An Investigation of Atf3, an Adaptive-Response Gene, in Breast Cancer Chemotherapy and Stress Response

An investigation of Atf3, an adaptive-response gene, in breast cancer chemotherapy and stress response

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Swati Jalgaonkar

Graduate Program in Molecular, Cellular and Developmental Biology

The Ohio State University

2016

Dissertation Committee:

Dr. Tsonwin Hai, Advisor

Dr. Sujit Basu

Dr. James Jontes

Dr. Mariano Viapiano

Swati Jalgaonkar

2016

Abstract

Activating Transcription factor 3 (Atf3) is induced by perturbations in the cellular environment (stress). Overwhelming evidence in literature links ATF3 with diverse human diseases associated with inflammation, including cancer. Here, I explore two distinct and novel aspects of ATF3 function: (1) the role of ATF3 in the host—the organism carrying cancer—in the context of breast cancer chemotherapy. We previously reported that ATF3 expression in the host promotes breast cancer metastases in several mouse models and the data are clinically relevant. Given the importance of host-ATF3 in breast cancer progression, and because ATF3 is induced by numerous chemotherapeutic drugs, in this study we further asked if host-ATF3 modulates chemotherapy efficacy in breast cancer. This is an important issue because clinically about one-third of the breast cancer patients display chemoresistance and disease relapse; an emerging concept in the field suggests that host-derived factors contribute to this.

To investigate the role of host-ATF3 in the context of breast cancer chemotherapy, we injected the same cancer cells into the fat pad of wild type (WT) mice and Atf3-null mice

(ATF3 KO), and treated them with paclitaxel (PTX)—a common clinical drug—or saline

(control). Strikingly, we found that in WT mice PTX treatment aggravated breast cancer metastases at a dose that was therapeutic for the primary tumor. By contrast, PTX did not enhance breast cancer metastases in the KO mice. I focused on the analyses of primary tumors to elucidate how the host-ATF3 and PTX may affect cancer cell escape from the

tumors. My analysis reveals that host-ATF3 regulates key features within the primary tumor that may contribute to cancer cell escape—an early step in metastases: (i) decreased pericyte coverage on tumor vasculature (indicative of leaky vasculature), (ii) an increased ability of a sub-set of macrophages to induce cancer cell invasion, and (iii) higher “tumor microenvironment for metastases” (TMEM)—sites for cancer cell escape from primary tumor—in the PTX-treated WT mice. Importantly, the functional consequence is the increased circulating tumor cells in PTX-treated WT mice. Taken together, the data identifies potential ways in which host-ATF3 promotes an overall environment conducive for cancer cell dissemination from the primary tumor.

(2) Development of a novel mouse model to trace stressed cells in vivo. So far there are no animal models that can be used to trace stressed cells in vivo. Since Atf3 transcription is rapidly and transiently induced in numerous cell types by varied stressors, we propose that we can use Atf3 as a “handle” to study cellular fates in diverse stress paradigms.

Briefly, the proposed transgenic mice would contain two alleles: (i) an inducible Cre driven by the entire Atf3 genomic locus (the Atf3-Cre* allele) and (ii) a ROSA26 reporter allele. The rationale is that the Atf3-Cre* mice will express Cre* only in stressed cells. Here, I generated and characterized a transgenic mouse line, Atf3-Cre*, that can be crossed with commercially available ROSA reporter mice (ROSA26-lox-STOP-lox- reporter) to generate the final double transgenic mice for tracing purposes.

Taken together, work presented in this dissertation identifies ATF3 as a key molecule that coordinates host responses to breast cancer chemotherapy and also provides a proof-of- concept for using Atf3 gene as a tool to study cellular stress response in vivo. iii

This work is dedicated to

Aai, Pappa, Sayalee and Gaurav—you are my reservoir of strength.

Acknowledgments

My experiences in graduate school have been both deeply humbling and undeniably educative. Throughout, there have been challenges that I found myself ill-prepared for, and which have tested my ability to adapt. While persevering through these challenges I have relied heavily upon the support of those around me. I take this opportunity to thank the many individuals who have assisted and influenced me during the transformative years in graduate school.

First of all, I am thankful to my advisor, Dr. Tsonwin Hai, for providing me with a rigorous scientific environment that encouraged inquiry and reason. Her constant emphasis on not assuming, planning and asking questions has been instrumental in my development as a researcher and as an individual. Perhaps I am most thankful for the time

Dr Hai invests in training all her pupils to hone their scientific capabilities, their communication and managerial skills. Dr Hai’s extensive training molds her students into multifaceted, independent researchers and for that I am forever grateful.

I am also thankful to the members of my graduate dissertation committee, Dr. Sujit Basu,

Dr. James Jontes, and Dr. Mariano Viapiano, who have always been approachable, pro- active and forthcoming with their time and guidance. My research has benefitted directly through discussions with them and by their diverse expertise.

To quote an African proverb that seems particularly relevant, “If you want to go fast, go alone. If you want to go far, go together.” I could not have come this far without the support of other members of Dr. Hai’s team: Yi Seok Chang, Justin Middleton and

Chinami Ikeda. Yi Seok and I have collaborated for many years and he is also a friend.

We faced numerous challenges together and having him to discuss issues with helped. I have also been influenced by past members of Dr. Hai’s lab—Dr. Erik Zmuda, Dr.

Stephen McConoughey and Dr. Christopher Wolford—who cultivated very high research standards in the laboratory that I have continuously tried to strive for.

Through the uncertainties of graduate school, my friends have played an integral role in helping me retain my sanity. Without mentioning names, I am appreciative of all their delightful company, moral support and encouragement whenever things looked bleak.

Finally, I am unable to adequately express in words my gratitude towards my understanding and accommodating family—my parents, my sister, Sayalee and my husband, Gaurav—who have made untold sacrifices to see me fulfill a personal goal. It seems to me that they have all selflessly rearranged their lives to enable mine. My parents and Sayalee have always voiced an unflinching faith in my abilities that has helped me commit to countless difficult decisions, including the one to move thousands of miles away from home. My loving husband, Gaurav has been fully supportive and encouraging of my ambitions, sans which it would have been impossible for me to advance both my professional and personal goals. His giving nature inspires me daily and I am a better person because of him. I am forever obliged to you all.

