clonal Proportion
1.00
0.75
0.50
0.25
0.00
Donor 8
CTRL WT ITD
Donor 9
CTRL WT ITD
Donor 10
CTRL WT ITD
Alpha TCR
A)
B)
Alpha Subunit
CTRL FLT3-ITD FLT3-WT
0
500
1000
1500
2000
Alpha Inverse Shannon Index
Donor 8
Donor 9
Donor 10
Donor JB
Donor LG
Donor CR
0.0000
0.0002
0.0004
0.0006
Jensen Shannon Index
Alpha ITD vs CTRL
V Genes
J Genes
C)
D o n o r 8
D o n o r 9
D o n o r 1 0
0.0000
0.0002
0.0004
0.0006
0.0008
Jensen Shannon Index
Alpha ITD vs WT
V Genes
J Genes

61

Isolation of top TCRs in FLT3-ITD stimulated T cells
While we aimed to identify common T cell clones in FLT3-ITD stimulated groups
that were not present in the FLT3-WT stimulated groups in a similar fashion to stated
previously, the algorithm did not identify any candidates. Therefore, the top ten CDR3s
for both the TCR  (Table 2.5, 2.7, and 2.9) and TCR  (Tables 2.6, 2.8, and 2.10) were
identified for each patient. After this, estimates were made regarding peptide processing
based on conserved peptide-MHC interaction of valines amino acids at the carboxy
terminals and leucines occupying the second amino acid of the amino terminal for antigen
presentation (Jeremy M. Berg 2002). The processed peptides were then presented on
Chimera to determine if CDR3s would present obvious chemical interactions, and it was
determined that no obvious steric clashes existed between the top CDR3s and the
peptides (Fig 2.11). This indicates that these presented peptides may be capable of
binding to CDR3 regions and causing downstream T cell activation.

Table 1.5 Donor 8 Top CDR3s for TCR  Subunit after Coculture with peptide-loaded
DCs
CDR3 Rank Amino Acid Sequence Number of Clones
1 CASSLGGDQPQHF 2607
2 CASSPTSGNYNEQFF 2230
3 CASSLLSPTKQYF 2158
4 CASSYDGVQETQYF 2070
5 CASSLARETQYF 1778
6 CASSQSNLAGPPYNEQFF 1602
7 CASSFGPFGGNTIYF 1433
8 CASSQSGFTSYNEQFF 1410
9 CASSFRPSTDTQYF 1401
10 CASSSGQGKVFNQPQHF 1272

62

Table 2.6. Donor 8 Top CDR3s for TCR  Subunit after Coculture with peptide-loaded
DCs
CDR3 Rank Amino Acid Sequence Number of Clones
1 CATASLVSLAGGTSYGKLTF 9990
2 CAARGYSTLTF 7597
3 CAASSSGNTGKLIF 5029
4 CILRSGNTGKLIF 3314
5 CAYRSGNYGGSQGNLIF 2878
6 CILRTGGYQKVTF 2792
7 CALAATGRRALTF 2633
8 CATDAITGRRALTF 2602
9 CAVKFFTGGGNKLTF 2362
10 CAPARYHARLMF 2361

Table 2.7. Donor 9 Top CDR3s for TCR  Subunit after Coculture with peptide-loaded
DCs
CDR3 Rank Amino Acid Sequence Number of Clones
1 CASSQPAPNEQFF 1260
2 CASSTSYNEQFF 1095
3 CASSTGGGGEQYF 1011
4 CASKVAGGYEQFF 579
5 CASSYSRETQYF 543
6 CASSGRLGTEAFF 521
7 CASSPRDTHYEQYF 467
8 CASSSGQATYEQYF 452
9 CASWAGTNTEAFF 445
10 CASTANSGTEAFF 444

63

Table 2.8. Donor 9 Top CDR3s for TCR  Subunit after Coculture with peptide-loaded
DCs

CDR3 Rank Amino Acid Sequence Number of Clones
1 CAASKGFGNVLHC 1893
2 CAVDPAGYALNF 1585
3 CVVTAKAAGNKLTF 1554
4 CAQGQGAQKLVF 1420
5 CAAKAAGNKLTF 1345
6 CVVSVLRYMGGGADGLTF 1036
7 CAVKLGTNAGKSTF 1029
8 CAMNSGGSNYKLTF 942
9 CAVRPGSNDYKLSF 883
10 CAARNTGGFKTIF 862

Table 2.9. Donor 10 Top CDR3s for TCR  Subunit after Coculture with peptide-loaded
DCs
CDR3 Rank Amino Acid Sequence Number of Clones
1 CALGSLAGNNRKLIW 4249
2 CSVQDGYEQYF 4023
3 CSASKDSQETQYF 3159
4 CSARGEIGETQYF 1785
5 CASSWTSGLTDTQYF 1507
6 CASSQAFDRGQYF 1300
7 CASSSASPGQGLGYTF 1148
8 CASSSRGQGTNEKLFF 1132
9 CASSLVASGTYQETQYF 1090
10 CASSLTGGYEQYF 1067

64

Table 2.10. Donor 10 Top CDR3s for TCR  Subunit after Coculture with peptide-loaded
DCs
CDR3 Rank Amino Acid Sequence Number of Clones
1 CGTELLKDTGANNLFF 13441
2 CAAVDTGGFKTIF 9095
3 CALTGGGNKLTF 7387
4 CAVTNQAGTALIF 6418
5 CASFSDGQKLLF 6417
6 CAVNNARLMF 4038
7 CAVEDGSSGSARQLTF 3742
8 CAVFYSSASKIIF 3583
9 CATDAGSNDYKLSF 3569
10 CAYRSAQGAQKLVF 2972

Figure 0.11 Chimera Binding of CDR3 regions and Peptides Corresponding with FLT3-
ITD Peptides

65
2.4. Discussion
Due to the limited amount of T cells that can be taken from patients with AML at
diagnosis, it was difficult to perform a detailed analysis on ITD-reactive T cells. In addition,
T cells from patients with AML exhibit abnormalities and defects that may prevent the
ability of measure T cell activation and expansion in vitro (Le Dieu et al. 2009). As a
result, it is crucial to explore alternate allogeneic lymphocyte sources such as PBMCs
from healthy donors for the presence of FLT3-ITD reactive T cells, as there may be TCRs
with binding affinities capable of inducing T cell activation upon stimulation with the FLT3-
ITD antigen. Once identified, these cells can be used to generate stable T cell populations
and clones that can be used in other patients with similar characteristics and FLT3-ITD
mutations. Currently, studies have indicated that the number of T cells specific for specific
MHC/peptide complexes in both antigen-primed and unprimed circulating lymphocyte
populations can be lower than 1 in 10
6

lymphocytes (Chaux et al. 1999). Therefore,
laboratory techniques to detect as well as expand T cells against FLT3-ITD mutations
must be highly sensitive. To solve this problem, we aimed to utilize a method in which
large numbers of CD8+

