Genetic Approaches to Study Transcriptional Activation and Tumor Suppression: a Dissertation

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2012-05-01

Genetic Approaches to Study Transcriptional Activation and Tumor Suppression: A Dissertation

Ling Lin University of Massachusetts Medical School

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GENETIC APPROACHES TO STUDY

TRANSCRIPTIONAL ACTIVATION AND TUMOR SUPPRESSION

A Dissertation Presented

LING LIN

Submitted to the Faculty of the

University of Massachusetts Graduate School of Biomedical Sciences, Worcester

In partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

MAY 1, 2012

INTERDISCIPLINARY GRADUATE PROGRAM

GENETIC APPROACHES TO STUDY

TRANSCRIPTIONAL ACTIVATION AND TUMOR SUPPRESSION

A Dissertation Presented By

LING LIN

The signatures of the Dissertation Defense Committee signify completion and approval as to style and content of the Dissertation

Michael Green, Thesis Advisor

Paul Kaufman, Member of Committee

Kirsten Hagstrom, Member of Committee

Sharon Cantor, Member of Committee

The signature of the Chair of the Committee signifies that the written dissertation meets the requirements of the Dissertation Committee

Scot Wolfe, Chair of Committee The signature of the Dean of the Graduate School of Biomedical Sciences signifies that the student has met all graduation requirements of the school

Anthony Carruthers, Ph.D., Dean of the Graduate School of Biomedical Sciences Program May 1, 2012 DEDICATION

I would like to dedicate this work to my parents Jingfang Yao and Feng

Lin. Their support and encouragement of my pursuits in science helped me conquer all the difficulties in the past eight years. I wish to share my joy of success with them.

iii

ACKNOWLEDGEMENTS

First of all, I would like to thank Dr. Michael Green for providing me the

opportunity to study the cutting edge of current science. His intelligence and

expertise always inspire me and guide me to improve myself, scientific and

personal. He forgave the mistakes that I made and has been patient with me even when experiments are not working. My graduate studies would not be completed without his guidance and support.

I would also like to thank Lynn Chamberlain. Lynn helped me with many laborious experiments and helped me get organized. Her companion for the last eight years is a great support.

I would also thank Green lab members, who provided great suggestions and discussions in many experiments.

Lastly, I would thank my Advisory Committee for their support and advice during my graduate studies.

ABSTRACT

The development of methods and techniques is the driving force of scientific research. In this work, we described two large-scale screens in studying transcriptional activation and tumor suppression.

In Part I, we studied transcriptional activation mechanisms by deriving and characterizing activation defective mutants. Promoter-specific transcriptional activators stimulate transcription through direct interactions with one or more components of the transcription machinery, termed the “target”. The identification of direct in vivo targets of activators has been a major challenge. We perform a large-scale genetic screen to derive and characterize tra1 alleles that are selectively defective for interaction with Gal4 in vivo. Utilizing these mutants, we demonstrated that Tra is an essential target for Gal4 activation, Gal4 and Tra1 bind cooperatively at the promoter and the Gal4–Tra1 interaction occurs predominantly on the promoter. In addition, we demonstrated that the Gal4- interaction site on Tra1 is highly selective.

In Part II, we described a functional genomics approach to discover new tumor suppressor genes. A goal of contemporary cancer research is to identify the genes responsible for neoplastic transformation. Cells that are immortalized but non-tumorigenic were stably transduced with pools of short hairpin RNAs

v (shRNAs) and tested for their ability to form tumors in mice. ShRNAs in any

resulting tumors were identified by sequencing to reveal candidate TSGs, which

were then validated both experimentally and clinically by analysis of human

tumor samples. Using this approach, we identified and validated 33 candidate

TSGs. We found that most candidate TSGs were down-regulated in >70% of

human lung squamous cell carcinoma (hLSCC) samples, and 17 candidate

TSGs negatively regulate FGFR signalling pathway, and their ectopic expression inhibited growth of hLSCC xenografts. Furthermore, we suggest that by examining at the expression level of TSGs in lung cancer patients, we can predict their drug responsiveness to FGFR inhibitors. In conclusion, we have identified many new lung squamous cell cancer TSGs, using an experimental strategy that can be broadly applied to find TSGs in other tumor types.

TABLE OF CONTENTS

PART I Studying Transcriptional Activation Mechanisms by Deriving and 1 Characterizing Activation Defective Mutants CHAPTER I 1.1 General Introduction 2 1.1.1 Transcription 3 1.1.2 Pol II Transcription 3 General transcription factors (GTF) 4 PIC formation 4 PIC activation 5 Recycle of transcription factors 5 1.1.3 Transcription regulation 5 Trans-regulating factors 6 Repressors and mechanisms 6 Activators and mechanisms 6 Cis-regulating factors 7 Chromatin structure and functions 8 Chromatin modification 9 Classes and functions 9 Acetylation and transcription 9 1.1.4 Tra1 in transcriptional regulation 11 Tra1 functions 11 Tra1 structure 11 1.1.5 Gal4-directed transcriptional regulation 12 Gal4 structure 12 Gal4 functions 12 Gal4 regulation 13

vii Gal4 targets 14 Chapter I Figures 16 Chapter I Table 24 CHAPTER II 25 1.2 Analysis of Gal4-directed transcription activation using Tra1 mutants selectively defective for interaction with Gal4 Abstract 26 Introduction 27 Results 29 Materials and Methods 39 Discussion 45 Chapter II Figures 48 Chapter II Tables 72 REFERENCE 89

PART II 105 Identification of New Tumor Suppressor Genes Through a Large-Scale shRNA Screen for Mouse Tumorigenesis CHAPTER I 2.1 General Introduction 106 2.1.1 Cancer 107 2.1.2 Oncogenes 109 History of oncogene discovery 109 The role of oncogene in cancer development 109 Activation of oncogenes 110 Classification of oncogenes 111 Receptor tyrosine kinases 111 2.1.3 FGFR1 111 Structure 111

viii Activation 112 Signaling 112 Oncogenic functions 113 2.1.4 Tumor Suppressor Genes 114 Mechanisms of tumor suppressor 114 History of tumor suppressor genes discovery 115 Genome-wide RNAi screen 116 2.1.5 Targeting Drug Development 117 Chapter I Figures 119 Chapter I Table 125 Chapter II 128 Identification of Squamous Cell Lung Cancer Tumor Suppressor Genes that Negatively Regulate FGFR1 Signaling Abstract 129 Introduction 131 Results 134 Materials and Methods 144 Discussion 149 Chapter II Figures 154 Chapter II Tables 177 Chapter III 186 General discussion 186 REFERENCE 190

LIST OF TABLES

Table 1.1.1 Histone acetylation enzymes and complexes

Table 1.2.1 Microarray data comparing gene expression in a tra1-2ts strain

relative to a wild-type TRA1 strain

Table 1.2.2 List of the top 20 genes most affected by Tra1 inactivation

Table 1.2.3 Microarray data comparing gene expression in a tra1-mut1 strain

relative to a wild-type TRA1 strain

Table 1.2.4 Microarray data comparing gene expression in a tra1-mut8 strain

relative to a wild-type TRA1 strain

Table 1.2.5 List of yeast strains used in this study

Table 1.2.6 Primer sequences for qRT-PCR, ChIP and yeast strain

construction

Table 2.1.1 Genetic alterations in FGFR1 and related cancer

Table 2.1.2 Altered expression of FGFR1 in cancer

Table 2.1.3 Current status of FGFR-targeting therapies

Table 2.2.1 List of 41 shRNAs recovered from tumors

Table 2.2.2 List of 32 candidate TSGs that validate in colony formation and

mouse tumorigenesis assays

Table 2.2.3 Summary of NCBI SKY/MFISH & CGH data for candidate TSGs

x Table 2.2.4 Summary of Oncomine data analysis querying whether candidate

TSGs are down-regulated in cancer versus normal tissue

Table 2.2.5 List of catalog numbers for shRNAs obtained from Open

Biosystems

Table 2.2.6 List of primers used for quantitative real-time RT-PCR and for

cloning TSGs

Table 2.2.7 Source and catalog numbers for human cDNAs used to clone

TSGs

LIST OF FIGURES

Figure 1.1.1 Formation of the PIC on the promoter occurs in a series of

ordered steps

Figure 1.1.2 SAGA is a modular complex with five distinct domains

Figure 1.1.3 Structure of Tra1 and Gal4

Figure 1.1.4 Gal4 regulation

Figure 1.2.1 Isolation of tra1 mutants that cannot support growth on galactose

Figure 1.2.2 Analysis of tra1 mutants that cannot support growth on galactose

Figure 1.2.3 Development of a bimolecular fluorescence complementation

assay for detecting interactions between activators and Tra1 in

vivo

Figure 1.2.4 Identification of Tra1 mutants that are unable to interact with Gal4

Figure 1.2.5 Identification of Tra1 mutants that are unable to interact with

HATs complexes

Figure 1.2.6 Gal4 and Tra1 bind cooperatively to the GAL1 promoter

Figure 1.2.7 Schematic diagram of the on/off DNA strategy

Figure 1.2.8 Gal4 and Tra1 interaction occurs on the promoter

Figure 1.2.9 Gcn4 functionally interacts with the Tra1 GID mutants

Figure 1.2.10 The Gal4-interaction site on Tra1 is highly selective

Figure 1.2.11 Tra1 GID mutants can interact with various activators

Figure 1.2.12 Microarray data shows that Tra1 GID mutants are Gal4 specific

xii Figure 2.1.1 Transformation is a multistep process

Figure 2.1.2 Schematic structure of FGFR1

Figure 2.1.3 Strategy of Pooled RNAi Library Screen

Figure 2.2.1 large-scale shRNA screen for the identification of candidate TSGs

Figure 2.2.2 shRNA Knocking Down Efficiency

Figure 2.2.3 Down-regulation of candidate TSGs in hLSCC samples

Figure 2.2.4 Down-regulation of candidate TSGs in human lung

adenocarcinoma samples

Figure 2.2.5 Knocking down TSGs in SA cell line activates FGFR signaling

pathway

Figure 2.2.6 Model of TSGs negatively regulating FGFR1 signaling

Figure 2.2.7 Gene repressed by FGFR1 signaling at transcriptional level

Figure 2.2.8 Knocking Down TSGs Induces transformation of lung epithelia

cells

Figure 2.2.9 SA cells with TSGs knocking down are sensitive to FGFR

inhibitor

Figure 2.2.10 Candidate TSG promoter hypermethylation in hLSCC samples.

Figure 2.2.11 Over-expression of candidate TSGs inhibits growth of hLSCC

cells

Figure 2.2.12 Mechanisms of candidate TSGs inhibiting growth of hLSCC cells

xiii PREFACE

The work contained within this thesis is represented in the following publications:

Lin, L., L. Chamberlain, et al. (2012). "Analysis of Gal4-directed transcription activation using Tra1 mutants selectively defective for interaction with Gal4." Proc

Natl Acad Sci U S A 109(6): 1997-2002.

Lin L, Wajapeyee N, Chamberlain L, Zhu LJ, Green MR

Identification of Squamous Cell Lung Cancer Tumor Suppressor Genes that

Negatively Regulate FGFR1 Signaling. (manuscript in preparation).

xiv

PART I

STUDYING TRANSCRIPTIONAL ACTIVATION

MECHANISMS BY DERIVING AND CHARACTERIZING

ACTIVATION DEFECTIVE MUTANTS

CHAPTER I

GENERAL INTRODUCTION

2 1.1.1 Transcription

Transcription is a process of copying DNA into a sequence complementary RNA molecule. Messenger RNA (mRNA) is then tailored

(splicing) and translated into a protein, which has cellular functions. Non-coding

RNAs function in translation or gene regulation. Transcription is the first step of gene expression.

The regulation of transcription directly influences cell viability, fitness, response to environment, etc. During the transcription process, DNA sequence is read by RNA polymerases. Eukaryotes have several types of RNA polymerases. RNA polymerase I synthesizes ribosomal RNA (rRNAs), which are the RNA components of ribosome. RNA polymerase II (Pol II) synthesizes mRNAs and most small nuclear RNAs (snRNAs) and microRNAs. RNA polymerase III synthesizes transfer RNAs (tRNAs), rRNA 5S and other small

RNAs. RNA polymerase IV and V are only found in plants. RNA Pol II is the most studied type of RNA polymerase, because it transcribes all the coding genes.

1.1.2 Pol II Transcription

In eukaryotes, transcriptional regulation mechanisms are conserved from single cell to multiple cell organisms. The discovery of RNA polymerase in 1959 by Weiss and Gladstone launched the field of transcription studies (Weiss and

Gladstone, 1959). Ten years later, the purification of three eukaryotic RNA

3 polymerases by Roeder and Rutter provided the foundation for the study of gene expression regulation (Roeder and Rutter, 1969). These purified RNA polymerases can initiate transcription at selected promoters in vitro with the help of basal or general transcription factors (GTFs). In vivo, successful promoter specific transcription requires additional apparatuses such as activators, chromatin-modification factors, elongation factors, etc. By sequentially recruiting these factors, eukaryotic transcription works in multi-steps: pre-initiation complex

(PIC) assembly, PIC activation, initiation, promoter clearance, elongation and termination.

General transcription factors (GTF)

“Basal” transcription, which only occurs in vitro, requires PIC, which consists of RNA Pol II and GTFs. Core RNA Pol II has 12 subunits (Rpb1-

Rpb12). They are structurally and functionally conserved from yeast to human.

Pol II alone cannot recognize specific promoters without additional factors.

“Activated” transcription, which occurs in vivo, requires additional regulatory molecules. GTFs include TFIIA, TFIIB, TFIID, TFIIE, TFIIF, and TFIIH, named in the order of discovery by chromatographic elution (Matsui et al 1980).

PIC formation

Formation of PIC on promoter occurs in a series of ordered steps (Figure

1.1.1): (1) TFIID recognizes of a promoter and forms the nucleus of transcription initiation complex; (2) TFIIB recognizes the TFIID-promoter complex and forms

4 DNA binding (DB) complex; (3) TFIIF/pol II complex is recruited to the promoter;

(4) recruitment of TFIIE and TFIIH completes the PIC formation.

PIC activation

Immediately following the PIC formation is PIC activation (DNA melting),

which form an “open” initiation complex. Unlike other steps, DNA melting

requires energy. The helicase activity of ERCC3 (excision repair cross-

complementing rodent repair deficiency, complementation group 3) in TFIIH facilitates DNA melting of a 10bp region upstream of the start site. TFIIE assists melting by stabilizing the single-stranded region near the start site. During this

process, carboxy-terminal domain (CTD) of polymerase II is phosphorylated,

which allows promoter clearance and elongation for synthesizing longer

transcripts.

Recycle of transcription factors

PIC components will be released from Pol II in the order of TFIIB, TFIIE

and TFIIH, and recycled for the next round of transcription initiation. After

transcription termination, Pol II CTD will be dephosphorylated, and recycled for

the PIC assembly.

1.1.3 Transcriptional regulation

Transcriptional regulation is conducted at different levels: cis-regulators,

which are DNA elements and trans-regulators, such as transcription factors,

chromatin structures, etc.

5 Trans-regulating factors

Repressors and mechanisms. The isolation and mechanism study of lac repressor by Gilbert and Ptashne proved the visionary hypothesis of repressor proposed by Jacob and Monod in 1961 and evidenced the existence of trans-acting factors that could regulate transcription by binding to the upstream

DNA sequences (Jacob 1961, Gilbert 1966, Ptashne 1967). Transcription repressors can be divided into two classes: general repressors and gene specific repressors. General repressors function through interacting with the TATA- binding protein (TBP) or preventing PIC assembly. Gene specific repressors function via: (1) binding to activators, for example, Gal80 represses Gal4 function by binding to its activation domain, (2) competing for activator binding sites, (3) interacting with transcription apparatuses, and (4) modifying chromatin by recruiting histone deacetylases (HDAC) to specific locations.

Activators and mechanisms. The existence of activators was not accepted until 1970 when Beckwith and Ptashne discovered and characterized an activator of the lac operon (Zubay et al, 1970; Emmer et al, 1970). A single activator usually can activate multiple genes and a single gene can be regulated by multiple activators as well. Therefore, activators regulate gene expression in a coordinated and combinatorial fashion. Activators stimulate transcription by several mechanisms: (1) recruiting chromatin modifying complexes to the promoters, (2) recruiting transcription initiation apparatus to the promoters, (3) influencing the activity of the transcription apparatus.

6 Transcription activators have several broad classes based on their activation domains: proline-rich, glutamine-rich, and acidic activators.

Mammalian proline-rich or glutamine-rich activator domains have no function in yeast (Ponticelli et al, 1995; Kunzler et al, 1994). Acidic activators function in all eukaryotes from yeast to human. Yeast acidic activators are modular proteins with two functionally and physically distinct domains. One is specific DNA- binding domain that reads promoter sequence. One domain is a short acidic region that can recognize transcription co-factors and the interaction with cofactor is independent on the structural complementarity (Sadowski et al, 1988).

Cis-regulating factors

The concept of promoter was first proposed by Ippen in 1968, and in 1971

Eron and Block confirmed it by in vitro transcription experiments (Ippen 1968,

Eron 1971). The first detailed transcription factor binding sequence was described by Tjian in 1978 using SV40 DNA. For most protein-coding genes, their promoters consist of a core promoter and transcriptional regulator binding sequences. The core promoter includes the transcription start site and TATA

box. Transcriptional regulator binding sequences include upstream activating sequences (UASs), enhancers, upstream repressing sequences (URSs), and silencers. UASs usually refer to the activator binding elements that are close to

the transcription start site. Enhancers refer to the clusters of DNA binding

sequences for transcriptional regulators distant from the start site, and it is

independent of orientation.

Chromatin structure and function

Throughout the 1960s, DNA was thought to be “naked” in a linear form, until 1974, Roger Kornberg presented the “nucleosome hypothesis”, in which eukaryotic DNA is packaged around histones to form nucleosome arrays

(Kornberg et al, 1974). By packaging DNA into chromatin, DNA can fit into the limiting confines of nucleus and be strengthened for mitosis and meiosis. In addition, the chromatin structure can prevent DNA damage, control gene expression and DNA replication. Chromatin consists of repeating nucleoprotein units named nucleosomes and linker DNA. A nucleosome contains three parts:

(1) nucleosome core, which is 146bp of DNA wrapped in 1.67 left-handed superhelical turns around the histone octamer, (2) the linker DNA connects adjacent histone octamers, (3) linker histone, H1, that binds the linker DNA and nucleosome core, and forms the exit/entry of the DNA strand on the nucleosome.

Transcription inhibition caused by packaging promoters in nucleosomes was found in vitro by Kornberg and colleagues in 1987, and was not accepted until it is proven in vivo by Han and Grunstein in 1988 (Knezetic and luse, 1986; Lorch et al., 1987; Han and Grunstein, 1988). Histone-DNA contact and higher order of chromatin structures can restrict access of transcription factors to the promoter elements. Disruption of nucleosome structure facilitates activator binding and transcription apparatus recruitment. However, in some cases, activators bind to

8 specific promoters in the distinct physical context provided by nucleosome

(Chavez 1997, Cirillo 1999, Schild 1993).

Chromatin modification

Classes and functions. Higher order of chromatin structures, usually mediated by modification of N-terminal tails of histones, are correlated with transcription regulation (Durrin et al, 1991). Histones can be modified by acetylation, phosphorylation, methylation ubiquitination and sumoylation.

Histone acetylation is the best understood histone modification. Chromatin with histone hyperacetylation is more accessible to transcription apparatus and therefore is associated with active transcription. Histone acetylation activates transcription through several mechanisms: (1) providing greater access to DNA sequence for the transcription apparatus and its regulators by disrupting higher order chromatin structures, (2) decreasing their affinity for DNA or neighboring nucleosomes by disrupting nucleosome structure, (3) promoting or suppressing interactions with specific transcription factors. Histone phosphorylation and ubiquitination are correlated with increased transcriptional activity. Whether

Histone methylation activates or represses transcription depends on the types of methylation of H3, H4 and H2.

Acetylation and transcription. Although RNA synthesis can be affected by histone acetylation was discovered early in 1964 by Allfrey et al, the link between transcription and acetylation wasn’t established until Brownell and

Schreiber found that a histone acetyltransferase (HAT) and a histone de-

9 acetyltransferase (HDAC) they identified are homologs to yeast transcription

factors Gcn5 and Rpd3 respectively (Allfrety et al, 1963; Brownell et al, 1996;

Schreiber et al 1996). Since then, a large number of HATs have been

discovered and are found to be previously identified transcriptional coactivators

(Table 1.1.1). HATs can be divided into two classes based on their cellular

locations: type A HATs are localized in nuclei and acetylate nuclear factors; type

B HATs are localized in cytoplasm and acetylate newly synthesized histones in

the process of histone assembly. In vivo, HATs function in association with

multisubunit complexes. Each HAT may show different substrate specificity in

different complexes. For example, Gcn5 is the catalytic component in SAGA,

ADA and SLIK (SAGA like) /SALSA (SAGA altered) complexes (Grant et al 1997,

Sterner et al 2002). SAGA (Spt-Ada-Gcn5 Acetylatransferase complex) is a 1.8-

MDa complex, comprises of four classes of proteins: (1) Ada proteins (Ada1,

Ada2, Ada3, Gcn5 and Ada5); (2) TBP-related Spt proteins (Spt3, Spt7, Spt8 and

Spt20); (3) TBP-associated factors (TAF5, TAF6, TAF9, TAF10 and TAF12); and

(4) Tra1. SAGA complex has a variant, SALSA (or SLIK), which functions in

retrograde response pathway with a truncated form of Spt7 and lacking Spt8

(Grant 2002). The structure of SAGA complex was revealed by Schultz lab using

immuno-electron microscopy method (Wu et al, 2004). They showed that SAGA

is a modular complex with five distinct domains (Figure 1.1.2). Domain II&IV

contain TAFs that may provide histone folding functions. Domain III is a central

architectural domain with HAT activity. Domain V may represent TBP interacting

10 surface. Domain I contains a 400kDa protein, Tra1, representing the activator interacting surface.

1.1.4 Tra1 in transcriptional regulation

Tra1 functions

Tra1 is the yeast homolog of human TRRAP

(Transformation/Transcription domain-Associated Protein). TRRAP was originally discovered by McMahon and Cole in 1998 (McMahon et al, 1998), who found TRRAP bound to c-Myc and E2F and mediated transformation. Yeast homolog Tra1 is a 400kDa protein and belongs to the phosphatidylinositol 3- kinase (PI3K)-related protein kinase (PIKK) protein family, which regulates signaling pathways by serine/threonine phosphorylation (Figure 1.1.3). Tra1 is an essential gene for yeast viability and a scaffold protein in SAGA, ADA and NuA4 acetyltransferase complexes (Grant et al, 1998; Saleh et al, 1998). Tra1 regulates many cellular functions, such as cell cycle progression, cell dividing,

DNA repair, etc. Given its gigantic size and essential role in cell viability, it is challenging to study the structure and the functions of Tra1.

Tra1 structure

Although Tra1 retains the structure of the PI3K domain, it does not have the catalytic function, because it lacks several critical residues essential for protein phosphorylation (Knutson and Hahn, 2010). Mutiu and Brandl using targeted mutagenesis identified functional residues that are critical to cell

11 viability, temperature sensitive growth and growth on 6% ethanol (Mutiu et al

2007). Knutson and Hahn made 42 tra1 mutants with ~100 aa deletions and

identified the regions that are critical for cell viability, association with SAGA and

NuA4 complexes (∆24), HAT specificity and activity (∆32), activator interaction

(e.g. ∆2, ∆5) (Knutson, 2011).

1.1.5 Gal4-directed transcriptional regulation

Gal4 Structure

Gal4 is one of the best investigated activators of yeast transcription. Gal4

is an 881aa protein member of the zn(II)2Cys6 (zinc cluster) family proteins

(Figure 1.1.3). It has a Zn-Cys DNA binding domain, a linker domain, a

dimerization domain and two acidic activation domains (Lohr, 1995, FASEB).

The consensus DNA binding site for Gal4 is a 17mer sequence 5’-CGG-N11-

CGG-3’. Using X-ray crystallography, Marmorstein and Harrison showed that

N’terminal fragment (DNA binding domain) binds to the consensus UAS as a dimer (Marmorstein, 1992). Reece and Ptashne further identified that a 19aa region at the C-terminal to the Zn-Cys cluster directs the specificity of Gal4 binding. The gene structure of Gal4 is illustrated in Figure 1.1.3.

Gal4 functions

Gal4 regulates expression of most genes in galactose utilization pathway when glucose or other carbon sources are not available. These genes function in transportation of galactose into the cell and its metabolism through the

12 glycolytic pathway. There are two classes of GAL genes that are required for the growth of yeast on galactose: structural genes (GAL1, GAL10, GAL2 and GAL7) and regulatory genes (GAL4, GAL80 and GAL3). Transcription induction of the

GAL structural genes by galactose is dependent on Gal4 binding to the UAS in their promoters. The number of Gal4 binding sites and the space between the triplets in the consensus sequence determine their affinity for Gal4 activator and lead to differential activation (Lohr, 1995). Besides GAL genes, some genes that are required for global adaptation to growth on galactose, such as MTH1, PCL10 and FUR4, are also regulated by Gal4 (Ren, 2000).

Gal4 regulation

Gal4 activating transcription is the earliest and most well studied model system for investigating transcription regulation in yeast (Figure 1.1.4). In the absence of galactose, Gal4 is inactivated by binding of Gal80 at its activation domain, rendering incapable to interact with transcriptional co-factors (Wu 1996;

Carrozza, 2002). In the presence of galactose, the interaction between Gal3 and

Gal80 causes conformational change in the Gal80-Gal4 complex, exposes the

Gal4 activation domain to the transcription co-factors and leads to the activation of transcription (Zenke, 1996; Leuther, 1992). This is confirmed by in vivo protein-protein interaction assay, fluorescence resonance energy transfer

(FRET), showing that Gal4 interacted with Gal80 in the presence of galactose

(Bhaumik, 2004). Peng and Hopper believed that Gal3 interacted with Gal80 and dissociated it from Gal4 by “shuttling” it to the cytoplasm (Peng and Hopper,

13 2000, 2002). However, our work showed that Gal80 was still associated with

Gal4 in nucleus after galactose induction.

Gal4 targets

Gal4 activates transcription by recruiting transcription co-activators and general transcription factors to the UAS through its activation domain. The pivotal questions are how does it recruit other factors and what are the targets.

There are many proteins that have been shown to interact with Gal4-activation domain: TBP (Melcher, 1995; Wu, 1996), TFIIB (Wu, 1996), Gal11 (Jeong,

2001), Srb10 (Ansari, 2002) and SWI/SNF (Yudkovsky, 1999) were identified interacting with Gal4 AD using in vitro binding assay with purified proteins; Srb4 was identified using affinity chromatography, label transfer affinity-photo-crosslinking and surface plasmon resonance (Koh, 1998); Sug1 (Gonzalez, 2002) and

Sug2 (Chang et al, 2001) were identified to interact with Gal4 using in vitro binding assay and in vivo cytotrap assay; SAGA was identified as Gal4 AD target using label transfer affinity-photo-cross-linking assay (Brown, 2001) and FRET

(Bhaumik, 2004).

Gal4 alone is sufficient to recruit SAGA to the UAS, suggesting that it’s a direct target of Gal4 activation domain (Bhaumik et al 2001, Bryant et al 2003,

Larschan et al 2001, 2005). In addition, Bhaumik and Green showed that SAGA works as a scaffold for PIC assembly at the promoter instead of (or other than)

HAT, because its catalytic subunit, Gcn5, is not required for SAGA recruitment and PIC formation (Bhaumik et al 2001). Furthermore, they showed that one of

14 the 22 subunits of SAGA complex, Tra1, is the direct target of Gal4 activation domain, and the interaction happens on the promoter (Bhaumik et al 2004).

In many of these target finding experiments, mutations in Gal4 activation domain were used to compromise its interaction with proposed targets, therefore confirm the specificity of the interaction. However, we think it’s not sufficient to prove the direct interaction(s) in vivo. What has been lacking is a reciprocal mutation in a target that prevents interaction with Gal4 and does not disrupt interactions between the Gal4 AD and any of its other putative targets. In the next chapter, we described Tra1 mutants that are selectively defective for interaction with Gal4, and use them to study how Gal4 stimulates transcription in vivo and the basis by which Tra1 is recognized by Gal4 and other activators.

15 Figure 1.1.1

AD DBD A UAS TATA INR

HAT(SAGA)

TFIIB

TRA1

AD TFIIA TFIID DBD B UAS TATA INR

TFIIH TFIIE

RNA polymerase II

TFIIF

HAT(SAGA)

TFIIB

TRA1

AD TFIIA TFIID DBD C UAS TATA INR

16 Figure 1.1.1. Formation of the PIC on the promoter occurs in a series of ordered steps: (A) Activator binds to the promoter and recruits transcription co-activators.

(B)TFIID recognizes of a promoter and forms nucleus of transcription initiation complex; TFIIB recognizes the

TFIID-promoter complex and form DNA binding (DB) complex; (C) TFIIF/pol II complex is recruited to the promoter. Recruitment of TFIIE and TFIIH completes the

PIC formation.

AD: activation domain; DBD: DNA binding domain; UAS: upstream activation sequence; HAT: Histone acetyltransferase; SAGA: saga spt-ada-gcn5- acetyltransferase.

17 Figure 1.1.2

TBP interacting

IV: TAFs Histone folding HAT function

III: HATs

II: TAFs I: Tra1

Activator interacting

18 Figure 1.1.2. SAGA is a modular complex with five distinct domains. Domain II&IV contain TAFs that may provide histone folding functions. Domain III is a central architectural domain with HAT activity. Domain V may represent TBP interacting surface. Domain I contains a

400kDa protein, Tra1, representing the activator interacting surface.

19 Figure 1.1.3

A HEAT FAT FRB PI3K FATC

TRA1

B Activation Gal80 binding Activation Dimerization DNA binding DNA

Gal4

20 Figure 1.1.3. (A) Structure of Tra1. Tra1 consists of a

HEAT domain (Huntingtin, elongation factor 3, PR65/A, and TOR), a FAT domain (FRAP, ATM, and TRRAP), a FRB domain (FKBP12 rapamycin binding), a PI3K domain and a

FATC domain (FAT C-terminal). (B) Gal4 has a DNA binding domain, a linker domain, a dimerization domain and two acidic activation domains.

21 Figure 1.1.4

Non-inducing condition cytoplasm

nucleus

Gal80 Gal4 Gal4 X UAS TATA

Galactose-inducing condition cytoplasm

Gal3 nucleus Gal80 ? Gal3

Gal80 mediator Gal4 Gal4 Tra1 Pol II>F Spt3

UAS TATA

22 Figure 1.1.4. In the absence of galactose, Gal4 is inactivated by binding of Gal80 at its activation domain, rendering incapable to interact with transcriptional co- factors. In the presence of galactose, the interaction between Gal3 and Gal80 causes conformational change in the Gal80-Gal4 complex, exposes the Gal4 activation domain to the transcription co-factors and leads to the activation of transcription.

