Faculty of Science and Technology MASTER’S THESIS Study program/ Specialization : Masters of Science in Biological Chemistry Spring semester , 2011… [601144]

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Faculty of Science and Technology

MASTER’S THESIS

Study program/ Specialization :

Masters of Science in Biological Chemistry Spring semester , 2011

Open Access

Writer:
Gurpartap Singh
…………………………………………
(Writer’s signature)
Faculty supervisor : Peter Ruoff

External supervisor(s): Oddmund Nordgård

Titel of thesis:

Molecular Detection of Tumor Cells in Regional Lymph Nodes and Blood Samples from
Patients Undergoing Surgery for Non -Small Cell Lung Cancer (NSCLC)

Credits (ECTS):
60
Key words :

Non-Small Cell Lun g Cancer, Lymph Node
Metastases, Circulating Tumor Cells, RT –
PCR,

Pages: 63
+
Enclosure: 4

Stavanger, June 15/2011
Date/year

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Molecular Detection of Tumor Cells in Regional Ly mph
Nodes and Blood Samples from Patients Undergoing
Surgery for Non -Small Cell Lung Cancer ( NSCLC )

Gurpartap Singh
Master’s Thesis in Biological Chemistry
Faculty of Science and Technology

University of Stavanger
Autumn 2011

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Acknowledgement

First of all I would like to convey my sincere gratitude to Almighty God for his grace in the
completion of my project. I am indebted to the entire staff of Molecular Biology Lab at
Stavanger University Hospital (SUS) for their kind help during this project. I o we my grateful
acknowledgement to my teachers who made me confident enough to undertake and
complete this project.
Thanks to University of Stavanger for accepting me as student: [anonimizat], I got familiar with the Norwegian
Culture and Education System. I express my heartiest gratitude towards the entire faculty of
Biological Chemistry at UIS for providing good education and knowledge. At UIS I got
familiar with the research activities a nd research environment in Norway that is going to be
a boon for my career.
I would like to convey my sincere gratitude to Oddmund Nordgård for accepting me as a
project student: [anonimizat], sincerity and
punct uality that he showed towards me throughout this project. He had been a best
supervisor to me and guided me wherever I needed help. He never ignored me or made
delays for the availability of the things that I asked for. He always showed his cooperation to
me while working in the lab. I will not forget to mention about Rune , Satu and Kjersti for
helping me in the lab work.
This project would not have been a success without the collaboration of Brustgun and
Helland. They were very much cooperative in provid ing the samples that we needed at
various stages of the experiment phase and for providing us sufficient funds.
I wish to express special thanks to Prof. Peter Ruoff, Cathrine Lillo, Sigrun Reumann, and
Lutz Eichacker for being good teachers to me.
Last bu t not the least, I express my deep sense of gratitude and indebtedness to my family
for showing their moral and financial support throughout my tenure of study. Finally, I
would like to give my heartiest apologies for all those helping hands names of whom I would
have missed unintentionally.

GURPARTAP SINGH

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Preface

This thesis is part of the course of Masters of Science in Biological Chemistry at University of Stavan ger.
This report is written in resp onse to project done at Stavanger University Hospital under the supervision
of Oddmund Nordgård . The project was a collaborated project between Oddmund N ordgård and
Brustgun/Helland research group ( Norwegian Radium Hospital, Oslo).
The project aims to detect molecular metastases in a group of patients undergoing surgery for Non –
Small Cell Lung Cancer at Radium Hospital. All the patient samples tested in t he project belongs to
patients at Raium Hospital and were analyzed at Molecular Biology lab at Stavanger university Hospital
(SUS). All the normal samples tested here were taken randomly at SUS.
A poster was presented about the findings of this study at AACR (American Association of Cancer
Research) co nferen ce on April 2, 2011 and the work was highly admired by the visitors. This study will be
published in few months as a research article.

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Table of Contents
Contents Page No.
1. INTRODUCTION ………………………….. ………………………….. ………………………….. …………………………. 8
1.1. Non -small cell Lung Cancer ………………………….. ………………………….. ………………………….. ……. 8
1.1.1. Definition ………………………….. ………………………….. ………………………….. ……………………. 8
1.1.2. Histopathology and Classification ………………………….. ………………………….. ………………. 10
1.1.3. Epidemiology ………………………….. ………………………….. ………………………….. ……………… 12
1.1.4. Detection and Treatment ………………………….. ………………………….. …………………………. 12
1.2. Regional Lymph Node Metastases ………………………….. ………………………….. …………………….. 13
1.2.1. Metastasis and Its impact ………………………….. ………………………….. …………………………. 14
1.2.2. Detection Strategies ………………………….. ………………………….. ………………………….. ……. 14
Routine analysis (H & E staining) ………………………….. ………………………….. ………………………….. …. 14
1.3. Circulating Tumor Cells in Blood ………………………….. ………………………….. ……………………….. 15
2. Methods and Materials ………………………….. ………………………….. ………………………….. …………….. 17
2.1. Methods ………………………….. ………………………….. ………………………….. ………………………….. 18
2.1.1. DNAse Treatment ………………………….. ………………………….. ………………………….. ……….. 18
2.1.2. Reverse Transcription ………………………….. ………………………….. ………………………….. ….. 18
2.1.3. QPCR (Quantitative Polymerase Chain Reac tion) ………………………….. ………………………. 20
2.1.4. Relative Quantification of mRNA ………………………….. ………………………….. ……………….. 23
2.1.5. Agarose gel electrophoresis of DNA ………………………….. ………………………….. ……………. 23
2.1.6. Purification of PCR Products ………………………….. ………………………….. ……………………… 24
2.1.7. Sequencing of PCR Product ………………………….. ………………………….. ……………………….. 25
2.1.8. Cell Culturing ………………………….. ………………………….. ………………………….. ……………… 26
2.1.9. Resuscitation of Frozen Cells ………………………….. ………………………….. …………………….. 27

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2.1.10. Subculturing Cells ………………………….. ………………………….. ………………………….. ……….. 27
2.1.11. Cryopreservation of Cells ………………………….. ………………………….. ………………………….. 28
2.1.12. RNA isolation from cells ………………………….. ………………………….. ………………………….. .. 28
2.1.13. Measurement of nucleotide concentrati on using NanoDrop ………………………….. ……….. 29
2.2. Materials ………………………….. ………………………….. ………………………….. ………………………….. 30
3. Results ………………………….. ………………………….. ………………………….. ………………………….. ………. 32
3.1. Select ion of Candidate markers ………………………….. ………………………….. ………………………… 33
3.1.1. Markers ………………………….. ………………………….. ………………………….. …………………….. 34
3.1.2. Designing Primer Sets ………………………….. ………………………….. ………………………….. ….. 35
3.1.3. Choosing the best primer set and Validation of Primers ………………………….. ……………… 35
3.1.4. Choosing a Calibrator ………………………….. ………………………….. ………………………….. ….. 40
3.2. Marker valid ation in lung tumors, normal lymph nodes and normal blood ……………………….. 41
3.3. Marker validation in tumors, patient lymph nodes and patient blood ………………………….. ….. 45
3.4. Comparison with Clinicopathological Data ………………………….. ………………………….. ………….. 49
4. Discussion ………………………….. ………………………….. ………………………….. ………………………….. ….. 51
4.1. Relevance of RT -PCR and Molecular Markers ………………………….. ………………………….. ……… 51
4.2. Lymph Node Metastases ………………………….. ………………………….. ………………………….. …….. 52
4.3. Circulating Tumor Cells ………………………….. ………………………….. ………………………….. ……….. 55
4.4. Future Research ………………………….. ………………………….. ………………………….. ………………… 56
5. Conclusion ………………………….. ………………………….. ………………………….. ………………………….. ….. 57
6. Index of Figures and Tables ………………………….. ………………………….. ………………………….. ……….. 58
7. References ………………………….. ………………………….. ………………………….. ………………………….. …. 60
Appendix 1 ………………………….. ………………………….. ………………………….. ………………………….. ………… 64
Appendix 2 ………………………….. ………………………….. ………………………….. ………………………….. ………… 66

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Abstract
The spread of metastases to regional lymph nodes in patients undergoing surgery for non -small cell lung
cancer (NSCLC) is routinely detected by histopathological examinations of tissue sections from retrieved
regional lymph nodes. If the patient’s lymph nodes do not show presence of metastas es they are
defined as LN – patients. In many cases the LN – patients get the disease back and die of it. This may be
due to insensitivity of the routine investigation to detect micrometastases. The aim of the study was to
find a group of patients where lymp h node metastasis is not shown by routine investigation but they still
have occult metastases in regional lymph nodes using more sensitive method that can be used on whole
lymph nodes. Besides this the other aim was to detect circulating tumor cells (CTCs) , the cells that are
shed from primary tumor in to the circulation system, from blood.
The 5 out of 7 (CK19, SFTPA, SFTPB, SFTPC, EPCAM, CEACAM and PVA) mRNA molecules have been
selected as potential markers for detection of micrometastases by RT -PCR. The markers were selected
from scientific research articles and cancer databases followed by their optimization and validation to
ensure pure PCR product. A calibrator cDNA was included in each plate to compare the results from
different runs. Certain cell li nes were cultured to select the calibrator expressing all marker mRNAs
included in the study. The verification of the PCR product was done by agarose gel electrophoresis and
DNA sequencing. In the initial phase 7 makers were quantified in 16 tumors, 16 nor mal lymph nodes and
12 blood samples to select best candidate markers. Some of these markers (CK19, CEACAM and PVA)
have already been quantified in many previous studies but SFTPA and SFTPC are new markers and
quantification method has been established for these markers in this study. The markers selected in
initial phase were quantified in tumors, lymph nodes and blood samples for a cohort of 55 patients
operated for non -small cell lung cancer (NSCLC) at Norwegian Radium Hospital. The relative
quantificati on of each marker was determined using 2 ΔΔCt method.

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1. INTRODUCTION
Lung cancer is one of the most common cancers we have and a large number of people die of this
disease every year. The disease is often discovered in a late stage, but also in earlier stages lung ca ncer
patients have worse outcom e than patients with other cancers. Even w ithout spreading to other organs ,
during stage I, the survival rate of lung cancer is under 70%. In comparison, for example, breast cancer
there is 95% survival in stage I [1].
First location of spread in most of the lung cancer patients is lymph nodes. Pre-operative assessment of
spread to lymph nodes is important for consideration of further treatment. Lymph nodes will be
removed during surgery to be analyzed using light microscopy for review. If there are areas of tumor
cells in lymph nodes, the patient is offered additional chemotherapy.
In most cases the spread of cancer to lymph nodes remain unnoticed due to insensitivity of the clinical
methods and the patients have low survival rate even after the diagno sis. More advanced and sensitive
methods are needed to help this out.
1.1. Non -Small Cell Lung Cancer

1.1.1. Definition

Lung Cancer, like all other cancers, results from an acquired abnormality in the body's basic unit of life,
the cell. Normally, the body maintains a system of control mechanisms for cell growth, so that cells
divide to produce new cells only when new cells are needed. Disruption of this control system results in
an uncontrolled division and proliferation of cells that eventually forms a mass known a s a tumor.
Although it can arise in any part of the lung; 90% -95% of the lung cancers arise from epithelial cells,
bronchi and bronchioles [2]. Sometimes it may also arise from the other supporting tissues within the
lungs like blood vessels. Lung cancers arise through a multistep process involving many genetic and
epigenetic changes that includes damage of many key cell -cycle genes [3]. The alterations may
accumulate in bronchial epithelium leading to clonal cell expansion. In some cancer patients clonal c ells
does not exist only as malignant cells but also as histologically normal appearing areas adjacent to
tumors [4].
Some cancer patients demonstr ate chromosomal abnormalities that damage tumor suppressor genes or
have mutations in oncogenes [5]. The muta tions are common at chromosome regions 3p (that includes
the FHIT, a tumor suppressor gene mutated in over 70% lung cancers), 9p (that includes p16INK4a,
p19ARF genes, which are involved in the RB signaling pathway), 13q (RB) and 17p (TP53) [6].

