ABSTRACT EXAMINING BIOMARKERS IN AGGRESSIVE TUMOR …
Transcript of ABSTRACT EXAMINING BIOMARKERS IN AGGRESSIVE TUMOR …
ABSTRACT
EXAMINING BIOMARKERS IN AGGRESSIVE TUMOR TYPES OF THYROID CANCER
Nearly 2,000 Americans die from thyroid cancer each year and its incidence is
steadily increasing. The follicular variant of papillary thyroid cancer (FVPTC) is the
second most common type of well-differentiated thyroid cancer, although very little
information is available on its tumor behavior. The purpose of this study was to evaluate
whether tumor profiles with high angiogenic activity (blood vessel-forming biomarkers)
correlate with invasiveness and metastatic pattern. Uncovering differences in angiogenic
activity may provide a strong indicator of tumor aggressiveness. I recruited 35 archival
FVPTC tumor tissue specimens and optimized tissue recovery for laser microdissection
by deparaffinization and staining. Laser capture microdissection (LCM) was performed
with multiple 2000 µm diameter cuts to separate tumor tissue from adjacent normal
control tissue. From this micro-dissected FVPTC material, RNA was extracted and
quantified for downstream PCR analyses of common angiogenic factor expression. I
evaluated the expression levels of specific angiogenic factors using quantitative PCR.
While I did not find any distinct signatures of disease behavior for FVPTC, I optimized
procedures to successfully dissect FVPTC tissue and extract RNA from the FVPTC tissue
samples.
Jazmin Cheatham May 2021
EXAMINING BIOMARKERS IN AGGRESSIVE TUMOR TYPES OF
THYROID CANCER
by
Jazmin Cheatham
A thesis
submitted in partial
fulfillment of the requirements for the degree of
Master of Science in Biology
in the College of Science and Mathematics
California State University, Fresno
May 2021
APPROVED
For the Department of Biology:
We, the undersigned, certify that the thesis of the following student meets
the required standards of scholarship, format, and style of the university and
the student's graduate degree program for the awarding of the master's
degree. Jazmin Cheatham
Thesis Author
Jason Bush (Chair) Biology
Joseph Ross Biology
Larry Riley Biology
For the University Graduate Committee:
Dean, Division of Graduate Studies
AUTHORIZATION FOR REPRODUCTION
OF MASTER’S THESIS
X I grant permission for the reproduction of this thesis in part or in its
entirety without further authorization from me, on the condition that
the person or agency requesting reproduction absorbs the cost and
provides proper acknowledgment of authorship.
Permission to reproduce this thesis in part or in its entirety must be
obtained from me.
Signature of thesis author:
ACKNOWLEDGMENTS
I would like to start out by thanking God for giving me the strength to complete
this task. I would like to thank Dr. Bush for giving me this opportunity to work and grow
both as a scientist and individual in his lab. This project could not have been done
without his support and guidance. Lastly, I want to thank my family for their continuous
support and belief in my dreams. They are my motivation always and forever.
To the future students that continue work on this project, never let anyone tell you
what you cannot do. Believe in yourself even if no one else does.
TABLE OF CONTENTS
Page
LIST OF TABLES ....................................................................................................... vii
LIST OF FIGURES ..................................................................................................... viii
INTRODUCTION ...........................................................................................................1
Histology of the Thyroid Gland ................................................................................1
Thyroid Cancer Detection and Treatment .................................................................4
Angiogenic Markers of FVPTC ...............................................................................6
Problems in Thyroid Cancer Analyses......................................................................8
Objectives .............................................................................................................. 10
MATERIALS AND METHODS ................................................................................... 11
Sample Collection .................................................................................................. 11
Deparaffinization/Staining Protocol ....................................................................... 11
Laser Capture Microdissection (LCM) ................................................................... 13
RNA Extraction and Quantification........................................................................ 14
Reverse Transcriptase Polymerase Reaction and Primer Optimization.................... 15
Agarose Gel Electrophoresis .................................................................................. 16
Quantitative Real Time Polymerase Chain Reaction............................................... 17
Statistical Analysis ................................................................................................. 18
RESULTS AND DISCUSSION .................................................................................... 19
Evaluation of FVPTC Tumor Tissue Specimens..................................................... 19
Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) on Extracted RNA to Evaluate Levels of bFGF, HIF-1α, and TGF-α ............................... 29
Angiogenic Factor Expression................................................................................ 29
Real-Time Polymerase Chain Reaction (qPCR) on Extracted RNA to Evaluate Amplication of bFGF, HIF-1α, and VEGF ................................................. 34
CONCLUSION ............................................................................................................. 39
Page
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REFERENCES .............................................................................................................. 42
APPENDIX: SUPPLEMENTARY DATA .................................................................... 48
LIST OF TABLES
Page
Table 1. Comparison of the five main subtypes of Thyroid Cancer ..................................4
Table 2. Summary of Primer Sets. The GAPDH at 99bp was used when the GAPDH at 225bp was no longer available. ....................................................................... 17
Table 3. LCM laser settings. Settings were optimized to prevent breakage of surrounding tissues, repeat laser cutting, and removal of unwanted tissue. LCM was performed using these settings for subsequent experiments. ............... 20
Table 4. Pooled (includes tumor and normal tissue combined) thyroid tissue cuts. * = data unavailable. ................................................................................................ 22
Table 5. Total Area of FVPTC tumor tissue cuts compared to total RNA concentration. Cuts refer to amount of circular dissections per LCM tissue slide. .................................................................................................................. 23
Table 6. Quantification yields of RNA from FVPTC tumor tissue extraction. Extraction procedures were carried out according to the standard protocol unless otherwise noted (indicated by modified technique). ................................. 26
Table 7. RNA Samples. RNA taken from control and tumor tissue cuts extracted separately. * = Data unavailable. ........................................................................ 28
Table 8: RNA Samples for RT-PCR. Control and tumor tissue cuts extracted separately. All 4 primers were used for all samples. * = data unavailable. .......... 31
Table 9. Average Cq values for bFGF and HIF-1α. ....................................................... 38
LIST OF FIGURES
Page
Figure 1. Histology of the thyroid gland. .........................................................................2
Figure 2. High-power histological view of thyroid cancer subtypes .................................3
Figure 3. Cellular Origin of Thyroid Malignancy.. ..........................................................4
Figure 4. FVPTC patient sample slides ......................................................................... 12
Figure 5. FVPTC tissue slides before staining. .............................................................. 13
Figure 6. Depicting LCM procedures ............................................................................ 14
Figure 7. Pro-angiogenic and housekeeping gene primer optimization. ......................... 16
Figure 8. Images of FVPTC stained tissue .................................................................... 20
Figure 9. Area of FVPTC tissue type. ........................................................................... 21
Figure 10. RNA concentration determined from tissue cuts. .......................................... 24
Figure 11. Cuts and RNA concentration obtained from FVPTC tissue samples. ............ 25
Figure 12. Extraction method effects on RNA concentration. ........................................ 27
Figure 13. RT-PCR optimization results at different RNA concentrations. .................... 30
Figure 14. Semi-quantitative RT-PCR results................................................................ 33
Figure 15. Densitometry analysis of relative expression ................................................ 34
Figure 16. Normal amplification curve using dilution series. ......................................... 36
Figure 17. Amplification and melting curve using FVPTC tissue samples ..................... 37
INTRODUCTION
Thyroid cancer is the most common endocrine malignancy in the United States
and accounts for an estimated amount of 52,890 cases that were diagnosed in 2020
(American Cancer Society database, 2020). Angiogenesis or the formation of new blood
vessels is known to play a vital role in the proliferative success of the follicular variant of
papillary thyroid cancer (FVPTC), the second most frequently occurring thyroid tumor
type (Redler et al., 2013). The question we are asking is: can we refine, using a
systematic molecular approach, signatures for angioinvasive FVPTC associated with its
aggressive tumor type. The level at which angiogenic factors are expressed dictates the
proliferative success seen in thyroid tumors (Bunone et al., 1999).
Histology of the Thyroid Gland
The thyroid is a gland responsible for synthesizing hormones that regulate
homeostasis and metabolism (Figure 1, left). Part of the endocrine system, with two
connected lobes located in the neck, the gland controls the release of hormones mediated
by thyrotropin-releasing hormone (TRH) from the hypothalamus and the thyroid
stimulating hormone (TSH) from the pituitary gland (Wilkes et al., 2013). TRH triggers
the pituitary gland to make TSH which then instructs the thyroid gland to uptake iodine
from the bloodstream. Iodine is required to make the thyroid's two principal products:
thyroxine (T4) and triiodothyronine (T3), generally known collectively as thyroid
hormone (TH). Follicular cells secrete T4 to target cells and which is then converted to
the T3 or the active form of the hormone (Figure 1, right).