23 Table 1.1.1 Histone acetylation enzymes and complexes

Acetylation Hat1 subunit of the Hat1p-Hat2p HAT complex, acetylates nuclear and cytoplasmic histone H4 Gcn5 Subunit of the ADA and SAGA HAT complex, modifies histone H2B and H3 Hpa2 Acetylates histones H3 and H4, autoacetylation Esa1 MYST family, essential for viability, acetylates histone H4 Sas3 MYST family, subunit of NuA3 complex, acetylates histone H3, transcriptional cilencing Elp3 Subunit of elongator complex, acetylates histone H3 and H4 TAFII145 Subunit of TAFIID

SAGA Tra1, Ada2, Gcn5, Spt3, Spt7, Spt8, Interacts with acidic Spt20, Taf5, Taf6, Taf9, Taf10, Taf12, activation domains and TBP. Chd1Sgf11, Sgf29, Sgf73, Ubp8, Ngg1, HAT activity stimulates Hfi1, Sus1 transcription. Acetylates histone H3 and H2B SLIK Tra1, Ada2, Gcn5, Spt3, Spt7(truncated HAT activity stimulates form), Spt20, Taf5, Taf6, Taf9, Taf10, transcription. Acetylates Taf12, Chd1Sgf11, Sgf29, Sgf73, Ubp8, histone H3 and H2B Ngg1, Hfi1, Sus1 NuA4 Tra1, Eaf1, Eaf3, Eaf5, Eaf6, Eaf7, Ynf2, Interacts with acidic Swc4, Esa1, Epl1, Yaf9 activation domains. HAT activity stimulates transcription in vitro. Acetylates histone H4 and H2A NuA3 Eaf6, Taf14, Nto1, Sas3, Yng1 Acetylates hitstone H3 ADA Ahc1, Ada2, Ahc2, Ngg1, Sgf29, Gcn5 Acetylates histone H3 and H2B Elong Elp2, Elp3, Elp4, Iki1,Iki3 Transcription elongation. ator Acetylates histones H2A, H2B, H3 and H4

CHAPTER II

Analysis of Gal4-directed transcription activation

using Tra1 mutants selectively defective

for interaction with Gal4

Abstract

Promoter-specific transcriptional activators (activators) stimulate

transcription through direct interactions with one or more components of the

transcription machinery, termed the “target”. The identification of direct in vivo

targets of activators has been a major challenge. Previous studies have provided

evidence that the Tra1 subunit of the yeast SAGA (Spt-Ada-Gcn5-

acetyltransferase) complex is the target of the yeast activator Gal4. However,

several other general transcription factors, in particular the mediator complex,

have also been implicated as Gal4 targets. Here, we performed a large-scale genetic screen to derive and characterize tra1 alleles that are selectively defective for interaction with Gal4 in vivo (Gal4 interaction defective (GID) mutants). In contrast to wild-type Tra1, Tra1 GID mutants are not recruited by

Gal4 to the promoter and cannot support Gal4-directed transcription, demonstrating the essentiality of the Gal4–Tra1 interaction. In yeast strains expressing a Tra1 GID mutant, binding of Gal4 to the promoter is unexpectedly also diminished, indicating that Gal4 and Tra1 bind cooperatively. Consistent with cooperative binding, we demonstrate that the Gal4–Tra1 interaction occurs predominantly on the promoter and not off DNA. Finally, we show that although

Tra1 is targeted by other activators, these interactions are unaffected by GID mutations, revealing an unanticipated specificity of the Gal4–Tra1 interaction.

26 Introduction

Transcription is a highly regulated process. The basal transcription requires minimal protein apparatus for accurate transcription initialtion: General transcription factors (GTFs) and the subunits of RNA polymerase II. Transcription initiation by RNA polymerase II involves the assembly of general transcription factors on the core promoter to form a preinitiation complex (PIC). In the cells, genes packaged into chromatin are in general repressed. Therefore, assembly of the GTFs at the promoter must be triggered by activator proteins. Promoter- specific activator proteins (activators) can recruit GTFs to a sequence specific promoter to accelerate PIC formation and induce the expression of genes

(Roeder, 1996; Orphanides, 1996; Ptashne, 1997; Lee, 2000). Activators can stimulate transcription at various steps: (1) remove repressors from promoter; (2) recruit GTFs and pol II to the promoter; (3) induce conformational change of PIC;

(4) induce covalent modification of proteins in the PIC; and (5) stimulate promoter clearance and elongation. Activators can recruit factors that remodel nucleosomes and make promoter sequences more accessible for GTFs and pol

II. H3 and H4 histones are reversibly acetylated. The acetylation of the lysine residues reduces the interaction between histone and DNA by neutralizing their positive charge. Activators that recruit histone acetyltransferase complex stimulate transcription by removing the histones athe the promoter. Activators are modular proteins that contain a DNA-binding domain (DBD) and an activation domain (AD). Activator-mediated stimulation of PIC assembly is believed to result

27 from a direct interaction between the AD and one or more components of the transcription machinery, termed the “target”. The unambiguous identification of the direct in vivo targets of activators has been a major challenge in the field.

28 Results

Isolation of tra1 Mutants that Cannot Support Growth on Galactose.

The strategy we used to derive Tra1 mutants that fail to interact with the

Gal4 AD is summarized in Figure. 1.2.1.A and described below. We generated a

library of random tra1 mutants by in vitro hydroxylamine mutagenesis of a low- copy plasmid expressing TRA1. The library was transformed into a haploid yeast tra1-∆ strain that was complemented by wild-type (WT) TRA1 expressed on a

low-copy URA3-containing plasmid. Following eviction of the WT TRA1 plasmid

on media containing 5-fluoroorotic acid (5-FOA), strains harboring the tra1

mutants were analyzed for growth on media containing glucose (YPD), but not

galactose (YPG). This approach yielded 13 tra1 mutants that were unable to

support growth on YPG (Figure. 1.2.1.C).

Sequence analysis revealed that the 13 tra1 mutants represented eight

distinct alleles (Figure. 1.2.1.B). Mutants 2-7 were found to contain the same

mutations and therefore, of these mutants, only tra1-mut2 was further analyzed.

Immunoblot analysis showed that all of the Tra1 mutants were expressed at

levels comparable to that of WT Tra1 (Figure 1.2.2.A).

To confirm that the tra1 mutants selectively abolished GAL gene

expression, we monitored expression of two GAL genes, GAL1 and GAL3, by

quantitative RT-PCR (qRT-PCR). In strains harboring the eight tra1 mutants,

transcription of GAL1 and GAL3 in galactose-containing media was severely

compromised relative to that observed in a WT TRA1 strain (Figure. 1.2.2.B). By

29 contrast, expression of RPS5 and RPS0B, which are SAGA-independent genes, were unaffected by the tra1 mutants (Figure. 1.2.2.C).

Development of a Bimolecular Fluorescence Complementation Assay for

Detecting Interactions Between Activators and Tra1 in vivo.

To detect direct interactions between the mutant Tra1 proteins and Gal4 in vivo, we developed a bimolecular fluorescence complementation (BiFC) assay, which has been used for the in vivo detection of a wide variety of protein–protein interactions (Akman, 2009; Sung, 2007). The BiFC assay is based on the formation of a fluorescent complex comprising two fragments of yellow fluorescent protein (YFP), which are brought together by association of two interacting proteins fused to the fragments (reviewed in Kerppola 2008).

To verify the feasibility of this approach, we first performed a series of control experiments that monitored the interaction between Tra1 and two SAGA- dependent activators, Gal4 and Gcn4. The experimental strategy for detecting activator–Tra1 interactions using the BiFC assay is shown in Figure. 1.2.3.A. We derived a pair of haploid yeast strains, one in which the endogenous Tra1 protein was tagged at the C-terminus with the N-terminal fragment of a YFP variant known as Venus (Nagai, 2002) (Tra1-VN), and a second strain of opposite mating type in which the endogenous activator was tagged at its C-terminus with the C-terminal Venus fragment (Gal4-VC or Gcn4-VC). The two haploid strains

30 were then mated, and the resulting diploid cells were analyzed by fluorescence

microscopy for a nuclear BiFC signal.

As described above, because of Gal80-mediated inhibition, the Gal4–Tra1 interaction is expected to occur in the presence but not the absence of galactose.

Figure. 1.2.3.B (top panel) shows that a BiFC signal was detected in cells expressing Tra1-VN and Gal4-VC and grown in YPG but not YPD. Identical results were obtained when the Venus fragment was fused to Tra1 at its N- terminus (VN-Tra1) (Figure. 1.2.3.B, bottom panel). Thus the BiFC assay detected the Gal4−Tra1 interaction, which, as expected, was galactose dependent.

A Gcn4–Tra1 interaction is expected to occur only under conditions of amino acid starvation. Figure. 1.2.3.C shows that a nuclear BiFC signal was not detected in cells expressing Tra1-VN and Gcn4-VC grown in nutrient-rich media

(YPD), but was readily detected in amino acid-starved cells grown on histidine

(His)-lacking medium in the presence of the competitive inhibitor 3-aminotriazole

(3-AT). Collectively, the results of Figure. 1.2.3 demonstrate that the BiFC assay can be used to detect interactions between activators and Tra1.

Identification of Tra1 Mutants that are Unable to Interact with Gal4

We used the BiFC assay to analyze the interaction between Gal4 and the

Tra1 mutants. In these and subsequent BiFC experiments described below, the endogenous activator was tagged at the C-terminus with VC in a haploid tra1-∆

31 strain that was complemented by WT TRA1 expressed on a URA3-containing

plasmid. The strain was then transformed with a low-copy plasmid expressing a

Tra1 mutant protein fused to VN at either the C-terminus (Tra1-mut1, -mut2, -

mut8, -mut9, -mut12 and -mut13) or N-terminus (Tra1-mut10 and -mut11), and

the BiFC signal was monitored following eviction of the WT TRA1 plasmid. As

expected, in cells expressing WT Tra1-VN, a nuclear BiFC signal could be

detected upon growth on galactose (Figure. 1.2.4). By contrast, a BiFC signal

was not detected in cells expressing any of the Tra1 mutants.

We have previously proposed that Tra1 must be incorporated into an

intact SAGA complex for interaction with Gal4 in vivo (Bhaumik et al 2004).

Therefore, one explanation for the inability of the Tra1 mutants to interact with

Gal4 is a failure to be incorporated into SAGA. To address this issue, we analyzed the stable association between Tra1 mutants and the SAGA subunit

Spt20 in a co-immunoprecipitation assay. Figure. 1.2.5.A shows that Tra1-mut1 and Tra1-mut8 co-immunoprecipitated with Spt20 at levels comparable to that of

WT Tra1. By contrast, the other mutants were completely (Tra1-mut2, -mut9, - mut10, -mut12 and -mut13) or partially (Tra1-mut11) defective for interaction with

Spt20. Tra1 is also a subunit of another HAT complex known as NuA4 (Allard et al 1999). Figure. 1.2.5.B shows that all Tra1 mutants co-immunoprecipitated with the NuA4 subunit Eaf1, indicating that they were all efficiently incorporated into the NuA4 complex. Collectively, these results indicate that Tra1-mut1 and Tra1- mut8 are incorporated into the SAGA complex but are unable to interact with

32 Gal4 and thus can be classified as Gal4 interaction defective (GID) mutants.

Tra1-mut8 is of particular importance because it contains only a single amino acid substitution at position 400 (H400Y) (see Figure. 1.2.1.B). The inability of

Tra1-mut1 and Tra1-mut8 to interact with Gal4 and support transcription of GAL genes conclusively establishes that the Gal4–Tra1 interaction is essential for

Gal4-directed transcription activation.

Gal4 and Tra1 Bind Cooperatively to the GAL1 Promoter

The interaction between Gal4 and Tra1 is expected to result in recruitment of the SAGA complex to the promoters of GAL genes. Therefore, in a yeast strain expressing a Tra1 GID mutant, the SAGA complex should not be recruited to

GAL genes. To confirm this prediction, we performed a series of chromatin immunoprecipitation (ChIP) assays. As expected, in galactose association of

Tra1-mut1 and Tra1-mut8 with the GAL1 promoter was substantially reduced relative to WT Tra1 (Figure. 1.2.6.A). Likewise, association of Spt20 with the

GAL1 promoter was comparably reduced in the two Tra1 GID mutant strains. By contrast, recruitment of Tra1-mut1 and Tra1-mut8 to the promoter of RPS0B, a

NuA4-dependent gene, was comparable to that of WT Tra1.

Figure. 1.2.6.A also shows that in raffinose Gal4 binding was roughly equivalent in the WT Tra1 and Tra1 GID mutant strains. Unexpectedly, in galactose binding of Gal4 to the GAL1 promoter was substantially reduced in the

Tra1 GID mutant strains. We interpret this result to indicate that the Gal4–Tra1

33 interaction enhances the ability of Gal4 to bind the promoter. Binding of Gal4 to the GAL1 promoter was also reduced in an spt20-∆ strain (Figure. 1.2.6.B), which does not support the Gal4–Tra1 interaction (Bhaumik et al 2002).

Collectively, the results of Figures. 1.2.6.A and B indicate that as a result of the

Gal4–Tra1 interaction, Gal4 and Tra1/SAGA bind cooperatively to the promoter.

The Gal4–Tra1 Interaction Occurs Predominantly on the Promoter and Not

Off DNA.

An important, unresolved question is whether in vivo the interaction between an activator and its target occurs predominantly on the promoter or whether the activator–target interaction is sufficiently stable that it occurs off

DNA. To address this issue, we used the BiFC assay to monitor the interaction between Tra1 and a LexA-Gal4 AD fusion-protein that can bind to DNA only in yeast strains engineered to contain LexA-binding sites. A summary of the experimental design is shown in Figure. 1.2.7.A and discussed below. We first constructed a haploid tra1-∆ strain harboring plasmids expressing Tra1-VN and a

LexA DBD-Gal4 AD fusion-protein tagged with the C-terminal Venus fragment

[LexA(DBD)-Gal4(AD)-VC]. This strain was transformed with a series of constructs to derive three strains, the first of which (strain 1) expressed a high- copy plasmid containing four LexA-binding sites located upstream of a GAL1- lacZ reporter gene (West et al 1984). Strain 2 was identical to strain 1 except that the construct lacked upstream LexA-binding sites (West et al 1984). In strain 3

34 the GAL1 core promoter sequence, which is the site at which SAGA and other

PIC components are recruited, was deleted from the GAL1-lacZ reporter gene.

Immunoblot analysis confirmed that the LexA(DBD)-Gal4(AD)-VC fusion-protein

was expressed equivalently in the three strains (Figure. 1.2.7.B).

We analyzed the interaction between the Gal4 AD and Tra1 in these three

strains using the BiFC assay. Figure. 1.2.8.A shows that a nuclear BiFC signal was detected in strain 1 but not in strain 2, indicating a requirement for LexA- binding sites. As expected, in strain 1, the nuclear BiFC signal was observed only in galactose. The nuclear BiFC signal was also absent from strain 3, which lacked the GAL1 core promoter. Collectively, these results indicate that the Gal4

AD–Tra1 interaction occurs predominantly on DNA, and is dependent upon both activator-binding sites and the core promoter. Previous report shows that Gal4 binding to the promoter is independent of Gal4-Tra1 interaction by chromatin immunoprecipitation that facilitated by formaldehyde crosslinking (Bauhmik,

2002). By using BiFC assay, we could monitor the real-time interaction between activator and Tra1 that not affected by crosslinking reagent.

As a control, we performed an analogous experiment to monitor the interaction between Gal4 and its negative regulator Gal80. Figure. 1.2.8.B shows that a nuclear BiFC signal was detected in all three yeast strains indicating, as expected, that the Gal4–Gal80 interaction is not dependent upon either activator binding sites or the core promoter. Notably, unlike the Gal4–Tra1 interaction, the

Gal4–Gal80 interaction occurred in both raffinose and galactose, which is in

35 agreement with previous biochemical experiments showing that in galactose

Gal80 remains physically associated with Gal4 at a second site (Sil et al 1999).

The Gal4-Interaction Site on Tra1 is Highly Selective

We next performed experiments to determine whether the Tra1 GID

mutants were selectively defective for interaction with the Gal4 AD or were

unable to interact with other activators that also targeted Tra1. Figure. 1.2.9.A

shows that both Tra1 GID mutant strains grew on His-lacking medium containing

3-AT, indicating that the mutants could support Gcn4-directed transcription.

Furthermore, a nuclear BiFC signal could be detected in strains expressing

Gcn4-VC and either WT Tra1 or a Tra1 GID mutant (Figure. 1.2.9.B). Thus, unlike Gal4, Gcn4 functionally interacts with Tra1 GID mutants.

To further investigate the selectivity of the Gal4 interaction site on Tra1, we sought to identify other activators that interact with Tra1 in the SAGA complex and determine their sensitivity to the Tra1 GID mutations. Toward this end, we first identified Tra1-dependent genes by comparing the mRNA population of a

WT TRA1 strain to that of a strain bearing a temperature-sensitive tra1 allele

(tra1-2ts; ref. Kulesza et al 2002) under non-permissive conditions. Following inactivation of TRA1, ~3% of yeast genes were down-regulated greater than two- fold (Table 1.2.1), consistent with a previous study that analyzed other tra1 alleles (Mutiu et al 2007).

36 To identify a set of Tra1-dependent genes that were also SAGA-

dependent, we analyzed the 20 genes most affected by TRA1 inactivation (see

Table 1.2.2) for dependence on Spt20. We first mined a published expression

profiling study for genes whose transcription is compromised in an spt20-∆ strain

(Lee et al 2000) and then confirmed these in silico results by qRT-PCR. This

combined analysis identified 11 genes whose transcription was compromised by

inactivation of Tra1 or loss of Spt20 (Figure 1.2.10.A). In addition, ChIP analysis

showed, as expected, that Tra1 was bound to the promoters of all 11 genes

(Figure. 1.2.10.B). Significantly, the two Tra1 GID mutants did not significantly

affect transcription of any of these 11 Tra1- and SAGA-dependent genes (Figure

1.2.10.A), suggesting that Tra1 GID mutants might be specific to Gal4 activation.

Next, we tested the ability of the activators that mediate expression of the

11 Tra1- and SAGA-dependent genes to interact with WT Tra1 and the Tra1 GID mutants. To identify activators involved in regulating expression of the Tra1- and

SAGA-dependent genes, we searched published genome-wide ChIP-microarray

(ChIP-chip) studies (Harbison, 2004; Iyer, 2001; Lee, 2002; Lieb, 2001; Ren,

2000). Using this approach, predicted activators could be identified for 10 of the

11 Tra1- and SAGA-dependent genes analyzed above (Table 1.2.2).

We selected five activators for further analysis: Cbf1, Fkh2, Mcm1, Reb1, and Zap1. Figure 1.2.11 shows a nuclear BiFC signal was detected in yeast strains expressing WT Tra1-VN and either Cbf1-VC, Fkh2-VC, Mcm1-VC, Reb1-

VC or Zap1-VC. Thus, as predicted, all five of these activators directly interact

37 with Tra1. Notably, a nuclear BiFC signal was also observed with all five

activators in strains expressing either Tra1 GID mutant. Thus, consistent with the

transcription results of Figure. 1.2.10.A, these activators directly interact with

Tra1, but this interaction is insensitive to the GID mutations.

Finally, in an independent approach, we attempted to identify genes

whose transcription was affected by the Tra1 GID mutants by comparing

genome-wide expression profiles of yeast strains harboring either WT TRA1,

tra1-mut1 or tra1-mut8 grown in YPD. Remarkably, we found only two genes

(GSC2 and HSP30) whose expression was affected more than 2-fold by the tra1- mut1 mutation and no genes that were affected more than 2-fold by the tra1- mut8 mutation (Figure 1.2.12.A and Tables 1.2.3 and 1.2.4). Moreover, even for

GSC2 and HSP30, qRT-PCR did not confirm the difference in expression levels

observed by microarray analysis and instead revealed that expression of these

two genes was comparable in tra1-mut1, tra1-mut8, and the WT TRA1 strains

(Figure 1.2.12.B). Collectively, these results indicate that remarkably few, and

possibly no other, yeast activators target the same Tra1 region at which Gal4

interacts.

38 Material and Methods

Plasmid and strain construction

Plasmids pRS414-pDED1-myc-TRA1 and pRS416-pDED1-myc-TRA1

were constructed by cloning the myc-TRA1 fragment from myc-TRA1-YCplac11

(Saleh et al 1998) into pRS414 (CEN TRP1) or pRS416 (CEN URA3),

respectively, using restriction enzyme Not I and Sal I.

Strain LLY154 is a haploid tra1-∆ strain derived from MDC1 (Kulesza et al

2002) in which the wild-type TRA1 plasmid was replaced with pRS416-pDED1-

myc-TRA1. For the qRT-PCR experiments shown in Figure 1.2.2, RNA was

prepared from LLY154 transformed with a pRS414-based wild-type or mutant

TRA1 plasmid following eviction of pRS416-pDED1-myc-TRA1 on 5-FOA. For

the experiment shown in Figure 1.2.10, gene expression was analyzed in

LLY154 harboring plasmids expressing TRA1, tra1-mut1 or tra1-mut8, in a

haploid tra1-2ts (MDC3) or isogenic wild-type strain (MDC1; (Kulesza et al 2002),

or in a haploid spt20-∆ strain (YDA352) or isogenic wild-type strain (FY23;

(Winston et al 1995).

To generate Tra1-VN- and VN-Tra1-tagged strains for the BiFC assay in

Figure 1.2.3, the N-terminal fragment of Venus was PCR amplified from plasmid

pFA6a-VN-His3MX6 and pFA6a-KanMX6-PCET1-VN, respectively (Sung et al

2007), using primers (listed in Table 1.2.6) and transformed into haploid MATa

strain BY4741 (Giaever et al 2002). To generate Gal4-VC and Gcn4-VC-tagged strains, the C-terminal fragment of Venus was PCR amplified from plasmid

39 pFA6a-VC-His3MX6 (Sung et al 2007) and transformed into haploid MATα strain

BY4742 (Giaever et al 2002). Pair-wise haploid strain combinations were mated, and the BiFC signal in the resulting diploid cells was examined.

For monitoring interactions between activators and Tra1 mutants, activators were C-terminally tagged at their endogenous locus, as described above, in strain LLY197, a haploid tra1-∆ strain harboring pRS416-pDED1-myc-

TRA1. The strains were then transformed with a plasmid expressing wild-type or mutant Tra1-VN (or VN-Tra1) [constructed by PCR amplifying the N-terminal fragment of Venus from plasmid pFA6a-VN-KanMX6 (Sung et al 2007) and inserting it into pRS414-based wild-type and mutant Tra1 plasmids], and the

BiFC signal was monitored following eviction pRS416-pDED1-myc-TRA1 on 5-

FOA.

To construct LexA(DBD)-Gal4(AD)-VC, the C-terminal Venus PCR fragment from pFA6a-VC-TRP1 (Sung et al 2007) was cloned into plasmid pSH17-4 (Wu et al 2000), which expresses a LexA(DBD)-Gal4(AD) fusion protein. The plasmid expressing LexA(DBD)-Gal4(AD)-VC was co-transformed into strain LLY210 (a haploid tra1-∆ strain harboring a plasmid expressing Tra1-

VN) together with pSH18-34 (harboring 4 LexA operators upstream of the GAL1- lacZ reporter gene; (West et al 1984), a derivative of pSH18-34 in which the

GAL1 promoter had been deleted; pSH18-34∆2) or LR1∆1 (harboring a GAL1- lacZ reporter gene but lacking the LexA operators; (West et al 1984). For Figure

1.2.8, Gal80 was C-terminally tagged at its endogenous locus by PCR amplifying

40 the N-terminal Venus fragment from pFA6a-VN-TRP1 (Sung et al 2007) and transforming it into W303, generating LLY326, which was then co-transformed with plasmids LexA(DBD)-Gal4(AD)-VC and pSH18-34, LR1∆1 or pSH18-34∆2.

Strains were grown in galactose or raffinose, and cells were monitored for a BiFC signal.

For the immunoblot experiment of Figure 1.2.2, whole cell extracts were prepared from strain LLY154 transformed with a pRS414-based WT or mutant

Tra1 plasmid following eviction of pRS416-pDED1-myc-TRA1 on 5-FOA. For the

co-immunoprecipitation experiments of Figure 1.2.5, Spt20 or Eaf1 was C-

terminally tagged at its endogenous locus with an HA epitope by PCR amplifying

the HA tag from pFA6a-3HA-KanMX6 (Longtine et al 1998) and transforming it

into strain LLY154. The strains were then transformed with pRS414-based WT or

mutant Tra1 plasmids, and extracts were prepared following eviction of pRS416-

pDED1-myc-TRA1.

ChIP was performed using strain LLY154 transformed with pRS414-based

WT or mutant Tra1 plasmid or FY23 and YDA352 (Figure 1.2.6).

TRA1 Mutagenesis Screen

Plasmid pRS414-pDED1-myc-TRA1 was mutagenized by treatment with hydroxylamine solution [1M hydroxylamine (Sigma), 50 mM sodium pyrophosphate (pH 7.0), 100 mM NaCl, and 2 mM EDTA] at 75˚C for 30 mins

(Guthrie et al 2004). The mutagenized library was amplified in bacteria and then

41 transformed into a haploid tra1-∆ strain LLY154 (see Table 1.2.5). Cells were plated on –Trp 5-FOA media, and ~1200 5-FOA-resistant colonies were patched and replica plated onto YPD and YPG media containing 20 µ g/ml antimycin A

(Sigma). Colonies able to grow on YPD but not YPG were selected, and the plasmid was isolated and sequenced. Strains carrying tra1-mut1 and tra1-mut8 were also analyzed for growth on –His media containing 50 mM 3-AT.

Quantitative RT-PCR

Total RNA was extracted (Schmitt et al 1990), and reverse transcription was performed using SuperScript II Reverse Transcriptase (Invitrogen) followed by qPCR using Fast SYBR Green Master Mix (Applied Biosystems) using the primers listed in Table S6. For the experiments shown in Fig. 1B and S1D, cells were grown in 2% raffinose followed by 2% galactose for 5 mins.

Bimolecular Fluorescence Complementation Assay

The BiFC signal in cells was examined by fluorescence microscopy using a Zeiss AXIO Imager Z2 microscope. A total of 100 cells from at least 7 different fields were counted; representative examples are shown.

Immunoblotting and Co-immunoprecipitation Assays

For Figure 1.2.2, whole cell extracts were prepared as previously described

(Brown et al 2001) and blots were probed with an anti-myc (Santa Cruz) or anti-

42 actin (Abcam) antibody. For Figure 1.2.7, extracts were prepared from strains

grown in galactose or raffinose medium, and blots were probed with an anti-LexA

(Santa Cruz) or anti-actin (Abcam) antibody. For the co-immunoprecipitation experiments of Figure 1.2.5, Spt20-HA or Eaf1-HA was immunoprecipitated with

an anti-HA antibody, and blots were probed with an anti-HA (Abcam) or anti-myc

(Santa Cruz) antibody.

Chromatin Immunoprecipitation

ChIP was performed as described previously (Harbison et al 2004) using

an anti-myc (Abcam), anti-HA (Abcam) or anti-Gal4 (Abcam) antibody. Following

reversal of the crosslinks, the DNA was PCR-amplified using gene-specific

primers (listed in Table 1.2.6).

Microarray Analyses

Strains MDC1 and MDC3 were grown at 30°C and shifted to 37°C for 60,

90 and 120 mins. Haploid TRA1, tra1-mut1 and tra1-mut8 strains were generated as described in Supplemental Methods. RNA was extracted according to standard protocols (Schmitt et al 1990) and hybridized to an Affymetrix YG-S98 array. The tra1-mut1/TRA1 and tra1-mut8/TRA1 experiments were done in duplicate. Statistical analyses were performed using R (Ihaka et al 1996). RMA method (Irizarry et al 2003) in Affy package from Bioconductor (Gentleman et al

2004) was used to summarize the probe level data and normalize the dataset to

43 remove across array variation. Limma package (Smyth 2004) with randomized block design was used to determine whether a gene’s expression level differs between mutant and WT regardless of time point. Genes with adjusted p-value using B-H method (Benjamini 1995) < 0.05 was considered significant. The microarray data from this publication have been deposited in NCBI’s Gene

Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE31391.

44 Discussion

The results presented in this manuscript definitively establish Tra1 as an

essential in vivo target of Gal4 by identifying the site on Tra1 at which Gal4

interacts and demonstrating the necessity of this interaction for Gal4-directed

transcription. For Tra1 to interact with Gal4 it first must be incorporated into an

intact SAGA complex. The amino acids in Tra1 that we find compromise

assembly into the SAGA complex are consistent with the results of a recent study

analyzing Tra1 functional domains (Knutson et al 2011). Our results indicate that

Tra1 does not have an intrinsic ability to interact with the Gal4 AD but rather requires proper presentation within the SAGA complex. It seems likely that this finding is relevant to the selective interaction of Gal4 with SAGA and not the

NuA4 complex, which also contains Tra1.

Several experimental observations have led to the suggestion that an activator will have multiple, functionally redundant targets. For example, in vitro protein–protein interaction experiments have shown that a single activator such as Gal4 (reeves, 2005; Wu, 1996; Melcher, 1995; Neely, 2002; Bryant, 2003;

Jeong, 2001; Koh, 1998; Park, 2000) or Gcn4 (Drysdale, 1998; Utley, 1998) can

interact with multiple components of the transcription machinery. Likewise, in

artificial recruitment experiments, a wide variety of transcription components can

stimulate transcription and thus could potentially function as targets (Ptashne,

45 1997). However, in contrast to this view, we demonstrate that interaction of Gal4 with a single site on Tra1 is required for Gal4-directed transcription.

The fact that many yeast activators contain acidic ADs, with apparently similar sequence features, has suggested that activators have common targets and recognition sites. Consistent with this idea, in vitro protein crosslinking experiments have shown that both the Gal4 and Gcn4 ADs interact with three common proteins; Tra1, Gal11 and Taf12 (Fishburn, 2005; Reeves, 2005).

Surprisingly, however, we find that the Gal4-interaction region on Tra1 is remarkably specific and that the Gal4 and Gcn4 ADs recognize Tra1 differentially. Our collective results suggest that at most very few, and likely no other, yeast activators functionally interact with the same region of Tra1 that is recognized by the Gal4 AD.

In this study, we have performed a series of BiFC experiments whose results show that the interaction between the Gal4 AD and Tra1 occurs predominantly on the promoter and not off DNA. These new results explain our previous finding that a Gal4 mutant lacking its DBD failed to interact with Tra1 in vivo (Bhaumik, 2004). Our results help explain how cellular activators avoid a transcription inhibitory process referred to as “squelching”, which occurs following over-expression of a strong AD, such as the herpes simplex virus VP16 AD (Gill,

1988; Triezenberg, 1988). Squelching results from the sequestration of the target by the activator off the promoter; the target is thus unavailable for promoter- bound activators resulting in transcriptional inhibition. By interacting with Tra1

46 predominantly on the promoter and not off DNA, Gal4 avoids squelching.

Squelching is dependent upon both the strength and concentration of the overexpressed AD (Gill, 1988; Triezenberg, 1988). Based upon these considerations, we speculate that cellular activator-target interactions are in general weak, thus ensuring that they occur only on the promoter where they are stabilized by the many other protein–protein and protein–DNA interactions in the PIC.

47 Figure 1.2.1

48 Figure 1.2.1. Isolation of tra1 mutants that cannot support growth on galactose. (A) Schematic for mutagenesis of TRA1 and selection of mutants that fail to grow on galactose. (B) Summary of the mutated residues in each of the tra1 mutants. (C) Growth of tra1 mutants

1-13 on YPD and YPG media supplemented with antimycin. Growth of WT TRA1 and gal4-∆ strains are shown as controls. Cells were spotted as 10-fold serial dilutions.

49 Figure 1.2.2

50 Figure 1.2.2. Analysis of tra1 mutants that cannot support growth on galactose. (A) Immunoblot analysis showing levels of WT and mutant Tra1 proteins. Actin

(Act1) was monitored as a loading control. (B) qRT-PCR analysis monitoring expression of GAL1 and GAL3 in strains expressing WT TRA1 or tra1 mutant grown in raffinose or galactose. (C) qRT-PCR analysis monitoring expression of RPS5 and RPS0B in strains expressing WT

TRA1 or each of the tra1 mutants grown in raffinose or galactose. Expression of each gene was normalized to that observed in the WT TRA1 strain grown in raffinose, which was set to 1. The fold induction in galactose in the

WT TRA1 strain is indicated. Error bars indicate standard deviation.