Vita

September 15th 1984 ...... Born—India

2003...... Higher Secondary Education (ISC Board),

G.D. Birla Centre for Education, India

August 2003 - May 2008 ...... 5 years Integrated M.Sc. Biotechnology,

Institute of Bioinformatics and

Biotechnology, University of Pune, India

September 2008 – April 2016 ...... Graduate Research Associate, Molecular

Cellular and Developmental Biology,

Department of Biological Chemistry and

Pharmacology, The Ohio State University,

USA

Publications

1. Chris C Wolford, Stephen J McConoughey, Swati P Jalgaonkar, Marino Leon, Anand S Merchant, Johnna L Dominick, Xin Yin, Yiseok Chang, Erik J Zmuda, Sandra A O’Toole, Ewan KA Millar, Stephanie L Roller, Charles L Shapiro, Michael C Ostrowski, Robert L Sutherland, Tsonwin Hai. Transcription factor ATF3 links host adaptive response to breast cancer metastasis. Journal of Clinical Investigation 2013; 123 (7), 2893-2906

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2. Tsonwin Hai, Swati Jalgaonkar, Chris C Wolford, Xin Yin. Immunohistochemical Detection of Activating Transcription Factor 3, a Hub of the Cellular Adaptive– Response Network. Methods in Enzymology 2011; 490, 175 (Invited and Peer Reviewed).

3. Nawneet K Kurrey, Swati P Jalgaonkar, Alok V Joglekar, Avinash D Ghanate, Prasad D Chaskar, Rahul Y Doiphode, Sharmila A Bapat. Snail and Slug Mediate Radioresistance and Chemoresistance by Antagonizing p53‐Mediated Apoptosis and Acquiring a Stem‐Like Phenotype in Ovarian Cancer Cells. Stem Cells 2009; 27 (9), 2059-2068.

Fields of Study

Major Field: Molecular, Cellular and Developmental Biology

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Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments ...... v

Vita ...... vii

List of Tables ...... xii

List of Figures ...... xiii

List of abbreviations…………………………...... xv

Chapter 1: Introduction…………………………...... 1

A. A background on ATF3: the key molecule for this dissertation. A.1 The ATF/CREB family of transcription factors…………………………...... 1 A.2 Activating Transcription Factor 3 (ATF3)………………………………….. 4 A.2.1 ATF3: A stress-inducible gene……………………………………. 5 A.2.2 ATF3: Transcriptional activator versus repressor…………………. 6 A.2.3 ATF3: Genomic organization………………………………...…… 7 A.2.4 ATF3: A hub of cellular adaptive-response network…………….... 8 A.2.5 ATF3 in diseases: Immune modulation as a unifying theme….…. 10 A.2.6 ATF3 in cancer……………………………………………….…... 12 A.2.6.1 ATF3 in cancer cells…………………………….……... 12 A.2.6.2 ATF3 in non-cancer host cells………………….……… 16 A.3 Summary…………………………………………………………………… 19

B. A background on breast cancer metastases: issues related to the dissertation. B.1 Breast cancer……………………………………………………………….. 20 B.1.1 Breast cancer: General background……………………….……… 20 B.1.2 Breast cancer: Pathology and heterogeneity…………………...… 21 B.1.3 Breast cancer: Cancer versus non-cancer cells in tumor…….….... 23 B.2 Metastasis: Mechanisms of cancer cell escape from the primary tumor….... 25 B.2.1 The hallmarks of cancer (defined by Hanahan and Weinberg)..…. 25 B.2.2 The invasion-metastatic cascade…………………………………. 25 B.2.3 The role of blood vessels in cancer cell escape…………………... 27 B.2.3.1 Angiogenesis: A historical perspective……………….... 27 B.2.3.2 Vessel morphogenesis and molecular signaling………... 28 B.2.3.3 Characteristics of tumor vasculature: "Leaky"…………. 33 B.2.3.4 Blood vessels: A "highway" for cancer cell escape…..... 34 B.2.4 Tumor-associated macrophages (TAMs) in cancer cell escape.…. 35 B.2.4.1 The tumor microenvironment…………………………... 35 B.2.4.2 TAMs: Relevance to cancer……………………………. 36 B.2.4.3 TAM polarization and location………………………… 37 B.2.4.4 Pro-metastatic functions of TAMs in breast cancer…..... 39 B.3 Breast cancer therapies and challenges…………………….………. 44 B.3.1 Breast cancer chemotherapy………………………..…….. 44 B.3.2 Chemoresistance and the role of TAMs…………….……. 46 B.3.3 Anti-angiogenesis therapy………………………………... 48 B.3.4 Other therapy options…………………………………….. 50 B.4 Summary…………………………………………………………… 51

C. A background on genetic cell tracing. C.1 Cell fate tracing…………………………………………………………….. 52 C.1.1 Historical perspective…………………………………………….. 52 C.1.2 Key experimental strategies……………………………………… 54

C.2 ROSA genetic tracing………………………………………………………. 57 C.2.1 Concept and origin……………………………………………….. 57 C.2.2 ROSA genetic tracing: Applications……………………………... 59 C.2.3 ROSA genetic tracing: Limitations and experimental controls….. 61 C.3 Summary…………………………………………………………………… 62

D. An overview of Chapters 2-4………………………………………….…….. 64 Tables and figures for Chapter 1………………………………………….…….. 66

Chapter 2: ATF3 in the host contributes to paclitaxel-aggravated breast cancer metastases: the role of ATF3 at the primary tumor site……………………………. 79 2.1 Summary……………………………………………………………………. 79 2.2 Introduction…………………………………………………………………. 81 2.3 Materials and methods……………………………………………………… 86 2.4 Results………………………………………………………………………. 95 2.5 Discussion…………………………………………………………………. 105 2.6 Acknowledgement………………………………………………………… 113 Figures for Chapter 2………………………………………………………….. 114