Thyroid cancer is the most diagnosed malignancy of the endocrine system and is
in the top five cancers diagnosed in the third and fourth decades of life (Nguyen et al.,
2015). Typically, thyroid cancer does not present any symptoms early in the disease. As
the disease grows some patients will experience a lump in the neck, difficulty
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Figure 1. Histology of the thyroid gland. The thyroid gland is a two-lobed structure
located in the neck region beneath the larynx (left). Follicular cells continuously
synthesize thyroid hormone and secrete it into the follicle lumen for iodination and
storage (right). Source: Thyroid gland images adapted from medicalterms.info.
swallowing, neck and throat pain, and changes in their voice including hoarseness
(Nguyen et al., 2015). Although, long term survival rates are higher in early-stage thyroid
cancers compared to other malignancies, many patients remain at risk for recurrent
disease with the possibility to metastasize (Lee et al., 2019). Often, recurrent tumors do
metastasize and are ultimately fatal and approximately 2,180 people die from thyroid
cancer each year (American Cancer Society Database, 2020).
Thyroid cancers are classified into five main histological groups: Follicular
Variant of Papillary Thyroid Cancer (FVPTC) (Figure 2), Papillary Thyroid Cancer,
Follicular Thyroid Cancer, Anaplastic Thyroid Cancer and Medullary Thyroid Cancer
(Table 1). Papillary Thyroid Cancer and Follicular Thyroid Cancer, are further classified
as well differentiated thyroid cancer usually with follicular growth pattern, varying
eosinophilic luminal colloid composition, and sclerosis while ATC and MTC are poorly
differentiated, aggressive and potentially fatal carcinomas (Figure 3) (Hakala et al.,
2016).
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.
Of the various histological groups, recent evidence demonstrates that the second
most common subtype of well-differentiated thyroid cancer is FVPTC (Nikiforov et al.,
2016). Thyroid tumors generally arise from follicular epithelial cells and there is clinical
evidence suggesting variation in the description of the tumor subtypes. FVPTC thyroid
tumors were originally classified as either encapsulated tumors (not anchored to
surrounding tissue) or non-encapsulated tumors. Most FVPTC tumors are solid tumors
with a grayish tan to brown color on the cut surface, cells with a follicular arrangement
and same nuclear features found in classic papillary thyroid cancer (Sobrinho-Simoes et
al., 2011). However, FVPTC is often misdiagnosed because of its similarity to classic
papillary thyroid cancer, often posing a challenge for clinicians (Gupta 2012). Moreover,
a study led by Nikiforov and colleagues (2016) called for FVPTC to be reclassified as
noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP).
According to the authors, adopting the new nomenclature correctly describes the indolent
behavior of the disease and removes the stigma attached to receiving a cancer prognosis.
Figure 2. High-power histological view of thyroid cancer subtypes. Minimally invasive
differentiated follicular variant of papillary thyroid Cancer (FVPTC), papillary thyroid
cancer, and follicular thyroid cancer (left, center, right). Compared to papillary thyroid
cancer and follicular thyroid cancer, FVPTC shows varying follicle sizes, nuclear
enlargement and elongation. Source: Nikiforov et al., 2016.
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Prognosis depends on patient age, size, staging of tumors, and responsiveness to
radioactive iodine. Liu (2008) demonstrated that spreading of the cancer from the lymph
nodes and recurrence were highest in those patients with unencapsulated (anchored to
surrounding tissue) invasive FVPTC, while patients with encapsulated, non-invasive
lesions have lower chance of tumor reoccurrence. The results of my study can effectively
create easily distinguishable signatures of specific disease behaviors and aid in early
detection and applicable therapies in a clinical setting. For the purposes of this study, we
will use the term FVPTC.
Table 1. Comparison of the five main subtypes of Thyroid Cancer
Subtype % Cases Diagnosed Distant Metastases Rate (%)
(lung)
Mortality Rate (%)
PTC 80-85 17 2.5
FTC 7-15 38 0.01
FVPTC 24-33 6.5 0.6
ATC 1-2 75 0.9
MTC 3-5 47 0.09
Thyroid Cancer Detection and Treatment
As in any cancer treatment, prevention is the most effective treatment. The use of
biomonitoring tools such as the cytokinesis block micronucleus (CBMN) assay can
identify individuals with a high risk of FVPTC (Pardini et al., 2017). The CBMN system
Source: Modified from Hugen et al., 2020.
Figure 3. Cellular Origin of Thyroid Malignancy. Source: Modified from Lee,
Stephanie; Pittas, Anastassios, personal communication, March 2017.
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measures DNA damage events and scores the level of cytotoxicity as a ratio of necrotic
and/or apoptotic cells (Fenech et al., 2007). One study demonstrated patients with
micronucleus frequencies in the medium to high tertials were more likely to develop
cancer (Bonassi et al., 2007). Thorough genetic biomonitoring tools may provide
valuable information about high-risk groups leading to earlier diagnosis and enhanced
cancer treatment.
Currently, treatment typically involves surgical removal of the entire thyroid
gland followed by radioactive iodine (RAI) ablation and endocrinotherapy (Perri et al.,
2014). About 90% of the time, patients are effectively cured while 10% may suffer from
recurrent disease and metastases. Treatment of recurrent disease comprises of a second
surgical operation, RAI and infrequently chemotherapy, although chemosensitivity of
thyroid cancer is relatively low (Perri et al., 2014). Therapies for recurrent FVPTC are
few and therefore, provides an incentive for generating additional therapies for these
patients.
One of the most studied pathways in thyroid cancer etiology is the MAP (mitogen
activated protein) kinase pathway and is known to play a major role in proliferative tumor
success. The MAPK pathway has been found to be repeatedly upregulated in many
thyroid diseases (Ancker et al., 2017). Mutations in certain upstream proteins including
RET (Rearranged during Transcription) produce ever-lasting stimulation of downstream
targets along the MAPK signaling pathway, occurring in about 40-50% of follicular
carcinomas (Nikiforov et al., 2013). The MAPK signaling cascade induces expression of
angiogenic markers such as bFGF, HIF-1α, and TGF-α (Burrows et al., 2011, Kondo et
al., 2007, Mincione et al., 2011). The up or down regulation of these growth and
transcription factors may have a profound impact on tumorigenesis and tumor
aggressiveness, making them important targets for disrupting the MAPK signaling
pathways (Nikiforov et al., 2013).
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The targeting of the MAPK pathway for therapeutic benefits has prompted several
clinical trials that disrupt this classic pro-survival pathway (Perri et al., 2014). The
intracellular pathway is a final step before the cell can undergo proliferation and growth
representing it as a key target therapy in fighting FVPTC and other thyroid carcinomas.
Multi-kinase inhibitors that directly affect the intracellular signaling to the pathway have
been introduced such as vandetanib, which has only been approved for medullary thyroid
cancer (Fallahi et al., 2019). More clinical trials evaluating toxicity and efficacy of other
potential drugs must be developed to aid in curing all types of thyroid cancer.
Angiogenic Markers of FVPTC
A key component to thyroid tumor success in proliferation and migration is the
establishment of a rich blood vessel supply. Angiogenesis is the formation of blood
vessels and is a significant indicator of tumor aggressiveness and a major cause of the
metastatic spread of thyroid cancer (Sprindzuk et al., 2010). Angiogenesis is carefully
regulated by the collaboration of angiogenic stimulators and inhibitors responsible for
tumor progression (Rajabi et al., 2019). Most notably are signaling proteins including the
basic fibroblast growth factor (bFGF), hypoxia inducible factor 1 alpha (HIF-1α) and
transforming growth factor alpha (TGF-α) (Rajabi et al., 2019, Garcia de la Torre et al.,
2006). Disruption of the function of these cytoplasmic proteins is a major contributor to
tumorigenesis, proliferation and several morphological and pathological changes seen in
thyroid carcinomas (Sprindzuk et al., 2010). Extensive research has allowed a distinct
angiogenic profile to be generated for classic papillary thyroid cancer and follicular
thyroid cancer, but unfortunately, not for FVPTC (Liu et al., 2018, Jia et al., 2020).
One potential source for identifying biomarkers for FVPTC is formalin-fixed
paraffin-embedded (FFPE) tissues. FFPE tissues provide an abundant histological archive
that remains largely under-utilized. FFPE samples are a great choice in preservation of
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tissue samples within the medical field for their cost effectiveness as they can be stored at
ambient temperatures rather than frozen over long periods of time (Kokkat et al., 2013).
Basic fibroblast growth factor-2 (bFGF or FGF2) is a member of related hairpin-
binding proteins known to play a role in the proliferation of endothelial cells and mitosis.