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Benign an d Malignant Tumors: Tumors can be class ified into Benign and Malignant.
Benign Tumors are those tumors that are not cancerous and are often localized without spreading to
other parts of body or nearby tissues [7]. These tumors grow slowly and are less harm ful. They can be
easily removed and patients have less chances of getting it back.
Table 1: Classification of Benign Lung Tumors [9]
Type Example

Laryngotracheobronchial Adenoma
Hamartoma

Parenchymal Fibroma
Hamartoma
Leiomyoma

Malignant Tumors refer to cancer where cells grow aggressively and invade other tissues and organs of
the body nearby and damage them. The cells of malignant tumors can pass into the bloodstream or
lymphatic system causing the spread of a tumor. This pro cess of spreading is termed metastasis.
Malignant tumors of the lung can be classified as follows:
Table 2: Classification of Primary Malignant Lung Tumors
Type Example
Carcinoma
Small Cell Oat Cell
Intermediate Cell
Combined
Non Small Cell Adenocarcinoma
Acinar
Bronchioloalveolar
Papillary
Adenosquamous

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Large Cell
Squamous Cell
Spindle Cell

1.1.2. Histopathology and C lassification

Lung cancers are broadly classified into two types:
 Small cell lung cancers (SCLC)
 Non -small cell l ung cancers (NSCLC)

Small cell lung cancer (SCLC): It is one of the most aggressive and rapidly growing lung cancers
comprising 20% of all lung cancers [2]. This type of cancer is strongly related to cigarette smoking. SCLC
often metastasizes rapidly to many sites and is discovered during late stages. These cancers have a
specific cell appearance under the microscope, the cells being smaller than the cells of Non -Small Cell
lung Cancer [10]. SCLC often remains central to the lung and grows along the wall of large bronchus [13].
The cells multiply quickly and form large tumors that spread throughout the body.
Non -small cell lung cancer (NSCLC): It is the most common type of lung cancers and accounts for about
80% of all lung cancers. NSCLC can be divided i nto three main types:
 Adenocarcinomas: This is found in the gland of the lung that produces muc ous and is the most
common type of NSCLC seen in Women and non smokers [10]. Adenocarcinomas comprise up to
50 % of Non Small Cell Lung cancers and it arises in the outer, or peripheral, areas of the lung. A
subtype of it is Bronchioloalveolar Carcinoma that develops frequently at multiple sites in the
lungs and spreads along the preexisting alveolar walls [2]. Sometimes adenocarcinomas arise
around a scar tissu e and are associated with asbestos exposure [11].
 Squamous C ell Carcinomas: These are also known as epidermoid carcinomas and accounts for
about 30 -40% of primary lung tumors. This type of cancer grows commonly in the central areas
around major bronchi in a stratified or pseudoductal arrangement. The cells have an epithelial
pearl formation with individual cell keratinization [11].
 Large C ell Carcinomas: The tumor cells are large and show no other specific morphological
traits. Sometimes they are referred to as undifferentiated carcinomas, and they are the least
common type of Non Small Cell Lung Cancer.

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The prognosis and treatment options depend on how widespread the disease is when diagnosed. The
TNM classification system is used to subgroup the patients according to the extent of the disease. The
method classify patients based on the size of primary tumor (T), degree of spread to lymph nodes (N) or
distant spread at the time of surgery (M). TNM classification is crucial for further treatment options and
must be present before treatment is initiated.
T-stage
This stage considers mainly the size of the primary tumor. From TX (positive cytology, but
unknown tumor) and T0 (not detected primary tumor) to T3 (tumor> 7 cm) and T4 (tumor invading
surrounding org an areas).
N-stage

• NX – Regional lymph nodes cannot be assessed
• N0 – no lymph node metastases
• N1 – The cancer has spread to lymph nodes within the lung and/or around the area where the
bronchus enters the lung (hilar lymph nodes) [12]. Met astasis to lymph nodes is on the same side as the
primary tumor.
• N2 – The cancer has spread to lymph nodes around the carina (point where the windpipe splits into
the left and right bronchi) or in the space behind the breast bone and in front of the h eart
(mediastinum) [12]. Metastasi s to mediastinal nodes is on the same side as the primary tumor .
• N3 – Metastasis to nodes on the opposite side of the lungs.

M-stage

• MX – distant spread cannot be assessed
• M0 – no distant metastases
• M1 – distant metastases
• M1a – distant spread to the lung on the opposite side of the main tumor
• M1b – distant metastases.

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1.1.3. Epidemiology

Lung Cancer is predominantly a disease of the elder persons. Nearly 70% of people diagnosed with lung
cancer a re above 65 years of age and less than 3% of lung cancers occur in the people below age of 45
years [2]. The incidence of lung cancer is strongly correlated with cigarette smoking, with 90 % of lung
cancers arising because of tobacco use.
The five year sur vival rate is up to 65% among the patients of NSCLC where the disease is detected in
early stages but the long term survival rate is 1% for those having metastases [13]. The prognosis for
adenocarcinoma is poor er than for squamous cell carcinoma , whereas f or large cell carcinoma it is
poorest.

Statistics in Norway:
Lung cancer accounted for 10.2% of the annual number of new cancer cases in Norway for
men and 9.1%for women in 2008 [14]. In 2008, 2,529 Norwegians got diagnosis of lung cancer,
including 1422 men and 1107 women. In 2007, 2100 patients died of lung cancer. The incidence of lung
cancer in men is declining, while for women, the number is doubled in the last 15 years. Today 22
percent of the Norwegian population smoke and the proportions are roughl y equal for both
sexes. Research shows that 80-90 percent of lung cancers are related to smoking.

1.1.4. Detection and Treatment

There are several symptoms connected with the presence of Lung Cancer that vary depending upon
where and how widespread the tumor is :
Symptoms related to the primary tumor: The growth and invasion of cancer in the lung tissues and
other surrounding areas may interfere with breathing that leads to some symptoms such as cough,
shortness of breath, wheezing, chest pain and coughing up blood (hemoptysis). In case the cancer has
invaded nerves it may cause shoulder pain that travels down the arm (Pan Coast’s syndrome) o paralysis
of the vocal cords leading to hoarseness. If it invades to esophagus it may cause difficulty in swallowing
(dysphasia). Sometimes obstruction of large may occur leading to collapse of a portion of lung and it
may cause pneumonia in the obstructed area [2].
Symptoms related to metastasis: if the lung cancer has spread to bones it may cause excruciating pain
in the bones. In case of spreading to bones it causes number of neurologic symptoms that may include
blurred vision, headaches, seizures, or, symptoms of stroke such as weakness or loss of sensation in
parts of the body.

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Paraneoplastic symptoms: Most frequently lung cancers are accompanied by symptoms that result
from production of hormone -like substances by t he tumor cells. The paraneoplastic syndromes occur
most commonly with SCLC but in some cases it may also be seen with some other type of tumor. A
release of parathyroid hormone like substance is the most frequent paraneoplastic syndrome seen with
NSCLC. T he release of this substance leads to elevated levels of calcium in the blood stream [2].
Nonspecific symptoms : some non specific symptoms may be seen in lung cancer such as weight loss,
weakness, and fatigue. Other psychological symptoms like depression and mood changes are also
common.
No symptoms: in around 25 % of patients with lung cancer the disease is discovered on a routine chest
X-ray or CT scan as a solitary mass (coin lesion). Some of these patients with small, single masses show
up no symptoms at the time cancer is discovered.
There are several ways to treat lung cancer. The treatment depends on the type of lung cancer and how
far it has spread. Treatments include surgery, chemotherapy, and radiation. People with lung cancer
often get more than one kind of the following treatment s.
 Surgery: Cancer tissues are removed by resection.
 Chemotherapy: This type of treatment involves the use of drugs to shrink or kill the cancer. The
drugs could be tablets or medicines given through an IV (intravenous) tube.
 Radiation: Radiation uses high -energy rays (similar to X -rays) to try to kill the cancer cells. The
rays are aimed at the part of the body where the cancer is.

Without treatment, lung cancer, almost without exception, eventually spread to other o rgans in the
body, either via the lymphatic system (the system that produces, stores, and carries the cells that
fight infections) or blood vessels. Spread to regional lymph nodes is the first and most common
distribution system. Hematogen spread to other organs also occurs frequently, the most common
localization is the other lung, bone, liver, adrenal glands and brain. Primary tumor can also spread by
direct tumor growth into adjacent organs.

1.2. Regional Lymph Node Metastases
Metastasis means the spread of cancer. Cancer cells can break away from a primary tumor and enter the
bloodstream or lymphatic system and spread to other parts of the body.
When cancer cells spread and form a new tumor in a different organ, the new tumor is called a
metastatic tumor. Th e cells in the metastatic tumor come from the original tumor. This means, for
example, that if breast cancer spreads to the lungs, the metastatic tumor in the lung is made up of
cancerous breast cells (not lung cells). In this case, the disease in the lung s is metastatic breast cancer

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(not lung cancer). Under a microscope, metastatic breast cancer cells generally look the same as the
cancer cells in the breast.

1.2.1. Metastasis and Its impact
Regional lymph node involvement in lung cancer is heterogeneous. From micrometastases i n
intrapulmonary lymph nodes, coded as N1 (mi), to bulky contralateral nodal disease, coded as N3, the
different situations in between vary in anatomic extent and prognosis. However, regardless of the
amount of tumor burden in the regional lymph nodes, the present nodal staging of the tumor, node and
metastases (TNM), described above, defines the extent of nodal involvement solely via anatomic
location.
Several studies have found that within every N category, there are prognostic modifiers. Thus, for
patho logically staged I tumors, the number of removed lymph nodes at thoracotomy seems to have
prognostic impact [16]. The involvement of hilar (main bronchi) lymph nodes is closely associated with
worse prognosis as compared to intrapulmonary lymph nodes in N1 patients. Other indicators of worse
prognosis in these patients include macroscopic nodal involvement, involvement of multiple nodal and
multiple nodal stations and metastatic involvement. Some other factors of adverse prognosis include
multilevel N2 dise ase and bulky disease, involvement of highest mediastinal lymph node and an extra
nodal extension.
1.2.2. Detection Strategies

Detection and Isolation of Lymph Nodes: Lymph nodes are collected by the pathologists in the
resection specimen by palpation and visual inspection. Following their detection lymph node biops y is
used to remove lymph nodes. The specimens are then formalin fixed and paraffin embedded. In this
process lymph nodes are cut during the surgery of patients having lung cancer. The isolated lymph
nodes are further analyzed for the presence or absence of tumor cells.
Detection of metastases in Lymph Nodes: The sections of isolated lymph nodes are analyzed using
various methods to follow the spread of the disease. Some of these methods are described below:
Routine analysis (H & E staining)

Hematoxylin -eosin staining (H & E staining) is based on staining of tissue sections by hematoxylin and
eosin dyes, making it easier to distinguish different types of cells visually. The method is not sensitive
enough to det ect small metastases but it can distinguish cancer cells from healthy cells by looking at the
cells’ shape, size and growth manner. Very small occurrences of cancer cells or single cells may be
difficult to detect.

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Immunohistochemistry
Metasta ses in regional lymph nodes can also be detected using sensitive immunohistochemical (IHC)
methods. The method is based on the binding of antibodies to epithelium -specific proteins on or inside
the cancer cell. The cells are then stained and visually separ ated under microscope. Cancer cells are
similar to the epithelial cells because they were originally epithelial cells, and these are not normally
present in the lymph nodes. Previous experiments have shown that this method is more sensitive than
routine an alysis based on H & E staining, since the color gives a greater contrast that makes the cells be
better separated from each other than with H & E staining. However, immunohistochemical methods
are difficult; both labour and time requiring observation by a skilled worker for a reasonable objective
evaluation. It is however a major advantage that IHC provides an opportunity to control tumor cell
morphology visually.
Reverse -transcrip tion PCR
Reverse transcription polymerase chain reaction (RT -PCR) -based detec tion of epithelium -specific mRNA
is a fast and simple method for detecting metastases. The principle behind this method is that small
amounts of cancer cells can be detected in clinical samples by amplifying specific mRNA that is
expressed selectively in c ancer cells, but not in the normal cells. The specific mRNA molecules that are
amplified using RT -PCR are known as markers (see method section).
The use of molecular markers in combination with RT -PCR defines a sensitive technology to detect even
a small a mount of metastases in regional lymph nodes and blood.

1.3. Circulating Tumor Cells in Blood

Circulating tumor cells (CTCs) are the cancer cells that escape into circulator system from the primary
tumor. While metastases directly lead to cancer recurrence, C TCs constitute a seed for metastases
indicating the metastatic potential of the disease [17].
Analyses of CTCs allow earlier detection of metastasis at an early stage. The molecular characterization
of CTCs may enable the treatment to be more effective and the removal of CTCs from circulation
minimizes the potential of metastases after surgery or therapy. Some previous studies have shown
presence of CTCs in peripheral blood and bone marrow has a prognostic significance f rom many tumors
[17]. The measurement of burden of CTCs can be used to monitor treatment response or relapse.
Molecular characterization of CTCs is helpful for the development of new methodologies for CTC
detection, enumeration, isolation and genetic analysis. The CTCs can be detected by the use of either
indirect or direct methods [31].

