University of Southern California Dissertations and Theses

Characterization of upregulated adhesion GPCRs in acute myeloid leukemia

(USC Thesis Other)

Characterization of upregulated adhesion GPCRs in acute myeloid leukemia

PDF

Download

Open document

Flip pages

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content CHARACTERIZATION OF UPREGULATED ADHESION GPCRs IN ACUTE MYELOID
LEUKEMIA

A Thesis
Presented to the Faculty of the Graduate School
of University of Southern California
In Partial Fulfillment of the Requirements for the Degree of
Master of Science
in Molecular Pharmacology and Toxicology

by
Jiawen Yang
May 2019

I

Statement

My first author paper Characterization of Upregulated adhesion GPCRs in Acute
myeloid leukemia is under revision by the journal called Translational Research. And
the abstract under the same title has been accepted by the 60
th
ASH annual meeting
and published online by the American Society of Hematology (Yang et al., 2018). The
content of this Master thesis will cover most part of the content in that paper and
abstract. A part of the analytical methods were adapted from a previous published
paper in our lab(Wu et al., 2018).

II
Abstract

Adhesion GPCRs have become increasingly evident in cancer research in recent years.
Yet, data supporting the contribution of this family of genes to hematological malignancies,
particularly acute myeloid leukemia are limited. Here, we use publically available genomic
data to characterize the expression of the 33 aGPCRs in patients with AML and examine
whether upregulation of these genes is associated with the clinical and molecular
characteristics of patients. Upregulation in one or more of eight aGPCR genes (ADGRB1,
CELSR2, ADGRD1, ADGRE1, ADGRE2, ADGRE5, ADGRG1, ADGRG3) was
significantly associated with shorter overall survival (OS) (median OS: 11.8 vs 55.4
months; p< 0.0001). Upregulation of these genes was also significantly associated with
worse OS in multivariate survival analysis (HR: 1.73; 95%CI 1.11 2.69; p=0.015) after
adjusting for age, molecular risk, and transplant status. High expression of the eight
aGPCRs was significantly associated with older age ( ≥60; p=0.011). Patients with high
aGPCRs were more frequently classified in the poor molecular risk status group and less
in the good risk group compared with patients with low aGPCRs levels (31% vs 17%
p=0.049 and 14% vs 28% p=0.027, respectively). Via Ingenuity Pathway Analysis (IPA),
we identified the interleukin-8 signaling was one of the most activated pathways in
patients with high aGPCRs expression. Overall, our data suggest that particular aGPCRs
are frequently upregulated in AML and associated with poor clinical outcome. Future
functional and mechanistic analysis are needed to address the role of aGPCRs in
AML(Yang et al., 2018).
III
List of Abbreviation

GPCR G protein-coupled receptor
aGPCR adhesion G protein-coupled receptor
GDP guanidine diphosphate
GTP guanidine triphosphate
β2AR β2 adrenergic receptor
PKA cAMP-dependent protein kinase A
GPS GPCR proteolytic site
EGF epidermal growth factor
LRRs leucine-rich repeats
PMNs polymorphonuclear cells
CNS central neural system
7-TM seven-transmembrane spanning domain
AML acute myeloid leukemia
RBC red blood cell
WBC white blood cell
BM bone marrow
PB peripheral blood
PLT platelet
IPA Ingenuity Pathway Analysis
FAB French–American–British classification
OS Overall survival
DFS Disease free survival
HR hazard ratio
CI confidence interval
iPSCs induced pluripotent stem cells
CSCs cancer stem cells
PSCs pluripotent stem cells
HSPCs hematopoietic stem and progenitor cells
HSCs hematopoietic stem cells
IV
List of Figures
Figure 3.1-1: Distribution of 33 adhesion GPCRs genes in 162 patients with and without
alteration
Figure 3.1-2: Heatmaps of the adhesion GPCR genes
Figure 3.2-1: Overall survival analysis of AML patients associated with 33 adhesion
GPCRs mRNA expression.
Figure 3.2-2: Disease-free survival analysis of AML patients associated with 33
adhesion GPCRs mRNA expression.
Figure 3.2-3: Survival analysis of patients with AML according to eight adhesion
GPCRs mRNA expression.
Figure 3.2-4: Survival analysis of AML associated with 33 adhesion GPCRs mRNA
expression in Metzeler 1 and 2 Leukemia datasets
Figure 3.2-5: Survival analysis of patients with AML according to eight adhesion
GPCRs mRNA expression and stratified by age
Figure 3.3-1: Eight adhesion GPCRs mRNA expression in patients with AML according
to molecular status, risk and FAB classifications
Figure 3.4-1: Correlation expression of the selected eight adhesion GPCRs
Figure 3.5-1: The methylation β value distribution of 109 patients in each of the eight
adhesion GPCR genes
Figure 3.5-2: Associations between methylation and individual adhesion GPCR mRNA
expression
Figure 3.6-1: Pathway analysis of the eight aGPCRs
V
List of Tables

Table 3.1-1: Upregulated and Downregulated population of each 33 genes in TCGA
dataset
Table 3.2-1: Multivariate analysis to assess the association between the combination of
eight aGPCRs expression levels and OS after adjusting for other factors
Table 3.3-1: Clinical characteristics of 173 AML patients according to eight adhesion
GPCRs expression Z-score ≥1
Table 3.4-1: Expression of eight adhesion GPCRs (Z-score ≥1 ）according to the top
mutations present in AML (n=173 patients)
Table 3.4-2: Expression of eight individual aGPCR genes according to the top
mutations presents in AML
Table 3.6-1: Ingenuity pathway analysis of differentially expressed adhesion GPCR
genes
1

Table of Contents

Statement.............................................................................................................................I
Abstract ..............................................................................................................................II
List of Abbreviation .......................................................................................................... III
List of Figures .................................................................................................................. IV
List of Tables ..................................................................................................................... V
Acknowledgement ............................................................................................................. 1
Introduction ........................................................................................................................ 2
1.1. Acute Myeloid Leukemia ....................................................................................................2
1.2 Epidemiology .......................................................................................................................2
1.3 Genomic Landscape............................................................................................................3
1.4 Current treatments and the limitations .............................................................................5
1.5 G protein-coupled receptor as an important drug target................................................7
1.6 The structure, distribution and function of Adhesion G protein-coupled receptor .. 10
1.7 The role of the aGPCRs in solid cancers and in hematological malignancies .......... 12
1.8 The role of aGPCRs in Acute Myeloid Leukemia ........................................................... 14
Materials and Methods .................................................................................................... 16
2.1 Patient’s data ...................................................................................................................... 16
2.2 Gene expression analyses ............................................................................................... 17
2.3 Statistical analyses ............................................................................................................ 17
2.4 Pathway analyses: ............................................................................................................. 19
Results .............................................................................................................................. 20
3.1 Genomic profile of adhesion GPCRs in AML samples ................................................. 20
3.2 Association between eight aGPCRs and shorter overall and disease-free survival . 24
3.3 Association between eight aGPCRs expression and patient primary characteristics
.................................................................................................................................................... 34
3.4 Association between eight aGPCRs expression and patient’s mutational status .... 39
3.5 Adhesion GPCRs are hypomethylated in patients with high gene expression ......... 47
3.6 IL-8 signaling pathway is activated in patients with high aGPCRs. ............................ 50
2
Discussion ........................................................................................................................ 53
References ....................................................................................................................... 58

1

Acknowledgement

First of all, I would like to express my sincere appreciation to my advisor Dr.
Houda Alachkar for her support and guidance for this paper. She helped me for the
whole time ， doing analysis and writing of this thesis. With her help, I have a chance
to make this thesis a journal paper and probably get it published. Also, I would like to
thank my program advisor Dr. Roger Duncan, who always gives me insightful
comments and encourage me to have more critical thinking when I am doing my project.

Besides, I would like to thank my lab mate Sharon Wu, who support me all the
time while I am writing this thesis. She helped me with the multivariate analysis part
and taught me how to conduct Fisher exact test on STATA, which is very useful for
both of my current and future study.

Finally, I would like to thank the Bioinformatics core at the Norris medical library,
University of Southern California and TCGA. University of Southern California School
of Pharmacy Seed Fund. This work was also supported by grant UL1TR001855 from
the National Center for Advancing Translational Science (NCATS) of the U.S. National
Institutes of Health.

2
Introduction

1.1. Acute Myeloid Leukemia
Acute Myeloid leukemia (AML) is a heterogeneous hematological malignancy
characterized by a clonal expansion of abnormal differentiated blasts derived from
primitive hematopoietic stem cell or progenitor cells. Consequences of the proliferation
include the accumulation of immature cells and myeloblasts in the bone marrow and
peripheral blood, instead of the functioned red blood cells (RBC), white blood cells (WBCs)
and platelets (PLT) (Khwaja et al., 2016), which lead to several symptoms including sever
infections, anemia, hemorrhage, even extramedullary disease in some of the patients.
Immediate diagnosis and therapy are necessary for AML patients to avoid the fatal risk
of tumor lysis syndrome or disseminated intravascular coagulation resulting from the rapid
proliferation of malignant blasts (Short et al., 2018).

1.2 Epidemiology
AML is the most common disease in acute leukemias in the USA, accounted for 65.7%
of the all diagnosed individuals, which is around two fold population of Acute lymphoid
leukemia (ALL)(Dores et al., 2012), and accounted for 1.1% of new cancer cases(Institute,
2018). In the USA, acute myeloid leukemia is predominant in male with a median age of
diagnosis at 68 years (Short et al., 2018), and the ratio to female normally ranges from
1.1 to 1.3 in population. The male to female ratio will increase to 1.8 when the age is 80-
84 years old.(Khwaja et al., 2016) (Dores et al., 2012) The common risk factors for AML
include the DNA-damaging agents exposure, for example, cigarette smoke, benzene,
ionizing radiation caused by radiation therapy, and cytotoxic chemotherapy for example,
3
alkylating agents and topoisomerase II inhibitors. Also, first-degree relatives of AML
patients have higher possibility of developing AML or similar malignancy (Khwaja et al.,
2016). Though AML is no longer an incurable disorder since 1970s, with the improvement
in chemotherapy, allogeneic HSCT and supportive care, it is still an obstacle for human
health, only 35%-40% of adult patients who are younger or equal to 60 years old, and
5%-15% of patients who are older than 60 achieve 5 year overall survival (Dohner et al.,
2015).

1.3 Genomic Landscape
AML is a clonal disorder which is characterized by defect in cell proliferation, survival and
differentiation. The transition from physiological status to pathological condition must
involve in multiple changes at molecular level, such as gene mutations, chromosome
translocation, chromosome gains or loss etc.

Given that AML derived from hematopoietic stem cells or progenitor cells, people
speculated that the accumulated mutation involved in the whole division or differentiation
process, eventually cause the AML. Genomic analyses result shows that common AML
mutations such as DNMT3A, TET2, ASXL, occur during the early stage in disease
evolution, within the asymptomatic clonal population(Short et al., 2018). These somatic
mutations can be detected in patients’ bone marrow and peripheral blood before any
symptom shows up. However, AML among all types of cancers, harbors far fewer point
mutations, eight mutations in average are detected by genome-wide sequencing
studies(Vogelstein et al., 2013).With the increasing of the next-generation sequencing,
4
more information regarding the spectrum and gene mutation, expression alternation,
mutual exclusivity, and epigenetic landscape contributing to the biology of the disease
are becoming available. Studies show that more than one mutations are required to
develop AML, and mutations can be grouped into different functional groups; genes that
encoding transcription factors whose function is regulating the cell differentiation and self-
renewal, such as RUNX1 and CEBPA; genes that activating cell signalings which are
related to cell proliferation and survival, such as FLT3, KIT, KRAS, and NRAS; or genes
involved in epigenetic modification of other genes, such as DNMT3AM, TET2, IDH1 and
IDH2.(Khwaja et al., 2016) The Cancer Genome Atlas (TCGA) Research Network have
conducted either whole-genome sequencing or whole-exome sequencing, along with
DNA-methylation analysis, RNA and microRNA sequencing on 200 AML patients’
samples. In which they reported 11 genes among all the detected genes that were
significantly mutated in AML. The 11 genes are FLT3, TP53, WT1, IDH1, IDH2, CEBPA,
RUNX1, NRAS, TET2, NPM1, DNMT3A(Dohner et al., 2015). Among which, FLT3,
NPM1, and DNMT3A are the most frequent mutant genes, they mutated in >25% of
patients(Khwaja et al., 2016). More mutational patterns and mutation related biological
changes in AML are waiting to be revealed and developed as potential therapeutic targets.

