Bernhard Mlecnik - Bioinformatics Graz · das Immunsystem eine wichtige Rolle im Erkennen und...

Bernhard Mlecnik

Database for cancer immunology

Master Thesis

1Institute for Biomedical Engineering, Graz University of

Technology, Graz, Austria

2Institut National de la Sante et de la Recherche Medical

Unite 255, Centre de Recherches Biomedicales des

Cordeliers, Paris, France

Supervisors: Dipl.-Ing. Robert Molidor1, Dr. Jerome Galon2, Ao.Univ.-Prof.

Dipl.-Ing. Dr.techn. Zlatko Trajanoski1

Evaluator: Ao.Univ.-Prof. Dipl.-Ing. Dr.techn. Zlatko Trajanoski1

Head of Institute: Univ.-Prof. Dipl.-Ing. Dr.techn. Gert Pfurtscheller1

Graz, February 2003

For my parents

Fur meine Eltern

Abstract

Abstract

Cancer is a worldwide public health problem. Each year, 6 million people die from cancer and 8,1

million new cases are diagnosed. In twenty years from now, the cancer burden will exceed 50% due to

the ageing of the population and their increasing exposure to risk factors. It is proven that the immune

system plays a major role in recognising and destroying tumour cells, and it is possible that it may induce

immunological responses, which may have therapeutical benefits against certain tumours.

The broad, long-term objective of the functional genomic studies in this thesis is to identify molecular

signatures and pathways in T-cells surrounding cancer. The specific aim of this thesis was to develop a

tumoral microenvironment (TME) database for storing and maintaining all the data which are arising

from different immunological experiments.

The data were obtained from cancer patients as well as from healthy donors. The used software tech-

nology was based on the newest Java-Client-Server technologies and applied Java Database Connectivity

(JDBC), Java Server Pages (JSP) and Enterprise Java Beans (EJB). The collected FACS (fluorescence ac-

tivated cell sorter) data was clustered using hierarchical clustering algorithm. The results demonstrated

that immunophenotypic and functional data can be used to group patients and controls into distinct

groups.

In future work, immunophenotypic and functional data will be integrated with microarray data in

order to explore new relations between expression patterns and cell surface markers.

Keywords: Cancer, Tumoral Microenvironment, T-Cells, Databases, Bioinformatics

i

Abstract

Kurzfassung

Krebs hat sich langst zu einem weltweiten Gesundheitsproblem entwickelt. Jahrlich sterben 6 Mil-

lionen Menschen an den Folgen einer Krebserkrankung und 8,1 Millionen neue Falle werden diagnos-

tiziert. In den kommenden zwanzig Jahren soll die Krebsrate um 50% steigen. Es ist bewiesen, dass

das Immunsystem eine wichtige Rolle im Erkennen und Zerstoren von Krebszellen einnimmt, wobei es

immunologische Reaktionen hervorrufen konnte, die therapeutisch gegen gewisse Krebsarten einsetzbar

waren.

Das Ziel langfristiger funktioneller genomischer Studien in dieser Diplomarbeit soll neue moleku-

lare Signaturen in T-Zellen aufdecken, die sich in unmittelbarer Umgebung eines Tumors befinden. Das

Ziel dieser Arbeit war es eine Datenbank zu entwickeln, die phenotypische wie funktionelle immunolo-

gische Daten speichern und verwalten soll, die wahrend verschiedner Experimente aufkamen, bzw. noch

aufkommen werden.

Die Softwaretechnologie zur Realisierung dieser Diplomarbeit basiert auf der neuesten Java-Client-

Server Technologie, unter Verwendung von Java Server Pages (JSP), Java Database Connectivity (JDBC)

und Enterprise Java Bean (EJB). Die gespeicherten FACS (fluorescence activated cell sorter) Daten wur-

den vereint und mit hierarchischen Cluster-Algorithmen geclustert. Es konnte gezeigt werden, dass

immunophenotypische und funktionelle Daten von Patienten und Kontrollpersonen verwendet werden

konnen, um sie in verschiedene Gruppen zu unterteilen.

In Zukunft sollen auch Microarray-Experimente mit den immunologischen Daten zusammengefuhrt

werden, um neue Zusammenhange zwischen intrazellularen Expressionsmustern und Oberflachenmark-

ern zu erforschen.

Schlusselworter: Krebs, Tumoral Micro Environment T-Zellen, Datenbanken, Bioinformatik

ii

Contents

Glossary viii

1 Introduction 1

1.1 Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

1.2 Tumoral microenvironment . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

1.3 Immunity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3.1 Innate and adaptive immunity . . . . . . . . . . . . . . . . . . . . . .4

1.3.2 B cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.3 T cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.3.4 Cluster Designation (CD) . . . . . . . . . . . . . . . . . . . . . . . .5

1.3.5 Cytokines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4 Tumour immunology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4.1 Immune surveillance . . . . . . . . . . . . . . . . . . . . . . . . . . .7

1.4.2 Tumour antigens . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

1.4.3 Immunotherapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

2 Objectives 10

2.1 Database development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

2.2 Application server deployment . . . . . . . . . . . . . . . . . . . . . . . . . .11

2.3 Web application development . . . . . . . . . . . . . . . . . . . . . . . . . . .11

3 Methods 12

iii

CONTENTS CONTENTS

3.1 Fluorescent-activated cell sorter (FACS) . . . . . . . . . . . . . . . . . . . . .12

3.1.1 FACS analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

3.1.2 Sample treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

3.1.3 Phenotype analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

3.1.4 Proliferation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .17

3.1.5 Cytokine secretion analysis . . . . . . . . . . . . . . . . . . . . . . . .18

3.2 Database development (Enterprise Information System (EIS)-Tier) . . . . . . .19

3.2.1 Relational databases . . . . . . . . . . . . . . . . . . . . . . . . . . .19

3.2.1.1 Normalisation . . . . . . . . . . . . . . . . . . . . . . . . .20

3.2.1.2 Integrity rules . . . . . . . . . . . . . . . . . . . . . . . . .20

3.2.2 Structured Query Language (SQL) . . . . . . . . . . . . . . . . . . . .21

3.2.2.1 Data Definition Language (DDL) . . . . . . . . . . . . . . .21

3.2.2.2 Data Manipulation Language (DML) . . . . . . . . . . . . .22

3.2.3 Java Database Connectivity (JDBC) . . . . . . . . . . . . . . . . . . .22

3.2.3.1 Two-tier and Three-tier Models . . . . . . . . . . . . . . . .23

3.3 Application server deployment (Middle-Tier) . . . . . . . . . . . . . . . . . .24

3.3.1 Enterprise Java Beans 2 (EJB2) . . . . . . . . . . . . . . . . . . . . .24

3.3.2 The Java 2 Enterprise Edition (J2EE) server . . . . . . . . . . . . . . .26

3.3.3 Java Cryptography Extension (JCE) . . . . . . . . . . . . . . . . . . .27

3.3.4 Extensible Markup Language (XML) . . . . . . . . . . . . . . . . . .27

3.3.5 JDOM (Java Document Object Model) . . . . . . . . . . . . . . . . .28

3.4 Web application development (WEB-Tier) . . . . . . . . . . . . . . . . . . . .28

3.4.1 Java Server Page (JSP) . . . . . . . . . . . . . . . . . . . . . . . . . .28

3.4.2 JSP Tag libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29

3.4.3 The Jakarta Struts Framework . . . . . . . . . . . . . . . . . . . . . .31

3.4.4 Struts Application Flow . . . . . . . . . . . . . . . . . . . . . . . . .31

4 Results 34

4.1 The Database Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

iv

CONTENTS CONTENTS

4.1.1 Patient and Experiment Table . . . . . . . . . . . . . . . . . . . . . .35

4.1.2 User Management Tables . . . . . . . . . . . . . . . . . . . . . . . . .35

4.1.3 Experiment Related Tables . . . . . . . . . . . . . . . . . . . . . . . .35

4.1.4 Application server connection . . . . . . . . . . . . . . . . . . . . . .38

4.2 The Web Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38

4.2.1 The TME.db Web Page . . . . . . . . . . . . . . . . . . . . . . . . . .39

4.2.2 User Management . . . . . . . . . . . . . . . . . . . . . . . . . . . .40

4.2.3 The Patient Overview . . . . . . . . . . . . . . . . . . . . . . . . . . .41

4.2.3.1 Cancer Information . . . . . . . . . . . . . . . . . . . . . .42

4.2.3.2 Biological Markers . . . . . . . . . . . . . . . . . . . . . .43

4.2.3.3 Treatments . . . . . . . . . . . . . . . . . . . . . . . . . . .43

4.2.3.4 FACS Phenotype Assays . . . . . . . . . . . . . . . . . . .44

4.2.3.5 FACS Proliferation Assays . . . . . . . . . . . . . . . . . .45

4.2.3.6 FACS Cytokine Secretion Assays . . . . . . . . . . . . . . .49

4.2.4 Basic Queries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49

4.2.5 Custom Queries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50

4.2.5.1 Building A Custom Query . . . . . . . . . . . . . . . . . . .52

4.2.5.2 Clustering the FACS data . . . . . . . . . . . . . . . . . . .55

5 Discussion 59

Bibliography 62

A Database Model 65

B Cluster Designation List 67

v

List of Figures

1.1 Tumour micro ecology [14] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 The principal components of the immune system [27] . . . . . . . . . . . . . .3

1.3 T cell encounters an APC [27] . . . . . . . . . . . . . . . . . . . . . . . . . .5

3.1 Left: FACS with two dye channels FL1-H and FL3-H. Right: Scatter plot with

histogram [27] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

3.2 Phenotype scatter plots. Day 0, cells withoutCD25+ markers. Right: Day 5

after stimulation, cells with an increased amount ofCD25+ markers . . . . . . 16

3.3 Proliferation scatter plots. Left: Day 0, cells stained with CFSE. Right: Day 5

after stimulation with Il-10 and IL-15. . . . . . . . . . . . . . . . . . . . . . .17

3.4 Cytokine secreting cell [20] . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

3.5 Multi Tier Model [11] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.6 EJB Model [1] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

3.7 JSP MVC model [23] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29

3.8 Process flow for displaying a JSP page within a Struts Project [5] . . . . . . . .32

4.1 TME Web Page Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

4.2 Add User Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40

4.3 Patient Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

4.4 Phenotype Assay Overview with scatter plot. . . . . . . . . . . . . . . . . . .44

4.5 Proliferation Assay Overview with: FSC-SSC-plot, scatter plot and histogram

plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46

vi

LIST OF FIGURES LIST OF FIGURES

4.6 Basic Query Page with certain selected Gates and Antigens . . . . . . . . . . .49

4.7 Custom List Section with selected experiment tab . . . . . . . . . . . . . . . .50

4.8 Custom Query Section with selected cancer tab . . . . . . . . . . . . . . . . .51

4.9 Custom Savings Section with saved query . . . . . . . . . . . . . . . . . . . .52

4.10 Custom Query Listings page with specified selections . . . . . . . . . . . . . .53

4.11 Patient FACS experiment list . . . . . . . . . . . . . . . . . . . . . . . . . . .54

4.12 Custom Query pages with specified selections . . . . . . . . . . . . . . . . . .54

4.13 File download . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .54

4.14 Hierarchical Cluster result of the FACS data . . . . . . . . . . . . . . . . . . .55

4.15 3D plot of the clusters within the 3 most influential Principle Components . . .57

vii

Glossary

APCs Antigen presenting cells, have the ability to present antigen particles bound to specific

receptors on their surface.

CDxx Cluster of Designation, terms lymphoid surface antigens.

CFSE Carboxy-fluorescein diacetate, succinimidyl ester, used for intracellular staining of

cells.

CSFs Colony-stimulating factors, have influence in controlling and directing the division and

differentiation of bone-marrow stem cells.

DDL Data Definition Language, is a sub section of SQL allowing the creation and deletion of

tables in the database.

DML Data Manipulation Language, is a sub section of SQL include the syntax for complex

queries as well as for updates, insertions and deletions of data records.

EJB Enterprise Java Bean, a business data object specification developed in java technology.

ELISA special kit for detecting particular cytokines secreted by APCs or other cytokine se-

creting cells.

viii

Glossary

FACS Fluorescent-activated cell sorter, designed for an automatic separation and analysis of

fluorescently stained cells.

Ficoll is used to separate immune cells from the blood.

FK Foreign Key, is a PK of a foreign data table.

HCL Hierarchical Clustering, cluster algorithm for creating a relational data tree (dendro-

gram).

IFNs Interferons, are mainly involved into the cell’s prevention of certain viral infections.

IgG Immune globulin G, soluble antibody secreted by B cells.

ILs Interleukins, cytokines mainly produced by T cells.

JCE Java Cryptography Extension, is a non-commercial cryptography extension for Java con-

taining a package which provides implementations for encryption.

JDBC Java Database Connectivity, Java software tool for accessing databases.

JSPs Java Server Pages, have been developed to provide an easy and intuitive way in creating

dynamical generated HTML pages.

MFI Mean fluorescent intensity, is measured within FACS experiments on stained antigens

bound to cell surfaces.

MHC Major Histocompatibility Complex, antigen presenting receptor.

NK Natural Killer cells, are a group of lymphocytes which have intrinsic ability to recognise

and destroy some virally infected cells.

ix

Glossary

PCA Principle Component Analysis, determines basis functions of a similarity matirx.

PHA Phytohemagglutin, mitogen for activating T cell receptors.

PK Primary Key, defines the uniqueness of each data record.

SQL Structured Query Language, non procedural language for accessing relational databases.

TCR T cell receptor, connects with the antigen presenting MHC receptor.

TGFs Transforming growth factors, have a partial effect in mediating inflammation reactions.

TME Tumoral microenvironment, comprises all the biomolecules and cells which surround a

tumour.

TNFs Tumour necrosis factors, have a partial effect in cytotoxic reactions against tumour

cells.

x

Chapter 1

Introduction

Cancer is a public health problem worldwide. Each year, 6 million people die from cancer

and 8.1 million new cases are diagnosed. The growth rate of cancer is now 2.1% per year,

a rate that exceeds the growth rate of the world’s population at 1.7 % per year. The leading

causes of worldwide cancer deaths are lung cancer, which accounts for over 900,000 deaths,

gastric cancer (600,000 deaths) and colorectal cancer (400,000 deaths) [3]. The occurring cases

of different types of cancer differ between developed and developing countries, whereby more

than 55% of the detected deaths occur in developing countries. The most common cancers in

developed countries are lung, stomach, breast, and colorectal cancer, whereas in developing

countries lung, stomach, breast, cervical, and oesophageal cancer accounts for the main part of

the occurring cases. The average 5-year survival is as low as 8 % in Europe and 14% in the

United States [3].

1.1 Cancer

The main indicator of cancer is the uncontrolled growth and dispersion of cells as a result of

abnormal changes to the genetic material contained in those cells. A single cell or group of cells

can undergo genetic events such as mutations, influenced by inherited or environmental factors

as well as a result of certain levels of hormones or growth factors, which may change the cells’

behaviours. These events, which may take years to arise, cause the cells to proceed down the

1

Introduction 1.2 Tumoral microenvironment

pathway to the development of cancer [2].