T cells can be analyzed for reactivity against FLT3-ITD peptides
and contrasted with those that were treated with FLT3-WT peptides.
We synthesized peptides based on the IEDB algorithm which estimates the best
possible processed peptides that may be presented by FLT3-ITD positive patients with
AML. Using these peptides, we stimulated fresh PBMCs that were isolated directly from
healthy donors to determine the presence of FLT3-ITD responsive T cells. After two
weeks to peptide stimulation, the results were quantified via flow cytometry. Then, we

66
used next-generation sequencing to quantify the overall clonal expansion and diversity of
the T cells to compare and contrast the different populations that were present after FLT3-
ITD or FLT3-WT peptide stimulation. Using R programing, we were able to separate out
the CDR3s that were present in the T cells from multiple patients that were stimulated
with FLT3-ITD peptides in vitro that were not present in the FLT3-WT peptide treated
group. These CDR3s were then imported into the EXSAN docking algorithm to determine
which CDR3’s would have the best binding affinity for the FLT3-ITD peptides presented
on the HLA:02:01 crystal structure. The EXSAN algorithm was able to provide a match
regarding the TCR β that would bind. However, we were unable to determine a potential
TCR α with the ability to bind the FLT3-ITD peptides.
The amount of shared FLT3-ITD-specific clones between the different healthy
donors was at a low frequency, which suggested a low expansion of T cells against the
ITD peptides. Therefore, to amend this issue, we cultured dendritic cells, which are
professional antigen-presenting cells, that can activate the T cells. In addition, we
incorporated an ELISpot assay at the end of the co-culture experiment to detect secretion
of IFN-γ, which provided an evidence of the T cell activation. These findings would support
evidence of T cell activity as well as a more potent clonal expansion that would be further
elucidated during sequencing. Certain peptides were identified in the T cell screen to
induce the secretion of IFN-γ and were selected for further analysis via sequencing of the
RNA from these samples to determine clonal expansion and quantify diversity of the TCR
repertoire. However, we were unable to apply the same algorithm that was previously
used, because no CDR3s were present in only the FLT3-ITD stimulated T cells that were
not present in the FLT3-WT stimulated T cells. Therefore, we used the top CDR3’s that

67
were expanded in the various FLT3-ITD stimulated T cells and used chimera to determine
if they were capable of binding to the corresponding peptides that elicited the secretion
of IFN-γ that was demonstrated previously.
The ELISpot screen conducted for the three patients did not reveal uniform results
that indicate which specific FLT3-ITD or WT peptide would elicit an immune response
from the CD8+ T cells. One possible explanation for this finding is that the donors did not
have the same MHC class I haplotype and therefore did not display the peptides in a
similar manner. For example, susceptibility to certain diseases such as lung cancer and
HIV-1 infection have been linked to certain MHC alleles (Ferreiro-Iglesias et al. 2018,
Fellay et al. 2007). This data indicates that certain MHC alleles are advantageous in the
context of certain diseases and therefore this observation may apply here regarding
FLT3-ITD peptide recognition. In addition, the peptides used for this project were
developed from IEBD, one specific algorithm of many others that are available that may
provide different peptides that may stimulate the CD8+ T cells more effectively. Ultimately,
this would result in a differential response in the amount of IFN-γ secreted by the CD8+
T cells.
Another relevant method that could provide additional insight is in vitro-transcribed
mRNA (IVT-mRNA) combined with electroporation as an antigen format instead of
loading peptides onto DCs (Foster, Barrett and Karikó 2019, Lee et al. 2020). Full-length
mRNA allows for coverage of all epitopes of a given protein and allows for a more donor-
specific peptide processing and presentation. The use of IVT-mRNA would potentially
also circumvent the need to predict and synthesize peptides with an algorithm that
provides estimates. Another advantage of IVT-mRNA electroporation is that mRNA is only

68
transient and bears no risk for insertional mutagenesis and therefore, can be conducted
relatively easily in good clinical practice conditions (Patel et al. 2019, Guan and
Rosenecker 2017).
Another issue that should be addressed is the design of the co-stimulation
experiments. Although the protocol for the development of DCs has been widely used
(Nair, Archer and Tedder 2012, Elkord et al. 2005), it would have been optimal to verify
that the APCs were present via a combination surface markers such as CD11b, CD11c,
and MHC class II prior to coculture (Collin and Bigley 2018, Musella et al. 2020). In
addition, similar studies that were attempting to isolate a functional TCR for a specific
peptide target used smaller amounts of T cells in coculture with DCs (Matsuda et al. 2018,
Yossef et al. 2018). DCs also comprise only 0.1-0.5% of human PBMC composition and
large amounts would be necessary to ensure every T cell in culture could potentially be
activated (Van Voorhis et al. 1982). This would make clonal expansion more obvious after
NGS and allow for the selection of a paired TCR β and TCR α subunits to be tested for
binding against the selected peptides from the ELISpot screen.
Other aspects for consideration include the different T cell subsets that were
isolated. For the experiments described previously, T cells were separated into CD8+,
CD4+, and CD4+CD25+ groups out after peptide stimulation (Fig. 2.4 and Fig. 2.5). In
the context of CD8+ T cells, four subsets have typically been used to describe this
population: naive, central memory, effector memory, effector, and stem cell memory
(TSCM) T cells (Gattinoni et al. 2017, Tsukahara et al. 2016). According to this classification
scheme, upon TCR stimulation via pathogens or neoantigens, T cells will undergo clonal
expansion, and move on to both effector function and memory formation (Khong and

69
Overwijk 2016). Therefore, it is possible that by sorting through the other T cell
populations according to more specific subsets, we may have observed a more obvious
clonal expansion after stimulation with the FLT3-ITD peptides.

Although the results of these experiments provide insight on a FLT3-ITD specific
TCR, further studies are required to explore the efficacy of this research and relate to
improvements in clinical care. The TCR should be cloned into the appropriate viral vector
to be used for transduction of T cells and functional studies including IFN-γ and targeted
lysis of FLT3-ITD positive cells. Furthermore, this method must be evaluated for clinical
efficacy, as other T cell therapies that have demonstrated promise have failed during
clinical trials. Therefore, we conclude that stimulation of CD8+ T cells with DCs loaded
with FLT3-ITD peptides demonstrates an immune response. The top resultant CDR3s
were tested using chimera to determine which ones were capable of binding to selected
peptides.