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In indirect Methods for detection of CTCs tumor specific genes circulating the blood were detected after
amplification by polymerase chain reaction (PCR). These methods have an advantage of being highly
sensi tive but sometimes they may have less specificity and reproducibility.
The direct methods are more advantageous than the direct methods: they provide morphological
confirmation of CTCs, allow quantitative analysis and isolation of CTCs for further analysis . Some of the
direct methods include size -selection methods, flow -cytometric methods and positive or negative
Immunomagnetic selection methods. In some cases these methods are also used in combination [31].
In both of the detection methods there is involve ment of further two steps: an enrichment step and a
detection step. The enrichment of CTCs can be performed by selecting the cells using tumor specific
markers (immunoseparation) or on the basis of morphological traits such as cell size or density. As
tumors display heterogeneity in cell size, density and marker expression, specificity and sensitivity is a
big issue with both techniques of enrichment.
Enrichment M ethods
Density Gradient Separation: in this method CTCs and mononuclear cells are separated f rom blood cells
and granulocytes on the basis of their density. CTCs have higher density (density <1.077 g/ml) than
blood cells [19].
Immunomagnetic Separation: the method relies on the positive selection of CTCs from blood samples
through their binding of antibodies coupled to magnetic beads targeting epithelial -specific antigens or
tumor specific cell surface antigens [19].

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2. Methods and Materials

Figure 1 A Flow chart displaying the main steps of methodology. It describes the step wise illustration of all the
methods performed at various stages during the project.

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2.1. Methods
2.1.1. DNAse Treatment
In this project we have treated RNA with DNAse before reverse transcription; reason being to make RNA
free of DNA that could be presen t. It is often that DNA can attach to RNA during isolation procedure
from patients. To ensure that only cDNA is amplified during qPCR and not DNA, RQ1DNAse is used to
degrade DNA.
RQ1DNAse is an endonuclease that degrades both the double stranded DNA and form 3’OH
oligonucleotide. It is an RNAse free enzyme and is ideal to use when it is critical to preserve RNA in the
sample. We also add RNAse inhibitor to avoid RNA degradation during the reaction.
Protocol:
1. Thaw RNA samples on crushed ice.
2. Make the mast er mix for all the proposed reactions[+1] by mixing the following per reaction:
 2μl 5x FSS (First Strand Synthesis) buffer
 1μl RQ1 DNAse
 0.25μl RNAse RNAseOUT inhibitor
3. Add 3μl of the master mix in 1.5ml RNAse free tubes. To this add 500ng RNA and up to7μl of RNAse
free water (Total volume in the tube should be 10μl). Mix by pipetti ng up and down several times.
4. Incubate tubes at 37oC for 30 minutes using water bath.
5. Add 1.0μl RQ1 stop solution. Mix well and spin down.
6. Incubate at 65oC for 10 minutes. This is done to inactivate the enzy me. Finally spin down the tubes.

2.1.2. Reverse Transc ription
After DNAse treatment mRNA samples are reverse transcribed. In this process mRNA is used as a
template to get a single stranded complementary DNA (cDNA). This reaction is catalyzed by Reverse
Transcriptase. The cDNA molecules generated in process c an be directly used as a template for further
amplification using PCR. Mostly the two processes are used in combination and the method is referred
to as RT -PCR.
Reverse transcription followed by the polymerase chain reaction (PCR) is the technique of choic e to
analyze mRNA expression derived from various sources. In this project we have used RT -qPCR
(Quantitative reverse transcription PCR). RT-qPCR is highly sensitive and allows quantification of rare
transcripts and small changes in gene expression.

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Proto col:
1. Make the master mix for all the proposed reactions [+1] by mixing the following per reaction:
 0.2 μl 1μg /μl random primer
 0.4 μl 25 mM dNTP
 0.4 μl DEPC H2O
2. Mix well and add 1.0 μl to each tube containing DNAse treated mRNA.
3. Incubate at 65oC for 5 minutes and immediately put on ice for minimum 2 minutes.
4. Make another master mix by mixing the follow ing per reaction:
 2 μl 5X buffer FSS
 2 μl 0.1 M DTT
 1 μl RNAse OUT RNAse inhibitor
 2 μl DEPC H2O
5. Mix well and add 7 μl to each tube containing mRNA.
6. Incubate tubes at 37oC water bath for 2 minutes.
7. Add 1 μl of MMLV Reverse transcriptase (RT) to each tu be except one that can be considered as –RT
sample.
8. Keep the tubes at room temperature for 10 minutes.
9. Transfer the tubes to 37oC water bath and keep them for 1 hour.
10. Shift the tubes to 65oC and keep them for 15 minutes.
11. Add 30 μl of dH 2O to all tubes in o rder to give a final concentration of reverse transcribed RNA 10
ng/μl.
12. Freeze the tubes at -20oC.

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2.1.3. QPCR (Quantitative Polymerase Chain Reaction)

PCR technology is widely used in quantifying DNA because the amplification of the target sequence
allows fo r greater sensitivity of detection . In an optimized reaction, the target quantity will
approximately double during each amplification cycle. In quantitative PCR (QPCR), the amount of
amplified product is linked to fluorescence intensity using a fluorescent signal that is measured in order
to calculate the initial template quantity at the end of the reaction (endpoint QPCR) or while the
amplification is still progressing (real -time QPCR).
Real -time PCR is a quantitative method involving the use of fluore scent molecules that binds to double
stranded DNA. SYBR Green I (used in this project) is a molecule that binds to all double stranded
DNA. When lying freely in a solution the fluorescent molecule emits weak fluorescence, but when it
binds to DNA fluoresce nce increases 1000 times. In each cycle of PCR reaction there occur increase in
the amount of DNA and thus the intensity of fluorescence also, that is measured after each cycle. In the
first cycle of real -time PCR the fluorescence generated from the ampli fication is higher than the
background signals, called "Ct" or threshold cycle. This Ct value can be correlated directly to the initial
concentration of DNA sample. The higher is the DNA concentration, the lower the Ct value.
PCR reaction performs by star ting at a lower temperature followed by a gradual increase in the
temperature. As the temperature increases, the double stranded DNA molecule denatures. Longer DNA
molecules will denature at higher temperatures than shorter fragments. DNA fragments denatu res by
releasing SYBR Green 1 molecules, resulting in a sudden decrease in fluorescence, which is registered as
a melting point. If you have more than one melting point of a PCR reaction, it has probably more than
one type of PCR product.

Protocol:
1. PCR was performed using SYBR Green Core kit from Eurogentec.
2. Make a m aster mix for the desired number of reactions [+1], for each marker gene that will be
amplified . Reagents are listed in Table 3 , which shows the amount used per reaction. SYBR Green 1
was d iluted 1:200 in DMSO solution provided with the kit.

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Table 3: Concentration of reaction mixtures for the markers
REAGENTS BCR CK19 SFTPA SFTPB SFTPC EPCAM CEA PVA
10 x PCR buffer 2.5 µl 2.5 µl 2.5 µl 2.5 µl 2.5 µl 2.5 µl 2.5 µl 2.5 µl
50 mM MgCl2 1.0 µl 1.0 µl 0.625 µl 1.0 µl 0.625 µl 0.625 µl 0.625 µl 0.875 µl
5 mM dNTP -U mix 1.0 µl 1.0 µl 1.0 µl 1.0 µl 1.0 µl 1.0 µl 1.0 µl 1.0 µl
10µM F – primer 0.75 µl 0.75 µl 0.75 µl 0.75 µl 0.75 µl 0.75 µl 0.5 µl 0.75 µl
10µM R – primer 0.75 µl 0.75 µl 0.75 µl 0.75 µl 0.75 µl 0.75 µl 0.5 µl 0.75 µl
1:200 SYBR
Green1 0.75 µl 0.75 µl 0.75 µl 0.75 µl 0.75 µl 0.75 µl 0.75 µl 0.75 µl
Hot Gold Star PCR
enzyme 0.125 µl 0.125 µl 0.125 µl 0.125 µl 0.125 µl 0.125 µl 0.125 µl 0.125 µl
dH2O 16.125
µl 16.125
µl 16.500
µl 16.125
µl 16.500
µl 16.500 µl 17.000
µl 16.250
µl
Total volume 23 µl 23 µl 23 µl 23 µl 23 µl 23 µl 23 µl 23 µl

3. Master Mix is mixed well and spun down shortly. Distribute master mix into desired number of wells
in a 96 well PCR pla te, pipetting 23 µl in each.
Table 4: Validated Primer Sets For each Marker
Primer Set Forward Primer Reverse Primer
SFTPA -F 5'-ttggaggcagagacccaagcag -3' 5’-ggctccaagaaatcagcgaccc -3’
SFTPB -E 5'-gtccagccctctccagtgtatc -3' 5'-gcccgtctcacttggcttttc -3'
EPCAM -B 5'-cgcagctcaggaagaatgtg -3' 5'-tgaagtacactggcattgacg -3'
CEACAM5 -C 5’-gggacctatgcctgttttgtctc -3’ 5’-gagcaaccccaaccagcac -3’
PVA -B 5'-ggcaaaaacgtgaatgggtga -3' 5'-gggttgcttggtaatctgaagta -3'

4. Thaw cDNA templates on ice in a separa te lab. Move the PCR plate to template lab. Vortex the tubes
containing cDNA shortly and spin them down.

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5. Add 2 µl of 10 ng/µl reverse transcribed RNA template into two wells for each marker gene. Also add
2 µl of –RT sample into the wells described for NO RT sample. Add 2 µl of dH2O in the wells labeled
for NTC. An exa mple plate set up is shown in figure 2 .

Figure 2: A picture showing view of Plate Set up used for qPCR run

6. Cover the PCR plate with foil lid and move it to the r oom where qPCR instrument is placed.
7. Adjust the qPCR instrument according the program mentioned in the table 5.
Table 5: PCR Program
Activation of Polymerase 95șC 10 min

40 Cycles 95șC 30 s
60șC (colored FAM fluorescence with stop) 60 s

Melting Curve 95șC 60 s
55șC 30 s
-> 95șC continuous colored fluorescence

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8. Run the temperature program.
9. Inspect the melting curve to determine the melting points and purity of the amplified PCR -products.

2.1.4. Relative Quantification of mRNA

Relative concentrations of DNA can be calculated in different ways, but we used 2ΔΔCt method [15]. This
method is useful for analysis of relative changes in gene expression. One or more "Housekeeping" gene
transcripts (genes that are believed to have th e same expression in all cells at any time) must be
included in the analysis, in this case BCR. Expression of the gene under study will be measured as
relative to the housekeeping gene. In addition, a reference sample (calibrator) is included in the
analys is. The method can be explained by the formula as under:
R (Relative Concentration) = 2ΔΔCt
ΔΔCt = (Ctsample – Ctcal) BCR – (Ctsample – Ctcal) Ck19
Where, (Ctsample – Ctcal) BCR means Ct value for BCR in the sample minus Ct Value for BCR in the
Calibrator and
(Ctsample – Ctcal) Ck19 means Ct value for gene under study (for e.g. CK19 in this case ) minus Ct value for
Calibrator for the gene of interest (CK19 in this case).
The 2ΔΔCt is applied to all the markers in order to determine the relative concentration of mRNA in the
sample.

2.1.5. Agarose Gel Electrophoresis of DNA

Agarose gel electrophoresi s is the easiest and commonest way of separating and analyzing DNA
fragments. The purpose of the gel electrophoresis might be to determine the fragment sizes, to estimate
relative amounts, or to isolate a particular fragment. The DNA is visualized in the g el by addition of
ethidium bromide. This reagent is fluorescent; it absorbs invisible UV light and transmits the energy as
visible orange light. Ethidium bromide binds strongly to DNA by intercalating between the bases and
produces increased fluorescence u pon binding.
The method is based on the movement of electrically charged DNA molecules in an electric field. In this
method 2% agarose is used to make a porous gel with a number of wells at one end. The gel is placed in
a buffer solution in an electrophor esis apparatus which sets up an electric field with a negative pole at

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one end where the wells are located, and a positive pole at the other end. Because DNA molecules are
negatively charged, these will start to move toward the positively charged electrode . Larger DNA
molecules will move more slowly through pores in the gel than smaller molecules and after a given time
the molecules separate by size. Separated DNA fragments can be visualized by adding ethidium bromide
to the gel during casting and exposing the gel to UV light after electrophoresis. DNA fragments that are
analyzed are compared with fragments with a size standard.
Protocol:
Casting of 2% Agarose Gel
1. 1.0g of agarose is mixed with 50 ml 0.5x TBE buffer in an Erlenmeyer flask.
2. Heat it for 2 min utes using microwave oven.
3. Let it cool to 50oC and add 2.5 µl of 10 µg/ µl ethidium bromide solution.
4. Mix it well and pour it in gel pouring chamber.
5. Place one or two combs in the chamber and let it cool for about 30 minutes.

Electrophoresis
1. Fill the electrophoresis chamber with 0.5x TBE buffer.
2. Mix the DNA sample and loading buffer in the appropriate dilution (1:3). Do the same for the
ladder.
3. Load the samples and ladder to the wells of the gel. Do not overfill the wells.
4. Run electrophoreses at 60V for (approx.) 90 minutes (depending upon the size of the product.
Take a picture of the gel illuminated by the UV light.