If cells divide abnormal in an early stage of development, they may evolve into a cell pop-

ulation that could be immortalized and which may lose the control mechanisms of normal cell

division, activity and interactions with neighbouring cells. Such immortalized cell populations

may evolve into malignant tumour cell populations, whose behaviour may violate the tissue

environment.

Once certain cell populations became malignant they may form solid tumours which invade

and destroy sane tissues as well as they may metastasize (spread) all over the body by releasing

tumour cells into the blood and lymph system, where they may continue to grow and develop

by forming new cancers [2].

1.2 Tumoral microenvironment

The tumoral microenvironment comprises all the

Figure 1.1: Tumour micro ecology

[14]

biomolecules and cells which surround the tumour and

have a major influence to its development and behaviour.

During the whole tumour aetiology, progression and

final metastases the tumour microenvironment of the

local host tissue may represent an active participant

in tumour-host interaction. Throughout all the cancer

stages the tumour-host interaction is accompanied by

enzyme and cytokine exchange of cancer and stromal

cells that change and modify the extra cellular matrix as well as survival and proliferation [14].

1.3 Immunity

All living species are threatened by other organisms constantly. This is the reason why many

species have tried to develop protections and defensive mechanisms against foreign aggressors

and intruders. Vertebrates have developed an own and very complex defensive mechanism

against intruding microorganisms, which is called the immune system. The meaning of the word

2

Introduction 1.3 Immunity

immunity derives from the Latin word immunis (unhurt, protected) and describes the protection

and immunity against particular infectious agents. During the encounter with foreign micro

organisms the immune system runs through a learn process whereby the recognition of the

infectious agents is a crucial step in immune defence. The most important task of the immune

system is to distinguish between own and foreign bio molecules to make sure that only foreign

intruders are attacked and destroyed [15].

The immune cells (leukocytes) are distinguished into three major subgroups (Fig.1.2):

• Lymphocytes: These kind of immune cells (B-, T-Cells) induces adaptive immune re-

sponses (adaptive immunity) and create specific memory cells to prevent further encoun-

ters with pathogens.

• Phagocytes: The main function of these cells (mononuclear phagocytes, neutrophils,

eosinophils) is the unspecific destruction (innate immunity) of foreign pathogens by en-

gulfing or releasing lytic granules to dissolve them.

• Auxiliary Cells: These cells (basophils, mast cells) are mainly involved in inducing in-

flammatory reactions which support and accelerate curing processes.

Figure 1.2: The principal components of the immune system [27]

3


1.3.1 Innate and adaptive immunity

Any immune response firstly recognises the pathogen or foreign material and eliminates it af-

terwards. There are different immune responses which fall into two categories: innate (or non-

adaptive) immune responses and adaptive immune responses. The main difference between

these two is that an adaptive immune response is highly specific against a particular pathogen.

The innate immune response does not alter during repeated encounters with infectious agents,

whereby the adaptive immune response improves with each successive encounter with the same

pathogen: in effect the adaptive immune response ’remembers’ the infectious agent and may

prevent it from causing a disease later [27]. The main tasks of the innate immune system are

non-specific recognition and digestion of foreign intruders and therefore it is also called the

first line of defence against infection. The major participants in this kind of immunity are the

phagocytic cells (Fig.1.2), the monocytes as well as the macrophages [27].

The strength of the adaptive immunity is the specific recognition of particular pathogens in

the host’s tissues and body fluids. Lymphocytes, which are distinguished into two major sub

groups, the B and the T lymphocytes (Fig.1.2), support the cells’ acting within the adaptive

immunity [27].

1.3.2 B cells

Every B cell bears a unique genetic information in its genome to encode its own very specific

surface marker which may only bind to one particular antigen. Once a B cell encounters a

specific antigen, fitting to its receptors, internal pathways are activated and the cell starts to

proliferate and differentiates itself into a plasma cell. Differentiated plasma cells raise a large

amount of soluble antibodies which are secreted afterwards. These antibodies are large glyco-

proteins situated in the blood which bind to the same type of antigen which initially encounters

the B cell’s receptors. So the antigen which has evoked an immunological response is covered

with antibodies all over, which may bind to the surface of a phagocyte that may engulf the

antigen and destroy it later [27].

4


1.3.3 T cells

Both B and T cells have the same precursors, the bone-marrow stem cells that are situated in

the cavities of the large bones. It is crucial that all specific immune cells are tested against auto-

immunity to prevent the body’s own proteins from being attacked. Mature lymphocytes which

show auto-immunity are detected and destroyed before they can enter the lymphatic system. T

cells migrate to the thymus where they mature and on passing the auto-immunity check (nega-

tive selection) they are released to the lymph system. Otherwise the cells are destroyed.

T cells are characteristic for detecting and bind-

Figure 1.3: T cell encounters an APC

[27]

ing unexceptionally to antigen presenting cells (APCs)

which have the ability to present antigen particles bound

to specific receptors on their surface. Because of their

far reaching genetic invariance these kinds of antigen

representing receptors are termed the major histocom-

patibity complex (MHC). The T cells in turn possess the T cell receptor (TCR) which may

connect with the antigen presenting MHC receptor (Fig.1.3). The T cells are distinguished into

two categories, the cytotoxic T cells (TC), which kill the APCs in case of an encounter, and

the T helper cells (TH) which initiate a secretion of soluble proteins called cytokines to induce

several different cell events. The cytotoxic cells use in addition to the TCR theCD8 (Cluster

Designation) receptor to detect the MHC of type I, whereas the T helper cells use theCD4 re-

ceptor to detect the MHC of type II. These complex recognition qualities are important security

mechanisms to make sure that there may not be any confusion in these complicated interactions

of cells [27].

1.3.4 Cluster Designation (CD)

Researchers in many scattered laboratories identified many of new lymphoid surface antigens

and termed them with self defined names. It became apparent that there were a vast amount

5


of various called antigens which seemed to be identically. Due to these confusions the cluster

designation (CD) system has been developed over the last few years. Now new investigated

antigens at first have to be termed ’CDw’ whereby ’w’ indicates the not yet being confirmed

label and within some years the label is changed to a true CD designation confirmed by an

international committee [6] [21]. A list of CD labels used in this thesis is given in appendix B.

1.3.5 Cytokines

All cells participating within an immune response, communicate among themselves by secret-

ing soluble molecules called cytokines. These molecules pertain to a large group of different

proteins which fall into several categories described below [27].

Interleukins (ILs)are mainly produced by T cells (IL − 1 to IL − 22), but there are also

many other kinds of cells which have the ability to secrete interleukins like some phagocytes

or tissue cells. They induce manifold cell activities, but their major function appears to be the

direction of other cells’ division and differentiation [27].

Interferons (IFNs)are mainly involved into the cell’s prevention of certain viral infections

whereby IFNα and/or IFNβ are produced and released during a cell’s infection. Certain acti-

vated T cells may also release another type of interferon, the INFγ [27].

Chemokinesdirect the cells’ movement around the body which goes from the blood stream

into the tissues and further on to appropriate locations within each tissue. They induce cells to

cross tissue boundaries but they also may have some certain functions in activating cells [27]

(Chemokines may be responsible for spreading and metastasis of cancer cells).

Colony-stimulating factors (CSFs)may have influence in controlling and directing the di-

vision and differentiation of bone-marrow stem cells. Blood leukocytes and their production

mostly depend on the CSFs’ balance too [27].

6

Introduction 1.4 Tumour immunology

Other cytokineslike the tumour necrosis factors (TNFs) and transforming growth factors

(TGFs) have a partial effect in mediating inflammation and cytotoxic reactions [27].

All these protein molecules are secreted by the white blood cells and act like hormones by

having a major influence in cells’ interactions and behaviour. Although a lot of cytokines are

already known there remains a vast of unidentified functions they may induce.

1.4 Tumour immunology

It is well known that tumours may induce immunological responses. In the early19th century

Paul Ehrlich was one of the first who suggested that tumours might be regarded as similar to

grafted tissue which may be rejected by the host, if causing an immunological response. A first

approach revealed a regression of grafted tumour tissue in the host, but it came in discredit as

it was apparent that the regression was caused by the foreignness of the MHC receptors (every

individuum bears its own kind of MHC receptors). Hence it was established that tumours might

be rejected in presence of antigenic disparity between tumour and host. Later, when inbred

rodents became available, studies on animals bearing a tumour expressing identical MHC anti-

gens were accomplished and the term immune surveillance became more and more important

[27].

1.4.1 Immune surveillance

At first it was suggested that the immune system surveys and recognises abnormal cells in a

very early stage in order to destroy them. There were proposals that the immune system plays

a major part in delaying growth and causing regression of already established tumours. Some

evidences which arose are outlined below:

• Spontaneous regression of tumours occurred.

7


• Postmortem data suggested that there may be more tumours than became clinically ap-

parent.

• Tumours arose frequently in immunosuppressed or immunodeficient individuals.

• Many tumours contained lymphoid infiltrates which may have been a favourable sign.

Despite these impressive evidences there was no proved correlation between immunosup-

pression and an increased tumour incidence. It rather seemed that the immune surveillance

acted against viruses which also might have caused tumoral spreading [27].

1.4.2 Tumour antigens

Abundant evidences have shown that almost all tumours indicate genetic alterations which lead

to the expression of mutated and sometimes overexpressed molecules. Tumour antigens were

first demonstrated by transplantation tests. When a tumour was grafted to an animal previously

immunized with inactivated cells of the same tumour, resistance to the graft was seen. There

are two known groups of tumour antigens:

• Tumour-specific antigenscaused by genetic mutations in tumour genes.

• Shared tumour antigensfound on several tumours induced by viruses.

It is important to explore a variety of new means which take the fact into account that the

immune system possesses the ability of recognising specific antigens on cells surfaces. This

specificity is of greatest importance in applying immunotherapies with T cells [27].

1.4.3 Immunotherapy

There is a long history behind immunotherapy but only during the last recent years it gained

more and more importance and is now established as a new reliable form of therapy for some

kinds of tumours. There are several forms of interventions like active or passive, specific or

non-specific treatments, but all of these remain currently in an experimental stage. Some of the

latest established therapies are listed below:

8


• Immunization with tumour antigens

• Immunotherapy with cytokine can cause tumour regression

• Immunization against oncogenic viruses

• Therapy with lymphokine-activated killer cells

• Immunotherapy with T cells

• Therapy with antibodies

As already mentioned all these therapies may only act against some particular forms of can-

cer and are therefore very restricted in their appliance. But nonetheless there are a lot of studies

engaged with the development of new means pertaining to this kind of therapeutic treatments

[27].

9

Chapter 2

Objectives

The aim of this thesis is to develop a database for different immunological data and make it

accessible via a web interface. The database should be maintainable by an administrator or

different users with particular access admissions. A basic demand was an encryption of data

pertaining to patient and user specific information. Different upload routines and input masks,

which should be accessible via a web browser, have to be written in order to fill the database

with the required data.

Several query algorithms have to be established with which the requested information should

be aligned in an appropriate way for the clustering software.

Finally the database’s re-obtained immunological data should be brought into a clusterable

form in order to apply the different cluster algorithms to observe different expression patterns

in the same way, as it is already used for microarray data [29]. The received data should contain

all the different experiment and patient information for each patient, all aligned in a matrix

which can be clustered with particular algorithms. Some cluster results should be shown to

demonstrate the functionality and necessity of this project.

The major goals of this project will be separated into three main parts:

10

Objectives 2.1 Database development

2.1 Database development

The first part is to develop and design a database for immunological data which will arise

within several different FACS analyses of different kinds of sample material (lung cancer tissue,

pleural liquid, etc.) from patients and healthy donors. The database should be flexible and easily

extendable to ensure the possibility of adding new tables and relations.

2.2 Application server deployment

For the second part the business logic should be programmed and deployed to an application

server to map the entity relations of the database in an appropriate and easy accessible way for

other client programs. The application server should bear the business logic and separate and

hide the data management layer from the accessing client layer. The data management should

contain the up- and download routines as well as the data maintaining methods.

2.3 Web application development

To make the data accessible for clients the third part should be the development of a web applica-

tion that communicates with the application server which processes the data from the database.

By using a browser the web server can be accessed by an appropriate web address. The server

creates and returns html pages which contain the requested information for the user.

11

Chapter 3

Methods

This chapter will give a brief survey of the FACS technology in respect to phenotypic and

functional analyses of immune cells. Further it will give a survey of current Java technologies

with regard to establishing server side applications with database connections. First it shows

the data storing layer, next it leads through the business logic up to the web server application

layer and finally faces the user’s web interface.

3.1 Fluorescent-activated cell sorter (FACS)

Flow cytometry is a powerful means in modern biology and has already gained a key position

in immunology and cell biology. It can be used to separate various kinds of cells using different

stainings of their diverse surface markers. It also allows examining the number of cell cycles

with intracellular immunofluorescence. The FACS was designed for an automatic separation

and analysis of fluorescently stained cells. The diluted cells pass through a thin vibrating nozzle

forming very small droplets which contain just one cell at a time. These cells are detected one by

one passing a laser beam which measures the wavelength and the intensity of their fluorescence

at a time. Dependent on this information the type and the size of one single cell can be examined

and displayed in an appropriate scatter plot (Fig.3.1). Newer FACS equipments may recognize

more than two different fluorescence colours at one time, thus it is possible to mark the cells’

surface antigens with several fluorescently stained antibodies to isolate different cell populations

12

Methods 3.1 Fluorescent-activated cell sorter (FACS)

[27] [10].

Figure 3.1: Left: FACS with two dye channels FL1-H and FL3-H. Right: Scatter plot withhistogram [27]

Every dot in a two dimensional scatter plot indicates a particular fluorescently marked cell

whereby the two detected fluorescent intensities (FI) are displayed in a logarithmic scale on each

axis. The mean fluorescent intensity (MFI) of one dye channel is the fluorescence intensity of

one species of equal stained antibodies adhered to the cell surface. The histogram plot indicates

the amount of cells with a certain fluorescent intensity.

3.1.1 FACS analyses

For this thesis various samples of patients and healthy donors were available. The samples were

obtained from several volunteers of different hospitals. Thus currently received samples are

listed in Table 3.1.

The used FACS (FACSCALIBUR from BECTON DICKINSON) was equipped with two

lasers allowing the recognition of four colour staining at one time. Hence for all FACS experi-

ments four different surface markers can be applied to examine four different surface antigens

simultaneously.

13


Donor Sample material Primitive cancer Cancer type Cancer subtype

Healthy blood, pleural liq-uid, purified Tcells

- - -

Patient tumour, lymphn-ode, pleural liq-uid, blood, not tu-moral biopsy

lung, breast,colon

KBP, MesM, K2 ADK, Kepi, K1P,NOS, Infl., Sark.

Table 3.1: Sample material list

3.1.2 Sample treatments

To prepare the different samples for the FACS analyses, several material treatments have to be

done, in order to extract the cells of interest.

Dilacerations are done to break up solid sample materials to bring them into an utilisable

form for further processing.

Ficoll is used on all sample materials to separate the immunologic cells of interest (leuko-

cytes) from the remaining blood compartment (red blood cells, dead cells, necrotic cells etc.).