70
Chapter 3: Midostaurin reduces Regulatory T cells markers in Acute Myeloid
Leukemia

Chapter is published: Midostaurin Reduces Regulatory T Cells Markers in Acute Myeloid
Leukemia. Scientific Reports. 2018. 17544

3.1. Introduction
Due to the prevalence of the FLT3-ITD mutation in AML patients, targeting the
FLT3 pathway through tyrosine kinase inhibitors has become a major focus in clinical
efforts for developing novel therapeutic agents. Currently, a class of drugs called receptor
TKIs have been heavily investigated in patients with FLT3-ITD positive AML and have
demonstrated promising results in clinical trials. Very recently, the first FLT3 inhibitor
midostaurin received FDA approval for treating patients with FLT3-ITD in combination
with standard chemotherapy prior to aHSCT (Fischer et al. 2010). However, one caveat
of this novel therapeutic approach is that the inhibitors are not specific to the FLT3
receptor and may affect other signaling pathways, including those involved in T cell
activation. In fact, previous in vitro studies have indicated that another TKI, sorafenib,
may inhibit proper T cell function (Cabrera et al. 2013). Therefore, if a T cell signaling
pathway is affected in a way that can enhance the GvL effect, it is possible that TKIs can
be used post-aHSCT for therapeutic benefit. In this study, we examined the effect of four
different TKIs sorafenib, midostaurin, tandutinib, and quizartenib on T cell populations in
blood samples obtained from both healthy donors and patients with AML. Assessment of
T cell populations, expression markers and cytokine levels showed that only midostaurin
treatment significantly reduced the regulatory T cells population in the healthy and
leukemic samples. These results indicate that further functional investigations are needed

71
to establish whether midostaurin may have potential benefit or drawback if used in post-
transplant setting.
3.2. Materials and Methods

Patient Samples
Blood samples were obtained from healthy donors or from patients with AML at diagnosis
from Norris Comprehensive Cancer Center at USC. All samples were collected after
obtaining written informed consent. The use of human materials was approved by the
University of Southern California Health Sciences Campus Institutional Review Board in
accordance with the Helsinki Declaration.
Isolation of Peripheral Blood Mononuclear Cells (PBMCs)
PBMCs from Healthy donors were drawn and collected in sterile EDTA tubes (BD
Vacutainer, Franklin Lakes, NJ). PBMCs were isolated by centrifugation over Ficoll-
Paque PLUS density gradients (GE Healthcare, Uppsala, Sweden). Blood was diluted 1:2
in PBS, over- laid on Ficoll lymphocyte separation medium, and centrifuged at 400 × g for
30 minutes at room temperature. PBMCs were collected and washed twice with
phosphate buffered saline (Sigma, St. Louis, MO). The final pellet was resuspended in
20% FBS RPMI and used in the proceeding experiments.
Cell Culture
PBMCs were isolated as previously stated. Immediately after isolation, PBMCs were
cultured in Roswell Park Memorial Institute 1640 1x (RPMI) medium supplemented with
20% fetal bovine serum (FBS) (Gibco, Gaithersburg, MD). In addition, PBMCs were
treated with IL-2 (5 ng/mL) and IL-7 (10 ng/mL) (Life Technologies, Carlsbad, CA), as well

72
as 1 μM of either Sorafenib, Midostaurin, Tandutinib, or Quizartenib. PBMCs were seeded
at 2 million cells per well in a 6 well plate (Genesee Scientific, San Diego, CA), and
allowed to incubate at 5% CO2, 37°C for 72 hours before analysis via flow cytometry or
quantitative polymerase chain reaction.
RNA Extraction
Cells were centrifuged at 1300 rpm for 5 minutes, supernatant was removed, and pellet
was resuspended in 500 μl of TRIzol reagent and allowed to incubate at room
temperature for 2.5 minutes. Subsequently, 200 μl of Chloroform was added to each
sample and shaken vigorously for 15 seconds and allowed to incubate on ice for 15
minutes. Samples were then centrifuged at 15000 × g for 15 minutes at 4 °C. The upper
aqueous phase was then transferred to an RNAse-free 1.5 mL Eppendorf tubes. 200 μl
of isopropanol was added to the samples and incubated on ice for 10 minutes. Then,
samples were centrifuged at 15000 × g for 15 minutes at 4 °C. Once complete, the pellet
was washed with 75% ethanol, and centrifuged at 15000 × g for 15 minutes at 4 °C. The
supernatant was discarded. The pellet was air-dried for 5 minutes and resuspended in 20
μl of DEPC-treated ddH20.
Quantitative Polymerase Chain Reaction (qPCR)
cDNA synthesis was performed with random hexamer primers using the Superscript first
strand synthesis system for qPCR (Invitrogen, Carlsbad, CA). qPCR was carried out
using the 7500 Real Time PCR system and Taqman assays from Applied Biosystems for
analysis of B2M, FOXP3, and Granzyme B. Expression of genes of interest were

73
normalized to B2M, the house-keeping gene. Subsequently, expression was normalized
to that of the negative control sample.
Flow Cytometry Analysis
The T cell population from PBMCs were analyzed via FACS after 72 hours of treatment
with tyrosine kinase inhibitors and IL-2/IL-7. T cell populations were identified through
PerCP-labelled anti-CD3 (eBioscience, San Diego, CA); specific T cell subpopulations
were identified with PE-labelled anti-CD4 (eBioscience, San Diego, CA), PE-Cyanine7-
labelled anti-CD8 (eBioscience, San Diego, CA), APC-labelled anti-CD25 (eBioscience,
San Diego, CA), and FITC-labelled anti-Foxp3 (eBioscience, San Diego, CA). Data was
acquired using the BD LSRII Flow Cytometer (BD Biosciences, San Jose, CA), and
analyzed using FACS Diva software (BD Biosciences, San Jose, CA). Cells were gated
for CD3+, then CD4+, then the percentage of CD4+ CD25+, and finally the percentage of
CD4+CD25+Foxp3+ populations were quantified.
Cytokine Measurement via Meso Scale Discovery Assay
Supernatants from PBMCs treated with tyrosine kinase inhibitors were stored at −80 °C
for analysis. Cytokine ELISAs were performed using electro-chemiluminescent multiplex
assays to determine the levels of four cytokines (IFN-γ, TNF-α, IL-10, TGFβ). Calibration
curves were prepared in the supplied assay diluents, with a range of 17500 to 0.93 pg/ml.
Cytokine concentrations were determined with MSD Workbench 3.0 software (Meso
Scale Discovery, Gaithersburg, MD, USA), using curve fit models (log or four-parameter
log-logistic).
Cell Viability

74
Cell viability was determined by Alamar Blue–based metabolic assay according to the
manufacturer’s instructions (Invitrogen, Carlsbad, CA). At 0 and 72 hours, 10 μl of Alamar
Blue reagent was added to each well containing 90 μl of resuspended cells; and
absorbance (ΔOD570 nm–600 nm) was measured on an automated 96-well
spectrophotometer after color development. In addition, cell viability was also determined
by Trypan Blue stain.
Mouse Studies
C57BL/6 CD45.1 and CD45.2 mice were purchased from Jackson Labs. Briefly CD45.2
mice irradiated on day 0 and were engrafted with 1 x 10
6
FLT3-ITD+/MLL-PTD+
(CD45.1+/CD45.2+) cells on day 1. Subsequently CD45.1mice were sacrificed and
spleens were homogenized. Half of the spleen cells were treated with midostaurin (1μM)
in R20 for one hour, while the other half were given only R20. 5 x 10
6
spleen cells were
engrafted on the mice on day 7 and again on day 14. On day 21, mice were sacrificed
and bone marrow cells were extracted from femurs. In addition, PBMCs were extracted
from blood samples and T cells were extracted from spleens. Finally, results were
quantified via flow cytometry via PerCP5.5-labelled anti-CD3 (eBioscience, San Diego,
CA), FITC-labelled anti-CD45.2 (eBioscience, San Diego, CA), and APC-labelled anti-
CD45.1.
Statistics
The data are presented as mean ± standard error (SE) and were repeated at least three
times. The Student t test was used to determine if the difference in mean between
samples was statistically significant: p < 0.05 was considered significant.