2.1.6. Purification of PCR Products

Principle
Prior to sequencing, the PCR product needs to be cleaned of buffers and other components from the
PCR reaction solution. This purification is done to get only those products that are amplified during the
PCR cycle and to filter the unwanted fragments. For cleaning and purifying the PCR product QIA quick
PCR purification kit is used. The system comb ines the "spin -column" technology with the selective
binding properties of a uniquely designed silica membrane. DNA adsorbs to silica membrane in the
presence of high salt concentration, while other components run through the column. We did this

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purificati on as we want to perform DNA sequencing for the PCR products of all markers. In order to
ensure the correct results from the Sequencing Data, purification of PCR products is a necessary.
Protocol:
1. Add 5 parts PBI buffer to 1 part of PCR product solution.
2. Check the color of the solution, if yellow it means pH < 7.5 if orange or purple then add 10 μl 3M
sodium acetate with pH 5.0 to make the solution yellow.
3. Transfer the contents into QIA quick column placed in a 2ml collecting tube.
4. Centrifuge the column for 1 minute at 1300 rpm.
5. Discard the contents in the collecting tube. And place the column back into the tube.
6. Centrifuge again for 2 minutes.
7. Transfer the column on to a new 1.5 ml tube and add 40 μl of dH2O.
8. Wait for 1 minute and again centrifuge.

2.1.7. Sequencing of PCR Product

Principle: Sanger Method (dideoxy method)
DNA sequencing enables us to perform a thorough analysis of DNA by providing the most basic
information of all the sequence of nucleotides. It can be used to locate regulatory and gene sequences,
to compare between homologous genes and to identify mutations.
Sanger’s method is also referred to as dideoxy sequencing or chain termination method. In this method
dideoxynucleotides (ddNTP’s) are also used in addition to normal nucleotides ( NTP’s). As the DNA
synthesizes, nucleotides are added on to the growing chain by the DNA polymerase. At the moment a
dideoxynucleotide is incorporated into the chain in place of normal nucleotide, the chain is terminated.
The key to this method is that al l the reactions start from the same nucleotide and end with a specific
base. Thus in a solution where the same chain of DNA is being synthesized over and over again, the new
chain will terminate at all positions where the nucleotide do not has the potentia l to be added because
of the integration of the dideoxynucleotides. In this way, bands of different lengths are produced.
We performed DNA sequencing in order to verify the identity of PCR products. The verification is done
to ensure that correct PCR prod uct is amplified during the PCR cycle for all the markers.

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Protocol:
1. According to the Big-Dye protocol we should use 1-3 ng PCR products if it has a size of 100 –
200 bp. The reaction solution was prepared according to Big Dye protocol shown in the table 6.
Table 6: Reaction mixture for Sequencing
Reagents Quantity
Big-Dye Version 3.1 1 µl
Sequencing Buffer (store at 4oC) 1 µl
Template (see table above) 200 ng
Primer 3.2 pmol
Deionised water q.s.
Total 10 µl

2. Perform cycle se quencing on therma l cycler as per the program described in table 7 .
Table 7: Thermal Cycler Program
1. 96 oC, 5 min
2. 25 Cycles` 96 oC, 10 sec
50 oC, 5 sec
60 oC, 4 min
3. 4 oC,

3. After this step we sent the reaction mix tures to the Sequencing Center in Bergen for purification
and capillary electrophoresis. The major and final steps of DNA sequencing are performed there.
2.1.8. Cell Culturing

Cell culture is the maintenance and growth of the cells of multicellular organisms outside the body in
specially designed containers and under precise conditions of temperature, humidity, nutrition, and
freedom from contamination. Cells are grown and maintained at an appropriate temperature and gas
mixture (typically, 37°C, 5% CO2 for mamma lian cells) in a cell incubator. Culture conditions vary widely

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for each cell type, and variation of conditions for a particular cell type can result in
different phenotypes being expressed. Aside from temperature and gas mixture, the most commonly
varied factor in culture systems is the growth medium. Recipes for growth media can vary in pH, glucose
concentration, growth factors, and the presence of other nutrients. The growth factors used to
supplement media are often derived from animal blood, such as calf serum.
In this project we performed cell culturing while growing certain cell lines in search for the cDNA that
can act as calibrator cDNA.

2.1.9. Resuscitation of Frozen Cells
Protocol:
1. Prepare a T25 flask appropriately labelled with Cell line name, passage number and date.
2. Add 12 ml of media in each flask.
3. Take the cryoculture tube out of liquid nitrogen and thaw them quickly using water bath at 37
oC.
4. Wipe of the tube with a 70% ethanol cloth. Add the cells into the flasks containing media.
5. Mix the contents of flask by moving a little.
6. Incubate the flasks at 37oC until sufficient confluency for subculturing.

2.1.10. Subculturing Cells
Protocol:
1. Heat PBS and trypsin at 37oC for 30 minutes.
2. Take cell culture flasks out of incubator.
3. Remove the media using pipette.
4. Wash the cells using PBS.
5. Add trypsin to the flask. Keep it at 37oC incubator for 3 -4 minutes (until all the cells detach from
the bottom). This is called trypsinization and it removes adherence of the cells from the surface
of the flask.
6. Now add 25 ml me dia to a new50 ml flask.
7. Add 5ml media to the flask containing trypsinized cells. This will stop the action of trypsin and
thereby protecting the cells.

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8. Mix the contents by pipetting up and down several times.
9. Put the necessary volume of the trypsinized ce lls into the 50 ml flask containing media, typically
1/5 of the cells. Incubate at 37oC for 48 hours.

2.1.11. Cryopreservation of Cells
Protocol:
1. Prepare freezing media using EMEM, 20%FBS, 10%DMSO.
2. Harvest the cells using PBS and trypsin.
3. Count the cells.
4. Add the freezing media and store them in small aliquot s using slowly cooling box at -80oC.
5. After 24 hours transfer the cell vials into liquid nitrogen cylinder .

2.1.12. RNA isolation from cells

The RNeasy procedure represents a well -established technology for RNA puri fication. This techno logy
combines the selective binding properties of a silica -based membrane with the speed of microspin
technology. A specialized high -salt buffer system allows up to 100 μg of RNA longer than 200 bases to
bind to the RNeasy silica membrane. Biological sampl es are first lysed and homogenized in the presence
of a highly denaturing guanidine -thiocyanate –containing buffer, which immediately inactivates RNases
to ensure purification of intact RNA. Ethanol is added to provide appropriate binding conditions, and th e
sample is then applied to an RNeasy Mini spin column, where the total RNA binds to the membrane and
contaminants are efficiently washed away. High -quality RNA is then eluted in 30 –100 μl water.
It is essential to use the correct amount of starting materi al in order to obtain optimal RNA yield and
purity. The minimal amount is generally 100 cells, while the maximum depends upon the RNA content of
the cell type, the binding capacity of the RNA easy spin column and the volume of buffer RLT used for
efficient lysis.
Protocol:
1. Trypsinize cells as described in the previous section and spin down using centrifuge.
2. Disrupt the cells by adding buffer RLT.
3. Pipette the lysate into QIAshredder spin column placed in a 2ml collection tube and centrifuge
for 2 minutes at full speed.

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4. Add 1 volume of 70% ethanol to the homogenized lysate and mix well by pipetting.
5. Transfer up to 700 µl of the sample to the RNA easy spin column placed in 2ml collection tube.
Close the lid and centrifuge at >8000 x g for 15 seconds. Discard the flow through.
6. Add 700 µl buffer RW1 to the RNA easy spin column and centrifuge it. Discard the flow
thorough.
7. Add 500 µl buffer RPE to the RNA easy spin column and centrifuge it. Repeat this step twice.
Discard the flow thorough.
8. Place the RNA easy sp in column in new collection tube and add 30 -50 µl of RNase free water.
Centrifuge it shortly. In this step we will get the RNA in the collection tube.

2.1.13. Measurement of nucleotide concentration using NanoDrop

There are several methods to establish the concentration of a solution of nucleic ac ids, including
spectrophotometric quantification and UV fluorescence in presence of a DNA dye. Nucleic
acids absorb ultraviolet light in a specific pattern. In a spectrophotometer , a sample is exposed to
ultraviolet light at 260 nm, and a photo -detector measures the light that passes through the sample. The
more light absorbed by the sample, the higher the nucleic acid concentration in the sample.
The Thermo Scientific NanoDrop 2000 is the only micro -volume spectrophotometer with a
patented sample retention technology that allows for sample volumes as small as 0.5 µL.
Protocol:
1. Clean the tip of NanoDrop where the sample is placed usin g a tissue paper and a drop of
distilled water.
2. Set up blank using 2µl of distilled water.
3. Take the first reading using 2µl of distilled water same as in previous step. The A260 value
should be around Zero. If it is more, then consider at as error correcti on value for further
readings.
4. Take more readings using 2 µl of nucleotide (DNA or RNA) sample.
5. Note down A260 and A260/A280 values for the successive readings.
6. Do not forget to clean the NanoDrop tip after taking each reading.

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2.2. Materials
Like every other experiments, materials remained an important part in this project as well. The materia ls
used here are listed in table 8 .
Table 8: Materials and their Suppliers
Materials Supplier
qPCR( Quantitative PCR machine) Stratagene
Nano Dr op Spectrophotometer 2000 Thermo Scientific
96 well PCR plates Sarstedt
Biosphere RNAse – and DNAse -free tubes Sarstedt
SYBR GREEN core PCR kit Eurogenetec
RNA easy Mini Kit for RNA isolation QIAGEN
BIG-DYE Version 3.1 Sequencing kit Applied Biosyste ms
RNA free tubes BIOSPHERE
MicroAmp Reaction Tube with Cap Applied Biosystems
QIA PCR purification kit QIAGEN
Centrifuge Thermo Scientific
Solutions and Chemicals:
5X FSS buffer (First strand Synthesis) Invitrogen
RQ1 RNAse free DNAse Promega
RNas e OUT RNase inhibitor Invitrogen
RQ1 DNAse Stop Solution Promega
Random Primers Promega
0.1 M DTT Invitrogen
MMLV Reverse Transcriptase Invitrogen
Agarose NA GE Health Care Biosciences

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Ethidium Bromide VWR International
1X TBE Buffer
54 g Trizma Bas e (Sigma Aldrich)
27.5 g Boric Acid (Calbiochem)
20 ml 0.5 M EDTA
dH 2O upto 1000 ml
Cell Culturing Medium
50 ml FBS (Fetal Bovine serum)
5ml Non Essential Amino acids
5ml Sodium Pyruvate
Sodium Bicarbonate
5ml Glutamine
2ml Pencillin
Freezing Medium
EMEM (Eagle’s Minimal Essential Medium)
20% FBS
10% DMSO

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3. Results
The main objective of the study was to find a set of gene transcripts (mRNA) that can be used to detect
tumor cells in regional lymph nodes and peripheral blood samples from patients operated for non-small
cell lung cancer (NSCLC).
Secondary objectives for the implementation of main goals:
 Establish qPCR reaction setup for quantification of selected tumor cell marker mRNA
 Quantitate markers in tumor samples, normal lymph nodes and normal blood to evaluate the
potential markers.
 Quantitate markers in lymph nodes and blood from patients who were operated for
lung cancer at the Radium Hospital.

Figure 3: A flow chart showing the different phases of Result Section
The f igure describes the main phases of the project . The primary phase was to select molecular markers
that can be quantified in the patient and normal samples to reveal the presence of metastases. The

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secondary was phase consists of two sections. In the first section the selected tumors are quantified in
16 samples of tumors and normal lymph nodes and 12 samples of blood. On the basis of the results from
this section a combination of best markers was selected to determine the relative quantification of the
mark ers in 55 samples of tumors, patient lymph nodes and patient blood.
3.1. Selection of Candidate markers
The makers are used to detect the presence of specific gene or fragment in the DNA molecule. The
primers are designed specific to the marker mRNA and added t o the PCR reaction mixture in order to
detect the expression of corresponding gene. The markers are highly specific in detection of expression
of a particular gene. The criteria adopted in the selection of markers in this project as follows:
 Markers should have high expression in the lung tumors.
 Markers should be absent or have little expression in normal lymph nodes and blood.
 Markers should be highly stable in terms of reproducibility .
Previous scientific research articles and DGED (Digital Gene Expre ssion Displayer) database searches are
certain sources that helped us in marker selection. CGAP (Cancer Genome Anatomy Project) helped us a
lot in this. The NCI's Cancer Genome Anatomy Project provides the information regarding the gene
expression profiles of normal, precancer and cancer cells. We selected 7 different markers (Table 9) for
our study.
Table 9: Marker genes and their ID
Gene name NCBI gene ID
CK19 3880
SFTPA 171327
SFTPB 6439
SFTPC 6440
EPCAM/TACSTD1 4072
CEA/CEA CAM5 1048
PVA/DSG3 1830

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3.1.1. Markers

CK19 (Cytokeratin 19)
Cytokeratins (CKs) constitute the largest intermediate filament protein subgroup and represent a
multigene family with more than 20 different types of polypeptides that are divided into acidic type I
(CK9 -CK20) and basic type II (CK1 -CK8) keratins.
SFTPA
Surfactant protein A (SFTPA) is an abundant, multifunctional protein that is secreted by airway epithelial
cells and functions as part of the innate immune response. SFTPA may be critical in protectin g the lungs
from infectious agents and environmental exposures early in life before the acquisition of specific
immunity [20].
SFTPB AND SFTPC
Surfactant proteins B and C are the genes critical for the function of pulmonary surfactant, a surface –
active ma terial that lines the lung alveoli. These genes are expressed in the developing lung epithelium
and in alveolar type II and bronchiolar Clara cells in the adult lung [21].
EPCAM (Epithelial Cell Adhesion Molecule)
This antigen is expressed on most normal e pithelial cells and gastrointestinal carcinomas and functions
as a homotypic calcium -independent cell adhesion molecule [22]. This gene encodes a carcinoma –
associated antigen and is a member of a family that includes at least two typeI membrane proteins.
CEACAM5 (Carcinoembryonic Antigen -related Cell Adhesion Molecule 5)
CEACAM5, also known as CEA or D66e, belongs to the large CEACAM subfamily of immunoglobulin
superfamily. CEACAM5 is expressed primarily by epithelial cells, and is synthesized as a glycopr otein
with a MW of 180 kDa comprising 60% carbohydrate.
PVA
Pemphigus vulgaris antigen (PVA or Dsg3) belongs to the desmoglein subfamily of the cadherin gene
superfamily. However, in contrast to most cadherins it does not bind alpha – or beta -catenins. PVA is a
calcium -binding transmembrane glycoprotein component of desmosomes in vertebrate epithelial cells
Desmosome is structure formed at the site of adhesion between two cells, consisting of a dense plate in
each adjacent cell separated by a thin layer of extracellular material [23].