The achieved leukocytes are purified, diluted and mixed with different fluorescent stained anti-

bodies later on to prepare it for the different FACS analyses.

Proliferation experiments are performed before and/or after T cell purification. The cells are

activatedandstimulated with different stimulus (cytokines, antibodies, mitogens etc.) and the

following proliferation was analysed by the FACS.

Purification of cell mixture with the monoclonal antibodies has to be done in order to isolate

T cells for proliferation assays and RNA extraction. There are several purification kits available

dependent on ensuing analyses and starting material. If there is a sufficient amount of cells

RNA extractions may be done for subsequent microarray experiments.

14


3.1.3 Phenotype analysis

A phenotypeis a physical manifestation of internally coded, inheritable information of ageno-

typewhich encodes and maintains the cells entire behaviours and structures [4]. Hence a phe-

notype analysis tries to examine different cell populations within a multicomponent mixture of

cells. The only way in doing this is to use different signs which are common within a partic-

ular cell subpopulation. In immunology the easiest way to distinguish between different cell

populations is to use monoclonal antibodies against the multiplicity of a cell’s surface markers

which are specific for a certain population.

The phenotype analysis starts with the proportioning of the prepared cell dilution into sev-

eral tubes. Each tube’s cell mixture is stained by using four different species of antibodies

marked with different fluorescent dyes. The fluorescent antibodies bind to their specific antigen

receptors and in subsequent FACS analyses they reveal the cells’ characteristics.

Tube FL1H FL2H FL3H FL4H

1 IgG1 IgG1 IgG1 IgG12 CD19 CD56 CD3 CD143 CD4 CD103 CD3 CD694 CD1a CD83 CD45 CD14

Table 3.2: FACS tube list

FL1H to FL4H mark the different fluorescent dyes. The IgG1 inTube 1 is an immuno glob-

ulin which binds with itsFC region end toFC receptors. Thus it is used for a calibrating process

to reveal the amount ofFC receptors on the cell’s surface because specific antigens may also

bind to theFC receptors and falsify the FACS result. The calibration process starts by inserting

the first tube (with the four different stained IgGs) into the FACS whereby each of the acquired

scatter plots indicates a cloud of points lying close together. This cloud is used to calibrate the

axes of the scatter plot by moving it into the left lower quadrant. All of the following analyses

use this adjustment, which defines if a cell has a positive (points above the axis) or negative

(point below the axis) expression of a certain surface marker.

15


The phenotype analyses shown below (Fig.3.2) were made at two different points in time

whereby the first scatter plot was captured at day zero and the second at day five after incubation

and activation with CD3/CD28 and stimulation with IL-2.

Figure 3.2: Phenotype scatter plots. Day 0, cells withoutCD25+ markers. Right: Day 5 afterstimulation, cells with an increased amount ofCD25+ markers

The first scatter plot shows two groups of cells,CD3+ (T cells; CD3 is a special T cell

marker, whereby the+ indicates a positive expression on the cell’s surface and the− a negative)

andCD3− but both of them areCD25−. The second scatter plot reveals a major increase of

CD25 markers on theCD3+ (T)cells’ surfaces after stimulation with IL-2 indicated by the

high MFI (mean fluorescence intensity) of theCD25 markers. This possibly may come from

the fact thatCD25 is a special receptor for IL-2 cytokines. IL-2 is known as an inducer of cell

proliferation but under certain conditions it also may cause apoptosis (self mediated cell death).

A listing of several used antibody combinations is given in Table 3.2.

For example inTube 2, following cell populations can be distinguished:CD3+ represent T

cells,CD19+ represent B cells,CD56+ are NK (natural killer) cells (CD3+ CD56+ cells are

NK T cells) andCD14+ are monocytes.

In Tube 3, CD3+ CD4+ cells areTH (T helper) cells,CD3+ CD69+ are activated T cells,

thereforeCD3+ CD4+ CD69+ cells are activatesTH cells, whereasCD3+ CD103+ represent

a subpopulation of regulatory T cells.

In Tube 4, CD45+ represent all hematopoetic cells,CD1a+ CD83+ are dendritic cells

(special APCs) andCD14+ CD1a− are monocytes.

16


3.1.4 Proliferation analysis

The proliferation analysis tries to examine the cells’ behaviour of cell division and augmentation

under certain activation and stimulation conditions. To observe cell proliferation Carboxy-

fluorescein diacetate, succinimidyl ester (CFSE) a red fluorescent dye is used for an intracellular

staining of all cells. Within each cell division the CFSE amount and therefore the MFI bisects

and diminishes.

Figure 3.3: Proliferation scatter plots. Left: Day 0, cells stained with CFSE. Right: Day 5 afterstimulation with Il-10 and IL-15.

Proliferation assays start with the same proportioning of the cell mixture into tubes as it

was already described for the phenotype analysis. Each tube stained with CFSE is stimulated

then with a different combination of cytokines followed by incubation for several days. FACS

experiments are made on different points in incubation time to record the cells’ behaviour in

respect of their different stimulation conditions. One of the four FACS’s dye channels is used

to detect the CFSE fluorescence intensity whereby the three remaining channels are used for

stained antibodies as supplied before.

To activate the cells, micro titer plates are filled with specific antibodies CD3/CD28 or

mitogens e.g. PHA (phytohemagglutin). The proliferation assays depicted above (Fig.3.3)

were made at different days of incubation. The first scatter plot indicates the initial state without

activation and stimulation at day zero. The other scatter plots were captured at day five after

17


stimulation with IL-10 or IL-15. The IL-15 stimulation reveals a considerably amount of cell

divisions ofCD3+ cells whereas IL-10 seems to inhibit cell proliferation.

3.1.5 Cytokine secretion analysis

Cytokine secretion assays use special Kits (e.g. ELISA) to detect particular cytokines secreted

by APCs or other cytokine secreting cells. The basic idea of such an assay is to detect cytokines

which are released under certain stimulation conditions.

Therefore this cytokine detection Kits provideCy-

Figure 3.4: Cytokine secreting cell

[20]

tokine Catch Reagentsand highly specificCytokine

Detection Antibodies. The Cytokine Catch Reagents

bind to the receptors of cytokine secreting cells and

may catch cytokines which are secreted by these cells.

When a cell has secreted its different species of cy-

tokines they diffuse to theCytokine Catch Reagents

and bind to them. After a certain incubation time the

different stainedCytokine Detection Antibodiesare mixed to the cell compound and each anti-

body binds to its specific kind of cytokine. Now the concentrations of certain secreted cytokine

species can be detected by a following FACS analysis which is performed in the same way than

for the phenotype analysis [20].

18

Methods 3.2 Database development (Enterprise Information System (EIS)-Tier)

3.2 Database development (Enterprise Information System

(EIS)-Tier)

3.2.1 Relational databases

Relational databases are rested upon the theory of relational mathematics based on the set the-

ory. The basic idea behind relational database models is a collection of two-dimensional tables,

linked among themselves by different keys. Real world objects are mapped by abstract entities

which are represented by their according tables [11]. Tables are storing information about in-

stances of entities, their attributes and their relations among each others. Every entity instance

consisting of a unique record (tuple) of data represents a row in the table . The uniqueness

of each data record is based on one well-defined primary key (PK) whose occurrence must be

unique within each table. To realise one to many (1:N) or many to one (N:1) interrelations, data

records must contain primary keys of foreign tables, called foreign keys (FK). An implementa-

tion of many to many (N:N) relations requires an additional table storing explicit allocations of

different foreign keys. The fact of defined relations allows the stored data to be broken down

into smaller logical and easier maintainable units. Some good reasons why relational databases

should be used are outlined below:

• Reducing of duplicate data:Leads to improved data integrity

• Data independence:Data can be thought of as being stored in tables regardless of how

physically stored

• Application independence:The database is independent of accessing systems and pro-

grams

• Data sharing with a multiplicity of users

• Single queries may retrieve data from more than just one table

19


3.2.1.1 Normalisation

Normalisation is used to break up araw database into logical and easy maintainable units called

tables. The idea is to create a level of minimized redundancy that allows two or more tables

to be joined within a single query. To realise such an implementation certain theoretical rules,

callednormal forms, have to be performed. There are six nested normal forms but in generally

the first three are used to meet the requirements of a well-formed business database.

First normal form (1NF): All attributes must be atomic and there must not be any repeating

values whereby each row/column intersection may have just one value [11].

Second normal form (2NF): The table must be in 1NF and there must not be any partial

dependencies in a table. Hence every non prime attribute must be fully functionally dependent

on a primary key.

Third normal form (3NF): table must be in 2NF and there must be no transitive dependencies

hence the value of a non-key attribute must not depend on another non-key’s value.

3.2.1.2 Integrity rules

There are three integrity rules which have to be performed in a well-designed database.

• Key constraint:Candidate keys are defined for each relation and must be unique for every

tuple in any relation instance of that schema.

• Entity integrity:All values pertaining to the primary (PK) must be no ’null’ values. Each

tuple must be uniquely identifiable.

• Referential integrity:There must not exist any foreign key (FK) in the database which is

not another table’s primary key [11] [25].

To prevent violations of integrity rules some safety precautions, like the interdiction of PK

alterations or a cascading alteration of all entries associated to the PK in case of an inevitable

change of the PK can be taken into account.

20


3.2.2 Structured Query Language (SQL)

The father of relational databases, and thus SQL, is Dr. E.F. ’Ted’ Codd who worked for IBM.

After Codd described a relational model for databases in 1970, IBM spent a lot of time and

money researching how to implement his ideas [9]. Now SQL has already evolved into a stan-

dard, open language without cooperative ownership and almost all nowadays available database

implementations are designed to meet the SQL standards. SQL pertains to the category of non

procedural languages called declarative languages. In opposition to procedural languages which

result in many lines of code, SQL results in just one statement of the desired demand. A single

database query consists of a SQL statement which includes all desired requests. This statement

is sent then to the database management system (DBMS) which executes a hidden internal code

and returns a somehow defined dataset [9]. There are two possibilities in accomplishing data

manipulations with SQL: commands which return demanded datasets are defined in the data

manipulation language (DML) and manipulating commands which alter the database’s internal

structures use the data definition language (DDL).

3.2.2.1 Data Definition Language (DDL)

The DDL is a sub section of SQL allowing the creation and deletion of tables in the database

as well as the definition of indexes (keys) and links between tables. It is also possible to enable

constraints among different tables, defined by foreign keys[7]. Some of the most important

DDL commands are listed below:

• CREATE TABLE -creates a new database table

• ALTER TABLE -alters (changes) a database table

• DROP TABLE -deletes a database table

• CREATE INDEX -creates an index (search key)

• DROP INDEX -deletes an index

21


3.2.2.2 Data Manipulation Language (DML)

The DML defines the second part of SQL commands. It includes the syntax for complex queries

as well as for updates, insertions and deletions of data records [7]. The four basic manipulation

commands are outlined below:

• SELECT -extracts data from a database table

• UPDATE -updates data in a database table

• DELETE -deletes data from a database table

• INSERT INTO -inserts new data into a database table

The basic body of almost all query statements is given in the following example:

• TheSELECT statement creates a recordset from existing tables according to the param-

eters that follow the statement.

• TheFROM command apprises the database engine to return all the fields in the selected

tables. The fields specified in the SQL statement become the columns in the new record-

set.

• TheWHERE condition restricts the rows returned to only rows containing the data spec-

ified in the SQL statement.

• TheORDER BY command notifies the database engine to sort the records before return-

ing them.

Example: SELECT address FROM patients WHERE ( name = ’...’)

3.2.3 Java Database Connectivity (JDBC)

JDBC is a low-level API (application programming interface) performed in Java programming

language [11] which allows an external access to any SQL database to manipulate and update

22


stored data. It provides library routines which supports the integration of direct SQL calls into

the Java programming environment. These routines support an interface which makes it very

easy to access the database by opening a connection and send SQL code to the database engine

which executes the demanded commands. Having accomplished the request the Java program

closes the connection and continues with its execution [31]. Java is already a well-established

web programming language and in combination with JDBC it becomes an extremely useful

tool in generating web based database applications. Due to Java’s platform independence it is

an extremely useful tool no matter which operating system is used [30]. Compendious JDBC

makes it possible to do three things:

• Establishes a connection to a database

• Sends SQL queries

• Processes and returns the results

3.2.3.1 Two-tier and Three-tier Models

A tier structure represents ab-

Figure 3.5: Multi Tier Model [11]

stract layers which communicate

among themselves via different in-

terfaces. Each tier performs its

own particular duties and inter-

acts with other layers to accom-

plish different tasks. This seg-

mentation into different tiers causes

a separation between user inter-

face and business logic which in-

tercommunicate via well-defined

interfaces [30].

23

Methods 3.3 Application server deployment (Middle-Tier)

Two-tier models may be java applications or applets which directly access the database.

Therefore a JDBC driver is needed which can communicate with the particular DBMS to send

SQL statements to the database. If the database is located on another machine it is called a

client/server configuration, whereby the accessing application acts as client. The connection

may be established via intranet or internet standard TCP/IP protocols [30].

Three-tier models (see Fig.3.5) use a middle layer the ’middle-tier’ which receives com-

mands from two different sides. The middle-tier conduces as service layer which executes

commands obtained from the user layer and sends them to the database. The database (EIS-

Tier) in turn processes the received SQL commands and returns the appropriate results to the

middle tier which then sends them to the Client-Tier. The major advance of a middle-tier is an

encapsulating of low-level calls hidden for the user who may access them by a high-level appli-

cation interface (Client-Tier). This architecture may also provide performance and maintaining

advantages [30].

3.3 Application server deployment (Middle-Tier)

3.3.1 Enterprise Java Beans 2 (EJB2)

EJBs are business data objects developed in java technology running in an EJB container sup-

ported by Java 2 Enterprise Edition (J2EE) application servers. The encapsulation mechanism

of EJBs allows the developer to concentrate on assignments belonging to his own business,

without caring about the beans’ interactions with the container. EJBs may be accessed by an

abundance of different users with appropriate admissions. The major advantage of EJBs is its

portability among a variety of application servers supporting the J2EE container specification

[13] [1]. The major improvement of EJB2 is the advanced EJB QL (Query Language) allowing

complex queries with optimised SQL statements mixed with Java code. EJBs fall into three sub

groups enumerated below:

Session beans:This kind of beans account for the first layer of an EJB structure model (see

Fig.3.6) seen by the client and mainly support getter and setter methods for the client. Thus

24


Session Beans comprise the main part of the client’s business logic for accessing the data layer.

As implied by their name, session beans only exist during on single session by executing one

specific remote method invocation. To speed up client connections session beans, once if they

were used, are sent to a pool where they wait for other invocations [1]. Session beans may be

subdivided into two groups:

• Stateless session beansdo not maintain their state among different method invocations.

• Stateful session beanshold the client state across method invocations.