Expressed in normal thyroid tissue, fibroblast growth factors (FGFs) and fibroblast
growth factor receptors (FGFR) are deregulated in thyroid tumors (Redler et al., 2013).
The down regulation of FGFR2 and FGF4 results from DNA methylation of the promoter
of the FGFR gene and may influence thyroid cancer proliferation by enhancing apoptosis
in tumor cells. This makes the expression of bFGF an excellent marker for FVPTC with
respect to normal thyroid tissue (Redler et al., 2013).
The hypoxia inducible factor 1 alpha (HIF-1α or HIF1A) is a two-subunit
transcription factor induced under low oxygen or hypoxic conditions and consequently
found active in many diseases involving low oxygen environments (Burrows et al.,
2011). HIF-1α controls many vital processes including vascular endothelial growth factor
(VEGF) signaling for angiogenesis and mitochondrial metabolism. It is a ubiquitous
protein and associated with tumor aggressiveness and poor prognosis. In normal
conditions (normoxia), HIF-1α is inactivated (Ding et al., 2016). Studies have
demonstrated the importance of the MAPK pathway to greatly increase HIF-1α
transactivation and signaling through selective activation of kinases by direct
phosphorylation of HIF-1α or its cofactors (Burrows et al., 2011). Enhanced expression
of HIF-1α promotes overall tumor survival and progression, regulation of factors
involved in angiogenesis such as VEGF, response to DNA damage, and apoptosis
(Burrows et al., 2011). Therefore, HIF-1α presents as a suitable maker for investigation in
this study.
Lastly, transforming growth factor alpha (TGF-⍺ or TGFA) is a member of the
epidermal growth factor family (EGF) and known to be upregulated in thyroid cancer cell
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lines (Degl'Innocenti et al., 2010). TGF-⍺ and the ligand receptor EGFR are tightly
regulated and vital for cell proliferation to activate the MEK/ERK and PI3K/AKT
pathways (part of the MAP Kinase pathway) in thyroid cancer (Degl'Innocenti et al.,
2010). Based on this evidence, exploring TGFA expression in FVPTC tissue could
provide another marker for tumor behavior.
Problems in Thyroid Cancer Analyses
The distinguishing criteria for which tumors will behave as benign or harmful
with the potential to metastasize remains a major concern in thyroid cancer research.
Currently, there is no reliable immunohistochemical or molecular marker characterized in
the literature for thyroid cancer invasion (Sobrinho-Simoes et al., 2011). Certain case
studies suggest that spreading of cancer to distant areas in the body occurs most often
when the initial diagnosis of thyroid cancer was of the follicular type or when
pathological diagnosis was inconclusive (Sobrinho-Simoes et al., 2011).
A substantial number of patients with localized FVPTC disease are cured
(Nikiforov et al., 2016). FVPTC shares similar nuclear characteristics as classic PTC as
well as thick colloid composition and monolayered sheets of follicular cells (Manimaran
et al., 2014). However, effective therapies remain unavailable for those patients with
recurrent and/or metastatic forms of thyroid cancer. There is strong evidence showing
that follicular thyroid carcinomas tend to metastasize via haematogenous route while
papillary thyroid neoplasias metastasize to the lymph node region (Garcia de la Torre et
al., 2006). Typically, patients with inconclusive test results undergo either surgery to
remove thyroid nodules or thyroidectomy and radioactive iodine ablation therapy for
tumors greater than 1.5 cm, although less than twenty percent of those surgically
removed are cancerous (Garcia de la Torre et al., 2006). Patients with indeterminate test
results and malignant tumors are often treated inadequately receiving multiple surgeries
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(Nikiforov et al., 2016). Therefore, there is need to provide more strict criteria in the
diagnosis of thyroid malignancies to reverse this trend. The identification and testing of
molecular markers may substantially increase the accuracy of diagnosis in unclear test
findings (Nikiforov et al., 2016).
Presently, diagnostic standards for FVPTC are clinically like classic PTC,
including multifocal tumor foci, nodal metastases, invasion of lymph nodes and cells
arranged as follicles with colloid in the center (Chakavarthy et al., 2018). These
similarities pose a substantial diagnostic problem due to the variation in colloid
composition in each of the follicles. In addition, fine needle aspiration cytology (FNAC)
requires the removal of thyroid gland tissue cells from nodules for microscopic
observation. However, this technique is limited by the number of cells that can be
collected and the discrepancy in operator experience (Chakavarthy et al., 2018). These
limitations lead to inconclusive diagnosis of FVPTC, therefore requiring more efficient
diagnostic markers.
Many tumors become less responsive to RAI ablation treatment leading to higher
chances of thyroid cancer recurrence (Tanaka et al., 2015). Currently, there are no
effective treatments for recurrent tumors leading to a more aggressive and persistent form
of the disease that is often fatal. In addition to distinguishing specific thyroid tumor
behaviors, the molecular mechanisms surrounding the formation and proliferation of
vessels within the lymphatic system are poorly understood (Garcia de la Torre et al.,
2006). Overall, it is still widely unknown whether angiogenesis and lymphatic
phenotypes affect tumor aggressiveness and metastasis or not (Garcia de la Torre et al.,
2006). This project addresses the importance of identifying which factors that can
effectively identify FVPTC.
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Objectives
We aim to define, using a systematic molecular approach, signatures for
angioinvasive FVPTC associated with its aggressive tumor type. We anticipate that the
expression level of angiogenic factors dictates the proliferative success of thyroid
cancers. This study was initiated to test the hypothesis that invasive, encapsulated
FVPTC has a distinct angiogenic profile compared to classic Papillary Thyroid Cancer
(PTC) and Follicular Thyroid Cancer (FTC).
In this thesis, the goal was to analyze the expression of angiogenic stimulators
bFGF, HIF-1α, TGF-α, and VEGF present in FVPTC with the following specific aims:
Aim 1. Process FVPTC tumor tissue specimens.
1. Optimize preparation of FVPTC tissue for microdissection (tissue slide
staining).
2. Separate tumor from adjacent normal control tissue by Laser Capture
Microdissection (LCM).
Aim 2. Determine expression levels of specific angiogenic markers bFGF, HIF-
1α, TGF-⍺, and VEGF in each FVPTC tumor tissue sample.
1. DNA and RNA extraction from FVPTC tumor tissue samples.
2. Quantitative PCR to identify expression levels of bFGF, HIF-1α, TGF-α,
and VEGF.
MATERIALS AND METHODS
Sample Collection
Thirty-five patient samples were identified, and all tissue specimens were
collected from a surgeon and pathologist to confirm original diagnosis and classify
tumors per standard protocol (Figure 4). This also eliminated any discrepancies in
diagnosis of the tumor tissues. Thyroid tumor samples derived from follicular thyroid
tumor tissue as well as normal thyroid tissue resected from the same patients were
collected. The tissue was placed on the FFPE membrane slides for laser microdissection
(Molecular Machines & Industries (MMI), Haslett, MI) obtained from Pathology
Associates (Clovis, CA) (Figure 5). The slides were labeled by the surgeon and
pathologist to clearly identify tumor thyroid tissue and normal thyroid tissue. While
formalin fixed and paraffin embedded procedures may result in fragmentation, several
measures were taken to optimize starting materials.
Deparaffinization/Staining Protocol
Deparaffinization, staining and dehydration was performed as follows: Two slides
were removed from the slide box and placed on a 56°C heating block for approximately
30 seconds. Each slide was placed into 25 ml of 100% xylene, followed by 100% ethanol
for 2 minutes. Then the slides were placed into 95 % ethanol and 75% ethanol for 1
minute each. Next, 100 µl of Paradise PLUS Staining Solution was added to the slide and
left for 15 minutes. The slides were placed into 25 ml 75%, 95%, and 100% ethanol for 1
minute, respectively. Tissue slides were now ready for LCM and were maintained at
room temperature for a maximum of 2 hours or stored at 4°C if LCM would not be
performed right away.
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Figure 4. FVPTC patient sample slides. Image depicts illustrations on a glass slide of the
thirty-five patient samples collected for the study. Dissected tissue arrived in triplicates on
FFPE tissue slides (not pictured). Glass slides represented exact replicas to FFPE tissue
slides, showing normal thyroid tissue and tumor thyroid tissue sections that were outlined
by purple or blue pen. They were used as a guide during dissection to keep tumor and
control cuts separate. T = tumor tissue and C = control.