Apart from testing the gene mutation at molecular level, karyotyping is frequently being
used as a diagnostic method to identify cytogenetic abnormalities and chromosomal
translocations; Based on the 2008 WHO classification of AML subtypes, there are seven
of them; 1. AML with t(8:21)(q22:q22); RUNX1-RUNX1T1, 2. AML with inv(16)(p13.1q22)
or t(16;16)(p13.1;q22);CBFB-MYH11, 3. APL with t(15:17)(q22:q12); PML-RARA, 4. AML
5
with t(9:11)(p22:q23); MLLT3-MLL, 5. AML with t(6:9)(p23:q34); DEK-NUP214, 6. AML
with inv(3)(q21q26.2) or t(3;3)(q21;q26.2); RPN1-EVI1, and 7. AML with t(1:22)(p13:q13);
RBM15-MKL1 ， among which the first three cytogenetic groups can be used as
diagnostic evidence alone for AML(Khwaja et al., 2016). Importantly, cytogenetic
abnormalities are considered as significant independent prognostic factors in AML(Estey,
2013; Mrozek et al., 2012). Patients are categorized as favorable, intermediate and poor
risk (overall survival) according to the cytogenetic information(Khwaja et al., 2016);
Patients who belong to the subtype t(8;21), inv(16) or t(16;16) usually have a good
prognosis (around 55% can be cured), patients who are cytogenetically normal usually
have an intermediate prognosis (around 40% can be cured), however patients who
belongs to the subtype t(6;9), inv(3) or monosomy 7 (-7) have a very poor prognosis.
Particularly, patients who belong to the subtype t(15;17), have an excellent prognosis
(around 85% can be cured), one of the reasons is that they can be treated efficiently with
all-trans retinoic acid(ATRA) and arsenic trioxide(ATO) (both of the drugs target to
degrade the aberrant protein that is generated from the chromosome translocation and
gene fusion on chromosome 17) (Khwaja et al., 2016).

1.4 Current treatments and the limitations
The current treatments normally include induction therapy and consolidation therapy. For
induction therapy, the most common used chemotherapy is continuous infusion of
cytarabine with an anthracycline, daunorubicin is also being used for induction therapy.
For patients who are younger or about 60 years old, a complete response can reach 60-
85%. While for patients older than 60, complete response rates are inferior (40% to
6
60%)(Dohner et al., 2015). For consolidation therapy, there are several strategies
included conventional chemotherapy and hematopoietic-cell transplantation. For the
latter option, people should concern about the risk of graft-versus-host disease (GVHD).
And for the chemotherapy, a severe cytotoxicity also can’t be ignored(Short et al., 2018).
Since AML are largely exposed in the blood, bone marrow, liver and spleen, the malignant
cells are accessible for drugs to target. There are some monoclonal antibodies targeted
to differentiation makers that are selective for AML, for example, Gemtuzumab
ozogamicin, a CD33 specific monoclonal antibody conjugated with a potent small
molecule calicheamicin. Though it has been withdrawn from the US market because of
the profound toxicity, is still being used in many other countries for treatment or
investigation (Bross et al., 2001; Giles et al., 2001; Khwaja et al., 2016). Other targeted
therapies include inhibitors that targets to abnormal signaling; some receptors or kinase
who can initiate downstream signaling pathway located on the cell surface, such as FLT3,
KIT, JAK2 in tyrosine kinase signaling pathway, MELK, MAPK, mTOR in
Serine/threonine kinases signaling pathway, the mutation of whom cause the abnormal
proliferation or survival features of AML. For example, FLT3, one of the most frequent
mutant gene in AML, encodes the FLT3 tyrosine kinase receptor. Midostaurin and
quizartinib are two kinds of inhibitors of FLT3 and are reported to induce significant
responses in monotherapy separately. Other examples include Ruxolitinib, Selumetinib,
Sirolimus/everolimus targeting to JAKs, MELK (for both MELK1 and MELK2), and mTOR
respectively(Khwaja et al., 2016). Besides the signaling pathway, there come drugs that
targets to epigenetic regulators such as histone deacetylases, for example, Vorinostat as
the first FDA approved drug of this kind, inhibits HDACs and results in the decrease of
7
acetylated histones and acetylated proteins, for instance, transcription factors that are
involved in cell cycle and apoptosis, thus interfere the proliferation of malignant
cells(Schaefer et al., 2009). Also, after the success of immune checkpoint inhibitors in
treating solid tumors, they are being used in hematological malignancies. For example,
Nivolumab and Ipilimumab are two immune checkpoint inhibitors targeted to PD1 and
CTLA4 respectively. Comparing to solid tumors, leukemic cells express several
checkpoint inhibitor receptors as well as ligands making them better potential direct
targets for these therapies(Boddu et al., 2018).

Though it seems that the biology, genetics and antigenic phenotype of AML have been
described well and several targeted therapies have been approved base on them. There
are problems still remain unsolved; for example, no truly specific antigen has been found
on the surface of leukemia cells so far, inhibitors are still not selective enough and the
incidence of unexpected toxicities caused by the off-target effect. Thus, AML is in a heavy
need for novel therapeutic strategies apart from the traditional chemotherapies and
transplantation. The advance of the G protein-coupled receptors (GPCRs) targeted drug
discovery and the accumulated evidence showing the relationship between aGPCRs
deregulation and AML provide novel ideas to cure the disease.

1.5 G protein-coupled receptor as an important drug target

The G protein-coupled receptor (GPCR) superfamily is a class of transmembrane
receptors found in eukaryotes and are characterized by the shared seven membrane-
spanning α-helical segments separated by alternating intracellular and extracellular loop
8
regions(Rosenbaum et al., 2009). They react to numerous stimulants, including growth
factors, hormones, peptide and non-peptide neurotransmitters, ions, odorant molecules,
light and lipid analogues from the surface of cells and through the signals transduction
cause a series of subcellular changes (Yona et al., 2008); The binding of the ligands
activates a conformational change of the receptor and subsequent exchange of guanidine
diphosphate(GDP) for guanidine triphosphate(GTP) and promoting the heterotrimeric G
proteins (composed of α-, β-, and γ-subunits) activation leading to the dissociation of Gα
from the dimeric Gβγ subunits which subsequently results in the modulation of
downstream effector proteins(Bourne et al., 1991). Since GPCRs can sense variety of
stimulants and the GPRC signal transductions are among the significant nodes for
external and internal communication, they regulate diverse physiological processes; For
example, the human β2 adrenergic receptor (β2AR) can regulate several signaling
pathways; it’s coupled with two kinds of G protein, Gαs and Gαi, which regulate adenylate
cyclase differentially. Following the activation of β2AR, Gαs was released to activate the
cAMP/PKA pathway to regulate the activity of several cellular proteins such as L-type
Ca
2+
and β2AR itself. Independently, the activation of β2AR can also lead to the
phosphorylation by a G-protein-coupled receptor kinase (GRK) and subsequent activate
the arrestin/MAP kinase pathway (Rosenbaum et al., 2009). It’s been said that GPCRs
acts like rheostas rather than on-off switches; even for a certain GPCR, the physiological
function can be different under different cellular environment or when people applied
different ligands(O'Hayre et al., 2014).

9
The genes coding for GPCRs are distributed on every human chromosome, there are at
least 800 GPCRs genes that have been identified from the human genome and are
important for regulating physiology. Based on the sequence and structural similarity,
GPCRs in vertebrates are divided into five families (Fredriksson et al., 2003); rhodopsin
(family A), secretin (family B), glutamate (family C), adhesion and Frizzled/Taste2. And
based on the classification from the guidelines of the International Union of Basic and
Clinical Pharmacology, GPCRs are divided in to four main groups; Class A, rhodopsin-
like receptors; Class B, secretin-like receptors; Class C included the metabotropic
glutamate and pheromone receptors, and Class D is comprised of frizzled receptors. In
the second kind of classification, the Adhesion GPCRs are classified as sub-class B2
under the Class B family. Based on the analysis, among all the families, Class A is the
largest and best-studied family, accounts for around 80% of the developed GPCRs
targeted drugs(Hauser et al., 2017). There is no precise number known for how many
GPCRs expressed in humans. However, it has been revealed that individual human
tissues, including cancer cells, express more than 100 different GPCRs(Insel et al., 2012).
Due to the expansion of sequencing technologies, combined with the pharmacological
experiments, the expression of GPCRs and their roles in the different tissues became
available.

To sum up, as the GPCRs form the largest human membrane protein family, regulate
numerous diverse physiological processes, can be drugged at the cell surface, and take
the advantage of the developing of structural biology and biotechnology breakthrough,
this kind of receptors have become the hottest targets among all the receptors in
10
pharmacology studies, and across a wide range of disorders. Notably, drugs that target
GPCRs account for nearly one-third of the global market of therapeutic drugs (Hauser et
al., 2017). As of November 2017, the number of the US or European Union approved
GPCRs targets for drugs reached to 134, among which 128 GPCRs are listed in the FDA
orange book. Based on the number of available drugs in the markets, the most frequent
targeted GPCRs are those known as histamine (HRH1), serotonin, dopamine, opioid, and
adrenergic receptors(Sriram and Insel, 2018).

1.6 The structure, distribution and function of Adhesion G protein-coupled
receptor

Adhesion G protein-coupled receptors (aGPCRs) family is second largest one of the five
GPCRs families, consisting of 33 different aGPCRs in human (Lagerstrom and Schioth,
2008). Adhesion-GPCRs are characterized by their large extracellular region that is linked
to a TM7 (seven-span transmembrane) moiety via a GPCR proteolytic site (GPS)-
containing stalk region. Their extracellular region contains various domains such as
epidermal growth factor (EGF)-like, leucine-rich repeats (LRRs), lectin-binding,
immunoglobulin (Ig) and cadherins, all of which are known to be involved in protein-
protein interaction and cell adhesion (Lagerstrom and Schioth, 2008; Paavola and Hall,
2012; Yona et al., 2008). Thus, they are hypothesized to potentially play a dual role in
cellular adhesion and signaling.

11
The 33 adhesion GPCRs are further divided into 9 subfamilies based on the similarity in
structure. Their expressions were reported to be restricted to specific cell types which
implies that these receptors exert different functions in distinct physiological
systems(Yona et al., 2008). Under the ADGRE and ADGRG sub-families, three gene
clusters have high expression level in the immune system (Lin et al., 2017).
CD97(ADGRE5) and EMR1-4(ADGRE1-4) which contain GPS motif and EGF-like repeat,
all belonging to ADGRE sub-family, located on chromosome 19p13.1(EMR2, EMR3 and
CD97 and chromosome 19p13.3 (EMR1 and EMR4) ) as two clusters, are expressed
mainly by leukocytes, and are reported to contribute to myeloid cell migration; both EMR2
(ADGRE2) and CD97(ADGRE5) ligation increase polymorphonuclear cells (PMNs)
adhesion and migration in the recruitment process(Kwakkenbos et al., 2005; Stacey et
al., 2003). The third gene clusters located on chromosome 16q21, encode the human
ADGRG sub-family members; GPR56, GPR97 and GPR144. GPR56 particularly, is a
versatile marker for all cytotoxic lymphocytes, and its upregulation is reported to
associated with ageing(Lin et al., 2017; Peters et al., 2015). BAI-1(ADGRB1) functions as
a phagocytic receptor for apoptotic cells on macrophage, promotes the phagocytosis
(Park et al., 2007). In neural system, CELSR1-3(ADGRC1-3) receptors with the extremely
long extracellular regions and special localization (cell-cell boundary, where it is thought
to undergo homotypic interaction), are highly expressed in the central neural system
(CNS) and coordinate neuronal development(Curtin et al., 2003; Takeichi et al., 2000;
Tissir et al., 2005). In hematological system, there are about one third of the 33 human
aGPCRs are expressed in hematopoietic stem, progenitor or mature cells, where they
define distinct cellular population. And 11 of the aGPCR (LPHN1, EMR1, EMR2, CD97,
12
GPR124, GPR125, CELSR3, GPR114, GPR126, GPR56 and GPR97) are relative highly
expressed in HSC-enriched CD34 CD45RA cord blood cells with reads per kilo base per
million mapped reads (RPKM) >1. The expression level of each aGPCR changes
differently with cell differentiation (Lin et al., 2017; Maiga et al., 2016).

Studies have showed that truncating the N termini of several distinct adhesion GPCRs
result in constitutively active receptors. Moreover, several genetic studies showed that
mutations or knockout of certain adhesion GPCRs cause serious physiological changes,
and potentially contribute to certain diseases (Koirala et al., 2009; Li et al., 2008; Piao et
al., 2004; Weston et al., 2004). Also, in different tissues, these receptors exhibit highly
discrete distribution patterns as mentioned before. Given the reasons above, the
adhesion GPCRs represent a huge family of potentially important drug targets that have
not yet been fully developed (Paavola and Hall, 2012).