Entity beans: These are beans persisted within the EJB container (see Fig.3.6) for a di-

rectly mapping of database entries. One entity bean corresponds exactly to one table within the

database, whereby each table’s entry accords with setter and getter methods defined in the en-

tity bean. These entity accessing methods may be invoked by session beans in case of a client’s

request. The entity layer therefore is a mediator between databases and session beans by hiding

the database’s specific accessing language from the developer [1]. Entity beans fall into two

groups:

• Container-managed persistence (CMP)- In case of a CMP the container must supply the

full synchronisation between the database and the entity layer. The container ensures the

consistency and integrity during the beans’ entire lifetime. The developer does not care

about how the beans access the database, but it is important that the underlying database

is relational in nature.

• Bean-managed persistence (BMP)- In case of a BMP the programmer is entirely respon-

sible for all the synchronising steps to connect the entity beans with the database. All the

necessary SQL statements and JDBC calls must be coded by the programmer. The ad-

vantage of this kind of persisting entity beans is the full control over all actions pertaining

the database, allowing an access optimisation [1].

Message driven beans:Message deliveries in contrast to method invocations are asyn-

chronous. Therefore an availability of the bean at the time of an occurring message can not be

25


assumed [1]. Hence this kind of beans must be driven by an asynchronous message receipt to

send information to the EJBs’ business logic.

Figure 3.6: EJB Model [1]

3.3.2 The Java 2 Enterprise Edition (J2EE) server

Every application server which wants to meet the EJB technology must confirm to the J2EE

container specifications. But most application servers support also a variety of other technolo-

gies which sometimes may cause a loss of the portability of J2EE applications across different

vendors [16],[18].

Some of the services provided by J2EE servers are outlined below:

• EJB:allows the user to call remote methods supported by the EJB technology via TCP/IP.

• HTTP (Hyper text transfer protocol):supports the accession of Java Server Pages (JSPs)

and servlets via a web browser.

• Authentication:increases security issues concerning the user loggings.

26


• JNDI (Java Naming and Directory Interface):enables the location of programs and ser-

vices within the J2EE platform.

3.3.3 Java Cryptography Extension (JCE)

JCE is a non-commercial cryptography extension for Java containing a package which provides

implementations for encryption, key generation and key agreement, and Message Authentica-

tion Code (MAC) algorithms [19]. It is an extremely valuable encipher tool for information of

a high security level. Restrictions to applets or application may be specified in certain ’jurisdic-

tion policy files’ dependent on their different jurisdiction context (location). Some features of

interest are listed below [19]:

• JCE is a pure java implementation

• Implementations and interfaces of ciphers, key agreements, MACs, etc.

• Support for the following algorithms by the SunJCE provider:

– DES

– DESede

– Blowfish

– PBEWithMD5AndDES

– Diffie-Hellman key agreement among multiple parties

– HmacMD5

– HmacSHA1

3.3.4 Extensible Markup Language (XML)

XML was developed by the W3C between 1996 and 1998 to provide a universal format for

describing structured documents and data; in other words, it allows data to be self-describing

[12]. XML tries to bring information into a clearly arranged text form storable in flat files.

27

Methods 3.4 Web application development (WEB-Tier)

Analogical to HTML (Hyper Text Markup Language) XML uses tags which may be defined

in DTDs (Document Type Definition) by the programmer arbitrarily [28]. DTDs describe in

a formal way which names are to be used for the different types of tag elements, where they

may occur, and how they all fit together. A well-defined tree structure makes it easy to parse

XML files to extract information. Different from HTML which is used to define the display of

web pages, XML’s is applied to store and transmit information whereby it is often used to save

configurations.

The most important reason of using XML files is their quality of storing information outside

of an program application. Hard coded (binary) constants may be separated into XML files

which are easily modified without changing the applications source code.

3.3.5 JDOM (Java Document Object Model)

JDOM is an open source library for Java-optimised XML data manipulation similar to DOM

(Document Object Model) but not build on it. The DOM model tries to represent documents

as a hierarchy of Node objects which may have child Nodes of various types. JDOM supplies

methods for an easy and efficient reading of XML files and is not an XML parser, but rather a

document object model that uses XML parsers to build documents whereby nearly any parser

may be used [8].

3.4 Web application development (WEB-Tier)

3.4.1 Java Server Page (JSP)

Java Server Page technology has been developed to provide an easy and intuitive way in creating

dynamical generated HTML pages. JSPs are like HTML pages but in addition to the static

HTML tags JSPs may contain Java code and specific tags, which account for the dynamic

generated part. By carrying the extension *.jsp the web engine of a JSP supporting web server

compiles the JSPs to servlets which are launched then in the web container to perform their

demanded tasks. Servlets running on the web server are similar to applets which are running

28


in a web browser. Servlets may invoke Enterprise Java Beans or create a direct connection

to a database and when they have finished their work, they send back dynamically generated

HTML pages which are displayed then by the client browser [17]. One of the most common

used architectures is depicted in Fig.3.7.

Figure 3.7: JSP MVC model [23]

3.4.2 JSP Tag libraries

JSP is a solution for creating and assembling together dynamical websites and it applies an

interconnection of various programming languages to control the entire web application. Hence,

JSP technology gains more and more complexity and becomes non transparent for a multitude

of web designers. The HTML sites mixed with a vast code of pure Java are very confusing and

programmers may quickly lose the plot. Therefore JSP developers came up with the idea to

create special tags for the code sections written in Java, by replacing pure Java with tags having

the same functionality. The intension was to create tags as they were already used for HTML or

XML, to perform a certain consistence within the static HTML code. These tags should enable

29


an easy accession and usage of Java extensions for non Java programmers and web designers

[5].

A Tag is represented by a well-defined syntax which exactly constitutes where it starts and

where it ends. Tags are enclosed by angled brackets which may bear attributes defining the

tag’s behaviours and it may embed information or further hierarchically arranged tags (in case

of XML files) in its body. An example is given below:

< tag1attribute1=’value1’ attribute2=’value2’ >

The tag‘s body ...

< / tag1>

Tags are used to store information in text oriented flat files and due to their well-structured

form they are easily parsed in turn. It depends on the application how to handle and translate

the parsed file’s information. So if the application is a web browser, the information file will

be interpreted in a graphical way displayed on a screen. In case of a JSP the engine of the

web server processes the *.jsp file and generates a servlet by using the JSP tags. Every JSP

tag possesses its own special Java tag class which defines the tag’s behaviours and contains the

pertaining Java code which was previously defined in the JSP. The attributes the tags possibly

have, are associated with the tags’ class initialising setter and getter methods. These tag classes

are used by the web engine to compile the servlet class which processes the associated tasks

and returns the result to the client’s web browser. A tag library now stores packages of different

tag classes in a clearly arranged way hidden for the JSP developer who merely sees the JSP tags

he is using [5]. Some of the major advantages of JSP tags:

• The average web designer can now maintain JSP sites

• Tags are portable within different web applications

• Tags speed up web developments by reusing Java code

• Tags are easily extendible by additional functionality

30


3.4.3 The Jakarta Struts Framework

Struts is the first open source framework supporting web design practises along with the thought

of the JSP Custom Tag technology. It was developed by Craig R. McClanahan who freely

offered the source to the Apache Software Foundation [5].

Struts implements the previous mentioned Model 2 JSP web application architecture (Fig.3.7)

that uses a servlet asController for dispatching the incoming requests, a Java Bean representing

theModelpart which stores the data for the request, and a JSP visualising the data to the user

accounts for theView part. Hence Struts represents the perfect decoupling of business logic,

application control and presentation [5] [24]. Other benefits to Struts are:

Extensive JSP Custom Tag libraries:which reduce the major part of Java scriptlet code

from the Java Server Pages and enable its reusability.

A generally reduce of code for web applications:by supplying a well tested base of

software technology.

A support of internationalisation for web applications: the web sites are dynamically

updated with the appropriate language of the operating system.

3.4.4 Struts Application Flow

The following characterisation of how Struts is handling a user request refers to Fig.3.8 depicted

below:

1. The first step to invoke a Struts web application is to open an appropriate web site which

sends a request to the Action Servlet controller by triggering a submit action (e.g. a button

or invoking a site).

2. Receiving the request the Action Servlet checks the Action Mappings and instantiates

an Action Form Bean which pertain to the invoking HTML form, to store then the form

fields’ information. The Action Form Bean bears a validation method which cancels the

user’s request if wrong parameters have been entered. In this case the Action Servlet

sends back the previous invoked JSP and indicates the occurred error messages.

31


Figure 3.8: Process flow for displaying a JSP page within a Struts Project [5]

3. If no insertion faults occur the Action Servlet calls the suitable Action Bean dependent

on the Action Mapping’s information.

4. This Action Bean may invoke the associated Action Form Bean’s methods to gather its

information in order to start data transactions with EJBs or databases directly.

5. Start of the data transaction.

6. After the data transactions are done the Action Bean may invoke the Action Form Bean

to store back new data again.

7. By having done this, the Action Bean calls a forward method which accesses the Action

Mappings again whereby these mappings now indicate the JSP page which should be

displayed next. Every forward requests a different or even the same JSP page depending

on the Action Bean’s state.

8. The Action Servlet sends a request the JSP claimed by the action forward and in case of

its first invocation, the web container will compile the JSP into a servlet class.

32


9. The JSP servlet calls and includes demanded tag libraries in its method, processing and

generating dynamically the HTML code by including the Action Form Bean’s data and

the JSP’s own HTML code.

10. The following response called by the JSP servlet returns the generated HTML code to the

Action Servlet.

11. The Action Servlet in turn induces its own response and delivers the HTML code back to

the browser. The browser parses the HTML code and visualises the web site for the user.

33

Chapter 4

Results

This chapter will present the developed data model for the Tumoral Micro Environment database

(TME.db) for storing the immunological data, which was obtained from different cancer pa-

tients and controls. Further the functionality of the developed web application will be shown by

giving some maintaining and querying examples. Finally some cluster experiments, performed

with re-obtained and particular aligned FACS and patient data, will be shown.

4.1 The Database Model

The first part for storing all the arising immunological data as well as the donor specific infor-

mation was to develop an appropriate, flexible and easily maintainable data model. The data

tables should be broken up into smallest possible units to ensure best flexibility among different

data tables. To realise an adequate model a relational database management system (Oracle)

was chosen for gathering the data. Oracle was the best choice because it has already been used

by the bioinformatics work group for several database projects like GOLD.db or MARS.db. All

considerations due to the table design were accomplished in regard to real world’s facts in order

to create an intuitive data model.

34

Results 4.1 The Database Model

4.1.1 Patient and Experiment Table

The core of the data model is embodied by thePatientsand theExperiments table represented

by the red tables (see appendix A). The patient table contains specific personal information

about the donors of the sample materials whereby all the data is encrypted with a special algo-

rithm supported by JCE. The patient table is linked to theHospitals table within a many to one

relation as well as one patient may have links to many experiments stored in the experiment

table.

TheExperiments table comprises all the information related to the experiment , and by hav-

ing an one to many relation to all the possible experiment tables, it links the database’s entire

available information (Therapies→ orange section, Cancers→ blue section, Biolmarkers→

yellow section, Proliferations→ pink section, Sampletreatments→ blue section, FACSLympho-

cytes(Phenotypes)→ light green section, Testmaterial→ grey section, Cytokineexperiments→

green section).

4.1.2 User Management Tables

The light orangetables depicted in appendix A store the user related information. The centre

of this section is represented by thePatientDBUserstable which stores the encrypted (by JCE)

user related information. Each user may save some specific query options which are stored in

the SavedQueryOptionstable. A many to many relation between thePatientDBUserstable

and thePatientDBUserRolestable, established by theUsersUserRolestable, enables multiple

user roles for one single user. The same relation construction is created with theUsersHospitals

to add to one user a variety of hospitals, which in turn are again linked with the patients table.

4.1.3 Experiment Related Tables

All the following explanations refer to appendix A whereby the description starts with the ther-

apies table and swift through the model counter clockwise.

35


The orange sectionstores possible therapeutic patient treatments like chemo therapies, x-

ray treatments etc. TheTherapies table contains all necessary ids and links (many to one) to

theTreatments table which stores the actual therapy name.

Theblue sectioncontains a collection of different cancer related tables like primitive cancer,

cancer type, cancer subtype, tumoral liquid, as well as different cancer stages. The Cancer table

stores solely the FKs (foreign keys) which link to the associated tables likePrimitiveCancers,

CancerType, CancerSubType, CancerTumoralLiquid andCancerStages. The cancer stage

table in turn contains again a tuple of FK which link to four different cancer stages;PStage,

TStage, NStageandMStage(particulars to these will be given in a later part of this chapter).

The yellow sectionrefers to certain patient stimulations with different biological markers

which may induce ascertained health effects. TheBiolFactors table contains the different stim-

ulation values and links with two FKs to theBiolMarkers table storing the different markers,

and to theTestType table which stores the test types of used stimulations. TheTestTypeBiol-

markers table enables a many to many relation between the latter two tables which defines the

affiliation of the test types to the biological markers.

The pink sectionpertains to the proliferation assays whereby each experiment may have

many proliferation experiments. To store and access the proliferation experiments’ information

more flexible, the data is split into a table containing particular experiment information about

the experiment’s handling and a FACS data specific table containing all the data processed by

the FACS analyses. As explained theProliferations table stores FACS experiment specific data

and links to theFACSCellProliferations table that stores the percentage of cell expressions and

pertaining MFI values. This table as well contains two FKs linked to theActivations and the

Stimulations table which save possible activations and stimulations for the proliferating cells.

The ActivationsStimulations table establishes again a many to many relation of these latter

tables and defines the stimulations which may account to appointed activations. At last the

stimulation table relates to theStimulationRangestable which contains particular stimulation

36


ranges comprising a min and max value for each stimulation. This is an important feature for

later queries to map the values to defined ranges, claimed by theGenesis softwarefor analysing

the data.

Theblue sectionis a special one which stores all the possible pre-treatments of one particu-

lar test material (e.g. blood or pleural liquid) for one single experiment. There are a lot of many

to many relations between these tables because a multiple performance of all different treatment

should be enabled. All these different treatment tables relate to the oneSampleTreatmentsta-

ble containing particular material treatments, which have no multiple occurrences. The other

tables store e.g. information about RNA extractions which may be performed with different

RNA-kits on even one sample material gathering RNA for microarray assays, or information

about different stimulations of the material before ficoll etc.

The light green sectionrelates to the phenotype analyses and uses a similar data model

as for the proliferation experiments. In this case theFACSLymphocytes table again stores

experiment specific information and links to theFACSLymphocyteGatescontaining all the

possible gates and theFACSLymphocyteTypescontaining all possibly occurring phenotypes.

TheFACSLymphocyteGatesTypestable characterises again a many to many interconnection

of the phenotypes pertaining to one single gate and theAntigenRangestable define particular

ranges of the expression of certain surface antigens and their MFI values. This form table model

was chosen to ensure the possibility of a dynamically update of gates and antigens at every time

to enlarge the storable data set (the same was applied for the proliferation and cytokine experi-

ments).

The grey section(table) defines all possible sample materials and was separated from the

experiment table to enable a later update of additional arising materials.

Thegreen sectiondescribes the cytokine experiment table relations with exact the same data

model as used for the phenotypes. These similarities in the data models allow a faster develop-

37

Results 4.2 The Web Application

ment of the accessing and querying software by enabling the use of equal code fragments for

all these demanding accessions.

