APOC2 presents a viable therapeutic target in cancer

PDF

Download

Open document

Flip pages

Download a page range

Download transcript

Copy asset link

Request this asset

Request accessible transcript

Transcript (if available)

Content

APOC2 PRESENTS A VIABLE THERAPEUTIC TARGET IN CANCER

by
Yuqiao Liu

A Thesis Presented to the
FACULTY OF THE USC SCHOOL OF PHARMACY
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
PHARMACEUTICAL SCIENCES

August 2021

Copyright 2021 Yuqiao Liu
ii

Acknowledgements
I’d like to thank my advisor Dr. Houda Alachkar. She always gives me suggestions in doing
research and mentors me in thesis writing. She helps me make it possible to publish part of the
data in this thesis as a journal paper: Deregulation of Apolipoprotein C2 gene in cancer, a
potential metabolic vulnerability
1
. I also appreciate her persistent encouragement and support
during my graduate study at USC.
I’d like to thank the members of my master’s thesis committee, Dr. Roger Duncan and Dr. Curtis
Okamoto, for their great guidance.
I’d like to thank my lab member Tian Zhang, who taught me the cloning technologies and helped
me with the survival analysis part. She was always there when I was in my first year and
answered all my questions regarding the experiments.
I’d like to thank my lab member Yiting Meng, who helped me with the gene expression analysis
part. I also want to thank my lab members Yang Zhao, Atham Ali, Lucas Gutierrez, John
Beckford, Lena Keossayan and Mohammed Almatani. Thank you all for your help and
inspiration.
Finally, I’d like to thank my parents for their unfailing love. Without them, I wouldn’t be the
person I am today.

iii

Table of Contents
Acknowledgements ......................................................................................................................... ii
List of Tables .................................................................................................................................. v
List of Figures ................................................................................................................................ vi
Abbreviations ................................................................................................................................ vii
Abstract .......................................................................................................................................... ix
Chapter 1 Introduction .................................................................................................................... 1
1.1 Cancer and cancer metabolism .............................................................................................. 1
1.2 Expression and Function of Apolipoprotein C2 (APOC2) .................................................... 4
1.3 Little is known about the role of APOC2 in cancer ............................................................... 6

Chapter 2 Deregulation of APOC2 Gene in Cancer ....................................................................... 8
2.1 Introduction ............................................................................................................................ 8
2.2 Materials and methods ......................................................................................................... 10
2.2.1 Patient data sets ............................................................................................................ 10
2.2.2 Gene expression analysis .............................................................................................. 10
2.2.3 DNA methylation analysis............................................................................................ 13
2.2.4 Ingenuity pathway analysis .......................................................................................... 13
2.2.5 Statistical analysis......................................................................................................... 14
2.3 Results .................................................................................................................................. 15
2.3.1 Patterns of APOC2 gene deregulation in cancer .......................................................... 15
2.3.2 APOC2 is upregulated in several malignancies ............................................................ 27
2.3.3 APOC2 is hypomethylated in several malignancies ..................................................... 31
2.3.4 Clinical attributes associated with alteration of APOC2 .............................................. 33
2.3.5 APOC2 deregulation is associated with TP53 mutation ............................................... 38
2.3.6 Molecular pathways associated with alteration of APOC2 .......................................... 41
2.3.7 APOC2 alterations are associated with shorter cancer overall survival ....................... 46
2.4 Discussion ............................................................................................................................ 50

iv

Chapter 3 Functional Characterization of APOC2 in Normal Mouse Primary Cells ................... 52
3.1 Introduction .......................................................................................................................... 52
3.2 Materials and methods ......................................................................................................... 54
3.2.1 RNA extraction and cDNA synthesis ........................................................................... 54
3.2.2 Plasmid constructs ........................................................................................................ 54
3.2.3 Cell line and transfection .............................................................................................. 55
3.2.4 Lentiviral production .................................................................................................... 55
3.2.5 Mouse primary cell infection ........................................................................................ 56
3.2.6 Quantitative PCR analysis ............................................................................................ 57
3.2.7 Western blot analysis .................................................................................................... 57
3.2.8 Proliferation assay ........................................................................................................ 58
3.2.9 Statistical analysis......................................................................................................... 58
3.3 Results .................................................................................................................................. 59
3.3.1 Apoc2 is knocked down in normal mouse primary cells .............................................. 59
3.3.2 Apoc2 is overexpressed in normal mouse primary cells ............................................... 61
3.3.3 Apoc2 is not essential in normal mouse primary cell proliferation .............................. 63
3.4 Discussion ............................................................................................................................ 66

Chapter 4 Conclusion .................................................................................................................... 68
References ..................................................................................................................................... 71

v

List of Tables
Table 2.1 Microarray datasets from GEO database associated with APOC2 expression analysis 11
Table 2.2 Clinical characteristics of the patient samples on cBioPortal ....................................... 18
Table 2.3 APOC2 gene deregulation in cancer ............................................................................. 19
Table 2.4 APOC2 mutations associated with hypertriglyceridemia ............................................. 23
Table 2.5 APOC2 mutations associated with cancer .................................................................... 25
Table 2.6 Chromosomal status associated with alteration of APOC2 .......................................... 36
Table 2.7 Genes with highest mutation frequencies in APOC2 altered and unaltered groups ..... 39
Table 2.8 TP53 mutation in APOC2 altered and unaltered groups in cancer ............................... 40
Table 2.9 z-scores of Ingenuity Pathway Analysis results ........................................................... 43
Table 2.10 -log(B-H p-value) of Ingenuity Pathway Analysis results .......................................... 44
Table 2.11 Association between APOC2 gene expression, alteration and cancer patient survival
....................................................................................................................................................... 48

vi

List of Figures
Figure 2.1 Patterns of APOC2 gene deregulation in cancer ......................................................... 17
Figure 2.2 Analysis of APOC2 expression in different cancers ................................................... 29
Figure 2.3 Analysis of APOC2 expression in different cancers (supplementary) ........................ 30
Figure 2.4 Promoter methylation level of APOC2 in cancers where APOC2 is upregulated ....... 32
Figure 2.5 Clinical attributes associated with alteration of APOC2 ............................................. 35
Figure 2.6 Ingenuity pathway analysis associated with alteration of APOC2 .............................. 43
Figure 2.7 Survival analysis for patients with different cancers associated with APOC2 alteration
and mRNA expression .................................................................................................................. 47
Figure 3.1 Apoc2 is knocked down in normal mouse primary cells ............................................. 60
Figure 3.2 Apoc2 is overexpressed in normal mouse primary cells ............................................. 62
Figure 3.3 Apoc2 is not essential in normal mouse primary cell proliferation ............................. 65

vii

Abbreviations

ACC acetyl-CoA carboxylase
AML acute myeloid leukemia
APOC2 Apolipoprotein C2
BLCA bladder urothelial carcinoma
BSA bovine serum albumin
DMEM Dulbecco’s Modified Eagle’s Medium
FA fatty acids
FAO fatty acid oxidation
FASN fatty acid synthase
FBS fetal bovine serum
FXR farnesoid X receptor
HDL high-density lipoprotein
HNSC head and neck squamous cell carcinoma
HSPC hematopoietic stem progenitor cell
HRP horseradish peroxidase
IDH isocitrate dehydrogenase
IPA Ingenuity Pathway Analysis
viii

LPL lipoprotein lipase
LXR liver X receptor
mApoc2 mouse Apolipoprotein c2
miRNA microRNA
OXPHOS oxidative phosphorylation
PI3K phosphatidylinositol 3-kinase
qPCR quantitative PCR
RXR retinoid X receptor
TALEN transcription activator-like effector nuclease
VLDL very low-density lipoprotein

ix

Abstract
Apolipoprotein C2 (APOC2) plays an essential role in lipid metabolism and fatty acids transport
to meet cells energy demands. Cancer cells require high level of energy to proliferate and invade
normal tissues. APOC2 genomics and transcriptomic alterations patterns in cancer remained
undiscovered. In this thesis, we characterize the deregulation of APOC2 in several types of cancer
by analyzing 46706 samples in 176 cancer studies, gene expression data in 13 datasets and gene
methylation data of 2121 samples in four cancers. We found that amplifications, mutations and
deep deletions are the main APOC2 gene alterations. Approximately 1% of all samples have at
least one of these genetic alterations. 19q gain status is present in 50% of samples with APOC2
alterations. APOC2 is significantly overexpressed in several malignancies compared with the
respective normal tissues. In these malignancies, we found lower promoter methylation level of
APOC2 in primary tumor than normal samples. Alterations and high expression level of APOC2
gene were found to be relevant to poor clinical outcome. Patients with APOC2 gene alterations
had significantly shorter overall survival (median survival, 37.97 vs 80.68 months; P<0.0001).
APOC2 gene is frequently mutated with TP53 (P<0.0001). In addition to lipid metabolism,
immune response related pathways were highly deregulated in APOC2 altered cancers. Our results
suggest that APOC2 gene upregulation and alterations occur frequently in cancer and that
functional and mechanistic studies are warranted to further establish APOC2 as a potential
therapeutic target. To establish APOC2 as cancer therapeutic target, investigating the role of this
gene in normal cells is needed. Here, we hypothesized that APOC2 is dispensable for the survival
of normal cells. By gain and loss of function approaches, we constructed lentiviral systems to
overexpress and knock down Apoc2 gene in normal mouse primary cells. Western blot and qPCR
were performed to confirm the overexpression and knockdown effects of the lentiviral particles.
x

We found that both the normal mouse primary spleen and bone marrow cells were still proliferating
up to five days after Apoc2 overexpression and knockdown, without statistically significant
difference from the control groups. Our research suggests that Apoc2 may not essential for the
proliferation of normal mouse primary spleen and bone marrow cells. While further phenotypic,
functional and mechanistic analysis are needed to confirm that Apoc2 is dispensable for normal
hematopoietic cells, these data are encouraging to further investigate the development of Apoc2
into a viable therapeutic target in cancer.
1

Chapter 1 Introduction
1.1 Cancer and cancer metabolism
Cancer is a group of diseases in which some of the body’s cells grow abnormally and invade or
spread to other parts of the body. As one of the leading causes of death in the world, cancer has a
prevalence of more than 6 million mortalities annually, and this number is increasing rapidly.
2-7
In
2020, almost 10 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred.
Female breast cancer was the most commonly diagnosed cancer in 2020, with an estimated 2.3
million new cases (11.7%), followed by lung (11.4%), colorectal (10.0%), prostate (7.3%), and
stomach (5.6%) cancers.
7

According to the original hallmarks of cancer, there are six capabilities which lead to the
development and progression of cancers. The acquired capabilities of cancer comprise self-
sufficiency in growth signals, insensitivity to antigrowth signals, evading apoptosis, limitless
replicative potential, sustained angiogenesis, as well as tissue invasion and metastasis.
8
In the last
decades, there are two emerging hallmarks of cancer, which are reprogramming of energy
metabolism and evading immune destruction.
9
Cancer cells require higher level of energy to
proliferate than normal cells. By metabolic reprogramming, cancer cells are able to rewire their
metabolism to obtain nutrients and support their growth and survival.
10

Metabolic pathways that are commonly reprogrammed include those that help tumor cells to get
access to plentiful nutrients and produce ATP from them, and generate biosynthetic precursors as
well as macromolecules.
10
An example of these pathways is the glycolysis related pathways.
Glycolysis is a pathway for catabolism of glucose. The process of glycolysis in normal cells
converts one molecule of glucose to two molecules of pyruvate, followed by the transportation of
2

pyruvate into the mitochondria. The energy released in this process is conserved in the form of
ATP and reducing equivalent NADH. In normal cells, glycolysis is prioritized when there’s limited
oxygen supply.
11
Tumor cells, on the other hand, preferentially utilize glycolysis even with
abundant oxygen supply and convert pyruvate into lactate, which has an extremely low energy
yield. As a very high glucose consumption rate is required for cancer proliferation, a high rate of
glucose catabolism into lactate is the most common metabolic phenotype across tumor cells.
12

Another type of reprogrammed pathways is those that support tumor cells in nutrient depletion
conditions by catabolizing macromolecules from inside or outside of the cell. An example of these
pathways is protein degradation pathways. These pathways can supply amino acids for tumor cells.
Intracellular proteins are recycled through autophagy, a highly regulated process through which
proteins are delivered to the lysosome and degraded
13
. Autophagy was found to contribute to
several types of cancer in murine models such as lymphoma and liver carcinoma
14
.
Targeting cancer metabolism could be useful in the clinic. For example, inactivation of lactate
dehydrogenase-A, which is a metabolic enzyme that converts pyruvate to lactate, contributes to
decreased tumorigenesis and disease regression in lung cancer murine models
15
.
A second example is targeting the mTOR pathway, which is an essential signaling pathway that
regulates metabolic processes. mTOR, a member of the phosphatidylinositol 3-kinase (PI3K)-
related kinase family of protein kinases, is highly involved in glucose, lipid, amino acid, nucleotide
metabolism and other biosynthetic pathways.
16,17
In acute lymphoblastic leukemia, targeting
multiple components of the PI3K/mTOR pathway can delay tumor progression, reduce tumor
burden and enhance the survival rate in murine xenograft models.
18,19
In bladder cancer, a novel
3

pyruvate dehydrogenase kinase 4 inhibitor, cryptotanshinone, was found to inhibit the invasiveness
and metastasis of cancer cells via mTOR related signaling pathway.
20

A third example is targeting isocitrate dehydrogenase isoform 1 (IDH1) and 2 (IDH2). Wild-type
IDH1 and IDH2 are key metabolic enzymes within tricarboxylic acid cycle and convert isocitrate
to α-ketoglutarate. Mutations in IDH1 and IDH2 affect approximately 20% of patients with acute
myeloid leukemia (AML) and generate an oncometabolite, D-2-hydroxyglutarate, which can lead
to hypermethylation of histones and DNA, resulting in cell abnormal differentiation.
21,22
These
mutations are highly associated with glucose, glutamine, NADPH, amino acid and lipid
metabolism.
23
Enasidenib and ivosidenib, two small molecular inhibitors targeting IDH2 and IDH1
gene mutations, respectively, have been proved by FDA for AML treatment.
24

Another example is targeting pathways associated with de novo fatty acid synthesis. Fatty acids
(FA) are critical in the biosynthesis of cell membranes and can function as energy sources as well
as signaling molecule. In the 1950s, it was first discovered that tumors are able to synthesize lipids,
and a following study determined that the large majority of lipids in tumor cells are obtained from
de novo synthesis instead of exogenous uptake.
25,26
De novo FA synthesis is a process first
converting acetyl-CoA groups to malonyl-CoA by the enzyme acetyl-CoA carboxylase (ACC),
and then assembling the fatty acid chain palmitate from malonyl-CoA via enzyme fatty acid
synthase (FASN). In lung cancer mouse models, ND-464, an allosteric inhibitor of ACC, was
found to have antitumor efficacy.
27
In lung, ovarian, prostate and pancreatic tumor xenograft
models, inhibition of FASN can decrease tubulin palmitoylation and disturb microtubule
organization, resulting in the suppression of tumor cell growth.
28