75
Ethics approval and consent to participate. This study was conducted according to
the approved IRB protocol.

3.3. Results

Midostaurin Reduces CD4 + CD25 + FOXP3 + T cell Population in healthy PBMC.
We isolated PBMCs obtained from healthy volunteers and from patients with AML. We
treated cells for 72 hours with either sorafenib, midostaurin, tandutinib, or quizartenib,
each at concentration of 1μM, combined with IL-2 (5 ng/ml) and IL-7 (10 ng/ml). We
used 1μM dose, which is significantly higher than the IC50 calculated for each inhibitor in
FLT3-ITD positive AML cells (MV4-11), but not effective in T cells (T ALL cell lines:
MOLT4 and RPMI8402) (Fig. 10) and is still achievable in blood of patients with AML. T
cell populations (CD3+, CD4+, CD8+ and CD4 + CD25+ cells) were evaluated by flow
cytometry analysis (Fig. 3.1). We found that PBMCs from Healthy donors treated with
midostaurin had a statistically significant decrease in mean CD4+ CD25+ T cell
population when compared with other treatment groups (N = 3, 80% decrease, P < 0.001,
Fig. 3.2). Sorafenib showed a modest decrease in mean CD4 + CD25+ T cells. On the
other hand, treatment with tandutinib and quizartenib did not affect CD4 + CD25+
population. No effect was observed on total lymphocytes, CD3+, CD8+, and CD4+ T cells,
when cells were treated with FLT3 inhibitors (Fig. 3.3). We also treated healthy PBMC
from three different donors with increasing concentrations of midostaurin (0.5 μM, 1 μM,
and 2 μM), and observed a dose response effect of midostaurin on CD4 + CD25 +
FOXP3+ cells (Fig. 3.4). Additionally, intracellular staining of FOXP3 demonstrated a

76
significant decrease of T cells in the midostaurin-treated group compared with the control
group (n = 3, 70–90% decrease, P < 0.001, Fig. 3.5).

Figure 3.1. IC50 of four FLT3 Inhibitors in MV4-11, RPMI8402, and MOLT-4 for A)
Midostaurin, B) Quizartenib, C) Sorafenib, D) and Tandutinib (N=3)
Table S3: Clinical information related to patients treated with midostaurin included in the study.
Patient
#
Age Diagnosis % PB
blasts
% BM
blasts
FLT3
status
Treatement with
midostaurin
Transplant
Status
Months
Post-
Transplant
Treatment
Outcome
Survival-
Months
Survival
Status
Patient
1
75 newly
diagnosed
AML
85% >90% FLT3-
ITD
Cytarabine-midostaurin
at induction
no n/a complete
remission
12
months
alive
Patient
2
48 post
transplant
relapsed
AML
58% >80% FLT3-
ITD
Cladribine-Cytarabine-
midostaurin at induction
yes 27 complete
remission
1.5
months
deceased
(disease
unrelated
cause)
Patient
3
37 newly
diagnosed
AML
57% 60% FLT3-
ITD
midostaurin starting 3
months post transplant
yes 4 complete
remission
11.5
months
alive
Patient
4
71 newly
diagnosed
AML
65% 94% FLT3-
TKD
consolidation AraC with
midostaurin
no n/a complete
remission
6.5
months
alive
Patient
6
41 newly
diagnosed
AML
rare(>1%) 26% FLT3-
ITD
consolidation
cytarabine/daunorubicin
with midostaurin
yes 3 complete
remission
5.5
months
alive

Supplemental Figures:

Figure S1: IC50 of four FLT3 Inhibitors in MV4-11, RPMI8402, and MOLT-4 for A)
Midostaurin, B) Quizartenib, C) Sorafenib, D) and Tandutinib (N=3).
A)
D)
B)
C)

77

Figure 3.2. Representative contour plots of T cell populations from Healthy donors treated
with inhibitors.

78

Figure 3.3. In vitro PBMCs were treated with 4 kinase inhibitors and normalized to IL-2/IL-
7 control population percentages

Figure 3.4. Combined quantification of multiple samples of PBMCs treated with 0.5, 1 and
2uM of midostaurin and normalized to IL-2/IL-7 control population percentages.

79

Figure 3.5. Midostaurin reduces CD4 + CD25 + FOXP3+ population in healthy PBMCs.
In vitro PBMCs were treated with 1μM midostaurin, T cell populations were analyzed by
flow cytometry (A) a representative figure. (B) Normalized to IL-2/IL-7 control population
percent.

Midostaurin Reduces FOXP3 mRNA expression and Modulates T cell Cytokine Activity
in healthy PBMC.
Treatment with midostaurin resulted in a statistically significant decrease in relative
FOXP3 mRNA expression compared with other treatment groups of the healthy PBMCs
(N = 5, 2-fold decrease, P = 0.02, Fig. 3.6A). On the other hand, sorafenib, tandutinib,
and quizartenib did not affect FOXP3 mRNA levels. Then, we measured
A)

80
the levels of interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), and IL-10 in the
supernatants of treated cells and compared it with untreated cells. We found that
midostaurin treatment resulted in significant decrease in IFN-γ, TNF-α, and IL-10 (N = 3.
95–98% decrease, P < 0.001 levels, Fig. 3.6B). Sorafenib showed an increase in IFN-γ
(N = 3, 5-fold increase, P = 0.04), TNF-α (N = 3, 4-fold increase, P = 0.02), and IL-10 (N
= 3. 2-fold increase, P = 0.17) levels (Fig. 3.6B). None of the kinase inhibitors exhibited
significant effect on the TGF β levels compared with that of the control groups (Fig. 3.8A).
The effects of different treatments on cell viability are shown in Fig. 3.6C. T cells express
very low level of FLT3 compared with myeloid leukemic cells. Thus, the effect of
midostaurin on T cell markers is likely a FLT3-independent effect.