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Laser Capture Microdissection (LCM)
Laser Capture Microdissection is a method used to accurately capture specific
tissue cells under a laser microscope. The process of LCM does not damage or alter the
tissue specimen in any way, making it an ideal choice for collection of RNA containing
cells. For LCM, I used the Olympus IX83 inverted UV laser microscope (Molecular
Machines & Industries (MMI), Haslett, MI) that portrayed the image of the tumor tissue
directly onto a large computer screen. Employing the MMI CellTools software, a single
cell (either normal or tumor tissue) was selected using the interactive pen screen on the
MMI CellCut software. Once the cells of interest were selected and ready for cutting, the
UV laser’s narrow beam allowed for precise drawing around the tissue or tissues of
interest while keeping unwanted tissue away.
Slides arrived from Pathology Associates on FFPE membrane slides marked to
separate normal (control) and tumor thyroid tissue. The dissected cells were cut into
about 10-20 individual circles approximately 1500-2000 µm in diameter and 10 µm thick
and placed in 1.5 ml MMI isolation caps with a diffuser (Figure 6). The diffuser was a
reaction tube that contained a protective membrane that adhered to the tissue cuts. The
laser beam originated from below the microscope stage and cut through the tissue and the
Figure 5. FVPTC tissue slides before staining. Archival FFPE blocks of tissue cut
and placed on membrane slides by Pathology Associates (Clovis, CA). Each slide
holder contained 3-4 membrane slides of each sample.
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membrane. The separated tissue and membrane were collected with the lid of the diffuser
and the dissected cell cuts adhered to the membrane. Vials were stored at minus 20°C.
This unique form of dissection proved beneficial where only the cells wanted for further
investigation were removed from the surrounding tissue, was gentler on the already
delicate tumor tissue, and minimized the probability of contamination from unwanted
tissue. The membrane and adhesive lid were chemically inert and did not affect
downstream applications. Normal tissue was collected from the same slides as tumor
tissue (see Figure 4).
RNA Extraction and Quantification
To isolate RNA and DNA from sections of tumor tissue slides, the AllPrep
DNA/RNA FFPE Kit (Qiagen, Hilden, Germany) for tissue extractions kits was used.
Extraction procedures were carried out according to kit instructions. The MMI cap was
inverted and flicked to coat and loosen cuts off the surface membrane of the isolation cap
and then centrifuged for 15 minutes at 20,000 x g to obtain the RNA-containing
supernatant and DNA containing pellet. The supernatant was treated with 1X DNase,
washed and eluted into a separate microcentrifuge tube. Next, 150 µl of buffer PKD was
Figure 6. Depicting LCM procedures. Tissue slides were placed on the microscope
stage with a coverslip and secured with stage clips. The MMI cap (right) was opened
to expose the diffuser located at the top of the cap and secured upside down onto the
microscope nosepiece to ensure direct contact with FFPE tissue slide. After
dissections were made in the FFPE tissue, the cap would pick up the cells wanted for
further investigation. Tissue cuts adhered to MMI cap diffuser and were removed
from surrounding tissue.
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added to the tube and mixed by vortexing. 10 µl of 20 mg/ml proteinase K was added,
mixed by vortexing and incubated at 56°C for 3 hours. The tube was placed upside down
onto a heating block to ensure all sample tissue would be lysed. Finally, the sample was
washed and purified using the RNeasy MinElute spin column. The column was placed
inside a 2 ml collection tube (supplied by kit) and the purified sample was incubated in
free RNase water at 37°C for one minute. The final elution volume was ~ 30 µl and the
sample was stored at -80°C.
For RNA quantitation, approximately 2.0 µL of extracted RNA from the FVPTC
tumor tissue sample was quantified using the NanoVue™ Spectrometer at 280 nm (GE
Healthcare Life Sciences, Waukesha, WI).
Reverse Transcriptase Polymerase Reaction and Primer Optimization
To verify RT-PCR kit stability and primer reliability, cell pellets from human
embryonic kidney cells (HEK 293), HeLa and MDA MB-231 cultures were spun down
and washed for extraction of RNA. These cells lines offered an abundance of biological
material to test, optimize, and validate PCR results that could be transferable to our
valuable and scarce human tissue samples as controls. For optimization, the isolated
RNAs were subjected to semi-quantitative RT-PCR to determine the expression level of
selected transcripts. Using bFGF, HIF-1α, and TGF-α primers (Table 2), reverse
transcription was carried out with the One-Step RT-PCR Kit (Qiagen). The transcripts
were subjected to RT-PCR for quantification from approximately 30 ng of RNA and a 12
µl reaction master mix. The RT-PCR master mix components were added together and
contained 7.5µl RNase free water, 5X RT-PCR buffer, 10 mM dNTPs, 0.6µM target
specific forward and reverse primer, and enzyme mix. The samples were tested on a
temperature gradient from 53°C to 60.1°C to determine proper primer efficiency using
the Mastercycler Pro S (Eppendorf, Hauppauge, NY) for 33 cycles. Based on the results,
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54.5°C proved most favorable for all three biomarkers (Figure 7). Each set of
experiments was repeated at least twice.
Band intensity and specificity comparison established optimal annealing
temperature. Transcription levels were then normalized to the stably expressed
housekeeping gene glyceraldehyde 3-phosphate dehydrogenase (GAPDH). The
expression level of RNAs were analyzed and compared to the existing information
present for the FVPTC tissues. All samples were run at the optimized temperature of
54.5°C. VEGF was not used in RT-PCR experiments.
Agarose Gel Electrophoresis
The RT-PCR products were combined with 7l 6x loading buffer for a final
volume of 20l. The final volume was loaded into 2% agarose precast gels with ethidium
bromide (Fisher Scientific, USA) along with 10 l of 50bp ladder (New England Biolabs,
Figure 7. Pro-angiogenic and housekeeping gene primer optimization. Temperature
gradient was run from 53-61°C. RNA was extracted from HEK 293 cells. GAPDH
used as a control. Molecular weight ladder (MW) is the 50bp ladder (Invitrogen).
Bright bands at 150 bp for bFGF, 166 bp for HIF-1α, 180 bp for TGF-⍺, and 225 bp for
GAPDH.
bFGF (150bp)
TGF-⍺ (180bp) GAPDH (225bp)
HIF-1⍺ (166bp)
17 17
Ipswich, MA) and electrophoresed at 100 mV for 30 minutes. The gel was imaged using
the Alpha ImagerHP.
Primer Sequence Annealing
Temp (°C) Product
Length (bp)
bFGF F’-
CTTCGCCAGGTCATTGAGAT 54.5 150
R’-
AGTATTCGGCAACAGCACAC
HIF1-⍺ F’-
GCACAGGCCACATTCACGTA 54.5 166
R’-
TCCAGGCTGTGTCGACTGAG
TGF-⍺ F’-
CAAATGGCTCAGGAGACAAT 54.5 180
R’-
GGTTGGCTGCTGTCTATCTT
GAPDH F’-
CCTGCACCACCAACTGCTTA 54.5 225
R’-
CCCATTCCCCAGCTCTCATAC
GAPDH F’-
GATTCCACCCATGGCAA 54.5 99
R’-
TTCCACTCACTCCTGGAA
VEGF F’-
CTGTTCCGAGGTTGCCCT 54.5 120
R’-
CAGGACCAACAGCCACTATGA
Quantitative Real Time Polymerase Chain Reaction
The isolated RNAs were subjected to qPCR to determine the expression levels of
selected transcripts. Using specific primers (Table 2), real time PCR was carried out with
the Express One-Step SYBR GreenER kit, with premix ROX (Invitrogen).
The same extracted RNA material was subjected to qPCR for quantification with
the MyGo Mini (Azura Genomics Inc, Raynham, MA) using approximately 10 ng of
Table 2. Summary of Primer Sets. The GAPDH at 99bp was used when the GAPDH
at 225bp was no longer available.
18 18
RNA and 20 µl reaction master mix. The qPCR master mix contained Taq DNA
Polymerase, reverse transcriptase for one step qRT-PCR, SYBR GreenER dye, uracil-
DNA-glycosylase (UDG), and target specific forward and reverse primer. Next, 14 PCR
reaction tubes (7 reactions in duplicate) were set up with the control RNA (HEK) and the
GAPDH primer. Using a gradient method, different concentrations of control RNA (3,
10, and 100 ng) were tested with the internal controls established by the stably expressed
GAPDH. Each primer (forward or reverse) concentration in the mixture was adjusted to a
final concentration of ~ 200nM. qPCR amplification of RNAs were analyzed and
compared to the existing information present for the FVPTC tissues. All samples were
run at the optimized temperature of 60°C.
Statistical Analysis
For qualitative difference of RT-PCR, spot densitometry analysis was used to
measure each band’s integrated density value (IDV) and was calculated with the formula:
%Relative Gene Expression = Target Gene IDV/Control Gene IDV x 100.