4

1.2 Expression and Function of Apolipoprotein C2 (APOC2)
APOC2 is a single polypeptide chain of 79 amino acid residues and is a constituent of
chylomicrons, very low-density lipoprotein (VLDL), low-density lipoprotein, and high-density
lipoprotein (HDL). The human APOC2 gene is located on the long arm of chromosome 19, with
close proximity to the APOC1 and APOE genes. APOC2 is expressed primarily in the liver
29
, and
is then secreted into the plasma. APOC2 binds lipoprotein lipase (LPL) and lipids in the plasma.
Besides being expressed in the liver, APOC2 is also expressed in the intestine
29
and in the
macrophages
30
. The mouse Apoc2 (mApoc2) gene, similarly, is contained within the Apoe-c1-c2
gene cluster. In the four exons of mApoc2, the major liver start site shows 62% homology with the
human promotor of APOC2. The exon 2 of mApoc2 includes part of the 5’ untranslated region and
the signal peptide, exons 3 and 4 of mApoc2 include 79 amino acids of the protein coding
sequences, and exon 4 includes the 3’ untranslated region, which is similar to the structure of
human APOC2 gene.
31
mApoc2 is expressed in fetal liver, adult liver, intestine and peritoneal
macrophages.
31

Different regulation patterns of APOC2 transcription are found in different tissues. In liver,
APOC2 transcription is regulated by the farnesoid X receptor (FXR) and retinoid X receptor (RXR),
which also regulate genes associated with bile acid synthesis and transport.
32
By the stimulation
of bile acids, FXR/RXR heterodimers bind to response elements of the hepatic control region 1
and 2, which enhances the activity of APOC2 promotor.
33
In macrophages, liver X receptor (LXR)
and RXR regulates APOC2 transcription. Two macrophage-specific multi-enhancer elements are
found to be essential for triggering the activity of APOC2 promoter by LXR and RXR.
30
In
intestine, the regulation of APOC2 transcription is not fully elucidated.
5

APOC2 plays an important role in lipid metabolism, it participates in hydrolysis of triglycerides,
VLDLs and HDLs to release FA. APOC2 also functions as a physiological activator of LPL via
direct helix-helix interactions between APOC2 and the loop covering the active site of LPL
34
.
Specifically, certain amino acid residues in the C-terminal helix of APOC2 are identified to be
essential for LPL activation.
35
In addition to the C-terminal helix, APOC2 contains two other
amphipathic helices, which facilitate lipid binding.
36
A recent study uncovered a unique function of APOC2 in hematopoietic stem progenitor cell
(HSPC) maintenance. APOC2 is required for LPL-mediated release of essential a fatty acid,
docosahexaenoic acid, which regulates HSPC expansion.
37

Particular mutations in the APOC2 gene have been identified in premature vascular disease,
encephalopathy, severe hypertriglyceridemia and recurrent pancreatitis. These mutations are
associated with loss of functional C-terminal, so that APOC2 is unable to activate LPL. Patients
with an insertion mutation c.274dupC causing translation reading frame shift beginning at residue
70 presented fasting chylomicronemia and an elevation of VLDL. This mutation was related to
premature ischemic vascular disease.
38
A nonsense mutation c.255C > A of APOC2 gene was
found in an infant presented with massive hyperchylomicronemia and a severe encephalopathy.
39

In addition, APOC2 deficiency with a del-ins mutation c.86A > CC was found to be associated
with severe hypertriglyceridemia.
40

6

1.3 Little is known about the role of APOC2 in cancer
Cancer cells require FA for proliferation and survival. FA is essential for membrane biosynthesis
and cellular signaling of cancer cells. FA also acts as a fundamental energy source during
conditions of metabolic stress.
41
In addition to de novo lipogenesis, selected breast cancer and
sarcoma cells were found to fuel their growth using LPL-mediated lipolysis to obtain FA from
circulating diet-derived lipoproteins.
42
In hepatocellular carcinoma (HCC), it was found that high
expression of LPL was associated with poor prognosis. Besides, human HCC cells depended on
both de novo synthesis and exogenous FA uptake for survival.
43
Enhanced FA uptake and
upregulation of the gene encoding LPL were also found to accelerate progression of melanocyte
neoplasia in zebrafish.
44
Based on its role in the release of FA and the activation of LPL, APOC2
is possibly linked to cancer development.
In solid cancer, high APOC2 serum level is associated with significantly shorter survival after
tumor resection in patients with pancreatic cancer. In addition, it has also been shown that the
recombinant APOC2 increased both the growth and invasion of pancreatic cancer cell lines in a
dose-dependent manner.
45
Very little is known about the expression patterns and the roles of
APOC2 in other malignancies.
Recently, we revealed a new role of APOC2 in AML. We reported that APOC2 is upregulated and
related to poor clinical outcome in AML. APOC2 physically binds to CD36 and triggers the
phosphorylation of LYN and ERK proteins, which are downstream targets of CD36, leading to
enhanced bioenergetic level of AML cells. Targeting APOC2 or CD36 can suppress cell
proliferation, result in apoptosis in vitro and delay leukemia development in murine models. This
study suggests that APOC2-CD36 signal axis can be a novel therapeutic target in AML and CD36
7

antibody as a viable treatment strategy to develop into the clinic.
46
Whether APOC2 would also
be a therapeutic target in other cancers remains to be explored. To address whether APOC2 has a
broader role in cancer development, we leveraged public genomics and transcriptomic data to
characterize the deregulation of APOC2 in cancer. In addition, whether targeting APOC2 in normal
cells would result in any phenotypic changes and lead to cell death requires further study. To help
determine the effect of targeting APOC2 in normal cells, we engineered an overexpression and a
knockdown lentiviral expression system to perform gain and loss of function approaches of the
Apoc2 gene in normal mouse primary cells and assess the phenotypic changes in the cells.

8

Chapter 2 Deregulation of APOC2 Gene in Cancer
2.1 Introduction
APOC2, an activator of LPL, participates in the hydrolysis of triglycerides, VLDLs and HDLs to
release free fatty acids. APOC2 plays an important role in lipid metabolism as well as fatty acids
transport to meet the energy demands of the cells. Because cancer cells require high level of energy
to proliferate and invade normal tissues, I hypothesized that APOC2 is possibly related to cancer
development.
Even though little is known about the function of APOC2 in cancer development, the function of
CD36, APOC2’s partner, is more elucidated. CD36 is a transmembrane glycoprotein and is also
known as FA translocase and scavenger receptor class B type 2. CD36 is found to accelerate tumor
growth and promote tumor metastasis. In a cervical cancer xenograft model, dietary oleic acid
upregulated the expression of CD36 and stimulated tumorigenesis. The inhibition of CD36 can
prevent the tumor-enhancing effect. Furthermore, oleic acid facilitated tumor development by
activating Src kinase and the downstream ERK 1/2 pathway in a CD36-dependent manner.
47
In
glioblastoma, cancer stem cells expressing high levels of CD36 were found to enhance self-
renewal and tumor initiation.
48
Cytarabine is a chemotherapy medication used to treat AML. In
cytarabine-resistant human AML cells, upregulated CD36 expression, elevated fatty acid oxidation
(FAO) and high oxidative phosphorylation (OXPHOS) were observed. Targeting mitochondrial
metabolism via CD36-FAO-OXPHOS can enhance cytarabine’s antileukemic effects.
49

To further explore the relation between APOC2 and cancer development, a study to investigate
and summarize the deregulation patterns of APOC2 in all types of cancer is necessary. In this
chapter, I first utilized public genomics and transcriptomic data including cBioPortal, Oncomine
9

and UALCAN datasets to study the expression and methylation conditions of APOC2 gene in
cancer. Then the association between APOC2 alterations and other important gene mutations,
cellular pathways as well as clinical outcomes were analyzed. These analyses can provide an
insight into establishing APOC2 as a potential target for cancer.

10

2.2 Materials and methods
2.2.1 Patient data sets
Patient’s genetic alterations and clinical data were downloaded from cBioPortal
50,51
(https://www.cbioportal.org/) on April 8, 2020. We analyzed data in 176 cancer studies that were
manually curated including The Cancer Genome Atlas (TCGA) and non-TCGA studies with
46706 non-overlapping samples. Survival data were downloaded from the TCGA database
available at cBioPortal. Patient’s APOC2 expression data in different types of cancer were
downloaded from the Oncomine database (https://www.oncomine.org/resource/main.html).
Patient’s gene methylation data were downloaded from TCGA analysis available at the UALCAN
database.
52

2.2.2 Gene expression analysis
APOC2 gene expression levels in various cancer types were obtained from the Oncomine database.
The significantly overexpressed APOC2 in cancer tissues compared to normal tissues was
identified in Oncomine datasets with setting the threshold parameters by P< 0.05, fold-change >2,
and gene rank in the top 10%. For each specific cancer study that was identified on Oncomine,
APOC2 expression raw data were obtained from the NCBI-GEO database of cancer types vs.
healthy tissues shown in Table 2.1. The gene microarray data were analyzed using the GEO query
package in R
53
, also all data omitting negative values were log (base 2) transformed and median-
centered normalized using the DescTools package in R. The mean fold change between cancer
11

group and healthy control was compared, and we used an unpaired t-test in R to calculate the p-
value for each test.

Table 2.1 Microarray datasets from GEO database associated with APOC2 expression
analysis

Cancer Type GEO
Accession
Tissue Platform
Glioblastoma GSE4536 Glioblastoma Affymetrix Human Genome U133
Plus 2.0 Array
Glioblastoma GSE2223 Glioblastoma SHFK
Glioblastoma GSE2223 Anaplastic
Oligoastrocytoma
SHFK
Breast Cancer GSE1477 Invasive Lobular
Breast Carcinoma
Protein Design Labs Hu03 Custom
Affymetrix GeneChip Array
Breast Cancer GSE1477 Invasive Ductal Breast
Carcinoma
Protein Design Labs Hu03 Custom
Affymetrix GeneChip Array
Breast Cancer GSE3744 Ductal Breast
Carcinoma
Affymetrix Human Genome U133
Plus 2.0 Array
Lymphoma GSE2350 Lymphoma's
Centroblastic
Lymphoma
Affymetrix Human Genome U95A
Array; Affymetrix Human Genome
U95 Version 2 Array
12

Lymphoma GSE2350 Diffuse Large B Cell
Lymphoma
Affymetrix Human Genome U95A
Array; Affymetrix Human Genome
U95 Version 2 Array
Lymphoma GSE2350 Burkitt's Lymphoma Affymetrix Human Genome U95A
Array; Affymetrix Human Genome
U95 Version 2 Array
Colorectal
Cancer
GSE9348 Colorectal Carcinoma Affymetrix Human Genome U133
Plus 2.0 Array
Hypopharyngeal
Cancer
GSE2379 Head and Neck
Squamous Cell
Carcinoma
Affymetrix Human Genome U95A
Array
Renal Tumor GSE11151 Papillary Renal Cell
Carcinoma
Affymetrix Human Genome U133
Plus 2.0 Array
Renal Tumor GSE11151 Chromophobe Renal
Cell Carcinoma
Affymetrix Human Genome U133
Plus 2.0 Array
Renal Tumor GSE11151 Clear Cell Renal Cell
Carcinoma
Affymetrix Human Genome U133
Plus 2.0 Array
Melanoma GSE7553 Cutaneous Melanoma Affymetrix Human Genome U133
Plus 2.0 Array
Melanoma GSE7553 Skin Squamous Cell
Carcinoma
Affymetrix Human Genome U133
Plus 2.0 Array
Gastric Cancer GSE19826 Gastric Cancer Affymetrix Human Genome U133
Plus 2.0 Array

13

2.2.3 DNA methylation analysis
APOC2 promoter methylation data of 2121 samples in colon adenocarcinoma, breast invasive
carcinoma, stomach adenocarcinoma and kidney renal clear cell carcinoma in TCGA analysis were
downloaded from the UALCAN database
52
. The gene symbol “APOC2” was searched and
methylation analysis was queried. Beta (β) value is the ratio of the methylated probe intensity and
the sum of methylated and unmethylated probe intensity. Beta (β) value indicates level of DNA
methylation ranging from 0 (unmethylated) to 1 (fully methylated). We compared methylation
beta (β) values associated with probes that correlate with APOC2 gene between tumors and healthy
controls. The ID of the probes in Illumina Infinium Human methylation 450 BeadChip were
cg22164781, cg27436184, cg10169327, cg14723423 and cg01958934.

2.2.4 Ingenuity pathway analysis
From cBioPortal, we queried samples with APOC2 mutation, copy number alterations as well as
mRNA expression z-scores threshold of ±2 in cancers where APOC2 was upregulated. Ingenuity
pathway analysis (IPA) (QIAGEN Inc., https://digitalinsights.qiagen.com/products-
overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/) was used to
identify potential signaling pathways. The expression log ratio of the significant genes in these
cancers were imported into IPA. We used comparison analysis to evaluate common pathways that
were associated with APOC2 alterations and high mRNA level.
14

2.2.5 Statistical analysis
Kaplan-Meier survival curves were generated for the comparison of overall survival (OS) between
patients with APOC2 alterations including copy number alterations as well as mutations and those
without, and between patients with low (Z < -2) and high (Z > 2) APOC2 expression level. P-
value for OS was calculated using the Mantel-Cox test in GraphPad Prism 7.0 (GraphPad Software,
Inc.). P-value for clinical attributes associated with chromosomal gain and loss was calculated
using Chi-squared test. Kruskal-Wallis test was used to calculate p-value for clinical attributes
associated with fraction genome altered, mutation count and MSIsensor score. P-value for APOC2
alteration association with gene mutations was calculated by Fisher exact test. Student’s t-test was
used to calculate the p-value for APOC2 methylation. The data in gene expression analysis and
DNA methylation analysis were log transformed and most of them were normally distributed.
Mann-Whitney test was used for the APOC2 gene expression data in invasive ductal breast
carcinoma, early stage colorectal tumor and hypopharyngeal cancer, which were not normally
distributed. P-value less than 0.05 was considered statistically significant.

15

2.3 Results
2.3.1 Patterns of APOC2 gene deregulation in cancer
Data of APOC2 gene alteration were downloaded from curated set of non-redundant studies
available on cBioPortal. In 176 studies of 34 different cancers and 46706 samples (Table 2.2),
amplification, mutation and deep deletion were the major APOC2 gene alterations that were
observed (Figure 2.1A). Approximately 1% (251 patients) of all patients have at least one of these
genetic alterations in APOC2. APOC2 gene alterations occurred at relatively higher frequency in
bladder cancer, (9.72%), endometrial carcinoma (3.24%), cervical squamous cell carcinoma
(3.19%) and pancreatic adenocarcinoma (2.72%). Gene amplification is the most common
deregulation found for APOC2 in cancer. For several cancer types such as pancreatic cancer,
bladder cancer and prostate cancer, amplification is the only type of alteration that affected the
APOC2 gene. Although mutations in APOC2 were rare, they still occurred in 2.3 % of non-
melanoma skin cancer, 0.51% endometrial carcinoma, 0.34% esophagogastric adenocarcinoma
and at lower frequencies in other types of cancer (Table 2.3). APOC2 mutations occurred in 26
different positions as missense mutations (20 cases) and truncating mutations (6 cases) (Figure
2.1B). Deep deletions in APOC2 occurred in 1.36% of diffuse glioma, 0.52% prostate
adenocarcinoma, 0.32% B-lymphoblastic leukemia/lymphoma and at lower frequencies in other
types of cancer (Table 2.3).
Mutation in the APOC2 gene causing APOC2 deficiency has been associated with
hypertriglyceridemia (HTG) and deregulation of lipid metabolism.
54
We compared the APOC2
mutations that were frequent in HTG summarized in Table 2.4 associated with those that were
identified in different types of cancer summarized in Table 2.5.
40,55-59
We found that the APOC2
16

“c.10C>T” mutation found in bladder urothelial carcinoma was also reported in patients with HTG.
This mutation introduces a stop codon and results in termination at position 4. The mutation
“c.122A>C”, which converted the lysine codon at position 41 into threonine, found in HTG
(ApoCII-v), also occurred in germinal center B-cell like diffuse large B-cell lymphoma. However,
other mutations were completely different between HTG and cancer.