81

Figure 3.6. Midostaurin Reduces FOXP3 Expression and T cell cytokines. (A) Effect of
various inhibitors on FOXP3 mRNA expression in healthy PBMCs (N = 5, p < 0.05). (B)
Cytokine levels in picograms/ml were measured in cell treated supernatants and
normalized to IL -2/IL-7 treated group (n = 3 p < 0.05). (C) PBMCs were treated with four
different kinase inhibitors for 72 hours and showed a 20% reduction in viability for both
healthy (N = 3) and AML (N = 5) groups. MV4-11 cells were used as a positive control for
kinase inhibitors and showed over 50% reduction in viability (N = 8).

Midostaurin-treated PBMCs from Patients with AML Display a Decrease in Regulatory T
cells.
To validate these findings in AML; PBMCs isolated from blood of patients with AML were
treated with midostaurin for 72 hours in the presence of IL-2 and IL-7, and compared with
untreated cells. Our initial results obtained via flow cytometry (Fig. 3.7A)-found a
significant decrease in the CD4 + CD25+ T cell population (N = 6, 30% decrease, P =

82
0.001, Fig. 3.7B) in midostaurin treated samples compared with their respective controls.
PBMCs extracted from patients with AML and treated with
midostaurin also displayed a slight decrease in FOXP3 mRNA expression compared to
the untreated PBMCs; however, the difference was not statistically significant (N = 6, 4–
55% decrease, P = 0.30, Fig. 3.7C). In addition, cytokine analysis of supernatants of AML-
PBMCs treated with midostaurin showed a change in IFN-γ (N = 5, 25% decrease, P =
0.002), TNF-α (N = 3, 10% increase, P = 0.42), and IL-10 (N = 2, 50% decrease, P =
0.001) when compared to the control group (Fig. 3.7D). IL-10 levels were below the
detection level of our assay in three patient’s samples. Also, similar to the healthy PBMCs,
TGFβ levels were not significantly changed following midostaurin treatment of AML
samples (Fig. 3.8). Viability assay confirmed that treatment with FLT3 inhibitors
decreased cell viability in AML PBMCs by about 17–33%, however, treatment with FLT3
inhibitors decreased cell viability of MV4-11 cells (an AML cell line that carries the FLT3-
ITD mutation) by more than 50% (Fig. 3.6C). Although the percentage of T cells was very
low in the blood obtained from the diagnostic samples obtained from patients with AML,
the observed trend was similar to that found in healthy PBMC.

83

Figure 3.7. Midostaurin reduces Tregs in AML cells. (A) Representative contour plots of
T cell populations from patients with AML treated with midostaurin. (B) In vitro PBMCs
were treated with one of the four kinase inhibitors and normalized to IL-2/IL-7 control
population percentages (n = 4, p < 0.05). (C) FOXP3 mRNA expression in AML PBMCs
(n = 4) treated ex vivo with 1uM of midostaurin. (D) Cytokine levels in picograms/ ml were
measured in AML cells treated supernatants and normalized to IL-2/IL-7 treated group (N
= 5).
C)

84

Figure 3.8. Midostaurin does not affect TFGβ levels. A) TGFβ levels in picograms/ml were
measured in cell treated supernatants and normalized to IL-2/IL-7 treated group for
healthy patients (n=3, p=0.42) B) TFGβ levels in picograms/ml were measured in cell
treated supernatants and normalized to IL-2/IL-7 treated group for patients with AML (n=5,
p=0.10).

Midostaurin reduces Tregs markers in patients with AML.
Because midostaurin is currently FDA approved for treatment of AML in combination with
chemotherapy, we validated our findings in patients who received midostaurin. We
obtained samples from three patients with AML at sequential times before and after
midostaurin treatment. CD4+CD25+ cells population decreased approximately four
weeks after the initiation of midostaurin treatment compared with time points before the
treatment in two patients (Fig. 3.9B). In one patient, CD4+CD25+ cells were already low
at base line and no change was observed following midostaurin treatment. We also
assessed FOXP3, CD25 and Granzyme B mRNA levels in five patients. We found a
consistent decrease in FOXP3 and CD25 levels and increase in Granzyme B levels in
three out of five patients, the two patients where no significant change observed also had
very low base level of FOXP3 and CD25 mRNA before treatment (Fig. 3.9C–E).

85

Figure 3.9. Midostaurin alters CD4 + CD25 + T cell population and T cell gene expression
markers in patients with AML. (A) Table indicating time points before and after
midostaurin treatment for patient samples. (B) Levels of CD4 + CD25+ population before
and after midostaurin treatment. (C) FOXP3 and (D) CD25 mRNA normalized to CD3 and
CD4 expression before and after midostaurin treatment. (E) Expression change of GZMB
mRNA normalized to CD3 and CD8 mRNA expression before and after midostaurin
treatment.

Treatment of T cells with midostaurin decreases leukemia engraftment in the spleen and
the blood but not bone marrow
CD45.2 C57BL/6 mice were engrafted with FLT3-ITD+ leukemia cells (CD45.1 and
CD45.2 double positive) and promptly engrafted with CD45.1 splenocytes treated with
midostaurin to determine whether this will improve the anti-leukemia effects of engrafted
T cells. The results showed that treatment with midostaurin prior to engraftment of T cells
resulted in lower engraftment of leukemia in the spleen (Fig 3.10A) and blood (Fig 3.10B)
but not the bone marrow (Fig 3.10C). This may suggest that T cells treated with
midostaurin exhibit enhanced anti-leukemia activity compared to T cells treated with PBS.

86
However, treatment with midostaurin did not result in higher T cell engraftment in any of
these sites (Fig 3.10 D-F).

Figure 3.10. Treatment of T cells with midostaurin decreases leukemia engraftment.
Leukemia engraftment determined by flow cytometry in the A) spleen B) blood, and C)
bone marrow. T cell engraftment population percentage in homogenized spleens. T cell
engraftment population percentage in the D) spleen, E) blood, and F) bone marrow
normalized to the leukemia control groups.
Blood Leukemia Analysis Spleen Leukemia Analysis
Mi dostaurin/T cells/Leukemia
T c e lls /L e u k e m ia
L e u k e m ia
0
50
100
150
Spleen Leukemia Analysis
CD45.1+CD45.2+
Average CD45.1+/CD45.2
Population Percentage
*
*
Mi dostaurin/T cells/Leukemia
T c e lls /L e u k e m ia
L e u k e m ia
0
50
100
150
CD45.1+CD45.2+
Average CD45.1+/CD45.2
Population Percentage
*
*
Bone Marrow Leukemia Analysis
Mi dostaurin/T cells/Leukemia
T c e lls /L e u k e m ia
L e u k e m ia
0
50
100
150
CD45.1+CD45.2+
Average CD45.1+/CD45.2
Population Percentage
A) B) C)
Blood Leukemia Analysis Spleen Leukemia Analysis Bone Marrow Leukemia Analysis
Mi dostaurin/T cells/Leukemia
T c e lls /L e u k e m ia
L e u k e m ia
0.0
0.1
0.2
0.3
0.4
0.5
Mi dostaurin/T cells/Leukemia
T c e lls /L e u k e m ia
L e u k e m ia
0.00
0.02
0.04
0.06
0.08
Mi dostaurin/T cells/Leukemia
T c e lls /L e u k e m ia
L e u k e m ia
0.000
0.001
0.002
0.003
D) E) F)
Normalized CD3+
Normalized CD3+
Normalized CD3+