For quantitative difference of qPCR, the average cycle number (Cq) values for the
housekeeping gene and gene of interest in control and experimental conditions were
calculated yielding 4 values: Gene of Interest Experimental (IE), Gene of Interest Control
(IC), Housekeeping Gene Experimental (HE), and Housekeeping Gene Control (HC).
The ∆Cq values for experimental and control conditions were calculated using the
differences between the experimental values (IE-HE) and differences between the control
values (IC-HC) to give ∆CTE (change in experimental) and ∆CTC (change in control).
Then, the difference between the ∆Ct experimental and control conditions (∆CTE-∆CTC)
was calculated to yield the double delta Cq value (∆∆Cq).
RESULTS AND DISCUSSION
The primary goals of my thesis were to develop procedures and test the utility of
semi-quantitative RT-PCR on clinical archival tissue with the following aims:
Aim 1: Evaluate FVPTC tumor tissue specimens.
Aim 2: Determine expression levels of specific angiogenic markers bFGF, HIF-
1α, TGF-⍺ and VEGF in each FVPTC tumor tissue sample.
My primary scientific question sought to address a known clinical problem: can
we refine, using a systematic molecular approach, signatures for angioinvasive FVPTC
associated with its aggressive tumor type. I hypothesized that invasive, encapsulated
FVPTC has a distinct angiogenic profile compared to classic Papillary Thyroid Cancer
(PTC) and Follicular Thyroid Cancer (FTC).
Evaluation of FVPTC Tumor Tissue Specimens
Laser Capture Microdissection (LCM)
In general, RNA extracted from FFPE materials is chemically altered and yield
can be affected by quality of the sample, time, formalin fixation, and proficiency of
microdissection (Datta et al., 2015). My study design involved a small range of tissue
cuts from each sample to reduce variation and reduce impact of storage and RNA
degradation. Adjacent normal thyroid tissue and tumor thyroid tissue could be separated
from each other and allow for the RNA to remain intact during the extraction process.
Settings for LCM were optimized to maximize tissue cuts and material available
from slides. Cuts referred to the number of circular dissections per tissue slide (Figure 8).
I found that velocity, laser focus and power, and tissue section thickness affected the
integrity of the tissue (Table 3).
LCM cut velocity below 117 µm/s resulted in tissue sections that were not
successfully dissected from the paraffin-embedded slide. Under this condition, tissue
20 20
from the slides could not be removed completely and dissection would need to take place
a second time. If the speed was above 117 µm/s, the laser lost focus and would not
properly excise the section leading to tissue left behind on the slide. Thus, when the cap
was placed onto the slide to load tissue sections onto it, the cut would not lift from the
slide and another dissection was required to fully lift the sample. In addition, LCM was
performed to ensure tumor tissue was dissected in 2000 µm in diameter. When tissue cuts
were between 1500-2000 µm across, the tissue remained more intact for downstream
Cut Velocity 117 µm/s
Laser Focus 2600
Laser Power 83%
Tissue Cut Diameter 1500-2000 µm
Tissue Section Thickness 10 µM
Figure 8. Images of FVPTC stained tissue. LCM microscope images before
microdissection (A) and after microdissection (B). Cuts are 2000µm across.
A B
Table 3. LCM laser settings. Settings were optimized to prevent breakage of
surrounding tissues, repeat laser cutting, and removal of unwanted tissue. LCM
was performed using these settings for subsequent experiments.
21 21
applications. Larger than 2000 µm in diameter often led to removal of unnecessary
sections of the slide and made it difficult to separate normal thyroid tissue from tumor
thyroid tissue. Each slide was marked previously by the pathologist who performed the
biopsy to show the separation between tumor and normal tissue. Between 60-75% of each
slide’s area contained tumor tissue and 10-25% of the slide’s area consisted of control
tissue (Figure 9). Thyroid tumor tissue accounted for more than half of the slides’ area
and could therefore justify the lower concentration of extracted RNA seen in control
thyroid tissue samples. LCM offered a more precise and less invasive method of
capturing delicate cells from tissue (Datta et al., 2015).
RNA Extraction from FVPTC Tumor Tissue Samples
Normal thyroid tissue and adjacent tumor thyroid tissue cuts were gathered and
purified to collect extracted RNA. Review of the literature suggested that extraction of
DNA and RNA from FFPE tissues would prove difficult considering the diminished
quality of the sample after fixation and RNA degradation (Liu et al., 2008). During the
25
75
Area Percentage of Tissue Type Per Slide
Control Tissue Tumor Tissue
Figure 9. Area of FVPTC tissue type. Chart depicting percentage of tissue
type found on each FFPE slide due to the size of biopsy material.
22 22
first six trials (staining, LCM cutting, RNA extraction and quantification of one slide was
considered a trial), cuts from FVPTC normal and tumor tissue were combined (pooled)
into one vial and extracted together (Table 4). The number of cuts were adjusted and
captured from each slide to determine the maximum yield in RNA concentration we
could obtain (Table 5).
Table 4. Pooled (includes tumor and normal tissue combined) thyroid tissue cuts. * =
data unavailable.
RNA
Tissue
Source
Pooled Number
of Cuts
Average
concentra
tion ng/ul
Concentr
ation
divided
by buffer
volume
ng of
RNA/
area used
Average
A260/A28
0
Average
A260/A23
0
S1245371
1B Pooled 10 8.8 0.29 1.47 * *
S1245371
1B Pooled 10 9.9 0.33 1.65 * *
S1134331
H Pooled 19 8.3 0.28 1.38 * *
S1223015
1B Pooled 16 6.3 0.21 1.05 * *
S0841981
F Pooled 40 7.1 0.24 1.18 1.922 0.076
S1013115
1B Pooled 26 1.6 0.05 0.27 0.97 0.007
S1013115
1B2 Pooled 32 6.6 0.22 1.10 8.75 0.006
S1013115
1B3 Pooled 30 8.3 0.28 1.38 1.697 0.047
S0613611
H Pooled 26 8.4 0.28 1.40 1.667 0.371
S1013115
1B4 Pooled 22 7 0.23 1.17 1.81 0.091
S1245371
1G Pooled 32 6.6 0.22 1.10 2.098 0.041
23 23
Trial # # Tissue Cuts Total Area
(# cuts x πr2) (µm2) RNA Concentration
(ng/µl)
1 12 37,680,000 10
2 13 40,820,000 11.2
3 8 25,120,000 10.5
4 15 47,100,000 12.7
5 10 31,400,000 11.4
6 15 47,100,000 24.3
7 4 12,560,000 12.4
8 4 12,560,000 27.6
9 10 31,400,000 7
10 14 43,960,000 58
11 6 18,840,000 10.4
12 11 34,540,000 26
13 7 21,980,000 4.4
14 16 50,240,000 20.8
15 6 18,840,000 5.4
16 15 47,100,000 10.3
17 5 15,700,000 2.2
18 8 25,120,000 72
19 5 15,700,000 3.7
20 10 31,400,000 28.4
21 4 12,560,000 13.6
22 4 12,560,000 28.8
23 10 31,400,000 28.4
24 14 43,960,000 63.6
25 15 47,100,000 82
Table 5. Total Area of FVPTC tumor tissue cuts compared to total RNA concentration.
Cuts refer to amount of circular dissections per LCM tissue slide.
24 24
Trials 4 and 16 both had 15 tissue cuts yet yielded RNA concentrations below 20
ng/µl at 12.7 and 10.3 ng/µl, respectively. However, trial 25 also had 15 cuts and yielded
an RNA concentration of 82.3 ng/µl. Thus, the number of cuts from the thyroid tissue did
not necessarily result in higher RNA concentrations and vice versa (Figure 10). I
discovered that the slides did not contain the same amount of tissue area, as this was
dependent on the size of biopsy material, the tumor area compared to adjacent normal
tissue, and limited to the sectioning available. More tumor tissue was available for
sectioning on the slides based on tissue collection during surgery which may have
contributed to the higher RNA concentration seen in thyroid tumor tissue compared to
normal thyroid tissue (Figure 11).
Figure 10. RNA concentration determined from tissue cuts. Shows average RNA
concentration extracted from samples based on the number of cuts taken from
neighboring normal and FVPTC tumor tissue.
25 25
Next, each purified sample of FVPTC control and tumor tissue was eluted into 30
µl of RNase free water. For RNA quantitation, approximately 2.0 µl of extracted RNA
from the FVPTC tissue sample was quantified using the NanoVue™ Spectrometer at 280
nm. The RNA concentration results were between 4.1 ng/µl to 8.3 ng/µl (Table 6). In
order to improve RNA concentration for further downstream analysis, a few
modifications were introduced into the previous extraction technique. The first of these
changes included warming the RNase free water to 37°C before eluting the sample
through the spin column. However, this resulted in an RNA concentration of 6.3 ng/µl
which was worse than the two other trials using the same number of tissue cuts (trials 2
and 4). Therefore, I did use this modified technique on subsequent trials (Table 7).