1.7 The role of the aGPCRs in solid cancers and in hematological malignancies

The accumulation of gene mutations and deregulation eventually results in an abnormal
cell growth, leading to tumor formation. Invasion and metastasis abilities are two very
important cancer hallmarks, and are used to evaluate the tissue malignancy (Aust et al.,
2016). The role of adhesion GPCRs in cancer has gained increasing attention in recent
years. All aGPCRs share similar structures and several similar functions relating to
migration, adhesion, and polarity that contribute to cancer hallmarks such as metastasis,
invasion, and angiogenesis (Aust et al., 2016). Alternations in homeostasis of several
13
aGPCRs have been observed in cancer; the mRNA expression data in the Cancer Cell
Line Encyclopedia (CCLE) shows that several aGPCR genes such as ADGRA3
(GPR125), ADGRB2 (BAI2), ADGRC2 (CELSR2) and ADGRC3 (CELSR3),
ADGRE5(CD97), ADGRG1(GPR56) have higher mRNA expression level in hundreds of
cancer cell lines comparing to their corresponding normal cells, which is consistent with
increased studies reporting on the deregulation of aGPCRs in several types of cancers
(Aust et al., 2016). Of all the aGPCRs, the roles of CD97(ADGRE5), and BAI1 (ADGRB1)
in cell migration, cell invasion(Aust et al., 2002; He et al., 2015; Liu et al., 2005; Steinert
et al., 2002) and angiogenesis(Fukushima et al., 1998; Hatanaka et al., 2000; Kang et al.,
2006; Kaur et al., 2005; Kaur et al., 2009; Kudo et al., 2007; Lee et al., 2001; Nishimori
et al., 1997) in cancer has been the best elucidated(Aust et al., 2016). It has been
reported that CD97 has the pro-tumorigenic activity; the extracellular N-terminus-
fragments of CD97 can be released from the receptor and join the body circulation. As
the fragments circulating in body, it may participate in effects far away from the original
site and modulate heterotypic tumor stromal interaction. CD97 also stimulates
angiogenesis through binding integrin 5 1 and v3 in vivo (Wang et al., 2005).
Compare to the normal tissues, malignant tissues usually show an increased and/or in
expression of CD97. Exceptions only show in cell lines derived from neuroblastoma and
small cell lung cancer. In fibrosarcoma cell line, CD97 overexpression was found to
stimulate cell motility, increase the proteolysis of matrix metalloproteinases, and the
secretion of chemokines in vitro, and promote tumor growth in mice model(Galle et al.,
2006). In prostate cancer, CD97 expression is upregulated, and its heterodimerization
with lysophosphatidic acid receptor (LPAR) lead to enhanced RHO-GTP level, which is
14
relevant to the metastasis of prostate cancer. Whereas a decreased bone metastasis was
found in the CD97 knockdown prostate cancer cell(Ward et al., 2011).

1.8 The role of aGPCRs in Acute Myeloid Leukemia
Recent published transcriptomic analysis of GPCRs has identified several aGPCRs
genes—CD97(ADGRE5), EMR1(ADGRE1), EMR2(ADGRE2) and GPR114(ADGRG5)—
that were deregulated in AML compared with normal cells (Maiga et al., 2016). Other
studies have shown that both CD97(ADGRE5) and GPR56(ADGRG1) contribute to the
engraftment and migration of primary leukemia cells in mice(Daria et al., 2016; Pabst et
al., 2016; Wobus et al., 2015). CD97 is reported to enhance AML cells (MV4-11 cell line)
migration toward the fetal calf serum and lysophosphatidic acid (LPA) (Wobus et al.,
2015). Besides, CD97 has also been identified as a leukemic stem cell marker in AML
(Bonardi et al., 2013) and was reported to be associated with FLT3-ITD (Wobus et al.,
2015). The potential role of GPR56 in AML was substantiated by additional in vivo and in
vitro studies (Daria et al., 2016; Maiga et al., 2016; Pabst et al., 2016; Saito et al., 2013).
GPR56 accelerates myeloid leukemogenesis in collaboration with HOXA9 was found to
completely change the molecular phenotype of Hoxa9-transduced cells affecting G
protein-coupled receptors and integrin signaling (Daria et al., 2016). Another study
reported that the down regulation of the GPR56 in EVI1 highly expressing leukemia cells
can decrease the cellular adhesion and growth ability, suggesting that GPR56 has the
potential to become a novel molecular target in certain type (EVI1
high
) of leukemia(Saito
et al., 2013). Yet the signaling pathways of aGPCRs and the molecular mechanisms
underlying aGPCR signal transduction remain unknown.
15

Because of the collected evidence of the role different aGPCRs play in AML and the
functional and structural similarity among them, we speculated that aGPCRs are
deregulated in AML and that upregulation of aGPCRs may affect AML progression via a
common signaling pathway. Here, we performed a comprehensive analysis of the
expression of 33 aGPCRs in patients with AML and assessed the association between
each gene and patients’ clinical outcome. Genes that were significantly associated with
shorter overall survival were combined into one group for further analysis to test their
association with patients’ clinical and molecular characteristics as well as clinical outcome.
To gain insight into the mechanistic role of aGPCRs in AML, we conducted Ingenuity
Pathway Analysis (IPA) to identify potential pathways common among aGPCR genes that
are associated with poor survival.

16
Materials and Methods

2.1 Patient’s data

We analyzed data from the cancer genome atlas (TCGA) dataset of 173 patients with
AML with complete clinical and RNA expression data for each patient. Patients in this
dataset were all diagnosed and received treatment according to National Comprehensive
Cancer Network (NCCN) guidelines between November 2001 and March 2010 (Ley et
al., 2013), as well as the stratification of molecular risk. The subtype classification of these
patients were assigned based on the French-American-British (FAB) classifications.
Among the 173 patients, 91 patients were aged < 60 years old, which took 52.6% of the
total, and 82 patients were aged ≥60 years old, which took 47.4% of the total patients.
The treatments for patients who are in the intermediate and poor cytogenetic risk groups
were not exactly same; allogeneic stem cell transplants were started differently,
depending on whether the surgeries were applicable or if there were available matched
donors. The most frequent somatic mutations in AML and genes expression level were
assessed in the 173 patients, such as IDH1, IDH2, NPM1, FLT3, TET2. Other information,
for example, patients’ survival data, clinical characteristics, gene expression (Z-score),
mutations, and gene methylation β values were downloaded from the TCGA database on
July 3
rd
, 2018 via cBioPortal (Cerami et al., 2012; Gao et al., 2013).

To analyze differential expression of the aGPCR genes in normal tissue vs AML we
utilized the Andersson Leukemia dataset (GSE7186) (Andersson et al., 2007) with 6
samples from healthy bone marrow and 23 samples from patients with AML, the
17
Haferlach Leukemia dataset (Haferlach et al., 2010) with 74 samples from healthy donor
PBMCs and 542 samples from patients with AML and the Valk Leukemia dataset
(GSE1159) (Valk et al., 2004) with 5 samples from healthy donor bone marrow, 3 samples
from CD34+ PBMC and 285 samples from patients with AML. We also validated our
survival analysis in two Metzeler (GSE12417 n=79 and n=163) (Metzeler et al., 2008)
Leukemia dataset and the Bullinger (GSE425 n=119) (Bullinger et al., 2004) Leukemia
dataset. All these datasets were downloaded from Oncomine.

2.2 Gene expression analyses
Analyzed RNA sequencing data in TCGA study were downloaded via cBioportal(Cerami
et al., 2012; Gao et al., 2013). We dichotomized patients into two groups based on
aGPCRs expression values (Z-scores); Z ≥1 and Z<1. Survival analysis were performed
using this cut-off for each of the 33 genes, genes associated with shorter survival outcome
(N=8) were included in the expanded combined analysis. In combined analysis, patients
who had at least one of the eight genes whose Z score ≥1 were categorized as high
expression; patients who did not have any of the eight genes whose Z score <1 were
characterized as low expression.

2.3 Statistical analyses

The overall survival (OS) was defined as the time period of patients from the date of
diagnosis till the date of death due to any reason. The disease-free survival (DFS) was
defined as the time length after diagnosis and removal from the study due to signs or
symptoms of relapse, lack of complete remission, or death. We conducted the Kaplan–
18
Meier survival analysis by comparing the overall and disease-free survival between
patients with high (Z ≥1) and low (Z<1 ) aGPCRs expression levels. OS and DFS are
considered undefined when the curve does not cross 50%. For the combined 8-aGPCRs
overall survival analysis, patients were grouped into higher expression level (Z score ≥
1) if they have at least one of the eight genes whose Z score ≥1; patients were grouped
into low expression level (Z score <1) if none of the eight genes had a Z-score > 1.
GraphPad Prism software package (ver. 6.0; GraphPad Software Inc., La Jolla, CA, USA)
was used to generate figures. In this study, we also used Mann–Whitney U’s non-
parametric test for continuous variables and Fisher’s exact test for categorical variables
to determine associations between aGPCRs expression level and patient
clinical/molecular characteristics by using STATA 15.1 SE. Multivariate survival analysis
by using the Cox Proportional Hazards Model was also conducted in Stata 15.1 SE
software. The multivariate analysis assessed the association between the combination of
eight aGPCRs expression levels and OS/DFS after adjusting for other factors.
Additionally, we performed survival analysis after dichotomizing patients according to age
into younger (< 60) and older ( ≥60) and excluding M3 subtype AML patients who have
t(15:17) translocation. A statistical cut-off of p<0.05 was used for inclusion of variables
from univariate analysis to multivariate analysis.

Spearman and Pearson correlation analyses was conducted to assess the correlation of
gene expression between each two genes of the eight identified genes. The scatter plots
show the correlation between two genes were generated using cBioPortal co-expression
analysis with the mRNA expression (RNA Seq V2 RSEM) data and used the log2-
19
transformed graph. Distribution figures of methylation β values and heatmaps were
generated using R studio 3.5.1. For the primary analysis (8-aGPCR combined analysis),
we used a P value of 0.05/34 =0.00147 as significant to correct for the multiple hypothesis
testing.

2.4 Pathway analyses:

The mRNA enrichment analysis data was downloaded from TCGA database on
cBioportal— using Z-score>±2 as the threshold and only included genes with enrichment
p-value < 0.05 and exclude the patients who has downregulation in any of the eight genes.
Ingenuity Pathway Analysis (IPA, version 01-13) was used to predict the potential
pathways. Ingenuity Pathway Analysis was used to identify potential common pathways
among the eight aGPCR genes that were associated with poor survival. The pathways
were built based on the Ingenuity Knowledge Base and confined in mammalian species
(human, mouse, rat) and experimental data only (no prediction).

20
Results

3.1 Genomic profile of adhesion GPCRs in AML samples

aGPCRs exhibited a wide spectrum of genomic and transcriptome alternations in AML
with frequencies that ranged from 0.6% for ADGRF2 to 35% for ADGRG5 (Figure 3.1-1).
Each of the 33 aGPCR genes was found to be genetically or transcriptionally altered in
at least one patient with AML. Most alterations were mRNA upregulation and mRNA
downregulation with very few amplification, deep deletion, and missense mutation
alterations (Table 3.1-1). Two patients had fusions in ADGRG7. There were genomic and
transcriptional alternations in all of the 33 adhesion GPCR genes in 154 (95.1%) of the
162 patients with AML (Figure 3.1-1). The heatmap shows the mRNA expression level of
33 adhesion GPCRs, they are at the different expression level. (Figure 3.1-2)

21

Figure 3.1-1. Distribution of 33 adhesion GPCRs genes in 162 patients with and
without alteration (set Z score ≥ 1 as the threshold). The data was acquired from acute
myeloid leukemia (TCGA NEJM 2013) and plotted using Oncoprint from the cBioportal.
The blue rectangle corresponds for eight adhesion GPCRs whose upregulation associate
with worse outcome. The percentage of patients with genomic or transcriptome
alternations are listed to the left.

22

Table 3.1-1. Upregulated and Downregulated population of each 33 genes in
TCGA dataset

Genes Upregulated patients Downregulated patients
ADGRB1 23 0
CELSR2 19 0
ADGRD1 16 0
ADGRE1 21 0
ADGRE2 28 25
ADGRE5 22 10
ADGRG1 20 0
ADGRG3 23 0
ADGRA1 4 0
ADGRA2 27 0
ADGRA3 22 27
ADGRB2 15 0
ADGRB3 9 0
CELSR1 20 0
CELSR3 15 0
ADGRD2 13 0
ADGRE3 14 0
ADGRE4P 12 0
ADGRF1 5 0
ADGRF2 1 0
ADGRF3 20 18
ADGRF4 3 0
ADGRF5 15 0
ADGRG2 20 11
ADGRG4 15 0
ADGRG5 25 32
ADGRG6 7 0
ADGRG7 2 0
ADGRL1 16 0
ADGRL2 15 0
ADGRL3 13 0
ADGRL4 14 0
ADGRV1 6 0

23

Figure 3.1-2. Heatmaps of the adhesion GPCR genes. Heatmap of the 33 genes in
adhesion GPCRs family according to the TCGA dataset. The red rectangles indicate the
eight selected adhesion genes. (colored by mRNA expression value).