Chapter 3: Tracing stressed cells in vivo: the Atf3-Cre* mice……………………. 133 3.1 Summary…………………………………………………………………... 133 3.2 Introduction………………………………………………………………... 134 3.3 Materials and methods…………………………………………………….. 138 3.4 Results……………………………………………………………………... 147 3.5 Discussion…………………………………………………………………. 153 3.6 Acknowledgement………………………………………………………… 155 Tables and figures for Chapter 3………………………………….…………… 156

Chapter 4: Future perspectives…………………………………………………...… 170

References…………………………………………………………………………….. 176

List of Tables

Table 1.1. ATF3 modulates the expression of inflammatory mediators…………… 66

Table 1.2. ATF3 function in cancer cells: Oncogene versus tumor suppressor……. 67

Table 1.3. A partial list of known regulators of angiogenesis (proteins)…………... 68

Table 1.4. Sub-classifications of the monocyte/macrophage cells of the myeloid lineage………………………………………………………………………………...… 69

Table 3.1. List of primers used with sequences. ………………………………..... 156

Table 3.2. PCR screening of live births from pro-nuclear injection to identify transgenic founders……………………………………………………………………. 157

Table 3.3. Identification of mouse line(s) with germline transgene transmission... 158

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List of Figures

Figure 1.1. A dendrogram of ATF/CREB proteins based on their amino acid sequences in the bZip region…………………………………………………………… 70

Figure 1.2. The genomic organization of mouse Atf3 gene………………………… 71

Figure 1.3. ATF3 as a hub of cellular adaptive-response network………………..... 72

Figure 1.4. ATF3 expression in the stromal immune cells of human melanoma and breast cancer……………………………………………………………………………. 73

Figure 1.5. Architecture of the mammary glands and breast cancer progression….. 74

Figure 1.6. The invasion-metastatic cascade……………………………………….. 75

Figure 1.7. Architecture of normal blood vessel versus tumor blood vessel……….. 76

Figure 1.8. An overview of the different TAM location within the primary tumor…77

Figure 1.9. ROSA genetic tracing method………………………………………….. 78

Figure 2.1. Host-ATF3 contributes to PTX-aggravated breast cancer metastases.....114

Figure 2.2. ATF3 in the host promotes tumor angiogenesis and PTX reduces vessel pericyte coverage in a host-ATF3 dependent manner……………………………….... 115

Figure 2.3. Analysis of immunofluorescent images for microvessel density and pericyte coverage using ImageJ-Fiji…………………………………………………... 117

Figure 2.4. ATF3 in the host promotes a pro-angiogenic subset of TAMs……….. 119

Figure 2.5. Gating strategy for the analyses of TEM numbers in primary tumors

(immunophenotyping)………………………………………………………………… 121

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Figure 2.6. PTX treatment affects the property of WT TEMs……………………. 123

Figure 2.7. Gating strategy and post-sort analysis for tumor-derived TEMs...... 125

Figure 2.8. ATF3 in the host increases the abundance of the TMEM structures in the mouse primary tumors……………………………………………………………….... 127

Figure 2.9. Clinical relevance of ATF3 and TEK (human TIE2) in breast cancer... 129

Figure 2.10. A working model for the mechanisms by which the host-ATF3 contributes to PTX-aggravated breast cancer metastases……………………………………….…. 131

Figure 3.1. Design of the transgenic construct……………………………….….… 159

Figure 3.2. Traditional cloning of mCherry-P2A-CreERT2 into the FNF vector..... 160

Figure 3.3. A schematic for BAC recombineering method to swap Cre*-NF into the

Atf3-BAC………………………………………………………………………….…... 161

Figure 3.4. Screening of recombined Atf3-Cre*-NF BAC clones by PCR….……. 162

Figure 3.5. Removal of Neo to derive the final Atf3-Cre* BAC transgene………. 163

Figure 3.6. Restriction enzyme digestion and field inversion electrophoresis of Atf3-

Cre*………………………………………………………………………………….… 164

Figure 3.7. Estimation of transgene copy number by qualitative PCR……….….... 165

Figure 3.8. Qualitative analysis of transgene expression in LPS-treated mice liver: acute stress paradigm……………………………………………………….……….… 166

Figure 3.9. Co-immunofluorescence for ATF3 and mCherry in LPS-treated liver of line#1……………………………………………………………………………….…. 167

Figure 3.10. Co-immunofluorescence for ATF3 and mCherry in PyMT tumors in line#1: chronic stress paradigm………………………………………….………….… 168

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List of abbreviations

AmpR Ampicillin resistance gene ANG Angiopoietin ANOVA Analysis of Variance AP-1 Activator protein-1 APC Allophycocyanin ATF Activating Transcription Factor Atf3 Activating Transcription Factor-3 gene ATF3 Activating Transcription Factor-3 protein Atf3-IRIS Atf3 Inducible Reporter Indicating Stress BAC Bacterial artificial chromosome bFGF basic fibroblast growth factor β-gal beta-galactosidase BK5 Bovine keratin bZip basic zipper C/EBP CCAAT-enhancer-binding proteins C57BL/6 C57 black 6; inbred laboratory mouse strain; coat color black CAF Cancer-associated fibroblasts cAMP cyclic Adenosine Monophosphate CCL chemokine (C-C motif) ligand CD11b cluster of differentiation molecule 11B (myeloid cell marker) CD31 cluster of differentiation molecule 31 (endothelial marker) CD4 cluster of differentiation molecule 4 (helper T-cell marker)) CD45 cluster of differentiation molecule 45 (hematopoietic marker) CD68 cluster of differentiation molecule 68 (macrophage marker) CD8 cluster of differentiation molecule 8 (cytotoxic T-cell marker)