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3.1.2. Designing Primer Sets
The designing of primer sets was an important step in the experimental phase because we want to
quantify the marker mRNAs. We took help of in -silico tools to design the primer sets. Primer3 and
Primer Bis earch were mainly used for this. The primer sets were also verified manually.
To start with we designed two sets of primers for each marker and tested them. For some of the
markers (like SFTPA, SFTPB and CEACAM) we had to design more primer sets because th e first primer
sets did not performed well in terms of a pure and efficient PCR reaction.
Finally we have 6 sets for SFTPA, 6 sets for SFTPB, 2 sets for EPCAM, 4 sets for CEACAM and 2 sets for
PVA. SFTPC and CK19 primers were already validated in the pre vious study in the lab.
3.1.3. Choosing the Best Primer Set and Validation of Primers
To choose the best primer set for each marker we adopted specific criteria that involve:
 High amplification efficiency (Low Ct Value for a certain cDNA template)
 Amplification of a pure PCR product of correct size
 No unwanted PCR products in the NTC (No Template Control)
 Higher reproducibility

Figure 4: Amplification Curves for different primer sets of SFTPA. It may be seen that there are many unwant ed
products present in NTC. NTC

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Figure 5: Dissociation curves for different primer sets of SFTPA. It may be seen that the unwanted products have
different melting points than the correct SFTPA.
During the selection of best primer set for each marker we used identical reaction set up for all the
primer sets of each marker. The only difference in the reaction mixture was the different primer set
(table 10) .
Table 10: Primer sequences of various primer sets for all markers
MARKERS FORWARD PRIMER REVERSE PRIMER

SFTPA A 5'-ccacactccacgacttyagacatc -3' 5'-gactgcccattgctggagaagac -3'
B 5'-ccacactccacgacttyagacatc -3' 5'-ggcatcaaaagtgatggactgccc -3'
C 5'-gagcctgaaaagaaggagcagcgac -3' 5'-ttcacttcgcacgcagcaccagag -3'
D 5'-tggagagtgtggagagaagg -3' 5'-agtcgtggagtgtggcttg -3'
E 5'-ttggaggcagagacccaagcag -3' 5'-atcagcgacccacacacagag -3'
F 5'-ttggaggcagagacccaagcag -3' 5’-ggctccaagaaatcagcgaccc -3’

A 5'-tgaggacatcgtccacatcc -3' 5'-ccaggaact tcctcatcgtgt -3'
B 5'-acatgtgggagccgatgac -3' 5'-cctccttggccatcttgttaag -3'

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SFTPB C 5'-gccaaggaggccattttccagg -3'
5'-tgagcagcttcaaggggaggac -3'
D 5'-acagccccgacctttgatgagaac -3' 5'-cccgtctcacttggcttttcctttg -3'
E 5'-gtccagccctctccagtgtatc -3' 5'-gcccgtctcacttgg cttttc-3'
EPCAM A 5'-ataacctgctctgagcgagtg -3' 5'-tgaagtgcagtccgcaaact -3'
B 5'-cgcagctcaggaagaatgtg -3' 5'-tgaagtacactggcattgacg -3'

CEACAM5 A 5'-agacaatcacagtctctgcgga -3' 5'-atccttgtcctccacgggtt -3'
B 5'-tttctccctatgtggtcgctccag -3' 5'-agcagatttttattga acttgtgc -3'
C 5’-gggacctatgcctgttttgtctc -3’ 5’-gagcaaccccaaccagcac -3’
D 5’-gaggctcctgctcacagcc -3’ 5’-tcaatagtgagcttggcagtgg -3’

PVA
A 5'-ggcagtctggaaccatgagaa -3' 5'-tcctggccatcgtcttcct -3'
B 5'-ggcaaaaacgtgaatgggtga -3' 5'-gggttgcttggtaatctgaagta -3'

On the basis of the criteria adopted we selected one primer set for each marker.
 SFTPA -F
 SFTPB -E
 EPCAM -B
 CEACAM5 -C
 PVA-B
Later on the reaction set up for the selected primer sets were optimized using MgCl2 and different
primer concentrations. For example : in case of SFTPA we made reaction 4 reaction mixtures having
different concentration of MgCl2 (i.e. 0.25 µM, 0.5 µM, 0.75 µM and 1.0 µM). Similarly the primer set
for CEACAM wa s optimized using different concentrations of Primer in three reaction mixture s (0.1 µM,
0.2 µM and 0.3 µM)
The motive behind the optimization was to get clear NTC and high reproducibility. The final
concentrations used are described in method section (Table 3) .

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Figure 6: Dissociation curves before MgCl 2 titration. There are some unwanted products in the NTC.

Figure 7: Dissociation Curves after MgCl 2 titration. Here only pure product is amplified.

The figures 6 & 7 clearly show the be nefit of Primer calibration. Previously there w ere so many
unwanted products in the NTC whereas after MgCl 2 titration we got clear NTC.
Also to verify that these primer sets amplify the correct product, we performed agarose gel
electrophoresis using the PCR product of all markers. All of the prime r sets produced correct PCR
products (figure 8) .

39 | P a g e
The sizes of the PCR products for the markers are as follows:

Figure 8: Agarose gel electrophoresis of PCR products of markers. The wells labelled as follows: 1: 1000bp
ladd er, 2: EPCAM (size=88bp), 3: PVA (size = 105bp), 4: CEACAM (size=151bp), 5: SFTPC (size=140bp).
A second verification was done for these primer sets. The PCR products for these primer sets were
purified and used sequenc ed. The sequencing data also pro ved t he PCR products to be the correct
products of the primer sets.

Figure 9: A chromatogram showing Sequence information for reverse primer of SFTPB
1 2
3 4
5

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To verify the information from sequencing data, the nucleotide sequence from the chromatogram of
reverse primer of SFTPB was used in blast search. The result file from the blast produced 100 % identity
of the sequence with the SFTPB gene and none of the file relating SFTPB gene was available with lower
identity and no other mismatch i s seen .
The pairwise alignment result from the blast search is as under:
Query 2 AGGTCGGGGCTGTGGATACACTGGAGAGGGCTGGACA 38
|||||||||||||||||||||||||||||||||||||
Sbjct 1278 AGGTCGGGGCTGTGGATACACTGGAGAGGGCTGGACA 1242

3.1.4. Choosing a Calibrator

The pur pose behind the selection of calibrator was to find a sample that can be included in all runs to be
able to compare the results from different runs, and to normalize the values according to 2ΔΔCt method.
A calibrator is a cDNA molecule that has the high ex pression of all the markers under study and is used
for calculation of R values using 2∆∆CT method.
We tested many different cDNA as calibrator, starting with a cell line cDNA (SKMES -1 and A549) as
calibrator. These two cell lines are lung cancer cell li nes and they did not have good expression for all
the markers under study. For some markers like EPCAM, CEACAM and CK19 they had high expression
but for others the expression was very much low giving a large Ct values.
We tested some other cDNA molecules (for example Universal Reference RNA) to be used as a calibrator
but most of them did not have high expression for one or the other marker. Later we quantified all the
markers in another cell NCI -H441. This cell line had good expression for all the markers except SFTPC
and PVA. We noticed that in some the patient tumor samples the two markers had high expression and
we designed new calibrator by mixing RNA of NCI -H441 and RNA of two tumor samples that had high
expression of the markers (SFTPC and PVA). The quantity in which the RNA was mixed is as under:
10 µg of NCI -H441 mRNA + 5 µg of LCa 194 T1 + 5 µg of LCa 202 T1
Where , LCa 194 T1 and LCa 202 T1 refers to the patient number whose cDNA has high expression for
SFTPC and PVA.
This mixture of mRNA was rev erse transcribed and used as a modified calibrator. This new calibrator has
the expression for all the marker mRNAs .

41 | P a g e

Figure 10: Amplification Curves for calibrator cDNA describes that the calibrator has good expression of all t he
markers.

Figure 11: Dissociation Curves of all the markers in the Calibrator cDNA. All the markers have different melting
points.

3.2. Marker Validation in Lung Tumors, Norm al Lymph Nodes and Normal
Blood
The 7 marker s CK19, SFTPA, SFTPB, SFTPC, EPCAM, CEACAM and PVA, were quantified in 16 tumor
samples, 12 normal blood samples and 16 normal lymph nodes samples in order to decide the best
marker combination to continue with.

42 | P a g e
This project is done in collaborat ion with the research group of Brustgun/Helland at Norwegian Radium
Hospital. The tumor samples used here were sent from Oslo and they belong to a study cohort recruited
among patients undergo ing surgery for NSCLC at the Norwegian Radium Hospital, Oslo. We got tot al
RNA from normal lymph nodes from Stavanger University Hospital (SUS). The normal lymph nodes
tested are not from the lung but instead they are from the colon, but it is expected that the expression
of these markers does not differ much between the two. It was reverse transcribed in the lab and then
mRNA markers were quantified in these using the qPCR program. The normal blood samples were taken
from healthy control persons recruited at the Norwegian Radium Hospital.
The mRNA quantification data obtained for each marker from each sample was registered in a MySQL
database. For each of the markers we generated stripcharts using the values from mRNA quantification.
Before this many rounds of quality control were performed on the data available from the study . The
quality control was established on the data to check that Ct values of the Calibrator for the markers does
not differ more than 2 Ct values in all the successive runs. All the plates, where such kind of difference is
observed, have been reanalyzed.
We generated the strip charts for each marker, showing the expression level in tumors, normal lymph
nodes and normal blood . An example plot is shown in the figure 12 .

Figure 12: An example figure showing e xpression level of SFTP B in tumors, normal lymph nodes and normal
blood respectively.

43 | P a g e

We can see that there is great difference in expression level of the marker between tumors and normal
samples.
To decide which marker combination was optimal we evaluated the markers accor ding to following
criteria.
 High s pecificity Index
 Complementary Primary tumor expression level
Specificity Index
Specificity indexes are generated according to Ohlson et al., by computing median tumor level divided
by highest normal level (either LNs or blood). The figure 13 shows the specificity indexes for all the
markers.

Figure 13: Specificity Indexes for all markers

The specificity indexes of all marker tells us which markers are best for the lymph nodes and which
markers are best for the blood, like by observing the above picture we can see that for blood CK19,

44 | P a g e
SFTPA, SFTPB, SFTPC and CEACAM scores best where as for lymph nodes CK19, SFTPA, SFTPB and SFTPC
scores best. PVA has very poor specificity index for both bloo d and lymph nodes.
Principal Component Analysis
We also performed Principal Component analysis to select the best candidate markers because PCA is a
very powerful way to identify correlations in a data material.
Principal component analysis (PCA) involves a mathematical procedure that transforms a number of
(possibly) correlated variables into a (smaller) number of uncorrelated variables called principal
components. The first principal component accounts for as much of the variability in the data as
possib le, and each succeeding component accounts for as much of the remaining variability as possible.
The data is analyzed is using PCA when we many variables (for example 7 Variables in this study). The
figure 14 shows a biplot of the first two principal compo nents.