17

Figure 2.1 Patterns of APOC2 gene deregulation in cancer. (A) Mutation and copy number
alteration analysis of APOC2 in different cancers determined by cBioPortal. The alterations
include amplification (red), deep deletion (blue) and mutation (green). “+” indicates the existence
of corresponding alteration type. “-” indicates lack of corresponding alteration type. (B) Mutation
diagram of APOC2 in cancer. The diagram presents the mutation sites and frequencies of APOC2.
Green spots represent missense mutations. Black spots represent truncating mutations.

18

Table 2.2 Clinical characteristics of the patient samples on cBioPortal

Total
Diagnosis age, y (%)
≤5 2864 (6.4)
5-20 2807 (6.3)
20-35 975(2.2)
36-50 3016 (6.7)
50-65 6370 (14.2)
65-80 5423 (12.1)
≥80 813 (1.8)
NA 22478 (50.3)
Sex, No. (%)
Female 20224(45.2)
Male 19825 (44.3)
NA 4702 (10.5)
Race, No. (%)
White/Caucasian 11499 (25.7)
Black/African American 1325 (3.0)
Asian 797 (1.8)
Other/NA 31134 (69.5)

19

Table 2.3 APOC2 gene deregulation in cancer

Cancer type Frequency Total case
number
Amplification Mutation Deep
deletion
Bladder Cancer 9.72%
(7 cases)
— — 72 (9.72%)
Endometrial Carcinoma 2.56%
(15 cases)
0.51%
(3 cases)
0.17%
(1 case)
586 (3.24%)
Cervical Squamous Cell
Carcinoma
3.19%
(8 cases)
— — 251 (3.19%)
Pancreatic Adenocarcinoma 2.72%
(5 cases)
— — 184 (2.72%)
Ovarian Epithelial Tumor 1.88%
(11 cases)
— 0.68%
(4 cases)
584 (2.57%)
Pancreatic Cancer 2.36%
(16 cases)
— — 678 (2.36%)
Skin Cancer, Non-Melanoma — 2.3%
(4 cases)
— 174 (2.3%)
Prostate Cancer 2.03%
(13 cases)
— — 639 (2.03%)
Adrenocortical Carcinoma 2.02%
(2 cases)
— — 99 (2.02%)
20

Sarcoma 1.18%
(3 cases)
— 0.78%
(2 cases)
255 (1.96%)
Soft Tissue Sarcoma 1.85%
(7 cases)
— — 379 (1.85%)
Diffuse Glioma 0.39%
(2 cases)
— 1.36%
(7 cases)
514 (1.75%)
Bladder Urothelial Carcinoma 1.46%
(6 cases)
0.24%
(1 case)
— 411 (1.7%)
Esophagogastric
Adenocarcinoma
1.35%
(8 cases)
0.34%
(2 cases)
— 592 (1.69%)
Non-Small Cell Lung Cancer 1.11%
(21 cases)
0.32%
(6 cases)
0.11%
(2 cases)
1899 (1.53%)
Salivary Cancer 0.94%
(2 cases)
— 0.47%
(1 case)
212 (1.42%)
Prostate Adenocarcinoma 0.87%
(10 cases)
— 0.52%
(6 cases)
1151 (1.39%)
Hepatocellular Carcinoma 1.36%
(5 cases)
— — 369 (1.36%)
Invasive Breast Carcinoma 0.91%
(10 cases)
0.09%
(1 case)
0.18%
(2 cases)
1096 (1.19%)
21

Pleural Mesothelioma 1.15%
(1 case)
— — 87 (1.15%)
Wilms Tumor 1.06%
(2 cases)
— — 188 (1.06%)
Colon Adenocarcinoma 0.94%
(1 case)
— — 106 (0.94%)
Breast Cancer 0.78%
(24 cases)
0.03%
(1 case)
0.1%
(3 cases)
3090 (0.91%)
Head and Neck Squamous Cell
Carcinoma
0.76%
(4 cases)
— — 523 (0.76%)
Melanoma 0.23%
(3 cases)
0.15%
(2 cases)
0.23%
(3 cases)
1318 (0.61%)
Hepatobiliary Cancer 0.13%
(1 case)
0.26%
(2 cases)
— 777 (0.39%)
B-Lymphoblastic
Leukemia/Lymphoma
— — 0.32%
(3 cases)
939 (0.32%)
Salivary Gland Cancer 0.21%
(2 cases)
— 0.11%
(1 case)
949 (0.32)
Bone Cancer — 0.31%
(1 case)
— 324 (0.31%)
22

Renal Clear Cell Carcinoma 0.2%
(1 case)
— — 511 (0.2%)
Glioblastoma 0.17%
(1 case)
— — 593 (0.17%)
Leukemia 0.08%
(1 case)
— 0.08%
(1 case)
1221 (0.16%)
Colorectal Adenocarcinoma — 0.08%
(1 case)
— 1213 (0.08%)
Mature B-Cell Neoplasms — 0.04%
(1 case)
0.04%
(1 case)
2800 (0.07%)

23

Table 2.4 APOC2 mutations associated with hypertriglyceridemia

ApoCII Variant
Mutation
Type
Nucleotide
Change Protein Change
ApoCII (-190T>A) Promoter c.-190T>A N/A
ApoCII (-86A>G) Promoter c.-86A>G N/A
ApoCII (PARIS1) Missense c.1A>G M1V
ApoCII (PARIS2,
BARCELONA)
Nonsense c.10C>T R4*
ApoCII (JAN, VEN) FS del c.70delC fsQ24
ApoCII (SHANGHAI) Del-ins c.86A>CC fsD29
ApoCII (NIJMEGEN) FS del c.118delG fsV40*
ApoCII variant
(ApoCII-v)
Missense c.122A>C K41T
ApoCII (WAKAYAMA) Missense c.142T>C W48R
ApoCII (BARI) Nonsense c.177C>G Y59*
ApoCII (PADOVA) Nonsense c.177C>A Y59*
ApoCII (SAN
FRANCISCO)
Missense c.178G>A E60K
ApoCII (PHILADELPHIA)
Missense c.215G>C R72T
ApoCII (AFRICAN) Missense c.229A>C K77Q
24

ApoCII (AUCKLAND) Nonsense c.255C>A Y85*
ApoCII (TORONTO) FS del c.270delT fsT90
ApoCII (ONTARIO) Nonsense c.274C>T Q92*
ApoCII (ST. MICHAEL) FS ins c.274dupC fsQ92
ApoCII (HONGKONG) Missense c.281T>C L94P
ApoCII (HAM, TOK) Splice c.55+1G>C
Splice NT 2 G+1
to C
ApoCII (TUZLA) Deletion Loss of Ex 2,3,4 untranslated
ApoCII (COLOMBIA) FS del c.133_134delTC fsS67

25

Table 2.5 APOC2 mutations associated with cancer

Cancer Type
Mutation
Type
Nucleotide
Change
Protein
Change
Stomach Adenocarcinoma Missense c.242C>A A81D
Tubular Stomach Adenocarcinoma FS del c.25_26del L9Vfs*10
Lung Adenocarcinoma Missense c.257C>A T86K
Lung Adenocarcinoma Nonstop c.305A>T *102Lext*23
Lung Squamous Cell Carcinoma Missense c.128C>G S43C
Lung Squamous Cell Carcinoma Missense c.156G>C K52N
Lung Adenocarcinoma Missense c.100C>G P34A
Bladder Urothelial Carcinoma Nonsense c.10C>T R4*
Cutaneous Melanoma Missense c.100C>T P34S
Hepatocellular Carcinoma Missense c.26T>C L9P
Hepatocellular Carcinoma Missense c.202G>T D68Y
Melanoma Missense c.5G>A G2D
Cutaneous Squamous Cell Carcinoma Missense c.295G>A G99R
Skin Cancer, Non-Melanoma Missense c.295G>A G99R
Breast Invasive Ductal Carcinoma Splice X71_splice
Colon Adenocarcinoma Missense c.163G>A A55T
26

Skin Cancer, Non-Melanoma Missense
c.295_296delins
AA G99K
Skin Cancer, Non-Melanoma Missense c.124G>A E42K
Osteosarcoma Missense c.58G>A V20I
Lung Adenocarcinoma Missense c.100C>A P34T
Germinal Center B-cell Like Diffuse
Large B-cell Lymphoma Missense c.122A>C K41T
Breast Invasive Carcinoma FS del c.166del Q56Rfs*12
Lung Adenocarcinoma Splice c.216-3C>T X72_splice
Uterine Serous Carcinoma/Uterine
Papillary Serous Carcinoma
Missense c.197C>A A66D
Uterine Endometrioid Carcinoma Missense c.265T>C F89L
Uterine Endometrioid Carcinoma Missense c.253T>C Y85H

27

2.3.2 APOC2 is upregulated in several malignancies
In addition to the major genomic alterations, we also assessed APOC2 transcriptional upregulation.
When compared with its levels in normal tissues, APOC2 was found to be significantly upregulated
in several malignancies. Analysis of brain cancer datasets showed that APOC2 was significantly
upregulated in tumor tissues compared with the corresponding normal tissues. In the GSE4536
dataset, APOC2 mRNA was greatly amplified in 22 patient-derived glioblastoma stem cells
compared with three normal neural stem cells (33.7-fold, P < 0.0001; Figure 2.2A). Similar results
were obtained in the GSE2223 dataset (Figure 2.3A, 6.1-fold, P < 0.0001; Figure 2.3B, 3.2-fold,
P = 0.006). Analysis of breast cancer datasets showed that APOC2 is also overexpressed. In the
GSE1477 dataset, APOC2 mRNA was upregulated in 29 specimens from invasive ductal breast
cancer compared with 7 specimens obtained from normal breast tissue (2.9-fold, P = 0.0073;
Figure 2.2B). Consistently, increased APOC2 mRNA was detected in samples taken from invasive
lobular breast cancer patients (Figure 2.3C, 3.587-fold, P = 0.006). Consistent results were shown
in the GSE3744 dataset (Figure 2.3D, 2.5-fold, P < 0.0001). APOC2 exhibited a higher expression
level in the investigation of lymphoma. In the GSE2350 dataset, we noticed that APOC2 was
greatly overexpressed in 28 centroblastic lymphoma samples compared with 20 normal B cells
(10.1-fold, P < 0.0001; Figure 2.2C). APOC2 upregulation was also observed in diffuse large B
cell lymphoma and Burkitt’s lymphoma in the GSE2350 dataset (Figure 2.3E, 2.1-fold, P < 0.001;
Figure 2.3F, 3.7-fold, P < 0.001). GSE9348 dataset suggested that APOC2 mRNA expression was
significantly higher in 70 patients with early stage colorectal tumor compared with 12 healthy
controls (3.8-fold, P <0.0001; Figure 2.2D). We also observed upregulated APOC2 expression in
34 hypopharyngeal cancer samples compared with 4 control samples in the GSE2379 dataset (2.1-
fold, P = 0.003; Figure 2.2E). In the assessment of APOC2 level in renal cancer, GSE11151 dataset
28

revealed that APOC2 mRNA expression was significantly higher in 26 samples obtained from
clear cell renal cell carcinoma tissue compared with 5 samples obtained from normal kidney tissue
(5.9-fold, P < 0.001; Figure 2.2F). Higher APOC2 mRNA levels were also observed in papillary
renal cell carcinoma and chromophobe renal cell carcinoma in the same dataset (Figure 2.3G, 5.0-
fold, P < 0.001; Figure 2.3H, 5.1-fold, P = 0.002). The analysis of melanoma dataset implicated
that APOC2 expression was upregulated in the early stage of skin cancer. In the GSE7553 dataset,
the higher expression of APOC2 mRNA was measured in 11 skin squamous cell carcinoma
samples compared with 4 normal skin samples (2.3-fold, P = 0.023; Figure 2.2G). Lastly, we found
that APOC2 mRNA was overexpressed in 12 samples from gastric cancer tissue compared with
15 samples from normal gastric tissue in the GSE19826 dataset (4.8-fold, P = 0.002; Figure 2.2H).

29

Figure 2.2 Analysis of APOC2 expression in different cancers. APOC2 expression raw data
was obtained from the GEO database. The mean fold change was compared between tumor
groups and healthy tissues in (A) patient-derived glioma stem cells, (B) invasive ductal breast
carcinoma, (C) centroblastic lymphoma, (D) early stage colorectal tumor, (E) hypopharyngeal
cancer, (F) clear cell renal cell carcinoma, (G) skin squamous cell carcinoma, and (H) gastric
cancer. The p-values were calculated using unpaired t-test, with the exception of panel (B), (D)
and (E), which were calculated using Mann-Whitney test. (****, P < 0.0001; ***, P < 0.001; **,
P < 0.01; *, P < 0.05)

30

Figure 2.3 Analysis of APOC2 expression in different cancers. (supplementary) APOC2
expression raw data was obtained from the GEO database. The mean fold change was compared
between tumor groups and healthy tissues in (A) glioblastoma, (B) anaplastic oligoastrocytoma,
(C) invasive lobular breast carcinoma, (D) ductal breast carcinoma, (E) diffuse large B cell
lymphoma, (F) Burkitt’s lymphoma, (G) papillary renal cell carcinoma, and (H) chromophobe
renal carcinoma. The p-values were calculated using unpaired t-test. (****, P < 0.0001; ***, P <
0.001; **, P < 0.01)

31

2.3.3 APOC2 is hypomethylated in several malignancies
To examine whether epigenetic changes may at least partially explain APOC2 upregulation in
cancer compared with normal tissues, we compared methylation beta (β) values associated with
probes that correspond to APOC2 gene between tumor samples and healthy controls from TCGA
DNA methylation available in the UALCAN database. We analyzed four types of cancer in which
APOC2 was upregulated. The promoter methylation level of APOC2 was significantly lower in
patients with primary tumor compared with controls in breast invasive carcinoma (median β value:
0.809 vs 0.852; P < 0.0001; Figure 2.4A), colon adenocarcinoma (median β value: 0.7 vs 0.81; P
< 0.0001; Figure 2.4B), kidney renal clear cell carcinoma (median β value: 0.79 vs 0.83; P <
0.0001; Figure 2.4C) and stomach adenocarcinoma (median methylation beta value: 0.676 vs
0.772; P = 0.116; Figure 2.4D).