The two remaining tables in the right upper corner have no linkage at all. One of these

stores constant values important for queries and the other one stores the number of primary

keys already given to certain updatable tables. For each new insertion into one of these tables

the number of its pertaining given PK is increased by one to ensure the integrity rule of key

constrains (see section 3.2.1.2 for integrity rules).

4.1.4 Application server connection

To get the data available for Java [8] the open source technology of the JBoss (http://www.jboss.org)

application server was used which supports the J2EE technology. Hence the EJB technology

could be used to map entity relations to the database’s tables. Nearly all tables were mapped by

entity beans to ensure an easy access to the data, but in some cases there was no necessity of

this because no frequently maintaining was claimed. Most of the data manipulations are done

between session and entity beans, but all the queries are done by session beans directly by using

JDBC connections to the database because of its swiftness. The EJBs contain all the business

logic to access the database and hide all the data gathering manipulations from the web server

side which only sees the methods which may invoke them.

4.2 The Web Application

The developed web application is based on the Jakarta Struts Framework and uses JSPs to gen-

erate the users view in the web browser. For the web server Tomcat4.0.6 is used which is open

source technology supported by the Jakarta project as well. The Struts application was deployed

to the web server’s web container and can be accessed by the addresshttps://tme.genome.tugraz.at.

38

http://www.jboss.org

https://tme.genome.tugraz.at


4.2.1 The TME.db Web Page

If one has appropriate access admissions (password, username) he may log into the TME.db

and the Web Page will be populated with its permitted tool buttons due to his given user roles.

All the following explanations refer to an administrator account with all possible permissions.

The displayed web page (Fig.4.1) shows a toolbar on its top and bottom with the possibilities

of adding or listing patients and different database query options.

Figure 4.1: TME Web Page Overview

On the left side a panel is shown which enables certain search options for particular patient

IDs or FACSIDs of different experiments. The page’s centre shows an additional link to the

39


administration page which allows to add/edit user as well as hospital entries. There is also the

possibility to add/edit Gates, Antigens, Activations and Stimulations for the different FACS

analyses.

4.2.2 User Management

To add a new user all the required fields have

Figure 4.2: Add User Panel

to be populated (Fig.4.2). Particular user roles

define the new user’s permissions whereby a user

may have multiple user roles. These user roles

are important in respect to different users which

may not see the entire available information (e.g.

Clinicians may not see immunological data from

the FACS experiments). A ’select hospitals’ field

restricts the user’s view to patients pertaining to

the selected hospitals. Hence the user may only

see patients of specified hospitals. The ’period

of validity’ field restricts the user account to a

certain expiration date. A hierarchical listing of

available user roles is given below:

• GUEST: role is assigned for default without any permissions.

• VIEW HOSPITAL INFO: permits the clinicians an insight to particular patient infor-

mation like cancer, given biological markers or the patient’s treatments.

• EDIT HOSPITAL INFO: includes the previous roles and allows the clinicians to mod-

ify the patient information (add/edit).

• DELETE HOSPITAL INFO: includes the previous roles and allows a deletion of pa-

tients as well as patient information.

40


• VIEW IMMUNOLOGICAL INFO: includes previous VIEW roles and allows the im-

munologists to view the immunological experiments (phenotype, cytokine and prolifera-

tion analyses).

• EDIT IMMUNOLOGICAL INFO: includes previous VIEW and EDIT roles permis-

sions with additional modifying rights of immunological data.

• DELETE IMMUNOLOGICAL INFO: includes all previous permissions and the dele-

tion rights of immunological information.

• ADMIN: includes all roles and the right of maintaining users, hospitals and specific per-

missions already mentioned in section 4.2.1.

4.2.3 The Patient Overview

Figure 4.3: Patient Overview

41


The Patient Overview joins together the entire given patient information (Fig.4.3). The top

of the page contains the patient’s particulars and if appropriate permissions are given (section

4.2.2) the patient information may be changed or deleted. Every patient may have many exper-

iments whereby each of them is displayed in a separately tabbed panel.

The header of the experiment panels shows the experiment’s specific information as well as

options to alter and delete it. There is also a link to the sample treatments page which contains

information about certain performed treatments of the experiment’s sample material (Fig.4.4).

Below the description header the experiment specific performed assays are shown, each pictured

with a particular icon (Cancer Information, Biological Markers, Treatments, FACS Phenotype

assays, FACS Cytokine Secretion assays, FACS Proliferation assays).

4.2.3.1 Cancer Information

This panel provides specific cancer information about the experiment’s sample material.

• Primitive Cancer:describes an umbrella term of a certain kind of cancer like lung, colon,

breast etc.

• Cancer Type:characterises a specific occurrence of certain forms of cancer.

• Cancer Sub Type:represents a particular sub specification of a cancer type.

• Tumoral Liquid: bears important information dependent on having tumoral cells in it or

not.

• P-Stage:constitutes if the detected tumour is malignant or not [26].

• T-Stage:distinguishes the tumoral stage into different states [26]

– A Tx tumour has a proven existence but cannot be assessed.

– A T1 cancer is less than 3cm in size and completely surrounded by lung tissue.

– A T2cancer is larger then 3cm without invading structures in the middle of the chest.

– A T3 cancer is of any size invading chest structures and it is still savely resectable.

42


– A T4 is a tumour of any size invading vital structures and is unresectable.

• N-Stage:refers to the involvement of cancer into lymph nodes and is distinguished into

different stages [26]:

– N0 refers to the absence of any lymph node involvement.

– N1 refers to the presence of cancer in the hilar lymph nodes.

– N2 refers to an involvement of the mediastinal lymph nodes on the cancer side.

– N3 cancers involve the lymph nodes on the other side of the chest, or in the supra-

clavicular area.

• M-Stage:is used to define the presence of metastasis [26]:

– M0 implies the absence of any evidence of cancer spread to other organs.

– M1 implies cancer spread to any organ.

The M-Stages may be subdivided into more precise stages.

To all these cancer information values were given to score their occurrence in queries later on.

4.2.3.2 Biological Markers

Biological markers are given to organisms to reveal physiological and biochemical responses

provoked by them. To identify potential markers several factors like the correlation between

response and effect, the feasibility of determining them or the specificity of the response need

to be investigated. In this project the availability of biological marker types likeIHC or ELISA ,

used on patients, should be taken into consideration by scoring them with specific values. These

kinds of markers may reveal certain kinds of cancers or their occurrence. But for the moment

there are no markers available at all and still remain in prospective.

4.2.3.3 Treatments

The treatments refer to certain therapies which were applied to the patient like chemo therapies

or radiological treatments. These patient treatments serve as additional information which may

43


be of interest for querying the database. So far all patients were analysed before chemotherapies

or radiotherapies have been applied.

4.2.3.4 FACS Phenotype Assays

FACS Phenotype Assays try to examine different cell populations within a compound of various

cells (see section 3.1.3). The following example will give an explanation of how the phenotype

data is achieved from the FACS and how it is stored in the database.

When the FACS analyses of all prepared tubes have finished the MFI values and the per-

centage of the different labelled surface markers are stored into a special file format. Then all

the data of interest are extracted into a text file which may be uploaded to the database in turn.

During the upload process the text file is parsed and potential errors in its content are logged

and displayed afterwards. A screen shot of one particular phenotype analysis is given in Fig.4.4

(right side). It contains all determined data pertaining to one single FACS experiment. The tabs’

label (e.g. CD3, nonT(cells), non-Tum etc.) indicates the specific gate in which the different

stained antigens were detected.

Figure 4.4: Phenotype Assay Overview with scatter plot.

On the left side of Fig.4.4 one single phenotype scatter plot is shown which was done to ex-

amine theCD127+ cells within theCD3 gate (that means the percentage of all T cells bearing

CD127 markers is determined). Below the plot the list of the particular determined percentage

44


and MFI values is given which directly result form the FACS analysis. The first column defines

the four quadrants of the scatter plot, the second refers to the percentage of gated cells (in this

case the ’% Gated’ refers to all cells which are alive), the third (% Total) displays the percentage

of cells in a quadrant as regard to all detected cells (dead cells included) and the X, Y Geo Mean

columns indicate the logarithmic scaled MFI of the stainedCD markers in each quadrant (in

this caseCD127 refers to the X Mean andCD3 to the Y Mean).

As depicted in Fig.4.4 theCD3 gate surrounds the both upper quadrants, hence the percent-

age of all T cells which areCD127+ is calculated as following:

CD3 CD127 % =UR of % Gated · 100

UR of % gated + UL of % Gated=

20, 58 · 100

20, 58 + 7, 84= 72, 41%

The MFI value of the T cells which areCD127+ is the X Geo Mean of the upper right

quadrant (Fig.4.4).

CD3 CD127 MFI = 530, 08

This tuple of phenotype parameters is determined for each antigen within each of the given

gates whereby each gate represents a particular phenotype.

4.2.3.5 FACS Proliferation Assays

FACS Proliferation Assays are performed in order to observe cell proliferation under certain

stimulation conditions (see section 3.1.4). The following example will give an explanation of

how the proliferation data is determined from a FACS analysis.

The proliferation FACS analysis follows the same data flow as already mentioned for the

phenotype analysis the only difference is that there may be more than just one analysis for one

experiment (e.g analyses on day 6, 7, etc. Fig.4.3). The FACS data is stored into an spread

sheet and after performing some modifications the data is uploaded into the database. The

following example will give explanation of how the data is modified before the upload process

45


is accomplished.

Figure 4.5: Proliferation Assay Overview with: FSC-SSC-plot, scatter plot and histogram plot.

Figure 4.5 shows a particular proliferation data set with a FSC(Forward Scatter)-SSC(Side

Scatter) plot (see Fig.3.1), a proliferation scatter plot and a histogram plot of the previous scatter

plot. Below these three plots a screen shot of the web site displaying the already modified and

stored proliferation data. Both the FSC and the SSC are determined by the FACS, whereby the

FSC indicates information related to the cell surface by diffracting the FACS’s laser light, and

the SSC reveals the cell’s intracellular granularity by reflecting the light. Hence the FSC-SSC

plot gives information about the cells’ structure through which the cells are characterised into

lymphocytes, monocytes, neutrophiles etc. The FACS software enables the possibility of setting

gates (regions) on the plots to frame certain populations of cells. Below the FSC-SSC plot four

gated regions and their appropriate percentage of cells are listed. The histogram plot shows the

counted cells with their appropriate CFSE fluorescent intensity distribution.

46


All used values for the following calculations are listed in the FACS plot (Fig.4.5) whereby

% Gated refers to the cells gated with R1 and % Total refers to all cells, dead cells included.

The region R1 is set to include all living cells for the following analsyses. Dead cells are

displayed in a different part of the FSC-SSC plot because of their changed cell characteristics.

Thus R1 is the first parameter of interest because all following experiments refer solely to

these R1 gated cells. For the given FACS example the cells have been activated with ’0’ (no

activation) and stimulated with ’IL-4’.

Thepercentage of living cellscorrespond to the R1 gated cells:

0 IL− 4 % Survival = 80, 3%

The next value of interest is thepercentage of proliferating cellswhich are displayed in

the FACS plot next to the FCS-SSC plot. The proliferating cells are situated in the Upper-Left

(UL) and Lower-Left (LL) quadrant, thus the their percentage is:

0 IL− 4 % ProliferatingCells = % of UL + % of LL = 15, 20 + 2, 17 = 17, 37%

The percentage of proliferating T cells refers to the percentage of proliferatingCD3+

cells and is calculated as followed:

0 IL− 4 % ProlifTCells =% of UL · 100

% of UL + % of UR=

15, 2, 58 · 100

15, 2 + 68, 36= 18, 19%

The next value is thepercentage of non proliferating cellswhich is the percentage of all

cells in the right quadrants:

0 IL− 4 % NonProliferatingCells = % of UR + % of LR = 68, 36 + 14, 27 = 82, 63%

Thepercentage of CD3 cellsis the percentage of all T cells (CD3+), thus the sum of the

percentage of both upper quadrants:

47


0 IL− 4 % CD3 = % of UR + % of UL = 68, 36 + 15, 2 = 83, 56%

TheMFI of the proliferating cells is the Geo Mean determined by the M1 range depicted

in the histogram plot (Fig.4.5):

0 IL− 4 MFI ProliferatingCells = 58, 21

TheMFI of the proliferating T cells is the X Geo Mean of the UL quadrant:

0 IL− 4 MFI ProlifTCells = 93, 42

TheMFI of the non proliferating cells is the X Geo Mean of the UR quadrant:

0 IL− 4 MFI NonProliferatingCells = 835, 51

TheMFI of the CD3 cells is the Y Geo Mean of the UL quadrant:

0 IL− 4 MFI CD3 = 1113, 28

The undergonecell cyclesare calculated as followed:

0 IL− 4 cellcycles =ln(MFI NonProliferatingCells

MFI ProlifTCells)

ln(2)=

ln(835,5193,42

)

ln(2)= 3, 16Cycles

Thefold increase/decreasedetermines the relative increase/decrease of the proliferation of

the stimulated(in this case with IL-4) T cells in respect to the unstimulated(0) T cells:

0 IL− 4 foldincrease/decrease =% ProlifTCells(IL− 4)

% ProlifTCells(0)=

18, 19

6, 406= 2, 83%

48


4.2.3.6 FACS Cytokine Secretion Assays

Cytokine Secretion Assays are designed for flow cytometric detection of various antigen-specific

T cells according to their secretion of effector cytokines (see section 3.1.5) [20]. Although the

database model design already includes the fields for cytokine secretion assays no usable data

has been performed yet.

4.2.4 Basic Queries

Figure 4.6: Basic Query Page with certain selected Gates and Antigens

Finally after all the data is stored in the database certain query procedures should be avail-

able for gaining desired information in an appropriate way. The basic query options make

available particular queries to the database in regard to obtain global information.

The basic queries are split into following subsections:

• DB Information Tab: These queries provide listings of certain database information like

patient list, patient’s cancer list, experiment list. The returned list is stored into a text file

which may be modified with an editor afterwards.

• All Data Queries Tab: These kinds of queries return a text file as well, but the file

has a special format so that it can be opened by theGenesis Clustering software[29] to

cluster the contained information (see a later section). This information may be result

from patient specific data combined with different experiment related data.

49


• The Phenotype, Cytokine and Proliferation Query Tabsenable more specific data

queries for particular selected items (Fig.4.6) in respect to the selected tab. These queries

create text files for theGenesis Clustering Software[29] as well and may be clustered

afterwards.

4.2.5 Custom Queries

The Custom Queries were designed to provide an abundance of possibilities in creating self

defined database queries whereby the major objective of these queries is to fit together a variety

data information. The queries may create lists with patient and experiment information as well

as text files with data for theGenesis Clustering Software.

The custom query page is separated into the sections:

• Listings Tab: This section provides search functions with multiple selections of different

search options resp. restrictions. These search options are again subdivided into several

groups of appropriate selections with respect to the patient data (patient, experiment,

cancer, treatments and sample treatments.).

Figure 4.7: Custom List Section with selected experiment tab

After the desired search options have been selected there are two possible choices of

listings. The first will create a list with patient specific cancer data according to the pre-

vious selection parameters and the second choice will create a list with FACS experiment

specific information.

