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Exploring the effects of CXCR4 inhibition on circulating tumor cell populations in metastatic prostate cancer

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Exploring the effects of CXCR4 inhibition on circulating tumor cell populations in metastatic prostate cancer

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Content 1

Exploring the effects of CXCR4 inhibition on
circulating tumor cell populations in
metastatic prostate cancer

Joseph Bae
May 2018
Master of Science in Medical Biophysics
Keck School of Medicine
University of Southern California
2

Table of Contents

Abstract
1. Introduction
2. Specific Aims
3. Experimental
3.1 Clinical Samples
3.2 Analysis of HD-CTCs via the HD-SCA assay
3.3 Single-cell genomic analysis
4. Results
4.1 HD-CTC kinetics post-inhibitor administration
4.2 Phenotypic analysis of HD-CTCs
4.3 Genotypic analysis of HD-CTCs
5. Discussion
6. Future Directions
7. Conclusions
8. Work Referenced
3
5
9
10
10
12
16
16
16
21
26
30
36
37
39

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Abstract
CXCR4 is a chemokine receptor that may play a role in the development
of bone metastases in metastatic prostate cancer patients. Inhibitors to the
receptor have been developed in an effort to eliminate bone metastases by
mobilizing them into the blood where they may be more effectively targeted
by chemotherapy.
To study the effects of a CXCR4 inhibitor on three Stage 4 prostate
cancer patients, circulating tumor cells (CTCs) and metastatic tumor cells
(MTCs) were studied using the High Definition Single Cell Analysis (HD-
SCA) platform. The HD-SCA enables the morphometric, phenotypic, and
genomic analysis of single cells and was used here to identify and
characterize cells in the blood and bone marrow of patients receiving the
CXCR4 inhibitor treatment. The primary clinical goal in the study was to
enumerate the levels of High-Definition circulation tumor cells (HD-CTCs)
present in the blood before and after using the CXCR4 inhibitor.
Additionally, further analysis into the characteristics of the HD-CTCs
identified was also performed. Heterogeneity in phenotype and genotype
were studied in the patients, and several distinct populations were
identified.
Ultimately, peaks in the HD-CTC population were observed at the one
hour timepoint for two of the three patients studied. The cells constituting
this peak were almost exclusively of a large, elongated cell subtype with a
low to moderate CK expression, vimentin positive, and AR negative
phenotype. Genomic profiles of these cells were free of mutations. In
addition, other CTC subpopulations were observed in the bone marrow and
blood at different timepoints of the treatment schema with varying
phenotypes. One phenotypic population possessed a higher level of
4

cytokeratin expression, and frequently with nuclear androgen receptor
expression. In contrast to the predominant population, these cells were
genomically altered with clonal copy number variation structures. Mutations
in these cells included hallmark amplifications and deletions of genes
commonly mutated in prostate cancer. Finally, CTC-small CK cells were
observed and also revealed phenotypic and genotypic heterogeneity. The
clinical relevance of each of these different cell types as biomarkers or
therapeutic targets in prostate cancer may be subject to further studies in
the future.
5

1. Introduction
Cancer is a disease characterized by rapid and uncontrolled
proliferation of cells. Cancer can begin in a variety of sites in the body
including organs and the blood and lymph streams. The rapid growth of
cancer cells leads to the development of solid tumors in organs and
frequently results in the presence of tumor cells in the liquid compartments.
Cancers are named for the site in which a primary tumor is discovered. For
example, if an individual’s cancer originated as a tumor in the prostate, then
they have developed prostate cancer.
In their landmark paper, Hanahan and Weinberg describe several
“hallmarks” of the disease including self-sufficiency in growth signals,
insensitivity to anti-growth signals, the ability to evade apoptosis, sustained
angiogenesis, tissue invasion and metastasis, and limitless replicative
potential (8). Each of these traits addresses various resource needs of rapidly
dividing cells or attempts to avoid regulation by the body.
Of particular relevance to the present work is cancer’s tendency to
metastasize, or spread to other tissues. Metastasis is responsible for up to
90% of cancer-caused deaths in humans, and results in the development of
secondary tumors at metastatic sites in the body (4).
Prostate cancer is among the most common type of cancer developed
in men in the United States and kills on the order of 25,000 men in the
country each year (10). It is most commonly diagnosed in men over the age
of 50 with the median age of patients set at 66 (10). Approximately 1 in 7
men will develop the cancer in his lifetime, and currently there are more
than 2.9 million men alive who have been diagnosed with the disease (10).
6

The severity and extent of prostate cancer can be graded on a four
stage system similar to other cancers as well as a prostate cancer specific
system known as the Gleason score. Staging of prostate cancer can be done
as follows. Stage 1 is the least severe form of the disease. It consists of a
small tumor found only in part of the prostate and is difficult to detect.
Stage 2 still corresponds to a tumor that is located only in the prostate, but
that tumor might be larger or more advanced in its development. Stage 3 of
the disease signifies that the tumor has spread beyond the outer layer of the
prostate, possibly to the seminal vesicles. However, it is distinguished from
Stage 4 by not having spread to other nearby or distant organs. Stage 4 is
the most advanced form of the disease in which the cancer has spread to
other organs or metastatic sites.
Prostate cancer is treated using different methods dependent on how
advanced the disease has become. Earlier stages of prostate cancer are often
treated through the use of radical prostatectomy or “watchful waiting” (7).
Radical prostatectomy physically removes the prostate and surrounding
tissues to extract the tumor, and has been shown in certain circumstances
to improve survival rates in patients over watchful waiting (7). Watchful
waiting is the process of waiting for the disease to progress before taking
action which can be beneficial in older patients in which surgery may be
more risky than the tumor itself (5, 7, 9). Prostate cancer may not always
be so aggressive as to require immediate action. In later stage patients,
combinations of hormone therapy and chemotherapy are used to reduce
tumor size and growth (5, 9). Prostate cancer is generally driven by
androgens and therefore initially responds well to androgen deprivation
therapy either alone or in combination with other therapies (1, 9, 13, 16).
However, advanced prostate cancer inevitably tends to castrate resistant
7