32

Figure 2.4 Promoter methylation level of APOC2 in cancers where APOC2 is upregulated.
Analyses for the promoter methylation level of APOC2 in tumor groups and healthy tissues were
performed for (A) breast invasive carcinoma, (B) colon adenocarcinoma, (C) kidney renal clear
cell carcinoma, and (D) stomach adenocarcinoma. The p-values were calculated using Student’s
t-test. (****, P < 0.0001; ns, not significant)

33

2.3.4 Clinical attributes associated with alteration of APOC2
We compared the frequencies of clinical attributes among patients with APOC2 alterations
including copy number alterations as well as mutations and those without. Our analyses revealed
that samples in APOC2 altered group had a higher fraction of genome alterations (median fraction
of genome altered: 0.334 vs 0.163; P < 0.0001; Figure 2.5A), a higher mutation counts (median
mutation count: 68 vs 14; P < 0.0001; Figure 2.5B) and a higher MSIsensor score (median
MSIsensor score (log2): -3.001 vs -4.059; P < 0.001; Figure 2.5C) compared with samples in
APOC2 unaltered group.
Because the APOC2 gene is located in a cluster with other related apolipoprotein genes on
chromosome 19, we also obtained 19q status data from cBioPortal and compared their frequencies
between APOC2 altered and unaltered groups. In APOC2 altered group (n = 58), 50% of the
samples (29 samples) had 19q gained status and 10.34% of the samples (6 samples) had 19q lost
status. In APOC2 unaltered group, only 13.01% of the samples had 19q gained and 10.87% had
19q lost status (n = 8890; P < 0.0001; Figure 2.5D). Similarly, 19p status was also associated with
APOC2 alterations. In APOC2 altered group (n = 88), 10.23% of the samples (9 samples) had 19p
gained while 32.95% of the samples (29 samples) had 19p lost status. In APOC2 unaltered group,
9.1% of the samples had 19p gained and 13.43% had 19p lost status (P < 0.0001; Figure 2.5E). In
addition, for 10p status, in APOC2 altered group (n = 115), 28.7% of the samples (33 samples) had
10p gained status and 15.65% of the samples (18 samples) had 10p lost status. In APOC2 unaltered
group, 9.96% of the samples had 10p gained and 19.77% of the samples had 10p lost status (P <
0.0001; Figure 2.5F). Thus, we observed significant increase in gain of 19q and 10p in APOC2
altered groups compared with unaltered groups. Other chromosomal statuses that were
significantly associated with APOC2 alteration are summarized in Table 2.6 and Figure 2.5.
34

35

Figure 2.5 Clinical attributes associated with alteration of APOC2. (A) Fraction genome
altered in APOC2 altered and unaltered group. (B) Mutation count in APOC2 altered and
unaltered group. (C) MSIsensor score in APOC2 altered and unaltered group. The frequencies of
chromosomal gain and lost in APOC2 altered and unaltered groups were compared in
chromosome (D) 19q, (E) 10p, (F) 19p, (G) 15q, (H) 12p, (I) 16p, (J) 22q, (K) 17p, (L) 5p, (M)
2p, (N) 12q, (O) 4q, (P) 5q, (Q) 9p, (R) 3q, (S) 1q, (T) 4p, and (U) 20q. APOC2 altered groups
included patient samples with APOC2 mutation and copy number alterations. The differences
between groups in panel (A), (B) and (C) were analyzed by Kruskal-Wallis test. The differences
between groups associated with chromosomal gain and loss were calculated using Chi-squared
test. (****, P < 0.0001; ***, P < 0.001; **, P < 0.01)

36

Table 2.6 Chromosomal status associated with alteration of APOC2

Chromosomal
Status
p-Value Frequency in altered group
% (N)
Frequency in unaltered group
% (N)
Gained Lost Not Called Gained Lost Not Called
19q Status <10
-10
50%
(29)
10.34%
(6)
39.66% (23) 13.01%
(1157)
10.87%
(966)
76.12%
(6767)
10p Status 3.46E-
10
28.7%
(33)
15.65%
(18)
55.65% (64) 9.96% (947) 19.77%
(1879)
70.27%
(6678)
19p Status 4.31E-7 10.23%
(9)
32.95%
(29)
56.82% (50) 9.1% (809) 13.43%
(1194)
77.47%
(6887)
15 (15q) Status 9.43E-7 6.67%
(7)
38.1%
(40)
55.24% (58) 5.26% (470) 18.48%
(1652)
76.26%
(6815)
12p Status 1.39E-6 33.63%
(38)
13.27%
(15)
53.1% (60) 17.5%
(1635)
8.07% (754) 74.43%
(6953)
16p Status 4.64E-6 14.29%
(16)
24.11%
(27)
61.61% (69) 13.73%
(1304)
10.05%
(954)
76.22%
(7237)
22 (22q) Status 1.57E-5 9.8%
(10)
43.14%
(44)
47.06% (48) 6.89% (637) 24.56%
(2270)
68.55%
(6337)
17p Status 5.35E-5 3.51%
(4)
55.26%
(63)
41.23% (47) 5.22% (497) 35.27%
(3360)
59.52%
(5670)
5p Status 6.22E-4 29.66%
(35)
13.56%
(16)
56.78% (67) 23.84%
(2279)
6.16% (589) 69.99%
(6690)
2p Status 1.60E-3 20.95%
(22)
1.9% (2) 77.14% (81) 11.13%
(1045)
6.75% (634) 82.12%
(7710)
37

12q Status 2.04E-3 18.75%
(21)
14.29%
(16)
66.96% (75) 11.03%
(1005)
8.66% (789) 80.31%
(7315)
4q Status 2.35E-3 1.79%
(2)
37.5%
(42)
60.71% (68) 3.11% (282) 23.55%
(2138)
73.34%
(6657)
5q Status 3.12E-3 5.66%
(6)
35.85%
(38)
58.49% (62) 7.91% (706) 22.1%
(1973)
69.99%
(6247)
9p Status 4.02E-3 14.29%
(17)
31.93%
(38)
53.78% (64) 7.06% (637) 28.68%
(2589)
64.27%
(5802)
3q Status 4.23E-3 32.71%
(35)
5.61%
(6)
61.68% (66) 19.92%
(1773)
7.89% (702) 72.19%
(6424)
1q Status 4.91E-3 41.82%
(46)
1.82%
(2)
56.36% (62) 28.28%
(2596)
4.43% (407) 67.29%
(6178)
4p Status 5.29E-3 5.04%
(6)
35.29%
(42)
59.66% (71) 4.87% (467) 22.86%
(2192)
72.26%
(6928)
20q Status 7.08E-3 38.74%
(43)
5.41%
(6)
55.86% (62) 29.63%
(2822)
2.32% (221) 68.05%
(6482)

38

2.3.5 APOC2 deregulation is associated with TP53 mutation
We also examined the association between APOC2 gene alteration and patient’s molecular
characteristics particularly the presence of common somatic mutations. We found that APOC2
alterations were more frequent in cancers with TP53 gene mutations (Fisher exact test, P < 0.0001;
Table 2.7). We also analyzed this association in several cancers where TP53 was highly mutated
and APOC2 was upregulated (Table 2.8). In breast cancer, 46.74% of the samples in APOC2
altered group had TP53 mutation, while 34% of the samples in APOC2 unaltered group had TP53
mutation (P = 0.00923). In patients with colorectal adenocarcinoma, 78.13% of the samples in
APOC2 altered group had TP53 mutation, while TP53 was mutated in 58.54% of the samples in
APOC2 unaltered group (P = 0.02). APOC2 altered group included patient samples with APOC2
mutation and copy number alterations. A list of other genes that were also associated with APOC2
deregulation are summarized in Table 2.7.

39

Table 2.7 Genes with highest mutation frequencies in APOC2 altered and unaltered groups

Gene
Altered (n = 132)
N (%)
Unaltered (n = 10305)
N (%) p-Value
TP53 78 (59.09%) 3761 (36.50%) 1.36E-7
TTN 50 (37.88%) 3086 (29.95%) 0.0319
MUC16 43 (32.58%) 1971 (19.13%) 1.88E-4
CSMD3 32 (24.24%) 1318 (12.79%) 2.68E-4
RYR2 31 (23.48%) 1289 (12.51%) 4.04E-4
USH2A 27 (20.45%) 1103 (10.70%) 7.97E-4
LRP1B 27 (20.45%) 1270 (12.32%) 5.79E-3
FLG 25 (18.94%) 1146 (11.12%) 5.85E-3
RYR1 23 (17.42%) 841 (8.16%) 4.78E-4
XIRP2 23 (17.42%) 874 (8.48%) 8.05E-4

40

Table 2.8 TP53 mutation in APOC2 altered and unaltered groups in cancer

Cancer Type Altered Unaltered p-Value
Breast Cancer 46.74% (43/92) 34.00% (616/1812) 9.23E-03
Colorectal Adenocarcinoma 78.13% (25/32) 58.54% (288/492) 0.02
Brain Lower Grade Glioma 69.57% (16/23) 48.14% (233/484) 0.036
Pancreatic Adenocarcinoma 88.89% (8/9) 59.12% (94/159) 0.071
Skin Cutaneous Melanoma 33.33% (6/18) 16.81% (58/345) 0.077

41

2.3.6 Molecular pathways associated with alteration of APOC2
We further evaluated the APOC2-associated cell signaling pathways through IPA. We analyzed
patient’s data from breast cancer, cervical squamous cell carcinoma, head and neck squamous cell
carcinoma (HNSC), as well as bladder urothelial carcinoma (BLCA), where alterations including
mutation, amplification and high mRNA level were found in APOC2. Expression log ratio of genes
significantly expressed in these cancers were imported into IPA. Some pathways relevant to lipid
metabolism were found to be modulated, these pathways changes include the downregulation of
PPAR signaling and the upregulation of Phospholipase C signaling. In addition, some pathways
related to innate and adaptive immune response were altered (Figure 2.6). The dendritic cells
maturation pathway, Th1 and Th2 activation pathway, pathways involved in the cross talk between
dendritic cells and natural killer cells, and iCOS-iCOSL signaling are all upregulated. On the
contrary, downregulation of PD-1 and PD-L1 cancer immunotherapy pathway was observed
(Table 2.9 and Table 2.10).

42

43

Figure 2.6 Ingenuity pathway analysis associated with alteration of APOC2. Four data sets
were included in the ingenuity pathway analysis (IPA) in which genetic alterations including
amplification, mutation and high mRNA level were found in APOC2. Gene expression log ratios
of significantly altered genes in patients with APOC2 alterations compared with patients without
APOC2 alterations were the input for the IPA. Pathways shown in orange are upregulated while
pathways shown in blue are downregulated. Abbreviations: HNSC: head and neck squamous cell
carcinoma, BLCA: bladder urothelial carcinoma.

Table 2.9 z-scores of Ingenuity Pathway Analysis results

Canonical Pathways breast

cervical

HNSC

BLCA
Dendritic Cell Maturation 5.75 5.396 5.209 4.841
Role of NFAT in Regulation of the Immune Response 5.093 5.745 5.425 4.768
Crosstalk between Dendritic Cells and Natural Killer Cells 5.396 5.385 5 4.472
PKCθ Signaling in T Lymphocytes 5.259 5.014 5.657 4.315
Th1 Pathway 4.644 5.568 5.396 4.017
Neuroinflammation Signaling Pathway 4.781 4.667 4.459 5.345
iCOS-iCOSL Signaling in T Helper Cells 3.618 4.914 5.292 4.379
TREM1 Signaling 5.568 3.873 4.243 4.123
Production of Nitric Oxide and Reactive Oxygen Species in
Macrophages 4.429 4.2 4.899 3.53
Systemic Lupus Erythematosus In T Cell Signaling
Pathway 2.528 4.95 4.564 4.218
IL-8 Signaling 3.015 3.71 3.9 4.243
Natural Killer Cell Signaling 4.041 3.812 4.629 2.271
Phospholipase C Signaling 2.794 3.71 4.472 3.528
PD-1, PD-L1 cancer immunotherapy pathway -2.53 -4.271 -3.651 -4.041
Tec Kinase Signaling 2.535 3.9 4.379 3.578
CREB Signaling in Neurons 1.2 1.857 5.24 5.963
Calcium-induced T Lymphocyte Apoptosis 3.128 3.5 4.123 3.464
Breast Cancer Regulation by Stathmin1 2.55 1.633 4.323 5.455
Fcγ Receptor-mediated Phagocytosis in Macrophages and
Monocytes 3.212 3.273 3.742 3.441
IL-17 Signaling 4.003 3.606 3.638 2.4
Integrin Signaling 2.188 2.982 3.771 4.7
Leukocyte Extravasation Signaling 2.714 4.041 3.962 2.921
Type I Diabetes Mellitus Signaling 4.849 3.207 3.606 1.89
Inhibition of ARE-Mediated mRNA Degradation Pathway 3.838 3.464 3.317 2.84
Role of Pattern Recognition Receptors in Recognition of
Bacteria and Viruses 3.651 3.207 3.606 2.84
44

CD28 Signaling in T Helper Cells 3.124 3.578 3.578 2.982
HIF1α Signaling 2.846 3.5 2.673 3.4
Cardiac Hypertrophy Signaling (Enhanced) 2.288 2.137 4.459 3.515
IL-15 Production 2.959 3.357 3.771 2.294
Colorectal Cancer Metastasis Signaling 1.604 3.153 3.9 3.656
Regulation Of The Epithelial Mesenchymal Transition By
Growth Factors Pathway 1.897 2.668 3.578 4.017
Chemokine Signaling 3.53 3 2.887 2.714
IL-15 Signaling 2.837 3.317 3.207 2.714
Hepatic Fibrosis Signaling Pathway 2.429 3.13 3.138 3.207
B Cell Receptor Signaling 3.098 2 3 3.578
Ephrin Receptor Signaling 2.596 1.897

2.84 4.264
Th2 Pathway 3 3.024 3.024 2.414
Systemic Lupus Erythematosus In B Cell Signaling
Pathway 3.215 2.846 3.162 2.16
PPAR Signaling -4.49 -2 -2.53 -2.324

Table 2.10 -log(B-H p-value) of Ingenuity Pathway Analysis results

Canonical Pathways breast

cervical HNSC BLCA
Th1 and Th2 Activation Pathway 13.96854 24.4442 33.01883 13.72257
Th1 Pathway 12.55284 21.59075 30.07198 12.5141
Th2 Pathway 10.96492 22.07059 26.5514 12.5141
iCOS-iCOSL Signaling in T Helper Cells 16.76354 16.2991 21.58176 6.322849
Natural Killer Cell Signaling 15.82531 15.37647 19.55529 4.100786
PD-1, PD-L1 cancer immunotherapy pathway 10.62646 15.33793 20.21685 7.184974
CD28 Signaling in T Helper Cells 13.78833 13.47955 19.31728 5.484102
Role of NFAT in Regulation of the Immune Response 17.27199 9.831735 16.81221 6.650397
T Cell Exhaustion Signaling Pathway 11.39984 12.99322 17.31697 6.648047
Dendritic Cell Maturation 14.90541 11.62038 13.39921 7.184974
Crosstalk between Dendritic Cells and Natural Killer
Cells 12.98429 15.78971 13.44477 4.100786
Altered T Cell and B Cell Signaling in Rheumatoid
Arthritis 14.12739 9.631984 17.31697 4.521195
T Helper Cell Differentiation 8.37569 11.6564 15.6229 5.559847
Antigen Presentation Pathway 10.98603 12.70031 10.15273 6.940501
Communication between Innate and Adaptive Immune
Cells 11.64356 9.77544 13.52918 4.521195
45