50


• Queries Tab: The query section enables a creation of specific text files which contain

appropriate data for theGenesis Cluster Software[29]. The data for the specific formatted

text file is aligned into a matrix, whereby each column of the matrix corresponds to a pa-

tient specific sample material and each row represents a particular performed experiment

with particular markers (phenotype, proliferation etc.) and/or patient information (e.g.

sex, cancer type, etc.). The query options now allow to select these specific patient and/or

experiment data which will be aligned. The query section is again separated into several

tabs (patient, experiment, cancer, etc.) containing the different selection options.

Figure 4.8: Custom Query Section with selected cancer tab

Before creating the data matrix all the different values have to be scaled between 2 and

-2. The patient-, experiment and cancer information are already scored with fixed values

between 2 and -2, hence only the FACS specific data remains to be transformed. The

percentage of the FACS data has a linear scale whereas the MFI values have a logarithmic

scale. To map these value symmetrically between two defined mapping ranges following

general mapping equations are given:

51


1. Linear Mapping:

MappedV al =(MaxMap−MinMap) · V alToMap

MaxV alRange−MinV alRange− MaxMap−MinMap

2

2. Logarithmic Mapping:

MappedV al = (MaxMap−MinMap)·log10 ( V alToMap

MinV alRange)

log10 (MaxV alRangeMinV alRange

)−MaxMap−MinMap

2

• Savings Tab:The savings section allows each user to store and maintain its own specific

list and query options.

Figure 4.9: Custom Savings Section with saved query

4.2.5.1 Building A Custom Query

To describe a custom query application flow an example will show of how such a query is per-

formed. It is important to know which specific information is required, otherwise the basic

queries are the better solution to find some valuable information.

Thus an exemplification will be constituted in order to perform a specific query flow:

• All healthy donors and all patients with lung or colon cancer which are in the tumoral

T-Stages 0-4 should be selected.

• After the selection of previously defined donors, the ’Genesis file’ should be generated

with following data:

52


– All phenotype gates except the ’nonT’ and ’CD4’ gate whereby all antigens pertain-

ing to the remaining gates should be included.

– All survival and cell cycles information of the proliferation analyses, whereby only

the ’PHA’ activated and ’InterLeukin’ stimulated assays should be included.

• After retrieving the specified data file a hierarchical clustering (HCL) analysis should be

performed by using theGenesis Clustering Software.

• Having clustered with the HCL analysis a survey of the principle components and poten-

tially selected clusters should be given.

So the first step is the retrieval of previously specified patients by using the Listings page to

select the particular patient related characteristics (Fig.4.10).

Figure 4.10: Custom Query Listings page with specified selections

It is important to check the boxes next to the selection field, because otherwise the selection

will not be included within the query. After all selections are done one has the choice between

the patients cancer and FACS list. For this experiment the FACS list was chosen to display the

patients experiments (Fig.4.11).

The FACS experiment list indicates with the FACSIDs whether patients have some exper-

iments or not. If there are some patients without data they can be unselected and will not be

included into the following queries. If the list conforms to certain self defined specifications,

53


Figure 4.11: Patient FACS experiment list

it may be saved as a text file for further handlings. A click onto the ’Go’ button in upper right

corner (Fig.4.11) will lead the user to the query page.

Figure 4.12: Custom Query pages with specified selections

Having checked all demanded options on the query page

Figure 4.13: File download

(Fig.4.12) one may select the alignment of the created data

matrix (patient/markers and vice versa). Now the query can

be sent off and one has to wait for the result file. When the

download site is displayed (Fig.4.13) the file can be down-

loaded to the PC by following the given instructions.

How to use the Genesis software and how the received file information can be clustered will

be described in the next section.

54


4.2.5.2 Clustering the FACS data

TheGenesis Softwarewas developed by Alexander Sturn, a member the bioinformatics group

of the TU-Graz. Genesis is a clustering tool supporting several cluster algorithms and may use

a vast of different distance measurements. For further information about clustering see [29].

Following the task defined in the previous section, a clustering of the FACS query data

is performed, using theHierarchical Cluster (HCL) algorithm. Due to the data mapping

(see custom queries 4.2.5) no normalisations are required, thus the ’Euclidean Distance’dE =√∑ni=1(xi − yi)2 may serve as clustering distance.

Figure 4.14: Hierarchical Cluster result of the FACS data

55


After having opened the data file with Genesis one may perform the clustering with the pre-

vious mentioned settings and obtain a cluster with a hierarchical tree (dendrogram) on both sides

of the data matrix, which denotes the relationship between particular clustered rows respectively

columns (Fig.4.14). The shorter the branches are which connect two vectors respectively clus-

ters, the closer is their relation. Thus the hierarchical tree reveals the relationship of one vector

among each others.

Now particular clusters can be chosen and marked with different colours. As one can see

the cluster tree ramifies into two main branches: The first solely consists in cancer patients and

the second chiefly comprises healthy donors, whereby some of the patients are dispersed among

them. The patient main branch spreads into three clearly identifiable subbranches. As one may

see (Fig.4.14) there are definitely perceivable differences in the expression of e.g. theCD62L

(see appendix B for appropriate characteristics) marker onCD3+ T cells (CD3CD62L %).

Nearly all of the patientsCD3+ cells show a rather weak expression of theCD62L marker on

their surface whereas most of the healthy donors indicate an abundant appearance of these.

Now the last assignment of the previous task, thePrinciple Component Analysis(PCA)

will be performed. PCA tries to assess the main expression patterns which are common within

most of the experiment data. Hence one searches expression patterns along the patient data (in

this case the rows of the matrix in Fig.4.14) in order to reveal the main trends of the data points.

The algorithm of the PCA uses the Single Value Decomposition Method in order to find the

Eigenvalues and Eigenvectors of the previous created (HCL) similarity matrix system. In this

special case one will receive 19 Eigenvectors (each experiment accounts for one dimension,

thus there are 19 dimensions) whereby each of the Eigenvectors is linear independent from

each other and indicates a basis function of this 19 dimensional space. Hence each Eigenvector

accounts for a specific trend of the data information and each data row of the matrix can be

displayed as a linear combination of those different weighted Eigenvectors. Each Eigenvector

normally possesses a specific Eigenvalue which indicates the importance of the Eigenvector

among the others. The higher the Eigenvalue is the more influence the Eigenvector gains in

respect to the main trends of the data. Thus the PCA determines all these values and sorts the

56


Eigenvectors in regard to the Eigenvalues in descending order.

Figure 4.15: 3D plot of the clusters within the 3 most influential Principle Components

The 3D plot of the PCA (Fig.4.15) takes the first three Eigenvectors with the three biggest

Eigenvalues, Principal Component 1 (PC1) to Principle Component3 (PC3) (Fig.4.15), (hence

the three main trends of the experiment data) and maps them to the three axes of a three dimen-

sional space. PC1 to x-axis, PC2 to the y-axis and PC3 to the z-axis. Now each of data matrix’s

rows (each row accounts for a patient or healthy donor) can be displayed as a sphere in the

3D plot whereby the sphere indicates the data row’s main trend by placing it to the appropriate

57


location in the 3D space. Thus spheres which are close together in this space have nearly the

same trend respectively expression of the data points.

The different selected clusters of the HCL tree are dyed in the PCA plot as well and one can

easily assess whether the clusters in the 3D space are close together or not. So the PCA can be

used for a better visualisation and verification of cluster algorithms.

As one can see in Fig.4.15 the classified patients (dyed with warm colours) are lying close

together whereas the healthy donors (dyed with cool colours) are displayed in another location

of the space. The black coloured spheres indicate not classified donors.

58

Chapter 5

Discussion

The specific aim of this thesis was to develop a tumoral microenvironment (TME) database

for storing and maintaining all the data which are arising from different immunological ex-

periments. This information comprises pathological information, patient related information,

experiment related information, and data from clinical treatments. This database was especially

developed for tumour microenvironment related data, but the flexible design suggests that it can

be used for other cancer related information as well.

As the whole system depends on the data layer, the development of it was the crucial point.

It is obvious that the design of databases for clinical as well as immunological data has to be

very flexible, to be able to adapt or even upgrade the whole system to new scientific insights

without major changes. The usage of relational databases together with latest Java technologies

(EJB, JDBC, etc. ) on the server side enables a fulfillment of all these demands.

As this database is designed for clinicians and immunologists which are working in dif-

ferent locations, it is important to provide an easy way to give the clients access to the stored

information. Thus a web application is the best solution to provide an easy data accession. The

flexible user management and the secure web connection allow an accession and insight to the

data to only those people having appropriate admissions. For users who need an evaluation and

analysis of their data, appropriate query methods are given, which bring the information into

59

Discussion

desired formats. The best option for a quick data exploring are the basic queries. To get deeper

insights into the data and create queries on parameters to which one is interested in, the custom

queries represent the best solution. The custom queries further provide save options for each

user in order to store specific query parameters in the database.

Security issues need to be treated in a highly specific manner in regard to the interest of

patients. Therefore an encryption of identifiable or indirectly identifiable personal patient infor-

mation was applied. Secured web protocols for data encryption are established and the database

management systems are hidden behind firewalls ensuring best opacity against non authorised

accessors. In terms of storing tumour and immunological related information, a consent proce-

dure proposes [22] that identifiable patient information would require a written informed con-

sent from the patient for further usage, and coded respectively anonymous information could be

collected on an opt-out basis.

Cluster algorithms applied to query data sets have shown that it is possible to distinguish

differences in certain pattern expressions. This demonstrated that the data visualisation with

clustering tools (e.g. HCL) and projection methods (e.g. PCA) are powerful means for further

evaluations of stored data information.

In future work, immunophenotypic and functional data will be integrated with microarray

data in order to explore new relations between expression patterns and cell surface markers.

In conclusion this tool brings together a vast of different data information in order to inves-

tigate new patterns to distinguish between patients and healthy donors. Hence, this database

should become a valuable mean in immunological and cancer related studies.

60

Acknowledgements

At first I want to thank my supervisor Robert Molidor who supported me with all his knowl-

edge and friendship through out my studies. As well I would like to thank my supervisors

Zlatko Trajanoski and Jerome Galon who gave me the great opportunity to perform the studies

for my thesis at the INSERM255 Unit.

Further I want thank Jerome Galon and the members of my work group at INSERM255,

Anne Costes, Mathieu Camus and Arnaud van Cortenbosh, who supported me with their broad

immunological knowledge. It was a great experience and pleasure to work with them.

Special thanks to the members of the bioinformatics group, Alexander Sturn for supporting

me with knowledge about clustering tools, and Michael Maurer for assisting with his experience

in Struts technology.

Finally I want to thank my family for supporting me with all their love and faith through out

my entire studies. I want to express my sincere gratitude.

61

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Appendix A

Database Model

See next page . . .

65

Database Model

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OLI

FTC

ELLS

: N

UM

BER

(20,

5)

MFI

NO

NP

RO

LIFE

RA

TIN

GC

ELL

S :

NU

MB

ER

(2...

MFI

CD

3 : N

UM

BER

(20,

5)

FOLD

INC

RE

AS

EDE

CR

EA

SE

: N

UM

BER

(20,

5)

DES

CR

IPTI

ON

: V

ARC

HA

R2(

255)

CEL

LCYC

LE :

NU

MBE

R(2

0, 5

)

(from

TM

E)

0..1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>

0..1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>

SAM

PLE

ACTI

VA

TIO

NS

BEFO

RE

SA

MP

LETR

EA

TME

NTI

D :

FLO

AT(

126,

...A

CTI

VATI

ON

ID :

FLO

AT(1

26, 0

)

(from

TM

E)

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

SAM

PLE

SEC

ON

DP

OS

SELE

CTI

ON

S

SA

MP

LETR

EA

TME

NTI

D :

FLO

AT(

126,

...

PO

SSE

LEC

TIO

NID

: FL

OA

T(12

6, 0

)

(from

TM

E)

10.

.*1

0..*

<<N

on-Id

entif

yin.

..

SAM

PLE

FIR

STN

EGSE

LEC

TIO

NS

SAM

PLE

TREA

TME

NTI

D :

FLO

AT(1

26, 0

)N

EGSE

LEC

TIO

NID

: FL

OAT

(126

, 0)

(from

TM

E)

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

SAM

PLE

STIM

ULA

TIO

NSA

FTE

R

SA

MP

LETR

EA

TME

NTI

D :

FLO

AT(

...S

TIM

ULA

TIO

NID

: FL

OAT

(126

, 0)

(from

TM

E)

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

SAM

PLER

NAE

XTR

ACTI

ON

S

SA

MP

LETR

EA

TME

NTI

D :

FLO

AT(

126.

..R

NA

EX

TRA

CTI

ON

ID :

FLO

AT(

126,

0)

(from

TM

E)

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

SAM

PLES

ECO

ND

NEG

SELE

CTI

ON

S

SA

MP

LETR

EA

TME

NTI

D :

FLO

AT(

126.

..N

EGSE

LEC

TIO

NID

: FL

OAT

(126

, 0)

(from

TM

E)

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

SAM

PLEF

IRST

POSS

ELEC

TIO

NS

SAM

PLET

REA

TMEN

TID

: FL

OA

T(12

6, 0

)P

OS

SE

LEC

TIO

NID

: FL

OAT

(126

, 0)

(from

TM

E)

1 0..*1 0..*

<<N

on-Id

entif

ying

>>

SAM

PLE

RN

AAM

PLI

FIC

ATIO

NS

SA

MP

LETR

EA

TME

NTI

D :

FLO

AT(

126,

...R

NA

AM

PLI

FIC

ATI

ON

ID :

FLO

AT(

126,

0...

(from

TM

E)

1

0..*

1

0..*

<<N

on-Id

entif

yin.

..

SAM

PLEA

CTI

VATI

ON

SAFT

ER

SA

MP

LETR

EA

TME

NTI

D :

FLO

AT.

..AC

TIVA

TIO

NID

: FL

OAT

(126

, 0)

(from

TM

E)

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

SAM

PLES

TIM

ULA

TIO

NSB

EFO

RE

SA

MP

LETR

EA

TME

NTI

D :

FLO

AT(

126,

...S

TIM

ULA

TIO

NID

: FL

OAT

(126

, 0)

(from

TM

E)

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

TRE

ATM

ENTS

TRE

ATM

EN

TID

: FL

OA

T(12

6,...

TYP

E : V

AR

CH

AR

2(25

5)D

ES

CR

IPTI

ON

: V

AR

CH

AR

2(...

(from

TM

E)

CAN

CER

SUBT

YPE

S

CA

NC

ER

SU

BTY

PE

ID :

FLO

AT(

1...

TYP

E : V

AR

CH

AR

2(50

)V

ALU

E :

NU

MBE

R(2

0, 5

)D

ES

CR

IPTI

ON

ID :

VA

RC

HA

R2(

2...

(from

TM

E)

CAN

CE

RTU

MO

RA

LLIQ

UID

TUM

OR

ALL

IQU

IDID

: FL

OA

T(12

6...TY

PE

: VA

RC

HA

R2(

50)

VA

LUE

: N

UM

BER

(20,

5)

DE

SC

RIP

TIO

NID

: V

AR

CH

AR

2(2..