24
3.2 Association between eight aGPCRs and shorter overall and disease-free
survival

We compared overall survival and disease-free survival (OS and DFS, respectively)
between patients with high (Z≥1) and patients with low (Z<1) expression of each of the
33 adhesion GPCR genes (Figure 3.2-1 and Figure 3.2-2 separately). Expression data
for GPR123(ADGRA1), GPR111(ADGRF2), GPR115(ADGRF4), GPR112(ADGRG4),
GPR128(ADGRG7) genes were not available therefore we cannot exclude their possible
association with clinical outcome. Otherwise, we found that eight of the 33 aGPCRs
[BAI1(ADGRB1), CELSR2(ADGRC2), GPR133(ADGRD1), EMR1(ADGRE1),
EMR2(ADGRE2), CD97(ADGRE5), GPR56(ADGRG1), GPR97(ADGRG3)] were
associated with shorter OS and/or DFS (P<0.05). Patients with high expression (Z≥1) of
each gene of the identified eight genes had significantly shorter median OS as well as
DFS than patients with Z<1 of that particular gene (Figure 3.2-1; ADGRB1: OS 8.20 vs
21.50 months, p=0.0157; CELSR2: OS 10.00 vs 21.50 months, p=0.0488; ADGRD1: OS
11.10 vs 20.50 months, p=0.0085; ADGRE1: OS 10.60 vs 20.50 months, p=0.0198;
ADGRE5: OS 7.35 vs 24.10 months, p=0.0015; ADGRG1: OS 6.80 vs 22.30 months,
p=0.0057; ADGRG3: OS 10.00 vs 21.50 months, p=0.0177. Figure 3.2-2; ADGRE2: DFS
12.10 vs 18.20 months, p=0.0374. ). In the combined analysis of the expression data of
the eight genes (patients with at least one of the eight genes have high (Z≥1) expression
are grouped in the 8-aGPCRs high expressers, and patients with all eight genes have
expression Z<1 are grouped in the 8-aGPCRs low expressers group), we found that
patients in the 8-aGPCR high group have significantly shorter OS (median: 11.8 months
vs 55.4 months; p< 0.0001) (Figure 3.2-3 a.) and DFS (median: 12 months vs Undefined;
p< 0.0001) (Figure 3.2-3 b.). than patients in 8-aGPCR low group. Similarly, when we
25
excluded patients with M3 AML, (as these patients generally have overall better outcome
and are treated differently, we excluded them to avoid the bias in analysis.) the
association between high aGPCRs and survival remained significant (OS median:11.80
months vs 53.90 months p=0.0001; DFS median:11.90 months vs 35.60 months
p=0.0002; Figure 3.2-3 c, d.). We also validated the result in Metzeler leukemia 1 and 2
datasets by using the similar method. This time we dichotomize the patients by the top
10% expression patients and the rest of the patients (Figure 3.2-4, a. ADGRG1/GPR56
from Metzeler leukemia 1 dataset OS median:209.5 days vs 186.5 days, p=0.0034;
b.ADGRE5/CD97 from Metzeler leukemia 2 dataset OS median: 186.5 days vs 606.0
days, p=0.0473; c. ADGRE2/EMR2 from Metzeler leukemia 2 dataset OS median: 93.00
days vs. 606.0 days, p=0.0131; d. ADGRG1/GPR56 from Metzeler leukemia 2 dataset
OS median:165.5 days vs. 624.0 days, p=0.0026.) We performed survival analysis of the
combination of the top 10% expressers of any of the eight genes in this dataset. (OS
median: 471.0 days vs 1278 days p=0.1010, Figure 3.2-4 e). Though the association did
not reach statistical significance, we observed the same trend as in TCGA dataset.
Survival analysis stratified by age showed also the significant association between high
aGPCRs expression and overall survival in younger patients (age<60) (median OS in
younger: 17.0 VS Undefined p=0.0003) but not in older patients (age≥60) (Figure 3.2-5.
a, b). Importantly, multivariate survival analysis showed that the upregulation of the eight
aGPCRs were associated with significantly shorter overall survival when adjusted by age,
molecular risk, transplant status (all patients: HR=1.73, 95%CI=1.11-2.69, P=0.015;
younger patients: HR=2.94, 95%CI=1.49-5.79, P=0.002; Table 3.2-1)

26

27

Figure 3.2-1. Overall survival analysis of AML patients associated with available 29
individual adhesion GPCRs mRNA expression.

28

29

Figure 3.2-2. Disease-free survival analysis of AML patients associated with
available 29 individual adhesion GPCRs mRNA expression.
30

Figure 3.2-3. Survival analysis of patients with AML according to eight adhesion
GPCRs mRNA expression. Patients with at least one of the eight genes (ADGRB1
(BAI1), ADGRC2 (CELSE2), ADGRE1 (EMR1), ADGRE2 (EMR2), ADGRE5 (CD97),
ADGRG1 (GPR56), ADGRG3 (GPR97), ADGRG5 (GPR133)) have high (Z≥1)
expression are grouped in the 8-aGPCRs high expressers, and patients with all eight
genes have expression Z<1 are grouped in the 8-aGPCRs low expressers group, survival
analysis were compared between the high and low expressors. a. Overall survival of 173
AML patients in high expresser group (Z score ≥1) and low expresser group (Z score
<1). b. Disease-free survival of 171 AML patients in high expresser group (Z score ≥1)
and low expresser group (Z score <1). c. Overall survival of 157 AML patients (excluded
those patients with M3 FAB type.) in high expresser group (Z score ≥1) and low
expresser group (Z score <1). d. Disease-free survival of 157 AML patients (excluded
those patients with M3 FAB type.) in high expresser group (Z score ≥1) and low
expresser group (Z score <1).
31

Figure 3.2-4. Survival analysis of AML associated with 33 adhesion GPCRs mRNA
expression in Metzeler 1 and 2 Leukemia datasets. a. Overall survival analysis of
AML patients in Metzeler leukemia 1 dataset, associated with ADGRG1(GPR56). b,c,d.
Overall survival analysis of AML patients in Metzeler leukemia 2 dataset, associated
with ADGRG1(GPR56), ADGRE2(EMR2), ADGRE5(CD97). e. Overall survival analysis
of the combination of the top 10% expressers of any of the eight gene in the Metzeler
leukemia 2 dataset.

a b
c
d
32
a.

b.

Figure 3.2-5. Survival analysis of patients with AML according to eight adhesion
GPCRs mRNA expression and stratified by age. Patients with at least one of the eight
genes (ADGRB1 (BAI1), ADGRC2 (CELSE2), ADGRE1 (EMR1), ADGRE2 (EMR2),
ADGRE5 (CD97), ADGRG1 (GPR56), ADGRG3 (GPR97), ADGRG5 (GPR133)) have
high (Z ≥1) expression are grouped in the 8-aGPCRs high expressers, and patients with
all eight genes have expression Z<1 are grouped in the 8-aGPCRs low expressers group,
survival analysis were compared between the high and low expressors. a. Overall survival
of 80 young AML patients (age <60). b. Overall survival of 77 old (age ≥60).
0 20 40 60 80
0
50
100
Months
Percent survival
Z³1
Z<1
p=0.6641
n=57
n=20
0 50 100 150
0
50
100
Months
Percent survival
Z³1
Z<1
p=0.0004
n=44
n=36
0 20 40 60 80
0
50
100
Months
Percent survival
Z³1
Z<1
p=0.6641
n=57
n=20
0 50 100 150
0
50
100
Months
Percent survival
Z³1
Z<1
p=0.0004
n=44
n=36
33
Table 3.2-1. Multivariate analysis to assess the association between the
combination of eight aGPCRs expression levels and OS after adjusting for other
factors

a. Cox Proportional Hazards modeling for overall survival in patients comparing patients with
high (Z>1) adhesion GPCRs expression and low (Z<1) adhesion GPCRs expression
(N=169).
Variable Hazard Ratio 95% CI p-value
Age 1.02 1.01 1.04 0.008
Molecular Risk
Intermediate 3.02 1.50 6.06 0.002
Poor 5.78 2.68 12.5 <0.001
Transplant Status
(Y/N)
0.44 0.28 0.69 <0.001
aGPCRs (Z>1) 1.73 1.11 2.69 0.015

b. Cox Proportional Hazards modeling for overall survival in patients comparing patients with
high (Z>1) adhesion GPCRs expression and low (Z<1) adhesion GPCRs expression in
younger patients (N=89).
Variable Hazard Ratio 95% CI p-value
Age 1.00 0.97 1.03 0.987
Molecular Risk
Intermediate 5.64 1.99 15.96 0.001
Poor 4.41 1.36 14.3 0.013
Transplant Status
(Y/N)
0.71 0.34 1.46 0.348
aGPCRs (Z>1) 2.94 1.49 5.79 0.002
34
3.3 Association between eight aGPCRs expression and patient primary
characteristics

Next, we analyzed the identified eight aGPCRs mRNA expression (log2-transformed) of
AML patients based on molecular risk, cytogenetic status and FAB (French-American-
British) subtypes (Figure 3.3-1). We found that median BAI1(ADGRB1), CELSR2 and
GPR56(ADGRG1) (Figure 3.3-1 a, b, g) mRNA expression were significantly higher in
patients with poor molecular risk compared with patients with intermediate or good
molecular risk. Except for EMR1(ADGRE1) mRNA expression, which was significantly
higher in patients with poor molecular risk compared to the patients with good molecular
risk while significantly lower than the patients with intermediate risk (Figure 3.3-1 d.)
However, EMR1(ADGRE1) and CD97(ADGRE5) mRNA expression were significantly
higher in cytogenetically normal patients than in cytogenetically abnormal ones (Figure
3.3-1 f.). When associating the aGPCRs expression with FAB subtypes, we found that
median CELSR2 mRNA expression was significantly higher in M5 compared to M1-M4
AML. (Figure 3.3-1 b.); median GPR133 (ADGRD1) mRNA expression was significantly
higher in M4 compared to M1-M3 FAB classes (Figure 3.3-1 c.); While, median
EMR1(ADGRE1) mRNA expression was significantly lower in M3 FAB class compared
to M1, M4, M5 FAB classes (Figure 3.3-1 d.). Also, according to the comprehensive high
(Z≥1) and low (Z<1) expression of the eight aGPCRs we set patients into two categories
and associate the expression with clinical characteristics (Table 3.3-1). There were 64
patients have low (Z<1) aGPCRs expression while 109 patients have high(Z≥1) aGPCRs
expression. Patients with high expression level have higher risk than patients have low
expression level (Poor: 31% vs 17% p=0.049; Good: 14% vs 28% p=0.027). We also
35
found out that high aGPCRs expression is significantly associated with age. There was
no significant association between high(Z≥1) aGPCRs expression and sex, FAB subtypes,
WB count (which contains the peripheral blood and bone marrow blast percentage),
cytogenetic status and transplant status.

36

poor
intermediate
good
0
5
10
15
BAI1 expression
*
poor
intermediate
good
0
2
4
6
8
10
CELSR2 expression
*
*
Normal
Abnormal
0
5
10
15
BAI1 expression
Normal
Abnormal
0
2
4
6
8
10
CELSR2 expression
M0
M1
M2
M3
M4
M5
M6
M7
0
5
10
15
BAI1 expression
M0
M1
M2
M3
M4
M5
M6
M7
0
2
4
6
8
10
CELSR2 expression
***
**
***
****
BAI1
a
CELSR2
b
poor
intermediate
good
-2
0
2
4
6
8
10
GPR133 expression
poor
intermediate
good
0
5
10
15
EMR1 expression
*** ****
*
Normal
Abnormal
-2
0
2
4
6
8
10
GPR133 expression
Normal
Abnormal
0
5
10
15
EMR1 expression
****
M0
M1
M2
M3
M4
M5
M6
M7
-2
0
2
4
6
8
10
GPR133 expression
**
***
****
M0
M1
M2
M3
M4
M5
M6
M7
0
5
10
15
EMR1 expression
*** ** *
c
d
GPR133
EMR1
37

Figure 3.3-1. Eight adhesion GPCRs mRNA expression in patients with AML
according to molecular status, risk and FAB classifications. log2 mRNA expression
for all the eight genes were categorized by risk status, molecular status and FAB (from
left to right) classification: *p < 0.05; **p<0.01; ***p < 0.001. a. BAI; b. CELSR2; c.
GPR133; d. EMR1; e. EMR2; f. CD97; g. GPR56; h. GPR97.
poor
intermediate
good
6
8
10
12
14
16
EMR2 expression
poor
intermediate
good
8
10
12
14
16
CD97 expression
Normal
Abnormal
6
8
10
12
14
16
EMR2 expression
Normal
Abnormal
8
10
12
14
16
CD97 expression
*
M0
M1
M2
M3
M4
M5
M6
M7
6
8
10
12
14
16
EMR2 expression
**
*
M0
M1
M2
M3
M4
M5
M6
M7
8
10
12
14
16
CD97 expression
*
EMR2
CD97
e
f
poor
intermediate
good
0
5
10
15
GPR56 expression
*
poor
intermediate
good
0
5
10
15
GPR97 expression
Normal
Abnormal
0
5
10
15
GPR56 expression
Normal
Abnormal
0
5
10
15
GPR97 expression
M0
M1
M2
M3
M4
M5
M6
M7
0
5
10
15
GPR56 expression
***
# ***
#
M0
M1
M2
M3
M4
M5
M6
M7
0
5
10
15
GPR97 expression
*
*
GPR56
GPR97
g
h
38

Table 3.3-1. Clinical characteristics of 173 AML patients according to eight adhesion GPCRs expression Z-score ≥
1
Characteristic Z-score (<1) (n=64) Z-score ( ≥1) (n=109) p value
age, median(years) 54.5

61

0.052
young 42 66% 49 45% 0.011
old 22 34% 60 55%

Sex

Female (n, %) 30 47% 51 47% > 0 .9 9 9
Male (n, %) 34 53% 58 53%

FAB

M0 3 5% 13 12% 0.173
M1 18 28% 26 24% 0.589
M2 15 23% 23 21% 0.709
M3 8 13% 8 7% 0.285
M4 12 19% 22 20% > 0 .9 9 9
M5 4 6% 14 13% 0.205
M6 1 2% 1 1% > 0 .9 9 9
M7 1 2% 2 2% > 0 .9 9 9
WB count, median 13.95

27.1

0.062
In mean 28.098

36.631

0.062
%BM blast, median 71.5

73

0.765
%PB blast, median 37

39

0.908
Risk status

poor 11 17% 34 31% 0.049
Intermediate 34 53% 58 53% > 0 .9 9 9
good 18 28% 15 14% 0.027
Cytogenetic status