prostate cancer (CRPC) in which androgen deprivation therapies begin to
fail (5, 13).
Once prostate cancer stops responding to androgen deprivation
therapies, chemotherapy is often the next step in treating the disease.
Docetaxel has been shown to have both palliative and survival based effects,
but these benefits have been modest at best. At this point, the disease has
progressed to a point that patients are at a high risk of serious
complications and death. Metastasis is inevitable.
Prostate cancer is notable as a cancer whose development is often
somewhat predictable. In up to 80% of patients who form metastases,
secondary tumors will be found in the bone marrow (2). These metastases
are particularly dangerous and can cause severe pain and fracturing of the
bones. A possible explanation for the high prevalence of metastatic
development to the bone marrow is the expression of the chemokine
receptor CXCR4 on prostate cancer cells.
CXCR4 is a chemokine receptor present on several different cell
types including hematopoietic stem cells, certain white blood cells (WBCs),
and certain cancer cells (11, 14). It plays a role in cell migration and
retention, and is implicated as a therapeutic target in diseases such as HIV
and cancer. Its ligand, CXCL12, binds to the CXCR4 receptor in order to
mediate these migration and retention pathways. The proposed mode of
action of this interaction is as follows. When CXCR4 is bound to CXCL12,
a series of signaling processes occur to trigger downstream upregulation of
integrins and other proteins. These integrins anchor the cell to ligands in its
environment. This interaction plays a role in migration and retention of cells
because cells expressing CXCR4 will become trapped in areas in which
CXCL12 is in high concentrations because of the above process. CXCL12 is
8

a small molecule ligand secreted by osteoblasts found on the bone
endosteum and is present in high concentrations in the bone marrow near
the growth plates of long bones (15). Because of this and the interaction
between CXCR4 and CXCL12, it is possible that prostate cancer cells that
are shed into the circulation from the primary tumor home to the bone
marrow where they are retained in the surrounding microenvironment to
eventually form metastatic tumors.
In the following work, a CXCR4 inhibitor was studied as a treatment
for prostate cancer patients with bone metastases. By inhibiting the
CXCR4-CXCL12 axis, the drug seeks to flush metastatic tumor cells from
the bone marrow into the blood. In theory, the inactivation of the CXCR4-
CXCL12 axis will result in the down-regulation of integrins on the surface of
tumor cells and thereby allow them to unbind from their environment in the
bone marrow and migrate into the blood. This will reduce the tumor load in
the bone marrow and release these cancerous cells into the peripheral blood
where they are more susceptible to chemotherapy. To study the effects of
this drug, primarily the kinetics of tumor cell release, we examined both
circulating tumor cells (CTCs) and metastatic tumor cells (MTCs).
CTCs are defined as cancer cells found in the peripheral blood of
cancer patients. MTCs are cancerous cells found in bone marrow aspirates
of patients with known metastases. These cells are not found in patients
without cancer, and likely contain the population of cells that cause
metastasis in cancer patients. In prostate cancer it has been shown that the
presence of CTCs in the blood is correlated with poor prognosis (19).
Despite this, the CXCR4 inhibitor is being developed to remove cells from
the bone marrow where they may be more harmful and resistant to
chemotherapy.
9

To study CTCs, the Kuhn Lab employs the High Definition Single
Cell Analysis (HD-SCA) platform. This platform has been adapted for use
with blood, bone marrow, and solid tissue samples, providing a consistent
method to analyze each of these different sample types. For the purposes of
this study, I have analyzed both blood and bone marrow aspirate samples.
2. Specific Aims
The present work was conducted in an effort to study the specific
effects of a CXCR4 inhibitor on the kinetics and characteristics of CTCs
and MTCs present in the bloodstream and bone marrow aspirates of a
prostate cancer patient. These efforts have been organized to address the
following goals.
Specific Aim 1: To determine whether CTC concentration increased
in the blood as a result of the administration of a CXCR4 inhibitor to the
prostate cancer patients studied.
To do so, it was necessary to observe the kinetics of CTC and MTC
levels in the blood and bone marrow compartments prior to and following
administration of the CXCR4 inhibitor.
Secondary analysis of the genotypic and phenotypic properties of
CTCs and MTCs found in the blood and bone marrow would also provide
further evidence for the effectiveness of the inhibitor.
Specific Aim 2: To further characterize the effect of the CXCR4
inhibitor on the patients studied.
To do so, it was necessary to monitor and perform the same analysis
as above on CTCs as well as other cell populations in order to determine the
effects of the inhibitor on various biomarkers.

10

3. Experimental
3.1 Clinical samples
The patient population included three stage four prostate cancer
patients with bone metastases treated with the CXCR4 inhibitor at the
University of Washington Medical Center (Patient 1) and Johns Hopkins
Hospital (Patients 2 and 3). The intended sampling schedule for the trial is
shown in Figure 1. Not all samples were collected as planned, due to poor
state of the patients. Specifically, bone marrow aspirates, as well as blood
samples beyond 24h post treatment were not collected for Patients 2 and 3.
All samples provided are listed in Table 1. Bone marrow aspirates are
obtained by drilling into the bone and aspirating bone marrow. Both blood
and bone marrow aspirates were collected into 10-ml Cell-Free DNA BCT
Streck tubes (STRECK, Omaha, USA, Cat#62790315) and shipped to the
Kuhn Lab at USC.

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Patient 1 Patient 2 Patient 3
Round 1 Round 2 Round 1 Round 2
Blood Pre-
treatment
Blood Pre-
treatment
Blood Pre-
treatment
Blood Pre-treatment
Bone
Marrow Pre-
Treatment
Bone Marrow
Pre-
Treatment
Blood 1hr Blood 1hr
Blood 1hr Blood 1hr Blood 2hr Blood 2hr
Blood 2hr Blood 2hr Blood 3hr Blood 3hr
Blood 3hr Blood 3hr Blood 4hr Blood 4hr
Blood 4hr Blood 4hr Blood 5hr Blood 5hr
Blood 5hr Blood 5hr Blood 6hr Blood 6hr
Blood 6hr Blood 6hr Blood 24hr Blood 24hr
Blood 24hr Blood 23hr
Blood 26hr
Blood 50hr
Blood 74hr
Blood 96hr
Bone Marrow
96hr