51 Figure 1.2.3

A B

52 Figure 1.2.3. Development of a bimolecular fluorescence complementation assay for detecting interactions between activators and Tra1 in vivo. (A)

Schematic diagram depicting the BiFC assay. Tra1 is tagged at the C-terminus with the N-terminal Venus fragment (VN), and the activator (Act) is tagged at the C- terminus with the C-terminal Venus fragment (VC). (B)

BiFC assay monitoring interaction between Tra1 and Gal4 in vivo, as evidenced by intense YFP signal (arrowheads) in YPG. The Tra1–Gal4 interaction occurs in the nucleus, as evidenced by co-localization (arrowheads) with the

DNA stain 4',6-diamidino-2-phenylindole (DAPI). Tra1 was tagged at either the C-terminus (top panels) or N-terminus

(bottom panels). (C) BiFC assay monitoring interaction between Tra1 and Gcn4 in vivo, as evidenced by intense

YFP signal in response to amino acid starvation (–His + 3-

AT media).

53 Figure 1.2.4

54 Figure 1.2.4. Identification of Tra1 mutants that are unable to interact with Gal4. BiFC assay monitoring the interaction between Gal4 and the mutant Tra1 proteins.

55 Figure 1.2.5

56 Figure 1.2.5. Identification of Tra1 mutants that are unable to interact with HATs complexes. (A) Co- immunoprecipitation assay. Spt20-HA was immunoprecipitated with an anti-HA antibody, and the immunoprecipitate analyzed for the presence of Tra1. The levels of Spt20 and Tra1 in the input extract are shown.

(B) The Tra1 mutants are efficiently incorporated into the

NuA4 complex. Eaf1-HA was immunoprecipitated with an anti-HA antibody, and the immunoprecipitate analyzed for the presence of Tra1. The levels of Eaf1 and Tra1 in the input extract are shown.

57 Figure 1.2.6

58 Figure 1.2.6. Gal4 and Tra1 bind cooperatively to the

GAL1 promoter. (A) ChIP assay monitoring recruitment of

Tra1, Spt20 and Gal4 to the GAL1 and RPS0B promoters in strains expressing WT TRA1, tra1-mut1 or tra1-mut8 and grown in media containing galactose or raffinose. (B)

ChIP assay monitoring recruitment of Gal4 to the GAL1 promoter in a WT SPT20 or spt20-∆ strain. Error bars indicate standard deviation.

59 Figure 1.2.7

60 Figure 1.2.7. Schematic diagram of the on/off DNA strategy. (A) Schematic diagram of the BiFC-based strategy to detect whether the Gal4–Tra1 interaction occurs predominantly on or off DNA. (B) Immunoblot analysis confirming expression of the LexA(DBD)-

Gal4(AD)-VC fusion protein in strains harboring either

Tra1-VN or Gal80-VN grown in galactose or raffinose.

61 Figure 1.2.8

62 Figure 1.2.8. Gal4 and Tra1 interaction occurs on the promoter. (A) BiFC assay monitoring the interaction between Tra1-VN and LexA(DBD)-Gal4(AD)-VC in the three yeast strains grown in galactose or raffinose. (B)

BiFC assay monitoring the interaction between Gal80-VN and LexA(DBD)-Gal4(AD)-VC in the three yeast strains grown in galactose or raffinose.

63 Figure 1.2.9

64 Figure 1.2.9. Gcn4 functionally interacts with the Tra1

GID mutants. (A) Growth of tra1-mut1 and tra1-mut8 on

His-lacking media containing or lacking 3-AT. Growth of

WT TRA1 and gcn4-∆ strains are shown as controls. (B)

BiFC assay monitoring the interaction between Tra1-VN and Gcn4-VC in His-lacking media containing or lacking 3-

AT.

65 Figure 1.2.10

66 Figure 1.2.10. The Gal4-interaction site on Tra1 is highly selective. (A) qRT-PCR analysis monitoring expression of 11 Tra1- and SAGA-dependent genes in tra1-ts, spt20-∆, tra1-mut1 and tra1-mut8 strains. Gene expression is presented relative to that observed in a WT strain, which was set to 1 (indicated by the red line). (B)

ChIP analysis monitoring binding of Tra1 to the promoters of the 11 Tra1- and SAGA-dependent genes. Error bars indicate standard deviation.

67 Figure 1.2.11

68 Figure 1.2.11. Tra1 GID mutants can interact with various activators. BiFC analysis monitoring the ability of

WT Tra1, Tra1-mut1 or Tra1-mut8 to interact with various activators.

69 Figure 1.2.12

70 Figure 1.2.12. Microarray data shows that Tra1 GID mutants are Gal4 specific. (A) Scatter plot analyses comparing gene expression in a WT TRA1 strain and tra1- mut1 strain (top) or tra1-mut8 strain (bottom). The red line represents no change in gene expression; the blue dotted line represents 2-fold down-regulation. Gray circles represent all the genes on the array; black circles represent genes with a p-value <0.05; red circles represent genes with a p-value<0.05 and down-regulated more than 2-fold. (B) qRT-PCR analysis monitoring expression of GSC2 and HSP30 in tra1-mut1 and tra1- mut8 strains relative to that observed in a WT TRA1 strain, which was set to 1. Error bars indicate standard deviation.

71 Table 1.2.1. Microarray data comparing gene expression in a tra1-2ts strain relative to a wild-type TRA1 strain (p<0.05).

log 2 fold change Probe Gene symbol 120 min 90 min 60 min 10012_at NA -1.226372245 -1.207768354 -1.088297812 10154_at IDP2 -1.062025648 -1.397346472 -2.097301878 10204_at ZRT2 -1.693546497 -1.694102453 -1.9383917 10238_at BUD20 -1.750503576 -1.795024945 -1.565246542 10268_at REX2 -1.255752195 -1.164401017 -1.224383784 10282_at NA -1.531035433 -1.974016187 -1.59479357 10377_at NA 1.440655369 1.653805439 1.459506989 10378_at AQY2 1.301360348 1.601900901 1.258684398 10415_at MMP1 -1.322554037 -1.608306622 -1.401658201 10478_at PCK1 -1.293644718 -1.282967759 -1.252414073 10479_at UBP11 -1.300677363 -1.27364203 -1.013231909 10497_at SIS2 -1.252818834 -1.231091171 -1.300768796 10501_at ECM4 -1.001039214 -1.23475752 -1.336766778 10601_at SFK1 -1.05344171 -1.275306044 -1.171416271 10852_at DAN1 1.758400415 2.591673792 1.547578953 10858_s_at THI11 1.214394359 1.395405163 1.350608614 10954_at LIA1 -1.375695121 -1.642771735 -1.345510776 11127_at ALB1 -1.325608385 -1.34043061 -1.079525381 11133_at NCA3 -2.016193799 -2.416885115 -2.163019521 11177_at FMP33 -1.535237707 -1.29764351 -1.396321813 11226_at ACO2 1.692816942 1.418572159 1.657043875 11228_at PHO90 -1.132894771 -1.102094894 -1.165011219 11260_at OPT1 -1.598875666 -1.628453295 -1.204435409 11262_s_at PHO11 -1.49630264 -2.016522899 -2.104202627 11342_at MAK16 -1.056651274 -1.432647125 -1.05656865 11370_at CYC3 -1.369717708 -1.259428941 -1.08470426 11389_at ECM1 -1.634960648 -1.714500625 -1.435056855 3189_i_at COS8 1.673309659 2.025539479 1.884785592 3259_at NA 1.316981342 1.612948588 1.406599971 3316_f_at NA 1.184817697 1.347626442 1.392113641 3358_f_at NA 1.250214504 1.045026222 1.015535932 3361_f_at NA 1.415801349 1.677852533 1.188172238 3368_f_at NA 1.192894452 1.131515 1.167871189 3371_f_at NA 1.893949049 1.848916649 1.074171085 3372_f_at NA 1.252031885 1.372837615 1.53997973 3418_f_at NA 1.650029693 1.783416026 1.129191255 3438_f_at NA 1.153403486 1.474104928 1.406412669 3476_f_at NA 1.219314979 1.770233827 1.237017432 3479_f_at NA 1.13734296 1.160289726 1.200664265 3482_f_at NA 1.697940527 1.857024486 1.246425058

72 3491_f_at NA 2.01701709 1.80687404 1.283049356 3508_f_at NA 1.199963494 1.10408395 1.007203658 3514_f_at NA 1.4641883 1.789969773 1.260490591 3548_s_at NA 1.649591911 1.831869171 1.628764795 3563_s_at NA 1.404690762 1.357392858 1.270205922 3594_f_at NA 1.687428704 1.691750573 1.144044298 3598_f_at NA 1.302151446 1.464556019 1.327377947 3645_f_at NA 1.058435983 1.122623103 1.085881394 3687_f_at NA 1.195607967 1.082197877 1.089775105 3695_f_at NA 1.123799689 1.050095761 1.086556152 3707_f_at NA 1.196868317 1.84150666 1.214973265 3709_f_at NA 1.583758884 1.839268552 1.184649556 3716_f_at NA 1.517062975 1.328377265 1.509759953 3758_f_at NA 1.583597896 1.781075676 1.193950851 3764_s_at NA 2.481400306 2.14278409 1.068471933 3766_s_at NA 2.381487663 2.310779975 1.239327073 3767_s_at NA 2.069032787 2.49199005 1.105300263 3769_s_at NA 1.232606256 2.234937299 1.216994864 3794_f_at NA 1.016381195 1.784031442 1.131791624 3803_f_at NA 1.225986232 1.54525296 1.449226313 3813_f_at NA 2.058513645 1.716623277 1.342865844 3814_f_at NA 1.343798436 1.034031015 1.192923085 3821_f_at NA 2.11747248 1.383283141 1.352090785 3827_f_at NA 1.15195968 1.485584325 1.320939804 3840_f_at NA 1.31947678 1.899737437 1.166644167 3867_s_at NA 1.530869411 1.728419633 1.514660293 3884_f_at NA 1.7451141 1.632430561 1.227354629 3924_f_at NA 1.04279452 1.878273994 1.158755642 4070_at LYS1 1.650724845 1.979292924 1.901143433 4109_at BAR1 2.269907778 1.632377873 1.151711513 4385_at SPL2 -2.963481901 -3.967203495 -3.216817334 4386_at ARO9 1.881736181 1.938215698 1.815695735 4390_at NA -1.07971541 -1.545567189 -1.026451368 4425_at GAR1 -1.025980243 -1.380629771 -1.142676877 4428_at HXT4 -1.152164879 -1.062658621 -1.222481608 4506_at NA -1.782036847 -1.653810522 -1.49785359 4561_s_at NA 1.318713765 1.358559904 1.283820774 4583_at SNF6 -1.160956469 -1.327787652 -1.123321353 4584_at RIM4 1.83492348 1.851553314 1.036397929 4801_at HIP1 -1.573368309 -1.378332661 -1.065595404 4813_at NA 1.455183565 1.57618898 1.392299632 4832_at ATF2 1.204774527 1.078300867 1.067023028 4842_at BTN2 2.087277044 1.61624537 1.335585976 4856_at CYS4 -1.13283771 -1.44113781 -1.287177133 4884_i_at TPO2 1.649076161 1.617706435 1.610188624 4961_at NA -2.408236232 -3.175988828 -2.509424446 4994_at NA -1.407296483 -1.332803497 -1.34130352

73 5030_at AGA2 1.565750381 1.279898565 1.194119501 5073_at DBP3 -1.753055449 -1.630129836 -1.137786371 5418_at NA 1.222144693 1.18106564 1.249299795 5553_at BUR6 1.302064445 1.366792201 1.277357579 5566_at NSA2 -1.71226706 -1.706840852 -1.062014311 5626_at RAD51 -1.996859608 -2.106117223 -1.809696074 5654_at SER3 2.023708952 2.043562387 1.518183429 5669_g_at FCY2 -1.553462513 -1.716628852 -1.112283902 5774_at PRB1 1.062843303 1.223606984 1.016633859 5807_at NA 1.114135951 1.407971005 1.332776314 5927_at STP4 -1.229512878 -1.320963511 -1.096955696 5940_at EMI2 -1.291517468 -1.303423682 -1.643470032 6020_r_at MFA1 1.898381709 1.514081909 1.540448747 6049_at UTP5 -1.162193042 -1.237781251 -1.117126423 6077_at ARO10 2.308300344 2.449547721 2.053347253 6100_at VID21 -1.182472328 -1.355770088 -1.097637966 6140_at SUM1 -1.272076063 -1.476580474 -1.061554989 6201_at PHM6 -1.032407245 -1.501811996 -1.069264226 6225_at SWM1 -1.008429008 -1.308110429 -1.126135694 6547_at LHP1 -1.411653576 -1.797606056 -1.278970296 6549_at KNH1 -1.275016056 -1.215475496 -1.462990771 6597_at LYS21 1.116486658 1.013240983 1.277301307 6598_at STF1 -1.656991103 -1.778303083 -1.675129689 6803_at NA -1.602812585 -1.749766965 -1.155203598 6847_g_at NA -1.530738573 -1.673350898 -1.103803499 6896_at GFD2 -1.445552695 -1.591454003 -1.222336991 7041_s_at REI1 -1.401543984 -1.147939236 -1.010736831 7069_at PHO89 -2.503977896 -2.767795659 -2.860615012 7141_at SWC5 -1.048700677 -1.281535063 -1.033660575 7314_at ZTA1 -1.17062068 -1.478569392 -1.677063425 7321_at NA -1.251903099 -1.551365772 -1.266507207 7334_at FUR4 -1.078044562 -1.698202099 -1.2813895 7342_at CDS1 -1.670652184 -1.435031 -1.118520787 7348_at HMT1 -1.391186039 -1.663279486 -1.30016823 7413_at URA7 -1.053633004 -1.352299477 -1.133564762 7476_at NA 1.085936664 1.059971876 1.067810269 7480_at SRO77 1.185588893 1.303254457 1.375337017 7581_at DPM1 -1.152418059 -1.276581928 -1.134475204 7587_at NOC4 -1.437844264 -1.328762466 -1.040177587 7705_at TIF5 -1.244558998 -1.165681119 -1.026684255 7762_at RPA135 -1.124629988 -1.380771839 -1.301632455 7779_at VTC3 -1.032394156 -1.440360856 -1.133699116 7842_at NOG1 -1.037288051 -1.167197447 -1.242620155 7968_at PRM3 1.009103304 1.562059867 1.305429901 8004_at RBD2 -1.263894032 -1.622639403 -1.421099628 8056_at NA 1.325978148 1.114167905 1.050726373 8161_at PRE10 -1.103490156 -1.114015107 -1.039912497

74 8186_at NA -1.212722769 -1.518279607 -1.308317842 8203_at VTS1 -1.352692379 -1.523772776 -1.243778792 8241_at MCH5 -1.979615186 -1.857201354 -1.98965766 8294_at TPO4 1.05295604 1.415287096 1.115166897 8380_at GAC1 -1.117067866 -1.254129824 -1.399384206 8387_at GSP2 -1.011384844 -1.205741659 -1.155062716 8392_at PNO1 -1.218703267 -1.608700345 -1.328932841 8445_at LEU9 -1.259761381 -1.553940362 -1.19576111 8459_at BUD21 -1.256649017 -1.315002726 -1.02981905 8473_at TMA46 -1.306530465 -1.713773351 -1.463901523 8505_at HMS1 1.193320725 1.688812373 1.12109662 8522_at RSB1 -1.329883044 -1.429946965 -1.258654409 8650_at TRM10 -1.274527458 -1.639366951 -1.290153472 8683_at GRE2 -1.119489139 -1.363424375 -1.258363188 8714_at NA 2.212995366 1.194655247 1.236773664 8808_at LYS9 2.244712898 2.274634949 2.957207553 8811_at NOG2 -1.146876988 -1.314533053 -1.096270779 8929_at NA -2.277537975 -2.086489943 -2.406972879 8981_at RPS7B -1.475543515 -1.565764084 -1.123391523 9023_at MFA2 1.509525881 1.389804511 1.318109629 9027_at AAH1 -2.286343878 -2.467192932 -2.073835226 9032_at IPI3 -1.070736902 -1.189154601 -1.160922076 9068_at DUG3 -1.142812885 -1.390121488 -1.027935402 9150_at PCL1 1.318949524 1.749086757 1.643209794 9193_at NA 2.34707451 2.294445897 1.026687324 9309_at ADH2 -1.600901037 -2.012313222 -1.740172961 9339_at HAS1 -1.35700518 -1.321860344 -1.19658027 9452_at RGM1 -2.300959152 -2.199347063 -1.83996093 9515_at NA -1.150737949 -1.009873126 -1.276454584 9545_at NA -1.186881424 -1.077135163 -1.378960759 9637_at ERG5 1.322483728 1.219480714 1.038951437 9760_at PGA3 -1.131001678 -1.365484033 -1.433424088 9762_at PHO84 -1.426957046 -1.312848521 -1.848028623 9764_at GTR1 -1.078301943 -1.335501187 -1.25391772 9774_at DAT1 -1.461455646 -1.498636278 -1.247005569 9853_at FPR4 -1.213093437 -1.283012062 -1.060745604 9868_at COX19 -1.820645205 -2.044719937 -1.498669773 9885_at CAR2 1.705506715 1.773749023 1.340872619 9898_at NA -1.247535322 -1.57140508 -1.131618907

75 Table 1.2.2. List of the top 20 genes most affected by Tra1 inactivation, as assessed by expression profiling. For those genes also affected by SAGA inactivation, the predicted activators are listed.

76 Gene Function Fold Putativ down- e regul activat ation ors SPL2 Suppressor of plc1 deletion; downregulates low-affinity 9.30 Aro80, phosphate transport during phosphate limitation Cbf1; Pho4; Cbf1, Pho4, Pho2 PHO89 Involved in phosphate metabolism; Na+/Pi co-transporter; 7.26 Pho4; transcription regulated by Pho4 Pho4, Aft2 YGR035C Unknown; transcription activated by Yrm1 and Yrr1 5.69 YNL058C Unknown 5.30 Ndd1, Mcm1; Fkh2, Mcm1, Ndd1 NCA3 Nuclear control of ATPase; functions with Nca2 to regulate 4.48 mitochondrial expression of F0-F1 ATP synthase subunits PHO11 Involved in phosphate metabolism; acid phosphatase; 4.30 Pho4 phosphate starvation-induced transcription coordinately regulated by Pho4 and Pho2 IDP2 Cytosolic NADP-specific isocitrate dehydrogenase; 4.28 AAH1 Adenine aminohydrolase; transcriptionally regulated by 4.21 nutrient levels and growth phase MCH5 Riboflavin transporter 3.97 Put3 ZRT2 Zinc-regulated transporter; transcription induced under low- 3.83 Zap1 zinc conditions by Zap1 PHO84 Involved in phosphate metabolism; high-affinity inorganic 3.60 Pho4; phosphate transporter; regulated by Pho4 and Spt7 Cbf1, Pho4 RGM1 Putative transcriptional repressor with proline-rich zinc 3.58 Reb1 fingers RAD51 Strand exchange protein involved in recombinational repair 3.50 Mbp1, of double-strand breaks Swi6; Swi6 ADH2 Glucose-repressible alcohol dehydrogenase; 3.34 transcriptionally regulated by Adr1 SOL4 Suppressor of Los1-1; 6-phosphogluconolactonase 3.23 ZTA1 Zeta-crystallin; NADPH-dependent quinone reductase 3.20 STF1 Stabilizing factor; involved in regulation of the mitochondrial 3.19 F1-F0 ATP synthase EMI2 Early meiotic induction; required for transcriptional 3.12 induction of early meiotic-specific transcription factor IME1 RFU1 Inhibits Doa4 deubiquitinating activity 3.02 BUD20 Involved in bud-site selection 2.96

Table 1.2.3. Microarray data comparing gene expression in a tra1- mut1 strain relative to a wild-type TRA1 strain (p<0.05).

ID Gene symbol Fold change P value 2595 3310_s_at YDR034C-A -1.207130626 0.001779359 1545 2207_s_at YGR032W -1.19256344 0.012666811 3189 3913_s_at YAR010C -1.013557778 0.005681208 6130 6854_at YCR021C -1.005050451 0.000142935 4174 4898_at YGR108W -0.882156111 0.000596586 2272 2944_g_at YLL021W -0.874190892 0.003812645 1544 2206_g_at YGR032W -0.872542119 0.00423667 8980 9704_at YML052W -0.852927463 0.001119004 6217 6941_s_at YER190C-A -0.851942748 0.007222219 5203 5927_at YDL048C -0.84978062 0.002388645 2504 3187_s_at YBL111C -0.84015179 0.073767104 3468 4192_at YIL119C -0.827467305 0.007772781 3225 3949_i_at YNL044W -0.817149365 0.01218005 6832 7556_at YPL189C-A -0.812754741 0.000632008 2298 2970_s_at YKL182W -0.804534336 0.029141472 70 10069_f_at YLR264W -0.798251776 0.017974932 4698 5422_s_at YER190C-A -0.783115415 0.001070263 6041 6765_at YCL048W-A -0.772422209 0.005972537 69 10068_i_at YLR264W -0.762286462 0.089983193 5600 6324_g_at YDR133C -0.739842079 0.016506907 9144 9868_at YLL018C-A -0.718216349 0.098525152 2164 2836_g_at YLR342W -0.704534724 0.085787013 4896 5620_at YER091C -0.700606395 0.030026657

78 2948 3672_f_at YBL005W-B -0.67181392 0.004216251 8622 9346_at YMR295C -0.670228666 0.033400865 7107 7831_at YPL058C -0.667407491 0.096374758 757 10756_at YKL163W -0.665228757 0.093656654 2292 2964_at YKL182W -0.66111955 0.035734882 3668 4392_at YHR143W -0.657233394 0.035233362 7836 8560_at YOL002C -0.643088742 0.000753064 4310 5034_at YGL028C -0.639647293 0.080791361 8129 8853_at YNR005C -0.637146572 0.004837066 1999 2666_s_at YNL054W -0.63380518 0.024161365 4820 5544_at YER150W -0.630562676 0.011902742 3856 4580_at YHL028W -0.627175751 0.002473332 4119 4843_at YGR143W -0.621938131 0.017701583 3191 3915_s_at YAR009C -0.619467852 0.006385464 5331 6055_at YDR404C -0.618070705 0.015048041 1976 2643_s_at YOR153W -0.617999736 0.043771507 8172 8896_at YNL046W -0.614783895 0.001163966 1124 11123_at YJL079C -0.613590464 0.019653135 3611 4335_at YHR175W -0.610404855 0.039227401 5193 5917_at YDR538W -0.606220723 0.067066862 5148 5872_at YDR374W-A -0.604194051 0.011629082 3519 4243_at YIL158W -0.603320387 0.013139729 7798 8522_at YOR049C -0.59964098 0.001705804 3455 4179_at YIL087C -0.595911291 0.00460624 3340 4064_at YIR028W -0.592191668 0.003179717 5756 6480_at YDR019C -0.590975313 0.010036226

Table 1.2.4. Microarray data comparing gene expression in a tra1-mut8 strain relative to a wild-type TRA1 strain (p<0.05).

ID Gene symbol Fold change P value 2595 3310_s_at YDR034C-A -1.097067224 0.002980552 9145 9869_at YLR264C-A -0.922627325 0.08557862 6130 6854_at YCR021C -0.895472494 0.000289464 6217 6941_s_at YER190C-A -0.804113967 0.009515161 4160 4884_i_at YGR138C -0.768574006 0.000623581 3189 3913_s_at YAR010C -0.729111841 0.025169612 5148 5872_at YDR374W-A -0.666964525 0.00730186 7836 8560_at YOL002C -0.666094003 0.000613788 3340 4064_at YIR028W -0.651389945 0.001906604 123 10122_at YLR230W -0.63627118 0.023959236 5203 5927_at YDL048C -0.613805619 0.012079149 431 10430_at YKL183C-A -0.609352353 0.056935467 388 10387_at YLL044W -0.601495203 0.042955871 5297 6021_f_at YDR461W -0.587224505 0.007250929

80 Table 1.2.5. List of yeast strains used in this study.

Strain Genotype Reference FY23 MATa leu2∆1 trp1∆63 ura3-52 Winston 1995 YDA352 MATa leu2∆1 trp1∆63 ura3-52 spt20::URA3 This study BY4741 MATa his3∆1 leu2∆0 met15∆0 ura3∆0 Giaever 2002 LLY074 MATa his3∆1 leu2∆0 met15∆0 ura3∆0 TRA1-VN This study LLY071 MATa his3∆1 leu2∆0 met15∆0 ura3∆0 VN-TRA1 This study BY4742 MATα his3∆1 leu2∆0 lys2∆0 ura3∆0 Giaever 2002 LLY080 MATα his3∆1 leu2∆0 lys2∆0 ura3∆0 GAL4-VC This study LLY082 MATα his3∆1 leu2∆0 lys2∆0 ura3∆0 GCN4-VC This study MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 Kulesza 2002 MDC1 tra1::HIS3+[TRA1/CEN/LEU2] MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 Kulesza 2002 MDC3 tra1::HIS3+[tra1-2ts/CEN/LEU2] LLY154 MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study tra1::HIS3+[myc-TRA1/CEN/URA3] MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 LLY129 tra1::HIS3 EAF1-HA+[myc-TRA1/CEN/URA3] This study MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 LLY131 tra1::HIS3 SPT20-HA+[myc-TRA1/CEN/URA3] This study MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study LLY197 tra1::LEU2+[myc-TRA1/CEN/URA3] MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study LLY210 tra1::LEU2+[myc-VN-TRA1/URA3/CEN] MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study LLY240 tra1::LEU2 GAL4-VC+[myc-TRA1/CEN/URA3] MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study LLY241 tra1::LEU2 ZAP1-VC+[myc-TRA1/CEN/URA3] MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study LLY242 tra1::LEU2 CBF1-VC+[myc-TRA1/CEN/URA3] LLY238 MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study

81 tra1::LEU2 CIN5-VC+[myc-TRA1/CEN/URA3] MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study LLY245 tra1::LEU2 REB1-VC+[myc-TRA1/CEN/URA3] MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study LLY246 tra1::LEU2 MCM1-VC+[myc-TRA1/CEN/URA3] MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study LLY247 tra1::LEU2 RAP1-VC+[myc-TRA1/CEN/URA3] MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study LLY278 tra1::LEU2 GCN4-VC+[myc-TRA1/CEN/URA3] MATa ura3-52 lys2-801 ade2-101 trp1∆63 his3∆200 leu2∆1 This study LLY279 tra1::LEU2 FKH2-VC+[myc-TRA1/CEN/URA3] MATa leu2-3,112 trp1-1 can1-100 ura3-1 ade2-1 his3- Veal 2003 W303 11,15 MATa leu2-3,112 trp1-1 can1-100 ura3-1 ade2-1 his3- This study LLY326 11,15 GAL80-VN

82 Table 1.2.6. Primer sequences for quantitative real-time RT-PCR (qRT-PCR) analysis, chromatin immunoprecipitation (ChIP) and yeast strain construction.

qRT-PCR Gene Forward or Reverse Sequence (5’  3’) AAH1 Forward CGGTCGAGGAATTAAACGAA Reverse CCACGCCAAATCAAAGAAAT ADH2 Forward ACTACGCCGGTATCAAATGG Reverse TCGTGGGTGTAACCAGACAA GAL1 Forward GGATCGAGCACGAATAAAGG Reverse ATTCAATATCGCCGTTCCAG GAL3 Forward ACCCTTAGTAGGTGCGCAGA Reverse GCGCAAGTAAATGCAGATGA GSC2 Forward GGTACAGCACCGGTTTAGGA Reverse AAACAAAATCCGCTGCAAAC HSP30 Forward CTAATTGCAGTCAGCCGTGA Reverse TCCGTAGCATGGTGATGAGA MCH5 Forward TCGCTACCGCCAACTCTACT Reverse TAGTGGGCGGGAACACTTAC PHO11 Forward GTGATTTGCCGGAAAGTTGT Reverse TTTTGGCTTTGCTGACAGTG PHO84 Forward TCTGCAAACCACTGTTGCTC Reverse CACCACCGATACCAATACCC PHO89 Forward AGGCGCAAGAGTTTCTGGTA Reverse ACCAACAAGACGAGCCAATC RAD51 Forward AGATATTGAGGCCACCAACG Reverse AGGCAGCTTCATCGTATGCT

83 RPS5 Forward ACTGACCAAAACCCAATCCA Reverse TTGACGTCTAGCAGCACCAC RPS0B Forward CAATCGCCGGTAGATTCACT Reverse GGCCTGAGCGTCTAATCTTG RGM1 Forward CTCCTGCTAGCCCACAAGAC Reverse GCAATGGTATGGGAGCAGAT SPL2 Forward GGTCACCAGCATAAGGGAAG Reverse TTGAGCCTCCTGCACTCTTT YNL058C Forward CCAAGTCCCTTCCATCGTTA Reverse CTGCTGTAGATGGGGGTGTT ZRT2 Forward AAAAAGAACCCGGCTCAAAT Reverse CATGGGAACTGGTTGGAACT

ChIP Gene Forward or Reverse Sequence (5’  3’) AAH1 Forward TTGCAAATTTTCATCGCTTA Reverse ATGCGATGAGGTCAGTTTCC ADH2 Forward AACGGCTTTCGCTCATAAAA Reverse CCAAAAAGTGAACCCCATTT GAL1 Forward CGTTCCTGAAACGCAGATGT Reverse GCCAGGTTACTGCCAATTTT MCH5 Forward CCAAAGCGCAGTATTCAACC Reverse TTCCATCTTTTCCCTCGTGT PHO11 Forward TACTGGGAGCAGGAGTTGCT Reverse AAAAACAAAACCGCATCACC PHO84 Forward TGAAGTCATCTCCGGGTAGTG Reverse GAGGCGGTTAATCAATGAAAA PHO89 Forward AAGGATCCAAAGTGGCTGTG Reverse CAGCACGTGGGAGACAAATA

84 RAD51 Forward TGCACCACTACCGTTCTTCA Reverse GGGTAACGAAGTGTGGCCTA RGM1 Forward AGAATTTGCTCAAGCCCTCA Reverse CGCCTTTTATAGTGCCCATC RPS0B Forward TGGGCATTACGTTTTCAACA Reverse TGTCATGAATGCATGGGTTT SPL2 Forward TGCCCATGGGAAGTCATAGT Reverse CTCTCTTTTCCCACGTGCTC YNL058C Forward TTGACAAGGTATGAGTCTAGTTGATT Reverse CATGAAACCCTTAATATGATTTCC ZRT2 Forward GGTTATATGCGACTCCTTCTCG Reverse CGGTCTAACCAACAGGGTGT

Yeast strain construction Genomic tagging Gene Forward or Sequence (5’  3’) Reverse CBF1-VC Forward GAAACAAGAGAACGAAAGAAAAAGCACTAG GAGCGATAATCCACATGAGGCTGATTACAA GGACGACGATGACAAGGGTCGACGGATCCC CGGGTT Reverse ACGCAGATACATAGGGAGACTCGAAATACAT TTAGCTATCTATTTTTAACTCTCGATGAATTC GAGCTCGTT CIN5-VC Forward AAACAGTGAATTGAAAAAAATGATTGAATCA TTAAAGTCGAAATTAAAAGAAGATTACAAGG ACGACGATGACAAGGGTCGACGGATCCCCG GGTT Reverse TGTGCTTTGAAAACTTTTAAGATGTTACTAGT ACTAATAATTATTCATTATTTCGATGAATTCG AGCTCGTT FKH2-VC Forward CAAGGAACTAATACTAGATACGGATGGTGCA AAGATCAGTATTATCAACAACGATTACAAGG ACGACGATGACAAGGGTCGACGGATCCCCG GGTT Reverse AGCCATTTCTCATTCATTTCTTTAGTCTTAGT GATTCACCTTGTTTCTTGTCTCGATGAATTC