87

3.4. Discussion
FLT3 and FLT3 ligand (FLT3L) signaling has been shown to indirectly expand Treg
through increasing dendritic cell number (Swee et al. 2009). Therefore, it is plausible that
mechanisms responsible for activating FLT3 signaling pathways may also cause an
expansion in regulatory T cells and thus induce a repressive immune response and
immune evasion in leukemic cells. As a result, we hypothesized that inhibiting FLT3
signaling pathways with FLT3 inhibitors would affect T cell populations and particularly
regulatory T cells. Unlike the second generation FLT3 inhibitors, sorafenib and quizartinib,
the first generation FLT3 inhibitors midostaurin and tandutinib are less specific and inhibit
a wide range of tyrosine kinases. Unexpectedly, only midostaurin but not other FLT3
inhibitors resulted in the significant decrease in Tregs both in healthy PBMCs and AML
PBMCs. This suggests that mechanisms other than those mediated by FLT3 signaling
pathways are responsible for this reduction in the Treg. Previous studies have
demonstrated that IL-2 and IL-7 are important for maintaining homeostasis of Treg cells
(Simonetta et al. 2012, Almeida et al. 2002). IL-7 induces the expression of CD25 on the
surface of T cells and expand Treg (Simonetta et al. 2012). Also, IL-2 has been shown to
increase the CD4 + CD25+ T cell population in cancer patients by 4-fold in vivo, as well
as increase their suppressive capabilities in vitro (Ahmadzadeh and Rosenberg 2006). In
addition, there is evidence that induced Tregs (iTregs) require a constant supply of IL-2
for proper development and function (Almeida et al. 2002). Whether midostaurin
interferes with IL-2 and IL-7 signaling pathways and downstream targets is unclear.
Midostaurin inhibitory effects on JAK/STAT and PI3K/AKT signaling pathways which are
also downstream of IL-2 and IL-7 may potentially be a plausible mechanism. Previous

88
studies have demonstrated that PI3K/AKT inhibitors can decrease Treg populations
without affecting other T cell populations (Abu-Eid et al. 2014). STAT5 is crucial for IL-2
response, Treg cell development, and for the expression of FOXP3 (Passerini et al. 2008,
Antov et al. 2003, Burchill et al. 2007). In fact, STAT5 is a transcription factor that also
functions downstream of FLT3-ITD, making it a potential target for midostaurin (Okutani
et al. 2001). Recent studies have reported that tyrosine kinase inhibitors increase the cell
surface localization of FLT3-ITD (Reiter et al. 2017). Although the study did not address
the effect of FLT3 inhibition on Tregs, this suggests a dual effect of midostaurin in
enhancing FLT3-directed immunotherapy of AML. Although the mechanism by which
midostaurin reduces Tregs is not clear, our data indicates possible off-target effects of
this multi-kinase inhibitor on T cell signaling pathways that is differentially specific to Treg
cells. A limitation of the study is the lack of comprehensive profiling of T cell markers, in
order to exclude potential effects of midostaurin on other T cell populations.
We conclude that midostaurin demonstrated a decrease in Treg populations both
in vitro and in some patients with AML. According to our in vivo results, this led to
increased anti-leukemia activity when compared to the control groups with the exception
of the bone marrow. It is possible that T cells were unable to have an impact on the
leukemia cells in the bone marrow because some studies have demonstrated that this
location can serve as an immune-privileged site to certain myeloid-derived cells (Mercier,
Ragu and Scadden 2011). This would prevent the T cells from mounting an appropriate
immune response against the AML cells that are harbored in the bone marrow. In addition,
our results also displayed no change in T cell engraftment compared to the control group,
indicating that more T cell activity was associated with

89
the group that was treated with midostaurin prior to engraftment.
Some of the limitations associated with this study was the lack of in vivo studies to
determine the impact of midostaurin-treated T cells on overall survival and progression
free survival. Because we believe treatment of T cells with midostaurin prior to
engraftment can lead to enhanced anti-leukemia activity, investigations into these
parameters would be appropriate to quantify the benefits of this treatment prior to
investigating in humans. In addition, the mechanism by midostaurin affects Treg markers
has still not been determined, as one study determined that treatment with midostaurin
did not result in decreased functionality of TCR signaling. However, this study used a
concentration of 50nM and therefore it is possible that these same signaling proteins
would be affected at higher concentrations. One proposed experiment to be conducted
to would be to isolate Tregs from PBMCs and treat them with midostaurin to investigate
which Treg-specific pathways are affected.
This suggests that the decreased Treg population may contribute to increased GvL
effect, however, more experiments must be done in the future to determine if this is
beneficial to patients. In light of the recent FDA approval of midostaurin combined with
chemotherapy in patients with AML, these results highlight a novel therapeutic advantage
of this drug that may be beneficial particularly in the post-transplant setting as well as in
combination with immunotherapy (Stone et al. 2017).

90
Chapter 4: Summary and Future Directions
T cell-focused cancer immunotherapy has emerged as a powerful therapy against
cancer. However, it has taken many years of basic science discoveries confirmed by
clinical trials to provide evidence for the impact of modulating the immune system to target
cancer. Although adverse effects have been shown to occur under certain circumstances,
these innovative immunomodulatory therapies are well tolerated when compared to
typical chemotherapeutic agents.
Immunotherapy has emerged as a standard cancer treatment and is currently
divided into different categories. The first category targets the immune checkpoint
blockade and is designed to encourage powerful T cell responses. Targets of this therapy
include CTLA-4 and PD1 proteins, which are utilized by cancer cells to prevent T cells
from functioning appropriately. One example of a CTLA-4 inhibitor is ipilimumab, which
has efficacy in treating malignancies such as melanoma, renal cell carcinoma, and
hepatocellular carcinoma (Waldman et al. 2020). In addition, PD1 inhibitors have been
approved for the treatment of cancers such as small cell lung carcinoma and melanoma
(Ai et al. 2020, Regzedmaa et al. 2019).
Another category of immunotherapy is adoptive cellular therapies, which are
categorized as the infusion of cancer-targeting immune cells into the body. One example
of this therapy is the use of tumor infiltrating lymphocytes (TIL) used for the treatment of
melanoma. One study utilized lymphodepletion prior to TIL therapy in 93 patients with
metastatic melanoma and demonstrated a complete tumor regression in 22% of patients,
whereby 19 were still in complete remission in a follow-up 3 years post-treatment