CTLA4 Signaling in Cytotoxic T Lymphocytes 9.560024 13.07387 13.44477 2.779854
PKCθ Signaling in T Lymphocytes 13.38516 8.453346 13.97238 2.421
Type I Diabetes Mellitus Signaling 14.12739 9.831735 11.13655 2.062291
Systemic Lupus Erythematosus In B Cell Signaling
Pathway 15.31973 7.577574 11.39626 2.191165
T Cell Receptor Signaling 13.16484 6.773308 12.51227 1.94637
Phagosome Formation 8.543273 8.061541 8.708587 8.980028
Graft-versus-Host Disease Signaling 8.37569 8.779995 10.85165 5.484102
Neuroinflammation Signaling Pathway 7.168107 7.492774 9.679055 7.170884
Autoimmune Thyroid Disease Signaling 5.953169 9.631984 10.70196 4.652253
Pathogenesis of Multiple Sclerosis 5.060983 10.16551 7.365341 7.285324
Allograft Rejection Signaling 4.062415 8.453346 10.93531 3.366138
B Cell Development 4.389133 6.835779 8.435663 5.163868
Role of Pattern Recognition Receptors in Recognition of
Bacteria and Viruses 6.844017 5.570442 8.794573 1.908191
OX40 Signaling Pathway 3.697071 7.318297 8.815089 3.112476
IL-4 Signaling 8.319988 4.954016 6.83618 2.618265
Calcium-induced T Lymphocyte Apoptosis 5.209927 6.608053 9.434611 1.451488
Tec Kinase Signaling 5.280867 5.172187 10.42861 1.817021
TREM1 Signaling 7.399895 4.335155 8.516418 2.437071
Granulocyte Adhesion and Diapedesis 8.170097 5.172187 4.237728 4.630398
Phospholipase C Signaling 9.68848 4.308972 5.122313 2.913021
Leukocyte Extravasation Signaling 4.369033 6.845884 7.365341 3.287256
Production of Nitric Oxide and Reactive Oxygen Species
in Macrophages 5.935789 5.449946 8.815089 1.202467
Primary Immunodeficiency Signaling 5.794237 6.73663 7.486297 0.898645
Atherosclerosis Signaling 3.2687 3.704161 6.963551 6.940501

46

2.3.7 APOC2 alterations are associated with shorter cancer overall survival
In order to establish the association between APOC2 gene deregulations and clinical outcome, a
survival analysis of all patients in different datasets were performed. We found that patients with
APOC2 gene alterations (including copy number alterations and mutations) have significantly
shorter overall survival in comparison to those without alterations (median survival, OS: 37.97 vs
80.68 months; P < 0.0001; Figure 2.7A). We further performed separate analysis to examine the
association between APOC2 and gene alterations including gene expression upregulation in each
cancer type independently. In esophageal adenocarcinoma, patients with APOC2 upregulation had
shorter overall survival (median survival, OS: 13.48 vs 28.11 months; P = 0.0363; Figure 2.7B).
In stomach adenocarcinoma, patients with APOC2 upregulation exhibited significantly shorter
overall survival (median survival, OS: 14.96 vs 36.00 months; P = 0.0072; Figure 2.7C) compared
to those with low APOC2 expression. In ovarian serous cystadenocarcinoma, we studied the
survival of patients with mutation and high mRNA level, as well as patients with high mRNA level
only. Patients with APOC2 upregulation and mutation tended to have significantly shorter overall
survival than those with low gene expression and wild type APOC2 (median survival, OS: 35.80
vs 44.51 months; P = 0.0058; Figure 2.7D). This association remained significant when we
considered upregulation of APOC2 gene expression only and regardless of APOC2 mutational
status (median survival, OS: 16.93 vs 44.32; P = 0.0221; Figure 2.7E). In uterine carcinosarcoma,
patients with amplification and high APOC2 expression exhibited significantly poorer overall
survival (median survival, OS: 12.97 vs 27.55 months; P = 0.0071; Figure 2.7F) compared with
patients with low APOC2 expression. The relationship between APOC2 gene expression, alteration
and cancer patient survival was summarized in Table 2.11.

47

Figure 2.7 Survival analysis for patients with different cancers associated with APOC2
alteration and mRNA expression. (A) Overall survival of cancer patients with and without
APOC2 alterations including APOC2 copy number alterations as well as mutation. (B) Overall
survival of esophageal adenocarcinoma patients with low (Z < -2) and high (Z > 2) APOC2
expression level. (C) Overall survival of stomach adenocarcinoma patients with low (Z < -2) and
high (Z > 2) APOC2 expression level. (D) Overall survival of ovarian serous cystadenocarcinoma
patients with APOC2 high (Z > 2) expression level and mutations compared patients with low (Z
< -2) expression level and wild type APOC2. (E) Overall survival of ovarian serous
cystadenocarcinoma patients with low (Z < -2) and high (Z > 2) APOC2 expression level. (F)
Overall survival of uterine carcinoma patients with APOC2 amplification and high (Z > 2)
expression level compared with low (Z < -2) APOC2 expression level. The p-values were
calculated using Mantel-Cox test.

48

Table 2.11 Association between APOC2 gene expression, alteration and cancer patient
survival

Cancer type Survival
(p-Value)
Number of altered samples /
total number of samples
Colorectal Adenocarcinoma 0.562 36/594
Breast Invasive Carcinoma 0.346 52/994
Glioblastoma Multiforme 0.974 5/378
Cervical Squamous Cell Carcinoma 0.111 20/275
Esophageal Adenocarcinoma 0.0363 10/181
Stomach Adenocarcinoma (mutation + high
mRNA)
0.162 22/407
Stomach Adenocarcinoma (high mRNA only) 0.0072 13/407
Stomach Adenocarcinoma (TCGA, Firehose
Legacy)
0.0108 13/478
Head and Neck Squamous Cell Carcinoma 0.633 12/488
Kidney Renal Clear Cell Carcinoma (TCGA,
Pan-Cancer Atlas)
0.307 2/352
Liver Hepatocellular Carcinoma 0.698 26/348
49

Ovarian Serous Cystadenocarcinoma
(mutation + high mRNA)
0.0058 16/201
Ovarian Serous Cystadenocarcinoma (high
mRNA only)
0.0221 10/201
Pancreatic Adenocarcinoma 0.634 10/168
Skin Cutaneous Melanoma 0.494 18/363
Sarcoma 0.380 10/251
Thyroid Carcinoma 0.0260 17/480
Papillary Thyroid Carcinoma 0.0438 15/388
Uterine Corpus Endometrial Carcinoma 0.412 25/507
Uterine Carcinosarcoma (amplification + high
mRNA)
0.0071 6/56

50

2.4 Discussion
Metabolic reprogramming is considered to be a hallmark of cancer. Cancer cells are able to rewire
their metabolism to support oncogenesis and tumor proliferation.
41
Lipogenesis, lipid uptake and
storage are elevated in human cancers to satisfy the demands of increased membrane biogenesis
and energy generation.
60,61
Both deficiency and overexpression of APOC2 are associated with
hyperlipidemia in transgenic mice, indicating the complex role of APOC2 in lipid homeostasis to
balance the activation of LPL and its lipid binding capacity.
62,63
However, the role of APOC2 in
cancer is not fully elucidated.
After analyzing public genomics and transcriptomic data, APOC2 was found to be upregulated
and hypomethylated in several types of cancer such as breast invasive carcinoma, gastric cancer
and kidney renal clear cell carcinoma. This could be relevant to the enhanced energy requirement
in cancer cells. A recent study in gastrointestinal stromal tumor (GIST) demonstrated that
upregulation of APOC2 was associated with GIST cell proliferation, migration and invasion.
64

Besides epigenetic mechanisms such as DNA methylation, transcription factor STAT1 was found
to bind on multienhancer 2 and RXRα located on APOC2 promotor to upregulate APOC2
expression in macrophages.
65
More research is needed to address the mechanisms of APOC2
upregulation in cancer cells.
Our study revealed that APOC2 is associated with poor clinical outcome. Whether APOC2
elevation in the serum also correlated with poor clinical outcome and thus can be utilized as a
biomarker for cancer prognosis would require further validations. Upregulation of APOC2 was
also observed in metastatic tumors such as fast-growing lymphoma and breast invasive carcinoma,
indicating that APOC2 could possibly be a prognostic marker in cancer.
51

Moreover, APOC2 is relevant to TP53 mutations. As a tumor suppressor, P53 is well-known for
its function in apoptosis, cell arrest and senescence. Several recent studies suggest that the
additional function of P53 in metabolic regulation may contribute to tumor suppression.
66
P53
regulates lipid metabolism through different mechanisms such as binding to the promoter region
of SREBP-1 to repress SREBP-1 expression, which results in downregulation of enzymes involved
in fatty acid synthesis in obese mice.
67
Thus, P53 mutations may contribute to the elevation in de
novo lipogenesis to fuel the growth of cancer cells. However, what is the mechanistic association
between P53 mutations and APOC2 deregulation needs to be explored. The relation between
APOC2 alterations and immune related signaling pathway deregulation is particularly interesting.
Because MSIsensor score can be used to predict patient’s responses to immunotherapy, higher
MSIsensor score in APOC2 altered group compared with unaltered group further indicates the
relation between APOC2 and immune system. Considering that APOC2 is a secreted
apolipoprotein that has been shown to act upon the CD36 receptor, it is plausible that APOC2 may
play a role in the interaction between cancer cells and the immune microenvironment. Recent
studies have shown that CD36 can mediate the metabolic adaptation, which supports regulatory T
cell survival and function in tumors. Intratumoral Treg cells demonstrate elevated lipid metabolism
and CD36 expression.
68
Whether APOC2 contribute to the metabolic adaptation of intratumoral
immune cell requires further study.
Taken together, our data suggest that besides its role in lipid metabolism, APOC2 is also involved
in cancer development. In addition, APOC2 alterations and upregulation are associated with poor
cancer clinical outcomes. Our study sheds the light on a lipoprotein that is previously not studied
in the context of cancer biology and suggests that APOC2 may potentially present a therapeutic
target in different malignancies.
52

Chapter 3 Functional Characterization of APOC2 in Normal Mouse Primary Cells
3.1 Introduction
In the previous chapter, my data suggested that APOC2 may potentially present a therapeutic target
in several types of cancer. A recent study of our lab revealed a new role of APOC2 in AML. We
reported that APOC2 was upregulated and relevant to poor clinical outcome in AML. APOC2
binds to CD36 and stimulates the phosphorylation of LYN and ERK proteins, which are
downstream targets of CD36, leading to enhanced bioenergetic level of AML cells. Targeting
APOC2 or CD36 can suppress cell proliferation, promote apoptosis in vitro and delay leukemia
progression in mouse models, which established the APOC2-CD36 signal axis as a novel
therapeutic target in AML.
46
To further address APOC2 as a cancer therapeutic target,
investigating the role of this gene in normal cells is needed.
APOC2 loss-of-function models are usually engineered to study cardiovascular diseases such as
hypertriglyceridemia. To create APOC2 knockout model in severe hypertriglyceridemia, one of
the studies designed a CRISPR/Cas 9 system to target exon 2 of Apoc2 gene from golden Syrian
hamster.
69
Another study used zinc finger nucleases to create Apoc2 mutant mice, which produced
a form of Apoc2 with an uncleaved signal peptide and resulted in hypertriglyceridemia.
62
A
zebrafish model of hypotriglyceridemia utilized a transcription activator-like effector nuclease
(TALEN) technique. The TALEN sequences were designed to bind on the left and right side of
the target site at exon 3 of the Apoc2 gene, which is located in front of LPL-binding domain.
70
In
all these different models, few limited hematopoietic phenotypes were observed, and mostly
recovered with age.
53

In this chapter, I hypothesized that APOC2 is dispensable for the survival of normal cells. To help
determine the effect of targeting APOC2 in normal cells, I engineered an overexpression and a
knockdown lentiviral expression system to perform gain and loss of function approaches of the
Apoc2 gene in normal mouse primary cells and assess the phenotypic changes in the cells. RNA
interference technique was used and shRNA sequences were designed to knock down Apoc2 gene.
If normal cells can still proliferate after APOC2 knockdown, and targeting APOC2 can suppress
tumor growth, then APOC2 is possible to be a therapeutic target.

54

3.2 Materials and methods
3.2.1 RNA extraction and cDNA synthesis
C57BL/6J mice were purchased from The Jackson Laboratory (Bar Harbor, ME, USA). Total RNA
was isolated from the liver of Cd45.2 mice using the TRIzol Reagent (Invitrogen, Carlsbad, CA,
USA). 1µg of total RNA was used for cDNA synthesis using the SuperScript® IV First-Strand
Synthesis System (Thermo Fisher, Waltham, MA, USA).

3.2.2 Plasmid constructs
The pLKO.1-TRC cloning vector was a gift from David Root (Addgene plasmid # 10878;
http://n2t.net/addgene:10878; RRID:Addgene_10878)
71
.Two pairs of small hairpin RNAs
(shRNAs) of Apoc2, 5’-
CCGGGTCATCCTGATGTTGGGAAATCTCGAGATTTCCCAACATCAGGATGACTTTTT
G-3’ and 5’-
AATTCAAAAAGTCATCCTGATGTTGGGAAATCTCGAGATTTCCCAACATCAGGATGA
C-3’; 5’-
CCGGGCAGGGCTCCCTCTTAAGTTACTCGAGTAACTTAAGAGGGAGCCCTGCTTTTT
G-3’ and 5’-
AATTCAAAAAGCAGGGCTCCCTCTTAAGTTACTCGAGTAACTTAAGAGGGAGCCCTG
C-3’, were inserted into the pLKO.1-TRC vector. The PLVX-Apoc2-AcGFP-N1 plasmid was
constructed by cloning the Apoc2 cDNA into the EcoRI/BamHI sites of the PLVX-AcGFP-N1
vector (Clontech). The PCDH-EF1-FHC was a gift from Richard Wood (Addgene plasmid #
55

64874; http://n2t.net/addgene:64874; RRID:Addgene_64874)
72
. The PCDH-Apoc2-Flag-HA
plasmid was constructed by cloning the Apoc2 cDNA into the EcoRI/BamHI sites of the PCDH-
EF1-FHC vector. All constructs were confirmed by sequencing (GENEWIZ, South Plainfield,
NJ, USA).