Table 1: Clinical samples received. The following samples were
received from clinical sites. Most patients did not complete the
idealized sampling timeline.
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3.2 Analysis of CTCs via the HD-SCA assay
To study CTCs, the Kuhn Lab employs the High Definition Single
Cell Analysis (HD-SCA) platform (Figure 2). The platform was designed for
detection and characterization of CTCs in blood but has also been adapted
for use with bone marrow aspirates and solid tissue touch prep samples,
providing a consistent method to analyze each of these different sample
types. For the purposes of this study, I have analyzed both blood and bone
marrow aspirate samples. The cells found in these samples will all be
henceforth referred to as High-Definition circulating tumor cells (HD-CTCs)
regardless of which compartment they were found in.
Figure 1: Intended clinical sampling schema. Samples were drawn
at the clinical sites and shipped to the Kuhn laboratory at the
USC campus. All samples were processed in the lab within 24
hours of being drawn.
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To begin the analysis pipeline, both blood and bone aspirate samples
were drawn from patients and deposited into Streck blood tubes at the
partner clinical site. Once the samples had been collected into tubes, they
were shipped from the partner clinical site to our lab on the University Park
Campus at the University of Southern California. Once the samples were
received, they were processed within 24 hours as follows.
First, the plasma was collected and stored at -80C, for circulating
tumor DNA analysis (not included in the current study). Next, non-
nucleated cells (red blood cells) were lysed and removed from the sample.
The remaining nucleated cells were plated onto custom adhesive Marienfield
microscope slides (Marienfeld, Lauda-K önigshofen, Germany) with a density
of approximately three million cells per slide. These nucleated cells included
WBCs as well as the HD-CTCs. Bovine serum albumin was added for
cryoprotection, before drying, barcoding, and storing slides at -80 C until
analysis. This enabled the storage of large numbers of samples for an
indefinite period of time prior to analysis.
Once desired, microscope slides were thawed and stained using the
following: DAPI (4',6-Diamidino-2-Phenylindole, Dihydrochloride,
Cat#D1306, Invitrogen, Waltham, MA) to identify nuclei, a mix of
cytokeratin (CK) 19 (1:100; Dako, Carpinteria, USA, Cat#M0888) and pan-
CK antibodies (1:100; Sigma-Aldrich, St. Louis, USA, Cat#C2562) with an
Alexa Fluor 555 secondary antibody (Invitrogen, Carlsbad, USA,
Cat#A21127) to identify epithelial cells, and an anti-CD45 Alexa Fluor
647–conjugated antibody (1:125; Biorad, Hercules, USA,
Cat#MCA87A647X) to identify leukocytes. In this study an anti-androgen
receptor (AR) rabbit monoclonal antibody (1:250; Cell Signaling
Technology, Danvers, USA, Cat#5153) and an anti-vimentin rabbit
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polyclonal antibody (1:100; Sigma-Aldrich, St-Louis, USA,
Cat#HPA001762) were also used with an Alexa Fluor 488 secondary
antibody (Invitrogen, Carlsbad, USA, Cat#A11034).
Following staining, slides were scanned using automated microscopy
machines taking 10x images of the entire slides. Using these images, a
machine learning algorithm was used to select cells of interest based upon
nuclear size, CD45 expression, and CK expression standard of the mean
values. These candidate cells were then reviewed and classified by a lab
member. A strength of the HD-SCA platform is that it enables the
identification of several disease-related cells in the blood and bone marrow.
Based primarily upon nuclear size, CK expression, and CD45 expression,
cells can be classified as an HD-CTC or WBC as well as several other
categories (Figure 3). An HD-CTC is characterized as having high CK
expression to denote its epithelial origin, negativity for CD45, and enlarged
nuclear size. A cell with the same characteristics but a smaller nuclear size
is said to be a CTC-small CK. Cells that are morphologically distinct but
with little to no CK expression and CD45 negativity are classified as CTC-
low CK. Cells with a disrupted or damaged nucleus and/or cytoplasm are
classified as apoptotic, or cell-free DNA producing cells. CD45 positive cells
are tagged as WBCs. Fourth color stains such as vimentin and AR stains
can be used to further subclassify cells based upon phenotype.
Candidate cells of interest can be subjected to further
characterization. This can follow one of two workflows. First, the cell is
imaged at higher resolution 40x after which the workflows diverge and cells
can be prepared for either genomic or proteomic profiling. In the current
study, we only applied genomic profiling, as explained below. The proteomic
profiling has been described elsewhere (6, 12).
15

Figure 2: HD-SCA workflow. Blood and bone marrow samples are
received and stored at -80 C. They are later thawed, scanned, and
imaged. Individual cells are then elected to undergo targeted
proteomics or genomic analysis.
Figure 3: Populations detected by the HD-SCA. Each of the above cell
populations can be distinctly identified using the HD-SCA platform. HD-
CTCs are CK+, CD45-, and have enlarged nuclei. CTC-Small CKs are
CK+, CD45-, but do not have enlarged nuclei. CTC-Low CKs have
greatly enlarged nuclei but are CK dim or negative and CD45-.
Apoptotic or cell free DNA producing cells are CK+ and CD45- but may
have ruptured or damaged nuclear or cytoplasmic features.
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3.3 Single-cell genomic analysis
If the sample was to be studied using genomic approaches, the first
step was to isolate the single cell. This was achieved through the use of a
micropipette that was affixed to a robotic arm. The capillary was guided to
the cell on the microscope slide and used to aspirate the single cell before
placing it into a PCR tube. This single cell was then used for whole genome
amplification (Sigma-Aldrich, St. Louis, USA, Cat#WGA4). Amplified and
purified DNA was then transferred to AFA fiber pre-slit snap-cap
microtubes (Covaris, Woburn, USA, Cat# 520077) and sonicated to 200 bp
fragments. Library preparation was then completed using the DNA Ultra
Library Prep Kit and Multiplex Oligos for Illumina (New England Biolabs,
Ipswich,USA, Cat#E7370 and E7600). Completed libraries were submitted
for copy number variation (CNV) sequencing. CNV sequencing was
performed by the UPC Genomic Core and produced CNV profiles revealing
amplification and deletion events in individual cell samples. This enabled
the direct comparison of different cells’ genomic profiles and could be
matched to hallmark mutations of the cancer.
4 Results
The results acquired in this project can be divided into several
categories. Data regarding the number of cells detected in each sample and
the genomic and phenotypic profiles of these cells all contribute to
characterizing a patients’ response to the CXCR4 inhibition treatment.
4.1 HD-CTC kinetics post-inhibitor administration
In the first phase of the study, the kinetics of cell release upon
administration of the CXCR4 inhibitor was investigated (Figure 4) The
intent was to apply the results in a second phase (not part of the current
17

study) in which the inhibitor would be combined with chemotherapy at a
timepoint at which the cells in the circulation had reached a maximum
concentration.
Figure 4A shows HD-CTC enumeration data for the three patients
for the first 24 hours following treatment, with blood samples collected at
baseline, and hourly for the first 6 hours post treatment, and finally at 24
hours. Note that Patient 1 underwent treatment with the inhibitor twice.
The first round of treatment was interrupted 24 hours post inhibitor
administration due to an imminent need for radiation therapy, and repeated
six weeks later once the patient was assessed as fit to continue the study.
Patient 3 did not have HD-CTCs found in the majority of samples and only
two HD-CTCs were found in total across the samples received for that
patient (below the threshold for HD-CTC positivity). Notably, the
concentration of HD-CTCs was significantly higher in Patient 2 than in
Patient 1; however, both Patients 1 and 2 had a peak in HD-CTC
concentration at one hour after administration of the CXCR4 inhibitor, of
an average of 2.7 times the baseline (pre-treatment) HD-CTC level. The
baseline level of HD-CTCs (pre-treatment) was higher at the second round
of administration for Patient 1 (14.9 HD-CTCs/mL vs 31.3 HD-CTCs/mL),
however the early peak in HD-CTCs were in both rounds observed at 1 hour
post treatment. The second round of treatment for Patient 2 continued
beyond the first 24 hours, according to the intended sampling schema
(Figure 1), with daily doses of the inhibitor for four consecutive days
(Figure 4B). From day 2 and on, blood was collected at 2 hours after the
daily dose (e.g. dose at 24 hours, blood draw at 26 hours), with an
additional blood and bone marrow sample collected at the 96 hour
timepoint. Only a moderate level of HD-CTCs (below baseline) was
18