.

(from

TM

E)

CAN

CE

RTY

PES

CA

NC

ER

TYP

EID

: FL

OA

T(12

6,...

TYP

E : V

AR

CH

AR

2(50

)V

ALU

E :

NU

MBE

R(2

0, 5

)D

ES

CR

IPTI

ON

ID :

VA

RC

HA

R2.

..

(from

TM

E)

PR

IMIT

IVE

CA

NC

ER

S

PR

IMIT

IVE

CA

NC

ER

ID :

FLO

AT(

1...

TYPE

: V

ARC

HA

R2(

255)

VAL

UE

: NU

MBE

R(2

0, 5

)D

ES

CR

IPTI

ON

ID :

VA

RC

HA

R2(

25...

(from

TM

E)

MST

AGES

MST

AGE

ID :

FLO

AT(1

26, 0

)TY

PE :

VAR

CH

AR

2(50

)V

ALU

E : N

UM

BER

(20,

5)

DE

SC

RIP

TIO

NID

: V

AR

CH

AR

2...

(from

TM

E)

NS

TAG

ES

NST

AGE

ID :

FLO

AT(1

26, 0

)TY

PE

: V

ARC

HA

R2(

50)

VALU

E : N

UM

BER

(20,

5)

DE

SC

RIP

TIO

NID

: V

AR

CH

AR

2...

(from

TM

E)

PST

AG

ES

PST

AGE

ID :

FLO

AT(

126,

0)

TYP

E : V

AR

CH

AR

2(50

)VA

LUE

: N

UM

BER

(20,

5)

DE

SC

RIP

TIO

NID

: V

AR

CH

AR

2...

(from

TM

E)

TSTA

GES

TSTA

GE

ID :

FLO

AT(1

26, 0

)TY

PE

: VAR

CH

AR

2(50

)V

ALU

E :

NU

MB

ER

(20,

5)

DE

SC

RIP

TIO

NID

: V

AR

CH

AR

2...

(from

TM

E)

CAN

CER

STAG

ES

CA

NC

ER

STA

GE

ID :

FLO

AT(

1...

PID

: FL

OAT

(126

, 0)

TID

: FL

OAT

(126

, 0)

NID

: FL

OA

T(12

6, 0

)M

ID :

FLO

AT(1

26, 0

)

(from

TM

E)

0..1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>

0..1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>

0..1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>0.

.1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>

HO

SP

ITAL

S

HO

SPI

TALI

D :

FLO

AT(1

26, 0

)H

OS

PIT

ALN

AM

E :

VA

RC

HA

R2.

..S

TREE

T : V

ARC

HA

R2(

300)

ZIP

: V

AR

CH

AR

2(30

0)C

ITY

: V

ARC

HA

R2(

300)

CO

UN

TRY

: V

AR

CH

AR

2(30

0)P

HO

NE

: VA

RC

HA

R2(

300)

FAX

: VA

RC

HA

R2(

300)

EM

AIL

: V

AR

CH

AR

2(30

0)D

ES

CR

IPTI

ON

ID :

VA

RC

HA

R2.

..

(from

TM

E)

SAV

EDQ

UE

RY

OPT

ION

S

SA

VE

DQ

UE

RY

OP

TIO

NID

: FL

OA

T(12

6...

USE

RID

: FL

OAT

(126

, 0)

SH

OR

TDE

SC

RIP

TIO

N :

VA

RC

HA

R2(

25...

DES

CR

IPTI

ON

: V

ARC

HA

R2(

400)

(from

TM

E)

US

ER

SH

OS

PITA

LS

USE

RID

: FL

OAT

(126

, 0)

HO

SP

ITA

LID

: FL

OA

T(12

6...

(from

TM

E)

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

PAT

IEN

TDB

US

ER

S

US

ER

ID :

FLO

AT(1

26, 0

)U

SER

NA

ME

: VAR

CH

AR2(

300)

PA

SS

WO

RD

_ : V

ARC

HA

R2(

300)

CH

AN

GE

PA

SS

WO

RD

: V

AR

CH

AR

2(5..

.D

ATE

OFP

AS

SW

OR

DC

HA

NG

E :

DA

T...

FULL

NAM

E : V

ARC

HAR

2(30

0)EM

AIL

: VAR

CH

AR

2(30

0)IP

AD

DR

ES

S :

VA

RC

HA

R2(

300)

DE

AC

TIV

ATE

D :

VA

RC

HA

R2(

5)D

ESC

RIP

TIO

NID

: V

ARC

HA

R2(

255)

(from

T...0.

.1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>

10.

.*1

0..*

<<N

on-Id

entif

yin.

..

PAT

IEN

TDB

US

ERR

OLE

S

RO

LEID

: FL

OAT

(126

, 0)

RO

LEN

AM

E : V

ARC

HAR

2(30

)D

ES

CR

IPTI

ON

: V

AR

CH

AR

2...

(from

TM

E)

USE

RS

USE

RR

OLE

S

US

ER

ID :

FLO

AT(

126.

..R

OLE

ID :

FLO

AT(

126,.

..

(from

TM

E)

10.

.*1

0..*

<<N

on-Id

entif

yin.

..

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

CYT

OK

INE

EXP

ER

IME

NTS

CY

TOK

INE

EX

PE

RIM

EN

TID

: FL

OA

T(12

6...

EXP

ERIM

ENTI

D :

FLO

AT(1

26, 0

)FA

CSF

ILEN

AME

: VAR

CH

AR

2(25

5)E

XPER

IME

NTB

Y : V

ARC

HA

R2(

255)

AN

ALY

SIS

BY

: VAR

CH

AR2(

255)

DES

CR

IPTI

ON

: VA

RC

HAR

2(25

5)

(from

T...

0..10..*

0..10..*

<<N

on-Id

entif

ying

>>

CA

NC

ER

S

CAN

CER

ID :

FLO

AT(1

26, 0

)E

XP

ERIM

ENTI

D :

FLO

AT(1

26, 0

)P

RIM

ITIV

EC

AN

CE

R :

FLO

AT(

126...

TYPE

ID :

FLO

AT(1

26, 0

)SU

BTY

PEID

: FL

OAT

(126

, 0)

STAG

EID

: FL

OAT

(126

, 0)

TUM

OR

ALL

IQU

IDID

: FL

OA

T(12

6...D

ES

CR

IPTI

ON

ID :

VA

RC

HA

R2(

2...

(from

TM

E)

0..1 0..*

0..1 0..*

<<N

on-Id

entif

ying

>>

0..1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>

0..1

0..*

0..1

0..* <<N

on-Id

entif

ying

>>

0..1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>0.

.10.

.*0.

.10.

.*

<<N

on-Id

entif

ying

>>

PATI

ENTS

PATI

ENTI

D :

VAR

CH

AR

2(30

0)H

OSP

ITAL

ID :

FLO

AT(1

26, 0

)D

ATEO

FBIR

TH :

DAT

ES

EXE

: CH

AR

(2)

DES

CR

IPTI

ON

ID :

VA

RC

HA

R2(

250)

PATI

ENTP

K : F

LOAT

(126

, 0)

NAM

E : V

AR

CH

AR

2(30

0)FI

RST

NAM

E : V

ARC

HAR

2(30

0)M

ID :

VAR

CH

AR

2(30

0)

(from

TM

E)

0..1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>

PR

OLI

FER

ATI

ON

S

PR

OLI

FER

ATI

ON

ID :

FLO

AT(

126,

0)

EXP

ERIM

ENTI

D :

FLO

AT(1

26, 0

)D

UP

LIC

ATE

PR

OLI

FID

: FL

OA

T(12

6...

DU

PLI

CA

TEN

UM

BE

R :

FLO

AT(

126.

..FA

CSF

ILEN

AME

: VAR

CH

AR

2(25

5)E

XPER

IMEN

TBY

: VAR

CH

AR

2(25

5)A

NA

LYS

ISB

Y : V

ARC

HA

R2(

255)

INC

UBA

TIO

NTI

ME

: FL

OAT

(126

, 0)

DES

CR

IPTI

ON

: V

AR

CH

AR

2(25

5)

(from

TM

E)

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>0.

.10.

.1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>

SAM

PLET

REA

TMEN

TS

SAM

PLET

REA

TMEN

TID

: FL

OA

T(12

6, 0

)E

XP

ER

IME

NTI

D :

FLO

AT(

126,

0)

DIL

ACER

ATIO

N :

VAR

CH

AR2(

1)FI

CO

LL :

VAR

CH

AR2(

1)FA

CS

EX

PE

RIM

EN

T : V

AR

CH

AR

2(1)

CFS

EP

RO

LIFE

RA

TIO

N :

VA

RC

HA

R2(

1)C

YTO

KIN

ESEC

RET

ION

: VA

RC

HA

R2(

1)IN

CU

BTI

ME

BE

FOR

E :

NU

MBE

R(2

0, 5

)IN

CU

BTIM

EAFT

ER :

NU

MBE

R(2

0, 5

)TI

MEB

IOPS

TOEX

P : N

UM

BER

(20,

5)

CD

19 :

NU

MB

ER

(20,

5)

CD

3 : N

UM

BER

(20,

5)

CD

56 :

NU

MB

ER

(20,

5)

CD

14 :

NU

MB

ER

(20,

5)

CD

4 : N

UM

BER

(20,

5)

CD

8 : N

UM

BER

(20,

5)

CAN

CER

CEL

LS :

NU

MBE

R(2

0, 5

)O

THE

RS

: N

UM

BE

R(2

0, 5

)D

ESC

RIP

TIO

N :

VAR

CH

AR2(

255)

(from

TM

E)

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

10.

.*1

0..*

<<N

on-Id

entif

yin.

..

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

1

0..*

1

0..*

<<N

on-Id

entif

yin.

..

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>

1

0..*

1

0..*

<<N

on-Id

entif

ying

>>1 0.

.*1 0..*

<<N

on-Id

entif

ying

>>

TES

TMA

TER

IAL

MAT

ERIA

LID

: FL

OAT

(126

, 0)

NAM

E : V

AR

CH

AR

2(25

0)V

ALU

E :

NU

MB

ER

(20,

5)

DE

SC

RIP

TIO

NID

: V

AR

CH

AR

2(2..

.

(from

TM

E)

THE

RA

PIE

S

THE

RA

PIE

ID :

FLO

AT(

126,

0)

EX

PE

RIM

EN

TID

: FL

OA

T(12

6...

TRE

ATM

EN

TID

: FL

OA

T(12

6, ...

DE

SC

RIP

TIO

N :

VA

RC

HA

R2(

...

(from

TM

E)0.

.10.

.*0.

.10.

.*

<<N

on-Id

entif

ying

>>

AN

TIG

EN

RA

NG

ES

TYP

EID

: FL

OAT

(126

, 0)

MIN

EX

P :

NU

MB

ER

(10,

0...

MA

XE

XP

: N

UM

BE

R(1

0, ...

MIN

MFI

: N

UM

BER

(10,

0)

MA

XM

FI :

NU

MB

ER

(20,

...

(from

BIO

LMAR

KER

S)

FAC

SLY

MP

HO

CY

TES

LYM

PHO

CYT

EID

: FL

OAT

(126

, 0)

EXP

ERIM

ENTI

D :

FLO

AT(1

26, 0

)TY

PE

ID :

FLO

AT(1

26, 0

)E

XPR

ES

SIO

N :

NU

MBE

R(2

0, 5

)M

EA

NFL

UO

RE

SC

EN

CE

: N

UM

BE

R(2

0...

DES

CR

IPTI

ON

ID :

VAR

CH

AR

2(25

0)G

ATEI

D :

FLO

AT(1

26, 0

)

(from

T...

FAC

SLYM

PHO

CYT

EGAT

ES

ID :

FLO

AT(1

26, 0

)G

ATE

: V

AR

CH

AR

2(40

)D

ES

CR

IPTI

ON

ID :

VA

RC

HA

R2(

2...

(from

TM

E)0.

.10.

.*0.

.10.

.*

<<N

on-Id

entif

ying

>>

FAC

SLY

MP

HO

CY

TETY

PES

TYPE

ID :

FLO

AT(1

26, 0

)TY

PE :

VAR

CH

AR

2(60

)D

ES

CR

IPTI

ON

ID :

VA

RC

HA

R2.

..

(from

TM

E)

1

0..1

1

0..1

<<N

on-Id

entif

ying

>>

0..1

0..*

0..1

0..*

<<N

on-Id

entif

ying

>>

FAC

SLY

MPH

OC

YTEG

ATE

STY

PES

GAT

EID

: FL

OA

T(12

6, 0

)TY

PEI

D :

FLO

AT(

126,

0)

(from

TM

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66

Appendix B

Cluster Designation List

CD antigen Cellular expression Functions Other names

CD1a,b,c,d Cortical thymocytes, Langerhans cells, Den-

dritic cells, B cells (CD1c), Intestinal epithe-

lium, smooth muscle, blood vessels (CD1d)

MHC class I-like molecule, associated with b2-

microglobulin. May have specialised role in presentation

of lipid antigens

-

CD2 T cells, thymocytes, NK cells Adhesion molecule, binding CD58 (LFA-3). Binds Lck in-

tracellularly and activate T cells

T11, LFA-2

CD2R Activated T cells activation-dependent conformational form of CD2 T11-3

CD3 Thymocytes, T cells Associated with the T cell antigen receptor. Required for

cell surface expression of and signal transduction by TCR.

Cytoplasmic domains contain ITAM motifs and bind cyto-

plasmic tyrosine kinases.

T3

CD4 Thymocyte subsets, helper and inflammatory T

cells (about two thirds of peripheral T cells),

monocytes, macrophages

Coreceptor for MHC class II molecules. Binds Lck on cy-

toplasmic face of membrane. Receptor for HIV-I and HIV-2

gp120.

T4, L3T4

CD5 Thymocytes, T cells, subset of B cells Binds to CD72 T1, Ly1

CD6 Thymocytes, T cells, B cell CLL unknown. T12

CD7 Pluripotential hematopoietic cells, thymocytes,

T cells

unknown, cytoplasmic domain binds PI-3 kinase on

crosslinking. Marker for T cell ALL and pluripotential stem

cell leukemias

-

CD8 Thymocyte subsets, cytotoxic T cells (about

one third of peripheral T cells)

Coreceptor for MHC class I molecules. Binds lck on cyto-

plasmic face of membrane

T8, Lyt2,3

continued on next page

67


continued from previous page


CD9 Pre-B cells, monocytes, eosinophils, basophils,

platelets, activated T cells, brain and peripheral

nerves, vascular smooth muscle

mediates platelet aggregation and activation via FcgRIIa,

may play a role in cell migration

-

CD10 B and T cell precursors, bone marrow stromal

cells

zinc metalloproteinase, marker for pre B ALL Neutral endopeptidase, Common

Acute Lymphocytic Leukemia Antigen

(CALLA)

CD11a lymphocytes, granulocytes, monocytes and

macrophages

aL subunit of integrin LFA-1 (associated with CD18) ; binds

to CD54 (ICAM-1), ICAM-2 and ICAM-3

LFA-1

CD11b myeloid and natural killer cells aM subunit of integrin CR3 (associated with CD18) ; binds

CD54, complement component iC3b and extracellular ma-

trix proteins

Mac-1

CD11c myeloid cells aX subunit of integrin CR4 (associated with CD18) ; binds

fibrinogen

CR4, p150, 95

CDw12 monocytes, granulocytes, platelets unknown -

CD13 myelomonocytic cells zinc metalloproteinase aminopeptidase N

CD14 myelomonocytic cells receptor for complex of LPS and LPS binding protein (LBP) -

CD15 neutrophils, eosinophils, monocytes branched pentasaccharide, expressed on glycolipids and

many cell surface glycoproteins; the sialylated form is a lig-

and for CD62E (ELAM)

Lewsi-x (Lex)

CD16a neutrophils, NK cells, macrophages component of low affinity Fc receptor, FcgRIII, mediates

phagocytosis, cytokine production and ADCC.