cDNA complementary deoxyribonucleic acid ChlR Chloramphenicol resistance gene cm centimeter CKO conditional knock-out CLDN1 claudin-1 Cre Causes Recombination (enzyme) Cre* mCherry-P2A-CreERT2-Frt Cre*-NF mCherry-P2A-CreERT2-Frt-Neo-Frt CRE site cAMP response element site CREB cAMP Response element binding protein cRNA complementary ribonucleic acid CSF-1 colony stimulating factor-1 CTC circulating tumor cells CTL cytotoxic T-cell CX3CL chemokine (C-X3-C motif) ligand CXCL chemokine (C-X-C motif) ligand 10 CXCR Chemokine (C-X-C Motif) Receptor DCIS Ductal carcinoma in situ DLL4 Delta-Like Ligand-4 DMEM Dulbecco's Modified Eagle's Medium DNA deoxyribonucleic acid Dpn1 restriction enzyme from Diplococcus pneumoniae G41 E1A adenovirus early region 1A EC endothelial cell ECM extracellular matrix EcoR1 restriction enzyme EDTA ethylene diamine tetra acetic acid EGF Epidermal growth factor EGFR Epidermal growth factor receptor

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Ena/VASP Ena/vasodilator-stimulated phosphoprotein-like ER estrogen receptor ERK extracellular signal-regulated kinase ERT2 Two estrogen ligand- binding domain EST Expressed Sequence Tag f/f floxed gene F1 1st filial F480 macrophage marker FBS fetal bovine serum FGF Fibroblast growth factor FGFR Fibroblast growth factor receptor FITC fluorescein isothiocyanate Flk1 Fetal Liver Kinase 1 Flp flippase enzyme Frt Flp recombinase target FNF Frt-Neo-Frt FSC forward scatter FVB/N inbred laboratory mouse strain; albino GFP Green Fluorescent Protein

H2O2 hydrogen peroxide Her2 human epidermal growth factor receptor H&E hematoxylin and eosin HGF Hepatocyte growth factor HIF-1 Hypoxia-inducible factor-1 HIF-2 Hypoxia-inducible factor-2 (5’Ho)- 5’ PCR arm with homology in ATF3 intro1-exon B area before Atf3 start (3’Ho)- 3’ PCR arm with homology in ATF3 exon E (after Atf3 codon) HR hormone receptor HRP Horseradish peroxidase

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Hsp90 heat shock protein 90 IACUC Institutional Animal Care and Use Committee iCre inducible Cre (enzyme with improved mammalian codon usage) IDC Invasive ductal carcinoma IgG immunoglobulin G IHC immunohistochemistry IL interleukin ILC Invasive lobular carcinoma IM inflammatory monocyte i.p. intra-peritoneal IPA Ingenuity Pathway Analysis IRIS Internal ribosome entry site i.v. intra-venous JNK c-Jun N-terminal kinase KanR kanamycin resistance gene kb kilo base pairs KD knock-down kDa kilo Dalton KO knock-out LCIS Lobular carcinoma in situ LPS lipopolysaccharide LRF-1 Liver regenerative factor-1 LysM myeloid/monocyte/macrophage selective lysosome promoter MAPK mitogen activated protein kinase mM milli molar MMP matrix metalloproteinase MMTV Mouse mammary tumor virus mRNA messenger ribonucleic acid

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MVT-1 cMyc and VEGFA transgene containing murine breast cancer cell line in FVB/N genetic background Neo Neomycin resistance gene Nru1 restriction enzyme ns not significant P/S penicillin/ streptomycin P1 alterrnate Atf3 promoter P2A picorna virus self-cleaving peptide sequence Pac1 restriction enzyme PBS phosphate buffer saline PCR polymerase chain reaction PE phycoerythrin PGK/Em7 phosphoglycerate kinase eukaryotic promoter/EM7 prokaryotic promoter PR progesterone receptor PTX paclitaxel PyMT Polyoma Virus Middle T antigen qPCR quantitative polymerase chain reaction R26R ROSA26 reporter RBC red blood cell RNA ribonucleic acid RNAi ribonucleic acid inhibitor ROSA reverse orientation splice acceptor RT reverse transcription Sal1 restriction enzyme SEM standard error of mean siRNA silencing ribonucleic acid SMA smooth muscle actin SSC side scatter STOP transcriptional stop sequence

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SW102 recombineering bacterial strain SW105 recombineering bacterial strain with L-arabinose inducible flp gene TAF Tumor angiogenesis factor TAM tumor-associated macrophage TBE tris-borate EDTA buffer TBST tris buffer saline tween buffer TEK Tyrosine Endothelial Kinase TEM TIE2 expressing monocyte/macrophage TIC tumor initiating cell TIE2 Tunica Interna Endothelial Cell Kinase-2 TIE2hi TIE2 high expression TLR toll-like receptor TMEM tumor microenvironment of metastasis TNBC triple negative breast cancer TSA Tyramide Signal Amplification TSS transcription start site uPA urokinase-type plasminogen activator UTR untranslated region VEGF vascular endothelial growth factor VEGFR vascular endothelial growth factor receptor WT wild type YFP Yellow Fluorescent Protein

Chapter 1

Introduction

The principal goal of my dissertation is to advance the current knowledge of the physiological role of the protein, Activating Transcription factor 3 (ATF3), in stress biology and in metastatic breast cancer, especially in the context of cancer chemotherapy.

In subsections of this chapter I will introduce three relevant concepts pertaining to my research namely, A) the ATF/CREB family of transcription factors and ATF3, B) breast cancer pathology and the role of the tumor microenvironment in breast cancer metastasis, and C) analyses of cell fate using the ROSA tracing technique. I will conclude each subsection by highlighting its relevance to the overall research presented in this thesis.

Finally, I will conclude Chapter 1 with two hypotheses that I tested in my dissertation work and the rationale behind them. I will present my data in Chapters 2 and 3, and then conclude my dissertation with Chapter 4 on future perspectives.

A. A background on ATF3: the key molecule for this dissertation

A.1 The ATF/CREB family of transcription factors

The Activating Transcription Factor/ cAMP Response Element Binding (ATF/CREB) family of transcription factors collectively refers to a group of basic leucine zipper (bZip) transcription factors that bind to the ATF/CRE consensus site in target gene promoters to regulate their transcription [1-3]. Here, I summarize the pivotal studies that led to defining the ATF/CREB family of transcription factors.