3.2.3 Cell line and transfection
HEK293T cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM; Thermo Fisher)
with 10% fetal bovine serum (FBS; Invitrogen) and 1% antibiotics (Invitrogen). HEK293T cells
were transfected with the pLKO.1-shApoc2-1 plasmid (shApoc2-1), pLKO.1-shApoc2-2 plasmid
(shApoc2-2), PLVX-Apoc2-AcGFP-N1 plasmid (PLVX-Apoc2) and PCDH-Apoc2-Flag-HA
plasmid (PCDH-Apoc2), respectively. The pLKO.1 or PLVX or PCDH plasmid was co-
transfected with packaging plasmids pMD2.G and psPAX. Calcium Phosphate Mammalian
Transfection Kit (Takara, Mountain View, CA, USA) was used for transfection.

3.2.4 Lentiviral production
The culture medium was collected 48 hours and 72 hours post transfection. After being filtered by
0.45- µm sterile filter and concentrated in PEG at 4℃ overnight, the viral particles in the medium
were centrifuged at 2300 rpm for 30 min. The viral pellets were resuspended in RPMI 1640
medium (Thermo Fisher) with 20% FBS (Invitrogen) and 1% antibiotics (Invitrogen), and then
stored in -80℃.
56

3.2.5 Mouse primary cell infection
C57BL/6J mice were euthanized using isoflurane and dissected to obtain primary spleen and bone
marrow cells. The spleen was mashed and filtered by 70- µm cell strainer. The bone marrow cells
were flushed out by PBS (Thermo Fisher) from the femur and then filtered by 70- µm cell strainer.
The primary spleen and bone marrow cells were cultured in RPMI 1640 medium (Thermo Fisher)
with 20% FBS (Invitrogen) and 1% antibiotics (Invitrogen). Primary cells were infected by the
produced lentivirus once a day for two days.
For Apoc2 knockdown using shApoc2-1 and shApoc2-2 lentivirus, puromycin was added to the
culture medium to select the cells successfully infected by the lentivirus 48 hours and 96 hours
after the second infection. Doxycycline was added to the culture medium to induce shRNA
expression 48 hours after the second puromycin selection. Cells were collected 48 hours after
adding doxycycline to extract RNA and perform quantitative PCR analysis. Cells together with
the culture medium were collected 72 hours after adding doxycycline to perform western blot and
determine transfection efficiency.
For Apoc2 overexpression using PLVX-Apoc2 and PCDH-Apoc2 lentivirus, puromycin was
added to the culture medium to select the cells successfully infected by the lentivirus 48 hours and
96 hours after the second infection. Cells were collected 48 hours after the second puromycin
selection to extract RNA and perform quantitative PCR analysis. GFP signals of the cells infected
by PLVX vector and PLVX-Apoc2 lentiviral particles were detected 48 hours after the second
puromycin selection. Cells together with the culture medium were collected 72 hours after the
second puromycin selection to perform western blot and determine transfection efficiency.

57

3.2.6 Quantitative PCR analysis
Quantitative PCRs (qPCRs) were performed using the Applied Biosystems
TM
PowerUp
TM

SYBR
TM
Green Master Mix (Thermo Fisher). Samples were analyzed using the QuantStudio 12K
Flex Real-Time PCR System (Thermo Fisher). The primer sequences of Apoc2 used for qPCR
were 5’-TACTGGAGTGAGCCAGGATAG-3’ (forward primer) and 5’-
CAGGAATAGAGCCAGGAAGAAC-3’ (reverse primer). Relative gene expression levels were
normalized to Gapdh and were calculated using the 2
(- ∆∆CT)
method. The primer sequences of
Gapdh used for qPCR were 5’- CTCCCACTCTTCCACCTTCG-3’ (forward primer) and 5’-
GCCTCTCTTGCTCAGTGTCC-3’ (reverse primer). Each assay was performed with technical
replicates of three wells per sample.

3.2.7 Western blot analysis
To perform western blot analysis, cells together with the culture medium were mixed with 2x
sample buffer containing 2x Laemmli sample buffer (Bio-Rad Laboratories, Hercules, CA, USA)
and BetaME (Bio-Rad Laboratories). The proteins were separated by electrophoresis using SDS-
PAGE gel, and transferred to PVDF membrane (Bio-Rad Laboratories). Membranes were blocked
with 5% bovine serum albumin (BSA; VWR, Solon, OH, USA) for 1 hour, then incubated with
APOC2 polyclonal antibody (Invitrogen, PA5-102480,1:1000), Horseradish peroxidase (HRP)-
conjugated anti-Rabbit secondary antibody (Invitrogen, 1:5000), and HRP-conjugated GAPDH
monoclonal antibody (Thermo Fisher, HRP-60004, 1:5000). Protein bands were visualized using
the Pierce ECL Western Blotting Substrate Reagent (Thermo Fisher) and ChemiDoc Touch
Imaging System (Bio-Rad Laboratories).
58

3.2.8 Proliferation assay
The infected cells were collected 48 hours after the second puromycin selection to perform
proliferation assay. For Apoc2 knockdown, each cell group was separated into two groups,
doxycycline was added into one group, and the same amount of culture medium was added into
the other group. Trypan blue assay was used to count the cells continuously for five days to
determine cell proliferation status.

3.2.9 Statistical analysis
The lentiviral infections of normal mouse primary cells were performed three times. qPCR was
performed twice for the normal mouse primary spleen and bone marrow cells infected by
knockdown lentiviral particles. qPCR was performed once for the normal mouse primary spleen
and bone marrow cells infected by overexpression lentiviral particles. Western blot and
proliferation assay were performed once for mouse primary spleen and bone marrow cells infected
by knockdown and overexpression lentiviral particles. Data were represented as the mean ±
standard error of the mean (SEM). One-way ANOVA and Student’s t-test were used to calculate
p-value for proliferation assay in GraphPad Prism 7.0 (GraphPad Software, Inc., San Diego, CA,
USA). P-value less than 0.05 was considered statistically significant.
59

3.3 Results
3.3.1 Apoc2 is knocked down in normal mouse primary cells
To determine the knockdown efficiency of Apoc2 lentiviral particles in normal mouse primary
spleen and bone marrow cells, qPCR and western blot analyses were performed. In normal mouse
primary spleen cells, both pairs of shRNAs reduced the Apoc2 mRNA level compared with the
scramble control group. The second pair of shRNA had better effect in knocking down Apoc2
mRNA compared with the first pair of shRNA (relative mRNA expression: 0.166 vs 0.386; Figure
3.1A). In normal mouse primary bone marrow cells, the second pair of shRNA reduced the Apoc2
mRNA level compared with the scramble control group (relative mRNA expression: 0.00014 vs
1; Figure 3.1B). Because the bone marrow cells were very few and the knockdown effect was not
easy to detect using western blot, we only obtained the western blot result in normal mouse primary
spleen cells (Figure 3.1C). We found a leakage in the first shRNA lentiviral particle. Even without
being induced by doxycycline, the Apoc2 protein level was decreased compared with the scramble
control group. For the second shRNA lentiviral particle, we confirmed that doxycycline could
induce the knockdown effect to reduce Apoc2 protein level.

60

Figure 3.1 Apoc2 is knocked down in normal mouse primary cells. The knockdown
efficiency of Apoc2 was measured by qPCR in (A) normal mouse primary spleen cells and (B)
normal mouse primary bone marrow cells; as well as (C) western blot in normal mouse primary
spleen cells. Doxycycline was used to induce the shRNA expression to knock down Apoc2 gene.

61

3.3.2 Apoc2 is overexpressed in normal mouse primary cells
To determine the overexpression efficiency of Apoc2 in normal mouse primary spleen and bone
marrow cells, qPCR and western blot analyses were performed. In normal mouse primary spleen
cells, PLVX-Apoc2 increased the Apoc2 mRNA level compared with the PLVX vector control
(relative mRNA expression: 1.401 vs 1; Figure 3.2A). From the western blot result, PCDH-Apoc2
overexpressed Apoc2 protein compared with the PCDH vector control (Figure 3.2C). In normal
mouse primary bone marrow cells, Apoc2 mRNA overexpression was verified in both PLVX-
Apoc2 group and PCDH-Apoc2 group. PLVX-Apoc2 elevated the Apoc2 mRNA level compared
with the PLVX vector control (relative mRNA expression: 1.511 vs 1; Figure 3.2B). PCDH-Apoc2
elevated the Apoc2 mRNA level compared with the PCDH vector control (relative mRNA
expression: 30.202 vs 1; Figure 3.2B). From the western blot result, an increase in Apoc2 protein
level was observed in the PCDH-Apoc2 group compared with the PCDH vector group (Figure
3.2D). We also detected GFP signals in the cells infected by PLVX lentiviral particles. GFP
Fluorescence was captured in both normal mouse primary spleen and bone cells infected by
PLVX-Apoc2 and PLVX vector control (Figure 3.2E) further confirming that the lentiviral
infection worked. It is not clear why the qPCR results were not as expected and not always
consistent with the western blot; it is possible that the qPCR primers require validation to confirm
their specificity.

62

Figure 3.2 Apoc2 is overexpressed in normal mouse primary cells. The overexpression
efficiency of Apoc2 was measured by qPCR in (A) normal mouse primary spleen cells and (B)
normal mouse primary bone marrow cells; as well as western blot in (C) normal mouse primary
spleen cells and (D) normal mouse primary bone marrow cells. GFP signals of the normal mouse
primary cells infected by PLVX lentiviral particles were also detected (E).

63

3.3.3 Apoc2 is not essential in normal mouse primary cell proliferation
Proliferation assay was performed on normal mouse primary cells infected by lentiviral particles
transduced with both shApoc2 plasmids and overexpressed Apoc2 plasmids. For knockdown
groups, in spleen and bone marrow cells infected by shApoc2 lentiviral particles, there wasn’t any
significant cell number difference between the shApoc2 groups and the scramble control group
(Figure 3.3A & B). For overexpression groups, slight yet statistically significant cell number
reduction was found in spleen cells infected by PLVX-Apoc2 on day3 and day4 compared with
those infected by PLVX vector. The cell numbers of the spleen cells infected by PCDH-Apoc2
and PCDH vector were significantly increased throughout the five days (Figure 3.3C). In bone
marrow cells infected by lentiviral particles transduced with overexpressed Apoc2 plasmids, there
wasn’t any significant cell number difference between the overexpression groups and the vector
control groups (Figure 3.3D). Overall, both the normal mouse primary spleen and bone marrow
cells were still proliferating after lentiviral particle infection, indicating that Apoc2 is not essential
for their proliferation.

64

65

Figure 3.3 Apoc2 is not essential in normal mouse primary cell proliferation. Proliferation
assay was performed on (A) normal mouse primary spleen cells infected by shApoc2 lentiviral
particles, (B) normal mouse primary bone marrow cells infected by shApoc2 lentiviral particles,
(C) normal mouse primary spleen cells infected by overexpression lentiviral particles, and (D)
normal mouse primary bone marrow cells infected by overexpression lentiviral particles. The
cell number difference between groups on each day were analyzed using one-way ANOVA test
and Student’s t-test. (***, P < 0.001; **, P < 0.01; *, P < 0.05)

66

3.4 Discussion
Metabolic reprogramming is a hallmark of cancer. Based on the role of APOC2 in lipid metabolism
to release FA, and the increasing demand of FA for cancer cells, it’s possible that APOC2 is
relevant to cancer development. The novel role of APOC2-CD36 signal axis as a novel therapeutic
target in AML and CD36 antibody as a viable treatment strategy to develop into the clinic have
been established.
46
However, the function of APOC2 in other types of cancers are not fully
elucidated.
To define whether Apoc2 is essential for normal cell proliferation, we engineered an
overexpression and a knockdown lentiviral expression system to perform gain and loss of function
approaches of Apoc2 gene in normal mouse primary spleen and bone marrow cells. We constructed
lentiviral plasmids and produced lentiviral particles to infect normal mouse cells, then performed
qPCR and western blot analyses to measure mRNA and protein level of Apoc2. We confirmed that
the second shRNA lentiviral particle had better efficiency in decreasing Apoc2 mRNA level and
can be induced by doxycycline to reduce Apoc2 protein level. We also confirmed that the PCDH-
Apoc2 lentiviral particle had better efficiency in overexpressing Apoc2 mRNA and protein. Our
research suggested that both the normal mouse primary spleen and bone marrow cells were still
proliferating after lentiviral particle infection, indicating that Apoc2 is not essential for the
proliferation of normal mouse primary bone marrow and spleen cells, thus could potentially act as
a therapeutic target without normal cell toxicity.
A limitation of this study is the lack of in vivo experiments. Engraftment of leukemia cells infected
by the lentiviral particles transduced with both shRNA and Apoc2 overexpression plasmids to the
C57BL/6J mice could be performed to test the in vivo effect of the lentiviral particles. We could
67

also test the differences between leukemia development in leukemic mouse models engrafted with
Apoc2 knockdown and overexpression lentiviral particles.
Mutation in the APOC2 gene causing APOC2 deficiency is associated with hypertriglyceridemia.
54

Whether the triglyceride level is elevated in normal mouse cells infected with the shRNA lentiviral
particles needs to be explored. The mechanism of mouse normal cell proliferation with APOC2
knockdown, which possibly leads to lower FA level, also requires further study.
The overexpression and knockdown lentiviral expression systems could be used to infect normal
cells from other mouse tissues such as liver, to further confirm the potential of APOC2 as
therapeutic target in other types of cancer.