Figure 14: Principle Component Analysis

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The picture above reveals that SFTPA and SFTPB are very well correlated, whereas CK19, EPCAM AND
CEA have also somewhat relatedness. SFTPC also has good expression but it expresses independent of
rest of the markers. We decided to use one marker from each group in a multimarker assay.
The two strategies adopted above solved the problem of marker choice. By combining the results of
Specificity indexes ad PCA we finalized CK19, SFTPA , SFTPC, CEACAM and PVA.
PVA has very poor specificity index, but we wanted to evaluate it further despite this, because of its
independent expression pattern in primary tumors. All the calculations and data handling procedures
were performed using R (a programming language) .
3.3. Marker Validation in Tumors, Patien t Lymph Nodes and Patient Blood

This phase constitute the final phase of the experiment, where the motive was to quantify the selected
mRNA markers in large number of tumors, patient lymph nodes a nd patient blood. In this phase we
performed our analysis over a cohort of 55 patients that includes tumor samples, lymph nodes and
blood, all three kinds of samples from the same patients. Relative mRNA quantification of each marker
in each patient was ca lculated and the data was stored in the database. Quality checks were again
applied to new data obtained.
Stripcharts were generated for all the markers. We placed a cut value for the samples from patient
lymph nodes and patient blood. For the patient LNs cut off values adopted is as above the maximum
value of expression in normal LNs and for patient blood the cut off is placed at the maximum value of
expression in normal blood.
Cutoffs are established to define a sample as LN+/LN – and CTC+/CTC -. The other reason was to establish
multimarker panel consisting of several markers that were good in combination and can reveal the
presence of micrometastases. For patient LNs, those having expression level above the cut -off for at
least one of the markers (CK19, C EA, and PVA) were declared as LN+, i.e. positive for lymph node
metastasis, and others were considered as LN -, i.e. negative for lymph node metastasis.
Similarly for blood, patients with expression level above the cut -off for at least one of the markers we re
declared CT+, i.e. positive for circulating tumor cells, and others are declared CT -, i.e. without circulating
tumor cells.
For SFTPA and SFTPC we cannot adopt the same cutoff value for LNs as we adopted for other marker s;
the reason being expression l evel in patient lymph nodes was generally much higher than normal lymph
nodes from lung. So the normal lymph node level did not seem to be representative for normal lung
lymph nodes for these markers. Also because of high back ground levels of these marke rs in patients
LNs these markers could not be the part of classification on the basis of LN status.

46 | P a g e
Below are the figures showing relative mRNA quantification for all markers.

Figure 15: CK19 : The picture shows that there are ma ny patients that have expression higher than cutoff for
lymph nodes where as very few for the blood. Here solid line defines cutoff for the lymph nodes and dashed line
define cutoff for blood samples .

Figure 16: CEACAM5 : In this case also, we can see that there are some patients that have mRNA expression
higher than cutoff for both LNs and blood, but as compared to CK19 the LN+ seems few than the former.

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Figure 17: PVA : It does not seem to show some interesting results. We can see that there are very few patients
that have mRNA expression higher than cutoff for LNs and for blood none of the patients seems CTC+. As
mentioned earlier that PVA has its own independent expression than other markers. So th e picture also explains
the same.

Figure 18: Stripchart illustrating the relative quantification of SFTPA in the Patient Samples.

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Figure 19: Stripchart showing expression of SFTPC in patient samples.
On the basis of criteria adopted for stating a sample as LN+ or LN – and CTC+ or CTC -, the patients are
classified . The data obtained is mentioned in the table 11.
Table 11: Summary of LN and CTC status
Variables Positive Negative
Lymph Node Metastases 31 24
Blood CTCs 25 30
We have obtained significant number of patients that are positive for LN metastasis and blood CTCs. We
examined whether there was any association between the detection of lymph node metastases and
blood CTCs, bu t got no significant findings (table 12 ).
Table 12: Association among LN metastases and CTCs
BLOOD

Lymph
Nodes Positive Negative
Positive 11 13
Negative 12 19

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3.4. Comparison with Clinicopathological Data
Here the data obtained from the relative mRNA quantification for CK19, CEACAM and PVA were
compared against the clinicopathological data (obtained from our collaborators in Oslo) for the patients
studied.
Table 13: Prevalance of LN+ and CTC+ level accord ing to clinicopathological variables
Variable All Patients ( N=55) CTC+ (N=25) CTC- (N=30) LN+ (N=31) LN-(N=24)
Sex–-no (%)
Male
Female
23
32
11 (48)
14 (44)
12 (52)
18 (56)
15 (65)
16 (50)
8 (35)
16 (50)
Histology – no (%)
Adenocarcinoma
Sq Cell Ca rcinoma
Others
34
13
8
15 (44)
7 (54)
3 (37 )
19 (56)
6 (46 )
5 (63 )
23 (67)
4 (31)
3 (37 )
11 (33)
9 (69)
5 (63)
C-Stage
Ia
IIa
Ib
IIb
IIIb
16
1
24
9
5
7 (44)
0
13 (54)
2 (22)
3 (60)
9 (56)
1 (100)
11 (46)
7 (78)
2 (40)
8 (50)
0
13 (54)
6 (66)
4 (80)
8 (50)
1 (100)
11 (46)
3 (34)
1 (20)
pT-Grade
1
2
3
4
13
32
7
3
5 (38)
18 (56)
0
2 (67)
8 (62)
14 (44)
7 (100)
1 (33)
5 (38)
20 (62)
4 (57)
2 (67)
8 (62)
12 (38)
3 (43)
1 (33)
pN-Grade
0
1
43
12
20 (46)
5 (42)
23 (54)
7 (58)
23 (53)
8 (67)
20 (47)
4 (33)

50 | P a g e
As we can see (Table 13) , the frequency of LN+ and CTC+ vary on the basis of certain characteristic
features of patients. We tested for associations between any of the clinicopathological parameters and
the CTC and LN status of the patien ts by Fisher's exact test. The only significant (borderline) association
we found was that more patients with adenocarcinoma had positive molecular LN status compared to
the patients with squamous cell carcinoma (P=0.06).
23 (67 % ) patients out of 34 adenocarcinoma patients , were found positive for LN+ but the number is
much less for the patients with squamous cell carcinoma (only 36%). More over future survival data f or
the patients will reveal the clinical relevance of our findings.
If we see the resul ts on the basis of the cancer stage, it reveals that the frequency increases with the
stage of cancer, like 80 % of the patients having cancer stage IIIb have developed lymph node metastasis
and 60 % of patients are positive for CTCs. Also the patients wit h low cancer stages have shown some
tendency (50%) of LN+ that makes the findings non significant.
By looking at the T – grade, it is seen that with the increase in tumor area , frequency of LN+ and CTC+
increases. The %age is less among other patients havin g small tumor area.
The most interesting result in the table is seen for N -stage. For N0 patients 53 % patients are found
positive for lymph node metastases in our study. Similarly 46 % of patients are found positive for CTCs.
These N0 patients were declar ed as LN – and CTC – by other methods .
Among node positive ( N1) patients in the table, we found 8 (67 % ) patients out of 12 patients as LN+.

51 | P a g e
4. Discussion
In this study we established a multimarker panel consisting of CK19, CEA and PVA to classify the
patien ts according to LN metastases and CTCs. The markers were quantified in a cohort of 55 patients
and the findings were compared against the clinicopathological data. The study was designed to detect
the presence of tumor cells in regional lymph nodes and cir culating blood.
The presence of metastatic tumor spread to lymph nodes is recognized in many patients with different
tumor types. But in some other patients this spread of tumor to lymph node and blood remain
unnoticed and it keeps the disease alive in pa tients in spite of their surgery. There are certain reasons
behind the hidden metastases;
1. Insensitivity for detection of isolated tumor cells
2. Limited amount of LN sampling from patients.
These limitations can be overcome by use of the more advanced methods that analyze the LNs at
multiple sections and are sensitive enough to detect even small amount of tumors cells in the LNs.
These days many modern molecular techniques are being used for this (like RT -PCR).
There are many such reports in the literature, va st majority of which have used RT -PCR to detect tumor
cells in histologically negative lymph nodes and in cases where clinical data is available, the studies
have shown prognosis sign ificance of RT -PCR positivity [24 , 25].
The recurrence of disease in pati ents of non-small cell lung cancer ( NSCLC ) may also occur due to the
presence of circulating tumor cells ( CTCs ). The micrometastases may be initiated by the presence of
CTCs in the peripheral blood. Different methods have been used to detect CTCs from the blood , for e.g.
RT-PCR, in some previous studies [32 , 36, and 37 ].

4.1. Relevance of RT -PCR and Molecular Markers

Reverse transcription quantitative PCR (RT-qPCR) is a combination of excellent sensitivity and
specificity, low contamination risk, and high spee d PCR technology [24 ] and provides the opportunity to
establish sensitive and specific ways to analyze regional LNs [2]. This technology has been adopted in
many studies related to detection of occult metastases in non -small cell lung cancer (NSCLC) patien ts
[25, 26, 27, and 28].
The ability of real time RT -PCR to detect metastatic NSCLC has been demonstrated in a study relating
EUS-FNA procurement [5 ]. In a reference study, 5 different markers ( lunx , KS1/4 , CEA, CK19 , and muc1 )

52 | P a g e
have been used to test with RT-PCR in order to detect over expression of any one of these genes in the
patient DNA and to provide patients with NSCLC staging. In this study the method has been tested over
9 mediastinal LNs containing metastatic NSCLC (5 adenocarcinomas, one large ce ll carcinoma, one
squamous cell carcinoma and two uncharacterized carcinomas) and 30 cervical lymph nodes were used
as a negative control.
In many studies relating NSCLC metastases, RT -PCR has been used with molecular markers (genes)
where the program is trained to look for the presence of marker genes in the c DNA molecule . In
different studies many different genes has been used as molecular markers. We also used 7 m arkers in
our primary study includ ing, CK19, SFTPA, SFTPB, SFTPC, EPCAM, CEA and PVA. Most of these markers
have already been used for detection of occult disease but fewer of them (SFTPA and SFTPC) are new
and very little information is available regarding them in the previous literature.
In another study by Salerno and his colleagues [30 ], RT-PCR assay has been used to detect occult tumor
cells in lymph nodes of 28 patients with NSCLC. They examined 88 N0 nodes for the expression of
mRNA transcripts for mucin -1(MUC1) and detected the presence of the gene in 37.5% of N0 nodes.
In our study we used reverse transcription quantitative PCR (RT-qPCR) to detect the presence of tumor
cells in lymph nodes and blood samples. We selected specific markers and designed primer sets specific
to the marker genes. The primers for all markers were validated an d PCR products were verified using
gel electrophoresis and DNA seque ncing . The validation of primer sets was a crucial step as we wanted
to ensure that only correct product is amplified during PCR cycl e while minimizing the amplification of
unwanted produ cts. At various times during the validation of primer sets we had to design new primer
sets for many markers. Incorrect primer sets may also enhance the amplification of unwanted products.
Sometimes unwanted products may also have the same threshold value of fluorescence (Ct Value) as
the correct product and may give error prone results . In order to minimize this risk, PCR products were
verified prior to the quantification of markers in the patient samples. The optimization of marker assays
had been a great obstacle in this study.
4.2. Lymph Node Metastases
It is seen that LN metastases is quite common in non-small cell lung cancer ( NSCLC ) patients and its
staging varies according to certain characteristic features of the lung cancer. LN metastases is often
hidd en and remained unnoticed by regular clinical investigations. Many scientific studies are being
carried on this in order to find solution to this.
After establishment of reaction setup for quantification of mRNA markers, while optimizing primers
sets by va rying MgCl 2 and primer concentration, non -specific PCR products were no longer detected as
side products during amplification of cDNA. Agarose gel electrophoresis confirmed the accuracy of the
PCR products from the size of fragments generated. As the unwa nted PCR products are no longer
found, patient samples were used as template for quantification of mRNA markers, which was the
biggest concern for the reaction scheme accepted and used.