85 GAGCTCGTT GAL4-VC Forward ATGGATGATGTATATAACTATCTATTCGATGA TGAAGATACCCCACCAAACCCAAAAAAAGAG GGTCGACGGATCCCCGGGTT Reverse TGAGATGGTGCACGATGCACAGTTGAAGTG AACTTGCGGGGTTTTTCAGTATCTACGATTC ATTTCGATGAATTCGAGCTCGTT GAL80- Forward TTAATCGAGAGCGTTTATAAAAGTAACATGA VN TGGGCTCCACATTAAACGTTAGCAATATCTC GCATTATAGTTTAGGTCGACGGATCCCCGG GTT Reverse TTACATAGATATATACTCAGTATTCGTTTTTA TAACGTTCGCTGCACTGGGGGCCAAGCACA GGGCAAGATGCTTTCGATGAATTCGAGCTC GTT GCN4-VC Forward TCGAAAAATTATCACTTGGAAAATGAGGTTG CCAGATTAAAGAAATTAGTTGGCGAACGCC GGATCCCCGGGTTAATTAA Reverse TCGAAAAATTATCACTTGGAAAATGAGGTTG CCAGATTAAAGAAATTAGTTGGCGAACGCC GGATCCCCGGGTTAATTAA MCM1-VC Forward TGCTGCCTACCAACAATACTTTCAAGAACCG CAACAAGGCCAATACGAACAGAAATTGATCT CTGAAGAAGACTTGGGTCGACGGATCCCCG GGTT Reverse AGCTTTTTCCTCTTAATGCTCGTCTATGAATT ATATACGGAAATCGATAAGATCGATGAATTC GAGCTCGTT RAP1-VC Forward TGGAACTGGTAGAATGGAAATGAGGAAAAG ATTTTTTGAGAAGGACCTGTTAGATTACAAG GACGACGATGACAAGGGTCGACGGATCCCC GGGTT Reverse TGAAATAAAGGAGTAAAATAAGTTAAACAAT GATGTTACTTAATTCAATTACTCGATGAATTC GAGCTCGTT REB1-VC Forward GCTAGTTGATTATTTTAGCTCCAATATTTCAA TGAAAACAGAAAATGAACAGAAATTGATCTC TGAAGAAGACTTGGGTCGACGGATCCCCGG GTT Reverse CAAACATTATTGAGTTTTTCGCTTTCACCAAT TATATTTTCCGGAATCGATGAATTCGAGCTC GTT TRA1-VN Forward CCTAGACTGTATTGGCAGCGCTGTCAGTCC

86 AAGAAACTTAGCAAGAACAGACGTGAACTTC ATGCCATGGTTCGGTCGACGGATCCCCGGG TT Reverse ATTTTTTGAGGCTTTCTCTACCTTCATTAAAT ACAGAGAAAAGGCCGGAAACTAACCCATGT ATCAGCTTCGATGAATTCGAGCTCGTT VN-TRA1 Forward CATAAAGAAGATTAAATTCAGAGAAGATCTA TCACACGATATCTCCATAGTTACAAAGAATA CCGCATTTTGCCGGAATTCGAGCTCGTTTAA AC Reverse AGTGGCATCATCATCGCGAAACCTACTGGC GAATTGCTCGATCTGCTCAGTGAGTGACATA GTACCACCAGAACCCTCGATGTTGTGGCGG ATC ZAP1-VC Forward AAAATTGAACTCACAGAAAGTGAAATGCGCA TTGGAAAGAAAACCATATTTAGATTACAAGG ACGACGATGACAAGGGTCGACGGATCCCCG GGTT Reverse TTTATGTGATCCTGAAAACCGCCCTTCTTAC GTTGGACCATGATGATCACATTCGATGAATT CGAGCTCGTT

Confirmation of genomic tagging Gene Forward or Sequence (5’  3’) Reverse CBF1-VC Forward GCAGGAAGAACTGGGAAATG CIN5-VC Forward AAAATCGAAGCGCACAAAAG FKH2-VC Forward ATAGCGGAGGCAACAAGAGA GAL4-VC Forward CACAACCAATTGCCTCCTCT GAL80-VN Forward CTAGAGCAAACGACTTCCCG GCN4-VC Forward AATCCAGTGATCCTGCTGCT MCM1-VC Forward CAGCAACAAATGTCACAGCA RAP1-VC Forward TGTTAATCCTCCTCCCAACG REB1-VC Forward TGAAGCCTGGCATGAAATTA TRA1-VN Forward GGTTCAAACCAACGTTGACC VN-TRA1 Forward CCAGGAGCGCACCATCTTCTTCAA

87 Reverse TCGTGATTCACCAGATCGTC ZAP1-VC Forward TCGCGATTTCAAGTTCTTTG VC Reverse TTCTCGTTGGGGTCTTTGCTCAG VN Reverse TTGAAGAAGATGGTGCGCTCCTG

Plasmid tagging Gene Forward Sequence (5’  3’) or Reverse GAL4(AD) Forward ATGGATGATGTATATAACTATCTATTCGATGATGA -VC AGATACCCCACCAAACCCAAAAAAAGAGGGTCGA CGGATCCCCGGGTT Reverse AATGAAAGAAATTGAGATGGTGCACGATGCACAG TTGAAGTGAACTTGCGGGGTTTTTCAGTATCTACG ATTCATTTCGATGAATTCGAGCTCGTT TRA1-VN Forward TCCTAGACTGTATTGGCAGCGCTGTCAGTCCAAG AAACTTAGCAAGAACAGACGTGAACTTCATGCCAT GGTTCAGATCCATCGCCACCATGGT Reverse ATGACCATGATTACGCCAAGCGCGCAATTAACCC TCACTAAAGGGAACAAAAGCTGGAGCTCCTACTC GATGTTGTGGCGGA VN-TRA1 Forward GCGGCCGCATGGTGAGCAAGGGCGAGG Reverse GCGGCCGCCGCGCCCTACTCGATGTTG

Confirmation of plasmid tagging Gene Forward or Sequence (5’  3’) Reverse GAL4)AD)-VC Forward CACAACCAATTGCCTCCTCT Reverse TTCTCGTTGGGGTCTTTGCTCAG TRA1-VN Forward CCAGGAGCGCACCATCTTCTTCAA Reverse GGTGGCGATGGATCTGTTA VN-TRA1 Forward GAGCTCAGATCCATCGCCACCATGG Reverse GAGCTCTTAGAAAAACTCATCGAGCATC

88 REFERENCE

Akman G, MacNeill SA (2009) MCM-GINS and MCM-MCM interactions in vivo

visualised by bimolecular fluorescence complementation in fission yeast. BMC

Cell Biol 10:12.

Allard S, et al. (1999) NuA4, an essential transcription adaptor/histone H4

acetyltransferase complex containing Esa1p and the ATM-related cofactor Tra1p.

EMBO J 18:5108-5119.

Allfrey, V., Littau, V. C. & Mirsky, A. On the role of histones in regulation

ribonucleic acid synthesis in the cell nucleus. Proc. Natl Acad. Sci. USA 49, 414–

421 (1963)

Ansari AZ, Koh SS, Zaman Z, Bongards C, Lehming N, Young RA, Ptashne M

(2002) Transcriptional activating regions target a cyclin-dependent kinase. Proc

Natl Acad Sci USA 99: 14706–14709.

Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statis. Soc. B 57:289-300.

Bhaumik SR, Green MR (2001) SAGA is an essential in vivo target of the yeast

acidic activator Gal4p. Genes Dev 15: 1935–1945.

Bhaumik SR, Green MR (2002) Differential requirement of SAGA components for

recruitment of TATA-box-binding protein to promoters in vivo. Mol Cell Biol 22:

7365–7371.

Brent, R. & Ptashne, M. A eukaryotic transcriptional activator bearing the DNA specificity of a prokaryotic repressor. Cell 43, 729–736 (1985)

Brown CE, Howe L, Sousa K, Alley SC, Carrozza MJ, Tan S, Workman JL (2001)

Recruitment of HAT complexes by direct activator interactions with the ATM- related Tra1 subunit. Science 292: 2333–2337.

Brownell, J. E., J. Zhou, et al. (1996). "Tetrahymena histone acetyltransferase A: a homolog to yeast Gcn5p linking histone acetylation to gene activation." Cell

84(6): 843-851.

Bryant GO, Ptashne M (2003) Independent recruitment in vivo by Gal4 of two complexes required for transcription. Mol Cell 11: 1301–1309.

Carrozza MJ, John S, Sil AK, Hopper JE, Workman JL (2002) Gal80 confers specificity on HAT complex interactions with activators. J Biol Chem 277: 24648–

24652.

Chang C, Gonzalez F, Rothermel B, Sun L, Johnston SA, Kodadek T (2001) The

Gal4 activation domain binds Sug2 protein, a proteasome component, in vivo and in vitro. J Biol Chem 276: 30956–30963.

Chavez S, Beato M. 1997. Nucleosome-mediated synergism between transcription factors on the mouse mammary tumor virus promoter. Proc. Natl.

Acad. Sci. USA 94:2885–90

Cirillo LA, Zaret KS. 1999. An early developmental transcription factor complex that is more stable on nucleosome core particles than on free DNA. Mol. Cell

4:961–69

Dudley AM, Rougeulle C, Winston F (1999) The Spt components of SAGA facilitate TBP binding to a promoter at a post-activator-binding step in vivo.

Genes Dev 13:2940-2945.

Durrin, L. K., Mann, R. K., Kayne, P. S. & Grunstein, M. Yeast histone H4-N- terminal sequence is required for promoter activation in vivo. Cell 65, 1023–1031

(1991)

91 Drysdale CM, et al. (1998) The Gcn4p activation domain interacts specifically in

vitro with RNA polymerase II holoenzyme, TFIID, and the Adap-Gcn5p

coactivator complex. Mol Cell Biol 18:1711-1724.

Emmer, M., deCrombrugghe, B., Pastan, I. & Perlman, R. Cyclic AMP receptor

protein of E. coli: its role in the synthesis of inducible enzymes. Proc. Natl Acad.

Sci. USA 66, 480–487 (1970)

Fishburn J, Mohibullah N, Hahn S (2005) Function of a eukaryotic transcription

activator during the transcription cycle. Mol Cell 18:369-378.

Gentleman RC, et al. (2004) Bioconductor: open software development for

computational biology and bioinformatics. Genome Biol 5:R80.

Giaever G, et al. (2002) Functional profiling of the Saccharomyces cerevisiae genome. Nature 418:387-391.

Gilbert, W. & Müller-Hill, B. Isolation of the lac repressor. Proc. Natl Acad. Sci.

USA 56, 1891–1898 (1966)

Gill G, Ptashne M (1988) Negative effect of the transcriptional activator GAL4.

Nature 334:721-724.

Gonzalez F, Delahodde A, Kodadek T, Johnston SA (2002) Recruitment of a 19S proteasome subcomplex to an activated promoter. Science 296: 548–550.

Grant PA, et al. (1997) Yeast Gcn5 functions in two multisubunit complexes to acetylate nucleosomal histones: characterization of an Ada complex and the

SAGA (Spt/Ada) complex. Genes Dev 11(13):1640-50

Guthrie C, Fink GR (2004) Guide to Yeast Genetics and Molecular and Cell

Biology (Academic Press, New York) p 933.

Harbison CT, et al. (2004) Transcriptional regulatory code of a eukaryotic genome. Nature 431:99-104.

Herbig E, et al. (2010) Mechanism of Mediator recruitment by tandem Gcn4 activation domains and three Gal11 activator-binding domains. Mol Cell Biol

30:2376-2390.

Hope IA, Mahadevan S, Struhl K (1988) Structural and functional characterization of the short acidic transcriptional activation region of yeast

GCN4 protein. Nature 333:635-640.

93 Iyer VR, et al. (2001) Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature 409:533-538.

Ihaka R, Gentleman R (1996) R: A language for data analysis and graphics. J

Comput Graph Stat 5:299-314.

Irizarry RA, et al. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249-264.

Jacob, F. & Monod, J. Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3, 318–356 (1961)

Jeong C-J, Yang S-H, Xie Y, Zhang L, Johnston SA, Kodadek T (2001) Evidence that Gal11 protein is a target of the Gal4 activation domain in the mediator.

Biochemistry 40: 9421–9427.

Johnston M (1987) A model fungal gene regulatory mechanism: the GAL genes of Saccharomyces cerevisiae. Microbio Rev 51:458-476.

Johnston M, Dover J (1987) Mutations that inactivate a yeast transcriptional regulatory protein cluster in an evolutionarily conserved DNA binding domain.

Proc Natl Acad Sci USA 84: 2401–2405.

Johnston M, Carlson M (1992) Regulation of carbon and phosphate utilization.

The molecular and cellular biology of yeast Saccharomyces: Gene expression, ed al. EWJe (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY), pp

193-281.

Kerppola TK (2008) Bimolecular fluorescence complementation: visualization of molecular interactions in living cells. Methods Cell Biol 85:431-470.

Kulesza CA, et al. (2002) Adenovirus E1A requires the yeast SAGA histone acetyltransferase complex and associates with SAGA components Gcn5 and

Tra1. Oncogene 21:1411-1422.

Kunzler, M., G. H. Braus, et al. (1994). "Functional differences between mammalian transcription activation domains at the yeast GAL1 promoter." EMBO

J 13(3): 641-645.

Koh SS, Ansari AZ, Ptashne M, Young RA (1998) An activator target in the RNA polymerase II holoenzyme. Mol Cell 1: 895–904.

Kornberg, R. D. Chromatin structure: a repeating unit of histones and DNA.

Science 184, 868–871 (1974)

Larschan E, Winston F (2001) The S. cerevisiae SAGA complex functions in vivo as a coactivator for transcriptional activation by Gal4. Genes Dev 15: 1946–1956.

Larschan E, Winston F (2005) The Saccharomyces cerevisiae Srb8–Srb11 complex functions with the SAGA complex during Gal4-activated transcription.

Mol Cell Biol 25: 114–123.

Lee TI, et al. (2002) Transcriptional regulatory networks in Saccharomyces

cerevisiae. Science 298:799-804.

Lieb JD, Liu X, Botstein D, Brown PO (2001) Promoter-specific binding of Rap1

revealed by genome-wide maps of protein-DNA association. Nat Genet 28:327-

334.

Lohr D, Venkov P, Zlatanova J. Transcriptional regulation in the yeast GAL gene

family: a complex genetic network. FASEB J. 1995 Jun;9(9):777-87.

Knutson, B. A. and S. Hahn (2011). "Domains of Tra1 important for activator

recruitment and transcription coactivator functions of SAGA and NuA4

complexes." Mol Cell Biol 31(4): 818-831.

96 Kulesza CA, et al. (2002) Adenovirus E1A requires the yeast SAGA histone acetyltransferase complex and associates with SAGA components Gcn5 and

Tra1. Oncogene 21:1411-1422.

Leuther KK, Johnston SA (1992) Nondissociation of GAL4 and GAL80 in vivo after galactose induction. Science 256: 1333–1335.

Lohr D, Venkov P, Zlatanova J (1995) Transcriptional regulation in the yeast GAL gene family: a complex genetic network. FASEB J 9: 777–787.

Longtine MS, et al. (1998) Additional modules for versatile and economical PCR- based gene deletion and modification in Saccharomyces cerevisiae. Yeast

14:953-961.

MacIsaac KD, et al. (2006) An improved map of conserved regulatory sites for

Saccharomyces cerevisiae. BMC Bioinformatics 7:113.

Marmorstein R, Carey M, Ptashne M, Harrison SC (1992) DNA recognition by

GAL4: structure of a protein–DNA complex. Nature 356: 408–414.

97 McMahon, S. B., H. A. Van Buskirk, et al. (1998). "The novel ATM-related protein

TRRAP is an essential cofactor for the c-Myc and E2F oncoproteins." Cell 94(3):

363-374.

Melcher K, Johnston SA (1995) GAL4 interacts with TATA-binding protein and coactivators. Mol Cell Biol 15: 2839–2848.

Mutiu, A. I., S. M. Hoke, et al. (2007). "Structure/function analysis of the

phosphatidylinositol-3-kinase domain of yeast tra1." Genetics 177(1): 151-166.

Nagai T, et al. (2002) A variant of yellow fluorescent protein with fast and efficient

maturation for cell-biological applications. Nat Biotechnol 20:87-90.

Neely KE, Hassan AH, Brown CE, Howe L, Workman JL (2002) Transcription

activator interactions with multiple SWI/SNF subunits. Mol Cell Biol 22:1615-

1625.

Orphanides G, Lagrange T, Reinberg D (1996) The general transcription factors

of RNA polymerase II. Genes Dev 10:2657-2683.

Park JM, et al. (2000) In vivo requirement of activator-specific binding targets of mediator. Mol Cell Biol 20:8709-8719.

Ponticelli, A. S., T. S. Pardee, et al. (1995). "The glutamine-rich activation

domains of human Sp1 do not stimulate transcription in Saccharomyces

cerevisiae." Mol Cell Biol 15(2): 983-988.

Ptashne, M. Isolation of the lambda phage repressor. Proc. Natl Acad. Sci. USA

57, 306–313 (1967)

Peng G, Hopper JE (2000) Evidence for Gal3p's cytoplasmic location and

Gal80p's dual cytoplasmic–nuclear location implicates new mechanisms for

controlling Gal4p activity in Saccharomyces cerevisiae. Mol Cell Biol 20: 5140–

5148.

Peng G, Hopper JE (2002) Gene activation by interaction of an inhibitor with a cytoplasmic signaling protein. Proc Natl Acad Sci USA 99: 8548–8553.

Qiu H, et al. (2005) Interdependent recruitment of SAGA and Srb mediator by transcriptional activator Gcn4p. Mol Cell Biol 25:3461-3474.

Reece RJ, Ptashne M (1993) Determinants of binding-site specificity among yeast C6 zinc cluster proteins. Science 261: 909–911.

99 Reeves WM, Hahn S (2005) Targets of the Gal4 transcription activator in functional transcription complexes. Mol Cell Biol 25:9092-9102.

Ren B et al. (2000) Genome-wide location and function of DNA binding proteins.

Science 290: 2306–2309.

Roeder, R. G. & Rutter, W. J. Multiple forms of DNA-dependent RNA polymerase in eukaryotic organisms. Nature 224, 234–237 (1969)

Roeder RG (1996) The role of general initiation factors in transcription by RNA polymerase II. Trends Biochem Sci 21:327-335.

Rudolph H & Hinnen A (1987) The yeast PHO5 promoter: phosphate-control elements and sequences mediating mRNA start-site selection. Proc Natl Acad

Sci U S A 84:1340-1344.

Sadowski, I., J. Ma, et al. (1988). "GAL4-VP16 is an unusually potent transcriptional activator." Nature 335(6190): 563-564.

Saleh A, et al. (1998) Tra1p is a component of the yeast Ada.Spt transcriptional regulatory complexes. J. Biol. Chem. 273:26559-26565.

100 Schild C, Claret FX, Wahli W, Wolffe AP. 1993. A nucleosome-dependent static loop potentiates estrogen-regulated transcription from the Xenopus vitellogenin

B1 promoter in vitro. EMBO J. 12:423—33

Schmitt ME, Brown TA, Trumpower BL (1990) A rapid and simple method for preparation of RNA from Saccharomyces cerevisiae. Nucleic Acids Res 18:3091-

3092.

Sil AK, et al. (1999) The Gal3p-Gal80p-Gal4p transcription switch of yeast: Gal3p destabilizes the Gal80p-Gal4p complex in response to galactose and ATP. Mol

Cell Biol 19:7828-7840.

Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol

3:Article3.

Sterner DE, et al. (2002) SALSA, a variant of yeast SAGA, contains truncated

Spt7, which correlates with activated transcription. Proc Natl Acad Sci U S A

99(18):11622-7

101 Sung MK & Huh WK (2007) Bimolecular fluorescence complementation analysis system for in vivo detection of protein-protein interaction in Saccharomyces cerevisiae. Yeast 24:767-775.

Taunton, J., Hassig, C. A. & Schreiber, S. L. A mammalian histone deacetylase related to the yeast transcriptional regulator Rpd3p. Science 272, 408–411

(1996)

Tjian, R. The binding site on SV40 DNA for a T antigen-related protein. Cell 13,

165–179 (1978)

Triezenberg SJ, Kingsbury RC, McKnight SL (1988) Functional dissection of

VP16, the trans-activator of herpes simplex virus immediate early gene expression. Genes Dev 2:718-729.

Utley RT, et al. (1998) Transcriptional activators direct histone acetyltransferase complexes to nucleosomes. Nature 394:498-502.

Veal EA, Ross SJ, Malakasi P, Peacock E, & Morgan BA (2003) Ybp1 is required for the hydrogen peroxide-induced oxidation of the Yap1 transcription factor. J

Biol Chem 278:30896-30904.

102 Weiss, S. & Gladstone, L. A mammalian system for the incorporation of cytidine triphosphate into ribonucleic acid. J. Am. Chem. Soc. 81, 4118–4119 (1959)

West RW, Jr., Yocum RR, & Ptashne M (1984) Saccharomyces cerevisiae

GAL1-GAL10 divergent promoter region: location and function of the upstream activating sequence UASG. Mol Cell Biol 4:2467-2478.

Winston F, Dollard C & Ricupero-Hovasse SL (1995) Construction of a set of convenient Saccharomyces cerevisiae strains that are isogenic to S288C. Yeast

11:53-55.

Wu Y, Reece RJ, Ptashne M (1996) Quantitation of putative activator-target affinities predicts transcriptional activating potentials. EMBO J 15: 3951–3963.

Wu X, et al. (2000) The Ess1 prolyl isomerase is linked to chromatin remodeling complexes and the general transcription machinery. EMBO J 19:3727-3738.

Wu, P. Y., C. Ruhlmann, et al. (2004). "Molecular architecture of the S. cerevisiae SAGA complex." Mol Cell 15(2): 199-208.

103 Yudkovsky N, Logie C, Hahn S, Peterson CL (1999) Recruitment of the SWI/SNF chromatin remodeling complex by transcriptional activators. Genes Dev 13:

2369–2374.

Zenke FT, Engels R, Vollenbroich V, Meyer J, Hollenberg CP, Breunig KD (1996)

Activation of Gal4p by galactose-dependent interaction of galactokinase and

Gal80p. Science 272: 1662–1665.

Zubay, G., Schwartz, D. & Beckwith, J. Mechanism of activation of catabolite- sensitive genes: a positive control system. Proc. Natl Acad. Sci. USA 66, 104–

111 (1970)

104

PART II

IDENTIFICATION OF NEW TUMOR SUPPRESSOR

GENES THROUGH A LARGE-SCALE SHRNA SCREEN

FOR MOUSE TUMORIGENESIS

105

CHAPTER I

GENERAL INTRODUCTION

106

2.1.1 Cancer

Cancer formation is the transformation of normal cells into highly malignant invasive derivatives, via a series of premalignant states (Fearon &

Vogelstein, 1990). It involves dynamic genetic changes in the genome in a stepwise process, and each step enables precancerous cells to acquire one type of growth advantage. The theory that cancer is a genetic disease was first proposed by David von Hansemann in 1890. Later, in the early twentieth century, a zoologist named Theodor Boveri brought up a bold and visionary hypothesis that provided the foundation for viewing cancer as a genetic disease.

He believed that cancer origins from a single cell with genetic alterations and the genetic alterations caused by environment stimulation could endow unlimited cell growth. Boveri also proposed the concepts of cell cycle checkpoint, tumor suppressor genes and oncogenes. Many of his predictions are now explained by the molecular mechanisms that discovered using modern techniques.

Although cancer is a complex disease, its initiation and progression have several characteristic hallmarks that are shared by almost all types of cancers.

Weinberg, Pouyssegur, Elledge and their colleagues, summarized the hallmarks of cancer and their function in transformation process (Hanahan & Weinberg,

2000, 2011; Kroemer & Pouyssegur, 2008; Luo et al, 2009). Hallmarks of cancer can be categorized into two classes: “emerging hallmarks”, which initiate or

107 promote transformation progress, and “enabling characteristics”, that are resulted from “emerging hallmarks” and are the phenotypes shared by many cancer types. “Emerging hallmarks” include: (1) deregulation of growth promoting or inhibiting signals, (2) evasion of apoptosis or senescence, (3) unlimited replication (bypass cellular crisis), (4) metabolism reprogramming, (5) deregulation of angiogenesis switch, (6) avoidance of immune surveillance, (7) tissue invasion and metastasis (Hanahan & Weinberg, 2000, 2011; Kroemer &

Pouyssegur, 2008; Luo et al, 2009). “Enabling characteristics” are the results of genetic alterations and cellular changes from the “emerging hallmarks”. They include: (1) genome instability (DNA damage and DNA replication stress, mitotic stress), (2) cellular stress (proteotoxic stress, metabolic stress, oxidative stress),

(3) inflammatory responses. Transformation of human cells is a long process of accumulation of these cellular alterations, which are caused by alteration or deregulation of many genes (Summarized in Figure 2.1.1). The accumulation of these changes is more important than their order with respect to one another.

These genes may have functions that promote the transformation process or functions that prevent cells from transformation. Gaining oncogenic functions and losing tumor suppression functions of a normal cell lead to the transformation to cancer cell.

108 2.1.2 Oncogene

History of oncogene discovery

One hundred years ago, the discovery of Rous sarcoma virus by Dr.

Peyton Rous opened the field of tumor virology and led to the discovery of first oncogene Src and molecular mechanisms of cancer formation (Rous, 1911). In

1970s, in an effort to understand the function of viral oncogene Src and Abl,

Bishop and Hunter groups found that protein phosphorylation was important in the transformation process (Levinson et al 1978; Eckhart et al 1979). The next year, the tyrosine kinase receptor oncogene, EGFR (epidermal growth factor receptor), was found by Cohen, followed by the discovery of PDGF (platelet- derived growth factor) by Waterfield and Antoniades (Ushiro and Cohen 1980;

Waterfield et al 1983; Doolittle et al 1983). In 1982, Weinberg, Wigler and

Barbacid groups cloned the first cellular oncogene RAS and discovered a single amino-acid change that could constitutively active RAS protein and lead to transformation (Shih and Weinberg 1982; Goldfarb et al 1982; Pulciani et al

1982; Tabin et al 1982; Reddy et al 1982; Taparowsky et al 1982). Discoveries of RAS, EGFR, PDGF together with v-erbB suggest that oncogenes are components of the normal growth regulatory machinery. Later on, many other

oncogenes were found having functions upstream or downstream of these

kinases signal transducers.

The role of oncogene in cancer development

109 Oncogenes initiate and promote transformation during cancer development by regulating the “emerging hallmarks” of cancer. Oncogenes (1) allow cells to proliferate in an uncontrolled and unlimited manner that is resistant to programmed cell death, (2) adapt the cell metabolism to anaerobic, acidic and high stress environment, (3) signal surrounding non-malignant cells to secret growth factors, (4) change environment to favor cancer cell growth, (5) protect cells from killing by immune response, and (6) promote metastasis to distant location.

Activation of oncogenes

Proto-oncogenes are genes with normal cellular functions. They can be activated and converted to cancerous oncogenes by mutations or elevated expression, which includes epigenetic regulation, gene amplification and chromosome translocation. For example, mutations in RAS, which is a small

GTPase, can constitutively activate the signaling pathways that lead to cell growth and division, independent of extracellular signals (Tabin et al 1982;

Reddy et al 1982; Taparowsky et al 1982). Amplification of fibroblast growth factor receptor 1 (FGFR1) in lung squamous carcinoma activates MAPK and

AKT pathways and results in cell proliferation independent of ligand binding (Dutt et al 2011; Weiss et al 2010). BCR-ABL oncogene, resulted from translocation of chromosomes 9 and 22, can enhance cell division, inhibit DNA repair and cause chronic myeloid leukemia (CML) (Nowell, 1960). Promoter hypomethylation of

110 BCL-2 gene causes its overexpression in B-cell chronic lymphocytic lymphomas

(Hanada et al 1993).

Classification of oncogenes

Oncogenes can be grouped into seven categories based on their cellular functions: (1) growth factors (e.g. c-Sis); (2) tyrosine kinase receptors (e.g.

EGFR, FGFR); (3) transcription factors (e.g. MYC); (4) signal transducer, which includes cytoplamic tyrosine kinases (e.g. Src family), cytoplasmic serine/threonine kinases (e.g. Raf) and signal regulatory proteins (e.g. Ras); (5) chromatin remodelers (e.g. ALL1); (6) apoptosis regulators (e.g. Bcl-2 family); and (7) microRNAs (e.g. miR-17).

Receptor tyrosine kinases

Receptor tyrosine kinase (RTK) is a large family of cell surface receptors.

It is so called because RTK can catalyze the tyrosines on target proteins by transfer the γ phosphate of ATP to hydroxyl groups of tyrosines. RTK has very important cellular functions in regulating cell cycles, migration, metabolism, survival, proliferation and differentiation. The structure, activation and signaling of members in RTK family are similar (Hunter, 1998). Here we use FGFR as an example of receptor tyrosine kinases.

2.1.3 FGFR1

Structure There are five members of the FGF receptors: FGFR1(flg),

FGFR2(bek, KGFR), FGFR3, FGFR4 and FGFR1L(has no kinase domain). First

111 FGF receptor was designated as fms-like gene (flg) because it was isolated from

a human endothelial cell cDNA library by hybridizing at relaxed stringency using

the v-fms oncogene as a probe (Ruta et al 1988). Further analysis of its

structure and function revealed that flg was the receptor for acidic FGF (Ruta et

al 1989). As illustrated in Figure 2.1.2, the structure of FGFR1 consists of an

extracellular domain (EC), a transmembrane (TM) stretch, a juxtamembrane (JM)

domain, a kinase domain (KD) and a C-terminal (CT) (Klint, 1999). Three

phosphorylation sites, Y653, Y654 and Y730, appear to be involved in the kinase

regulation. Phosphorylation of Y766 binds to phospholipase Cr (PLCr).

Activation

FGFs are secreted glycoproteins that are sequestered by heparin sulphate

proteoglycans to the extracellular matrix (HPSGs). To signal, FGFs are liberated

from extracellular matrix and bind to cell surface HPSG and FGF receptors

(Harmer et al 2004, Mohammadi et al 2005). Crystal structure shows that they

form a stable 2:2 FGF: FGFR complex stabilized by heparin (Schlessinger,

2000). Dimerization of FGFR1 leads to trans-autophosphorylation of their

cytoplasmic domains (Schlessinger, 1988). Binding of FGF to FGFR stimulates

tyrosine kinase activity was first discovered in NIH3T3 cells (Coughlin et al 1988).

In addition, FGFR can also be activated by inhibition of protein tyrosine

phosphatases, receptor amplification or mutations, independent of ligand binding.

Signaling

112 Under normal conditions, FGF binding leads to kinase activation, tyrosine

phosphorylation of residues in the kinase domain and tyrosine phosphorylation of

the C-terminal tail of FGFR. The major effector of FGFR is the adaptor protein

FRS2 (FGFR substrate 2). FRS2 constitutively associates with FGFR

juxtamembrane domain independent of its activation and gets phosphorylated on

several Tyr and Ser/Thr residues following FGFR activation (Ong et al 2000).

pFRS2 functions as a docking site for Grb2 and activates MAPK and AKT

pathway through SOS and Gab1 respectively, and in turn regulates cell

proliferation and cell survival (Eswarakumar et al 2005; Altomare et al 2005).