68

Chapter 4 Conclusion
Taken together, this study is a combination of datasets exploration of APOC2 deregulation patter
in cancer and the lab work of gene editing study in normal mouse primary cells to establish whether
this gene is required for normal cells.
Here, we found that amplifications, mutations and deep deletions were the major APOC2 gene
alterations. 1% of all samples had at least one of these genetic alterations. APOC2 was significantly
upregulated in several malignancies compared with the healthy tissues. In these malignancies, we
found lower promoter methylation level of APOC2 in primary tumor than normal samples.
Alterations and high expression level of APOC2 gene were found to be relevant to poor clinical
outcome. Patients with APOC2 gene alterations had significantly shorter overall survival.
Alterations in the APOC2 gene were frequently found in patients with TP53 mutations (P<0.0001).
TP53, a well-known tumor suppressor, was also found to regulate lipid metabolism.
66,67
However,
the mechanistic association between P53 mutations and APOC2 deregulation needs to be explored.
In addition to lipid metabolism, immune response related pathways were highly deregulated in
APOC2 altered cancers. The function of APOC2 in the interaction between cancer cells and the
immune microenvironment requires further study.
By gain and loss of function approaches, we constructed lentiviral systems to overexpress and
knock down Apoc2 gene in normal mouse primary cells. Western blot and qPCR were performed
to confirm the overexpression and knockdown effects of the lentiviral expression systems. We
found that both the normal mouse primary spleen and bone marrow cells were still proliferating
up to five days after Apoc2 overexpression and knockdown, without statistically significant
difference from the control groups.
69

Our results suggest that APOC2 upregulation and gene alterations are frequently occurred in
cancer and are associated with poor clinical outcomes. Moreover, Apoc2 is dispensable for the
survival of normal mouse primary spleen and bone marrow cells. Based on the similarity between
human APOC2 and mouse Apoc2 genes, it’s possible that human APOC2 is also dispensable for
normal cell survival. All these data indicate that APOC2 presents a viable therapeutic target in
cancer.
Further study is needed to address the mechanism of APOC2 upregulation in cancer cells and the
mechanism of mouse normal cell proliferation with APOC2 knockdown. Whether APOC2
elevation in the serum also correlates with poor clinical outcome and thus can be utilized as a
biomarker for cancer prognosis requires further validation. In addition, the function of APOC2 in
normal cells from other tissues besides spleen and bone marrow also needs to be investigated.
Moreover, in the previous study of APOC2 in AML, our lab transduced normal CD34+ cord blood
cells with APOC2, CD36 or the combined overexpression lentiviral particles. Significant increase
in the number of viable cells was only found in CD34+ cells transduced with both APOC2 and
CD36, but not the APOC2 or CD36 overexpression groups, which suggests that maybe CD36 is
required for APOC2 function.
46
Whether CD36 is also required for the function of APOC2 in other
types of cancer besides AML requires further study.
In addition to APOC2, we also found that APOC1 was significantly upregulated in several types
of cancer and associated with poor clinical outcome. APOC3, which plays an opposite role of
APOC2 in lipid metabolism as it inhibits LPL, was often deleted in several types of cancer such
as melanoma. A more comprehensive analysis of the deregulation patterns of other apolipoproteins
needs to be performed.
70

In a recent study of possible agents for cardiovascular disease treatment, an APOC2 mimetic
peptide was synthesized to promote lipolysis of triglycerides on native lipoproteins by LPL.
73
An
inactive analog of APOC2 was also designed as control group, which provides inspiration for
targeting APOC2 in cancer. Besides targeting APOC2 directly, APOC2-related microRNA
(miRNA) could also be a target. In gastrointestinal stromal tumor, by miRNA profiling and target
gene prediction, miR-4510 was found to be significantly downregulated, which resulted in the
upregulation of APOC2 mRNA and protein expression. Overexpression of miR-4510 using miR-
4510 mimics suppressed tumor proliferation, migration and invation.
64
Development of APOC2
inhibitors such as inactive analog of APOC2, as well as mimics or inhibitor of APOC2 expression
regulator, and the functional characterization of their antitumor effects could be a topic in future
study.

71

References
1. Liu, Y., Meng, Y., Zhang, T., & Alachkar, H. (2021). Deregulation of apolipoprotein C2
gene in cancer: A potential metabolic vulnerability. Clinical and Translational
Medicine, 11(6), e406.
2. Parkin, D. M., Bray, F., Ferlay, J., & Pisani, P. (2005). Global cancer statistics, 2002. CA: a
cancer journal for clinicians, 55(2), 74–108.
3. Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., & Forman, D. (2011). Global cancer
statistics. CA: a cancer journal for clinicians, 61(2), 69–90.
4. Torre, L. A., Bray, F., Siegel, R. L., Ferlay, J., Lortet-Tieulent, J., & Jemal, A. (2015).
Global cancer statistics, 2012. CA: a cancer journal for clinicians, 65(2), 87–108.
5. Global Burden of Disease Cancer Collaboration, Fitzmaurice, C., Dicker, D., Pain, A.,
Hamavid, H., Moradi-Lakeh, M., MacIntyre, M. F., Allen, C., Hansen, G., Woodbrook, R.,
Wolfe, C., Hamadeh, R. R., Moore, A., Werdecker, A., Gessner, B. D., Te Ao, B.,
McMahon, B., Karimkhani, C., Yu, C., Cooke, G. S., … Naghavi, M. (2015). The Global
Burden of Cancer 2013. JAMA oncology, 1(4), 505–527.
6. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018).
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide
for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), 394–424.
7. Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F.
(2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality
Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians, 71(3), 209–
249.
8. Hanahan, D., & Weinberg, R. A. (2000). The hallmarks of cancer. Cell, 100(1), 57–70.
9. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: the next
generation. Cell, 144(5), 646–674.
10. DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G., & Thompson, C. B. (2008). The biology
of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell
metabolism, 7(1), 11–20.
11. Phan, L. M., Yeung, S. C., & Lee, M. H. (2014). Cancer metabolic reprogramming:
importance, main features, and potentials for precise targeted anti-cancer therapies. Cancer
biology & medicine, 11(1), 1–19.
12. WARBURG O. (1956). On the origin of cancer cells. Science (New York, N.Y.), 123(3191),
309–314.
13. Galluzzi, L., Pietrocola, F., Levine, B., & Kroemer, G. (2014). Metabolic control of
autophagy. Cell, 159(6), 1263–1276.
72

14. Galluzzi, L., Pietrocola, F., Bravo-San Pedro, J. M., Amaravadi, R. K., Baehrecke, E. H.,
Cecconi, F., Codogno, P., Debnath, J., Gewirtz, D. A., Karantza, V., Kimmelman, A.,
Kumar, S., Levine, B., Maiuri, M. C., Martin, S. J., Penninger, J., Piacentini, M.,
Rubinsztein, D. C., Simon, H. U., Simonsen, A., … Kroemer, G. (2015). Autophagy in
malignant transformation and cancer progression. The EMBO journal, 34(7), 856–880.
15. Xie, H., Hanai, J., Ren, J. G., Kats, L., Burgess, K., Bhargava, P., Signoretti, S., Billiard, J.,
Duffy, K. J., Grant, A., Wang, X., Lorkiewicz, P. K., Schatzman, S., Bousamra, M., 2nd,
Lane, A. N., Higashi, R. M., Fan, T. W., Pandolfi, P. P., Sukhatme, V. P., & Seth, P. (2014).
Targeting lactate dehydrogenase--a inhibits tumorigenesis and tumor progression in mouse
models of lung cancer and impacts tumor-initiating cells. Cell metabolism, 19(5), 795–809.
16. Laplante, M., & Sabatini, D. M. (2012). mTOR signaling in growth control and
disease. Cell, 149(2), 274–293.
17. Shimobayashi, M., & Hall, M. N. (2014). Making new contacts: the mTOR network in
metabolism and signalling crosstalk. Nature reviews. Molecular cell biology, 15(3), 155–
162.
18. Gazi, M., Moharram, S. A., Marhäll, A., & Kazi, J. U. (2017). The dual specificity
PI3K/mTOR inhibitor PKI-587 displays efficacy against T-cell acute lymphoblastic
leukemia (T-ALL). Cancer letters, 392, 9–16.
19. Tasian, S. K., Teachey, D. T., Li, Y., Shen, F., Harvey, R. C., Chen, I. M., Ryan, T., Vincent,
T. L., Willman, C. L., Perl, A. E., Hunger, S. P., Loh, M. L., Carroll, M., & Grupp, S. A.
(2017). Potent efficacy of combined PI3K/mTOR and JAK or ABL inhibition in murine
xenograft models of Ph-like acute lymphoblastic leukemia. Blood, 129(2), 177–187.
20. Kim, C. J., Terado, T., Tambe, Y., Mukaisho, K. I., Kageyama, S., Kawauchi, A., & Inoue,
H. (2021). Cryptotanshinone, a novel PDK 4 inhibitor, suppresses bladder cancer cell
invasiveness via the mTOR/β-catenin/N-cadherin axis. International journal of
oncology, 59(1), 40.
21. Figueroa, M. E., Abdel-Wahab, O., Lu, C., Ward, P. S., Patel, J., Shih, A., Li, Y., Bhagwat,
N., Vasanthakumar, A., Fernandez, H. F., Tallman, M. S., Sun, Z., Wolniak, K., Peeters, J.
K., Liu, W., Choe, S. E., Fantin, V. R., Paietta, E., Löwenberg, B., Licht, J. D., … Melnick,
A. (2010). Leukemic IDH1 and IDH2 mutations result in a hypermethylation phenotype,
disrupt TET2 function, and impair hematopoietic differentiation. Cancer cell, 18(6), 553–
567.
22. Lu, C., Ward, P. S., Kapoor, G. S., Rohle, D., Turcan, S., Abdel-Wahab, O., Edwards, C. R.,
Khanin, R., Figueroa, M. E., Melnick, A., Wellen, K. E., O'Rourke, D. M., Berger, S. L.,
Chan, T. A., Levine, R. L., Mellinghoff, I. K., & Thompson, C. B. (2012). IDH mutation
impairs histone demethylation and results in a block to cell
differentiation. Nature, 483(7390), 474–478.
23. Parker, S. J., & Metallo, C. M. (2015). Metabolic consequences of oncogenic IDH
mutations. Pharmacology & therapeutics, 152, 54–62.
73

24. Cerchione, C., Romano, A., Daver, N., DiNardo, C., Jabbour, E. J., Konopleva, M., Ravandi-
Kashani, F., Kadia, T., Martelli, M. P., Isidori, A., Martinelli, G., & Kantarjian, H. (2021).
IDH1/IDH2 Inhibition in Acute Myeloid Leukemia. Frontiers in oncology, 11, 639387.
25. MEDES, G., THOMAS, A., & WEINHOUSE, S. (1953). Metabolism of neoplastic tissue.
IV. A study of lipid synthesis in neoplastic tissue slices in vitro. Cancer research, 13(1), 27–
29.
26. Ookhtens, M., Kannan, R., Lyon, I., & Baker, N. (1984). Liver and adipose tissue
contributions to newly formed fatty acids in an ascites tumor. The American journal of
physiology, 247(1 Pt 2), R146–R153.
27. Svensson, R. U., Parker, S. J., Eichner, L. J., Kolar, M. J., Wallace, M., Brun, S. N.,
Lombardo, P. S., Van Nostrand, J. L., Hutchins, A., Vera, L., Gerken, L., Greenwood, J.,
Bhat, S., Harriman, G., Westlin, W. F., Harwood, H. J., Jr, Saghatelian, A., Kapeller, R.,
Metallo, C. M., & Shaw, R. J. (2016). Inhibition of acetyl-CoA carboxylase suppresses fatty
acid synthesis and tumor growth of non-small-cell lung cancer in preclinical models. Nature
medicine, 22(10), 1108–1119.
28. Heuer, T. S., Ventura, R., Mordec, K., Lai, J., Fridlib, M., Buckley, D., & Kemble, G.
(2017). FASN Inhibition and Taxane Treatment Combine to Enhance Anti-tumor Efficacy in
Diverse Xenograft Tumor Models through Disruption of Tubulin Palmitoylation and
Microtubule Organization and FASN Inhibition-Mediated Effects on Oncogenic Signaling
and Gene Expression. EBioMedicine, 16, 51–62.
29. Myklebost, O., Williamson, B., Markham, A. F., Myklebost, S. R., Rogers, J., Woods, D. E.,
& Humphries, S. E. (1984). The isolation and characterization of cDNA clones for human
apolipoprotein CII. The Journal of biological chemistry, 259(7), 4401–4404.
30. Mak, P. A., Laffitte, B. A., Desrumaux, C., Joseph, S. B., Curtiss, L. K., Mangelsdorf, D. J.,
Tontonoz, P., & Edwards, P. A. (2002). Regulated expression of the apolipoprotein E/C-I/C-
IV/C-II gene cluster in murine and human macrophages. A critical role for nuclear liver X
receptors alpha and beta. The Journal of biological chemistry, 277(35), 31900–31908.
31. Hoffer, M. J., van Eck, M. M., Havekes, L. M., Hofker, M. H., & Frants, R. R. (1993).
Structure and expression of the mouse apolipoprotein C2 gene. Genomics, 17(1), 45–51.
32. Kast, H. R., Nguyen, C. M., Sinal, C. J., Jones, S. A., Laffitte, B. A., Reue, K., Gonzalez, F.
J., Willson, T. M., & Edwards, P. A. (2001). Farnesoid X-activated receptor induces
apolipoprotein C-II transcription: a molecular mechanism linking plasma triglyceride levels
to bile acids. Molecular endocrinology (Baltimore, Md.), 15(10), 1720–1728.
33. Kardassis, D., Roussou, A., Papakosta, P., Boulias, K., Talianidis, I., & Zannis, V. I. (2003).
Synergism between nuclear receptors bound to specific hormone response elements of the
hepatic control region-1 and the proximal apolipoprotein C-II promoter mediate
apolipoprotein C-II gene regulation by bile acids and retinoids. The Biochemical
journal, 372(Pt 2), 291–304.
74

34. MacPhee, C. E., Hatters, D. M., Sawyer, W. H., & Howlett, G. J. (2000). Apolipoprotein C-
II39-62 activates lipoprotein lipase by direct lipid-independent
binding. Biochemistry, 39(12), 3433–3440.
35. Shen, Y., Lookene, A., Nilsson, S., & Olivecrona, G. (2002). Functional analyses of human
apolipoprotein CII by site-directed mutagenesis: identification of residues important for
activation of lipoprotein lipase. The Journal of biological chemistry, 277(6), 4334–4342.
36. Zdunek, J., Martinez, G. V., Schleucher, J., Lycksell, P. O., Yin, Y., Nilsson, S., Shen, Y.,
Olivecrona, G., & Wijmenga, S. (2003). Global structure and dynamics of human
apolipoprotein CII in complex with micelles: evidence for increased mobility of the helix
involved in the activation of lipoprotein lipase. Biochemistry, 42(7), 1872–1889.
37. Liu, C., Han, T., Stachura, D. L., Wang, H., Vaisman, B. L., Kim, J., Klemke, R. L.,
Remaley, A. T., Rana, T. M., Traver, D., & Miller, Y. I. (2018). Lipoprotein lipase regulates
hematopoietic stem progenitor cell maintenance through DHA supply. Nature
communications, 9(1), 1310.
38. Connelly, P. W., Maguire, G. F., & Little, J. A. (1987). Apolipoprotein CIISt. Michael.
Familial apolipoprotein CII deficiency associated with premature vascular disease. The
Journal of clinical investigation, 80(6), 1597–1606.
39. Wilson, C. J., Priore Oliva, C., Maggi, F., Catapano, A. L., & Calandra, S. (2003).
Apolipoprotein C-II deficiency presenting as a lipid encephalopathy in infancy. Annals of
neurology, 53(6), 807–810.
40. Jiang, J., Wang, Y., Ling, Y., Kayoumu, A., Liu, G., & Gao, X. (2016). A novel APOC2
gene mutation identified in a Chinese patient with severe hypertriglyceridemia and recurrent
pancreatitis. Lipids in health and disease, 15, 12.
41. Koundouros, N., & Poulogiannis, G. (2020). Reprogramming of fatty acid metabolism in
cancer. British journal of cancer, 122(1), 4–22.
42. Kuemmerle, N. B., Rysman, E., Lombardo, P. S., Flanagan, A. J., Lipe, B. C., Wells, W. A.,
Pettus, J. R., Froehlich, H. M., Memoli, V. A., Morganelli, P. M., Swinnen, J. V.,
Timmerman, L. A., Chaychi, L., Fricano, C. J., Eisenberg, B. L., Coleman, W. B., & Kinlaw,
W. B. (2011). Lipoprotein lipase links dietary fat to solid tumor cell proliferation. Molecular
cancer therapeutics, 10(3), 427–436.
43. Cao, D., Song, X., Che, L., Li, X., Pilo, M. G., Vidili, G., Porcu, A., Solinas, A., Cigliano,
A., Pes, G. M., Ribback, S., Dombrowski, F., Chen, X., Li, L., & Calvisi, D. F. (2017). Both
de novo synthetized and exogenous fatty acids support the growth of hepatocellular
carcinoma cells. Liver international : official journal of the International Association for the
Study of the Liver, 37(1), 80–89.
44. Henderson, F., Johnston, H. R., Badrock, A. P., Jones, E. A., Forster, D., Nagaraju, R. T.,
Evangelou, C., Kamarashev, J., Green, M., Fairclough, M., Ramirez, I. B., He, S., Snaar-
Jagalska, B. E., Hollywood, K., Dunn, W. B., Spaink, H. P., Smith, M. P., Lorigan, P.,
75