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In our study we included 7 markers in our primary study where the quan tificatio n was done using 16
tumors, 16 normal lymph nodes and 12 normal blood samples.
During se condary phase we quantified 5 (CK19, SFTPA, SFTPC, CEA and PVA) out of 7 markers over the
cohort of 55 patient samples including lymph nodes and blood. In our findings we got some interesting
results; more than 50 % of N0 p atients have shown node positivity based on the higher expression of
CK19 (figure 15 ). CEACAM mRNA expression was found higher in few patients. Combining the
expression data of three markers (CK19, CEA and PVA), lymph node positivity is detected in 23 (53 %)
out of 43 node negative ( N0) patients.
In the similar study by Liqiang and his colleagues [ 28] they have analyzed combination of molecular
markers to detect lymph node ( LN) metastases in N SCLC patients and found 100% accuracy with CK19
and TACSTD1 regardless of primary tumor histology. In their study CEA was declared as the best marker
next to TACSTD1, showing 95 % sensitivity and according to their findings CK19 seems less likely to
stand up as independent marker. These results are contradictory to our findings. In our study we found
that CK19 is the best marker , with high expression level in most patients as compared to other
candidate markers, for the detection of lymph node ( LN) metastas is. They found PVA and SCCA1/2 as
the best markers for squamous cell carcinoma and recommend ed PVA for use in a combination of
marker assay s, despite of its independent expression in some of the primary tumors where other
markers are not able to detect met astases.
In another study, different detection methods ( histopathology, Immunohistochemistry and RT -PCR)
were compared to detect lymph node metastases in 254 mediastinal lymph node s of 49 patients having
non-small cell lung cancer (NSCLC ) [31]. Out of 22 5 node negative (N0) patients based on
histopathological screening, 32 have been found positive for CK19 mRNA and 16 patients were found
to be upstaged for this marker . Similar to our findings this study also found CK19 as a good marker for
detection of micrometastases in NSCLC patients .
By comparing our findings with clinicopathological data (Table 13 ) it is seen that 23 (53% ) out of 40
node negative (N0) patients are found positive for lymph nod e metastases in our study. For 12 node
positive (N1) patients in the table 15, we found lymph node positivity in 8 ( 67 % ) patients . The other 4
(33 % ) node positive (N1) patients did not showed positivity in our study b ecause we tested only 1 or 2
lymph nodes from a patient , so it might be possible that during clinical investigat ion they had tested
some other lymph nodes . The other reason could be that the patients might have obtained further
chemotherapy treatm ents and thus the frequency of lymph node metastases had decreased. The
future clinical follow up data will give relevant facts about our findings.

CK19

CK19 showed good potential as a marker with 103 fold higher relative concentration higher in tumors as
compared against the highest concentration of CK19 mRNA in normal lymph nodes (figure 15 ). We
observed elevated levels of CK19 mRNA in som e patient lymph nodes that indicate the presence of

54 | P a g e
metastases. One can speculate that level variations may be related to the size of metastases, although
this cannot be verified. In 3 tumor samples , level of CK19 expre ssion are quite low and are easily
distinguishable from the other samples. These 3 samples were considered as outliers with no Ct value s
and there for omitted from analysis .
CEACAM
The expression level of CEACAM in tumor samples lie between 10-4 and 101, and is 105 times higher
than no rmal lymph nodes (figure 16 ). The expression of CEACAM varies among tumor samples and is
distributed evenly in the range. The variation of expression c an depend upon the histology of cancer,
stage of cancer and tumor size. Also in few tumor sampl es the expression level is low than the highest
level in normal blood . These samples were considered as outliers and were not included in the
statistics.
In some of the patient LNs the expression of CEACAM mRNA is much higher than the n ormal LNs and
equals the expressi on level in tumors. We can assume that these patients have occult metastases .
PVA
Through previous studies , PVA has not been described a s good independent marker for lymph node
metastasis detection by RT -PCR, but it is sugg ested of having good significan ce in a combination of
markers [28 ]. It is also stated that PVA is an excellent marker in Squamous Cell lung Ca rcinoma with
100% sensitivity [28 ]. In our study we found that PVA is higher in 6 LNs (figure 17 ).
SFTPA and SFT PC
The marker mRNA for the two genes has been found to have quite high level of expression in the lymph
nodes of non -small cell lung cancer (NSCLC) patients . As shown in the figure 18 most of the patient
lymph nodes have high expression levels of these mar ker mRNAs. It is unlikely to have lymph node
metastases in so many patients . Actually we have quantified these markers in normal lymph nodes
from colon sections and it did not seem any correlation for the expression of this marker with lymph
nodes from lun g cancer patients. To be of clinical relevance normal lymph nodes from lung are needed
to be analyzed for the expression of SFTPA and SFTPC .
However it is still believed that SFTPA and SFTPC would be the good markers, based on their high
values in tumor samples and lymph nodes from lung cancer patients. Since it could not be detected in
normal lymph nodes from lung , SFTPA and SFTPC could not set any limit for the detection of
micrometastases.
There appears to be good correlation between SFTPA mRNA leve l and SFTPC mRNA level in patient LNs
(results not shown). This strengthens the assumption that higher SFTPA and SFTPC mRNA levels
reflected the presence of tumor cells in lymph nodes.

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4.3. Circulating Tumor Cells
Like lymph node metastases, RT -PCR has also a significant role in the detection of CTCs (circulating
tumor cells) and accurate results have been achieved using the study. In most of the studies related to
CTC detection CEA has been used as potential candidate marker to detect the tumor cells in the b lood of
patients.
In this study we found that , inspite of being a good marker in multimarker panel for detection of lymph
node metastases, CEACAM is also the best marker for the detection of CTCs as well. It was found high
levels of expression (figure 16 ) of this marker in 7 out of 55 patients ( 12%).
In other relevant studies CEA has been extensively examined as a useful marker to distinguish lung
cancer, especially adenocarcinoma of the lung [33, 34]. In the study by Tanaka K and his colleagues, CEA
and SCC antigen has been used to classify patients according to CTC status. The examined the samples
taken from patients before surgery and after surgery. They performed analysis over 244 patients out of
34 (13.9%) patients were found CTC positive before surg ery and 41 (16.8 %) patients after the thoracic
procedure [35]. The findings seem quite relative to our study.
Our findings seem significant in relation to the study by Nakashima S et al, where CEA has been detected
in the blood of 31 out of 54 patients (57.4%). The found that incidence of total recurrence and blood
borne recurrence was greater in the patients that had high levels of CEA mRNA in the blood than other
patients with relatively low levels of CEA mRNA [36].
In other study by Wang JY, RT -PCR analy sis was performed using combination of markers (CK19, CK20,
hTERT, CEA). They found that healthy individuals were negative for CEA expression but patients having
surgery were having significant levels of the mRNA marker. According to their findings patient s with CEA
expression in their blood have significantly higher risk of post -operative metastases [37].
In some of the patient blood samples, expression of CK19 mRNA (figure 15) has been found above the
cutoff value but the difference is not much high than normal blood samples, so it is unlikely to consider
them CTC+ and the samples should be subjected to further analysis to verify CTCs, if present any . High
levels of CK19 may also be found in some patients showing CTC positivity but the marker is significan t
to use for detection of LN metastases.
In our study we found that out of 43 N0 patients 20 patients ( 46 % ) were found CTC+ (Table 13) and 5
patients ( 42% ) out of 12 N1 patients were also found CTC+ in our study. The patients found CTC+ here
can be provi ded with further investigations and treatment therapy in order to increase the survival
rate.

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4.4. Future R esearch

In the future , the mult imarker panel established in this study can be included in clinical
investigations to detect micrometastases in the pat ients offering surgery. Also some more mRNA
markers can be included in this multimarker panel. Early detection and sensitivity of the assay to
detect occult disease may help in improving the survival data of patients.
The future clinical follow up data fo r the patients included in the study should be observed to find a
clinical relevance of the findings. The markers should be quantified in more patients.

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5. Conclusion
To summarize the findings of the study we can describe the most important results bel ow:
 RT-PCR seems to have good sensitivity a nd specificity to detect micrometastases in non-
small cell lung cancer ( NSCLC ) patients.
 CK19, CEA and PVA mRNA have good prospects as in multi marker panel for detection
of tumor cells in regional lymph nodes fro m lung cancer patients. It was generally higher
level of CK19 and CEA mRNA in lymph nodes from lung cancer patients than from
normal lymph nodes .
 SFTPA and SFTPC mRNA might be good markers for the detection of lymph node
metastases in non -small cell lung c ancer (NSCLC) patients
 CEACAM mRNA is good marker in the detect ion of circulating tumor cells (CTCs ) in
the peripheral blood.
 Future c linical follow up data is important to define clinical relevance of the findings.

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6. Index of Figures and Tables
FIGURE 1 A FLOW CHART DISPLAYING THE MAIN STEPS OF ME THODOLOGY . IT DESCRIBES THE STEP WISE ILLUSTRATION OF ALL THE
METHODS PERFORMED AT VARIOUS STAGES DURIN G THE PROJECT . ………………………….. ………………………….. ……………. 17
FIGURE 2: A PICTURE SHOWING VIEW OF PLATE SET UP USED FOR Q PCR RUN ………………………….. ………………………….. ……… 22
FIGURE 3: A FLOW CHART SHOWING T HE DIFFERENT PHASES OF RESULT SECTION ………………………….. ………………………….. ….. 32
FIGURE 4: AMPLIFICATION CURVES FOR DIFFERENT PRIMER SETS OF SFTPA. IT MAY BE SEEN THAT T HERE ARE MANY UNWANT ED PRODUCTS
PRESENT IN NTC. ………………………….. ………………………….. ………………………….. ………………………….. ………….. 35
FIGURE 5: DISSOCIATION CURVES F OR DIFFERENT PRIMER SETS OF SFTPA. IT MAY BE SEEN THAT T HE UNWANTED PRODUCTS HAVE
DIFFERENT MELTING PO INTS THAN THE CORREC T SFTPA. ………………………….. ………………………….. ………………………. 36
FIGURE 6: DISSOCIATION CURVES BEFORE MGCL2 TITRATION . THERE ARE SOME UNWANT ED PRODUCTS IN THE NTC. ……………………. 38
FIGURE 7: DISSOCIATION CURVES AFTER MGCL2 TITRATION . HERE ONLY PURE PRODUC T IS AMPLIFIED …………………………… ……….. 38
FIGURE 8: AGAROSE GEL ELECTROPH ORESIS OF PCR PRODUCTS OF MARKERS . THE WELLS LABELLED AS FOLLOWS : 1: 1000 BP LADDER , 2:
EPCAM (SIZE=88 BP), 3: PVA (SIZE = 105 BP), 4: CEACAM (SIZE=151 BP), 5: SFTPC (SIZE=140 BP). ………………………….. 39
FIGURE 9: A CHROMATOGRAM SHOWING SEQUENCE INFORMATION FOR REVERSE PRIMER O F SFTPB ………………………….. ………… 39
FIGURE 10: AMPLIFICATION CURVES FOR CALIBRATOR CDNA DESCRIBES THAT THE C ALIBRATOR HAS GOOD E XPRESSION OF ALL THE
MARKERS . ………………………….. ………………………….. ………………………….. ………………………….. ………………….. 41
FIGURE 11: DISSOCIATION CURVES OF ALL THE MAR KERS IN THE CALIBRATOR C DNA. ALL THE MARKERS HAVE DIFFERENT MELTING PO INTS.
………………………….. ………………………….. ………………………….. ………………………….. ………………………….. …. 41
FIGURE 12: AN EXAMPLE FIGURE SHO WING EXPRESSION LEVE L OF SFTPB IN TUMORS , NORMAL LYMPH NODES A ND NORMAL BLOOD
RESPECTIVELY …………………………… ………………………….. ………………………….. ………………………….. ……………… 42
FIGURE 13: SPECIFICITY INDEXES FOR ALL MARKE RS ………………………….. ………………………….. ………………………….. ……….. 43
FIGURE 14: PRINCIPLE COMPONENT ANALYSIS ………………………….. ………………………….. ………………………….. …………….. 44
FIGURE 15: CK19: THE PICTURE S HOWS THAT THERE ARE MANY PATIENTS THAT H AVE EXPRESSION HIGHE R THAN CUTOFF FOR LY MPH
NODES WHERE AS VERY FEW FOR THE BLOOD . HERE SOLID LINE DEFIN ES CUTOFF FOR THE LY MPH NODES AND DASHED LINE DEFINE
CUTOFF FOR BLOOD SAM PLES. ………………………….. ………………………….. ………………………….. ………………………… 46
FIGURE 16: CEACAM5: IN THIS CASE ALSO , WE CAN SEE THAT THER E ARE SOME PATIENTS THAT HAVE M RNA EXPRESSION HIGHER TH AN
CUTOFF FOR BOTH LNS AND BLOOD , BUT AS COMPARED TO CK19 THE LN+ SEEMS FEW THAN THE F ORMER . ……………………… 46
FIGURE 17: PVA: IT DOES NOT SEEM TO S HOW SOME INTERESTING RESULTS . WE CAN SEE THAT THERE ARE VERY FEW PATIENT S THAT HAVE
MRNA EXPRESSION HIGHER TH AN CUTOFF FOR LNS AND FOR BLOOD NONE OF THE PATIENTS SEE MS CTC+. AS MENTIONED EARLIER
THAT PVA HAS ITS OWN INDEPEND ENT EXPRESSION THAN OTHER MARKERS . SO THE PICTURE ALSO E XPLAINS THE SAME . …………. 47
FIGURE 18: STRIPCHART ILLUSTRATI NG THE RELATIVE QUAN TIFICATION OF SFTPA IN THE PATIENT SAMPLES . ………………………….. .. 47
FIGURE 19: STRIPCHART SHOWING EX PRESSION OF SFTPC IN PATIENT SAMPLES . ………………………….. ………………………….. …… 48