observed after the 24 hours dose, and after the 72 hours dose and at 96
hours, the concentration was below the threshold for HD-CTC positivity.
This could have indicated that 1-2 doses of the inhibitor would have been
sufficient to mobilize all the tumor cells from the bone marrow. However, a
large peak observed at 50 hours, after the third dose at 48 hours,
contradicted this. How HD-CTCs seem to have been cleared from the
circulation after the first 2 days of treatment, to then reappear after the
third dose, and then drop to negligible levels is puzzling. In addition to
tumor cells, the drug is expected to also mobilize a significant number of
CXCR4-expressing WBCs, and thus the WBC peak correlating with the
HD-CTC peak at 50 hours, indicated that the drug indeed had an
significant effect on cell mobilization at this time point. Patient 1 also had
bone marrow aspirates drawn at the 96 hour timepoint, following four
consecutive days of CXCR4 inhibitor doses, unlike the other two patients
studied. This sample contained HD-CTCs at a concentration of 219 HD-
CTCs/mL (2.2 times the baseline level), indicating that MTCs were still
present in the bone marrow after four consecutive days of mobilization
treatment. The characteristics of these cells are reported in section 4.2.
Interestingly, trends in the WBCs count were highly similar in all
three patients and for Patient 1 in both rounds of administration, with a
steady increase in WBCs up to 4 hours post administration, which then
leveled out with a peak at 6 hours. At 24 hours post administration, the
WBC levels had again decreased almost to baseline levels. . As referred to
above, certain populations of WBCs do express CXCR4 and are expected to
be mobilized from the bone marrow. It is noteworthy however, that the
mobilization kinetics is more rapid for the HD-CTCs than for the WBCs.
19

CTC-small CK enumeration also revealed similar trends between the
two patients, though in this case we see a peak in this population at the 6
hour timepoint, corresponding with peaks in the WBCs counts for Patient 1,
and at 24 hours for Patients 2 and 3 (Figure 5).As described above, these
cells are defined as DAPI positive, CK positive, and CD45 negative cells of
a size comparable to surrounding WBCs.
In summary, the kinetics segment of the study suggests that
administration of the CXCR4 inhibitor indeed resulted in a release of tumor
cells into the circulation, with a peak at 1 hour post-administration for the
two patients that had positive HD-CTC counts. In contrast, the CTC-small
CK subtype peaked later, at 6-24 hours, in concordance with the increase in
WBC level in the circulation.
The kinetic analysis concluded the Kuhn lab’s role in the clinical
trial. With the downstream characterization tools available within the HD-
SCA technology, we however proceeded to explore the nature of the cell
subtypes observed in the circulation and bone marrow aspirate at the
different timepoints following CXCR4 inhibitor administration.

20

Figure 4: HD-CTC and WBC kinetics. In the plots above, orange bars
represent HD-CTC counts per mL (left axis) and blue lines represent
WBC counts in millions per mL (right axis). Of note, Patient 1 had two
separate rounds of treatment with the inhibitor. For Patient 3, the HD-
CTC counts were below 4 HD-CTCs/mL, which is the HD-SCA
threshold for HD-CTC positivity for prostate cancer. In (A) timepoints
span the baseline, hourly for six hours post-treatment, and at 23 or 24
hours. (B) displays data for Patient 1 over the entire course of treatment
with repeated inhibitor injections daily. Note that the x-axes are not
plotted in true time scale.
A
B
21

4.2 Phenotypic analysis of HD-CTCs
The phenotypic and proteomic characterization based on the 4-color
HD-SCA immunofluorescence revealed a striking heterogeneity in and
between the two patients with HD-CTCs. Note that in all figures the colors
blue, green, and red represent DAPI, CD45, and CK signal respectively. As
can be seen in Figure 6, several populations of HD-CTCs may be present at
any given timepoint in a patient. The cell images in Figure 6 are from
Patient 2, and reveal HD-CTCs with varying levels of CK, AR, and
vimentin positivity. Sister slides from the same samples had been previously
stained with AR instead of vimentin in the fourth channel, and although the
vimentin and AR were never measured on the same cell, the two markers
could be correlated through cell morphology and CK level. The three main
populations of cells (all classified as HD-CTCs in the HD-SCA assay) were
Figure 5: CTC-small CK and WBC kinetics. In the plots above, orange
bars represent CTC-small CK concentration per mL and blue lines
represent WBC concentration in millions per mL. Three patients are
presented with both data sets obtained from Patient 1. Timepoints span
the baseline, hourly for six hours post-treatment, and at 23 or 24 hours.
Note that the x-axis is not plotted in true time scale.
22

1) large, elongated cells with a low to moderate CK expression, vimentin
positive and AR negative, often present in small clusters of 2-3 cells, 2)
smaller, often round cells with very high CK expression, vimentin negative
and with a nuclear AR signal, and 3) smaller, round to oval cells with
moderate CK staining that frequently overlaps to a great extent with the
nuclear stain, and that are AR and vimentin negative. These three cell
types were present in differing ratios across the timeline of the patient’s
treatment, and some were found only at specific timepoints. Figure 7
demonstrates these ratios. Population 1 (CK+, AR-, vimentin+) were not
present at baseline, but appeared in high numbers at 1 hour post-treatment,
to then rapidly decrease. In contrast, population 2 (CK+++, AR+,
vimentin-) were already present at significant levels at baseline and were
then gradually cleared from the circulation during the first 6 hours post
treatment. At 24 hours, they were again present but at a lower level
compared to baseline. Finally, population 3 (CK++, AR-, vimentin-) were
present at low levels at baseline, peaked at 1 hour post-treatment and then
leveled out at approximately the baseline concentration.
In Patient 1 a somewhat more consistently homogenous population of
HD-CTCs and CTC-small CKs was observed, though there were some
differences in AR expression as well as in morphometric characterization of
the cells. For example, a majority of cells were AR- (henceforth population
4) and only a minority were AR+ (henceforth population 5) throughout all
timepoints, and in the 96 hour bone marrow aspirate several CK+, AR+
cells with damaged cytoplasmic features were found (henceforth population
6) (Figure 8). It is of note that no HD-CTCs observed in the 1 hour blood
sample were AR positive for either of the two rounds of Patient 1’s
treatment though a single CTC-small CK did express AR at this timepoint.
23

There were also variations observed in the CTC-small CK cell
population for both Patients 1 and 2 (Figure 9). Double positive cells with a
nuclear AR signal and CK expression (population 7) are found and can be
distinguished from an AR- population (population 8). A third unusual
population observed consists of cells with extremely high CK and
cytoplasmic AR expression and a notably low DAPI signal (population 9).
In both patients the AR- CTC-small CK population dominated across all
timepoints.
24