91
(Rosenberg et al. 2011). Another example of this form of cancer immunotherapy is CAR
T cell therapy, which is treatment that genetically alters a patient’s T cells with an artificial
T cell receptor capable of recognizing and targeting malignant cells. One example of this
therapy used in the clinic is Kymriah, which is a CAR T cell therapy targeting CD19 and
is indicated for relapsed or refractory B cell acute lymphoblastic leukemia (Liu et al. 2017).
AML is one of the most lethal forms of leukemia, which is compounded by the fact
that it has been shown to be difficult to treat. Currently, the combination of cytotoxic
chemotherapy and aHSCT are the only two treatments that have been demonstrated to
cure AML (Döhner et al. 2015a). With the exception of patients with favorable karyotypes,
chemotherapy is limited in its effectiveness due to the development of resistance in a
significant proportion of patients with AML. In fact, not many immunotherapies are
currently available for treating patients with AML, as most surface proteins expressed on
AML blasts are also expressed on hematopoietic stem and progenitor cells. Therefore,
potent immunotherapies against extracellular AML targets such as CD33 have led to
hematologic toxicity (Giles et al. 2001). In addition, the potential for life-threatening GvHD
effects associated with aHSCT limits this procedure to younger and healthier candidates
(Döhner et al. 2015a). Given the current lack of therapeutic options, it is important to
develop novel therapies that are applicable to patients classified as high-risk AML
subtypes that can work in conjunction with established treatments in a favorable way.
Although the current knowledge of the role of the immune system in controlling
AML is incomplete, it is well established that AML cells, display epitopes on their surface
that can activate immune responses (Boyer et al. 2000). In fact, the GvL effect associated

92
with aHSCT has shown that the immune system has the capacity to specifically eradicate
the leukemic cells that express particular antigens without damaging normal healthy cells
(Greiner et al. 2005). Therefore, by identifying AML-specific or -associated target antigens
and combining this knowledge with recent technological advances such as immune
checkpoint blockade and adoptive T cell therapy, it is possible to enable antigen-specific
immunotherapy as a viable therapy to cure AML (Gattinoni et al. 2017, Kadowaki and
Kitawaki 2011). Although late phase clinical trials have not yet been concluded, several
leukemia antigens have already demonstrated their potential clinical value in phase I trials
in patients AML (Anguille, Van Tendeloo and Berneman 2012, Greiner et al. 2012). Over
the past several years, an increasing number of AML antigens have been implicated in
the GvL effect associated with aHSCT or DLI, indicating their therapeutic relevance in the
context of passive immunotherapy (Smits et al. 2011).
Evidence from multiple studies indicate that certain T cell populations play a crucial
role as effectors of cellular cancer immunity and therefore, recent cancer research has
focused on the development of T cell-based immunotherapies (Barrett and Le Blanc
2010, Acheampong et al. 2018). However, the success of any T cell based
immunotherapeutic strategy critically depends on the chosen target antigen. Neoantigens
associated with leukemia such as the FLT3-ITD mutation are good potential targets for
immunotherapeutic interventions with potential for curative modality. By inducing an anti-
FLT3-ITD specific T cell response via either the adoptive transfer of the appropriate T
cells or by therapeutic vaccine, it can elicit an immune response capable of targeting the
malignant cells. However, it should be noted that because the ITD mutation is a repeat

93
sequence of the basic structure for the juxtamembrane domain of the FLT3 receptor and
therefore will not be an easy target for immunotherapy.
Due to the altered proliferation and weaker lytic capacity of T cells after aggressive
chemotherapy treatment in patients with AML, it is advised that efforts to identify FLT3-
ITD specific T cells should be derived from allogeneic sources (Le Dieu et al. 2009, Knaus
et al. 2018). More specifically, they should be utilized for the generation of ITD-specific T
cells, as healthy donors have not undergone intensive chemotherapy regiments, likely
have proper thymic function, and a more diversified T-cell repertoire (Hakim et al. 2005,
Onyema et al. 2015). However, one of the caveats of utilizing healthy donors is that the
frequency of leukemia-reactive T cells is theoretically low. Despite this, prior studies have
shown that T cell responses against several LAAs such as WT1, PRAME, RHAMM, and
others can be expanded from the peripheral blood of healthy donors (Rezvani et al. 2007,
Weber et al. 2009, Griffioen et al. 2006, Quintarelli et al. 2008, Grube et al. 2007).
Here, we demonstrated that certain FLT3-ITD peptides can be used to elicit an
immune response via ELISpot assay. Furthermore, we attempted to quantify the clonal
expansion of the T cells responsible for this immune response via sequencing and use of
diversity indices. We used in silico modeling to determine which of the most abundant
TCRs were capable of binding to the mutated FLT3-ITD peptides. However, future
experiments are required to verify these results, as the DNA for these TCRs should be
cloned into a viral vector and used to transduce other T cells. These T cells should be
tested to determine functionality against FLT3-ITD mutations. Based on the preclinical
evidence generated thus far, it is possible that this approach may be used in the clinic in

94
the future to target FLT3-ITD as well as other personalized mutations. The work here can
lay the foundation for a platform that can create future engineered TCRs to target
neoantigens from other cancer cells.
In addition, the targeting of quiescent and drug resistant leukemia cells is an
important goal regarding the improvement of survival and the curing of patients with AML
(Zhang, Gu and Chen 2019). Currently, small molecules such as TKIs appear to be a
therapeutically useful treatment for patients with FLT3-ITD

AML, however, they cannot
reliably eliminate minimal residual disease according to previous studies (Giles et al.
2003, Metzelder et al. 2010). However, certain research groups have demonstrated that
treatment with sorafenib post aHSCT can synergize with GvL effects after to induce
durable remissions in FLT3-ITD positive patients with AML (Metzelder et al. 2012).
Recently, the TKI midostaurin has obtained FDA approval as a FLT3 inhibitor for
pre-transplant patients with FLT3-ITD in combination with standard therapy. In addition
to their multi-kinase activity which may affect T cell signaling, FLT3-inhibitors induce
apoptosis of malignant cells which may also enhance antigen presentation to activate T
cells. Considering the increased clinical use of FLT3-inhibitors in patients with AML, and
the limited clinical benefit derived from their use as single agents, understanding how
FLT3-inhibitors affect T cell population and function is needed to facilitate and support
their observed clinical benefit. We examined the effect of four different FLT3 inhibitors
(midostaurin, sorafenib, tandutinib, and quizartenib) on T cell populations in PBMC
obtained from healthy donors and from patients with AML. Midostaurin exhibited a
significant decrease in CD4 + CD25 + FOXP3+ T cell population and FOXP3 mRNA