Normal (n, %) 30 47% 50 46% > 0 .9 9 9
Abnormal (n, %) 33 52% 57 52%

Transplant status

No (n, %) 34 53% 66 61% 0.345
Yes (n, %) 30 47% 43 39%
39
3.4 Association between eight aGPCRs expression and patient’s mutational
status

We also analyzed association of aGPCRs expression with patient’s mutational status
(Table 3.4-1). Patients in the high 8-aGPCRs were less likely to have CEBPA mutation
(%: 3.7 vs 14.1, P=0.017). However, analysis of the association between the individual
aGPCR genes and the presence of different AML mutations showed no association
between CEBPA mutations and any of the eight aGPCRs (Table 3.4-2 a-h). Yet, TP53
and NPM1 mutations were associated with several aGPCR genes: GPR133/ADGRG1
(TP53: 23.5% vs 6.4%, p=0.035), CD97/ADGRE5 (NPM1: 53.8% vs 23.1% p=0.003),
EMR1/ADGRE1 (IDH2: 22.7% vs 7.9% p=0.046; NPM1 63.6% vs 22.5% p<0.001) and
GPR97/ADGRG3 (NPM1: 47.8% vs 24.7%, p=0.026), in which high (Z>1) expression of
the particular gene was associated with higher frequency of the mutation.
We also assessed whether the expression levels of the eight aGPCR genes were
correlated with each other among the patients with AML. We tested each pair separately,
and found that four pairs of genes among the eight selected genes were correlated:
CD97/ADGRE5 vs. EMR2/ADGRE2 (Pearson correlation: 0.43, Spearman
correlation:0.45 p=1.59×10
-9
) (Figure 3.4-1 a), CD97/ADGRE5 vs. EMR1/ADGRE1
(Pearson correlation: 0.44, Spearman correlation:0.41 p=6.44×10
-8
)(Figure 3.4-1 b),
BAI1/ADGRB1 vs. GPR133/ADGRG1 (Pearson correlation: 0.46, Spearman correlation:
0.44 p=3.08×10
-9
)(Figure 3.4-1 c) and GPR97/ADGRG3 vs. GPR56/ADGRG1 (Pearson
correlation: 0.43, Spearman correlation:0.45 p=9.485×10
-5
) (Figure 3.4-1 d).

40
Table 3.4-1. Expression of eight adhesion GPCRs (Z-score ≥1 ）according to the
top mutations present in AML (n=173 patients)
Genes
Z-score (<1)
Z-score ( ≥1 ） p value

(n =6 4 ) (n =1 0 9 )

FLT3 (n, %)
15
2 3 . 4 %
34
3 1 . 2 %
0.299
TP53 (n, %)
3
4 . 7 %
11
1 0 . 1 %
0.259
WT1 (n, %)
6
9 . 4 %
4
3 . 7 %
0.176
IDH1 (n, %)
6
9 . 4 %
10
9 . 2 %
>0.999
IDH2 (n, %)
5
7 . 8 %
12
1 1 . 0 %
0.602
CEBPA (n, %)
9
1 4 . 1 %
4
3 . 7 %
0.017
RUNX1 (n, %)
7
1 0 . 9 %
8
7 . 3 %
0.417
NRAS (n, %)
5
7 . 8 %
7
6 . 4 %
0.762
TET2 (n, %)
7
1 0 . 9 %
8
7 . 3 %
0.417
NPM1 (n, %)
13
2 0 . 3 %
35
3 2 . 1 %
0.114
DNMT3A (n, %)
14
2 1 . 9 %
28
2 5 . 7 %
0.714

41
Table 3.4-2 Expression of eight individual aGPCR genes according to the top
mutations presents in AML

a. Expression of BAI1(ADGRB1) (Z-score ≥1) according to the top mutations
present in AML (n=173 patients)
Genes
Z-score (<1)
Z-score ( ≥1 ） p value

(n= 150) (n= 23)

FLT3(n, %)
40
26.7%
9
39.1%
0.223
TP53(n, %)
5
3.3%
9
39.1%
<0.001
WT1(n, %)
10
6.7%
0
0.0%
0.362
IDH1(n, %)
14
9.3%
2
8.7%
>0.999
IDH2(n, %)
17
11.3%
0
0.0%
0.133
CEBPA(n, %) 12
8.0%
1
4.3%
>0.999
RUNX1(n, %)
15
10.0%
0
0.0%
0.225
NRAS(n, %) 10
6.7%
2
8.7%
0.663
TET2(n, %)
11
7.3%
4
17.4%
0.119
NPM1(n, %)
40
26.7%
8
34.8%
0.456
DNMT3A(n, %)
34
22.7%
8
34.8%
0.204

42
b. Expression of CELSR2(ADGRC1) (Z-score ≥1) according to the top mutations
present in AML (n=173 patients)
Genes
Z-score (<1)
Z-score ( ≥1 ） p value

(n=152) (n=21)

FLT3(n, %)
45
29.6%
4
19.0%
0.440
TP53(n, %)
13
8.6%
1
4.8%
>0.999
WT1(n, %)
9
5.9%
1
4.8%
>0.999
IDH1(n, %)
16
10.5%
0
0.0%
0.224
IDH2(n, %)
15
9.9%
2
9.5%
>0.999
CEBPA(n, %) 13
8.6%
0
0.0%
0.372
RUNX1(n, %)
13
8.6%
2
9.5%
>0.999
NRAS(n, %) 12
7.9%
0
0.0%
0.365
TET2(n, %)
14
9.2%
1
4.8%
0.698
NPM1(n, %)
42
27.6%
6
28.6%
>0.999
DNMT3A(n, %)
37
24.3%
5
23.8%
>0.999

c. Expression of GPR133 (ADGRD1) (Z-score ≥1) according to the top mutations
present in AML (n=173 patients)
Genes
Z-score (<1)
Z-score ( ≥1 ） p value

(n=156) (n=17)

FLT3(n, %)
46
29.5%
3
17.6%
0.402
TP53(n, %)
10
6.4%
4
23.5%
0.035
WT1(n, %)
10
6.4%
0
0.0%
0.601
IDH1(n, %)
16
10.3%
0
0.0%
0.373
IDH2(n, %)
14
9.0%
3
17.6%
0.224
CEBPA(n, %) 11
7.1%
2
11.8%
0.620
RUNX1(n, %)
13
8.3%
2
11.8%
0.645
NRAS(n, %) 9
5.8%
3
17.6%
0.099
TET2(n, %)
15
9.6%
0
0.0%
0.367
NPM1(n, %)
46
29.5%
2
11.8%
0.158
DNMT3A(n, %)
38
24.4%
4
23.5%
>0.999
43
d. Expression of CD97 (ADGRE5) (Z-score ≥1) according to the top mutations
present in AML (n=173 patients)
Genes
Z-score (<1)
Z-score ( ≥1 ） p value

(n=147) (n=26)

FLT3(n, %)
38
25.9%
11
42.3%
0.101
TP53(n, %)
13
8.8%
1
3.8%
0.697
WT1(n, %)
10
6.8%
0
0.0%
0.362
IDH1(n, %)
15
10.2%
1
3.8%
0.472
IDH2(n, %)
14
9.5%
3
11.5%
0.724
CEBPA(n, %) 12
8.2%
1
3.8%
0.694
RUNX1(n, %)
15
10.2%
0
0.0%
0.131
NRAS(n, %) 10
6.8%
2
7.7%
>0.999
TET2(n, %)
15
10.2%
0
0.0%
0.131
NPM1(n, %)
34
23.1%
14
53.8%
0.003
DNMT3A(n, %)
33
22.4%
9
34.6%
0.215

e. Expression of EMR1(ADGRE1) (Z-score ≥1) according to the top mutations
present in AML (n=173 patients)
Genes
Z-score (<1)
Z-score ( ≥1 ） p value

(n=151) (n=22)

FLT3(n, %)
41
27.2%
8
36.4%
0.448
TP53(n, %)
14
9.3%
0
0.0%
0.221
WT1(n, %)
10
6.6%
0
0.0%
0.365
IDH1(n, %)
14
9.3%
2
9.1%
>0.999
IDH2(n, %)
12
7.9%
5
22.7%
0.046
CEBPA(n, %) 13
8.6%
0
0.0%
0.378
RUNX1(n, %)
14
9.3%
1
4.5%
0.696
NRAS(n, %) 11
7.3%
1
4.5%
>0.999
TET2(n, %)
14
9.3%
1
4.5%
0.696
NPM1(n, %)
34
22.5%
14
63.6%
<0.001
DNMT3A(n, %)
34
22.5%
8
36.4%
0.184
44
f. Expression of EMR2(ADGRE2) (Z-score ≥1) according to the top mutations
present in AML (n=173 patients)
Genes
Z-score (<1)
Z-score ( ≥1 ） p value

(n=142) (n=31)

FLT3(n, %)
40
28.2%
9
29.0%
>0.999
TP53(n, %)
12
8.5%
2
6.5%
>0.999
WT1(n, %)
9
6.3%
1
3.2%
0.693
IDH1(n, %)
13
9.2%
3
9.7%
>0.999
IDH2(n, %)
11
7.7%
6
19.4%
0.087
CEBPA(n, %) 12
8.5%
1
3.2%
0.468
RUNX1(n, %)
12
8.5%
3
9.7%
0.734
NRAS(n, %) 10
7.0%
2
6.5%
>0.999
TET2(n, %)
14
9.9%
1
3.2%
0.313
NPM1(n, %)
35
24.6%
13
41.9%
0.075
DNMT3A(n, %)
31
21.8%
11
35.5%
0.113

g. Expression of GPR56 (ADGRG1) (Z-score ≥1) according to the top mutations
present in AML (n=173 patients)
Genes
Z-score (<1)
Z-score ( ≥1 ） p value

(n=147) (n=26)

FLT3(n, %)
39
26.5%
10
38.5%
0.075
TP53(n, %)
10
6.8%
4
15.4%
0.083
WT1(n, %)
9
6.1%
1
3.8%
1
IDH1(n, %)
15
10.2%
1
3.8%
0.697
IDH2(n, %)
16
10.9%
1
3.8%
0.7
CEBPA(n, %) 12
8.2%
1
3.8%
1
RUNX1(n, %)
13
8.8%
2
7.7%
1
NRAS(n, %) 11
7.5%
1
3.8%
1
TET2(n, %)
12
8.2%
3
11.5%
0.411
NPM1(n, %)
39
26.5%
9
34.6%
0.2
DNMT3A(n, %)
35
23.8%
7
26.9%
0.426
45
h. Expression of GPR97 (ADGRG3) (Z-score ≥1) according to the top mutations
present in AML (n=173 patients)
Genes
Z-score (<1)
Z-score ( ≥1 ） p value

(n=150) (n=23)

FLT3(n, %)
38
25.3%
11
47.8%
0.044
TP53(n, %)
11
7.3%
3
13.0%
0.404
WT1(n, %)
8
5.3%
2
8.7%
0.624
IDH1(n, %)
13
8.7%
3
13.0%
0.451
IDH2(n, %)
15
10.0%
2
8.7%
1
CEBPA(n, %) 12
8.0%
1
4.3%
1
RUNX1(n, %)
14
9.3%
1
4.3%
0.696
NRAS(n, %) 10
6.7%
2
8.7%
0.663
TET2(n, %)
15
10.0%
0
0.0%
0.225
NPM1(n, %)
37
24.7%
11
47.8%
0.026
DNMT3A(n, %)
34
22.7%
8
34.8%
0.204

Figure 3.4-1. Correlation expression of the selected eight adhesion GPCRs.
Spearman's rho correlation analysis and Pearson correlation analysis between
expression levels of the eight aGPCR genes are conducted. Scatter-plots showing a log2
transformed mRNA expression levels of a. CD97/ADGRE5 and EMR2/ADGRE2, b.
CD97/ADGRE5 and EMR1/ADGRE1, c. BAI1/ADGRB1 and GPR56/ADGRG1, d.
GPR133/ADGRG1 and GPR97/ADGRG3. Expression correlations are shown in these 4
pairs of genes.
a
b
c d
47
3.5 Adhesion GPCRs are hypomethylated in patients with high gene expression

Upregulation of only a portion of the aGPCRs was explained by association with patient
mutational status, thus, we speculated that upregulation of the other portion of aGPCRs
could be explained by DNA methylation. We assessed the methylation β-value distribution
of each of the eight genes based on high (Z>1) and low (Z<1) expression. We found that
five of the eight genes— BAI1/ADGRB1, EMR1/ADGRE1, EMR2/ADGRE2, CELSR2,
CD97/ADGRE5—were hypomethylated (median β-value < 0.2) in the majority of the
patients (Figure 3.5-1). Additionally when we associated the gene expression level with
methylation β-value individually (Figure 3.5-2, we analyzed data in 25 aGPCR genes that
are available), we found that aGPCR methylation was significantly lower in the aGPCR
high (Z≥1) group for five of the eight aGPCRs genes: BAI1/ADGRB1, GPR133/ADGRD1,
EMR1/ADGRE1, GPR56/ADGRG1 and GPR97/ADGRG3 (Figure 3.4-2 in the red
rectangle; median methylation β value: BAI1 0.04560 vs 0.06087 p=0.0011, GPR133
0.5806 vs 0.8781 p=0.0005, EMR1 0.1341 vs 0.3178 p=0.0001, GPR56 0.4906 vs 0.6053
p<0.0001, GPR97 0.6471 vs 0.7567 p=0.0152). However, there was no significant
association between the level of methylation and clinical outcome.