The C-terminal phosphorylation of FGFR phosphorylates and activates

Phospholipase Cr (PLCr) through its Src homology 2 (SH2) domain (Peters et al

1992). Activated PLCr hydrolyses phosphatidylinosito-4,5-biphosphate (PIP2) to

phosphoatidylinositol-3,4,5-triphosphate (PIP3) and diacylglycerol (DAG), and then activate protein kinase C (PKC) and MAPK pathway (Klint et al 1999).

Oncogenic functions

Several types of genetic changes of FGFR1 can cause its oncogenic function: gene amplification, activating mutations, chromosomal translocations, single nucleotide polymorphisms and aberrant splicing. However, constitutive activation by amplification or mutation is the most common in cancer. Oncogenic

FGFR1 signaling can cause proliferation, survival, migration, invasion and angiogenesis, and has been found in many types of cancers (Table 2.1.1 and

2.1.2). FGFR1 amplification, results of chromosome amplification, is the most

113 common genetic alteration in lung squamous carcinoma (~20%) (Weiss et al

2010; Dutt et al 2011), and one of the most common focal amplification in

estrogen receptor (ER)-positive breast cancer (10%) (Courjal et al 1997;

Jacquemier et al 1994; Reis-Filho et al 2006). Amplification of FGFR1 has also

been found in oral squamous carcinoma (Freier et al 2007), ovarian cancer

(Gorringe et al 2007), bladder cancer (Simon et al 2001) and

rhabodomyosarcoma (Missiaglia et al 2009). Constitutive active FGFR TDII

(thanatophoric dysplasia type II) like mutation (FGFR1K656E, FGFR3K650E,

FGFR4K645E) can activate Stat1 and Stat3 (Hart et al 2000).

2.1.4 Tumor suppressor genes

Mechanisms of tumor suppressor

In the 1970s and early 1980s, more and more evidence hinted that there is a second type of growth-controlling genes that function to constrain or suppress cell proliferation, so called tumor suppressor genes (TSGs). TSGs negatively regulate cancer development by several different mechanisms: (1) repressing cell growth signaling pathways and inhibiting cell division, (2) blocking cell cycle in response to DNA or cellular damage, (3) initiating programmed cell death, (4) promoting cell adhesion to prevent metastasis, (5) functioning in DNA repair to reduce the risk of genomic instability. TSGs can be classified into three categories based on their functions: “caretaker”, “gatekeeper” and “landscaper”

(Kinzler and Vogelstein, 1997; Michor et al., 2004; Ashworth et al, 2011).

114 “Caretaker” genes maintain genome stability and prevent mutation acquisition.

For example, BRCA1 and BRCA2 play critical roles in DNA repair. “Gatekeeper” genes control cell growth, for example, p53 can induce cell cycle arrest or cell death. “Landscaper” genes control the microenvironment for tumor growth. For example, TSP1 has anti-angiogenesis function therefore cutoff the nutrient supply for tumor growth (Volpert et al, 2002).

History of tumor suppressor gene discovery

Due to the recessive nature of TSGs, TSGs discovery is more difficult than oncogenes. In early years, people discovered tumor suppressing elements using transfer approaches, which deliver intact or fragmented chromosomes, such as yeast artificial chromosome (YAC) or bacterial artificial chromosome (BAC), into cancer cells to monitor the inhibition of cell growth (Harris et al 1969; Fournier et al 1977; Stanbridge, 1976, 1992; Murakami 1995,1998, Todd 1996). The discovery of RB gene, the first tumor suppressor, confirmed the “two-hit hypothesis”: cancer is caused by at least two successive mutations, the first hit inactivates one allele of a TSG and the second hit results in the loss of the other allele (Friend et al 1986, Knudson, 1971). In cancer cells, loss of heterozygosity

(LOH) is considered the second hit of the “two-hit hypothesis”. Single nucleotide polymorphism (SNP)-based array was widely used to detect genome-wide copy number and LOH status to identify new TSGs. In early 1980s, fluorescence in situ hybridization (FISH) was developed to detect and localize the presence or absence of specific DNA sequences, which provided an alternative way to

115 identify TSGs. However, FISH is only useful to identify genes with known sequences and can only test one gene at a time. In 1990s, comparative genomic hybridization (CGH) was developed to analyze the copy number changes of

TSGs. However, this approach cannot detect chromosome aberrations, such as translocations or inversions. Many genes are silenced by epigenetic events, such as methylation, irrespective of their LOH status in tumors. During last decades, gene expression arrays and promoter methylation arrays were widely used in discovering genes that are differentially expressed in cancer cells and normal cells, which could be potential TSGs.

Genome-wide RNAi screen

The elucidation of the mechanism of RNAi by Mello and Fire in 1998 spurred an industry of RNAi biotechnology. RNAi is a naturally occurring gene expression regulation mechanism. In eukaryotes, double stranded interfering

RNA targets and degrades complementary mRNA, and results in selective gene silencing. By delivering short hairpin RNA (shRNA), the precursor of siRNA, into cultured cells, we can knock down a specific gene in very short time and at much lower cost, comparing to traditional targeted gene inactivation or knock-out techniques. The development of genome-wide RNA interference (RNAi) library made it possible to conduct high throughput loss of function screens for specific phenotypes with assistance from statistics and bioinformatics (Paddison et al

2004; Berns et al 2004). Pools of shRNA expressing vectors are introduced into cells by transfection or infection. Cells are then selected for certain phenotypes.

116 In positive screens, cells are selected for ability to transform, or induced drug resistance, etc. Transformed cells or surviving cells were then used for DNA isolation. shRNA sequence were amplified by PCR and sequenced. In negative screens, e.g. drug sensitivity, DNA from cell populations from before and after drug treatment are isolated, sequenced and compared for gene identification

(Figure 2.1.3).

Taking advantage of this technique, many new TSGs were identified. For example, REST is found as a tumor suppressor in breast cancer (Westbrook et al

2005); Sfrp1, Numb, Mek1 and Angpt2 are tumor suppressors in lymphoma (Bric et al 2009); XPO4 is a tumor suppressor for liver cancer (Zender et al 2008, etc).

From our study, we identified 24 new tumor suppressors for lung squamous carcinomas cancer.

2.1.5 Targeting Drug Development

Besides surgical removal, cancer therapies usually target cancer cells,

cancer vasculature, the immune system and even the bone marrow. After

decades of extensive studies of the molecular basis underpinning cancer

development, therapeutics targeting the hallmarks of cancer cells have been

rapidly developed. In cancer cells, signal transduction pathways are frequently

altered. Therefore, many drugs, such as tyrosine and threonine kinase inhibitors,

are developed directly targeting signal transducting molecules. Imatinib (Glivec),

a drug that treats chronic myelocytic leukemia (CML) by inhibiting BCR-ABL, is

117 the first tyrosine kinase inhibitor approved for clinical use (Druker et al 2001).

Gefitinib and Erlotinib, EGFR inhibitors, are subsequently approved for non-small cell lung cancer (NSCLC) treatment (Lynch et al 2004). However, lung squamous cell carcinomas (LSCC) bare a different genetic alternation that is not responsive to EGFR inhibitors. FGFR1 has been found amplified in more than

20% of LSCC patients. It suggests that FGFR1 can be a potential drug target for

LSCC treatment.

Drug development research on FGFR signaling pathway are majorly using small-molecule tyrosine kinase inhibitors, but blocking antibodies and ligand-trap approaches are also being developed (Table 2.1.3). Tyrosine kinase inhibitors

(TKIs) such as PD173074, ponatinib or TKI258 block FGFR signals and reduce

FRS2 tyrosine phosphorylation, causing the dissociation of Grb2 and therefore inhibit ERK and AKT activities. Blocking antibodies can decrease tumor cell proliferation and induce apoptosis same as TKIs, however, it may potentially recruit immune effector cells to the tumor and may lead to antibody-dependent cellular cytotoxicitiy. Many small molecule inhibitors of FGFR tyrosine kinase activity have been discovered and described in the literature. Many of these are currently in clinical trials in various types of cancers (Table 2.1.3). Our study suggests that the application of FGFR tyrosine kinase inhibitors is not restricted in LSCC. Patients missing FGFR negative regulating TSGs can be selected for treatment with FGFR inhibitors.

118 Figure 2.1.1

Genetic/epigenetic Characteristic alteration hallmarks

Normal cells self-sufficiency in growth Mutations in signals; insensitivity to TSGs/Oncogenes growth-inhibitory signals Hyperplasia Evasion of apoptosis; DNA Mutations in TSGs/ damage/replication stress; oncogens; LOH protecotoxic stress; mitotic Metaplasia stress, metabolic stress; oxidative stress Deregulation of telomerase Limitless replication;

Dysplasia Mutations in cellular Glycolysis metabolism; metabolism regulator; microenvironment Deregulation of change angiogenesis switch Carcinoma

Immune surveillance; eg.Mutations in Invasion and genes involved metastasis in cell adhesion

Metastasis

119 Figure 2.1.1. Transformation is a multistep process.

Genetic or epigenetic alterations in tumor suppressor genes or oncogenes lead to the acquiring cancer hallmarks. The accumulation of these changes, rather than their order, is most important.

120 Figure 2.1.2

Acid I box heparin Extra cellular domain II Ig-like domains FGF

III

Transmembrane domain

Juxtamembrane Y463P domain FRS2

Kinase domain Kinase Y583P/Y585P insert

Y653P/Y654P

Y730P

C-terminal tail Y766P PLCγ

(Klint et al, 1999)

121 Figure 2.1.2. Schematic structure of FGFR1. FGFR1 has

6 domains: (1) Extracellular domain contains three immunoglobulin (Ig)-like domains. Some splicing variants have two (II and III) Ig-like domains. (2) Transmembrane domain. (3) Juxtamembrane domain. (4) kinase domain with a short kinase insert. (5) C-terminal tail.

122 Figure 2.1.3

Pools of shRNA virus Select for certain phenotype infection

Positive negative screen screen

Isolate DNA PCR amplify the shRNA sequence

A T C G

Sequence identify shRNA

123 Figure 2.1.3. Strategy of Pooled RNAi Library Screen. Pools of shRNA expressing vectors are introduced into cells by transfection or infection. Cells are then selected for certain phenotypes. In positive screens, cells are selected for the ability to transform, drug resistance etc. Transformed cells or survived cells were then used to isolate DNA. shRNA sequence were amplified by PCR and sequenced. In negative screens, e.g. drug sensitivity screen, DNA from cell populations from before and after drug treatment are isolated, sequenced and compared for genes identification.

124 Table 2.1.1. Genetic alterations in FGFR1 and related cancer Amplification Lung squamous carcinoma (~20%), breast Courjal 1997, Jacquemier cancer (10%), ovarian cancer (~5%), bladder 1994, Gorringe 2007, Simon cancer (3%) and rhabodomyosarcoma (3%) 2001, Missiaglia 2009 Mutation P252S: Melanoma Lin, 2008; Ruhe, 2007 P252T, V664: Lung cancer Davies, 2005; Greenman, 2007 N546K, R576W: Glioblastoma Rand, 2005; Lew, 2009 K656E: Glioblastoma Network, 2008 S125L: Breast Greenman, 2007 Translocation Stem cell leukaeia and lymphoma syndrome Xiao 1998, Roumiantsev 2004 (or EMS), chronic myeloid leukaemia (rare)

125 Table 2.1.2 Altered expression of FGFR1 in cancer Cancer type Up-regulated (references) Brain Kroemer 2008; Luo 2009; Colotta 2009; Stratton 2009 Head and Olsen 2004; Zhang 2006; Ornitz 1996; Schlessinger 2000; Lew 2009; Chen neck 2007; Mohammadi 1996 Sarcoma Lu 2008; Parsa 2008; Murakami 2008 Soft tissue Beenkan 2009; Chin 2006; Toyokawa 2009 sarcoma Thyroid Ray 2004; Bernard-Pierrot 2008 Xian 2009, 2005; Reis-Filho 2007; Easton 2007; Hunter 2007; Meyer 2008; Lung Turner 2010; Sahadevan 2007; Giri 1999; Hamaguchi 1995; Freeman 2003 Grand 2004; Trudel 2006; Keats 2003; zingone 2010; Cha 2008; Ezzat 2002; Breast Itoh 1994; Cha 2009 Liver Jang 2001 Pancreas Gartside 2009; Cheng 2009; Knowles 2008; Tomlinson 2007; d’Avis 1998 Stomach Martinez-Torrecuadrada 2008; Hernanda 2008 Bladder Roumiantsev 2004, Guasch 2004; Chen 2004, Ren 2009; Demiroglu 2001 Dphna-Iken 1998; MacArthur 1995; Song 2002; Valta 2008; Maruyama- Prostate Takahashi 2008; Schimada 2005; Wang 1997 Testis Dellacono 1997 Colon Memarzadeh 2007 Uterus Joyce 2009; Abdel-Rahman 2008 Ovary Darby 2006; Murphy 2010 Skin Cho 2004; Tannheimer 2000 Leukaemia/M Cho 2007; Fogarty 2007; Knights 2010; Byron 2010; Claudio 2007 PD/lymphoma

126 Table 2.1.3. Current status of FGFR-targeting therapies Drug name Company Activity Clinical development Reference Small molecular tyrosine kinase inhibitors SUS402 Selective FGFR inhibitor NA Ropiquet 2000 PD173074 FGFR, PDGFR and VEGFR inhibitor NA Mohammadi 1998 Ponatinib(AP24534) Ariad Pharmaceuticals FGFR, BCR-ABL, PDGFR and VEGFR Phase II Joseph 2012 inhibitor Cediranib(AZD2171) AstraZeneca FGFR, PDGFR and VEGFR inhibitor Phase I (Gstric, Breast) Takeda 2007, Phase II (Endometrial) Wedge 2005 Dovitinib(TKI258) Novartis FGFR, PDGFR and VEGFR inhibitor Phase II (Breast, Bladder, Sarker 2008 Myeloma) Intedanib(BIBF1120) Boehringer Ingelheim FGFR, PDGFR and VEGFR inhibitor Phase III Hilberg 2008 Brivanib(BMS-582,664) Bristol-Myer Squibb FGFR and VEGFR inhibitor Phase II (Endometrial, Gastric, Chen 2005; Bladder) Huynh, 2008 E7080 Eisai FGFR, PDGFR and VEGFR inhibitor Phase I (solid malignancies), Matsui 2008 Phase II (Endometrial) TSU-68 (Orantinib) Taiho Pharmaceutical FGFR, PDGFR and VEGFR inhibitor Phase II/III Machida 2005 AZD4547 Astra Zeneca Pan-FGFR Phase I (solid malignacies), Phase I/II (Breast) BGJ398 Novartis Pan-FGFR Phase I (solid malignancies) Masitinib (AB1010) AB Science c-KIT, PDGFR, FGFR3 Phase III (Gastrointestinal Hahn, 2008 Stromal Tumors) SU6668 Sugen FGFR, PDGFR, VEFGR2 Phase I (Advanced Solid Fabbro, 2001 Tumors) Pazopanib Glaxo Smith Kline VEGFR, PDGFR, c-KIT, FGFR1/3 Phase III (Metastatic Renal Cell Kumar, 2007 (GW786034) Carcinoma) RO438596 Roche VEGFR, PDGFR, FGFR Marek 2009, McDermott, 2005 FGFR antibodies and FGF ligand traps IMC-A1 ImClone FGF1-IIIc-specific antibody NA Sun 2007 PRO-001 ProChon Biotech FGFR3-specific blocking antibody NA Trudel 2006 R3Mab Genentech FGFR3-specific antibody NA Qing 2009 1A6 Genentech FGF19-specifc antibody NA Desnoyers 2008 FP-1039 Five Prime FGF ligand trap (multiple FGFs) Phase I (solid malignancies) Martinez- Therapeutics Phase II (endometrial) Torrecuadrada 2005 (ref: Greulich & Pollock, 2011; Knights & Cook, 2010)

127

CHAPTER II

Identification of Squamous Cell Lung Cancer Tumor

Suppressor Genes that Negatively Regulate FGFR1

Signaling

128 Abstract

A goal of contemporary cancer research is to identify the genes

responsible for neoplastic transformation. Here we describe a functional

genomics approach to discover new tumor suppressor genes (TSGs). Cells that

are immortalized but non-tumorigenic were stably transduced with pools of short

hairpin RNAs (shRNAs) and tested for their ability to form tumors in mice.

ShRNAs in any resulting tumors were identified by sequencing to reveal

candidate TSGs, which were then validated experimentally and by expression

analysis of human tumor samples. Using this approach, we identified and

validated 24 TSGs that were down-regulated in >70% of human lung squamous cell carcinomas (LSCCs). Amplification of fibroblast growth factor receptor 1

(FGFR1), which aberrantly increases FGFR signalling, is the most common known genetic alteration in LSCC. Remarkably, we found that 17 of the 24 TSGs encode negative regulators of FGFR signalling. Knockdown of 11 TSGs in immortalized human bronchial epithelial cells results in increased total or phosphorylated levels of FGFR1 leading to transformation and sensitivity to

FGFR1 pharmacological inhibition. Our results indicate that many LSCCs without

FGFR1 amplification or activating mutations may respond to FGFR1 inhibitors and in a predictable manner.

129 Introduction

The conversion of a normal cell to a cancer cell is a stepwise process that involves the activation of genes that promote cancer, oncogenes, and the inactivation of genes that protect cells from cancer, such as tumor suppressor genes (TSGs). A goal of contemporary cancer research is to identify all genes responsible for neoplastic transformation. For example, the identification of new

TSGs has important implications for both diagnosing and treating cancer.

TSGs are defined by a series of characteristic properties (Weinberg 2006).

First, TSGs are inactivated in certain human tumor types either by deletion, mutation or a transcriptional repression mechanism including epigenetic silencing. Epigenetically silenced TSGs bear several hallmarks including repressive histone modifications and hypermethylated CpG-rich promoter regions

(Esteller 2007). Second, inactivation of a TSG in an appropriate cell type promotes one or more growth-related properties such as immortalization, proliferation, or tumorigenesis. Conversely, over-expression of a TSG in an appropriate cell type has one or more growth-suppressive effects such as inhibition of cell cycle progression or the induction of apoptosis or senescence.

One of the ongoing efforts to identify the genes responsible for tumor formation is partial or complete sequencing of cancer genomes (Mardis and

Wilson 2009). Although this approach has and will continue to reveal genes involved in tumorigenesis, there are specific challenges for discovery of TSGs solely by sequence analysis of cancer genomes. First, the genomes of cancer

130 cells are error-prone (Loeb 2001) and thus many of the mutations found in

cancer cells are not causally related to the transformed phenotype. Second, as

mentioned above, in many instances TSGs are inactivated by epigenetic

silencing rather than a mutational event, and thus would not be identified by

conventional genome-sequencing methods.

Fibroblast growth factors receptors (FGFR) are cell surface tyrosine

kinase receptors for fibroblast growth factors. They play crucial roles in

development, cell profleration, survival and migrations. FGFRs familiy consists

of four members (FGFR1-4) and one non-catalytic functional protein (FGFR1L).

FGFR signaling can be activated by (1) ligand induction (2) FGFR amplification

or activating mutations. Activation of FGFR1 leads to kinase activation, tyrosine

phosphorylation of residues in the kinase domain and the C-terminal tail. The

major effector of FGFR signaling is the adaptor protein FRS2 (FGFR substrate

2). FRS2 is constitutively associated with FGFR independent of its activation and gets phosphorylated on several Tyr residues following FGFR activation (Ong et al 2000). pFRS2 functions as a docking site for Grb2 and activates MAPK and

AKT pathway through SOS and Gab1 respectively, and in turn regulates cell proliferation and cell survival (Eswarakumar et al 2005; Altomare et al 2005). The

C-terminal phosphorylation of FGFR activates protein kinase C (PKC) and MAPK

pathways by phosphorylating and activating Phospholipase C r (PLCr) through its

Src homology 2 (SH2) domain (Peters et al 1992; Klint et al 1999). In cancer,

FGFR signaling pathway is usually constitutively activated by amplification or

131 mutation. Oncogenic FGFR1 signaling can cause proliferation, survival,

migration, invasion and angiogenesis. FGFR1 amplification is the most common

genetic alteration in lung squamous carcinoma (~20%) (Weiss et al 2010; Dutt et

al 2011), and one of the most common focal amplifications in estrogen receptor

(ER)-positive breast cancers (10%) (Courjal et al 1997; Jacquemier et al 1994;

Reis-Filho et al 2006). Amplification of FGFR1 has also been found in oral

squamous carcinoma (Freier et al 2007), ovarian cancer (Gorringe et al 2007),

bladder cancer (Simon et al 2001) and rhabodomyosarcoma (Missiaglia et al

2009). Amplification of FGFR1 activates MAPK but not AKT pathway (Weiss et al 2010).

Drug development research on inhibiting FGFR signaling pathway are primarily using small-molecule tyrosine kinase inhibitors, but blocking antibodies and ligand-trap approaches are also being developed. Tyrosine kinase inhibitors

(TKIs) such as PD173074, ponatinib or TKI258 block FGFR signaling and reduce

FRS2 tyrosine phosphorylations, cause the dissociation of Grb2 and therefore

decrease in ERK and AKT activity.

Functional genomics approaches for TSG discovery are highly

complementary to cancer genome sequencing efforts. In this regard, several

recent studies have described in vivo RNA interference (RNAi) screens in mice

that have successfully identified TSGs involved in liver cancer (Zender et al

2008) and genes that suppress lymphoma progression (Bric 2009, Meacham

2009). These studies were performed using a limited collection of shRNAs

132 targeting genes that had been previously implicated or suspected to be involved in cancer. Here we describe and demonstrate a large-scale RNAi-based functional approach for TSG discovery that is unbiased with regard to either the genes being targeted or cancer type. From the screen, we identified 24 TSGs in lung squamous carcinoma, suggesting that they can be used as new biomarkers in cancer diagnosis. Among these 24 TSGs, 17 can negatively regulate FGFR1 signaling pathway and 12 regulate expression or activation levels of the receptor.

Lung small air way cells are sensitive to FGFR1 inhibitors when knocking down individual TSGs. Therefore, we propose that the application of FGFR1 inhibitor can be expanded beyond FGFR1 amplification patients. Expression level of

TSGs can be used as marker for drug selection and to predict drug responsiveness.

133 RESULTS

A large-scale shRNA screen for the identification of candidate TSGs

To identify TSG candidates, we performed a large-scale shRNA screen for

tumorigenesis in mice, which is summarized in Figure. 2.2.1.A and is discussed

below. We used NIH 3T3 fibroblasts, which are immortalized but not transformed

and can be rendered tumorigenic by a wide range of oncogenic events (Stacey

and Kung, 1984; Ikawa et al 1988, Smith et al 1990; Velu et al 1987; Di Fiore et

al 1987).

Before initiating the primary screen, we performed a reconstruction

experiment to determine a minimal number of transformed NIH 3T3 cells required

for tumorigenesis in our xenograft system. In this approach, 1000, 100 or no

Kras-transformed NIH 3T3 cells were added to non-transformed NIH 3T3 cells to

achieve a total of 1x106 cells, which were then injected into the flanks of nude mice. As expected, no tumors formed in the sample lacking Kras NIH 3T3 cells.

However, tumors were observed in samples containing either 100 or 1000 Kras

NIH 3T3 cells (Figure. 2.2.1.B), indicating that 100 transformed NIH 3T3 cells

(and likely fewer) are sufficient to initiate a detectable tumor in a mouse

xenograft.

For the primary screen, a mouse shRNA library comprising 62,400

shRNAs directed against ~28,000 genes (Silva et al 2005) was divided into 10

pools, which were packaged into retrovirus particles and used to stably transduce

mouse NIH 3T3 cells. For each of the 10 stable cell populations, 1x106 cells were

134 injected subcutaneously into the flank of a nude mouse. Thus, each of the 6240

shRNAs in each pool was present in ~160 cells, which is greater than the 100

transformed NIH 3T3 cells found in the reconstruction experiment to be required

for tumor formation. We found that seven of the 10 shRNA pools gave rise to

detectable tumors. After two weeks, tumors were dissected and genomic DNA

was extracted. To identify the candidate shRNAs, the shRNA region of the

transduced virus was PCR amplified, cloned and sequenced (Gazin et al 2007).

We recovered 41 independent shRNAs from the seven tumors (Table 2.2.1).

To validate the candidates, single shRNAs directed against each gene

were stably transduced into NIH 3T3 cells, and the knockdown (KD) cell lines

were generated. We tested the ability of the NIH 3T3 KD cells lines to form

tumors in vivo following subcutaneous injection into nude mice. Figure 2.2.1.C shows that all 33 shRNAs promoted tumor formation, whereas a control non- silencing shRNA, as expected, did not. Quantitative real-time RT-PCR (qRT-

PCR) confirmed in all cases that expression of the target gene was decreased in the corresponding KD cell line (Figure 2.2.2).

Of the 33 candidate TSGs, we could identify unambiguous human

homologs in 28 cases, which are summarized in Table 2.2.2. These 28 human

TSG candidates are involved in diverse biological processes including signal

transduction, transcriptional regulation, cell growth/metabolism and DNA/RNA

metabolism. Significantly, several of the genes such as IGF2R (Probst et al 2009,

O’Gorman et al 1999; Motyka et al 2000; Nam et al 2010), SEMA3B (Castro-

135 Rivera et al 2008; Rolny et al 2008; Nair et al 2007), and STK11 (also known as

LKB1) (Mehenni et al 2005; Mehenni et al 2005) are well documented TSGs, validating the overall experimental strategy.

Down-regulation of candidate TSGs in human lung squamous cell carcinoma samples

To gain insight into the tumor types in which the candidate TSGs may play a role, we performed a series of cancer database searches. Analysis of the 28 human TSG candidates using the NCBI SKY/M-FISH and CGH database

(National Center for Biotechnology Information Spectral Karyotyping, Multiplex

Fluorescence In Situ Hybridization and Comparative Genomic Hybridization; ref.

23) revealed that all the genes have been found to harbor deletions in either one or both copies in multiple cancer types (Table 2.2.3). Furthermore, a search of the Oncomine cancer profiling database (Rhodes et al 2007) revealed that, with the exception of PKD1L3, all the candidate TSGs are significantly down- regulated in a variety of cancers compared to normal tissues (Table 2.2.4).

Notably, a large fraction of the candidate TSGs were down-regulated in lung cancers. To confirm and extend this finding, we analyzed expression of the

28 genes in a series of human lung squamous cell carcinoma (hLSCC), lung adenocarcinoma and normal lung samples. The qRT-PCR analysis of

Figure2.2.3 shows that 24 of the 28 candidate TSGs were down-regulated greater than 2-fold in ≥ 70% of the hLSCC samples analyzed. In several

136 instances, the candidate TSGs were down-regulated greater than 10-fold in all

(or almost all) hLSCC samples (e.g., ANGPT1, FLNA, IGF2R, NME4, SEMA3B

and STK11). Six genes were also down-regulated greater than 2-fold in ≥70% of

the lung adenocarcinoma samples (Figure 2.2.4).

Knocking down TSGs activates FGFR1 signaling pathway

It has been shown that FGFR1 amplification is the most common genetic

alteration in LSCC (~20%) (Weiss, 2010; Dutt, 2011). In order to test whether

knocking down TSGs can activate FGFR1 signaling pathway, we first knocked

down our candidate TSGs using gene specific shRNAs in SA (small airway) cells, which are immortalized but non-transformed human lung small airway epithelia cells. Then we examined the activation levels of FRS2, which is phosphorylated upon FGFR1 activation, in 24 TSG KD cell lines. As shown in Figure 2.2.5, we found 17 out of 24 TSG KD cell lines increased pFRS2 to the level comparable to

NCI-H520, while the total FRS2 expression levels were same as non-silencing control. We further analyzed the phospho-FGFR1 and total-FGFR1 levels. We found that knocking down 7 genes (FLNA, GAPVD1, MYD88, PIGH, PTGIS,

SFRS9, SPAST) increased the total FGFR1 levels, and knocking down 5 genes

(ANGPT1, DAPP1, FPR3, NME4, PTPN4) increased pFGFR1 levels without

elevated tFGFR1. However, Y653 phosphorylation of FGFR1 was not increased

in FLNA and MYD88 KD cell lines, although the total FGFR1 is increased. 6

genes (CDK5R1, DDX52, SDF2L1, SPOP, STK11, TXNRD1) increased pFRS2

137 level without elevated phospho- or total-FGFR1 levels. It is known that pFRS2 functions as a docking site for Grb2 and activates SOS and Gab1, and leads to activation of MAPK and AKT signaling pathways (Eswarakumar et al 2005;

Altomare et al 2005). We checked the levels of activated Akt and Erk, and found that, except PTGIS and SPAST, all TSGs KD cell lines with increased pFRS2 levels had increased pErk levels. DAPP1 could affect both MAPK and AKT signaling pathways. Four genes (IGF2R, LSM8, MAP1A, SEMA3B) inhibited

pErk level independent of FGFR1 signaling. KD IGF2R didn’t activate FGFR

signaling validated our results because IGF2R is a tyrosine kinase receptor

independent of FGFR signaling. Based on these results, we proposed a model

in Figure 2.2.6 showing possible mechanisms that the TSGs function by

negatively regulating FGFR1 signaling at different steps. To further understand

the relationship between our TSGs and FGFR1 signaling pathway, we knocked

down FGFR1 in NCI-H520 cell line, using FGFR1 specific shRNA, and analyzed

the mRNA levels of 24 TSGs. We found de-repression of 3 genes (ANGPT1,

DAPP1, TXNRD1) after FGFR1 knock down (Figure 2.2.7). These results

suggest that, these genes may have negative feedback effects on regulating

FGFR1 signaling pathway.

Knocking down TSGs induces transformation of lung epithelia cells

Next, we tested whether knocking down TSGs has the same

transformation function as FGFR1 amplification in lung epithelia cells. First of all,

138 we tested whether overexpression FGFR1 could transform mouse and human

cell lines. As shown in Figure 2.2.8.A, 1 million FGFR1 overexpressed NIH3T3

cells formed a tumor when subcutaneously injected into nude mice, while no

tumor formed in the empty vector control. This result is consistent with a

previous report that FGFR1 can transform NIH3T3 (Hart et al 2000), and further

confirmed that our TSG candidates from NIH3T3 screen might be related to

FGFR1 oncogenic pathway. Then we tested whether overexpression of FGFR1

can transform human SA cells. We found 5 million FGFR1 overexpressed SA cells formed a visible tumor in nude mice, while no tumor formed in the empty vector control (Figure 2.2.8.A).

Then we want to test whether the activation of FGFR1 pathway by knocking down our TSG candidates can cause transformation of human SA lung cells. In Figure 2.2.8.B, we performed in vitro soft agar colony formation assay,

and found 14 out of 17 FGFR1 regulating genes and 4 FGFR1 signaling independent genes can form colonies in soft agar (Figure 2.2.8.B). Then we injected knock down cell lines, that formed colonies in soft agar, into the flanks of

nude mice and found they can all form tumors in vivo (Figure 2.2.8.C). Our

results suggest that inhibiting these TSGs in normal cells can induce

transformation by activating FGFR1 oncogenic signaling.