Figure 6: Phenotypic HD-CTC populations in Patient 2. This figure
illustrates examples of the three HD-CTC populations found in samples
from Patient 2. In the images the red signal indicates CK positivity, the
green signal indicates CD45 positivity, and the blue signal indicates the
cell nuclei. In (A), the white signal demonstrates vimentin positivity and
in (B) the white signal indicates AR positivity. Cells can be sorted into
each of the three populations described by matching cellular morphology
including nuclear size, roundness, and CK expression.
Figure 7: Time evolution of HD-CTC population ratios in Patient 2. In
the figure above, blue bars indicate the per mL number of HD-CTCs
found that are vimentin+, CK+, and AR- (population 1). Red bars
indicate HD-CTCs that are vimentin-, CK+, and AR+ (population 2).
Yellow bars denote a population that is vimentin-, CK+, and AR-
(population 3).
25

Figure 9: AR expression in CTC-small CKs identified in Patient 2. Cells
in the three rows above come from populations 7, 8, and 9 in descending
order. Here AR signal is represented in white. The final population is
also characterized by low DAPI expression. These cells were present in
both Patients 1 and 2.
Figure 8: Phenotypic variation in Patient 1. The first row above shows
HD-CTCs from the blood with enlarged nuclei that are CK+, AR+, and
CD45- (population 4). The second row contains examples of HD-CTCs
found in the bone marrow of Patient 1 that have damaged or irregular
cell membranes (population 6).
26

4.3 Genotypic analysis of HD-CTCs
A subset of cells were isolated for genomic analysis on a single-cell
level, from bone marrow and blood pre-treatment, from blood at 1 hour
post-treatment, and from bone marrow at 96 hours following treatment. The
genomic profiles from Patient 1 are shown in Figure 10. In Figure 10A, a
copy number heat map consisting of results from several cells in several
columns is presented. This heat map consists of several different cell types
including HD-CTCs, CTC-small CKs, CTC-No CKs, and WBCs. These cells
are also spread across bone marrow and blood sample types as well as across
the different sample times throughout analysis. From these results, we were
able to build up a spatiotemporal genomic picture of the cells in circulation
and the bone marrow before and following treatment.
Additionally, we were able to note similarities and differences
between samples at various timepoints. For example, there seem to be three
clusters of distinct genomic profiles that could be identified in the above
heat map. First, there is a population of cells that share similar genomic
profiles. The second population is a group of cells that show genomic
abnormalities but do not share these abnormalities in a clonal manner.
Finally, there is a population of cells that display nearly no genomic
abnormalities.
To look more closely at the genomic data presented in the heat map,
it is possible to look at individual copy number variation profiles for each
cell. Figure 10B shows profiles from each of the cells in the clonal
population of Patient 1. These cells were all isolated from the bone marrow
sample collected at 96 hours after four consecutive days of treatment (one
dose per day), except for one cell that was isolated from the blood sample
drawn at 1 hour post treatment (following the first dose of CXCR4
27

inhibitor). Of note, the blood cell was of the CTC-small CK category, with
a relatively low CK expression (standard deviation of the mean of 4.6) and
also expressed AR. Among the HD-CTCs from the bone marrow, three
appeared to have ruptured cytoplasmic and membranous features whereas
the fourth did not. Although only a single blood cell that was isolated
showed this clonal structure, the results still indicate that clonal tumor cells
residing in the bone marrow could be observed in the blood as early as 1
hour post treatment, and that the tumor cells do not necessarily fall into
the classical HD-CTC (larger than WBC) category.
The observed clone had focal amplifications on the X chromosome as
well as on chromosome 8 on each of the profiles. These can be matched
potentially to AR amplifications and amplifications in the MYC gene
responsible for cell cycle progression and apoptosis. A full list of genomic
abnormalities demonstrated by this clonal population is given below in
Table 2.
From Patient 2, one CTC-small CK and several HD-CTC cells were
isolated from the pre-treatment and the 1 hour timepoint blood samples (no
bone marrow aspirate was collected from this patient). The results are
displayed in Figure 11 and contain cells from the HD-CTC and CTC-small
CK populations.
One clonal population could be identified in the Figure 11A. This
clone was found in both one hour and baseline samples, contained one CTC-
small CK and eight HD-CTCs, and possessed mutations in AR, RGMB,
CHD1, BRAF, EZH2, MYC, DAPK1, and SYK (Table 2). Individual
representative profiles of cells from the two clones are given in Figure 11B.
It is also of note to mention that cells in which there appear to be few or no
genomic aberrations came exclusively from the one hour timepoint.
28

Figure 10: Genomic data for Patient 1. (A) is a heatmap demonstrating
the various genomic features of cells found in samples drawn from
Patient 1. The x-axis lists various cells analyzed from this patient that
span all categories and the y-axis lists chromosome number. Three
distinct populations of cells are listed demarked by vertical lines.
Individual columns represent data from individual cells. Red bars
represent amplifications and blue ones represent deletions. (B) displays
cells from the clonal population consisting of cells 1-5. Individual copy
number variation profiles are paired with an image of the cell whose
genome is presented.
A
B
29

Figure 11: Genomic data for Patient 2. (A) is a heatmap demonstrating
the various genomic features of cells found in samples drawn from
Patient 2. The x-axis lists various cells analyzed from this patient that
span all categories and the y-axis lists chromosome number. Individual
columns represent data from individual cells. Red bars represent
amplifications and blue ones represent deletions. (B) displays cells from
the clonal population consisting of cells 1-9. Individual copy number
variation profiles are paired with an image of the cell whose genome is
presented.

A
B
30

5. Discussion
This study has outlined the use of the HD-SCA to perform an in
depth analysis of the effects of a CXCR4 inhibitor in prostate cancer
patients. Cell kinetics, proteomic analysis, and genomic characterization
have enabled the detailed description of HD-CTC and CTC-small CK
behavior following use of the drug, and this information has implications for
the clinical utility of the inhibitor.
The primary goal of the Kuhn lab’s participation in the CXCR4
inhibitor clinical trial was to monitor the number of HD-CTCs found in the
blood following treatment. Two of three patients demonstrated measurable
Gene Location Patient 1 Patient 2 Description
AR X Amplification N/A
Responsible for androgen
sensitivity in cells.
RGMB 6 Deletion Deletion
Potential role in bone
metastases development
CHD1 6 Deletion Deletion Chromatin remodeler
BRAF 7 Amplification Amplification
Proto-oncogene. Involved
in directed cell growth
EZH2 7 Amplification Amplification
Transcription factor.
Represses tumor
suppressor genes.
MYC 8 Amplification Amplification
Cell cycle regulation and
apoptosis
DAPK1 9 N/A Amplification
Associated with
programmed cell death.
SYK 9 N/A Amplification Cell growth modulator.
PTEN 10 Deletion N/A Tumor suppressor
TP53 16 Deletion N/A Tumor suppressor
Table 2: Genomic data for Patient 1 and 2. Presented below are various
genes found to be commonly present in prostate cancer and are also
found in one or both patients studied.
31