For the second modification, I separated control thyroid tissue and tumor thyroid
tissue cuts during LCM and introduced a longer proteinase K incubation time during the
extraction process (Figure 12A). Proteinase K works to degrade proteins and remove
Figure 11. Cuts and RNA concentration obtained from FVPTC tissue samples. Shows
the average number of cuts obtained from 20 FVPTC control and tumor tissue sample
slides (A). Depicts 20 extracted FVPTC tissue samples and the average RNA
concentration based on tissue type (B). More thyroid tumor tissue was available on each
slide which contributed to the higher RNA concentration observed in thyroid tumor
tissue.
A B
26 26
contaminants, such as nucleases, that may digest nucleic acids during purification. Thus,
by varying proteinase K digestion times during the extraction process, I could determine
which time resulted in less RNA degradation and high RNA concentration. Incubation
times for proteinase K digestion ranged from 15 minutes (as recommended by
manufacturer standard protocol), to 24 hours at 56°C. Quantified values showed a
dramatic improvement in quality and quantity of RNA extracted from FFPE tissue. RNA
concentration exhibited a ten-fold increase with the proteinase K incubation period of 3
hours at 56°C with an average of 83.4 ng/µl, making this the best condition for disruption
of crosslinking and maintenance of RNA integrity. At 24 hours, the RNA was severely
degraded, yielding zero concentration, and the sample was discarded. Thus, all
subsequent extractions were carried out using the 3-hour proteinase K digestion time to
maximize RNA extraction from the thyroid tissue (Figure 12B). Table 7 shows all
FVPTC samples that were used for LCM, RNA extraction, and quantification. In the
majority of the samples, the number of tumor tissue cuts was almost double the number
taken from normal tissue. Not all of the 35 patient samples provided were used due to
degradation or damage during experiments.
Trial # RNA Concentration (ng/µL)
Trial 1 (~10 cuts) 4.1 ng/µl
Trial 2 (~20 cuts) 8.3ng/µl
Trial 3 (~20 cuts) with 1st modified
technique
6.3ng/µl
Trial 4 (~20 cuts) 8.0ng/µl
Table 6. Quantification yields of RNA from FVPTC tumor tissue extraction.
Extraction procedures were carried out according to the standard protocol unless
otherwise noted (indicated by modified technique).
27 27
Figure 12. Extraction method effects on RNA concentration. RNA yields from
FVPTC tumor tissue extraction optimization (A). Extraction procedures were carried
out according to the standard protocol unless otherwise noted using the AllPrep
DNA/RNA FFPE or Qproteome FFPE DNA tissue extractions kits. Extended heating
referred to RNase free water warmed to 37°C to enhance elution yield. Chart depicts
average RNA concentration extracted from tumor tissue as proteinase K digestion time
was increased (B). Each average was calculated from replication of 3 separate samples.
B
A
28 28
Table 7. RNA Samples. RNA taken from control and tumor tissue cuts extracted
separately. * = Data unavailable.
RNA Tissue
Source
Tumor/
Control
Number
of Cuts
Average
[RNA]
ng/ul
ng of
RNA/ area
used
Average
A260/
A280
Average
A260/
A230
S09506201F1 Control 10 0.6 0.10 1.453 0.098
Tumor 15 6.4 1.07 2.761 0.086
S10499371D Control 10 11.4 1.90 * *
Tumor 15 24.3 4.05 * *
S11523971B Control * 12.4 2.07 1.843 0.253
Tumor * 27.6 4.60 1.651 0.311
S12220151B Control * 7.2 1.20 * *
Tumor * 51.8 8.63 * *
S1248371G Control 12 10 1.67 2.31 0.02
Tumor 13 11.2 1.87 2.163 0.108
S1148126W Control 7 2.3 0.38 0.887 0.084
Tumor 13 7.2 1.20 2.722 0.039
S12255631E Control * 10.4 1.73 * *
Tumor * 26 4.33 * *
S12255631J Control 6 5.4 0.90 0.805 0.011
Tumor 11 10.3 1.72 1.148 0.186
S11508171L Control 7 2.2 0.37 1.437 0.237
Tumor 16 72 12.00 1.547 1.09
S08411981F Control 5 6.5 1.08 * *
Tumor 8 82 13.67 * *
S0647393G Control 6 3.7 0.62 5.632 0.021
Tumor 15 28.4 4.73 1.761 0.222
S12220151B1 Control * 4.4 0.73 0.94 0.016
Tumor * 20.8 3.47 1.486 0.148
S12255671B Control * 6.7 1.12 1.05 0.108
Tumor * 21.2 3.53 1.395 0.624
S1245371B Control * 23.2 3.87 1.657 0.118
Tumor * 63.6 10.60 1.559 0.723
S11508171L2 Control * 13.6 2.27 1.498 0.04
Tumor * 28.8 4.80 1.636 1.161
S11508171C Control * 6.75 1.13 1.735 0.365
Tumor * 93.6 15.60 1.696 0.851
S1236381 Control 5 6.6 1.10 1.431 0.073
Tumor 10 65.8 10.97 1.68 0.588
S09506201F Control 8 4.6 0.77 2.556 0.0575
Tumor 15 27.4 4.57 1.801 0.3105
29 29
Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) on Extracted RNA to Evaluate Levels of
bFGF, HIF-1α, and TGF-α
Following primer optimization, I wanted to establish which concentration of RNA
produced the brightest bands. RNA was extracted from HEK 293 cells, as well as FVPTC
tumor and normal tissue and diluted with RNase free water to 30 ng/ul, 60 ng/ul, and 90
ng/ul. RT-PCR was performed using the housekeeping gene glyceraldehyde 3-phosphate
dehydrogenase (GAPDH) and results shows the lowest concentration of 30 ng/ul as
sufficient for producing clear bands (Figure 13). 30 ng/ul of RNA was used as the
standard for each subsequent RT-PCR run of FVPTC tumor and normal/control tissue.
Angiogenic Factor Expression
The expression levels of various angiogenic factors associated with thyroid
cancers were examined by the widely used technique of semi-quantitative RT-PCR. Gene
expression of the angiogenic factors bFGF, HIF-1α, and TGF-⍺ were analyzed in FVPTC
tissue taken from tumor patients (Table 8). Analysis of 20 independent FVPTC tumor
and control tissue samples revealed bFGF, HIF-1α, and TGF-⍺ present in the FVPTC
tumor and control tissue samples (Figure 13 and Appendices).
Using spot densitometry analysis on each band produced from RT-PCR, the
integrated density value (IDV) was calculated (Figure 15). Relative gene expression was
determined by comparing band brightness of the angiogenic factors bFGF, HIF-1α, and
TGF-⍺ against the brightness band of GAPDH in all of my experiments, and then
graphed as a percent. Out of the 20 FVPTC tumor samples analyzed, only slides
S1150817J-2 and S1245371B produced clear bands (Figure 14). Analysis showed bFGF
present in both the tumor and control thyroid tissue sample S1150817J-2. However,
bFGF is not expressed in normal thyroid tissue, therefore, the results of this experiment
point to possible contamination between the tumor and control thyroid samples (Jia et al.,
2020). HIF-1α expression is induced by hypoxic or low oxygen conditions and promotes
30 30
GAPDH 99bp
Figure 13. RT-PCR optimization results at different RNA concentrations. RNA was
extracted from HEK 293 cells to preserve thyroid samples and used as a control.
GAPDH was also used as an internal control at 30 ng/ul and at 60 ng/ul (A) and at 90
ng/ul (B). Molecular weight ladder (MW) is the 50bp ladder (Invitrogen).