139 SA cells with TSGs knocking down are sensitive to FGFR inhibitor

FGFR1 is a tyrosine kinase receptor. Many small molecules have been generated to inhibit the FGFR1 tyrosine kinase (PD173074, ponatinib, BIBF1120 etc) and they have been shown to inhibit cancer cell growth in vivo and in vitro

(Weiss et al 2010; Joseph et al 2012; Hilberg et al 2008). Some of these inhibitors are in the process of clinical trials for cancer treatment. Ponatinib is an oral tyrosine kinase inhibitor, and it is now in a pivotal phase II trial in patients

with chronic myeloid leukemia (CML) and acute lymphoblasic leukemia (ALL),

and it has been shown to inhibit kinase activity of FGFR1. We wanted to test

whether our TSGs KD cell lines are sensitive to Ponatinib. We treated cells with

500nM ponatinib for three days and found TSG KD cell lines that have increased

pFGFR1 levels are sensitive to Ponatinib compared to non-silencing control,

shown in Figure 2.2.9.A. Our results suggest that the application of Ponatinib in

lung squamous carcinoma might not be restricted to the patients with FGFR1

amplification. Lung cancer patients with repressed levels of these TSGs may

also be good candidates for Ponatinib treatment. To further confirm this, we first

tested the Ponatinib sensitivity of A427, which is a lung squamous carcinoma cell

line with normal copy of FGFR1. A427 showed similar sensitivity to Ponatinib

compare to FGFR1 amplified cell line NCI-H520, while a control cell line A549

with normal level of FGFR1 showed more resistance (Figure 2.2.9.C). Then, we

compared the mRNA level of 24 TSG candidates in A427 and SA or HBEC cell

lines. SA and HBEC are both immortalized but non-transformed lung epithelial

140 cells. And we found 3 genes (STK11, MYD88, DAPP1) are highly repressed in

A427 compare to SA or HBEC (Figure 2.2.9.B). So we confirmed that patients

with normal FGFR1 status but TSG deficient might be sensitive to FGFR1

inhibitor. Therefore, by examining the expression levels of TSGs in lung cancer

patients, we could predict drug responsiveness and treatment outcome.

Candidate TSG promoter hypermethylation in hLSCC samples

To determine whether down-regulation of any of the candidate TSGs was

a result of epigenetic silencing due to promoter hypermethylation, we performed

comprehensive DNA methylation analysis to quantify promoter DNA methylation

(Bibikova et al 2009). Genomic DNA was isolated from paired primary hLSCC

and normal lung squamous cell samples from 18 individuals, subjected to

bisulfite conversion, and hybridized to a genome-wide methylation array

containing >27,500 individual CpG sites spanning ~14,500 promoters. To verify

the quality of the methylation analysis, we first confirmed that well-established

tumor suppressor genes, whose promoters are known to be hypermethylated in

lung cancers. PTGIS (Stearman et al 2007; Gray et al 2009), showed

significantly higher promoter hypermethylation in lung cancer relative to normal

samples. The methylation heat map of Figure 2.2.10.A and histogram of Figure

2.2.10.B show that among the 26 candidate TSGs analyzed (GAPVD1 and

PKD1L3 were absent from the array), nine displayed a statistically significant increase in promoter hypermethylation in lung cancer samples relative to normal

141 samples. These results indicate that epigenetic silencing due to promoter hypermethylation represents one mechanism by which the candidate TSGs are down-regulated in human lung cancers.

Over-expression of candidate TSGs inhibits growth of hLSCC cells

To test whether ectopic expression of these candidate TSGs could inhibit cell proliferation, a representative subset were cloned into a retroviral vector co- expressing green fluorescent protein (GFP) and transduced into NCI-H520 hLSCC cells. Cells were selected with puromycin for 4 weeks and analyzed in a colony suppression assay. The results, shown in Figure 2.2.11.A and quantified in Figure 2.2.11.B, demonstrate that over-expression of all 16 genes tested reduced colony formation to varying extents. We then asked whether ectopic expression of the candidate TSGs could inhibit tumor growth in mouse xenografts. Retroviral vectors expressing 12 of the genes were transduced into

NCI-H520 cells, and 24 h later GFP-positive cells were isolated by fluorescence- activated cell sorting and injected subcutaneously into nude mice. Figure2.2.11.C shows that ectopic expression of all of the genes tested markedly suppressed tumor growth compared to the vector control.

TSGs have been found to inhibit tumor growth through a variety of mechanisms including negatively regulating cell cycle progression, inducing senescence or promoting apoptosis (Sheer et al 2004). To gain insight into the mechanism-of-action of the candidate TSGs, GFP-positive NCI-H520 cells

142 ectopically expressing 16 of the TSG candidates were analyzed for their ability to

induce apoptosis or senescence. We found that ectopic expression of 12 genes

induced apoptosis in NCI-H520 cells as evidenced by cleavage of Caspase 3.

(Figure 2.2.12.A and B). Furthermore, ectopic expression of six candidate TSGs induced senescence in NCI-H520 cells as evidenced by senescence-associated b-galactosidase staining (Figure 2.2.12.C and D).

143 Material and Methods

Cell lines and culture

NIH 3T3, K:Molv NIH 3T3 (referred to here as Kras NIH 3T3) and NCI-

H520 cells (ATCC) were grown in DMEM or RPMI medium, respectively, supplemented with 10% fetal bovine serum (Atlanta Biologicals) and 1X penicillin/streptomycin (Invitrogen). SA cells are obtained from Scott Randell at

University of North Carolina at Chapel Hill School of Medicine, cells were grown in BEGM medium from Lonza (CC-3170).

Large-scale in vivo RNAi screen

To determine the number of transformed cells that could potentially seed a tumor in our experimental system, 0, 100 or 1000 Kras-transformed NIH 3T3 cells were mixed with non-transformed NIH 3T3 cells to achieve a final population of 1x106 cells, which were then injected into the flanks of nude mice

(n=3). Tumor dimensions were measured every 1-2 days from the time of appearance of the tumors, and tumor volume was calculated using the formula

π/4 x (length) x (width)2.

The mouse shRNAmir library (release 2.16; Open Biosystems) was obtained through the University of Massachusetts Medical School (UMMS) RNAi

Core Facility. Ten retroviral pools, each comprising ~6240 shRNA clones, were generated with titers of ~2.6x105 pfu/ml, as previously described (Gazin et al

2004). Briefly, 1x106 NIH 3T3 cells were transduced at a multiplicity of infection

144 (MOI) of 0.2 with the retroviral stocks in 100 mm plates, and 2 days later selected for resistance to puromycin (1.5 µ g/ml) for 7 days. For each pool, 1x106 cells were then injected subcutaneously into the flank of a BALB/c nu/nu mouse

(Taconic Farms). Tumors were excised and dissected, and genomic DNA was extracted. To identify the candidate shRNAs, the shRNA region of the transduced virus was PCR amplified, cloned and sequenced (Gazin et al 2004). To validate the candidates, 2x106 NIH3T3 cells with individual shRNA KD (Table 2.2.5) were injected subcutaneously into nude mice (n=2); mice were sacrificed and photographed when the tumor size reached 1.5-2 cm. All animal experiments were performed in accordance with institutional and national guidelines and regulations and approved by the Institution Animal Care and Use Committee.

In vitro transformation assay

SA cells were infected with a lentivirus carrying an individual shRNA at high MOI. 1x104 cells were seeded in 6-well plates in soft agar. Colonies are stained with crystal violet and scored after 4 weeks.

Identification of human homologs

Human homologs were identified using NCBI’s HomoloGene

(www.ncbi.nlm.nih.gov/homologene). Human genes were considered to be homologs if they shared greater than 50% identity at the protein level with the mouse gene.

145

Analysis of gene expression in human lung cancer samples

Total RNA from 10 normal lung, 10 adenocarcinoma and 27 hLSCC

samples were obtained from the UMMS Cancer Center Tissue Bank. qRT-PCR was performed using primers listed in Table 2.2.6.

Analysis of promoter methylation in human lung cancer samples

Genomic DNA was extracted from ~200-300 mg of fresh-frozen primary hLSCC tissue and matching normal lung squamous cell tissue from 18 individuals (obtained from The Prince Charles Hospital and University if

Queensland Thoracic Research Center). High molecular-weight DNA was purified using the DNeasy Tissue Kit (Qiagen). Cleaned genomic DNA (1 µg) was bisulfite-converted using the EZ DNA Methylation Kit (Zymo Research), hybridized to multisample Illumina HumanMethylation27 v1.0 BeadChips. Images were processed, data extracted and control probes checked using the

BeadStudio Methylation Module (Illumina) using default settings. All CpGs located on the X-chromosome were removed to avoid gender-specific bias.

Percent methylation was calculated by measuring the intensity ratio of methylated to unmethylated DNA, giving a β-value between 0 (100% methylation) and 1 (0% methylation). For each gene, the fold change was calculated for each individual by dividing the percent methylation in the tumor sample by the normal control. A gene with a fold change ≥1.5 was considered

146 hypermethylated (i.e., ≥50% increase in methylation) in hLSCC compared to

normal lung, whereas a gene with a fold change <1.5 was considered to have no

change in methylation status. To evaluate the significance of any observed

number of hypermethylation events n for each gene, we estimated the probability

of obtaining the value n or more in random data drawn according to a null model

(i.e., individuals are independent and the hypermethylation rate is uniformly

distributed among genes). Statistical analyses were performed using R (Ihaka

and Gentleman 1996). The p-value for each gene was estimated based on binomial distribution.

Ectopic expression of TSGs and analysis of tumor suppressor activity

TSG cDNAs (Table 2.2.7) were cloned, by PCR (using primers listed in

Table 2.2.6) followed by restriction enzyme digestion, into MSCV PIG (Puro-

IRES-GFP) (Addgene). For some genes, a 3xFlag tag sequence was

incorporated into the primers for cloning in-frame with the target gene. Murine

stem cell viruses (MSCVs) carrying TSGs were packaged in 293T cells and used

to infect NCI-H520 cells. For the colony suppression assay, NCI-H520 cells

(5x103) were plated in 6-well plates, infected with retroviruses at a multiplicity of

infection (MOI) of 2 (or, in the case of IGF2R, transfected with a plasmid

expressing IGF2R or empty vector), puromycin selected for 4 weeks and stained

with crystal violet. For the mouse tumorigenesis assays, NCI-H520 cells were

infected with retroviruses and 2 d later FACS sorted for GFP-positive cells. 2x106

147 viable GFP-positive cells were injected subcutaneously into nude mice, and tumor dimensions were measured and calculated every 3-4 d [using the formula

(length x width2) x (π/4)]. For apoptosis assays, NCI-H520 cells were infected with retroviruses for 24 h, puromycin selected for 24 h, and recovered in fresh medium without puromycin for 3 d. Immunoblots were probed using an antibody against cleaved caspase-3 (Asp175) (Cell Signaling Technology) and quantified using Image J software (NIH). For cellular senescence assays, NCI-H520 cells were infected with retrovirus at a MOI of 2, and the medium was changed 24 h after infection. Cells were stained for β-galactosidase and visualized as previously described (Wajapeyee et al 2008).

148 DISCUSSION

In this report, we have described an experimental strategy for the systematic identification of candidate TSGs. Based upon this approach, and subsequent functional analyses, we have identified human genes that have all the expected properties of TSGs including: promotion of tumorigenesis following inactivation, frequent deletion or down-regulation in human tumor samples, and inhibition of colony formation and/or tumor growth upon over-expression.

Significantly, several of the genes we identified are well documented TSGs (e.g.,

STK11 and SEMA3B), thus validating the experimental approach. Although we performed the primary screen by analyzing tumor formation in mice, a viable alternative would have been to first analyze growth in soft agar. We felt that a compelling advantage of the tumorigenesis assay is that it is a more stringent test of the transformed phenotype, and would therefore reduce the number of false positives obtained in the primary screen. However, an advantage of using the soft agar assay as a primary screen is that it is likely to be more economical.

The relative advantages of the two approaches should be considered when designing other similar, large-scale screens.

Our reconstruction experiment indicates that that, in principal, the design of the primary screen was sufficient to analyze all 62,400 shRNAs in the library.

However, for several reasons we are reluctant to conclude that the screen was saturating and that all possible candidate TSGs were identified. In particular, transformed cells may have different growth rates depending upon the particular

149 shRNA present in the cell. Therefore, some shRNAs that induce transformation events may also cause cells to grow slowly and, as a consequence, those shRNAs may be relatively under-represented in the resulting tumors. In addition, we have found that not all shRNAs are equally efficacious and thus some candidate TSGs may be missed due to sub-optimal knockdown efficiency.

Nonetheless, our experiments successfully identified a large number of candidate TSGs and therefore we were not compelled to search more exhaustively for further candidates. However, additional candidate TSGs could be identified by, for example, injecting each pool into more mice and obtaining a greater number of shRNA sequences from each tumor using either conventional approaches or massively parallel short sequencing. In any case, the possibility that there are additional candidate TSGs that were not identified in our screen does not diminish the fact that the approach successfully resulted in the discovery of a large number of new TSGs.

As discussed above, there have been several previous studies in which

TSGs have been identified through in vivo RNAi screens using a limited collection of shRNAs that were selected based upon their previous implication in cancer (Bric et al, 2009; Meacham et al, 2009; Zender et al, 2008). The substantially smaller number of shRNAs screened in these studies increases the likelihood of complete coverage of the collection used. However, a significant disadvantage of this smaller scale approach is that the collection of shRNAs is biased and limited, precluding unexpected results from being obtained.

150 In this regard, several of the genes we found were previously

unrecognized TSGs, with well-established roles in processes other than growth

suppression. For example, MYD88 is a gene best known for its role in innate

immunity (reviewed in Arancibia et al 2007) and SFRS9 encodes a pre-mRNA

splicing factor (Screaton et al 1995). Furthermore, the TSGs we identified that

negatively regulate FGFR1 signaling could provide new hints for functional

studies of these TSGs. For example, PTPN4 is a known tyrosine phosphatase

(Wu et al 2006) and can dephosphorylate the immunoreceptor tyrosine-based activation motifs (ITAMs). We showed that knocking down PTPN4 increased

phospho level of FGFR1, suggesting that FGFR1 might be a potential substrate

of PTPN4. GAPVD1 has been shown to mediate ubiquitination and degradation

of EGFR (Xiong, 2007). From our experiments, we showed that KD GAPVD1

increased level of total FGFR1, therefore we think GAPVD1 might also mediate

ubiquitination and degradation of FGFR1. It is widely accepted that FGFR

signals promote angiogenesis, and people found FGFR1 regulates angiogenesis

through cytokines interleukin-4 and pleiotrophin in embryos. However, little is

known about FGFR1 induced angiogenesis in tumors. We found that Angpt1, a

known anti-angiogenesis protein, is repressed by FGFR1 overexpression and

knocking down ANGPT1 can further activate FGFR1 signaling, thus form a

negative feedback regulation of FGFR1. It might be one of the mechanisms of

angiogenesis in FGFR1 amplified lung cancer.

151 Our results highlight the power of unbiased, large-scale functional screening for cancer gene discovery. The shRNA screening approach we describe is a general strategy that can be used to discover new TSGs in any tumor type. A wide range of immortalized but non-transformed cell lines derived from different cell types are available and would presumably lead to the identification of distinct TSGs. Appropriate cell lines have been isolated from human tissue, such as MCF10A mammary epithelial cells, or experimentally derived from primary human cells using defined oncogenes (see, for examples,

Hahn et al 1999, Gupta et al 2005, Sato et al 2006, Radulovich et al 2008, Qian et al 2005).

152 Figure 2.2.1

153 Figure 2.2.1. Large-scale shRNA screen for the identification of candidate TSGs. (A) Schematic summary of the screen. (B) 0, 100 or 1000 Kras- transformed NIH 3T3 cells were mixed with non- transformed NIH 3T3 cells to achieve a final population of

1x106 cells, which were then injected into the flanks of nude mice (n=3) (C) Tumor formation assay. The NIH 3T3

KD cell lines were subcutaneously injected into nude mice, and tumors were photographed at various time points following injection.

154 Figure 2.2.2

155 Figure 2.2.2. shRNA knock down dfficiency. qRT-PCR analysis monitoring shRNA-mediated knockdown efficiency for each TSG (error bars represent SD, n=3).

Values are given relative to expression of each gene following treatment with a non-silencing (NS) shRNA, which was arbitrarily set to 1.

156 Figure 2.2.3

157 Figure 2.2.3. Down-regulation of candidate TSGs in human lung squamous cell carcinoma (hLSCC) samples. qRT-PCR analysis monitoring expression of each TSG in 27 hLSCC samples (error bars represent

SD, n=3). Values were normalized to the expression of the TSG in 10 normal lung samples, the average of which was set to 1. The red line indicates 2-fold down- regulation. Asterisks indicate samples whose fold down- regulation vastly exceeds that shown in the graph.

Samples have been re-ordered from least to most down- regulated gene.

158 Figure 2.2.4

159 Figure 2.2.4. Down-regulation of candidate TSGs in human lung adenocarcinoma samples. qRT-PCR analysis monitoring expression of each TSG in 10 human lung adenocarcinoma samples (error bars represent SD, n=3). Values were normalized to the expression of the

TSG in 10 normal lung samples, the average of which was set to 1. The red line indicates 2-fold down- regulation.

160 Figure 2.2.5

161 Figure 2.2.5. Knocking down TSGs in Small Airway

(SA) cell line activates FGFR signaling pathway. (A)

Western blots show the levels of key proteins in the

FGFR1 signaling pathway after treatment with non- silencing shRNA or shRNA targeting candidate TSGs.

Genes labeled in red have increased pFRS2 level, which indicates activation of FGFR1 signaling pathway. (B,C)

Western blots show the tFGFR1 and pFGFR1 levels of

KD cell lines that activate FGFR1 signaling pathway.

Genes labeled in red have increased tFGFR1 level.

Genes labeled in green have increased pFGFR1 without increase tFGFR1 level.

162 Figure 2.2.6

FGFR1

FLNA, GAPVD1, MYD88, PTGIS, SFRS9, SPAST

FRS2

Y436 CDK5R1, DDX52, SDF2L1, SPOP, ANGPT1, DAPP1, Y653P/ STK11, TXNRD1 FPR3, NME4, Y654P PTPN4 Y766P

IGF2R, LSM8, MAP1A, DAPP1 PIGH, SEMA3B pErk pAkt

ANGPT1, DAPP1, TXNRD1

163 Figure 2.2.6. Model of TSGs negatively regulating

FGFR1 signaling. 7 genes (FLNA, GAPVD1, MYD88,

PIGH, PTGIS, SFRS9, SPAST) inhibit total FGFR1 levels; 5 genes (ANGPT1, DAPP1, FPR3, NME4,

PTPN4) inhibit pFGFR1 levels without elevated tFGFR1.

Y653 phosphorylation of FGFR1 was not increased in

FLNA and MYD88 KD cell lines, although the total

FGFR1 is increased; 6 genes (CDK5R1, DDX52,

SDF2L1, SPOP, STK11, TXNRD1) inhibit pFRS2 levels without elevated phospho- or total-FGFR1 levels; Four genes (IGF2R, LSM8, MAP1A, SEMA3B) inhibit pErk level independent of FGFR1 signaling.

164 Figure 2.2.7

4 * 3.5 3 * 2.5 * 2 1.5 1 0.5

Fold de-repression after FGFR1 KD 0 FPR3

‐0.5 PIGH FLNA SPOP LSM8 PTGIS IGF2R SFRS9 SPAST STK11 NME4 SDF2L ZNF22 PTPN4 DAPP1 DDX52 TXNRD MYD88 MAP1A PKD1L3 CDK5R1 ANGPT1 GAPVD1 SEMA3B Candidate TSGs

165 Figure 2.2.7. Candidate TSGs repressed by FGFR1 signaling at the transcriptional level. Three candidate

TSGs are de-repressed after FGFR1 knock down in NCI-

H520, monitored by qRT-PCR.

166 Figure 2.2.8 A Vector FGFR1 OE

NIH3T3

35 FGFR1 pathway

0 Fold increase of colony number compare to NS

167 Figure 2.2.8. Knocking down TSGs induces transformation of lung epithelia cells. (A) 1 million

NIH3T3 cells overexpressing FGFR1 and 5 million SA cells overexpressing FGFR1 formed tumors when subcutaneously injected into the flank of nude mice, while no tumor formed in empty vector controls. (B) In vitro soft agar colony formation assay showed 14 out of

17 FGFR1 regulating genes and 4 FGFR1 signaling independent genes can form colonies in soft agar. (C)

KD cell lines that formed colonies in soft agar were injected into the flanks of nude mice and formed tumors in vivo.

168 Figure 2.2.9 A

B 2283 4184 C 80 FGFR1 amplification 70 + - - 60 NCIH520 A427 A549 50 40 DMSO SA 30 20 HBEC 10

relave fold of downregulaon Ponatinib 0

169 Figure 2.2.9. SA cells with TSGs knocked down are sensive to FGFR inhibitor. (A) Aer treatment with 500nM ponanib for three days, TSG KD cell lines that have increased pFGFR1 levels (see ﬁgure 2.2.5) were sensive to Ponanib compared to non‐silencing control. (B) 3 genes (STK11, MYD88, DAPP1) are highly repressed in A427 compared to SA or HBEC. (C)

A427 showed similar sensivity to Ponanib (500nM) compared to FGFR1 ampliﬁed cell line NCI‐H520, while a control cell line A549 with normal level of FGFR1 showed more resistance

170 Figure 2.2.10

171 Figure 2.2.10. Candidate TSG promoter hypermethylation in hLSCC samples. (A) Left, methylation heat map for candidate TSGs showing hypomethylation (red) or hypermethylation (green) in the tumor sample. Genes that are significantly hypermethylated (defined as a ≥50% increase in methylation) in tumor samples (p-value < 0.05) are indicated by an asterisk. Right, color key. (B) Percent of individuals with significant hypermethylation in nine candidate TSGs. The p-value for each gene is shown.

172 Figure 2.2.10 A

173 Figure 2.2.11. Over-expression of candidate TSGs inhibits growth of hLSCC cells. (A) Colony suppression assay. NCI-H520 cells infected with a retrovirus expressing each candidate TSG or empty vector were stained with crystal violet. (B) Quantification of the colony suppression assay shown in (A) (error bars represent SD, n=3). (C) Tumor formation assay. NCI-H520 cells ectopically expressing a TSG were injected subcutaneously into nude mice and tumor growth was monitored (error bars represent SD, n=3).

174 Figure 2.2.12 A

175 Figure 2.2.12. Mechanisms of candidate TSGs inhibiting growth of hLSCC cells. (A) Apoptosis assays.

NCI-H520 cells ectopically expressing each TSG were analyzed by immunoblot for cleaved caspase 3. (B).

Cleaved caspase 3 protein levels visualized on western blots were quantified and normalized to actin levels, and then to cleaved caspase 3 levels in the vector control, which was set to 1. The red line denotes a 2-fold increase. (C) Cellular senescence assays. NCI-H520 cells expressing each TSG were stained for senescence- associated β-galactosidase. (D) The percent positive cells was quantified by counting the blue stained cells

(error bars represent SD, n=3). The red line denotes a 2- fold increase relative to the vector control.

176 Table 2.2.1. List of 41 shRNAs recovered from tumors.

Pool ShRNAs recovered from the tumor

number

1 Dnajc12, Gapvd1, Mrps18c, Mtap1a, Sfrs9, Svs6, Zfp422

2 Ddx52

3 Angpt1, Cdk5r1, Dapp1, Efna3, Nme4, Nup205, Pkd1l3, Ptpn4,

Spast, Stk11

4 Flna, Fpr3, Gzma, Orc1l, Prl7a2, Gm11541, Sdf2l1, Sip1,

Riken4632401L01

6 Mad2l1, Slfn4

8 Dhrs9, Pigh

9 Cr1l, Igf2r, Lsm8, Myd88, Ptgis, Sema3b, Spop, Txnrd1, Wap,

Gm5058

177 Table 2.2.2. List of 32 candidate TSGs that validate in colony formation and mouse tumorigenesis assays.

Biological Gene Gene name Human process symbol homolog Cell signaling Angpt1 angiopoietin 1 ANGPT1 Dapp1 dual adaptor for phosphotyrosine and 3- DAPP1 phosphoinositides 1 Fpr3 formyl peptide receptor 3 FPR3 Gapvd1 GTPase activating protein and VPS9 domains 1 GAPVD1 Igf2r insulin-like growth factor 2 receptor IGF2R Myd88 myeloid differentiation primary response gene 88 MYD88 Pkd1l3 polycystic kidney disease 1 like 3 PKD1L3 Prl7a2 prolactin family 7, subfamily a, member 2 – Ptpn4 protein tyrosine phosphatase, non-receptor type 4 PTPN4 Stk11 serine/threonine kinase 11 STK11 Cell Cdk5r1 cyclin-dependent kinase 5, regulatory subunit 1 (p35) CDK5R1 growth/migration/ metabolism Flna filamin, alpha FLNA Mtap1a microtubule-associated protein 1 A MAP1A Nme4 non-metastatic cells 4, protein expressed in NME4 Sema3b sema domain, immunoglobulin domain (Ig), short basic SEMA3B domain, secreted, (emaphoring) 3B Slfn4 schlafen 4 – Spast spastin SPAST DNA/RNA Ddx52 DEAD (Asp-Glu-Ala-Asp) box polypeptide 52 DDX52 metabolism Lsm8 LSM8 homolog, U6 small nuclear RNA associated (S. LSM8 cerevisiae) Orc1l origin recognition complex, subunit 1-like (S. cerevisiae) ORC1L Sfrs9 splicing factor, arginine/serine rich 9 SFRS9 Immunity Cr1l complement component (3b/4b) receptor 1-like – Gzma granzyme A GZMA Lipid metabolism Pigh phosphatidylinositol glycan anchor biosynthesis, class H PIGH Ptgis prostaglandin I2 (prostacyclin) synthase PTGIS Nuclear transport Nup205 nucleoporin 205 NUP205 Transcriptional Spop speckle-type POZ protein SPOP regulation Zfp422 zinc finger protein 422 ZNF22 Protein Dnajc12 DnaJ (Hsp40) homolog, subfamily C, member 12 DNAJC12 folding/export/ metabolism Sdf2l1 stromal cell-derived factor 2-like 1 SDF2L1 Txnrd1 thioredoxin reductase 1 TXNRD1 Unknown Wap whey acidic protein –

178

Table 2.2.3. Summary of NCBI SKY/MFISH & CGH data for candidate TSGs.

Liver Lung Breast Kidney Bladder Ovarian Cervical Prostate Sarcoma Myeloma Leukem

Chromosome Colorectal Melanoma Pancreatic Lymphoma Gene location Brain&CNS ANGPT1 8q22.3-q23          CDK5R1 17q11.2             DAPP1 4q25-q27                DDX52 17q21.1             DNAJC12 10q22.1               FLNA Xq28              FPR3 19q13.3-q13.4               GAPVD1 9q33.3               GZMA 5q11-q12             IGF2R 6q26               LSM8 7q31.1-q31.3           MAP1A 15q13-qter               MYD88 3p22               NME4 16p13.3               NUP205 7q33             ORC1L 1p32             PIGH 14q11-q24             PKD1L3 16q22.2                PTGIS 20q13.13          PTPN4 2q14.2              SDF2L1 22q11.21               SEMA3B 3p21.3               SFRS9 12q24.31                SPAST 2p24-p21               SPOP 17q21.33             STK11 19p13.3             TXNRD1 12q23-q24.1               ZNF22 10q11               

 indicates a reported deletion of the gene in the cancer type.

179

Table 2.2.4. Summary of Oncomine data analysis querying whether candidate TSGs are down-regulated in cancer versus normal tissue.

dder

Gene Bla Brain&CNS Breast Cervical Colorectal Esophageal Gastric Head&Neck Kidney Leukemia Liver Lung Lymphoma Melanoma Myeloma Other Ovarian Pancreatic Prostate Sarcoma ANGPT1               CDK5R1        DAPP1        DDX52     DNAJC12            FLNA            FPR3    GAPVD1     GZMA         IGF2R    LSM8    MAP1A          MYD88    NME4     NUP205    ORC1L    PIGH        PKD1L3 PTGIS              PTPN4     SDF2L1    SEMA3B           SFRS9   SPAST             SPOP     STK11     TXNRD1         ZNF22       

 indicates one or more reports of significant down-regulation of the gene in cancer versus normal tissue.