levels of HD-CTCs before and after treatment, and in these patients a peak
in HD-CTC counts appeared at the 1 hour timepoint. At this timepoint, the
concentration of HD-CTCs was on average 2.7 times that of pre-treatment
levels. Despite differences in the absolute number of HD-CTCs found in the
blood between patients, the fact that there is an identical peak in HD-CTC
concentration and that this peak is also present during both rounds of
Patient 1’s treatment is consistent with the hypothesis that the inhibitor is
mobilizing cells into the blood, and suggests that there may be a very
specific timeline associated with the mechanism of this release. Additionally,
repeated rounds of treatment may be valuable or necessary for the
treatment of a patient, as evidenced by the fact that an increased number of
HD-CTCs were still found in Patient 1 during his second round of
treatment. It should also be noted that Patient 1 actually experienced a
higher peak in HD-CTC concentration at the 50 hour timepoint in his
second round of treatment. This result cannot be compared with either of
the two other patients who were only treated with a single dose and
followed for 24 hours, and may be a result of a confluence of factors
including the repeated inhibitor doses that he had received by this
timepoint.
Based solely upon the HD-CTC enumeration data requested of the
Kuhn lab, the one hour timepoint is the candidate for chemotherapy
administration to target the increased HD-CTCs observed in the blood.
However, increased phenotypic and genomic analysis was also performed to
further elucidate the characteristics of the cells found in the blood and bone
marrow.
Throughout the treatment of both Patients 1 and 2, several distinct
phenotypic populations were present. However, at the one hour timepoint,
32

one population of HD-CTCs dominated in both patients. In Patient 1,
population 4 constituted all detected HD-CTCs at the 1 hour timepoint,
while in Patient 2, population 1 made up the majority of HD-CTCs
observed (68%) at this timepoint. Both of these populations were
characterized by large, elongated cells with dim CK expression, negativity
for AR, and in population 1 were also positive for vimentin. The samples
from Patient 1 were never stained with vimentin, however based on
morphology, these cells also do appear to be similar, and quite possibly may
constitute a single phenotypic population. This hypothesis is also supported
by genomic data. In Patient 2, it was observed that no cells of population 1
contained any CNV abnormalities. The same result was found in the similar
cells of population 4 in Patient 1. From these observations, it is likely that
these two cell populations are identical in both patients and may be derived
from the tumor endothelium. These cells may have increased propensity for
blood transport, and are evidently the cells that are shed into the blood as a
result of the inhibitor. These cells may also be those that have undergone
epithelial-mesenchymal transition (EMT), and there is evidence to suggest
the importance of vimentin in the development of prostate cancer
metastases via EMT (17, 18). However, it is still possible that these cells
possess point mutations not detected through CNV analysis, and whether or
not these cells have the clinical significance that HD-CTCs with genetic
mutations might have remains to be elucidated.
In addition to the identification of populations 1 and 4 as the
dominant cell populations at the one hour timepoint, cells from populations
2 (CK+++, AR+, vimentin-), 3 (CK++, AR-, vimentin-), and 5 (CK+,
AR+) were identified in lower levels across various timepoints in both
patients and were also subjected to CNV analysis. These cells did tend to
33

exhibit genetic aberrations, and constituted several clonal populations in
both patients. In Patient 1, one clonal population was present composed of
HD-CTCs in the bone marrow and a CTC-small CK found in the blood.
There are several intriguing qualities about this population of cells. First,
the fact that a clonal population is found across the bone marrow aspirate
and blood compartment further supports the hypothesis that the CXCR4
inhibitor is flushing cells into the blood; the clonal population shares a
common origin, and therefore cells must have migrated from that origin to
be found in these two disparate compartments. Additionally, among the five
cells present in the clonal population there were three distinct phenotypes.
Three cells demonstrated damaged cytoplasmic features, were AR+, and
were classified as metastatic HD-CTCs found in the 96 hour bone marrow
aspirate. One further metastatic HD-CTC also from the 96 hour bone
aspirate possessed an intact cytoplasm with cytoplasmic AR expression. The
final cell was a CTC-small CK collected from the 1 hour blood sample and
expressed cytoplasmic AR. Because CNV analysis does not measure the
expression of genes beyond the number of copies present of a particular
sequence of DNA, it is possible that cells may all have the same genomic
copy number aberrations but are differentially expressing specific genes that
create the phenotypes observed. CNV analysis also fails to account for point
mutations that may be having effects on the phenotype of these cells.
However, it is also possible that nuclear size is not a significant distinction
between HD-CTC and CTC-small CK populations, at least in predicting
genotype, because these cells of vastly different morphology all shared
identical genomic profiles. Furthermore, population 6 (CK+++, AR+,
disrupted cytoplasm) only appeared in the 96 hour bone marrow timepoint.
It is possible that these cells possess damaged structures because they have
34

been exposed to successive rounds of CXCR4 inhibitor treatment. Because
HD-CTC levels did increase (2.2 –fold) in the bone marrow and because
these cells with cytoplasmic aberrations were morphologically distinct
(higher CK and AR expression, disrupted cytoplasm) from those found at
baseline, it is possible that these cells were disrupted by the use of the
CXCR4 inhibitor to the extent that they were now flowing in the bone
marrow rather than anchored to sites within the bone, and hence appeared
at a higher level in the bone marrow aspirate after treatment than before.
The 96h bone marrow aspirate was collected after four consecutive days of
treatment with daily doses of the CXCR4 inhibitor. Despite multiple doses,
it appears that these cells had not been fully released into the blood but
instead were mobilized within the bone marrow following the use of the
drug. If this is the case, the drug may potentially be harming the patient as
metastatic cells are now mobilized in a compartment where they are still
difficult to target by chemotherapy. Of note, the corresponding blood
sample that was collected at the 96 hour timepoint was below the threshold
for HD-CTC positivity, with only 2.4 HD-CTCs/mL of blood. Hence, no
significant level of tumor cells could be found in the circulation at the same
time as the highly genomically altered tumor cells were observed in the bone
marrow aspirate.
Furthermore, CTC-small CKs were found in each patient with highly
rearranged genomes that demonstrated clonality with other cells from the
same patient. Among the CTC-small CK cells studied in this project, only
two demonstrated genomic alterations while the rest did not. There is little
indication of what significance may be attributed to the cells that do not
demonstrate genomic alterations, but the presence of those that do
35