The members of the ATF family of transcription factors were discovered in the late 1980s by researchers trying to identify factor(s) involved in the coordinated regulation of a set of adenoviral genes. The transcription of adenoviral early genes (such as E1B, E2A/B, E3 and E4), requires the protein product of the viral immediate early gene, E1A [4-9].

However, the E1A protein does not directly bind to DNA [7]. Thus, the prevalent hypothesis was that E1A interacted with the target gene promoters via binding partner(s) of cellular (non-viral) origin [10]. The model of cellular cofactors was supported by transfection studies that demonstrate that no other viral protein was required for the E1A- inducibilty of the genes [11, 12]. Additionally, promoter-bashing experiments using reporter assays indicated that the transcription of viral early genes requires a minimal cis- acting DNA element in their promoters [8]. Using this cis-acting DNA element as a probe in DNA-binding assays, several groups were able to detect activities in nuclear extracts.

Three such activities are E2-EF for the E2 promoter, E3F1 for the E3 and E4F1 for the

E4 promoters [13-15]. Interestingly, the core binding sequences for all three of them are identical. Thus, Lee et.al. inferred that the E2-EF, E3F1 and E4-F1 activities were due to the same protein factor [15]. Since deletion of the binding sites resulted in reduction of promoter activity in the reporter assays, they deduced that the protein factor must be a transcriptional activator, and named it Activating Transcription Factor (ATF) [15].

At that time (the mid 1980’s), the conventional dogma was that each cis-acting element

(the DNA binding site) defines a single trans-acting factor [5, 11, 15]. Hai et.al, were the first to purify ATF from nuclear extract by chromatography using DNA-affinity column composed of tandem ATF consensus sequence (5'-TGACGTCA-3',) [16]. To their 2

surprise, they consistently detected not one, but a group of proteins that bound to the ATF consensus site, revealing the existence of a family of proteins. In a follow-up paper that cemented the idea of a transcription factor family, Hai et.al, isolated several cDNA clones using three ATF sites in tandem to probe two gt11 expression libraries [17]. Each of the eight cDNA clones that were studied further (named ATF1 to ATF8) were encoded by a different gene. Structurally, these ATF clones contain a basic leucine zipper (bZip) region that is responsible for their dimerization and binding to DNA. Interestingly, the ATF clones do not share much amino acid sequence homology except within the bZip region.

Even within the bZip region, the homology is limited to the bZip motif; the sequences outside the motif are different. These findings provided the first description of the ATF family of transcription factors and a surprisingly large group of proteins with similar

DNA binding selectivity, against the dogma at that time.

Remarkably, the ATF site is identical to the cAMP response enhancer (CRE) element—a palindromic (5’ TGACGTCA 3’) sequence identified by Montminy et.al. in the cellular

(non-viral), cAMP-responsive promoter of rat somatostatin gene and highly conserved in other cAMP-induced gene promoters [18]. Montminy et.al. isolated the CRE-binding protein (CREB) which binds to the CRE site and regulates transcription of target genes in response to increased cellular cAMP levels [19-23]. Isolation and characterization of a

CREB cDNA clone [24]demonstrated that CREB has a bZip domain for dimerization and

DNA binding, and shares about 70% overall amino acid homology with ATF1. Given these similarities between the ATF proteins and CREB, these distinct proteins are now classified together as the ATF/CREB family of transcription factors. 3

Taken together, the ATF/CREB family of transcription factors was classically defined by the following traits: 1) bind to the ATF/CRE site, 2) contain a bZip DNA-binding domain, and 3) form homodimers or heterodimers with other members within the family

[25]. Today, with a growing understanding of the bZip proteins, it is clear that the

ATF/CREB proteins can dimerize with other bZip proteins (such as c-Jun, JunD) and bind to DNA sites containing variations from their core consensus or sites composed of composite sequences. Thus, a more nuanced perspective for the ATF/CREB family of proteins is that they belong to a super-family of bZip proteins (total 56 in mammalian genome, Fig. 1.1) that can dimerize via the leucine zipper with selective partners and bind to a spectrum of DNA sites with variations from previously defined consensus sequences [1-3, 26]. The names of the proteins (such as ATF, CREB, Fos, Jun, C/EBP,

Maf) reflect the history of discovery, rather than their actual differences or similarities [3,

27].

A.2 Activating Transcription Factor 3 (ATF3)

The Activating Transcription Factor 3 (ATF3), a member of the ATF/CREB family of transcription factors, is central to my dissertation. The ATF3 cDNA clone was first isolated by Dr. Tsonwin Hai, during her post-doctoral research, from a serum-induced

HeLa cell cDNA library, based on its ability to bind to the ATF consensus sequence [16].

A few years later, Hsu et.al, isolated ATF3 cDNA clone from a library derived from regenerating rat livers using subtractive hybridization and named it the Liver

Regeneration Factor-1 (LRF-1) [28].

A.2.1 ATF3: A stress-inducible gene

Since establishing her own laboratory, Dr. Hai has focused on gaining mechanistic insight about the Atf3 gene and dissecting the physiological role of ATF3. The stimuli used to generate the cDNA libraries, from which the ATF3 clones were isolated, induce cell proliferation: serum stimulation and partial heptectomy (to induce liver regeneration). Thus, the early hypothesis was that ATF3 plays a role in proliferation.

However, ATF3 expression was not detected in most cell lines examined (unpublished data) and not in the embryonic rat liver on day 12/13 post-gestation (unpublished data)— a critical time for embryonic liver development and hepatocyte proliferation. These puzzling results led the group to consider the stimuli used in the generation of the libraries from a different perspective: stress.