FcgRIII

CDw17 neutrophils, monocytes, platelets lactosyl ceramide, a cell surface glycosphingolipid -

CD18 Leukocytes integrin b2 subunit, associates with CD11a,b and c. -

CD19 B cells, follicular dendritic cells forms complex with CD21 (CR2)and CD81 (TAPA-1);

coreceptor for B cells - cytoplasmic domain binds cytoplas-

mic tyrosine kinases and PI-3 kinase.

-

CD20 B cells Oligomers of CD20 may form a Ca2+ channel; possible role

in regulating B cell activation

-

CD21 mature B cells, FDC receptor for complement component C3d, EBV. With CD19

and CD81 forms coreceptor for B cells

CR2

CD22 mature B cells Adhesion of B cells to monocytes, T cells BL-CAM

CD23 mature B cells, activated macrophages,

eosinophils, follicular dendritic cells, platelets

low affinity receptor for IgE, regulates IgE synthesis; ligand

for CD19:CD21:CD81 coreceptor

FceRII


68




CD24 B cells, granulocytes unknown possible human homologue of mouse

Heat Stable Antigen (HSA) or J11d.

CD25 activated T cells, B cells, monocytes IL-2 receptor Tac

CD26 Activated B and T cells, macrophages Exopeptidase, cleaves N terminal X-Pro or X-Ala dipep-

tides from polypeptides.

Dipeptidyl peptidase IV

CD27 Medullary thymocytes, T cells, NK cells and

some B cells

binds CD70; can function as a costimulator for T and B cells-

CD28 T cell subsets, activated B cells Activation of naive T cells, receptor for costimulatory signal

(signal 2) binds CD80 (B7-1) and B7-2

Tp44

CD29 Leukocytes Integrin b1 subunit, associates with CD49a in VLA-1 inte-

grin

-

CD30 Activated T, B and NK cells, monocytes Binds CD30L; crosslinking CD30 enhances proliferation of

B and T cells

Ki-1

CD31 monocytes, platelets, granulocytes, T cell sub-

sets, endothelial cells

Adhesion molecule, mediating both leukocyte/endothelial

and endothelial/endothelial interactions

PECAM-1

CDw32 Monocytes, granulocytes, B cells eosinophils low affinity Fc receptor for aggregated Ig/immune com-

plexes

FcgRII

CD33 myeloid progenitor cells, monocytes unknown -

CD34 hematopoietic precursors, capillary endothe-

lium

Ligand for CD62 (L-selectin) -

CD35 Erythrocytes, B cells, monocytes, neutrophils,

eosinophils, FDC

Complement receptor 1, binds C3B and C4b, mediates

phagocytosis

CR1

CD36 platelets, monocytes, endothelial cells platelet adhesion molecule; involved in recognition and

phagocytosis of apoptosed cells

platelet GPIV, GPIIIb

CD37 mature B cells, mature T cells, myeloid cells unknown, may be involved in signal transduction; forms

complexes with CD53, CD81, CD82 and MHC class II.

-

CD38 early B and T cells, activated T cells, germinal

centre B cells, plasma cells

NAD glycohydrolase, augments B cell proliferation T10

CD39 activated B cells, activated NK cells,

macrophages, dendritic cells

unknown, may mediate adhesion of B cells -

CD40 B cells, macrophages, dendritic cells, basal ep-

ithelial cells

binds CD40L ; receptor for costimulatory signal for B cells,

promotes growth, differentiation and isotype switching of

B cells, cytokine production by macrophages and dendritic

cells

-


69




CD41 platelets, megakaryocytes aIIb integrin, associates with CD61 to form GPIIb, binds

fibrinogen, fibronectin, von Willebrand factor and throm-

bospondin

GPIIb

CD42a,b,c,d platelets, megakaryocytes binds von Willebrand factor, thrombin; essential for platelet

adhesion at sites of injury

a: GPIX b: GPIba C: GPIbb d: GPV

CD43 leukocytes, except resting B cells binds CD54 (ICAM-1) has extended structure, approx

45nm long and may be anti-adhesive

leukosialin, sialophorin

CD44 leukocytes, erythrocytes binds hyaluronic acid, mediates adhesion of leukocytes Hermes antigen, Pgp-1

CD45 all hematopoietic cells tyrosine phosphatase, augments signalling through antigen

receptor of B and T cells, multiple isoforms result from al-

ternative splicing (see below)

Leukocyte common antigen (LCA),

T200, B220

CD45RO T cell subsets, B cell subsets, monocytes,

macrophages

isoform of CD45 containing none of the A, B and C exons -

CD45RA B cells, T cell subsets (naive T cells) monocytes isoforms of CD45 containing the A exon -

CD45RB T cell subsets, B cells, monocytes,

macrophages, granulocytes

isoforms of CD45 containing the B exon T200

CD46 hematopoietic and non-hematopoietic nucle-

ated cells

membrane cofactor protein, binds to C3b and C4b to permit

their degradation by Factor I

MCP

CD47 all cells unknown, associated with Rh blood group -

CD48 leukocytes unknown Blast-1

CD49a activated T cells, monocytes, neuronal cells,

smooth muscle

a1 integrin, associates with CD29, binds collagen, laminin-

1

VLA-1

CD49b B cells, monocyte, platelets, megakaryocytes,

neuronal, epithelial and endothelial cells, osteo-

clasts

a2 integrin, associates with CD29, binds collagen, laminin VLA-2, platelet GPIa

CD49c B cells, many adherent cells a3 integrin, associates with CD29, bindslaminin-5, fi-

bronectin, collagen, entactin, invasin

VLA-3

CD49d Broad distribution includes B cells, thymocytes,

monocytes, granulocytes, dendritic cells

a4 integrin, associates with CD29, binds fibronectin,

MadCAM-1, VCAM-1

VLA-4

CD49e Broad distribution includes memory T cells,

monocytes, platelets

a5 integrin, associates with CD29, binds fibronectin, invasinVLA-5

CD49f T lymphocytes, monocytes, platelets,

megakaryocyes, trophoblast

a6integrin, associates with CD29, binds laminins, invasin,

merosin

VLA-6


70




CD50 thymocytes, T cells, B cells,monocytes, granu-

locytes

Binds integrin CD11a/CD18 ICAM-3

CD51 platelets, megakaryocytes av integrin, associates with CD61, binds vitronectin, von

Willebrand factor, fibrinogen and thrombospondin; may be

receptor for apoptotic cells

vitronectin receptor

CD52 thymocytes, T cells, B cells (not plasma cells),

monocytes, granulocytes, spermatozoa

unknown, target for antibodies used therapeutically to de-

plete T cells from bone marrow

CAMPATH-1 HE5

CD53 leukocytes unknown MRC OX44

CD54 hematopoietic and non-hematopoietic cells InterCellular Adhesion Molecule, (ICAM)-1 binds

CD11a/CD18 (LFA-1) and CD11b/CD18 (Mac-1)

integrins, receptor for rhinovirus

ICAM-1

CD55 hematopoietic and non-hematopoietic cells Decay Accelerating Factor (DAF), binds C3b, disassembles

C3/C5 convertase

DAF

CD56 NK cells isoform of Neural Cell Adhesion Molecule (NCAM), adhe-

sion molecule

NKH-1

CD57 NK cells subsets of T cells, B cells and mono-

cytes

oligosaccharide, found on many cell surface glycoproteins HNK-1, Leu-7

CD58 hematopoietic and non-hematopoietic cells Leukocyte Function-associated Antigen-3 (LFA-3), binds

CD2, adhesion molecule

LFA-3

CD59 hematopoietic and non-hematopoietic cells binds comlement components C8 and C9, blocks assembly

of membrane attack complex

Protectin, Mac inhibitor

CDw60 T cell subsets, platelets, monocytes 9-O-acetylated disialoyl group present on gangliosides, pre-

dominantly ganglioside D3

-

CD61 platelets, megakaryocytes, macrophages integrin b3 subunit, associates with CD41 (GPIIb/IIIa) or

CD51 (vitronectin receptor)

-

CD62E endothelium endothelium leukocyte adhesion molecule (ELAM),

bindssialyl-Lewis x, mediates rolling interaction of

neutrophils on endothelium

ELAM-1, E-selectin

CD62L B cells, T cells, monocytes, NK cells leukocyte adhesion molecule (LAM), binds CD34, Gly-

CAM, mediates rolling interactions with endothelium

LAM-1, L-selectin, LECAM-1

CD62P platelets, megakaryocytes, endothelium adhesion molecule, binds PSGL-1, mediates interaction of

platelets with endothelial cells, monocytes and rolling inter-

action of leukocytes on endothelium

P-selectin, PADGEM

CD63 activated platelets, monocytes, macrophages unknown, is lysosomal membrane protein translocated to

cell surface after activation

platelet activation antigen


71




CD64 monocytes, macrophages high affinity receptor for IgG, binds IgG3;IgG1;IgG4;IgG2,

mediates phagocytosis, antigen capture, ADCC

FcgRI

CD65 myeloid cells oligosaccharide component of a ceramide dodecasaccharide-

CD66a neutrophils unknown, member of carcinoembryonic antigen (CEA)

family (see below)

biliary glycoprotein-1 (BGP-1)

CD66b granulocytes unknown, member of carcinoembryonic antigen (CEA)

family

previously CD67

CD66c neutrophils, colon carcinoma unknown, member of carcinoembryonic antigen (CEA)

family

Nonspecific Crossreacting Antigen

(NCA)

CD66d neutrophils unknown, member of carcinoembryonic antigen (CEA)

family

-

CD66e adult colon epithelium, colon carcinoma unknown, member of carcinoembryonic antigen (CEA)

family

CarcinoEmbryonic Antigen (CEA)

CD68 monocytes, macrophages, neutrophils, ba-

sophils, large lymphocytes

unknown macrosialin

CD69 activated B cells, activated T cells, activated

macrophages, activated NK cells

unknown, early activation antigen Activation Inducer Molecule (AIM)

CD70 activated B cells, activated T cells,

macrophages

Ligand for CD27, may function in co-stimulation of B and

T cells

Ki-24

CD71 All proliferating cells, hence activated leuko-

cytes

transferrin receptor T9

CD72 B cells (not plasma cells) unknown, ligand for CD5 Lyb-2

CD73 B cell subsets, T cell subsets ecto-5O-nucleotidase, dephosphorylates nucleotides to al-

low nucleoside uptake

-

CD74 B cells, macrophages, monocytes, MHC class

II positive cells

MHC class II associated Invariant chain Ii, Ig

CD75 mature B cells, T cell subsets sialoglycan moiety, ligand for CD22, mediates B cell/B cell

adhesion

-

CD76 mature B cells, T cell subsets a 2,6 sialylated polylactosamine expressed on glycosphin-

golilpids and glycoproteins

-

CD77 germinal center B cells Neutral glycosphingolipid

(Gala1R©4Galb1R©4Glcb1R©ceramide), binds

Shiga toxin, Crosslinking induces apoptosis

Globotriaocylceramide (Gb3), Pk blood

group


72




Cdw78 B cells unknown Ba

CD79a,b B cells components of B cell antigen receptor analogous to CD3,

required for cell surface expression and signal transduction

Iga, Igb

CD80 B cell subset costimulator, ligand for CD28 and CTLA-4 B7 (now B7-1), BB1

CD81 lymphocytes associates with CD19, CD21 to form B cell coreceptor Target of AntiProliferative Antibody

(TAPA-1)

CD82 leukocytes unknown R2

CD83 Activated B cells, activated T cells, circulating

dendritic cells (veil cells)

- HB15

CDw84 monocytes, platelets, circulating B cells - GR6

CD85 monocytes, circulating B cells - GR4

CD86 monocytes, activated B cells, dendritic cells Ligand for CD28 and CTLA4 B7.2

CD87 granulocytes, monocytes, macrophages, T cells,

NK cells, wide variety of nonhematopioetic cell

types

Receptor for urokinase plasminogen activator uPAR

CD88 polymorphonuclear leukocytes, macrophages,

mast cells

Receptor for complement component C5a C5aR

CD89 monocytes, macrophages, granulocytes, neu-

trophils, B cell subsets, T cell subsets

IgA receptor FcaR

CD90 CD34+ prothymocytes (human) thymocytes, T

cells (mouse)

unknown Thy-1

CD91 monocytes, many nonhematopoietic cells a2 macroglobulin receptor -

CDw92 neutrophils, monocytes, platelets, endothelium - GR9

CD93 neutrophils, monocytes, endothelium - GR11

CD94 T cell subsets, NK cells - KP43

CD95 wide variety of cell lines in vivodistribution un-

certain

binds TNF-like ligand, induces apoptosis Apo-1, Fas

CD96 activated T cells, NK cells - T cell ACTivation Increased Late Expres-

sion (TACTILE)

CD97 activated B and T cells, monocytes, granulo-

cytes

Binds CD55 GR1


73




CD98 T cells, B cells, NK cells, granulocytes, all hu-

man cell lines

may be amino acid transporter 4F2, FRP-1

CD99 Peripheral blood lymphocytes, thymocytes unknown MIC2, E2

CD100 Hematopoietic cells unknown GR3

CD101 monocytes, granulocytes, dendritic cells, acti-

vated T cells

unknown V7, BPC#4

CD102 Resting lymphocytes, monocytes, vascular en-

dothelial cells (strongest)

binds CD11a/CD18 (LFA-1) but not CD11b/CD18 (Mac-1) ICAM-2

CD103 Intraepithelial lymphocytes, 2-6% of peripheral

blood lymphocytes

aE integrin HML-1, a6,aE integrin

CD104 CD4-CD8- thymocytes, neuronal, epithelial

and some endothelial cells, Schwann cells, tro-

phoblasts

integrin b4, associates with CD49f, binds laminins b4 integrin

CD120a hematopoietic and non-hematopoietic cells,

highest on epithelial cells

TNF receptor, binds both TNFa and TNFb TNFR-I

CD120b hematopoietic and non-hematopoietic cells,

highest on myeloid cells

TNF receptor, binds both TNFa and TNFb TNFR-II

CD122 Natural Killer cells, resting T cell subsets, some

B cell lines

IL-2 receptor b chain IL-2Rb

CD210 B cells Receptors involved with cell signaling and immune regula-

tion

CK

74

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