demonstrate the importance of further studying and potentially
subcategorizing CTC-small CK cells..
It was observed that peaks in CTC-small CK kinetics in both
patients did not occur simultaneously with peaks in HD-CTC concentration.
For Patient 1, a peak in CTC-small CKs occurred at the six hour timepoint,
concurrently with the peak in WBCs for that patient. In Patients 2 and 3,
CTC-small CKs peaked at the 23 and 24 hour timepoints and WBCs peaked
at the 6 hour timepoint. The increased level of WBCs following CXCR4
administration was expected. It has been demonstrated that CXCR4 is
expressed in certain WBCs, including some monocytes (3), and therefore
might also be affected by the inhibitor. As alluded to above, the nature of
CTC-small CKs has not been fully explored to date. It would however be
expected that if CTC-small CKs are also disease related and of prostate
origin in this instance, that they might also express CXCR4 and thus be
flushed from the bone marrow following inhibitor administration. The much
delayed peak in the circulation compared to the HD-CTCs may be
explained by different kinetics of these smaller cells from the larger tumor
cell populations. It was also observed that three distinct populations of
CTC-small CKs were observed. Of the two cells that were found to have
genomic alterations, one was a member of population 7 (CK++, AR-, small
cells) and the other was from population 8 (CK+, AR+ small cells). Cells
from population 9 (CK+, AR+++ very round small cells) failed genomic
amplification. Because of this and their low DAPI signal, it is possible that
these ‘cells’ are actually oncosomes, vesicles deriving from cancer cell
membranes that may carry limited amounts of genetic material. If this were
to be the case, their appearance post treatment with the CXCR4 inhibitor
can be attributed to CXCR4 receptors that would be on their surface as
36

they were emitted off of the cell membranes of prostate cancer cells. These
oncosome candidates were found in both patients, and have previously not
been observed in healthy blood donors or in prostate cancer patients who
have not been treated with a CXCR4 inhibitor. The nature of these AR
high and CK positive very round events remains to be explored.
6. Future Directions
The work presented has resulted in several intriguing observations
that might form the basis for further inquiry into the effect of a CXCR4
inhibitor on prostate cancer. An obvious improvement on the current study
would involve the study of an increased number of patients to determine the
consistency of maximum HD-CTC concentration at the 1 hour timepoint.
The nature of these cells, whether they are tumor endothelial cells, or cells
undergoing epithelial to mesenchymal transition, can be explored through
proteomics using the imaging mass cytometer which would allow researchers
to begin determining whether these cells are significant therapeutic targets
in prostate cancer despite their lack of genomic mutations, or if they might
have value as biomarkers. For example, studying whether these cells express
cell proliferation markers such as Ki-67 may be an indication of whether the
cells are active in the growth of tumors. Furthermore, single nucleotide
sequencing may be of use in further determining the characteristics of HD-
CTCs discovered in these patients and may be used to determine whether
this specific cell-type does indeed possess no genomic mutations.
Additionally, further work in determining the significance of the
CTC-small CK population in prostate cancer might now be performed to
assess whether the distinction based upon nuclear size is a valid indicator of
a difference in the clinical significance of these cells. It is possible that only
certain populations of these cells are of value to further research, and it may
37

be important to determine whether the double positive, low-DAPI cells do
fall into the CTC-small CK category or whether they may be vesicles with
other clinical impacts. Finally, this study suffered from a lack of complete
sampling timelines for two of the three patients studied. If future trials are
to be performed, this data may shed light on the kinetics of bone marrow
HD-CTC concentration as well as on the significance of the 50 hour
timepoint in the kinetics of the CXCR4 inhibitor.
7. Conclusion
The enumeration of HD-CTCs in three patients treated with a CXCR4
inhibitor suggests that the inhibitor does have an effect on the release of
HD-CTCs into the blood. Across two patients that demonstrated
measurable levels of HD-CTCs prior to treatment, HD-CTCs were found to
peak at the 1 hour timepoint following use of the CXCR4 inhibitor. Levels
of WBC and CTC-small CK populations also rose following treatment and
may reflect expression of CXCR4 on these cells as well. Based solely on the
HD-CTC enumeration data presented, the 1 hour timepoint appears to
mark the release of large numbers of cancer cells that would be vulnerable
to chemotherapy at this timepoint.
However, additional characterization of the cells found across different
timepoints revealed trends in phenotypic and genotypic diversity among the
cancer cells observed. HD-CTCs found at the 1 hour timepoint in Patient 1
were solely comprised of CK dim cells that did not express AR. In Patient 2
there was some heterogeneity though most cells expressed vimentin but
were CK dim and AR negative. It is likely that this is the population of
cells acted upon by the CXCR4 inhibitor as these were the cells present in
the highest concentration at the 1 hour timepoint. Genomic data further
38

elucidates the characteristics of these cells as they all did not display
apparent genomic aberrations in their CNV profiles.
In this clinical trial for a CXCR4 inhibitor, the hypothesis was that the
release of metastatic tumor cells from the bone marrow into the blood is
desirable because it renders these cells more susceptible to chemotherapy.
Whether or not the danger of these HD-CTCs in the bone marrow or the
effectiveness of this drug’s strategy is mitigated by the type of cells that are
released is unclear, but is now a known unknown that can be further
studied.

39

8. Works Referenced

1. Abrahamsson, P. (2017). Intermittent androgen deprivation therapy in
patients with prostate cancer: Connecting the dots. Asian Journal of
Urology, 4(4), 208-222.
2. Bertrand Tombal and Frederic Lecouvet, “Modern Detection of Prostate
Cancer's Bone Metastasis: Is the Bone Scan Era Over?,” Advances in
Urology, vol. 2012, Article ID 893193, 8 pages, 2012.
doi:10.1155/2012/893193
3. Caulfield, J., Fernandez, M., Snetkov, V., Lee, T., & Hawrylowicz, C.
(2002). CXCR4 expression on monocytes is up-regulated by
dexamethasone and is modulated by autologous CD3+ T cells.
Immunology, 105(2), 155–162. http://doi.org/10.1046/j.0019-
2805.2001.01359.x
4. Chaffer, C. L., & Weinberg, R. A. (2011). A perspective on cancer cell
metastasis. Science, 331(6024), 1559. Retrieved from
5. Eisenberger, M. A., & Saad, F. (2014). Introduction-Castration resistant
prostate cancer: A rapidly expanding clinical state and a model for
new therapeutic opportunities. In F. Saad, & M. A. Eisenberger
(Eds.), Management of castration resistant prostate cancer (pp. 3-8).
New York, NY: Springer New York.10.1007/978-1-4939-1176-9_1
6. Gerdtsson, Erik & Pore, Milind & Thiele, Jana-Aletta & Sandstrom
Gerdtsson, Anna & Daisy Malihi, Paymaneh & Nevarez, Rafael &
Kolatkar, Anand & Ruiz Velasco, Carmen & Wix, Sophia & Singh,
Mohan & Carlsson, Anders & J Zurita, Amado & Logothetis,
40