Partial heptectomy is an injury; serum stimulation can be used as a paradigm for wound healing, since normal cells are not exposed to serum except when the vessels are damaged, resulting in leakage of blood content [29]. Thus, a plausible hypothesis is that

ATF3 is induced by stress signals. Chen et.al. in the laboratory tested this hypothesis using different tissue-injury models and found that the ATF3 mRNA is low in un- stimulated tissues but is high in a variety of stressed tissues such as mechanically injured liver, toxin-treated liver, blood-deprived heart, post-seizure brain [30], and skin with incision wound (data not shown). Thus, they surmised that Atf3 is a “stress-response” gene induced by diverse physiological stresses [1, 2, 26].

A.2.2 ATF3: Transcriptional activator versus repressor

The name ATF implies that ATF3 is a transcriptional activator. However, work from Dr.

Hai’s laboratory established that, contrary to its name, ATF3 functions as a transcriptional repressor, when bound to the ATF sites as a homodimer [31, 32].

Although the molecular mechanisms by which ATF3 represses transcription have not been elucidated, indirect evidence support the notion that it does so by recruiting inhibitory co-factors to the target promoters [31]. In addition to homodimers, ATF3 can form selective heterodimers with other bZip proteins (such as c-Jun and Jun-B) [28].

These ATF3-Jun heterodimers have been demonstrated to activate transcription [28].

Interestingly, the ATF3 heterodimers can bind to DNA sequences with significant variations from the ATF consensus, thus expanding the target genes that they may regulate. Taken together, these studies indicate that ATF3 homodimers repress transcription, whereas ATF3 heterodimers (with ATF2, c-JUN, JUN-B, JUN-D, TAX and others) [33] activate transcription. Thus, depending on the cellular context and the binding partners, ATF3 may act as a transcriptional repressor or activator. Considering the diversity of target genes that can be regulated by ATF3, it is not surprising that ATF3 can either up- or down-regulate them.

A.2.3 ATF3: Genomic organization

The human Atf3 gene maps to chromosome 1q32.3 (chromosome 1 H6 in mouse), and is encoded on the plus strand. The promoter of the human Atf3 gene was originally identified to be the 5’2 kb region upstream of the transcriptional start site (TSS) [34]. It contains the consensus TATA box at the -30 position with respect to TSS, and results in promoter-activity in reporter assays [34]. It also contains an ATF/CRE site in the -90 position and binding sites for various transcription factors such as AP1, NF-kB,

Myc/Max and E2F [34]. This promoter is inducible by serum and subinhibitory concentrations of anisomycin which, in literature, is used to stimulate the stress-inducible

JNK/SAPK signaling pathway [34, 35]. Interestingly, the activation of Atf3 promoter downstream of activated JNK/SAPK pathway fits with the notion that Atf3 [34, 35] is a stress-response gene.

More recently, Miyazaki et.al. have described an alternate promoter, designated as P1, nearly 45kb upstream of the Atf3 gene in humans [36]. The P1 promoter is highly conserved in human and mouse and contains multiple TSS that transcribe mRNAs with different 5’ UTRs. Significantly, the P1 promoter is activated to differing extents when compared to the previously described proximal promoter, depending on the stimuli. For instance, the P1 promoter is more strongly activated by serum, whereas the proximal promoter is more efficiently activated by transforming growth factor – (TGF-) and

HRas. Furthermore, the P1 promoter is constitutively active in several cancer cell lines and accounts for high ATF3 protein levels in these cells. This context-depended differential use of Atf3 promoters, depending on extra-cellular signal, has important 7

implications in modulating cellular stress-response. Although the existence of the P1 promoter can be deduced from the Expressed Sequence Tags (ESTs), others have not been able to detect the full-length transcript derived from this promoter (personal communication to Dr. Hai). Thus, more work is required to address the issues surrounding this upstream promoter.

The mature transcript derived from the original TSS contains 4 introns and 5 exons: exon

A (167 bp), exon B (244 bp), exon C (105-108 bp), exon D (145 bp) and exon E (1395 bp) [34]. It encodes the so-called full length ATF3 protein (22 KD by calculation and ~

26-28 kD on gel). Fig. 1.2 summarizes the genomic organization and the domains encoded by the exons. Thus far, various alternatively spliced isoforms have been identified. Most are deduced from ESTs; only some of them have been demonstrated by the isolation of their corresponding full-length transcripts. However, their potential function and biological relevance are not well elucidated.

A.2.4 ATF3: A hub of cellular adaptive-response network

The induction of Atf3 gene is rapid (within 30 minutes to two hours in most cases) and transient (returns to basal within 1-2 hours) [34]. These, combined with the AUUUA sequences in its 3’ untranslated region (UTR), indicate that the Atf3 gene is an immediate early gene [30]. With the advancement in cRNA microarray, the list of stimuli that can induce ATF3 expression has grown extensively over the years, including injury, infection, oxidative stress, hypoxia, nutrient deficiency, alcohol, chemotherapeutic agents, genotoxic agents and others [3]. One intriguing feature of Atf3 induction is that it

is neither stimuli-specific nor tissue-specific; it is induced in many different cell types, both in vivo and in vitro, by many different extra-cellular and intra-cellular stimuli [1-3,

26]. Studies of signaling transduction have identified various pathways involved in Atf3 induction. For instance, the p38 MAPK pathway is necessary for various signals (such as anisomycin, IL-1β (interleukin 1β), TNFα (tumor necrosis factor α) and H2O2 to induce

Atf3. Prostaglandin induction of Atf3 in the bovine corpus luteum is mediated by the

ERK, JNK and p38 MAPK pathways [37]. Other pathways linked with Atf3 induction include the Smad, Myc, Ras and NF-b pathways [3] (Fig. 1.3).

Given that many different signaling pathways funnel through ATF3, we put forth the idea that ATF3 is a crucial “hub” in the cellular adaptive-response network—integrating, relaying and translating diverse external stimuli to coordinate cell fate [2, 26]. Indeed, a wealth of literature indicates that ATF3 functionally regulates critical cellular processes that determine cell fate such as cell cycle arrest, DNA repair, proliferation, apoptosis, cellular motility, metabolism, cell-cell communication, endoplasmic reticulum stress response, and others [3, 26, 38]. Although at first glance the literature appears to be confusing, indicating opposing roles of ATF3 (such as cell death vs. survival [26]), a detailed study of literature highlights the context-dependent nature of ATF3 function.