A
B
GAPDH 99bp
MW
50bp
MW
50bp
1 2 3 4 5 6 7 8 9 10 11
1 2 3 4 5 6 7
31 31
RNA Tissue
Source Tissue Type
Number of
Cuts Tissue area
cm
Average
concentratio
n ng/ul
Average
A260/A280
S1248371G Control 12 0.2 10 2.31
Tumor 13 0.2 11.2 2.163
S09506201F Control 8 0.2 10.5 2.071
Tumor 15 0.2 12.7 1.567
S10499371D Control 10 0.2 11.4 1.991
Tumor 15 0.2 24.3 1.649
S11523971B Control 4 0.2 12.4 1.843
Tumor 4 0.2 27.6 1.651
S1245371G Control * 0.2 7 1.651
Tumor * 0.2 58 1.57
S12255631E Control 10 0.2 10.4 1.925
Tumor 14 0.2 26 1.448
S12220151B1 Control * 0.2 4.4 0.94
Tumor * 0.2 20.8 1.486
S12255631J Control 6 0.2 5.4 0.805
Tumor 11 0.2 10.3 1.148
S11508171L Control 7 0.2 2.2 1.437
Tumor 16 0.2 72 1.547
S0647393G Control 6 0.2 3.7 5.632
Tumor 15 0.2 28.4 1.761
S08411981F Control 5 0.2 6.5 1.717
Tumor 8 0.2 82 5.632
S11508171J2 Control * 0.2 13.6 1.498
Tumor * 0.2 28.8 1.636
S1236381 Control 5 0.2 6.6 1.431
Tumor 10 0.2 65.8 1.68
S1245371B Control * 0.2 23.2 1.657 Tumor * 0.2 63.6 1.559
Table 8: RNA Samples for RT-PCR. Control and tumor tissue cuts extracted
separately. All 4 primers were used for all samples. * = data unavailable.
32 32
survival and proliferation within the tumor microenvironment. The nature of the hypoxic
environment within the tumor plays a vital part in the distribution of vasculature, as well
as how and when HIF-1α is expressed. Therefore, HIF-1α is not present in healthy tissue
(Ding et al., 2016).
This experimentation demonstrated HIF-1α expression absent in normal thyroid
tissue and substantially increased in FVPTC tumor tissue for sample S1150817J-2. These
early results are consistent with findings observed by Burrows, Resch and Cowen.
Congruent with literature, HIF-1α exhibited the brightest band in the thyroid
tumor sample when compared to control thyroid tissue for sample S1150817J-2. Analysis
of sample S1245371B revealed HIF-1α expression in normal thyroid tissue but no
expression was found in the tumor sample. This suggests possible tumor invasion into
normal thyroid tissue stimulating hypoxia and HIF-1α gene expression (Talks et al.,
2000). However, HIF-1α would have had to present in the tumor sample as well.
Finally, TGF-α was present only in RNA samples extracted from HEK cell lines
and was not found in significant amounts within any FVPTC tumor or control samples.
This suggests that either TGF-α was not expressed in the FVPTC samples or it would
have to be identified by a different molecular approach.
33 33
TGF-⍺ 180bp
GAPDH 99bp
HIF-1⍺ 166bp
bFGF 150bp
Figure 14. Semi-quantitative RT-PCR results. Lane numbers noted in white: TGF-⍺
in lanes 2-6 and GAPDH is in lanes 7-11 (A) and bFGF in lanes 2-6 and HIF-1⍺ in
lanes 7-11 (B). T = tumor tissue C = control tissue. Molecular weight ladder (MW)
is the 100bp ladder (Invitrogen).
A
B
MW
100bp
3 11 1 2 4 6
5 7 8
9 10
1 2 3 4 5 6
7 8
9 10 11
MW
100bp
34 34
Real-Time Polymerase Chain Reaction (qPCR) on Extracted RNA to Evaluate Amplication of
bFGF, HIF-1α, and VEGF
Pro-angiogenic and Housekeeping Gene
Primer Optimization
In an effort to use a more sensitive and quantitative approach than RT-PCR,
FVPTC samples were subjected to the Real-Time Polymerase Chain Reaction or qPCR.
The same HEK 293 RNA from the previous extraction was used to verify qPCR kit
stability and primer reliability. Next, 20 µl aliquots of qPCR master mix containing each
primer (GAPDH) was added to 3, 10, 30, and 100 ng of RNA and ran in a dilution series
0
5
10
15
20
25
30
35
40
45
50
BFGF HIF-1α TGFα
% R
elati
ve
Gen
e E
xp
ress
ion
% Relative Expression of Target Genes
HEKSample S11508171J-2 TUMOR TISSUESample S11508171J-2 CONTROL TISSUESample S1245371B TUMOR TISSUESample S1245371B CONTROL TISSUE
Figure 15. Densitometry analysis of relative expression. Target genes BFGF, HIF-1α,
and TGF-α graphed in comparison to GAPDH gene expression using ImageJ software.
Densitometry was performed on the agarose gel background comparing Figure 11 and
Figure 12 against each other. Bands that are present are represented as bars in this
graph (Figure 13) and lanes that did not yield bands are shown as blank bars (Figure
13).
35 35
to determine the lowest concentration that could be detected (Figure 16A). The HEK
RNA sample run produced a normal amplification curve showcasing a shift between the
3, 10, 30, and 100 ng. The 100 ng contained more RNA, and therefore, had a lower Cq or
cycle number compared to the sample containing 3 ng RNA, which had a higher Cq
number. The shift present in the plot was consistent with what was expected and
established in literature. Based on the results, 10 ng of RNA proved most favorable for
the biomarkers. Next, the melt curve analysis revealed a tight, single curve for GAPDH
across the control sample concentrations demonstrating high primer specificity (Figure
16B).
Following primer optimization, the isolated RNAs from the control and tumor
FVPTC thyroid tissue and each primer (bFGF, HIF-1α, and VEGF) were added to the
master mix and subjected to qPCR. TGF-α was not detected in prior reactions and was
not included in subsequent experiments (Figure 15). Analyses showed the FVPTC tumor
and control samples to produce poor amplification curves (Figure 17). The amplification
occurred later suggesting only a small amount of FVPTC tissue sample was available,
and thus, revealed no difference between tumor and control samples. qPCR was not
sensitive enough to detect sufficient differences in angiogenic marker expression most
likely due to degradation within FVPTC tissue samples.
To establish that the variations in the Cq values of the FVPTC tissue samples
were due to biological changes and not technical errors, the delta delta Cq (△△Cq)
values were calculated for each gene of interest. The delta delta Cq value determines the
fold change between tumor and control gene expression relative to experimental
conditions normalized by the housekeeping gene (GAPDH). A fold change above 1
indicates upregulation or increase in expression of the gene of interest relative to the
control (normal tissue). A fold change below 1 indicates downregulation or decrease in
36 36
expression of the gene of interest relative to the control. In other words, the control tissue
would have higher expression than the tumor tissue. However, we did not have enough
A
B
Figure 16. Normal amplification curve using dilution series. Graph depicts
amplification curve obtained with a dilution series using approximately 3, 10, 30, and
100 ng of HEK RNA and the housekeeping gene GAPDH. 10 ng was the chosen
concentration for following experimentations (A). Melt curve displaying single
amplicon for GAPDH. Indicates primer specific amplification (B).
37 37
B
A
Figure 17. Amplification and melting curve using FVPTC tissue samples. Graph
depicts amplification curve obtained using 10 ng of RNA extracted from FVPTC tumor
and control tissue (A). Melt curve displaying single amplicon for GAPDH, bFGF, HIF-
1α, and VEGF. Indicates primer specific amplification (B).
38 38
replicates to produce a reliable average for the Cq values for each primer and were only
able to test one of the FVPTC tumor and control samples (sample S123681). Therefore,
Cq values were calculated only for bFGF and HIF-1α. We were unable to quantify
reliable Cq values for VEGF and it was excluded (Table 9). Assuming 100%
amplification efficiency of the reference gene and target gene, there was less than a 1%-
fold change in bFGF and an even smaller decimal change observed in HIF-1α. A
minimum of 2-fold change would need to be observed in order to classify the fold
changes as significant for the purposes of this study (Livak et al., 2001). Therefore, these
Cq values are not meaningfully different.
Tumor Control
deltaCq
(△Cq)
deltadeltaCq
(△△Cq)
% Fold
Change x
100
GAPDH 24.29 30.18
bFGF 33.72 28.85 2.11 6.81 8.00E-03
HIF-1α 33.21 32.27 -1.33 10.76 3.30E-05
Table 9. Average Cq values for bFGF and HIF-1α.
CONCLUSION
My current project was conducted to create a distinct angiogenic profile for the
invasive, encapsulated FVPTC compared to classic Papillary Thyroid Cancer (PTC) and
Follicular Thyroid Cancer (FTC). Initial investigation suggested semi-quantitative PCR
was not sensitive enough to detect the angiogenic markers bFGF, HIF-1α, and TGF-α in
FVPTC normal and tumor tissue. I performed qPCR on the same samples as a more
sensitive and quantitative approach. Unfortunately, qPCR did not reveal any meaningful
differences in expression levels of the angiogenic markers. Tissue integrity and RNA
yield was primarily affected by the quality of the sample, preservation method and
microdissection efficiency. Ultimately, degradation of the RNA from the FVPTC tissues
may have contributed to the lack of expression seen in the FVPTC tissue.