48

Figure 3.5-1. The methylation β value distribution of 109 patients in each of the
eight adhesion GPCR genes. The 109 patients come from the TCGA dataset whose
methylation value are available. The X-axis represent the methylation β-value, the Y-axis
represent the density of 109 patients.

49

Figure 3.5-2. Associations between methylation and individual adhesion GPCR
mRNA expression. Adhesion GPCRs methylation β value comparison between
patients according to their expression (Z-score ≥ 1 and < 1). A non-parametric Mann–
Whitney U test was used to compare the median of methylation β value between the
groups. The red rectangle shows the eight selected aGPCR genes
50
3.6 IL-8 signaling pathway is activated in patients with high aGPCRs.

Since all the aGPCRs share similar structures, we speculated that genes in this family
may lead to an activation of a common pathways. To have a deeper investigation on the
mechanisms shared by aGPCRs and how they contribute to AML, we performed
knowledge-based pathway analysis. We used a Z-score>2 and excluded patients who
have downregulation in any aGPCR gene and conducted enrichment analysis between
the gene expression altered group (expression Z score>2) and unaltered group
(expression Z score≤2). We extracted genes that were significantly different between the
two groups (p-value<0.05). Next, the extracted genes were analyzed using IPA (QIAGEN
Inc., https://www.qiagenbioinformatics.com/products/ingenuitypathway-analysis) (Figure
3.6-1 a, b). Ingenuity Pathway Analysis identified the IL-8 (interleukin-8) signaling
pathway to be among the top enriched pathways in patients with high aGPCRs. Other
pathways that were found to be activated are the FCɣ receptor mediated phagocytosis in
macrophages and monocytes, integrin, B cell receptor and NGF signaling pathways
(Table 3.6-1).

Figure 3.6-1. Pathway analysis of the eight aGPCRs. a. Oncoprint of the eight aGPCRs genes with upregulation and
amplification (set Z score > ± 2 as the threshold and exclude those patients who have downregulation in any of the eight
genes.) b. IPA pathway enrichment analysis of the eight aGPCR genes. The x-axis shows enrichment p-values and ratios
for the eight aGPCRs expressions altered group compared with the unaltered group (Z-score > 2 as altered; Z-score < 2 as
unaltered). Bright orange are pathways with the most significant activation, bright blue are pathways with the most significant
inhibition.
A D G R B 1
C E L S R 2
A D G R D 1
A D G R E 1
A D G R E 2
A D G R E 5
A D G R G 3
A D G R G 1
10%
6%
6%
5%
4%
7%
3%
3%
G en etic A lteratio n Missense Mutation (unknown significance) Amplification mRNA Upregulation No alterations
b.
Gene Set / Pathway is altered in 51 (31.5%) of queried samples
162 patients
Z-Score > 2.00
-log(p-value)
Ratio
a.
EIF2 Signaling
Regulation of eIF4 and p70S6K Signaling
Hereditary Breast Cancer Signaling
Salvage Pathways of Pyrimidine Ribonucleotides
mTOR Signaling
Role of BRCA1 in DNA Damage Response
Fc γ Receptor-mediated Phagocytosis in Macrophages and Monocytes
Huntington's Disease Signaling
IL-8 Signaling
ATM Signaling
Telomerase Signaling
Pyridoxal 5'-phosphate Salvage Pathway
Integrin Signaling
Apoptosis Signaling
PI3K/AKT Signaling
Chronic Myeloid Leukemia Signaling
Cell Cycle: G1/S Checkpoint Regulation
B Cell Receptor Signaling
NGF Signaling
DNA Double-Strand Break Repair by Homologous Recombination
Acute Myeloid Leukemia Signaling
p70S6K Signaling
Cell Cycle: G2/M DNA Damage Checkpoint Regulation
Molecular Mechanisms of Cancer
Cyclins and Cell Cycle Regulation
0 5 10 15 20
0.0 0.2 0.4 0.6 0.8
Table 3.6-1. Ingenuity pathway analysis of differentially expressed adhesion
GPCR genes.
Ingenuity Canonical Pathways
-log(p-
value) Ratio
z-
score
EIF2 Signaling 1.48E+01 3.48E-01 -2.828
Regulation of eIF4 and p70S6K Signaling 8.96E+00 3.25E-01 0.209
Hereditary Breast Cancer Signaling 8.91E+00 3.33E-01 NaN
Salvage Pathways of Pyrimidine
Ribonucleotides 6.88E+00 3.51E-01 1.372
mTOR Signaling 6.69E+00 2.75E-01 0
Role of BRCA1 in DNA Damage Response 6.31E+00 3.62E-01 -1.5
FcŒ≥ Receptor-mediated Phagocytosis in
Macrophages and Monocytes 6.30E+00 3.44E-01 3.182
Huntington's Disease Signaling 6.18E+00 2.56E-01 0.392
IL-8 Signaling 5.87E+00 2.66E-01 3.286
ATM Signaling 5.73E+00 3.27E-01 -1.177
Telomerase Signaling 5.67E+00 3.08E-01 0.577
Pyridoxal 5'-phosphate Salvage Pathway 5.50E+00 3.69E-01 2.041
Integrin Signaling 5.48E+00 2.56E-01 3.501
Apoptosis Signaling 5.46E+00 3.23E-01 1.095
PI3K/AKT Signaling 5.26E+00 2.90E-01 -0.164
Chronic Myeloid Leukemia Signaling 5.25E+00 3.04E-01 NaN
Cell Cycle: G1/S Checkpoint Regulation 5.23E+00 3.58E-01 -0.277
B Cell Receptor Signaling 5.07E+00 2.58E-01 2.828
NGF Signaling 4.94E+00 2.88E-01 3.212
DNA Double-Strand Break Repair by
Homologous Recombination 4.69E+00 6.43E-01 NaN
Acute Myeloid Leukemia Signaling 4.69E+00 3.03E-01 1.732
p70S6K Signaling 4.69E+00 2.75E-01 1.333
Cell Cycle: G2/M DNA Damage Checkpoint
Regulation 4.69E+00 3.80E-01 2
Molecular Mechanisms of Cancer 4.68E+00 2.16E-01 NaN
Cyclins and Cell Cycle Regulation 4.63E+00 3.21E-01 -2.324

53
Discussion

Emerging evidence related to the role of aGPCRs in cancer initiation and growth have
suggested that they may present viable therapeutic targets (Bar-Shavit et al., 2016).
Among all the GPCR families, though adhesion GPCRs are the second largest GPCR
superfamily, there is no drug in the markets targets aGPCRs so far(Hauser et al., 2017).
This is likely due to the fact that most aGPCRs are still considered to be orphan receptors,
only a few ligands are founded to interact with aGPCRs, and some of which are
endogenous agonists for aGPCRs. Besides, for a number of identified interacting partners
to certain sub-families, their signaling effects remain unknown(Purcell and Hall, 2018).
However, adhesion GPCR members exhibit several properties as promising drug targets,
such as the large extracellular structure, very discrete patterns of distribution and the
involvement in a broad range of human diseases including cancer(Purcell and Hall, 2018).

Recent findings reporting the mechanoreceptive features of aGPCR(Scholz et al., 2015;
Scholz et al., 2016; Wilde et al., 2016) further supports their implication in cancer growth.
In addition to the genetic and epigenetic aberrations that differentiate cancer from normal
cells, cancer cells have developed biomechanical features that allow them to respond to
intrinsic and extrinsic mechanical cues (Scholz, 2018). Evidence show that GPCRs
deregulation is also associated with stem cell maintenance and induced pluripotent stem
cells (iPSCs) and cancer stem cells (CSCs) reprogramming; they are believed to play an
important role in regulating the biological properties of pluripotent stem cells (PSCs) and
CSCs. The roles of GPR124 and GPR126 in stem cell maintenance are good examples
(Choi et al., 2015). In hematopoiesis, particular aGPCRs are expressed distinctly on
54
hematopoietic stem and progenitor cells (HSPCs) and/or more differentiated peripheral
blood cells(Lin et al., 2017). Interestingly, certain aGPCR genes such as CD97, EMR1,
EMR2, EMR3 and GPR97 are expressed at a low level in HSPCs, but their expression
levels gradually increase upon differentiation and reach maximum expression levels in
mature peripheral blood granulocytes or in metamyelocytes. Whereas the expression of
GPR56 as well as LPHN1, GPR124, GPR125, CELSR3, GPR113, GPR114, and
GPR126 is high in hematopoietic stem cells (HSCs) but decrease with the process of
differentiation from HSCs to more mature cells.(Lin et al., 2017; Maiga et al., 2016). We
found different methylation patterns for the different aGPCRs genes. GPR56, in one,
shows hypermethylation in most of AML samples (relative differentiated samples)
whereas CD97, EMR1 and EMR2 show hypomethylation in most of AML samples. Thus,
it is reasonable to speculate that the epigenetic modification, for example, gene
methylation is one of the mechanisms to regulate the aGPCRs expression in the
hematopoietic system. A recent transcriptomic analysis has examined GPCRs in patients
with AML and shown deregulation of the expression of several GPCR genes including
members of the aGPCRs (Maiga et al., 2016). In consistent with this study, we found that
aGPCRs have a wide range of deregulation in AML patients from TCGA database. And
importantly, a large number of them were upregulation, suggesting that some of these
genes could serve as potential therapeutic targets.

In this study, we also found strong correlation of the expression between several aGPCRs,
particularly those found on the same chromosomes; suggesting similar upstream
mechanisms for their upregulations. Among the seven genes, EMR1, EMR2 and CD97
55
are located on the same chromosome, EMR1 is located on chromosome 19p13.3
whereas EMR2 and CD97 both located on chromosome 19p13.1. It’s known that for some
aGPCRs, such as EMR1, EMR2 and CD97, their N-terminal EGF-like domains are
encoded by single exons, which can give rise to isoforms with different numbers of EGF
domains during the alternative splicing (Lin et al., 2017). GPR56, GPR97 and GPR133
are located on the same chromosome 16q21, BAI1 is located on the chromosome 8q24.3.
GPR56 is reported to be a multifaceted biomarker for human cytotoxic lymphocytes, and
the second most highly upregulated gene associated with age in a meta-analysis of whole
blood gene expression in 14983 people(Peters et al., 2015). BAI1 and GPR97 are
reported to have mRNA expression in different hematopoietic lineage(Lin et al., 2017).

In addition to their ability to function through GPCR dependent signaling, aGPCR also
signals through 7-TM independent mechanisms. aGPCR feature a long extracellular N
terminal domain, longer than most other GPCRs and contains multiple conserved
domains that are involved in cell adhesion and cellular interaction, in addition to a GPCR
proteolysis site (GPS)(Purcell and Hall, 2018). Because these receptors provide a huge
opportunity for drug development in the form of agonists, antagonists and other
pharmacological intervention, functional and mechanistic characterization of these genes
in AML will deepen our understanding of their role in this disease.
Among the 33 aGPCR we investigated in this study, we found that eight aGPCRs—
BAI1(ADGRB1), CELSR2(ADGRC2), GPR133(ADGRD1), EMR1(ADGRE1),
EMR2(ADGRE2), CD97(ADGRE5), GPR56(ADGRG1) and GPR97(ADGRG3)—were
associated with poor clinical outcome. According to a recent study, GPR56 has been
56
identified as a leukemic stem cell marker for the majority of AML(Pabst et al., 2016). Also,
GPR56 was found to significantly accelerate HOXA9-induced leukemogenesis in mice,
providing evidence that GPR56 upregulation contributes to AML development and
establishing this gene as a potential novel target for antibody-mediated antileukemic
strategies (Daria et al., 2016). Besides, knocking down of GPR56 was found to decrease
the cellular adhesion and cell growth of EVI1 highly expressed leukemia cells in vitro and
result in a reduction of the in vivo repopulating ability of the HSCs, suggesting that GPR56
has the potential to become a novel molecular target in certain type (EVI1high) of
leukemia(Saito et al., 2013). CD97 has also been identified as a leukemic stem cell
marker in AML (Bonardi et al., 2013) and was reported to be associated with FLT3-ITD
(Wobus et al., 2015). Although less is known about the contribution of other aGPCRs in
AML, the high frequency of deregulated EMR1 and EMR2 in patients with AML suggests
a possible role in disease development. The contribution of aGPCRs to AML development
is further supported by the association between their upregulation and the patient’s
clinical outcome reported here.

The concept of studying genes as families or groups that function in a similar manner,
share similar a structure or work together to regulate a particular process is novel. It
enables the identification of larger patient population that are affected by similar molecular
and biological dysfunction that otherwise would not be identified. This will also facilitate
the identification of therapeutic approaches that would affect the mutual downstream
regulator, process or mechanism rather than the individual genes. Adopting this approach
led us to the identification of IL-8 as a possible mutual signaling pathway that is
57
deregulated in patients with upregulated aGPCRs. IL-8 also known as chemokine (C-X-
C motif) ligand 8 (CXCL8), a proinflammatory chemokine that is known to related with the
promotion of neutrophil chemotaxis and degranulation. There are multiple well studied
upstream signaling pathways associated with the IL-8 activation, such as Akt, PKC and
MAPK signaling that promotes angiogenesis, proliferation, and survival of endothelial and
cancer cells, and potentiates tumor and endothelial cells migration(Waugh and Wilson,
2008). IL-8 also plays a role in the induction and maintenance of EMT(Palena et al., 2012),
a process that is associated with more aggressive and invasive malignancies(Brabletz et
al., 2018). In fact, the upregulation of EMT marker vimentin was also associated with
poor overall survival in patients with AML(Wu et al., 2018). In acute myeloid leukemia, it
has been reported that IL-8 is significantly upregulated, and the expression of which
positively correlates with the tumor progression and recurrence. It plays an important role
in AML cell proliferation and cell cycle. Knocking down of IL-8 can cause an arresting of
G0/G1 cell cycle and increased apoptosis(Li et al., 2018). Besides, a study reported that
one of the human myeloid-restricted adhesion GPCRs, EMR2/ADGRE2, the activation of
which can promote the differentiation of THP-1 cell. Meanwhile, the activation of EMR2
can induce the expression of some subsequent pro-inflammatory mediators such as IL-8
and TNF-α(I et al., 2017).