Thus, a cogent summary of ATF3’s significance is that ATF3 functions to restore cellular homeostasis that is disturbed upon stress: for instance, a stressed cell might be arrested in the cell cycle or directed to apoptosis depending on the extent of damage sensed by the intra-cellular machinery. We propose that ATF3 plays a pivotal role in sensing the stress and directing subsequent adaptive processes. 9

A.2.5 ATF3 in diseases: Immune modulation as a unifying theme

Maladaptation to stressful stimuli that disturb homeostasis can lead to cellular dysfunction and disease. We posit that Atf3 induction—an immediate primary response to stress in many different cells and tissues—helps the cells to adapt to sudden alterations in its milieu [2, 26, 38]. However, an abnormal ATF3 status in cells or tissues can potentially lead to maladaptation under conditions of stress and therefore, to disease.

Supporting this idea, a growing body of literature implicates ATF3 in many different pathologies, including liver dysfunction [39, 40], diabetes [41-43], obesity[44], renal failure, pulmonary disease [2, 45], metabolic syndrome [46, 47], microbial infection [48-

50], hypospadias [51, 52], sclerosis and neuro-muscular diseases [53], cardio-vascular disease (such as heart failure and atherosclerosis) [54-56] [57], exposure stresses (such as ultra-violet radiation, cigarette smoke, etc.), various cancers [58], and others.

Significantly, the laboratory mice that lack Atf3—the whole body Atf3 knock-out (KO) mice—are viable and behave similarly to the wild type mice, except when subjected to stress (insult or injury). This suggests that ATF3 is non-essential for normal development, but plays a critical role under pathological conditions.

A recurrent underlying theme for ATF3-associated diseases is chronic inflammation [3], which is characterized by local and systemic changes in cytokine profiles and immune cell activation for extended periods of time [59]. Briefly, inflammation refers to the collective physiological response (to an injury) that triggers rapid flux of immune cells, progenitor cells, cytokines and chemokines at the site of injury/insult, in order to initiate and conduct the body’s repair mechanisms. However, chronic or persistent inflammation 10

is deleterious to the organism and results in conditions akin to sepsis [59-67]. A review of literature suggests that a crucial function of ATF3 appears to dampen the initial inflammatory response in order to restore balance, and minimize damage to self [3]. For instance, Glichrist et.al. demonstrated that ATF3 is a negative regulator of the lipopolysaccharide (LPS) activated Toll-Like Receptor 4 (TLR-4) pathway in murine macrophages [45]. TLR4 is a type-I cell membrane receptor that binds to bacterial LPS and other endogenous ligands released by stressed or injured cells [68]. Upon ligand binding, TLR4 is activated and induces the activation of NFkb pathways, resulting in the release of critical proinflammatory cytokines (such as IL6, IL12b). ATF3 directly represses LPS-induced IL6 and IL12b expression in macrophages [45]. ATF3 also represses other pro-inflammatory cytokines (such as tumor necrosis factor alpha, TNFA)

[69] and immune cells recruiting factors (such as CCL4). Thus, transient induction of

ATF3 is believed to be a mechanism to dampen inflammation, which when left unchecked can lead to over inflammation and disease. However, this is not the entire repertoire of ATF3 function. The role of ATF3 is complex, and again likely to be context-dependent. ATF3 has been shown to up-regulate cytokine genes, such as IL6,

TNFA, and CCL2. In addition, ATF3 modulates (up- or down-regulates) cytokine and chemokine gene expression not just in immune cells (including macrophages, dendritic cells, mast cells, and CD4 T cells) but also in non-immune cells, such as fibroblasts, adipocytes, and islet cells. Table 1.1 summarizes some of the literature. Ingenuity

Pathway Analyses (IPA) of global gene expression data showed that five immune-related functions are among the top 10 physiological functions regulated by ATF3 [70]. Taken

together, we propose that a unifying theme of ATF3 function—be it in immune or non- immune cells—is to modulate immune response, and is supported by literature [3, 58, 71,

72].

A.2.6 ATF3 in cancer

In recent years, research in Dr. Hai’s laboratory, including my own dissertation research, has focused on elucidating the multifaceted role of ATF3 in cancer. In this section, I will briefly review the literature that expounds the significance of Atf3 in the cancer context, by discussing the ATF3 functions in cancer cells themselves, and in non-cancer cells.

A.2.6.1 ATF3 in cancer cells

Reports in literature indicate that in cancer cells ATF3 can function both as an oncogene and as a tumor suppressor (Table 1.2). The mouse homolog of Atf3 (named TI-241) was identified by differential hybridization due to its elevated transcript levels in the highly metastatic melanoma cell line B16-F10, compared to the non-metastatic cell line B16-F1

[73, 74]. Functionally, ectopic-expression of ATF3 in the non-metastatic B16-F1cells converted them to be highly metastatic, suggesting that ATF3 is an oncogene [74].

Subsequently, the same group described an oncogenic role of Atf3 in a human colon cancer cell line; knock-down of ATF3 reduced tumor growth in vivo and increased survival [75]. However, research in other laboratories suggested that Atf3 functions as a tumor suppressor. Our laboratory reported that ATF3 suppresses Ras-mediated tumorigenesis by inducing cell cycle arrest and apoptosis [76]. Subsequently, another study has reported that Atf3 may represent a down-regulated tumor suppressor in colon

cancer [77]: knock-down of ATF3 in colon cancer cells promotes subcutaneous growth of tumors and hepatic metastases in nude mice, supporting a tumor suppressor role of Atf3.