Christopher & Merchant, Akil & B Hicks, James & Kuhn, Peter.
(2017). Multiplex protein detection on circulating tumor cells from
liquid biopsies using imaging mass cytometry. Convergent Science
Physical Oncology. 10.1088/2057-1739/aaa013.
7. Hammad, F. T. (2008), Radical Prostatectomy. Annals of the New York
Academy of Sciences, 1138: 267–277. doi:10.1196/annals.1414.032
8. Hanahan, D., & Weinberg, R. A.The hallmarks of cancer. Cell, 100(1),
57-70. 10.1016/S0092-8674(00)81683-9
9. Horwich, A. (Ed.). (2014). Systemic treatment of prostate cancer.
Retrieved from https://ebookcentral.proquest.com
10. Howlader N, Noone AM, Krapcho M, Miller D, Bishop K, Kosary CL,
Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer
EJ, Cronin KA (eds). SEER Cancer Statistics Review, 1975-2014,
National Cancer Institute. Bethesda, MD,
https://seer.cancer.gov/csr/1975_2014/, based on November 2016
SEER data submission, posted to the SEER web site, April 2017.
11. Ishii, T., Nishihara, M., Ma, F., Ebihara, Y., Tsuji, K., Asano, S., . . .
Maekawa, T. (1999). Expression of stromal cell-derived factor-1/Pre-
B cell growth-stimulating factor receptor, CXC chemokine receptor 4,
on CD34+ human bone marrow cells is a phenotypic alteration for
committed lymphoid progenitors. The Journal of Immunology,
163(7), 3612. Retrieved from
http://www.jimmunol.org/content/163/7/3612.abstract
12. Malihi, Paymaneh & Morikado, Michael & Welter, Lisa & T Liu, Sandy
& T Miller, Eric & Cadaneanu, Radu & Knudsen, Beatrice & Lewis,
Michael & Carlsson, Anders & Ruiz Velasco, Carmen & Kolatkar,
Anand & Rodriguez-Lee, Mariam & Garraway, Isla & B Hicks,
41

James & Kuhn, Peter. (2017). Clonal diversity revealed by
morphoproteomic and copy number profiles of single prostate cancer
cells at diagnosis. Convergent Science Physical Oncology.
10.1088/2057-1739/aaa00b.
13. Shore, N., Mason, M., & De Reijke, T. (2012). New developments in
castrate ‐resistant prostate cancer. BJU International, 109, 22-32.
14. Sun, X., Cheng, G., Hao, M., Zheng, J., Zhou, X., Zhang, J., … Wang,
J. (2010). CXCL12/CXCR4/CXCR7 Chemokine Axis and Cancer
Progression. Cancer Metastasis Reviews, 29(4), 709–722.
http://doi.org/10.1007/s10555-010-9256-x
15. Sun, Y.-X., Schneider, A., Jung, Y., Wang, J., Dai, J., Wang, J., Cook,
K., Osman, N. I., Koh-Paige, A. J., Shim, H., Pienta, K. J., Keller,
E. T., McCauley, L. K. and Taichman, R. S. (2005), Skeletal
Localization and Neutralization of the SDF-1(CXCL12)/CXCR4
16. Ting, Cha, Chang, Liao, & Yu. (2016). Successful downstaging of
isolated bony metastatic prostate cancer after metastasectomy and
androgen deprivation therapy. Formosan Journal of Surgery, 49(2),
70-73.
17. Quinn, M., Novakovic, K., Shumaker, D., Rabbitt, S., Pruitt, J., &
Helfand, B. (2013). Transcellular migration of prostate cancer cells
through endothelial cells requires vimentin. Journal Of Urology,
189(4), E328.
18. Xie, D., Gore, C., Liu, J., Pong, R., Mason, R., Hao, G., . . . Hsieh, J.
(2010). Role of DAB2IP in modulating epithelial-to-mesenchymal
transition and prostate cancer metastasis. Proceedings of the
42

National Academy of Sciences of the United States of America,
107(6), 2485-90.
19. Zheng, Y., Zhang, C., Wu, J., Cheng, G., Yang, H., Hua, L., & Wang,
Z. (2016). Prognostic Value of Circulating Tumor Cells in Castration
Resistant Prostate Cancer: A Meta-analysis. Urology Journal, 13(6),
2881-2888.

Abstract (if available)

Abstract CXCR4 is a chemokine receptor that may play a role in the development of bone metastases in metastatic prostate cancer patients. Inhibitors to the receptor have been developed in an effort to eliminate bone metastases by mobilizing them into the blood where they may be more effectively targeted by chemotherapy. ❧ To study the effects of a CXCR4 inhibitor on three Stage 4 prostate cancer patients, circulating tumor cells (CTCs) and metastatic tumor cells (MTCs) were studied using the High Definition Single Cell Analysis (HDSCA) platform. The HD-SCA enables the morphometric, phenotypic, and genomic analysis of single cells and was used here to identify and characterize cells in the blood and bone marrow of patients receiving the CXCR4 inhibitor treatment. The primary clinical goal in the study was to enumerate the levels of High-Definition circulation tumor cells (HD-CTCs) present in the blood before and after using the CXCR4 inhibitor. Additionally, further analysis into the characteristics of the HD-CTCs identified was also performed. Heterogeneity in phenotype and genotype were studied in the patients, and several distinct populations were identified. ❧ Ultimately, peaks in the HD-CTC population were observed at the one hour timepoint for two of the three patients studied. The cells constituting this peak were almost exclusively of a large, elongated cell subtype with a low to moderate CK expression, vimentin positive, and AR negative phenotype. Genomic profiles of these cells were free of mutations. In addition, other CTC subpopulations were observed in the bone marrow and blood at different timepoints of the treatment schema with varying phenotypes. One phenotypic population possessed a higher level of cytokeratin expression, and frequently with nuclear androgen receptor expression. In contrast to the predominant population, these cells were genomically altered with clonal copy number variation structures. Mutations in these cells included hallmark amplifications and deletions of genes commonly mutated in prostate cancer. Finally, CTC-small CK cells were observed and also revealed phenotypic and genotypic heterogeneity. The clinical relevance of each of these different cell types as biomarkers or therapeutic targets in prostate cancer may be subject to further studies in the future.

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Asset Metadata

Creator Bae, Joseph (author)

Core Title Exploring the effects of CXCR4 inhibition on circulating tumor cell populations in metastatic prostate cancer

School Keck School of Medicine

Degree Master of Science

Degree Program Medical Biophysics

Publication Date 05/01/2018

Defense Date 03/19/2018

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

Tag bone metastases,chemokine,circulating tumor cells,CTCs,CXCR4,CXCR4 inhibitor,HD-SCA,high-definition single cell analysis,liquid biopsy,Metastasis,OAI-PMH Harvest,prostate cancer

Format application/pdf (imt)

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Advisor Farley, Robert (committee chair), Kaslow, Harvey (committee member), Siemer, Ansgar (committee member)

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