LCM was optimized to isolate single cells (normal and tumor thyroid tissue) from
FFPE slides, an essential pre-requisite for downstream applications. I optimized
conditions for microdissection that allowed precise and repeatable cutting results from a
defined set of parameters. Normal thyroid tissue was successfully separated from FVPTC
tumor tissue and could be used for RNA extraction and PCR analysis.
The establishment of a reliable and reproducible extraction process was required
for suitable levels of RNA for downstream PCR analyses. RNA was successfully
extracted from FVPTC tissues using the protocols that I developed (Figure 12). Three-
hour incubation with proteinase K produced a nearly eight-fold increase in RNA quantity
(Figure 13). My data suggested that incubating samples with proteinase K for 3 hours at
56°C inactivates nucleases that may otherwise degrade the RNA, resulting in longer RNA
molecules being extracted at a higher concentration. Thus, proteinase K was
demonstrated as a vital component in my extraction protocol.
40 40
Finally, gene expression data showed that one of the three biomarkers (bFGF)
were present in FVPTC tumor and control tissues by qPCR (Figure 14). However,
comparison between expression levels of the biomarkers did not reveal a distinct
angiogenic pattern or statistically significant difference within the FVPTC tissue samples.
This was most likely due to the age of the FFPE samples we obtained and therefore the
quality of the RNA. Archival tissue was dated between 5-10 years (data not shown).
Therefore, I must formally reject my hypothesis.
A few limitations affected the results of my experiments. To begin with, during
LCM the tissue slide comes into direct contact with the mounting media causing slight
friction of the dry tissue section (Datta et al., 2015). Even with FFPE tissue preservation
and the most precise and mild cutting that laser microdissection offered, the tissue tended
to fall apart. This also lowered the probability of extracting quality RNA sufficient for
downstream processes. In addition, for RT-PCR I was only able to produce bands from
two of the 20 samples I tested. Likewise, I was only able observe 1 sample in qPCR and
neither method displayed differences in angiogenic expression levels. Ultimately, these
limitations did not allow me to accomplish all of my aims and objectives reliably.
Future studies should focus on expanding to a larger sample size, more replicates,
potentially diversifying the tumor subtype sample set, and using more sensitive
quantitative PCR (qPCR) techniques to further explore the value of bFGF, HIF-1α, TGF-
⍺, and VEGF along with additional angiogenic factors in FVPTC risk assessment. In
addition, it may prove helpful to mount equal areas of normal and tumor tissue on the
FFPE slides in anticipation of future biomedical research use. This would eliminate the
discrepancy between the tumor and control tissue’s RNA concentrations. Although I was
unable to produce any meaningfully significant results, I was still able to develop an
efficient workflow using FVPTC tissue. I gained knowledge about thyroid cancer,
histology, microdissection, tissue extraction techniques and PCR technology. It is my
41 41
hope this development of an efficient workflow will aid in future studies using FVPTC
tissue.
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48
APPENDIX: SUPPLEMENTARY DATA
49 49
RNA Tissue
Source Tumor/Control
Number of
Cuts Tissue area cm
Average
concentration
ng/ul
Average
A260/A280
S09506201F Control 8 0.2 10.5 2.071
Tumor 15 0.2 12.7 1.567
C T C T C T C T
1000 bp
350 bp
50 bp
Figure 1A: Shows RT-PCR of RNA expression levels from select FVPTC tumor tissue samples
going from left to right: BFGF, HIF-1⍺, TGF-⍺, and GAPDH. T=tumor tissue C=control tissue.
FVPTC tissue slide sample S09506201F
GAPDH 225 bp
TGF-⍺ 180 bp
HIF-1⍺ 166 bp
BFGF 150 bp
50 50
RNA Tissue
Source Tumor/Control
Number of
Cuts Tissue area
cm
Average
concentration
ng/ul
Average
A260/A280
S09506201F Control 8 0.2 10.5 2.071
Tumor 15 0.2 12.7 1.567
1000 bp
350 bp
50 bp
Figure 2A: Shows RT-PCR results going from left to right: BFGF, HIF-1⍺, TGF-⍺, and
GAPDH. T = tumor tissue C = control tissue. FVPTC tissue slide sample S09506201F.
T T C T C T C
GAPDH 225 bp
TGF-⍺ 180 bp
HIF-1⍺ 166 bp
BFGF 150 bp
51
RNA Tissue
Source Tumor/Control
Number of
Cuts Tissue area cm
Average
concentration
ng/ul
Average
A260/A280
S10499371D Control 10 0.2 11.4 1.991
Tumor 15 0.2 24.3 1.649
1000 bp
350 bp
50 bp
T T C T C T C
Figure 3A. RT-PCR results. Shows RT-PCR results going from left to right: BFGF, HIF-1⍺,
TGF-⍺, and GAPDH. T=tumor tissue C=control tissue. FVPTC tissue slide sample S10499371D
GAPDH 225 bp
TGF-⍺ 180 bp
HIF-1⍺ 166 bp
BFGF 150 bp
52
RNA Tissue
Source Tumor/Control
Number of
Cuts Tissue area cm
Average
concentration
ng/ul
Average
A260/A280
S11523971B Control 4 0.2 12.4 1.843
Tumor 4 0.2 27.6 1.651
1000 bp
350 bp
50 bp
T C T C T C T
Figure 4A. RT-PCR results. Shows RT-PCR results going from left to right: BFGF, HIF-
1⍺, TGF-⍺, and GAPDH. T=tumor tissue C=control tissue. FVPTC tissue slide sample
S11523971B
GAPDH 225 bp
TGF-⍺ 180 bp
HIF-1⍺ 166 bp
BFGF 150 bp
53
RNA Tissue
Source Tumor/Control
Number of
Cuts Tissue area cm
Average
concentration
ng/ul
Average
A260/A280
S12255631E Control 10 0.2 10.4 1.925
Tumor 14 0.2 26 1.448
GAPDH 225 bp
TGF-⍺ 180 bp
HIF-1⍺ 166 bp
BFGF 150 bp
1000 bp
350 bp
50 bp
Figure 5A. RT-PCR results. Shows RT-PCR results going from left to right: BFGF, HIF-1⍺,
TGF-⍺, and GAPDH. T=tumor tissue C=control tissue. FVPTC tissue slide sample S12255631E
T C T C T C T
54
RNA Tissue
Source Tumor/Control
Number of
Cuts Tissue area cm
Average
concentration
ng/ul
Average
A260/A280
S12255631J Control 6 0.2 5.4 0.805
Tumor 11 0.2 10.3 1.148
1000 bp
350 bp
50 bp
Figure 6A. RT-PCR results. Shows RT-PCR results going from left to right: BFGF, HIF-1⍺,
TGF-⍺, and GAPDH. T=tumor tissue C=control tissue. FVPTC tissue slide sample S12255631J
T C T C T C T
GAPDH 225 bp
TGF-⍺ 180 bp
HIF-1⍺ 166 bp
BFGF 150 bp
55
RNA Tissue
Source Tumor/Control
Number of
Cuts Tissue area cm
Average
concentration
ng/ul
Average
A260/A280
S11508171L Control 7 0.2 2.2 1.437
Tumor 16 0.2 72 1.547
T C T C T C T
1000 bp
350 bp
50 bp
Figure 7A. RT-PCR results. Shows RT-PCR results going from left to right: BFGF, HIF-1⍺,
TGF-⍺, and GAPDH. T=tumor tissue C=control tissue. FVPTC tissue slide samples
S11508171L
GAPDH 225 bp
TGF-⍺ 180 bp
HIF-1⍺ 166 bp
BFGF 150 bp
56
RNA Tissue
Source Tumor/Control
Number of
Cuts Tissue area cm
Average
concentration
ng/ul
Average
A260/A280
S08411981F Control 5 0.2 6.5 1.717
Tumor 8 0.2 82 5.632
C T C T C T C T
1000 bp
350 bp
50 bp
Figure 8A. RT-PCR results. Shows RT-PCR results going from left to right: BFGF, HIF-1⍺,
TGF-⍺, and GAPDH. T=tumor tissue C=control tissue. FVPTC tissue slide samples
S08411981F.
GAPDH 225 bp
TGF-⍺ 180 bp
HIF-1⍺ 166 bp
BFGF 150 bp
57
T C T C T C T
1000 bp
350 bp
50 bp
Figure 9A. RT-PCR results. Shows RT-PCR results going from left to right: BFGF, HIF-1⍺,
TGF-⍺, and GAPDH. T=tumor tissue C=control tissue. FVPTC tissue slide samples
S11508171J-2.
GAPDH 225 bp
TGF-⍺ 180 bp
HIF-1⍺ 166 bp
BFGF 150 bp
58
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