Claude, E., Williams, K. J., … Hurlstone, A. (2019). Enhanced Fatty Acid Scavenging and
Glycerophospholipid Metabolism Accompany Melanocyte Neoplasia Progression in
Zebrafish. Cancer research, 79(9), 2136–2151.
45. Xue, A., Chang, J. W., Chung, L., Samra, J., Hugh, T., Gill, A., Butturini, G., Baxter, R. C.,
& Smith, R. C. (2012). Serum apolipoprotein C-II is prognostic for survival after pancreatic
resection for adenocarcinoma. British journal of cancer, 107(11), 1883–1891.
46. Zhang, T., Yang, J., Vaikari, V. P., Beckford, J. S., Wu, S., Akhtari, M., & Alachkar, H.
(2020). Apolipoprotein C2 - CD36 Promotes Leukemia Growth and Presents a Targetable
Axis in Acute Myeloid Leukemia. Blood cancer discovery, 1(2), 198–213.
47. Yang, P., Su, C., Luo, X., Zeng, H., Zhao, L., Wei, L., Zhang, X., Varghese, Z., Moorhead,
J. F., Chen, Y., & Ruan, X. Z. (2018). Dietary oleic acid-induced CD36 promotes cervical
cancer cell growth and metastasis via up-regulation Src/ERK pathway. Cancer letters, 438,
76–85.
48. Hale, J. S., Otvos, B., Sinyuk, M., Alvarado, A. G., Hitomi, M., Stoltz, K., Wu, Q.,
Flavahan, W., Levison, B., Johansen, M. L., Schmitt, D., Neltner, J. M., Huang, P., Ren, B.,
Sloan, A. E., Silverstein, R. L., Gladson, C. L., DiDonato, J. A., Brown, J. M., McIntyre,
T., … Lathia, J. D. (2014). Cancer stem cell-specific scavenger receptor CD36 drives
glioblastoma progression. Stem cells (Dayton, Ohio), 32(7), 1746–1758.
49. Farge, T., Saland, E., de Toni, F., Aroua, N., Hosseini, M., Perry, R., Bosc, C., Sugita, M.,
Stuani, L., Fraisse, M., Scotland, S., Larrue, C., Boutzen, H., Féliu, V., Nicolau-Travers, M.
L., Cassant-Sourdy, S., Broin, N., David, M., Serhan, N., Sarry, A., … Sarry, J. E. (2017).
Chemotherapy-Resistant Human Acute Myeloid Leukemia Cells Are Not Enriched for
Leukemic Stem Cells but Require Oxidative Metabolism. Cancer discovery, 7(7), 716–735.
50. 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., Antipin, Y., Reva, B., Goldberg, A. P., Sander, C., &
Schultz, N. (2012). The cBio cancer genomics portal: an open platform for exploring
multidimensional cancer genomics data. Cancer discovery, 2(5), 401–404.
51. Gao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., Sun, Y.,
Jacobsen, A., Sinha, R., Larsson, E., Cerami, E., Sander, C., & Schultz, N. (2013).
Integrative analysis of complex cancer genomics and clinical profiles using the
cBioPortal. Science signaling, 6(269), pl1.
52. Chandrashekar, D. S., Bashel, B., Balasubramanya, S., Creighton, C. J., Ponce-Rodriguez, I.,
Chakravarthi, B., & Varambally, S. (2017). UALCAN: A Portal for Facilitating Tumor
Subgroup Gene Expression and Survival Analyses. Neoplasia (New York, N.Y.), 19(8), 649–
658.
53. Davis, S., & Meltzer, P. S. (2007). GEOquery: a bridge between the Gene Expression
Omnibus (GEO) and BioConductor. Bioinformatics (Oxford, England), 23(14), 1846–1847.
76

54. Breckenridge, W. C., Little, J. A., Steiner, G., Chow, A., & Poapst, M. (1978).
Hypertriglyceridemia associated with deficiency of apolipoprotein C-II. The New England
journal of medicine, 298(23), 1265–1273.
55. Wolska, A., Dunbar, R. L., Freeman, L. A., Ueda, M., Amar, M. J., Sviridov, D. O., &
Remaley, A. T. (2017). Apolipoprotein C-II: New findings related to genetics, biochemistry,
and role in triglyceride metabolism. Atherosclerosis, 267, 49–60.
56. Wiebusch, H., Nofer, J. R., von Eckardstein, A., Funke, H., Wahrburg, U., Martin, H.,
Köhler, E., & Assmann, G. (1995). Electrophoretic screening for human apolipoprotein C-II
variants: repeated identification of apolipoprotein C-II(K19T). Journal of molecular
medicine (Berlin, Germany), 73(7), 373–378.
57. Ueda, M., Dunbar, R. L., Wolska, A., Sikora, T. U., Escobar, M., Seliktar, N., deGoma, E.,
DerOhannessian, S., Morrell, L., McIntyre, A. D., Burke, F., Sviridov, D., Amar, M.,
Shamburek, R. D., Freeman, L., Hegele, R. A., Remaley, A. T., & Rader, D. J. (2017). A
Novel APOC2 Missense Mutation Causing Apolipoprotein C-II Deficiency With Severe
Triglyceridemia and Pancreatitis. The Journal of clinical endocrinology and
metabolism, 102(5), 1454–1457.
58. Johansen, C. T., Wang, J., McIntyre, A. D., Martins, R. A., Ban, M. R., Lanktree, M. B.,
Huff, M. W., Péterfy, M., Mehrabian, M., Lusis, A. J., Kathiresan, S., Anand, S. S., Yusuf,
S., Lee, A. H., Glimcher, L. H., Cao, H., & Hegele, R. A. (2012). Excess of rare variants in
non-genome-wide association study candidate genes in patients with
hypertriglyceridemia. Circulation. Cardiovascular genetics, 5(1), 66–72.
59. Pinilla-Monsalve, G. D., Lores, J., Pachajoa, H., López-Ponce de León, J. D., López, A.,
Rodríguez-Rojas, L. X., & Nastasi-Catanese, J. A. (2020). A Novel APOC2 Mutation in a
Colombian Patient with Recurrent Hypertriglyceridemic Pancreatitis. The application of
clinical genetics, 13, 63–69.
60. Röhrig, F., & Schulze, A. (2016). The multifaceted roles of fatty acid synthesis in
cancer. Nature reviews. Cancer, 16(11), 732–749.
61. Petan, T., Jarc, E., & Jusović, M. (2018). Lipid Droplets in Cancer: Guardians of Fat in a
Stressful World. Molecules (Basel, Switzerland), 23(8), 1941.
62. Sakurai, T., Sakurai, A., Vaisman, B. L., Amar, M. J., Liu, C., Gordon, S. M., Drake, S. K.,
Pryor, M., Sampson, M. L., Yang, L., Freeman, L. A., & Remaley, A. T. (2016). Creation of
Apolipoprotein C-II (ApoC-II) Mutant Mice and Correction of Their Hypertriglyceridemia
with an ApoC-II Mimetic Peptide. The Journal of pharmacology and experimental
therapeutics, 356(2), 341–353.
63. Shachter, N. S., Hayek, T., Leff, T., Smith, J. D., Rosenberg, D. W., Walsh, A.,
Ramakrishnan, R., Goldberg, I. J., Ginsberg, H. N., & Breslow, J. L. (1994). Overexpression
of apolipoprotein CII causes hypertriglyceridemia in transgenic mice. The Journal of clinical
investigation, 93(4), 1683–1690.
77

64. Chen, Y., Qin, C., Cui, X., Geng, W., Xian, G., & Wang, Z. (2020). miR-4510 acts as a
tumor suppressor in gastrointestinal stromal tumor by targeting APOC2. Journal of cellular
physiology, 235(7-8), 5711–5721.
65. Trusca, V. G., Florea, I. C., Kardassis, D., & Gafencu, A. V. (2012). STAT1 interacts with
RXRα to upregulate ApoCII gene expression in macrophages. PloS one, 7(7), e40463.
66. Liu, J., Zhang, C., Hu, W., & Feng, Z. (2019). Tumor suppressor p53 and
metabolism. Journal of molecular cell biology, 11(4), 284–292.
67. Yahagi, N., Shimano, H., Matsuzaka, T., Najima, Y., Sekiya, M., Nakagawa, Y., Ide, T.,
Tomita, S., Okazaki, H., Tamura, Y., Iizuka, Y., Ohashi, K., Gotoda, T., Nagai, R., Kimura,
S., Ishibashi, S., Osuga, J., & Yamada, N. (2003). p53 Activation in adipocytes of obese
mice. The Journal of biological chemistry, 278(28), 25395–25400.
68. Wang, H., Franco, F., Tsui, Y. C., Xie, X., Trefny, M. P., Zappasodi, R., Mohmood, S. R.,
Fernández-García, J., Tsai, C. H., Schulze, I., Picard, F., Meylan, E., Silverstein, R.,
Goldberg, I., Fendt, S. M., Wolchok, J. D., Merghoub, T., Jandus, C., Zippelius, A., & Ho, P.
C. (2020). CD36-mediated metabolic adaptation supports regulatory T cell survival and
function in tumors. Nature immunology, 21(3), 298–308.
69. Gao, M., Yang, C., Wang, X., Guo, M., Yang, L., Gao, S., Zhang, X., Ruan, G., Li, X., Tian,
W., Lu, G., Dong, X., Ma, S., Li, W., Wang, Y., Zhu, H., He, J., Yang, H., Liu, G., & Xian,
X. (2020). ApoC2 deficiency elicits severe hypertriglyceridemia and spontaneous
atherosclerosis: A rodent model rescued from neonatal death. Metabolism: clinical and
experimental, 109, 154296.
70. Liu, C., Gates, K. P., Fang, L., Amar, M. J., Schneider, D. A., Geng, H., Huang, W., Kim, J.,
Pattison, J., Zhang, J., Witztum, J. L., Remaley, A. T., Dong, P. D., & Miller, Y. I. (2015).
Apoc2 loss-of-function zebrafish mutant as a genetic model of hyperlipidemia. Disease
models & mechanisms, 8(8), 989–998.
71. Moffat, J., Grueneberg, D. A., Yang, X., Kim, S. Y., Kloepfer, A. M., Hinkle, G., Piqani, B.,
Eisenhaure, T. M., Luo, B., Grenier, J. K., Carpenter, A. E., Foo, S. Y., Stewart, S. A.,
Stockwell, B. R., Hacohen, N., Hahn, W. C., Lander, E. S., Sabatini, D. M., & Root, D. E.
(2006). A lentiviral RNAi library for human and mouse genes applied to an arrayed viral
high-content screen. Cell, 124(6), 1283–1298.
72. Yousefzadeh, M. J., Wyatt, D. W., Takata, K., Mu, Y., Hensley, S. C., Tomida, J., Bylund,
G. O., Doublié, S., Johansson, E., Ramsden, D. A., McBride, K. M., & Wood, R. D. (2014).
Mechanism of suppression of chromosomal instability by DNA polymerase POLQ. PLoS
genetics, 10(10), e1004654.
73. Amar, M. J., Sakurai, T., Sakurai-Ikuta, A., Sviridov, D., Freeman, L., Ahsan, L., &
Remaley, A. T. (2015). A novel apolipoprotein C-II mimetic peptide that activates
lipoprotein lipase and decreases serum triglycerides in apolipoprotein E-knockout mice. The
Journal of pharmacology and experimental therapeutics, 352(2), 227–235.

Asset Metadata

Creator Liu, Yuqiao (author)

Core Title APOC2 presents a viable therapeutic target in cancer

Contributor Electronically uploaded by the author (provenance)

School School of Pharmacy

Degree Master of Science

Degree Program Pharmaceutical Sciences

Degree Conferral Date 2021-08

Publication Date 07/28/2021

Defense Date 07/25/2021

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

Tag APOC2,cancer,OAI-PMH Harvest

Format application/pdf (imt)

Language English

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

Creator Email yuqiao@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC15640547

Unique identifier UC15640547

Legacy Identifier etd-LiuYuqiao-9911

Document Type Thesis

Format application/pdf (imt)

Rights Liu, Yuqiao

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 author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

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

Repository Email uscdl@usc.edu

Abstract (if available)

Abstract Apolipoprotein C2 (APOC2) plays an essential role in lipid metabolism and fatty acids transport to meet cells energy demands. Cancer cells require high level of energy to proliferate and invade normal tissues. APOC2 genomics and transcriptomic alterations patterns in cancer remained undiscovered. In this thesis, we characterize the deregulation of APOC2 in several types of cancer by analyzing 46706 samples in 176 cancer studies, gene expression data in 13 datasets and gene methylation data of 2121 samples in four cancers. We found that amplifications, mutations and deep deletions are the main APOC2 gene alterations. Approximately 1% of all samples have at least one of these genetic alterations. 19q gain status is present in 50% of samples with APOC2 alterations. APOC2 is significantly overexpressed in several malignancies compared with the respective normal tissues. In these malignancies, we found lower promoter methylation level of APOC2 in primary tumor than normal samples. Alterations and high expression level of APOC2 gene were found to be relevant to poor clinical outcome. Patients with APOC2 gene alterations had significantly shorter overall survival (median survival, 37.97 vs 80.68 months; P<0.0001). APOC2 gene is frequently mutated with TP53 (P<0.0001). In addition to lipid metabolism, immune response related pathways were highly deregulated in APOC2 altered cancers. Our results suggest that APOC2 gene upregulation and alterations occur frequently in cancer and that functional and mechanistic studies are warranted to further establish APOC2 as a potential therapeutic target. To establish APOC2 as cancer therapeutic target, investigating the role of this gene in normal cells is needed. Here, we hypothesized that APOC2 is dispensable for the survival of normal cells. By gain and loss of function approaches, we constructed lentiviral systems to overexpress and knock down Apoc2 gene in normal mouse primary cells. Western blot and qPCR were performed to confirm the overexpression and knockdown effects of the lentiviral particles. We found that both the normal mouse primary spleen and bone marrow cells were still proliferating up to five days after Apoc2 overexpression and knockdown, without statistically significant difference from the control groups. Our research suggests that Apoc2 may not essential for the proliferation of normal mouse primary spleen and bone marrow cells. While further phenotypic, functional and mechanistic analysis are needed to confirm that Apoc2 is dispensable for normal hematopoietic cells, these data are encouraging to further investigate the development of Apoc2 into a viable therapeutic target in cancer.