TABLE 1: CLASSIFICATION OF BENIGN LUNG TUMORS [9] ………………………….. ………………………….. ………………………….. …… 9
TABLE 2: CLASSIFICATION OF PRIMARY MALIGNANT LUNG TUMORS ………………………….. ………………………….. …………………… 9
TABLE 3: CONCENTRATION OF REAC TION MIXTURES FOR TH E MARKERS ………………………….. ………………………….. ………………. 21
TABLE 4: VALIDATED PRIMER SETS FOR EACH MARKER ………………………….. ………………………….. ………………………….. ……. 21
TABLE 5: PCR PROGR AM ………………………….. ………………………….. ………………………….. ………………………….. ………… 22
TABLE 6: REACTION MIXTURE FOR SEQUENCING ………………………….. ………………………….. ………………………….. …………… 26
TABLE 7: THERMAL CYCLER PROGRAM ………………………….. ………………………….. ………………………….. ……………………… 26
TABLE 8: MATERIALS AND THEIR SUPPLIERS ………………………….. ………………………….. ………………………….. ………………… 30
TABLE 9: MARKER GENES AND THEI R ID………………………….. ………………………….. ………………………….. …………………….. 33

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TABLE 10: PRIMER SEQUENCES OF VARIOUS PRIMER SE TS FOR ALL MARKERS ………………………….. …………………… 36
TABLE 11: SUMMARY OF LN AND CTC STATUS ………………………….. ………………………….. ………………………….. …………….. 48
TABLE 12: ASSOCIATION AMONG LN METASTASES AN D CTC S ………………………….. ………………………….. ………………………… 48
TABLE 13: PREVALANCE OF LN+ AND CTC+ LEVEL ACCORDING TO C LINICOPATHOLOGICAL V ARIABLES ………………………….. ………… 49

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Appendix 1
Relative Quantification of Markers in Patien t LNs and their Metastases Status
ID R_CK19 R_SFTPA R_SFTPC R_CEA R_PVA LN Status
Patient 1 4.52E -06 2.99E -05 0.00025 3.15E -05 0.001211 FALSE
Patient 2 1.75E -05 3.18E -05 9.38E -05 0.000177 0.000394 TRUE
Patient 3 1.66E -06 3.43E -05 8.54E -05 1.04E -05 0.006992 FALSE
Patient 4 2.06E -05 7.90E -06 7.33E -05 7.84E -06 0.004086 TRUE
Patient 5 1.61E-06 6.88E -06 6.66E -05 0.006684 8.44E -06 TRUE
Patient 6 1.93E -06 9.72E -06 7.81E -05 9.46E -06 0.000187 FALSE
Patient 7 1.45E -05 8.38E -06 0.00016 8.32E -06 0.002631 FALSE
Patient 8 3.06E -06 1.02E -05 9.51E -05 1.02E -05 0.002175 FALSE
Patient 9 2.07E -05 2.14E-05 0.0003 7.29E -06 0.004962 TRUE
Patient 10 1.68E -05 6.80E -06 6.32E -05 6.76E -06 0.001455 FALSE
Patient 11 2.92E -06 1.39E -05 0.000243 7.55E -06 0.001816 FALSE
Patient 12 3.72E -05 1.32E -05 8.60E -05 4.30E -05 0.002455 TRUE
Patient 13 2.29E -06 2.71E -05 8.78E-05 3.46E -05 0.00096 FALSE
Patient 14 2.36E -06 1.56E -05 0.000107 8.47E -06 0.000246 FALSE
Patient 15 2.09E -05 1.16E -05 0.00314 4.66E -05 0.000236 TRUE
Patient 16 1.16E -05 1.40E -05 0.003773 0.000189 0.000268 TRUE
Patient 17 2.06E -05 1.13E -05 0.006108 4.53E -05 0.000222 TRUE
Patient 18 1.99E -05 1.11E -05 0.000138 8.12E -06 0.007417 TRUE
Patient 19 2.17E -07 1.25E -05 0.003365 4.99E -05 0.003786 TRUE
Patient 20 4.32E -06 8.98E -06 0.000112 6.55E -06 0.0041 FALSE
Patient 21 1.00E -05 1.23E -05 0.003319 4.92E -05 0.006302 TRUE
Patient 22 6.76E -06 9.59E -06 0.002586 3.84E -05 3.52E -05 FALSE
Patient 23 9.36E -06 5.48E -05 0.000135 0.000559 0.006434 TRUE
Patient 24 6.69E -06 3.42E -05 0.000138 8.12E -06 0.008609 FALSE
Patient 25 2.13E -06 9.04E -06 0.000116 5.64E -05 0.0017 91 FALSE
Patient 26 1.82E -05 1.09E -05 0.000257 7.79E -06 0.003151 TRUE
Patient 27 4.25E -07 2.14E -05 0.000132 0.000464 2.46E -05 TRUE
Patient 28 3.22E -06 1.82E -05 0.000112 0.000452 2.10E -05 TRUE
Patient 29 3.79E -07 2.22E -05 0.000424 2.55E -05 8.42E -05 FALS E
Patient 30 6.18E -06 1.08E -05 6.63E -05 5.86E -06 0.00145 FALSE
Patient 31 6.64E -06 1.36E -05 0.00012 9.72E -06 0.003594 FALSE
Patient 32 2.44E -05 2.20E -05 0.000859 1.19E -05 0.005048 TRUE
Patient 33 5.19E -06 1.42E -05 0.000128 1.63E -05 0.000277 FALSE
Patient 34 1.58E -06 0.000126 0.000383 8.49E -06 6.08E -05 FALSE
Patient 35 1.13E -05 1.33E -05 0.000136 4.67E -05 1.54E -05 FALSE

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Patient 36 2.50E -05 1.21E -05 0.000134 5.52E -05 0.003484 TRUE
Patient 37 2.41E -05 1.02E -05 0.00024 7.34E -06 0.003273 TRUE
Patient 38 1.61E -05 2.93E -05 9.19E -05 1.17E -05 0.000225 FALSE
Patient 39 7.82E -06 3.90E -05 0.000342 9.46E -06 6.73E -05 FALSE
Patient 40 2.06E -05 1.77E -05 0.000109 9.59E -06 0.005962 TRUE
Patient 41 2.44E -05 1.01E -05 8.42E -05 6.69E -06 0.003195 TRUE
Patient 42 1.93E -06 5.17E -05 0.000703 1.41E -05 0.002734 FALSE
Patient 43 1.47E -05 4.71E -05 0.000261 1.17E -05 0.015251 TRUE
Patient 44 3.19E -06 6.23E -05 0.000248 2.01E -05 0.007573 FALSE
Patient 45 3.01E -06 6.06E -05 0.000196 0.011438 3.67E -05 TRUE
Patient 46 1.74E -06 5.58E-05 0.000171 1.48E -05 0.004826 FALSE
Patient 47 2.46E -06 2.88E -05 0.003721 1.91E -05 9.35E -05 TRUE
Patient 48 1.31E -05 8.95E -06 0.000213 4.82E -05 0.006237 FALSE
Patient 49 1.23E -06 1.04E -05 0.000264 2.03E -05 0.003893 FALSE
Patient 50 1.26E -06 1.21E -05 0.000107 8.70E -06 0.009855 FALSE
Patient 51 2.98E -05 7.98E -06 6.68E -05 5.30E -06 0.000187 TRUE
Patient 52 3.75E -06 2.94E -05 0.000181 0.045753 3.39E -05 TRUE
Patient 53 1.22E -05 3.93E -05 0.000457 3.02E -05 0.000531 FALSE
Patient 54 1.13E -05 1.31E -05 0.000 37 7.07E -06 0.003033 FALSE
Patient 55 1.68E -05 1.04E -05 8.72E -05 6.92E -06 0.002447 FALSE

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Appendix 2

Relative Quantification of Markers in Patient Blood and their CTC Status
ID R_CK19
R_SFTPA
R_SFTPC R_CEA R_PVA CTC status
Patient 1 0.006968 2.12138 81.8551 0.009131 0.001275 TRUE
Patient 2 0.007264 0.021493 0.646176 4.58E -05 0.000119 TRUE
Patient 3 0.000294 0.003331 0.011883 8.19E -05 9.00E -05 TRUE
Patient 4 0.000219 0.00099 0.019984 1.09E -05 0.04 4041 TRUE
Patient 5 0.0002 0.037292 0.732043 7.81E -05 0.002715 FALSE
Patient 6 0.000162 0.002677 0.089003 0.000108 0.000313 FALSE
Patient 7 0.000139 0.01243 0.534033 0.000124 0.000439 FALSE
Patient 8 9.64E -05 0.011842 0.179244 0.000146 6.82E -05 FALSE
Patient 9 0.000151 0.001145 0.040667 4.96E -05 6.12E -05 FALSE
Patient 10 0.000222 0.002781 0.010132 1.01E -05 0.012048 TRUE
Patient 11 7.65E -05 0.004158 0.068157 6.20E -06 3.20E -05 FALSE
Patient 12 0.000422 0.0041 0.06983 5.01E -05 6.23E -05 TRUE
Patient 13 0.000176 0.00285 0.006615 1.02E -05 0.004645 FALSE
Patient 14 0.00066 0.002036 0.023931 5.17E -05 6.82E -05 TRUE
Patient 15 0.005136 0.986233 0.768438 0.030927 0.00072 TRUE
Patient 16 0.000101 0.000427 0.016289 5.51E -06 2.84E -05 FALSE
Patient 17 0.009099 8.9383 0.699793 0.25 5.68E -05 TRUE
Patient 18 9.95E -05 0.00291 0.00013 1.19E -05 0.00403 FALSE
Patient 19 0.000218 0.001683 0.00173 2.19E -05 0.000691 FALSE
Patient 20 0.000316 0.024349 0.05672 0.000199 1.42E -05 TRUE
Patient 21 9.78E -05 5.66E -05 0.00011 6 3.97E -05 1.31E -05 FALSE
Patient 22 0.000366 0.003377 0.114229 3.23E -05 0.00011 TRUE
Patient 23 0.000571 0.002991 0.054598 0.008088 0.001141 TRUE
Patient 24 0.000137 0.002086 0.055169 0.000279 3.30E -05 FALSE
Patient 25 0.205898 0.949342 6.02099 0.0137 92 0.013936 TRUE
Patient 26 3.62E -05 0.005172 0.00982 0.000128 0.000774 FALSE
Patient 27 0.002489 0.077482 0.015357 0.032577 0.014478 TRUE
Patient 28 0.003879 0.327598 0.063373 0.002696 0.000457 TRUE
Patient 29 0.000531 0.01176 0.157127 0.000353 5.79E -05 TRUE
Patient 30 0.000921 0.008004 0.11344 0.001665 0.100481 TRUE
Patient 31 0.000166 0.000235 0.066064 4.81E -05 0.004245 FALSE
Patient 32 0.000153 0.001748 0.008461 1.51E -05 0.001316 FALSE
Patient 33 0.002481 0.048529 0.986233 0.000262 0.000833 TRUE

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Patient 34 0.00021 0.00222 0.034435 6.52E -05 0.000421 FALSE
Patient 35 0.001946 0.989657 25.5455 5.48E -05 0.002057 TRUE
Patient 36 0.000132 0.047696 0.012824 6.59E -05 0.001103 FALSE
Patient 37 0.537747 0.041955 0.042837 0.547147 0.000492 TRUE
Patient 38 0.000213 0.020546 0.156583 8.51E -05 0.001007 FALSE
Patient 39 0.000415 0.045594 0.49827 0.00141 0.006258 TRUE
Patient 40 0.00124 0.78187 6.7039 8.38E -06 0.012913 TRUE
Patient 41 8.08E -05 0.006684 0.052374 2.60E -05 0.000117 FALSE
Patient 42 0.013462 0.089003 0.427798 0.008116 0.000141 TRUE
Patient 43 0.000406 0.016688 0.319746 0.011478 0.000152 TRUE
Patient 44 0.000143 0.006434 0.046714 1.14E -05 0.009992 FALSE
Patient 45 0.000182 0.000865 0.051296 1.11E -05 0.005355 FALSE
Patient 46 0.007732 11.23 56 548.748 0.001791 0.000108 TRUE
Patient 47 0.000152 0.001258 0.023601 0.00441 0.00181 FALSE
Patient 48 0.000156 0.014378 0.130308 0.015303 0.02105 TRUE
Patient 49 0.000653 0.028262 0.99654 0.004843 0.000927 TRUE
Patient 50 0.000397 0.003734 0.076151 0.000274 9.48E -05 TRUE
Patient 51 0.000248 0.002072 0.054034 5.35E -05 0.000242 FALSE
Patient 52 0.630689 18.8959 24.847 0.035036 0.026553 TRUE
Patient 53 0.000656 0.005759 0.126745 0.00291 0.000356 TRUE
Patient 54 0.001182 0.00062 0.007017 0.000683 0.000131 TRUE
Patient 55 6.59E -05 0.000824 0.025471 0.00017 0.001157 FALSE