180 Table 2.2.5. List of catalog numbers for shRNAs obtained from Open Biosystems.

Gene Catalog number for 1st Catalog number for 2nd shRNA shRNA Angpt1 RMM1766-96738501 RMM1766-96739079 Cdk5r1 RMM1766-96739750 RMM1766-96738443 Cr1l RMM1766-97042824 RMM3981-98063631 Dapp1 RMM1766-96738885 RMM1766-96880910 Ddx52 RHS1764-9687389 RMM1766-96889298 Dnajc12 RMM1766-9106520 RMM1766-96743200 Flna RMM1766-96741212 RMM1766-9352033 Fpr3 RMM1766-96746297 RMM1766-98468326 Gapvd1 RMM1766-9336338 RMM1766-9336322 Gzma RMM1766-96745254 RMM1766-9106902 Igf2r RMM1766-97044218 RMM1766-98467508 Lsm8 RMM1766-97044512 RMM1766-96881489 Mtap1a RMM1766-9341607 RMM1766-96874116 Myd88 RMM1766-97042719 RMM3981-97065530 Nme4 RMM1766-96740301 RMM1766-96740215 Nup205 RMM1766-96873936 RMM1766-9355153 Orc1l RMM1766-96744068 RMM1766-96744205 Pigh RMM1766-96891459 RMM1766-96737813 Pkd1l3 RMM1766-9353837 RMM1766-9354529 Prl7a2 RMM1766-96744814 RMM3981-98069754 Ptgis RMM1766-97042638 RMM1766-96738598 Ptpn4 RMM1766-96740179 RMM1766-96740341 Sdf2l1 RMM1766-96743359 RMM3981-99012961 Sema3b RMM1766-97042710 RMM3981-9621999 Sfrs9 RMM1766-9106052 RMM3981-97059906 Slfn4 RMM1766-96878856 RMM1766-97043261 Spast RMM1766-96737853 RMM1766-96871825 Spop RMM1766-97044276 RMM3981-98497970 Stk11 RMM1766-96740219 RMM3981-9591552 Txnrd1 RMM1766-97042622 RMM1766-97042953 Wap RMM1766-97043220 RMM1766-97044227 Zfp422 RMM1766-9336451 RMM1766-96739222

181

Table 2.2.6. List of primers used for quantitative real-time RT-PCR and for cloning TSGs.

mGene Forward primer (5’  3’) Reverse primer (5’  3’) qRT-PCR Angpt1 (mouse) GTGCCGATTTCAGCACGAAG CCATGATTTTGTCCCGCAGT ANGPT1 (human) GGACAGCAGGAAAACAGAGC GGGCACATTTGCACATACAG Cdk5r1 (mouse) TCATGAGCTCCAAGATGCTG CCTGACCGCTCTCATTCTTC CDK5R1 (human) TGCTGACATGCCTGTACCTC TTGATGACAGAGAGGCAACG Cr1l (mouse) TGTCCGCCTTCAGTCTCTGC TGTTCTCATTTCACGTTGCTGCT Dapp1 (mouse) TGTGCAAAGACGGGAGTTGAA TCGGACGGTATCTTCTCCTTGA DAPP1 (human) CTCTGTGCAAAGACCGGAGT CCCTTGGTTGAGCTGTTTTC Ddx52 (mouse) CACAGTCCACAGCTTCAGAGCA TCCCCTATTCCCTGCTCTTCC DDX52 (human) TGCTAGCAAGAGGGATTGATT CCCTTATTCCCTGCTCTTCC Dnajc12 (mouse) TTCCCAGAGGAGGGCATCTC GTTCTGAGGGAGCGTCACCA DNAJC12 (human) TCTGGAATGTCACCCAGACA TCCTTTGCCTTCTGCAGTTT Flna (mouse) AAGGCCTGGGGCTAAGCAAG GCCCATGTGTTTCACCAGGA FLNA (human) CCAGAAGAGCAGCTTCACAG ACGTGCTTCACCAGGATCTC Fpr3 (mouse) TCTGTGTCCCCTGAATCTGGA TCCCAGCACACCAAGGAAGA FPR3 (human) GGGACTCTGGATTTTCACCA GTCACCCCAGAATGCAAAGT Gapvd1 (mouse) TGGCCAACGAGGACTCTGTC CTCCACGGCTGCTGTGAACT GAPVD1 (human) GCGGATGACTTTGTTCCTGT TGTGAACTGCATCCACCAAT Gzma (mouse) GTTGACTGCTGCCCACTGTA TGGTTCCTGGTTTCACATCA GZMA (human) CCTCCGAGGTGGAAGAGACT TTTCAAGGCCAAAGGAAGTG Igf2r (mouse) AGAAGAAGCTCGGGCGTGTC CTTGCCCGTCCTTGCCTAGT IGF2R (human) CCGACTGCCAGTACCTCTTC GTTCTGACAGCCCCTTGTG Lsm8 (mouse) CATGAGCGGGTGTTCAGCTC AGGCTCTGCTCGGATGTTCC LSM8 (human) CAGCTCTTCACAGGGGGTAG CTGCTCGAATATTCCCCAAA Mtap1a (mouse) TGGAAATGACCCTGCCAATG TGCTGCTGTTGCTCGTGTGT MAP1A (human) TCCAAAGGCCTAGTCAATGG CGGAAGAAGTCAAGGTCAGC Myd88 (mouse) TCCCAGTATCCTGCGGTTCA TCTGGCTCCGCATCAGTCTC MYD88 (human) GCACATGGGCACATACAGAC TAGCTGTTCCTGGGAGCTGT Nme4 (mouse) GGACAATCAGGGGCGACTTC ACCACCATCTGCCCAGTTCA NME4 (human) GACTTCAGCGTCCACATCAG CTGGAACCACAGCTGGATCT Nup205 (mouse) CGGAGAGTCGCTGCAAAAGA TTCCTGGACAGCCTCAGCAG NUP205 (human) TTTATTCTTTGGCGCCATCT GATTGGTTTCTGAGGCGAAG Orc1l (mouse) CCGTCGGTCAGGACTAGAGGA ACTGGGCTCCACCAGAAGGA ORC1L (human) GTCGATCAGGACTGGAGGAA CCATGGTCTCTGACATGGTG Pigh (mouse) TACTGAAGGAGCCGGGGAAG GCTGTGGCTTTCTGGTGTGC PIGH (human) CAGTGGAACCACATGGGATA TCTCCTGGCAGCTCCTGTAT Pkd1l3 (mouse) CAATCCTGGGCTCCCTTTTG GGTGCAGGCGGATTCCTAAC PKD1L3 (human) TGCAGCATCTCTGACTACCG GGACTGGGTCTAAAGCGAAA Prl7a2 (mouse) CAAAACTTGCAGAGCTTTTTGA CGGTGTATTCCACATTTCCTG Ptgis (mouse) AACCAGTGCCTGGGGAAGAG CTGGCTGCATCAGACCGAAG PTGIS (human) ACTGCCTGGGGAGGAGTTAT GATCTCCACATCTGCGTTGA Ptpn4 (mouse) CCTGGCCTGACCATGGAGTA CAATGAGACACATGGCAGTTTCC PTPN4 (human) GAGCCATGATGATCCAAACA CAAAGCCTTCTTCATAAACTTTCA Sdf2l1 (mouse) AACCTGCACACGCACCACTT TTGCCCAGAACATCGGACTG SDF2L1 (human GCACCTCTGTGTTCCTGTCA TTCCACGTATTGTGCGTGTT Sema3b (mouse) GCATGTGCAGTGGACCTTCC CAACCGCGACGCAAAGATAC SEMA3B (human) GGACCCAGGAAGGATAGAGG GGCTGCGAAAGATGGTAAAG

182 Sfrs9 (mouse) CGGAATGGGGATGGTTGAAT CAGACCGCGACCGTGAGTAG SFRS9 (human) ATATGCCCTGCGTAAACTGG AGCTGGTGCTTCTCTCAGGA Slfn4 (mouse) GAATGGGTGAAGCGCCAGAT TCTTGCAAGCCCAGATCCAA Spast (mouse) TGCAATCTGCTGGAGATGACAG TTTGGTAAGGACACATATACCCG TTT SPAST (human) CACTGGGTCCTATCCGAGAA GCTGACGCTGCGTTTTATTT Spop (mouse) CTGACCTCCACAGCGCAGAT GGAAAGGGCACTGTGCTGAA SPOP (human) GCCCTCTGCAGTAACCTGTC TTGACTTCCACCCAGAGGTC Stk11 (mouse) GCAGCAAGGTGAAGCCAGAA CCAACGTCCCGAAGTGAGTG STK11 (human) CATGACTGTGGTGCCGTACT TGTGACTGGCCTCCTCTTCT Txnrd1 (mouse) AGCAGCTGGACAGCACCATC TCTTGGCAACAGCATCCACA TXNRD1 (human) AGTAGGTCCACATGCACACG TTTGATCAAGTTCAAGCACAAAA Wap (mouse) CCAGCGACCGTGAGTGTTCT TCCAGGAGTGAAGGGTCTTGC Zfp422 (mouse) CAGCCAAGGAAAAGCCTATG TTCTCGTCCACGTTCCTTCT ZNF22 (human) CTCTCATCTGAGGCAGCACA GAGACCAGCCACAGACTTCC Cloning ANGPT1 GGAAGATCTTCCATGACAGTTTT CCGCTCGAGCGGTCAAAAATCTA CCTTTCCTTTG AAGGTCGAATC CDK5R1 CCGCTCGAGCGGATGGGCACG CCGCTCGAGCGGTCACCGATCC GTGCTGTCCCT AGGCCTAGGA DAPP1 CCGCTCGAGCGGATGGGCAGA CCGCTCGAGCGGCTATTTAAAGA GCAGAACTTCTA TGAACGACCGAG DDX52 GGAAGATCTATGGACTACAAAG CCGCTCGAGCGGTTAACTTTTGT ACCATGACGGTGATTATAAAGAT CTTCAAGAGCTA CATGACATCGATTACAAGGATG ACGATGACAAGATGGACGTCCA CGATCTCTT FPR3 GAAGATCTATGGACTACAAAGA CCGCTCGAGCGGTCACATTGCTT CCATGACGGTGATTATAAAGATC GTAACTCCGT ATGACATCGATTACAAGGATGA CGATGACAAGATGGAAACCAAC TTCTCCATT GZMA GAAGATCTATGAGGAACTCCTAT CCCTCGAGTTAAACTGCTCCCTT AGATT GATAG IGF2R CCGCTCGAGCGGATGGGGGCC CCGCTCGAGCGGTCAGATGTGTA GCCGCCGG AGAGGTCCTCG LSM8 GGAAGATCTATGGACTACAAAG CCGCTCGAGCGGTCAGTGTGCT ACCATGACGGTGATTATAAAGAT ACAGAATTTAAA CATGACATCGATTACAAGGATG ACGATGACAAGATGACGTCCGC TTTGGAGAA MYD88 GGAAGATCTTCCGAATTCCCGG GGAAGATCTTCCTCAGGGCAGG GATATCATGC GACAAGGCC PIGH GGAAGATCTTCCATGGAGGATG CCGCTCGAGCGGTCATGGGCTT AGCGGAGCTTT GTTGATGTGGC PTGIS GGAAGATCTTCCATGGCTTGGG GGAAGATCTTCCTCATGGGCGGA CCGCGCTC TGCGGTAG SDF2L1 GGAAGATCTTCCATGTGGAGCG CCGCTCGAGCGGTCAGAGTTCAT CGGGCCGC CGTGACCTGC SEMA3B CCGCTCGAGATGGACTACAAAG CCGGAATTCTCACCAGTGCGTTG ACCATGACGGTGATTATAAAGAT CGCT CATGACATCGATTACAAGGATG ACGATGACAAGATGAACTTCGT GAAGTTGCTG

183 SFRS9 CCGCTCGAGATGGACTACAAAG CCGGAATTCTCAGTAGGGCCTGA ACCATGACGGTGATTATAAAGAT AAGGAG CATGACATCGATTACAAGGATG ACGATGACAAGATGTCGGGCTG GGCGGAC SPOP GAAGATCTATGGACTACAAAGA CCGCTCGAGCGGTTAGGATTGCT CCATGACGGTGATTATAAAGATC TCAGGCGTTT ATGACATCGATTACAAGGATGA CGATGACAAGATGTCAAGGGTT CCAAGTCC STK11 GGAAGATCTATGGACTACAAAG CCGCTCGAGCGGTCACTGCTGC ACCATGACGGTGATTATAAAGAT TTGCAGGCC CATGACATCGATTACAAGGATG ACGATGACAAGATGGAGGTGGT GGACCCG

184

Table 2.2.7. Source and catalog numbers for human cDNAs used to clone TSGs.

Gene Source Catalog number ANGPT1 Open Biosystems EHS1001-99608061 CDK5R1 Open Biosystems MHS1010-58341 DAPP1 Open Biosystems MHS1010-7429735 DDX52 Open Biosystems MHS1011-9199110 DNAJC12 Open Biosystems MHS1010-58074 FPR3 Open Biosystems MHS1010-98684799 GZMA Open Biosystems MHS1011-76468 IGF2R ATCC 95661 LSM8 Open Biosystems MHS1011-60450 MYD88 Open Biosystems MHS1010-73828 NUP205 Open Biosystems EHS1001-99865050 PIGH Open Biosystems MHS1011-60796 PTGIS Open Biosystems MHS1010-98052790 SDF2L1 Open Biosystems MHS4426-99240278 SEMA3B Open Biosystems MHS1010-74089 SFRS9 Open Biosystems MHS1010-98052451 SPOP Open Biosystems MHS1010-57540 STK11 Open Biosystems MHS1011-62254 TXNRD1 Open Biosystems MHS1010-58057

185 REFERENCES

Adnane, J., Gaudray, P., Dionne, C. A., Crumley, G., Jaye, M., Schlessinger, J., Jeanteur, P., Birnbaum, D. and Theillet, C. (1991) BEK and FLG, two receptors to members of the FGF family, are amplified in subsets of human breast cancers. Oncogene 6, 659-663

Allerstorfer, S., Sonvilla, G., Fischer, H., Spiegl-Kreinecker, S., Gauglhofer, C., Setinek, U., Czech, T., Marosi, C., Buchroithner, J. and Pichler, J. (2008) FGF5 as an oncogenic factor in human glioblastoma multiforme: autocrine and paracrine activities. Oncogene 27, 4180-4190

Altomare, D. A. & Testa, J. R. Perturbations of the AKT signaling pathway in human cancer. Oncogene 24, 7455–7464 (2005).

Andre, F., Job, B., Dessen, P., Tordai, A., Michiels, S., Liedtke, C., Richon, C., Yan, K., Wang, B. and Vassal, G. (2009) Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization array. Clin. Cancer Res. 15, 441-451

Aplin A.E, A Howe, S.K Alahari, R.L Juliano. Signal transduction and signal modulation by cell adhesion receptorsthe role of integrins, cadherins, immunoglobulin-cell adhesion molecules, and selectins.. Pharmacol. Rev., 50 (1998), pp. 197–263

Arancibia, S.A., et al. Toll-like receptors are key participants in innate immune responses. Biol Res 40, 97-112 (2007).

190 Bass, A. J., Watanabe, H., Mermel, C. H., Yu, S., Perner, S., Verhaak, R. G., Kim, S. Y., Wardwell, L., Tamayo, P. and Gat-Viks, I. (2009) SOX2 is an amplified lineage-survival oncogene in lung and esophageal squamous cell carcinomas. Nat. Genet. 41, 1238-1242

Ashworth A, Lord CJ, Reis-Filho JS. Genetic interactions in cancer progression and treatment. Cell. 2011 Apr 1;145(1):30-8.

Baird, K., Davis, S., Antonescu, C. R., Harper, U. L., Walker, R. L., Chen, Y., Glatfelter, A. A., Duray, P. H. and Meltzer, P. S. (2005) Gene expression profiling of human sarcomas: insights into sarcoma biology. Cancer Res. 65, 9226-9235

Behrens, C., Lin, H. Y., Lee, J. J., Raso, M. G., Hong, W. K., Wistuba, I. I. and Lotan, R. (2008) Immunohistochemical expression of basic fibroblast growth factor and fibroblast growth factor receptors 1 and 2 in the pathogenesis of lung cancer. Clin. Cancer Res. 14, 6014-6022

Berger, W., Setinek, U., Mohr, T., Kindas-Mugge, I., Vetterlein, M., Dekan, G., Eckersberger, F., Caldas, C. and Micksche, M. (1999) Evidence for a role of FGF-2 and FGF receptors in the proliferation of non-small cell lung cancer cells. Int. J. Cancer 83, 415-423

Bernard St , R., Zheng, L., Liu, W., Winer, D., Asa, S. L. and Ezzat, S. (2005) Fibroblast growth factor receptors as molecular targets in thyroid carcinoma. Endocrinology 146, 1145-1153

Berns K, Hijmans EM, Mullenders J, Brummelkamp TR, Velds A, Heimerikx M, Kerkhoven RM, Madiredjo M, Nijkamp W, Weigelt B, Agami R, Ge W, Cavet G, Linsley PS, Beijersbergen RL, Bernards R. A large-scale RNAi screen in human

191 cells identifies new components of the p53 pathway. Nature. 2004 Mar 25;428(6981):431-7.

Bibikova, M., et al. Genome-wide DNA methylation profiling using Infinium assay. Epigenomics 1, 177-200 (2009).

Bouck, N, Stellmach, V ,S.C Hsu. How tumors become angiogenic. Adv. Cancer Res., 69 (1996), pp. 135–174

Bovee, J. V., van Den Broek, L. J., Cleton-Jansen, A. M. and Hogendoorn, P. C. (2000) Up-regulation of PTHrP and Bcl-2 expression characterizes the progression of osteochondroma towards peripheral chondrosarcoma and is a late event in central chondrosarcoma. Lab. Invest. 80, 1925-1934

Bric A, Miething C, Bialucha CU, Scuoppo C, Zender L, Krasnitz A, Xuan Z, Zuber J, Wigler M, Hicks J, McCombie RW, Hemann MT, Hannon GJ, Powers S, Lowe SW. Functional identification of tumor-suppressor genes through an in vivo RNA interference screen in a mouse lymphoma model. Cancer Cell. 2009 Oct 6;16(4):324-35.

Bruce A. Chabner & Thomas G. Roberts. Chemotherapy and the war on cancer Nature Reviews Cancer 5, 65−72 (2005)

T.M Bryan, T.R Cech. Telomerase and the maintenance of chromosome ends. Curr. Opin. Cell Biol., 11 (1999), pp. 318–324

T.M Bryan, A Englezou, J Gupta, S Bacchetti, R.R Reddel. Telomere elongation in immortal human cells without detectable telomerase activity. EMBO J., 14 (1995), pp. 4240–4248

192

Castro-Rivera, E., Ran, S., Brekken, R.A. & Minna, J.D. Semaphorin 3B inhibits the phosphatidylinositol 3-kinase/Akt pathway through neuropilin-1 in lung and breast cancer cells. Cancer Res 68, 8295-8303 (2008).

Chen, J. et al. FGFR3 as a therapeutic target of the small molecule inhibitor PKC412 in hematopoietic malignancies. Oncogene 24, 8259–8267 (2005).

Chesi M, Nardini E, Brents LA, Schrock E, Ried T, Kuehl WM and Bergsagel PL. Frequent translocation t(4;14)(p16.3;q32.3) in multiple myeloma is associated with increased expression and activating mutations of fibroblast growth factor receptor 3. Nat. Genet. 16, 260-264, (1997).

Chin, K., DeVries, S., Fridlyand, J., Spellman, P. T., Roydasgupta, R., Kuo, W. L., Lapuk, A., Neve, R. M., Qian, Z. and Ryder, T. (2006) Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 10, 529-541

Coughlin SR, Barr PJ, Cousens LS, Fretto LJ & Williams LT. Acidic and basic fibroblast growth factors stimulate tyrosine kinase activity in vivo. Journal of Biological Chemistry 263, 988–993. 1988

C.M Counter, A.A Avilion, C.E LeFeuvre, N.G Stewart, C.W Greider, C.B Harley, S Bacchetti. Telomere shortening associated with chromosome instability is arrested in immortal cells which express telomerase activity. EMBO J., 11 (1992), pp. 1921–1929

193 Courjal, F. et al. Mapping of DNA amplifications at 15 chromosomal localizations in 1875 breast tumors: definition of phenotypic groups. Cancer Res. 57, 4360– 4367 (1997).

L.M Coussens, Z Werb. Matrix metalloproteinases and the development of cancer. Chem. Biol., 3 (1996), pp. 895–904

A.F Chambers, L.M Matrisian. Changing views of the role of matrix metalloproteinases in metastasis. J. Natl. Cancer Inst., 89 (1997), pp. 1260– 1270

H. Davies et al. Somatic mutations of the protein kinase gene family in human lung cancer. Cancer Res., 65 (2005), pp. 7591–7595

De Boer, W. I., Houtsmuller, A. B., Izadifar, V., Muscatelli-Groux, B., Van der Kwast, T. H. and Chopin, D. K. (1997) Expression and functions of EGF, FGF and TGFbeta-growth-factor family members and their receptors in invasive human transitional-cell-carcinoma cells. Int. J. Cancer 71, 284-291

Dellacono, F. R., Spiro, J., Eisma, R. and Kreutzer, D. (1997) Expression of basic fibroblast growth factor and its receptors by head and neck squamous carcinoma tumor and vascular endothelial cells. Am. J. Surg. 174, 540-544

Desnoyers, L. R. et al. Targeting FGF19 inhibits tumor growth in colon cancer xenograft and FGF19 transgenic hepatocellular carcinoma models. Oncogene 27, 85–97 (2008).

Dewhirst MW, Cao Y, Moeller B. Cycling hypoxia and free radicals regulate angiogenesis and radiotherapy response. Nat. Rev. Cancer. 2008;8:425, 437.

194 Devilard, E., Bladou, F., Ramuz, O., Karsenty, G., Dales, J. P., Gravis, G., Nguyen, C., Bertucci, F., Xerri, L. and Birnbaum, D. (2006) FGFR1 and WT1 are markers of human prostate cancer progression. BMC Cancer 6, 272

Di Fiore PP, Pierce JH, Kraus MH, Segatto O, King CR, Aaronson SA. ErbB-2 is a potent oncogene when overexpressed in NIH/3T3 cells. Science. 1987 Jul 10;237(4811):178-82.

Doolittle, R. F. et al. Simian sarcoma virus onc gene, v-sis, is derived from the gene (or genes) encoding a platelet-derived growth factor. Science 221, 275–277 (1983)

Donnem, T., Al-Shibli, K., Al-Saad, S., Busund, L. T. and Bremnes, R. M. (2009) Prognostic impact of fibroblast growth factor 2 in non-small cell lung cancer: coexpression with VEGFR-3 and PDGF-B predicts poor survival. J. Thorac. Oncol. 4, 578-585

Douwes Dekker, P. B., Kuipers-Dijkshoorn, N. J., Baelde, H. J., van der Mey, A. G., Hogendoorn, P. C. and Cornelisse, C. J. (2007) Basic fibroblast growth factor and fibroblastic growth factor receptor-1 may contribute to head and neck paraganglioma development by an autocrine or paracrine mechanism. Hum. Pathol. 38, 79-85

Druker, B. J. et al. Efficacy and safety of a specific inhibitor of the BCR–ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med. 344, 1031–1037 (2001)

Dutt A, Ramos AH, Hammerman PS, Mermel C, Cho J, Sharifnia T, Chande A, Tanaka KE, Stransky N, Greulich H, Gray NS, Meyerson M. Inhibitor-sensitive

195 FGFR1 amplification in human non-small cell lung cancer. PLoS One. 2011;6(6):e20351.

Eckhart, W., Hutchinson, M. A. & Hunter, T. An activity phosphorylating tyrosine in polyoma T antigen immunoprecipitates. Cell 18, 925–933 (1979)

Edwards, J., Krishna, N. S., Witton, C. J. and Bartlett, J. M. (2003) Gene amplifications associated with the development of hormone-resistant prostate cancer. Clin. Cancer Res. 9, 5271-5281

Eron, L. & Block, R. Mechanism of initiation and repression of in vitro transcription of the lac operon of Escherichia coli. Proc. Natl Acad. Sci. USA 68, 1828–1832

Elbauomy, E. S., Green, A. R., Lambros, M. B., Turner, N. C., Grainge, M. J., Powe, D., Ellis, I. O. and Reis-Filho, J. S. (2007) FGFR1 amplification in breast carcinomas: a chromogenic in situ hybridisation analysis. Breast Cancer Res. 9, R23

Esteller, M. Epigenetic gene silencing in cancer: the DNA hypermethylome. Hum Mol Genet 16 Spec No 1, R50-59 (2007).

Eswarakumar, V. P., Lax, I. & Schlessinger, J. Cellular signaling by fibroblast growth factor receptors. Cytokine Growth Factor Rev. 16, 139–149 (2005).

D. Fabbro, P.W. Manley. Su-6668. SUGEN. Curr Opin Investig Drugs, 2 (8) (2001), pp. 1142–1148

196 Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917–921 (2005).

Fischer, H., Taylor, N., Allerstorfer, S., Grusch, M., Sonvilla, G., Holzmann, K., Setinek, U., Elbling, L., Cantonati, H. and Grasl-Kraupp, B. (2008) Fibroblast growth factor receptor-mediated signals contribute to the malignant phenotype of non-small cell lung cancer cells: therapeutic implications and synergism with epidermal growth factor receptor inhibition. Mol. Cancer Ther. 7, 3408-3419

J Folkman. Tumor angiogenesis. J.F Holland, R.C Bast, D.L Morton, E Frei, D.W Kufe, R.R Weichselbaum (Eds.), Cancer Medicine, Williams and Wilkins, Baltimore, MD (1997) 181–204.pp

Forozan, F., Veldman, R., Ammerman, C. A., Parsa, N. Z., Kallioniemi, A., Kallioniemi, O. P. and Ethier, S. P. (1999) Molecular cytogenetic analysis of 11 new breast cancer cell lines. Br. J. Cancer 81, 1328-1334

Fournier RE and Ruddle FH (1977b). Microcell-mediated transfer of murine chromosomes into mouse, Chinese hamster, and human somatic cells. Proc Natl Acad Sci U S A 74:319-323.

Freier, K., Schwaenen, C., Sticht, C., Flechtenmacher, C., Muhling, J., Hofele, C., Radlwimmer, B., Lichter, P. and Joos, S. (2007) Recurrent FGFR1 amplification and high FGFR1 protein expression in oral squamous cell carcinoma (OSCC). Oral Oncol. 43, 60-66

Friend SH, Bernards R, Rogelj S, Weinberg RA, Rapaport JM, Albert DM and Dryja TP. A human DNA segment with properties of the gene that predisposes to retinoblastoma and osteosarcoma. Nature 323: 643-646. (1986)

197

Freier, K. et al. Recurrent FGFR1 amplification and high FGFR1 protein expression in oral squamous cell carcinoma (OSCC). Oral Oncol. 43, 60–66 (2007).

Gazin, C., Wajapeyee, N., Gobeil, S., Virbasius, C.M. & Green, M.R. An elaborate pathway required for Ras-mediated epigenetic silencing. Nature 449, 1073-1077 (2007).

Gelsi-Boyer, V., Orsetti, B., Cervera, N., Finetti, P., Sircoulomb, F., Rouge, C., Lasorsa, L., Letessier, A., Ginestier, C. and Monville, F. (2005) Comprehensive profiling of 8p11-12 amplification in breast cancer. Mol. Cancer Res. 3, 655-667

Giri, D., Ropiquet, F. and Ittmann, M. (1999) Alterations in expression of basic fibroblast growth factor (FGF) 2 and its receptor FGFR-1 in human prostate cancer. Clin. Cancer Res. 5, 1063-1071

Giroux, V., Iovanna, J. & Dagorn, J. C. Probing the human kinome for kinases involved in pancreatic cancer cell survival and gemcitabine resistance. FASEB J. 20, 1982–1991 (2006).

Gogvadze V, Orrenius S, Zhivotovsky B. Mitochondria in cancer cells: what is so special about them? Trends Cell Biol. 2008;18:165, 173.

Goldfarb, M., Shimizu, K., Perucho, M. & Wigler, M. Isolation and preliminary characterization of a human transforming gene from T24 bladder carcinoma cells. Nature 296, 404–409 (1982)

198 Goldstein, M., Meller, I. and Orr-Urtreger, A. (2007) FGFR1 over-expression in primary rhabdomyosarcoma tumors is associated with hypomethylation of a 5' CpG island and abnormal expression of the AKT1, NOG, and BMP4 genes. Genes Chromosomes Cancer 46, 1028-1038

Gong, J., Morishita, A., Kurokohchi, K., Tani, J., Kato, K., Miyoshi, H., Inoue, H., Kobayashi, M., Liu, S. and Murota, M. (2010) Use of protein array to investigate receptor tyrosine kinases activated in gastric cancer. Int. J. Oncol. 36, 101-106

Gorringe, K. L. et al. High-resolution single nucleotide polymorphism array analysis of epithelial ovarian cancer reveals numerous microdeletions and amplifications. Clin. Cancer Res. 13, 4731–4739 (2007).

D.R Green, J.C Reed. Mitochondria and apoptosis. Science, 281, pp. 1309– 1312 (1998)

Greenman C, Stephens P, Smith R, et al. Patterns of somatic mutation in human cancer genomes. Nature 2007;446:153–8.

Greulich H, Pollock PM. Targeting mutant fibroblast growth factor receptors in cancer. Trends Mol Med. 2011 May;17(5):283-92

Gravdal, K., Halvorsen, O. J., Haukaas, S. A. and Akslen, L. A. (2006) Expression of bFGF/FGFR-1 and vascular proliferation related to clinicopathologic features and tumor progress in localized prostate cancer. Virchows Arch. 448, 68-74

Gruel, N., Lucchesi, C., Raynal, V., Rodrigues, M. J., Pierron, G., Goudefroye, R., Cottu, P., Reyal, F., Sastre-Garau, X. and Fourquet, A. (2010) Lobular

199 invasive carcinoma of the breast is a molecular entity distinct from luminal invasive ductal carcinoma. Eur. J. Cancer 46, 2399-2407

Guddo, F., Fontanini, G., Reina, C., Vignola, A. M., Angeletti, A. and Bonsignore, G. (1999) The expression of basic fibroblast growth factor (bFGF) in tumor- associated stromal cells and vessels is inversely correlated with non-small cell lung cancer progression. Hum. Pathol. 30, 788-794

Gupta, P.B., et al. The melanocyte differentiation program predisposes to metastasis after neoplastic transformation. Nat Genet 37, 1047-1054 (2005).

Hadari, H. Kouhara, I. Lax, J. Schlessinger. Binding of Shp2 tyrosine phosphatase to FRS2α is essential for fibroblast growth factor-induced PC12 cell differentiation. Mol. Cell. Biol., 18 (1998), pp. 3966–3973

Hadari, N. Gotoh, H. Kouhara, I. Lax, J. Schlessinger. Critical role for the docking-protein FRS2alpha in FGF receptor-mediated signal transduction pathways. Proc. Natl. Acad. Sci. USA, 98 (2001), pp. 8578–8583

W.C Hahn, C.M Counter, A.S Lundberg, R.L Beijersbgern, M.W Brooks, R.A Weinberg. Creation of human tumor cells with defined genetic elements. Nature, 400 (1999), pp. 464–468

K.A. Hahn, G. Ogilvie, T. Rusk, P. Devauchelle, A. Leblanc, A. Legendre et al. Masitinib is safe and effective for the treatment of canine mast cell tumors J Vet Intern Med, 22 (6) (2008), pp. 1301–1309

200 Hamaguchi, A., Tooyama, I., Yoshiki, T. and Kimura, H. (1995) Demonstration of fibroblast growth factor receptor-I in human prostate by polymerase chain reaction and immunohistochemistry. Prostate 27, 141-147

Hanada M, Delia D, Aiello A, Stadtmauer E, Reed JC. bcl-2 gene hypomethylation and high-level expression in B-cell chronic lymphocytic leukemia. Blood. 1993 Sep 15;82(6):1820-8.

D Hanahan, J Folkman. Patterns and emerging mechanisms of the angiogenic switch during tumorigenesis. Cell, 86 (1996), pp. 353–364

Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000 Jan 7;100(1):57- 70.

Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011 Mar 4;144(5):646-74. Review.

Harris H, Miller OJ, Klein G, Worst P and Tachibana T (1969). Suppression of malignancy by cell fusion. Nature 223: 363-368.

Hase, T., Kawashiri, S., Tanaka, A., Nozaki, S., Noguchi, N., Kato, K., Nakaya, H. and Nakagawa, K. (2006) Correlation of basic fibroblast growth factor expression with the invasion and the prognosis of oral squamous cell carcinoma. J. Oral Pathol. Med. 35, 136-139

Harmer, N. J. et al. Towards a resolution of the stoichiometry of the fibroblast growth factor (FGF)-FGF receptor-heparin complex. J. Mol. Biol. 339, 821–834 (2004).

201 Hart, K. C. et al. Transformation and Stat activation by derivatives of FGFR1, FGFR3, and FGFR4. Oncogene 19, 3309–3320 (2000).

Hilberg, F. et al. BIBF 1120: triple angiokinase inhibitor with sustained receptor blockade and good antitumor efficacy. Cancer Res. 68, 4774–4782 (2008).

S.R. Hubbard, M. Mohammadi, J. Schlessinger. Autoregulatory mechanisms in protein-tyrosine kinases. J. Biol. Chem., 273 (1998), pp. 11987–11990

Haugsten, E. M., A. Wiedlocha, et al. (2010). "Roles of fibroblast growth factor receptors in carcinogenesis." Mol Cancer Res 8(11): 1439-1452.

T. Hunter. The phosphorylation of proteins on tyrosineits role in cell growth and disease. Philos. Trans. R. Soc. Lond. B. Biol. Sci., 353 (1998), pp. 583–605

H. Huynh et al. Brivanib alaninate, a dual inhibitor of vascular endothelial growth factor receptor and fibroblast growth factor receptor tyrosine kinases, induces growth inhibition in mouse models of human hepatocellular carcinoma. Clin. Cancer Res., 14 (2008), pp. 6146–6153

Ihaka, R. & Gentleman, R. R: A language for data analysis and graphics. J. Comput. Graph Stat. 5, 299-314 (1996).

Iida, S., Katoh, O., Tokunaga, A. and Terada, M. (1994) Expression of fibroblast growth factor gene family and its receptor gene family in the human upper gastrointestinal tract. Biochem. Biophys. Res. Commun. 199, 1113-1119

Ippen, K., Miller, J. H., Scaife, J. & Beckwith, J. New controlling element in the lac operon of E. coli. Nature 217, 825–827 (1968)

202

Ikawa S, Fukui M, Ueyama Y, Tamaoki N, Yamamoto T, Toyoshima K. B-raf, a new member of the raf family, is activated by DNA rearrangement. Mol Cell Biol. 1988 Jun;8(6):2651-4.

Ishizuka, T., Tanabe, C., Sakamoto, H., Aoyagi, K., Maekawa, M., Matsukura, N., Tokunaga, A., Tajiri, T., Yoshida, T., Terada, M. and Sasaki, H. (2002) Gene amplification profiling of esophageal squamous cell carcinomas by DNA array CGH. Biochem. Biophys. Res. Commun. 296, 152-155

Jacquemier, J. et al. Expression of the FGFR1 gene in human breast-carcinoma cells. Int. J. Cancer 59, 373–378 (1994).

Ji, H., et al. LKB1 modulates lung cancer differentiation and metastasis. Nature 448, 807-810 (2007).