In conclusion, our study suggests that particular aGPCRs are frequently upregulated in
AML and that their overexpression is associated with poor clinical outcome and
activated IL-8 signaling pathway. Future functional and mechanistic analysis are needed
to address the role of aGPCRs in AML.
58
References

Andersson, A., Ritz, C., Lindgren, D., Eden, P., Lassen, C., Heldrup, J., Olofsson, T.,
Rade, J., Fontes, M., Porwit-Macdonald, A., et al. (2007). Microarray-based
classification of a consecutive series of 121 childhood acute leukemias: prediction of
leukemic and genetic subtype as well as of minimal residual disease status. Leukemia
21, 1198-1203.
Aust, G., Steinert, M., Schutz, A., Boltze, C., Wahlbuhl, M., Hamann, J., and Wobus, M.
(2002). CD97, but not its closely related EGF-TM7 family member EMR2, is expressed
on gastric, pancreatic, and esophageal carcinomas. Am J Clin Pathol 118, 699-707.
Aust, G., Zhu, D., Van Meir, E.G., and Xu, L. (2016). Adhesion GPCRs in
Tumorigenesis. Handb Exp Pharmacol 234, 369-396.
Bar-Shavit, R., Maoz, M., Kancharla, A., Nag, J.K., Agranovich, D., Grisaru-Granovsky,
S., and Uziely, B. (2016). G Protein-Coupled Receptors in Cancer. Int J Mol Sci 17.
Boddu, P., Kantarjian, H., Garcia-Manero, G., Allison, J., Sharma, P., and Daver, N.
(2018). The emerging role of immune checkpoint based approaches in AML and MDS.
Leuk Lymphoma 59, 790-802.
Bonardi, F., Fusetti, F., Deelen, P., van Gosliga, D., Vellenga, E., and Schuringa, J.J.
(2013). A proteomics and transcriptomics approach to identify leukemic stem cell (LSC)
markers. Mol Cell Proteomics 12, 626-637.
Bourne, H.R., Sanders, D.A., and McCormick, F. (1991). The GTPase superfamily:
conserved structure and molecular mechanism. Nature 349, 117-127.
Brabletz, T., Kalluri, R., Nieto, M.A., and Weinberg, R.A. (2018). EMT in cancer. Nat
Rev Cancer 18, 128-134.
59
Bross, P.F., Beitz, J., Chen, G., Chen, X.H., Duffy, E., Kieffer, L., Roy, S., Sridhara, R.,
Rahman, A., Williams, G., et al. (2001). Approval summary: gemtuzumab ozogamicin in
relapsed acute myeloid leukemia. Clin Cancer Res 7, 1490-1496.
Bullinger, L., Dohner, K., Bair, E., Frohling, S., Schlenk, R.F., Tibshirani, R., Dohner, H.,
and Pollack, J.R. (2004). Use of gene-expression profiling to identify prognostic
subclasses in adult acute myeloid leukemia. N Engl J Med 350, 1605-1616.
Cerami, E., Gao, J., Dogrusoz, U., Gross, B.E., Sumer, S.O., Aksoy, B.A., Jacobsen, A.,
Byrne, C.J., Heuer, M.L., Larsson, E., et al. (2012). The cBio cancer genomics portal:
an open platform for exploring multidimensional cancer genomics data. Cancer Discov
2, 401-404.
Choi, H.Y., Saha, S.K., Kim, K., Kim, S., Yang, G.M., Kim, B., Kim, J.H., and Cho, S.G.
(2015). G protein-coupled receptors in stem cell maintenance and somatic
reprogramming to pluripotent or cancer stem cells. BMB Rep 48, 68-80.
Curtin, J.A., Quint, E., Tsipouri, V., Arkell, R.M., Cattanach, B., Copp, A.J., Henderson,
D.J., Spurr, N., Stanier, P., Fisher, E.M., et al. (2003). Mutation of Celsr1 disrupts planar
polarity of inner ear hair cells and causes severe neural tube defects in the mouse. Curr
Biol 13, 1129-1133.
Daria, D., Kirsten, N., Muranyi, A., Mulaw, M., Ihme, S., Kechter, A., Hollnagel, M.,
Bullinger, L., Dohner, K., Dohner, H., et al. (2016). GPR56 contributes to the
development of acute myeloid leukemia in mice. Leukemia 30, 1734-1741.
Dohner, H., Weisdorf, D.J., and Bloomfield, C.D. (2015). Acute Myeloid Leukemia. N
Engl J Med 373, 1136-1152.
60
Dores, G.M., Devesa, S.S., Curtis, R.E., Linet, M.S., and Morton, L.M. (2012). Acute
leukemia incidence and patient survival among children and adults in the United States,
2001-2007. Blood 119, 34-43.
Estey, E.H. (2013). Acute myeloid leukemia: 2013 update on risk-stratification and
management. Am J Hematol 88, 318-327.
Fredriksson, R., Lagerstrom, M.C., Lundin, L.G., and Schioth, H.B. (2003). The G-
protein-coupled receptors in the human genome form five main families. Phylogenetic
analysis, paralogon groups, and fingerprints. Mol Pharmacol 63, 1256-1272.
Fukushima, Y., Oshika, Y., Tsuchida, T., Tokunaga, T., Hatanaka, H., Kijima, H.,
Yamazaki, H., Ueyama, Y., Tamaoki, N., and Nakamura, M. (1998). Brain-specific
angiogenesis inhibitor 1 expression is inversely correlated with vascularity and distant
metastasis of colorectal cancer. Int J Oncol 13, 967-970.
Galle, J., Sittig, D., Hanisch, I., Wobus, M., Wandel, E., Loeffler, M., and Aust, G.
(2006). Individual cell-based models of tumor-environment interactions: Multiple effects
of CD97 on tumor invasion. Am J Pathol 169, 1802-1811.
Gao, J., Aksoy, B.A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S.O., Sun, Y.,
Jacobsen, A., Sinha, R., Larsson, E., et al. (2013). Integrative analysis of complex
cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6, pl1.
Giles, F.J., Kantarjian, H.M., Kornblau, S.M., Thomas, D.A., Garcia-Manero, G.,
Waddelow, T.A., David, C.L., Phan, A.T., Colburn, D.E., Rashid, A., et al. (2001).
Mylotarg (gemtuzumab ozogamicin) therapy is associated with hepatic venoocclusive
disease in patients who have not received stem cell transplantation. Cancer 92, 406-
413.
61
Haferlach, T., Kohlmann, A., Wieczorek, L., Basso, G., Kronnie, G.T., Bene, M.C., De
Vos, J., Hernandez, J.M., Hofmann, W.K., Mills, K.I., et al. (2010). Clinical utility of
microarray-based gene expression profiling in the diagnosis and subclassification of
leukemia: report from the International Microarray Innovations in Leukemia Study
Group. J Clin Oncol 28, 2529-2537.
Hatanaka, H., Oshika, Y., Abe, Y., Yoshida, Y., Hashimoto, T., Handa, A., Kijima, H.,
Yamazaki, H., Inoue, H., Ueyama, Y., et al. (2000). Vascularization is decreased in
pulmonary adenocarcinoma expressing brain-specific angiogenesis inhibitor 1 (BAI1).
Int J Mol Med 5, 181-183.
Hauser, A.S., Attwood, M.M., Rask-Andersen, M., Schioth, H.B., and Gloriam, D.E.
(2017). Trends in GPCR drug discovery: new agents, targets and indications. Nat Rev
Drug Discov 16, 829-842.
He, Z., Wu, H., Jiao, Y., and Zheng, J. (2015). Expression and prognostic value of
CD97 and its ligand CD55 in pancreatic cancer. Oncol Lett 9, 793-797.
I, K.Y., Huang, Y.S., Hu, C.H., Tseng, W.Y., Cheng, C.H., Stacey, M., Gordon, S.,
Chang, G.W., and Lin, H.H. (2017). Activation of Adhesion GPCR EMR2/ADGRE2
Induces Macrophage Differentiation and Inflammatory Responses via
Galpha16/Akt/MAPK/NF-kappaB Signaling Pathways. Front Immunol 8, 373.
Insel, P.A., Snead, A., Murray, F., Zhang, L., Yokouchi, H., Katakia, T., Kwon, O.,
Dimucci, D., and Wilderman, A. (2012). GPCR expression in tissues and cells: are the
optimal receptors being used as drug targets? Br J Pharmacol 165, 1613-1616.
Institute, N.C. (2018). Cancer stat facts: leukemia—acute myeloid leukemia (AML)
(https://seer.cancer.gov/statfacts/html/amyl.html).
62
Kang, X., Xiao, X., Harata, M., Bai, Y., Nakazaki, Y., Soda, Y., Kurita, R., Tanaka, T.,
Komine, F., Izawa, K., et al. (2006). Antiangiogenic activity of BAI1 in vivo: implications
for gene therapy of human glioblastomas. Cancer Gene Ther 13, 385-392.
Kaur, B., Brat, D.J., Devi, N.S., and Van Meir, E.G. (2005). Vasculostatin, a proteolytic
fragment of brain angiogenesis inhibitor 1, is an antiangiogenic and antitumorigenic
factor. Oncogene 24, 3632-3642.
Kaur, B., Cork, S.M., Sandberg, E.M., Devi, N.S., Zhang, Z., Klenotic, P.A., Febbraio,
M., Shim, H., Mao, H., Tucker-Burden, C., et al. (2009). Vasculostatin inhibits
intracranial glioma growth and negatively regulates in vivo angiogenesis through a
CD36-dependent mechanism. Cancer Res 69, 1212-1220.
Khwaja, A., Bjorkholm, M., Gale, R.E., Levine, R.L., Jordan, C.T., Ehninger, G.,
Bloomfield, C.D., Estey, E., Burnett, A., Cornelissen, J.J., et al. (2016). Acute myeloid
leukaemia. In Nat Rev Dis Primers (England), p. 16010.
Koirala, S., Jin, Z., Piao, X., and Corfas, G. (2009). GPR56-regulated granule cell
adhesion is essential for rostral cerebellar development. J Neurosci 29, 7439-7449.
Kudo, S., Konda, R., Obara, W., Kudo, D., Tani, K., Nakamura, Y., and Fujioka, T.
(2007). Inhibition of tumor growth through suppression of angiogenesis by brain-specific
angiogenesis inhibitor 1 gene transfer in murine renal cell carcinoma. Oncol Rep 18,
785-791.
Kwakkenbos, M.J., Pouwels, W., Matmati, M., Stacey, M., Lin, H.H., Gordon, S., van
Lier, R.A., and Hamann, J. (2005). Expression of the largest CD97 and EMR2 isoforms
on leukocytes facilitates a specific interaction with chondroitin sulfate on B cells. J
Leukoc Biol 77, 112-119.
63
Lagerstrom, M.C., and Schioth, H.B. (2008). Structural diversity of G protein-coupled
receptors and significance for drug discovery. Nat Rev Drug Discov 7, 339-357.
Lee, J.H., Koh, J.T., Shin, B.A., Ahn, K.Y., Roh, J.H., Kim, Y.J., and Kim, K.K. (2001).
Comparative study of angiostatic and anti-invasive gene expressions as prognostic
factors in gastric cancer. Int J Oncol 18, 355-361.
Ley, T.J., Miller, C., Ding, L., Raphael, B.J., Mungall, A.J., Robertson, A., Hoadley, K.,
Triche, T.J., Jr., Laird, P.W., Baty, J.D., et al. (2013). Genomic and epigenomic
landscapes of adult de novo acute myeloid leukemia. N Engl J Med 368, 2059-2074.
Li, S., Jin, Z., Koirala, S., Bu, L., Xu, L., Hynes, R.O., Walsh, C.A., Corfas, G., and Piao,
X. (2008). GPR56 regulates pial basement membrane integrity and cortical lamination. J
Neurosci 28, 5817-5826.
Li, Y., Cheng, J., Jiang, Y., Ma, J., Li, Q., and Pang, T. (2018). CXCL8 is associated
with the recurrence of patients with acute myeloid leukemia and cell proliferation in
leukemia cell lines. Biochem Biophys Res Commun 499, 524-530.
Lin, H.H., Hsiao, C.C., Pabst, C., Hebert, J., Schoneberg, T., and Hamann, J. (2017).
Adhesion GPCRs in Regulating Immune Responses and Inflammation. Adv Immunol
136, 163-201.
Liu, Y., Chen, L., Peng, S., Chen, Z., Gimm, O., Finke, R., and Hoang-Vu, C. (2005).
The expression of CD97EGF and its ligand CD55 on marginal epithelium is related to
higher stage and depth of tumor invasion of gastric carcinomas. Oncol Rep 14, 1413-
1420.
Maiga, A., Lemieux, S., Pabst, C., Lavallee, V.P., Bouvier, M., Sauvageau, G., and
Hebert, J. (2016). Transcriptome analysis of G protein-coupled receptors in distinct
64
genetic subgroups of acute myeloid leukemia: identification of potential disease-specific
targets. Blood Cancer J 6, e431.
Metzeler, K.H., Hummel, M., Bloomfield, C.D., Spiekermann, K., Braess, J., Sauerland,
M.C., Heinecke, A., Radmacher, M., Marcucci, G., Whitman, S.P., et al. (2008). An 86-
probe-set gene-expression signature predicts survival in cytogenetically normal acute
myeloid leukemia. Blood 112, 4193-4201.
Mrozek, K., Marcucci, G., Nicolet, D., Maharry, K.S., Becker, H., Whitman, S.P.,
Metzeler, K.H., Schwind, S., Wu, Y.Z., Kohlschmidt, J., et al. (2012). Prognostic
significance of the European LeukemiaNet standardized system for reporting
cytogenetic and molecular alterations in adults with acute myeloid leukemia. J Clin
Oncol 30, 4515-4523.
Nishimori, H., Shiratsuchi, T., Urano, T., Kimura, Y., Kiyono, K., Tatsumi, K., Yoshida,
S., Ono, M., Kuwano, M., Nakamura, Y., et al. (1997). A novel brain-specific p53-target
gene, BAI1, containing thrombospondin type 1 repeats inhibits experimental
angiogenesis. Oncogene 15, 2145-2150.
O'Hayre, M., Degese, M.S., and Gutkind, J.S. (2014). Novel insights into G protein and
G protein-coupled receptor signaling in cancer. Curr Opin Cell Biol 27, 126-135.
Paavola, K.J., and Hall, R.A. (2012). Adhesion G protein-coupled receptors: signaling,
pharmacology, and mechanisms of activation. Mol Pharmacol 82, 777-783.
Pabst, C., Bergeron, A., Lavallee, V.P., Yeh, J., Gendron, P., Norddahl, G.L., Krosl, J.,
Boivin, I., Deneault, E., Simard, J., et al. (2016). GPR56 identifies primary human acute
myeloid leukemia cells with high repopulating potential in vivo. Blood 127, 2018-2027.
65
Palena, C., Hamilton, D.H., and Fernando, R.I. (2012). Influence of IL-8 on the
epithelial-mesenchymal transition and the tumor microenvironment. Future Oncol 8,
713-722.
Park, D., Tosello-Trampont, A.C., Elliott, M.R., Lu, M., Haney, L.B., Ma, Z., Klibanov,
A.L., Mandell, J.W., and Ravichandran, K.S. (2007). BAI1 is an engulfment receptor for
apoptotic cells upstream of the ELMO/Dock180/Rac module. Nature 450, 430-434.
Peters, M.J., Joehanes, R., Pilling, L.C., Schurmann, C., Conneely, K.N., Powell, J.,
Reinmaa, E., Sutphin, G.L., Zhernakova, A., Schramm, K., et al. (2015). The
transcriptional landscape of age in human peripheral blood. Nat Commun 6, 8570.
Piao, X., Hill, R.S., Bodell, A., Chang, B.S., Basel-Vanagaite, L., Straussberg, R.,
Dobyns, W.B., Qasrawi, B., Winter, R.M., Innes, A.M., et al. (2004). G protein-coupled
receptor-dependent development of human frontal cortex. Science 303, 2033-2036.
Purcell, R.H., and Hall, R.A. (2018). Adhesion G Protein-Coupled Receptors as Drug
Targets. Annu Rev Pharmacol Toxicol 58, 429-449.
Rosenbaum, D.M., Rasmussen, S.G., and Kobilka, B.K. (2009). The structure and
function of G-protein-coupled receptors. Nature 459, 356-363.
Saito, Y., Kaneda, K., Suekane, A., Ichihara, E., Nakahata, S., Yamakawa, N., Nagai,
K., Mizuno, N., Kogawa, K., Miura, I., et al. (2013). Maintenance of the hematopoietic
stem cell pool in bone marrow niches by EVI1-regulated GPR56. Leukemia 27, 1637-
1649.
Schaefer, E.W., Loaiza-Bonilla, A., Juckett, M., DiPersio, J.F., Roy, V., Slack, J., Wu,
W., Laumann, K., Espinoza-Delgado, I., and Gore, S.D. (2009). A phase 2 study of
vorinostat in acute myeloid leukemia. Haematologica 94, 1375-1382.
66
Scholz, N. (2018). Cancer Cell Mechanics: Adhesion G Protein-coupled Receptors in
Action? Front Oncol 8, 59.
Scholz, N., Gehring, J., Guan, C., Ljaschenko, D., Fischer, R., Lakshmanan, V., Kittel,
R.J., and Langenhan, T. (2015). The adhesion GPCR latrophilin/CIRL shapes
mechanosensation. Cell Rep 11, 866-874.
Scholz, N., Monk, K.R., Kittel, R.J., and Langenhan, T. (2016). Adhesion GPCRs as a
Putative Class of Metabotropic Mechanosensors. Handb Exp Pharmacol 234, 221-247.
Short, N.J., Rytting, M.E., and Cortes, J.E. (2018). Acute myeloid leukaemia. Lancet
392, 593-606.
Sriram, K., and Insel, P.A. (2018). G Protein-Coupled Receptors as Targets for
Approved Drugs: How Many Targets and How Many Drugs? Mol Pharmacol 93, 251-
258.
Stacey, M., Chang, G.W., Davies, J.Q., Kwakkenbos, M.J., Sanderson, R.D., Hamann,
J., Gordon, S., and Lin, H.H. (2003). The epidermal growth factor-like domains of the
human EMR2 receptor mediate cell attachment through chondroitin sulfate
glycosaminoglycans. Blood 102, 2916-2924.
Steinert, M., Wobus, M., Boltze, C., Schutz, A., Wahlbuhl, M., Hamann, J., and Aust, G.
(2002). Expression and regulation of CD97 in colorectal carcinoma cell lines and tumor
tissues. Am J Pathol 161, 1657-1667.
Takeichi, M., Nakagawa, S., Aono, S., Usui, T., and Uemura, T. (2000). Patterning of
cell assemblies regulated by adhesion receptors of the cadherin superfamily. Philos
Trans R Soc Lond B Biol Sci 355, 885-890.
67
Tissir, F., Bar, I., Jossin, Y., De Backer, O., and Goffinet, A.M. (2005). Protocadherin
Celsr3 is crucial in axonal tract development. Nat Neurosci 8, 451-457.
Valk, P.J., Verhaak, R.G., Beijen, M.A., Erpelinck, C.A., Barjesteh van Waalwijk van
Doorn-Khosrovani, S., Boer, J.M., Beverloo, H.B., Moorhouse, M.J., van der Spek, P.J.,
Lowenberg, B., et al. (2004). Prognostically useful gene-expression profiles in acute
myeloid leukemia. N Engl J Med 350, 1617-1628.
Vogelstein, B., Papadopoulos, N., Velculescu, V.E., Zhou, S., Diaz, L.A., Jr., and
Kinzler, K.W. (2013). Cancer genome landscapes. Science 339, 1546-1558.
Wang, T., Ward, Y., Tian, L., Lake, R., Guedez, L., Stetler-Stevenson, W.G., and Kelly,
K. (2005). CD97, an adhesion receptor on inflammatory cells, stimulates angiogenesis
through binding integrin counterreceptors on endothelial cells. Blood 105, 2836-2844.
Ward, Y., Lake, R., Yin, J.J., Heger, C.D., Raffeld, M., Goldsmith, P.K., Merino, M., and
Kelly, K. (2011). LPA receptor heterodimerizes with CD97 to amplify LPA-initiated RHO-
dependent signaling and invasion in prostate cancer cells. Cancer Res 71, 7301-7311.
Waugh, D.J., and Wilson, C. (2008). The interleukin-8 pathway in cancer. Clin Cancer
Res 14, 6735-6741.
Weston, M.D., Luijendijk, M.W., Humphrey, K.D., Moller, C., and Kimberling, W.J.
(2004). Mutations in the VLGR1 gene implicate G-protein signaling in the pathogenesis
of Usher syndrome type II. Am J Hum Genet 74, 357-366.
Wilde, C., Fischer, L., Lede, V., Kirchberger, J., Rothemund, S., Schoneberg, T., and
Liebscher, I. (2016). The constitutive activity of the adhesion GPCR GPR114/ADGRG5
is mediated by its tethered agonist. FASEB J 30, 666-673.
68
Wobus, M., Bornhauser, M., Jacobi, A., Krater, M., Otto, O., Ortlepp, C., Guck, J.,
Ehninger, G., Thiede, C., and Oelschlagel, U. (2015). Association of the EGF-TM7
receptor CD97 expression with FLT3-ITD in acute myeloid leukemia. Oncotarget 6,
38804-38815.
Wu, S., Du, Y., Beckford, J., and Alachkar, H. (2018). Upregulation of the EMT marker
vimentin is associated with poor clinical outcome in acute myeloid leukemia. J Transl
Med 16, 170.
Yang, J., Wu, S., and Alachkar, H. (2018). Characterization of Upregulated Adhesion
GPCRs in Acute Myeloid Leukemia. Blood 132, 1515.
Yona, S., Lin, H.H., Siu, W.O., Gordon, S., and Stacey, M. (2008). Adhesion-GPCRs:
emerging roles for novel receptors. Trends Biochem Sci 33, 491-500.