95
expression in healthy and AML PBMCs. Similarly, samples collected from patients with
AML treated with midostaurin showed a reduction in Treg markers. IFN-γ, TNF-α, and IL-
10 levels were also reduced following midostaurin treatment.
Future experiments should investigate the mechanism by which midostaurin
affects Treg populations. Certain studies have demonstrated that midostaurin can
modulate T cell populations via interruption of Lck signaling. Treating purified Tregs with
midostaurin to detect p-Lck levels may be an appropriate method for analyzing
midostaurin’s effects on T cell populations (Wolleschak et al. 2014). Considering the FDA
approval of midostaurin for use in patients with AML in the pre-transplant setting, our
findings may have important clinical implications, as they provide the rationale for
functional investigation of the use of midostaurin in post-transplant patients.
In conclusion, in this work, I established the feasibility of our developed strategy
for the identification of TCR-neoantigen pairs which leverages the depth of the TCR
sequencing approaches in addition to the structural docking prediction algorithms. With
this approach I identified TCR candidates that could be used to target FLT3-ITD mutations
based on the peptides provided by the IEDB algorithm. In addition, I determined that
midostaurin can affect Treg population markers as well as modulate certain T cell
cytokines. Additional experiments should be conducted to further validate these findings
and support the potential clinical implications of this data. Taken together, these findings
highlight the therapeutic potential of harnessing the immune system to target and treat
AML.

96
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Abstract (if available)

Abstract Acute Myeloid Leukemia (AML) is a hematologic malignancy characterized by great heterogeneity in the molecular genetic aberrations and clinical outcome. Unfortunately, with current induction and consolidation chemotherapy, the 5-year survival rate is only about 30%. Currently, the only treatment that has a curative potential in AML is allogeneic hematopoietic stem cell transplant (HSCT), in which stem cells are collected from a donor and transplanted into a patient to restore the normal hematopoietic function. One of the major benefit of HSCT is the emergence of graft vs leukemia (GvL) effect, where the T cells from the donor marrow can remove residual malignant cells thus, curing the patient of AML. T cells identify specific neoantigens associated with AML due to the specificity of their T cell receptors (TCR). ❧ Because T cells play an important role in the GvL effect, better understanding of the T cell repertoire and function in AML is necessary to enhance this phenomenon. FLT3-ITD, a common mutation in AML (30% normal karyotype patients with AML), is associated with poor outcome and thus presents a potential neoantigen that can be targeted by T cells. In fact, due to its prevalence in patients with AML, many tyrosine kinase inhibitors (TKIs) have been developed to target this mutation. Of these TKIs, midostaurin is the first in its class to receive FDA approval for treatment of AML in combination with standard chemotherapy in the pre-transplant setting. Although this TKIs can be effective, AML can develop resistance, thereby limiting the efficacy of this treatment. However, because multiple studies have demonstrated the effect of TKIs on T cell signaling pathways, it is possible that they can positively affect outcomes of immunotherapy. Therefore, it is possible that TKI treatment of patients with AML could affect T cells in a way that may lead to a synergistic response against AML blasts. ❧ Therefore, to study potential immunotherapeutic strategies against AML, this study has two main objectives: 1) to identify a FLT3-ITD reactive T cell receptor and 2) determine if TKIs will enhance the effect of GvL in cytotoxic T cells. To achieve these objectives, I propose the following aims: aim 1: develop a TCR based prediction platform that enables the identification of TCR clones that are specific to the FLT3-ITD neoantigen and aim 2: assess the effect of TKIs on T cell populations and function. ❧ In order to develop a platform to identify TCR clones against FLT3-ITD neoantigens, we selected FLT3-ITD sequences previously found in patients with AML. Using Immune Epitope Database and Analysis Resource, T cell epitope prediction and T cell epitopes-MHC binding prediction tools we generated peptides with high affinity to the top three most common HLA alleles. FLT3-ITD and FLT3-WT peptides were synthesized. Peripheral blood mononuclear cells (PBMC) from healthy donors were pulsed with the peptides pool in the presence of IL-2 and IL-7 and cultured for one week. Cells were analyzed by flow cytometry to assess changes in T cell populations. Two of the samples were magnetically enriched for CD3 positive cells (T cells) and then further sorted into the following T cell populations: CD8+, CD4+ and CD4+ CD25+. RNA was extracted from the collected cells, and 5’Race RNA based NGS was performed for TCRA and TCRB repertoire. Sequence reads were analyzed using MiXCR and “tcR” software. We compared the TCRB diversity and the V and J segment utilization difference between cells pulsed with FLT3-ITD and FLT3-WT peptides using the Inverse Simpson index (IS) and Jensen Shannon Divergence index (JSD), respectively. We also compared the TCRA and TCRB clonal expansion between FLT3-WT and FLT3-ITD peptide stimulated cells. In a second series of experiments, I also co-cultured T cells with isolate dendritic cells from the same healthy donors preloaded with either FLT3-WT or FLT3-ITD peptides. the co-culture and T cell stimulation was followed with measurement of T cell activation via detection of interferon-γ (IFN-γ) ELISpot assay. TCR-seq analysis was performed also on expanded T cells and top 10 CDR3s were tested in silico to detect binding between TCRs and FLT3-ITD peptides. It was estimated that resulting CDR3s provided no obvious steric clashes with processed peptides and would likely allow for T cell activation. ❧ In order to assess the effect of TKIs on T cells phenotypes and functions, T cells were obtained from healthy donors, and stimulated with four different TKIs (sorafenib, tandutinib, midostaurin, and quizartenib). Treatment with midostaurin but not the other three TKIs resulted in a significant decrease in CD4 + CD25 + FOXP3+ T cell population and FOXP3 mRNA expression in healthy and AML PBMCs. Similarly, samples collected from patients with AML treated with midostaurin showed a reduction in Tregs markers. IFN-γ, tumor necrosis factor-α (TNF-α), and IL-10 levels were also reduced following midostaurin treatment. My findings provide evidence that midostaurin may enhance the GvL effect via modulating the T cell population, repertoire, and function. Overall, this work explores the development of a platform that can incorporate information regarding the structure of the TCR and peptide/major histocompatibility complex to optimize cancer immunotherapy as well as discuss the possibilities of TKI effects on T cells to enhance the GvL effect.

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Asset Metadata

Creator Gutierrez, Lucas (author)

Core Title Investigating the effects of T cell mediated anti-leukemia activity in FLT3-ITD positive acute myeloid leukemia

School School of Pharmacy

Degree Doctor of Philosophy

Degree Program Clinical and Experimental Therapeutics

Publication Date 04/20/2021

Defense Date 11/20/2020

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

Tag midostaurin,OAI-PMH Harvest,T cells

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Alachkar, Houda (committee chair)

Creator Email lkgutier@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-449836

Unique identifier UC11667181

Identifier etd-GutierrezL-9508.pdf (filename),usctheses-c89-449836 (legacy record id)

Legacy Identifier etd-GutierrezL-9508.pdf

Dmrecord 449836

Document Type Dissertation

Rights Gutierrez, Lucas

Type texts

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...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA