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

vi vi

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

2 2

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).

3 3

.

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.

5 5

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).

6 6

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

7 7

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

8 8

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

9 9

(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.

13 13

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.

14 14

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.

15 15

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,

16 16

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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