Abstract (if available)

Abstract Adhesion GPCRs have become increasingly evident in cancer research in recent years. Yet, data supporting the contribution of this family of genes to hematological malignancies, particularly acute myeloid leukemia are limited. Here, we use publically available genomic data to characterize the expression of the 33 aGPCRs in patients with AML and examine whether upregulation of these genes is associated with the clinical and molecular characteristics of patients. Upregulation in one or more of eight aGPCR genes (ADGRB1, CELSR2, ADGRD1, ADGRE1, ADGRE2, ADGRE5, ADGRG1, ADGRG3) was significantly associated with shorter overall survival (OS) (median OS: 11.8 vs 55.4 months

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Clinical, functional and therapeutic analysis of CD99 in acute myeloid leukemia

PDF

Genomics and transcriptomic alterations of the glutamate receptors in acute myeloid leukemia

PDF

The immunomodulatory effects of midostaurin on T cells

PDF

Characterization of epithelial-mesenchymal transition in acute myeloid leukemia

PDF

Investigation of the synergistic effect of midostaurin, a tyrosine kinase inhibitor, with anti FLT3 antibodies-based therapies for acute myeloid leukemia

PDF

Investigating CD99 as a therapeutic target in acute myeloid leukemia

PDF

Maternal embryonic leucine zipper kinase (MELK) as a novel therapeutic target in the treatment of acute lymphoblastic leukemia

PDF

FLT3/CD99 bispecific antibody-based nanoparticles (BiAbs) for acute myeloid leukemia

PDF

Differences in the mutational landscape of clonal hematopoiesis of indeterminate potential among Races and between Male and Female patients with cancer.

PDF

Characterization of the interaction of nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain-containing protein 12 (Nlrp12) with hematopoietic cell kinase (Hck)

PDF

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

PDF

SGLT2 inhibitors reverse adipocyte differentiation and reduce daunorubicin metabolism in acute lymphoblastic leukemia treatment

PDF

Investigating the effect of FLT3 tyrosine kinase inhibitors and anti-FLT3 antibody-based therapy in acute myeloid leukemia

PDF

Disruption of immature Sertoli cell functions by endocrine disruptors and analgesic drugs

PDF

NMI (near-infrared dye conjugate MAO A inhibitor) outperformed FDA-approved prostate cancer drugs with a unique mechanism based on bioinformatic analysis of NCI60 screening data

PDF

Reprogramming exosomes for immunotherapy of acute myeloid leukemia

PDF

Application of elastin-like polypeptides to therapeutics in leukemia

PDF

APOC2 presents a viable therapeutic target in cancer

PDF

Structure-based computational analysis and prediction of TCR CDR3 loops in the TCR-peptide-MHC complex using solvation parameters and peptide molecular dynamics.

PDF

Solvation as a driving force for peptide docking to the major histocompatibility complex (MHC) class II molecules

Asset Metadata

Creator Yang, Jiawen (author)

Core Title Characterization of upregulated adhesion GPCRs in acute myeloid leukemia

School School of Pharmacy

Degree Master of Science

Degree Program Molecular Pharmacology and Toxicology

Publication Date 11/08/2019

Defense Date 04/01/2019

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

Tag AML,GPCRs,IL-8 signaling pathway,OAI-PMH Harvest,overall survival,pathway analysis

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Alachkar, Houda (committee chair), Culty, Martine (committee member), Duncan, Roger (committee member)

Creator Email jiawen_yang16@outlook.com,jiawenya@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-156954

Unique identifier UC11662913

Identifier etd-YangJiawen-7432.pdf (filename),usctheses-c89-156954 (legacy record id)

Legacy Identifier etd-YangJiawen-7432.pdf

Dmrecord 156954

Document Type Thesis

Format application/pdf (imt)

Rights Yang, Jiawen

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