Kadota, M., Sato, M., Duncan, B., Ooshima, A., Yang, H. H., Diaz-Meyer, N., Gere, S., Kageyama, S., Fukuoka, J., Nagata, T. and Tsukada, K. (2009) Identification of novel gene amplifications in breast cancer and coexistence of gene amplification with an activating mutation of PIK3CA. Cancer Res. 69, 7357- 7365

Kerr, J.F, Wyllie, A.H, Currie, A.R. Apoptosisa basic biological phenomenon with wide-ranging implications in tissue kinetics. Br. J. Cancer, 26 (1972), pp. 239– 257

K.W. Kinzler, B. Vogelstein. Cancer-susceptibility genes. Gatekeepers and caretakers. Nature, 386 (1997), pp. 761–763

203 Kin, M., Sata, M., Ueno, T., Torimura, T., Inuzuka, S., Tsuji, R., Sujaku, K., Sakamoto, M., Sugawara, H., Tamaki, S. and Tanikawa, K. (1997) Basic fibroblast growth factor regulates proliferation and motility of human hepatoma cells by an autocrine mechanism. J. Hepatol. 27, 677-687

Kobrin, M. S., Yamanaka, Y., Friess, H., Lopez, M. E. and Korc, M. (1993) Aberrant expression of type I fibroblast growth factor receptor in human pancreatic adenocarcinomas. Cancer Res. 53, 4741-4744

Kornmann, M., Ishiwata, T., Matsuda, K., Lopez, M. E., Fukahi, K., Asano, G., Beger, H. G. and Korc, M. (2002) IIIc isoform of fibroblast growth factor receptor 1 is overexpressed in human pancreatic cancer and enhances tumorigenicity of hamster ductal cells. Gastroenterology 123, 301-313

Knutsen, T., et al. The interactive online SKY/M-FISH & CGH database and the Entrez cancer chromosomes search database: linkage of chromosomal aberrations with the genome sequence. Genes Chromosomes Cancer 44, 52-64 (2005).

Komarova NL, Lengauer C, Vogelstein B, Nowak MA. Dynamics of genetic instability in sporadic and familial colorectal cancer. Cancer Biol. Ther. 2002;1:685, 692.

Koziczak, M., Holbro, T. & Hynes, N. E. Blocking of FGFR signaling inhibits breast cancer cell proliferation through downregulation of D-type cyclins. Oncogene 23, 3501–3508 (2004).

Klint, P. & Claesson-Welsh, L. Signal transduction by fibroblast growth factor receptors. Frontiers in Bioscience 4, 165–177 (1999).

204

Knights V, Cook SJ. De-regulated FGF receptors as therapeutic targets in cancer. Pharmacol Ther. 2010 Jan;125(1):105-17. Epub 2009 Oct 27.

Knudson AG. Mutation and Cancer: Statistical Study of Retinoblastoma". Proc Natl Acad of Sci 68 (4): 820–3. 1971

R. Kumar, V.B. Knick, S.K. Rudolph, J.H. Johnson, R.M. Crosby, M.C. Crouthamel et al. Pharmacokinetic–pharmacodynamic correlation from mouse to human with pazopanib, a multikinase angiogenesis inhibitor with potent antitumor and antiangiogenic activity. Mol Cancer Ther, 6 (7) (2007), pp. 2012–2021

Kwek, S. S., Roy, R., Zhou, H., Climent, J., Martinez-Climent, J. A., Fridlyand, J. and Albertson, D. G. (2009) Co-amplified genes at 8p12 and 11q13 in breast tumors cooperate with two major pathways in oncogenesis. Oncogene 28, 1892- 1903

Lee AC, Fenster BE, Ito H, Takeda K, Bae NS, Hirai T, Yu ZX, Ferrans VJ, Howard BH, Finkel T. Ras proteins induce senescence by altering the intracellular levels of reactive oxygen species. J. Biol. Chem. 1999;274:7936, 7940.

Letessier, A., Sircoulomb, F., Ginestier, C., Cervera, N., Monville, F., Gelsi- Boyer, V., Esterni, B., Geneix, J., Finetti, P. and Zemmour, C. (2006) Frequency, prognostic impact, and subtype association of 8p12, 8q24, 11q13, 12p13, 17q12, and 20q13 amplifications in breast cancers. BMC Cancer 6, 245

Leung, H. Y., Gullick, W. J. and Lemoine, N. R. (1994) Expression and functional activity of fibroblast growth factors and their receptors in human pancreatic cancer. Int. J. Cancer 59, 667-675

205 Leung, H. Y., Mehta, P., Gray, L. B., Collins, A. T., Robson, C. N. and Neal, D. E. (1997) Keratinocyte growth factor expression in hormone insensitive prostate cancer. Oncogene 15, 1115-1120

Levinson, A. D., Oppermann, H., Levintow, L., Varmus, H. E. & Bishop, J. M. Evidence that the transforming gene of avian sarcoma virus encodes a protein kinase associated with a phosphoprotein. Cell 15, 561–572 (1978)

E.D. Lew et al. The precise sequence of FGF receptor autophosphorylation is kinetically driven and is disrupted by oncogenic mutations. Sci Signal. 2009 Feb 17;2(58):ra6.

Lin, W. M. et al. Modeling genomic diversity and tumor dependency in malignant melanoma. Cancer Res. 68, 664–673 (2008).

Liu, Z., Ishiwata, T., Zhou, S., Maier, S., Henne-Bruns, D., Korc, M., Bachem, M. and Kornmann, M. (2007) Human fibroblast growth factor receptor 1-IIIb is a functional fibroblast growth factor receptor expressed in the pancreas and involved in proliferation and movement of pancreatic ductal cells. Pancreas 35, 147-157

Loeb, L.A. A mutator phenotype in cancer. Cancer Res 61, 3230-3239 (2001).

Lynch, T. J. et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 350, 2129–2139 (2004)

Scott W. Lowe, Enrique Cepero & Gerard Evan Intrinsic tumour suppression Nature 432, 307−315 (18 November 2004)

J. Luo, N.L. Solimini, S.J. Elledge. Principles of cancer therapy: Oncogene and

206 non-oncogene addiction. Cell, 136 (2009), pp. 823–837

Luo J, Emanuele MJ, Li D, Creighton CJ, Schlabach MR, Westbrook TF, Wong KK, Elledge SJ. A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell. 2009 May 29;137(5):835-48.

Lundberg AS, Weinberg RA. Control of the cell cycle and apoptosis. Eur J Cancer. 1999 Dec;35(14):1886-94.

MacKeigan, J. P., Murphy, L. O. & Blenis, J. Sensitized RNAi screen of human kinases and phosphatases identifies new regulators of apoptosis and chemoresistance. Nature Cell Biol. 7, 591–600 (2005).

Machida, S. et al. Inhibition of peritoneal dissemination of ovarian cancer by tyrosine kinase receptor inhibitor SU6668 (TSU-68). Int. J. Cancer 114, 224–229 (2005).

Mardis, E.R. & Wilson, R.K. Cancer genome sequencing: a review. Hum Mol Genet 18, R163-168 (2009).

Marek, L., Ware, K. E., Fritzsche, A., Hercule, P., Helton, W. R., Smith, J. E., McDermott, L. A., Coldren, C. D., Nemenoff, R. A. and Merrick, D. T. (2009) Fibroblast growth factor (FGF) and FGF receptor-mediated autocrine signaling in non-small-cell lung cancer cells. Mol. Pharmacol. 75, 196-207

Marzioni, D., Lorenzi, T., Mazzucchelli, R., Capparuccia, L., Morroni, M., Fiorini, R., Bracalenti, C., Catalano, A., David, G. and Castellucci, M. (2009) Expression of basic fibroblast growth factor, its receptors and syndecans in bladder cancer. Int. J. Immunopathol. Pharmacol. 22, 627-638

207 Matsui, J. et al. E7080, a novel inhibitor that targets multiple kinases, has potent antitumor activities against stem cell factor producing human small cell lung cancer H146, based on angiogenesis inhibition. Int. J. Cancer 122, 664–671 (2008).

L.A. McDermott, M. Simcox, B. Higgins, T. Nevins, K. Kolinsky, M. Smith et al. RO4383596, an orally active KDR, FGFR, and PDGFR inhibitor: synthesis and biological evaluation. Bioorg Med Chem, 13 (16) (2005), pp. 4835–4841

Mehenni, H., et al. LKB1 interacts with and phosphorylates PTEN: a functional link between two proteins involved in cancer predisposing syndromes. Hum Mol Genet 14, 2209-2219 (2005).

Meacham, C.E., Ho, E.E., Dubrovsky, E., Gertler, F.B. & Hemann, M.T. In vivo RNAi screening identifies regulators of actin dynamics as key determinants of lymphoma progression. Nat Genet 41, 1133-1137 (2009).

F. Michor, Y. Iwasa, M.A. Nowak. Dynamics of cancer progression. Nat. Rev. Cancer, 4 (2004), pp. 197–205

Missiaglia, E. et al. Genomic imbalances in rhabdomyosarcoma cell lines affect expression of genes frequently altered in primary tumors: an approach to identify candidate genes involved in tumor development. Genes Chromosom. Cancer 48, 455–467 (2009).

Mohammadi M, Dikic I, Sorokin A, Burgess WH, Jaye M, Schlessinger J. Identification of six novel autophosphorylation sites on fibroblast growth factor receptor 1 and elucidation of their importance in receptor activation and signal transduction. Mol Cell Biol. 1996 Mar;16(3):977-89.

208

Mohammadi, M. et al. Crystal structure of an angiogenesis inhibitor bound to the FGF receptor tyrosine kinase domain. EMBO J. 17, 5896–5904 (1998).

Mohammadi, M., Olsen, S. K. & Ibrahimi, O. A. Structural basis for fibroblast growth factor receptor activation. Cytokine Growth Factor Rev. 16, 107–137 (2005).

Morgan-Lappe, S. et al. RNAi-based screening of the human kinome identifies Akt-cooperating kinases: a new approach to designing efficacious multitargeted kinase inhibitors. Oncogene 25, 1340–1348 (2006).

Morrison, R. S., Yamaguchi, F., Saya, H., Bruner, J. M., Yahanda, A. M., Donehower, L. A. and Berger, M. (1994) Basic fibroblast growth factor and fibroblast growth factor receptor I are implicated in the growth of human astrocytomas. J. Neurooncol. 18, 207-216

Motoda, N., Matsuda, Y., Onda, M., Ishiwata, T., Uchida, E. and Naito, Z. (2011) Overexpression of fibroblast growth factor receptor 4 in high-grade pancreatic intraepithelial neoplasia and pancreatic ductal adenocarcinoma. Int. J. Oncol. 38, 133-143

Motyka, B., et al. Mannose 6-phosphate/insulin-like growth factor II receptor is a death receptor for granzyme B during cytotoxic T cell-induced apoptosis. Cell 103, 491-500 (2000).

Murakami YS, Brothman AR, Leach RJ and White RL (1995). Suppression of malignant phenotype in a human prostate cancer cell line by fragments of normal chromosomal region 17q. Cancer Res 55: 3389-3394.

209 Murakami Y, Nobukuni T, Tamura K, Maruyama T, Sekiya T, Arai Y, Gomyou H, Tanigami A, Ohki M, Cabin D, Frischmeyer P, Hunt P and Reeves RH. Localization of tumor suppressor activity important in nonsmall cell lung carcinoma on chromosome 11q. Proc Natl Acad Sci U S A 95: 8153-8158. (1998a).

Murakami Y and Sekiya T. Accumulation of genetic alterations and their significance in each primary human cancer and cell line. Mutat Res 400: 421- 437. (1998b)

Murphy, T., Darby, S., Mathers, M. E. and Gnanapragasam, V. J. (2009) Evidence for distinct alterations in the FGF axis in prostate cancer progression to an aggressive clinical phenotype. J. Pathol. 220, 452 460

Myoken, Y., Myoken, Y., Okamoto, T., Sato, J. D., Kan, M., McKeehan, W. L., Nakahara, M. and Takada, K. (1996) Immunohistochemical study of overexpression of fibroblast growth factor-1 (FGF-1), FGF-2, and FGF receptor-1 in human malignant salivary gland tumours. J. Pathol. 178, 429-436

Nair, P.N., McArdle, L., Cornell, J., Cohn, S.L. & Stallings, R.L. High-resolution analysis of 3p deletion in neuroblastoma and differential methylation of the SEMA3B tumor suppressor gene. Cancer Genet Cytogenet 174, 100-110 (2007).

Nam, E. & Park, C. Maspin suppresses survival of lung cancer xells through modulation of Akt pathway. Cancer Res Treat 42, 42-47 (2010).

T.C.G.A. Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455 (2008), pp. 1061–1068

Neve, R. M., Chin, K., Fridlyand, J., Yeh, J., Baehner, F. L., Fevr, T., Clark, L.,

210 Bayani, N., Coppe, J. P. and Tong, F. (2006) A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell 10, 515- 527

Nowell P, Hungerford D (November 1960). "A minute chromosome in chronic granulocytic leukemia"m. Science 132 (3438): 1497

O'Gorman, D.B., Costello, M., Weiss, J., Firth, S.M. & Scott, C.D. Decreased insulin-like growth factor-II/mannose 6-phosphate receptor expression enhances tumorigenicity in JEG-3 cells. Cancer Res 59, 5692-5694 (1999).

Ong SH, Guy GR, Hadari YR, Laks S, Gotoh N, Schlessinger J &Lax I. FRS2 proteins recruit intracellular signaling pathways by binding to diverse targets on fibroblast growth factor and nerve growth factor receptors. Molecular and Cellular Biology 20 979–989. 2000

Ong, Y.R. Hadari, N. Gotoh, G.R. Guy, J. Schlessinger, I. Lax. Stimulation of phosphatidylinositol 3-kinase by fibroblast growth factor receptors is mediated by coordinated recruitment of multiple docking proteins. Proc. Natl. Acad. Sci. USA, 98 (2001), pp. 6074–6079

Paddison PJ, Silva JM, Conklin DS, Schlabach M, Li M, Aruleba S, Balija V, O'Shaughnessy A, Gnoj L, Scobie K, Chang K, Westbrook T, Cleary M, Sachidanandam R, McCombie WR, Elledge SJ, Hannon GJ. A resource for large-scale RNA-interference-based screens in mammals. Nature. 2004 Mar 25;428(6981):427-31.

211 Penault-Llorca, F., Bertucci, F., Adelaide, J., Parc, P., Coulier, F., Jacquemier, J., Birnbaum, D. and deLapeyriere, O. (1995) Expression of FGF and FGF receptor genes in human breast cancer. Int. J. Cancer 61, 170-176

Peters, K. G. et al. Point mutation of an FGF receptor abolishes phosphatidylinositol turnover and Ca2+ flux but not mitogenesis. Nature 358, 678–681 (1992).

Pulciani, S. et al. Oncogenes in human tumor cell lines: molecular cloning of a transforming gene from human bladder carcinoma cells. Proc. Natl Acad. Sci. USA 79, 2845–2849 (1982)

Probst, O.C., et al. The mannose 6-phosphate/insulin-like growth factor II receptor restricts the tumourigenicity and invasiveness of squamous cell carcinoma cells. Int J Cancer 124, 2559-2567 (2009).

Qian, J., Niu, J., Li, M., Chiao, P.J. & Tsao, M.S. In vitro modeling of human pancreatic duct epithelial cell transformation defines gene expression changes induced by K-ras oncogenic activation in pancreatic carcinogenesis. Cancer Res 65, 5045-5053 (2005).

Qing, J. et al. Antibody-based targeting of FGFR3 in bladder carcinoma and t(4;14)-positive multiple myeloma in mice. J. Clin. Invest. 119, 1216–1229 (2009).

Radulovich, N., Qian, J.Y. & Tsao, M.S. Human pancreatic duct epithelial cell model for KRAS transformation. Methods Enzymol 439, 1-13 (2008).

V. Rand et al. Sequence survey of receptor tyrosine kinases reveals mutations in glioblastomas. Proc. Natl. Acad. Sci. U.S.A., 102 (2005), pp. 14344–14349

212

Richelda R, Ronchetti D, Baldini L, Cro L, Viggiano L, Marzella R, Rocchi M, Otsuki T, Lombardi L, Maiolo AT and Neri A. A novel chromosomal translocation t(4; 14)(p16.3; q32) in multiple myeloma involves the fibroblast growth-factor receptor 3 gene. Blood 90, 4062-4070. (1997).

Reddy, E. P., Reynolds, R. K., Santos, E. & Barbacid, M. A point mutation is responsible for the activation of transforming properties by the T24 human bladder carcinoma oncogene. Nature 300, 149–152 (1982)

Reis-Filho, J. S. et al. FGFR1 emerges as a potential therapeutic target for lobular breast carcinomas. Clin. Cancer Res. 12, 6652–6662 (2006).

Rhodes, D.R., et al. Molecular concepts analysis links tumors, pathways, mechanisms, and drugs. Neoplasia 9, 443-454 (2007).

Rolny, C., et al. The tumor suppressor semaphorin 3B triggers a prometastatic program mediated by interleukin 8 and the tumor microenvironment. J Exp Med 205, 1155-1171 (2008).

Roumiantsev, S. et al. Distinct stem cell myeloproliferative/T lymphoma syndromes induced by ZNF198–FGFR1 and BCR–FGFR1 fusion genes from 8p11 translocations. Cancer Cell 5, 287–298 (2004).

Rous, P. 1911. A sarcoma of the fowl transmissible by an agent separable from the tumor cells. J. Exp. Med. 13:397–411. doi:10.1084/jem.13.4.397

J.E. Ruhe et al. Genetic alterations in the tyrosine kinase transcriptome of human cancer cell lines. Cancer Res., 67 (2007), pp. 11368–11376

213

Ruta, M., Howk, R., Ricca, G., Drohan, W., Zabelshansky, M., Laureys, G., Barton, D. E., Francke, U., Schlessinger, J., Givol, D. A novel protein tyrosine kinase gene whose expression is modulated during endothelial cell differentiation. Oncogene 3: 9-15, 1988.

Ruta, M., Burgess, W., Givol, D., Epstein, J., Neiger, N., Kaplow, J., Crumley, G., Dionne, C., Jaye, M., Schlessinger, J. Receptor for acidic fibroblast growth factor is related to the tyrosine kinase encoded by the FMS-like gene (FLG). Proc. Nat. Acad. Sci. 86: 8722-8726, 1989.

Sahadevan, K., Darby, S., Leung, H. Y., Mathers, M. E., Robson, C. N. and Gnanapragasam, V. J. (2007) Selective over-expression of fibroblast growth factor receptors 1 and 4 in clinical prostate cancer. J. Pathol. 213, 82-90

Sarker, D. et al. A phase I pharmacokinetic and pharmacodynamic study of TKI258, an oral, multitargeted receptor tyrosine kinase inhibitor in patients with advanced solid tumors. Clin. Cancer Res. 14, 2075–2081 (2008).

Sato, M., et al. Multiple oncogenic changes (K-RAS(V12), p53 knockdown, mutant EGFRs, p16 bypass, telomerase) are not sufficient to confer a full malignant phenotype on human bronchial epithelial cells. Cancer Res 66, 2116- 2128 (2006).

Schaefer, K. L., Wai, D. H., Poremba, C., Korsching, E., van Valen, F., Ozaki, T., Boecker, W. and Dockhorn-Dworniczak, B. (2002) Characterization of the malignant melanoma of soft-parts cell line GG-62 by expression analysis using DNA microarrays. Virchows Arch. 440, 476-484

214 J. Schlessinger. Signal transduction by allosteric receptor oligomerization Trends Biochem. Sci., 13 (1988), pp. 443–447

J. Schlessinger, A.N. Plotnikov, O.A. Ibrahimi, A.V. Eliseenkova, B.K. Yeh, A. Yayon, R.J. Linhardt, M. Mohammadi. Crystal structure of a ternary FGF-FGFR- heparin complex reveals a dual role for heparin in FGFR binding and dimerization. Mol. Cell, 6 (2000), pp. 743–750

Screaton, G.R., et al. Identification and characterization of three members of the human SR family of pre-mRNA splicing factors. Embo J 14, 4336-4349 (1995).

Sherr, C.J. Principles of tumor suppression. Cell 116, 235-246 (2004).

Shih, C. & Weinberg, R. A. Isolation of a transforming sequence from a human bladder carcinoma cell line. Cell 29, 161–169 (1982)

Shin, E. Y., Lee, B. H., Yang, J. H., Shin, K. S., Lee, G. K., Yun, H. Y., Song, Y. J., Park, S. C. and Kim, E. G. (2000) Up-regulation and co-expression of fibroblast growth factor receptors in human gastric cancer. J. Cancer Res. Clin. Oncol. 126, 519-528

Shingu, K., Fujimori, M., Ito, K., Hama, Y., Kasuga, Y., Kobayashi, S., Itoh, N. and Amano, J. (1998) Expression of fibroblast growth factor-2 and fibroblast growth factor receptor-1 in thyroid diseases: difference between neoplasms and hyperplastic lesions. Endocr. J. 45, 35-43

Simon, R., Richter, J., Wagner, U., Fijan, A., Bruderer, J., Schmid, U., Ackermann, D., Maurer, R., Alund, G. and Knonagel, H. (2001) High-throughput

215 tissue microarray analysis of 3p25 (RAF1) and 8p12 (FGFR1) copy number alterations in urinary bladder cancer. Cancer Res. 61, 4514-4519

Silva, J.M., et al. Second-generation shRNA libraries covering the mouse and human genomes. Nat Genet 37, 1281-1288 (2005).

Smith MR, Heidecker G, Rapp UR, Kung HF. Induction of transformation and DNA synthesis after microinjection of raf proteins. Mol Cell Biol. 1990 Jul;10(7):3828-33.

J.W Shay, S Bacchetti. A survey of telomerase activity in human cancer. Eur. J. Cancer, 33 (1997), pp. 787–791

Spielberger, R. et al. Palifermin for oral mucositis after intensive therapy for hematologic cancers. N. Engl. J. Med. 351, 2590–2598 (2004).

Stacey DW, Kung HF. Transformation of NIH 3T3 cells by microinjection of Ha- ras p21 protein. Nature. 1984 Aug 9-15;310(5977):508-11.

Stanbridge EJ (1976). Suppression of malignancy in human cells. Nature 260: 17-20.

Stanbridge EJ (1992). Functional evidence for human tumour suppressor genes: chromosome and molecular genetic studies. Cancer Surv 12: 5-24.

Sugiura, K., Ozawa, S., Kitagawa, Y., Ueda, M. and Kitajima, M. (2007) Co- expression of aFGF and FGFR-1 is predictive of a poor prognosis in patients with esophageal squamous cell carcinoma. Oncol. Rep. 17, 557-564

216 Sun, H. D. et al. Monoclonal antibody antagonists of hypothalamic FGFR1 cause potent but reversible hypophagia and weight loss in rodents and monkeys. Am. J. Physiol. Endocrinol. Metab. 292, 964–976 (2007).

Suzui, H., Takahashi, J. A., Fukumoto, M., Hashimoto, N., Itoh, N., Hatanaka, M. and Kikuchi, H. (1994) Immunohistochemical study for basic fibroblast growth factor and fibroblast growth factor receptor I in pituitary adenomas. Neurosci. Lett. 171, 192-196

Szatrowski TP, Nathan CF. Production of large amounts of hydrogen peroxide by human tumor cells. Cancer Res. 1991;51:794, 798.

Tabin, C. J. et al. Mechanism of activation of a human oncogene. Nature 300, 143–149 (1982)

Takanami, I., Tanaka, F., Hashizume, T., Kikuchi, K., Yamamoto, Y., Yamamoto, T. and Kodaira, S. (1996) The basic fibroblast growth factor and its receptor in pulmonary adenocarcinomas: an investigation of their expression as prognostic markers. Eur. J. Cancer 32A, 1504-1509

Takeda M, Arao T, Yokote H, Komatsu T, Yanagihara K, Sasaki H, et al. AZD2171 shows potent antitumor activity against gastric cancer over-expressing fibroblast growth factor receptor 2/keratinocyte growth factor receptor. Clin Cancer Res 2007;13:3051–7.

Taparowsky, E. et al. Activation of the T24 bladder carcinoma transforming gene is linked to a single amino acid change. Nature 300, 762–765 (1982)

N.A Thornberry, Y Lazebnik. Caspases: enemies within. Science, 281 (1998), pp. 1312–1316

217

Theillet, C., Adelaide, J., Louason, G., Bonnet-Dorion, F., Jacquemier, J., Adnane, J., Longy, M., Katsaros, D., Sismondi, P. and Gaudray, P. (1993) FGFRI and PLAT genes and DNA amplification at 8p12 in breast and ovarian cancers. Genes Chromosomes Cancer 7, 219-226

Tomlinson, D. C., Lamont, F. R., Shnyder, S. D. and Knowles, M. A. (2009) Fibroblast growth factor receptor 1 promotes proliferation and survival via activation of the mitogen-activated protein kinase pathway in bladder cancer. Cancer Res. 69, 4613-4620

Turner, N., Pearson, A., Sharpe, R., Lambros, M., Geyer, F., Lopez-Garcia, M. A., Natrajan, R., Marchio, C., Iorns, E. and Mackay, A. (2010) FGFR1 amplification drives endocrine therapy resistance and is a therapeutic target in breast cancer. Cancer Res. 70, 2085-2094

Trudel, S. et al. The inhibitory anti-FGFR3 antibody, PRO-001, is cytotoxic to t(4;14) multiple myeloma cells. Blood 107, 4039–4046 (2006).

Ueba, T., Takahashi, J. A., Fukumoto, M., Ohta, M., Ito, N., Oda, Y., Kikuchi, H. and Hatanaka, M. (1994) Expression of fibroblast growth factor receptor-1 in human glioma and meningioma tissues. Neurosurgery 34, 221-225

Ushiro, H. & Cohen, S. Identification of phosphotyrosine as a product of epidermal growth factor-activated protein kinase in A-431 cell membranes. J. Biol. Chem. 255, 8363–8365 (1980)

Valve, E. M., Nevalainen, M. T., Nurmi, M. J., Laato, M. K., Martikainen, P. M. and Harkonen, P. L. (2001) Increased expression of FGF-8 isoforms and FGF receptors in human premalignant prostatic intraepithelial neoplasia lesions and prostate cancer. Lab. Invest. 81, 815-826

218 Veltman, J. A., Fridlyand, J., Pejavar, S., Olshen, A. B., Korkola, J. E., DeVries, S., Carroll, P., Kuo, W. L., Pinkel, D. and Albertson, D. (2003) Array-based comparative genomic hybridization for genome-wide screening of DNA copy number in bladder tumors. Cancer Res. 63, 2872-2880

Velu TJ, Beguinot L, Vass WC, Willingham MC, Merlino GT, Pastan I, Lowy DR. Epidermal-growth-factor-dependent transformation by a human EGF receptor proto-oncogene. Science. 1987 Dec 4;238(4832):1408-10.

Bert Vogelstein & Kenneth W Kinzler. Cancer genes and the pathways they control. Nature Medicine 10, 789−799 (2004) von Hansemann, D. Ueber asymmetrische Zelltheilung in epithel Krebsen und deren biologische Bedeutung. Virchow's Arch. Path. Anat. 119, 299 (1890)

Volm, M., Koomagi, R., Mattern, J. and Stammler, G. (1997) Prognostic value of basic fibroblast growth factor and its receptor (FGFR-1) in patients with non-small cell lung carcinomas. Eur. J. Cancer 33, 691-693

Volpert, O. V., Pili, R., Sikder, H. A., Nelius, T., Zaichuk, T., Morris, C., Shiflett, C. B., Devlin, M. K., Conant, K., Alani, R. M. Id1 regulates angiogenesis through transcriptional repression of thrombospondin-1. Cancer Cell 2: 473-483, 2002.

Wajapeyee, N., Serra, R.W., Zhu, X., Mahalingam, M. & Green, M.R. Oncogenic BRAF induces senescence and apoptosis through pathways mediated by the secreted protein IGFBP7. Cell 132, 363-374 (2008).

Warburg O. On the origin of cancer cells. Science. 1956;123:309, 314.

219 Waterfield, M. D. et al. Platelet-derived growth factor is structurally related to the putative transforming protein p28sis of simian sarcoma virus. Nature 304, 35–39 (1983)

Wedge SR, Kendrew J, Hennequin LF, Valentine PJ, Barry ST, Brave SR, et al. AZD2171: a highly potent, orally bioavailable, vascular endothelial growth factor receptor-2 tyrosine kinase inhibitor for the treatment of cancer. Cancer Res 2005;65:4389–400.

Weinberg RA. The molecular basis of oncogenes and tumor suppressor genes. Ann N Y Acad Sci. 1995 Jun 30;758:331-8. Review.

Wienberg, R.A. The Biology of Cancer, (Garland Science, 2006).

Weiss J, et al. Frequent and focal FGFR1 amplification associates with therapeutically tractable FGFR1 dependency in squamous cell lung cancer. Sci Transl Med. 2010 Dec 15;2(62):62ra93.

Westbrook TF, Martin ES, Schlabach MR, Leng Y, Liang AC, Feng B, Zhao JJ, Roberts TM, Mandel G, Hannon GJ, Depinho RA, Chin L, Elledge SJ. A genetic screen for candidate tumor suppressors identifies REST. Cell. 2005 Jun 17;121(6):837-48.

Woolley, P. V., Gollin, S. M., Riskalla, W., Finkelstein, S., Stefanik, D. F., Riskalla, L., Swaney, W. P., Weisenthal, L. and McKenna, R. J. (2000) Cytogenetics, immunostaining for fibroblast growth factors, p53 sequencing, and clinical features of two cases of cystosarcoma phyllodes. Mol. Diagn. 5, 179-190

220 W.E Wright, O.M Pereira-Smith, J.W Shay. Reversible cellular senescenceimplications for immortalization of normal human diploid fibroblasts. Mol. Cell. Biol., 9 (1989), pp. 3088–3092

Whitehurst, A. W. et al. Synthetic lethal screen identification of chemosensitizer loci in cancer cells. Nature 446, 815–819 (2007).

Wu, C. W., J. H. Chen, et al. (2006). "PTPN3 and PTPN4 tyrosine phosphatase expression in human gastric adenocarcinoma." Anticancer Res 26(2B): 1643- 1649.

Xiao, S. et al. FGFR1 is fused with a novel zinc-finger gene, ZNF198, in the t(8;13) leukaemia/lymphoma syndrome. Nature Genet. 18, 84–87 (1998).

Yamaguchi, F., Saya, H., Bruner, J. M. and Morrison, R. S. (1994) Differential expression of two fibroblast growth factor-receptor genes is associated with malignant progression in human astrocytomas. Proc. Natl. Acad. Sci. U.S.A. 91, 484-488

Yasumoto S, Burkhardt AL, Doniger J, DiPaolo JA. Human papillomavirus type 16 DNA-induced malignant transformation of NIH 3T3 cells. J Virol. 1986 Feb;57(2):572-7.

Zender L, Xue W, Zuber J, Semighini CP, Krasnitz A, Ma B, Zender P, Kubicka S, Luk JM, Schirmacher P, McCombie WR, Wigler M, Hicks J, Hannon GJ, Powers S, Lowe SW. An oncogenomics-based in vivo RNAi screen identifies tumor suppressors in liver cancer. Cell. 2008 Nov 28;135(5):852-64. Epub 2008 Nov 13.

221 Zhao, X., Weir, B. A., LaFramboise, T., Lin, M., Beroukhim, R., Garraway, L., Beheshti, J., Lee, J. C., Naoki, K. and Richards, W. G. (2005) Homozygous deletions and chromosome amplifications in human lung carcinomas revealed by single nucleotide polymorphism array analysis. Cancer Res. 65, 5561-5570

Zhang, H. et al. FP-1039 (FGFR1:Fc), a soluble FGFR1 receptor antagonist, inhibits tumor growth and angiogenesis (AACR–NCI–EORTC International Conference, San Francisco, 2007).

222