Thus, these early studies showed conflicting outcomes of ATF3 expression in cancer and pointed towards a context-dependent nature of ATF3. This conflicting literature is more the norm than the exception for ATF3. Depending on the oncogenic signal, the cell type and the cancer type studied, Atf3 can function as a tumor suppressor or as an oncogene (Table 1.2). To elaborate further on this point, below I summarize a few studies demonstrating a dichotomous role of ATF3 in three cancers:

(i) Prostate cancer: Bandyopadhyay et.al., identified ATF3 through a systematic microarray analysis of prostate cancer cells that were deficient in the tumor suppressor gene Drg1 [78]. They also demonstrated that ATF3 overexpression in the prostate cancer cells significantly enhanced their spontaneous lung metastases in mice, without affecting the primary tumor formation. The authors went on and showed that nuclear expression of

ATF3 in patient prostate tumor specimens positively correlated with metastasis. This was the first study examining the role of ATF3 in cancer using human tumor samples.

However, more recently, Wang et.al. published that ATF3 suppresses prostate cancer onset and development, induced by Pten deletion in the cancer cells, and thus, functions as a tumor suppressor [79].

(ii) Breast cancer: As described above, context-dependency is an explanation for the conflicting data in the field. However, this per se does not provide much insight, since the cell lines and cell types used by different investigators are completely different. Our laboratory addressed this issue by using a series of isogenic human mammary cell lines: 13

from immortalized but not transformed cell (MCF10A) to tumor-forming but not aggressive cells (MCF10AT1k (M-II), MCF20CA1h (M-III)) to aggressive metastatic cell (MCF10CA1a). Since the latter cell lines were sequentially derived from MCF10A, these cells have the same genetic background except the mutations that allow them to acquire different degrees of malignancy. Our laboratory (Xin et al.) found that ATF3 plays a dichotomous role in breast cancer progression, depending on the stage at which it is expressed [80]. In the untransformed MCF10A cells, which represent the early stage of tumorigenesis, ATF3 induces apoptosis. However, in the transformed and aggressive

MCF10CA1a cells, ATF3 expression protects them from apoptosis and increases their motility. Thus, in this model, the degree of malignancy of the cells affects ATF3 functions. In a follow up report, our laboratory provided further molecular understanding of Atf3 in MCF10CA1a cells. Xin et al. showed that ATF3 expression enhances the migration, invasion, and epithelial-to-mesenchymal transition of the cells. Importantly, it increases the tumor initiating cell (TIC, the so-called cancer stem cell) population, as evidenced by mammosphere assay and tumor formation in nude mice after serial dilution

[81]. Xin et al. also presented data suggesting that these oncogenic functions of Atf3 are achieved at least in part by its ability to form a positive feedback loop on TGF-, thus amplifying TGF- signal to induce epithelial-to-mesenchymal transition and TIC features

[81]. This Atf3-to-TGF- feedback loop has an intriguing implication. Atf3 is a stress- inducible gene. Thus, it is possible that stress signals can “jump start” the TGF- pathway via Atf3—in the absence of TGF-. The oncogenic role for Atf3 in breast cancer tumorigenesis was also reported by Wang et.al., who generated transgenic mice

expressing Atf3 driven by the bovine cytokeratin-5 (BK5) promoter [82]. The BK5.ATF3 mice express ATF3 in the ductal epithelial cells and develop metaplastic lesions in the mammary glands that progress to malignancy in the biparous animals [82].

These aforementioned studies were carried out in cultured cells or mouse models. The question then is whether Atf3 has any relevance to human breast cancer progression. Our laboratory addressed this issue by examining human breast tumor specimens and found that the Atf3 gene is amplified in ~80% of the specimens examined (38 out of 48 tumors had more than 2 copies of ATF3 gene) [80]. When analyzed, laser capture microdissection indicated that the amplification is in cancer cells not stromal cells.

Importantly, the ATF3 protein level is elevated in nearly 50% of them (23 out of 48), and among the 23 tumors with elevated ATF3 protein levels, none of them had mutations in the open reading frame of Atf3 [80]. Thus, this is not a case like p53—a tumor suppressor gene that has elevated expression in some cancer cells but is mutated. These data together—gene amplification, elevated expression, and lack of mutation—support an oncogenic role of Atf3 in human breast tumors at the late stage of disease, from which the clinical specimen were taken. We note that this is the only study thus far that has addressed the potential mutations of ATF3 in human tumors. Further study in this regard will provide useful information. In summary, ATF3 has a dichotomous role in mammary epithelial cells, at least in part dependent on the degree of malignancy of the cells. In addition, it is likely to be relevant to human breast cancer.

(iii) Glioma: A recent systematic study identified Atf3 as a tumor suppressor in Bmi1- depleted glioblastoma [83]. Mechanistically, in this model, ATF3 functions downstream of the ER stress pathway and the TGF- pathway to inhibit critical oncogenic networks such as the ERK/MAPK signaling. Interestingly, ATF3 is found to be in the nucleus of the low grade glioblastoma specimen but in the cytoplasm (excluded from the nucleus) of the high grade glioblastoma multiforme, suggesting a potential way to regulate ATF3 function by sub-cellular localization. However, another study published subsequently indicates that Atf3 is oncogenic—it is over-expressed in human glioma compared to the normal human brain tissue and siRNA mediated knock-down of ATF3 in U373MG glioblastoma cells reduces their proliferation and increases apoptosis [84]. Thus, as seen in other cancer types, Atf3 can function as an oncogene or a tumor suppressor in glioma.

A.2.6.2 ATF3 in non-cancer host cells

Cancer cells secrete factors to affect the bone marrows in the host—the organism bearing the tumor—resulting in the recruitment of various immune and mesenchymal precursor cells to the primary tumors. These recruited cells together with the resident host cells and the non-cellular components in the tumors, such as the extracellular matrix (ECM) and soluble factors within the milieu, are collectively referred to as the stroma or the tumor microenvironment. The non-cancer host cells (either the recruited or the resident cells) within the tumor microenvironment are untransformed and include fibroblasts, endothelial cells, immune cells, and adipocytes. They are subjected to various stress signals in the cancer environment such as hypoxia, nutrient depletion, and inflammatory cytokines. These host cells respond to the signals and cross talk with the transformed 16

cancer cells and among themselves. These complicated interactions eventually determine the disease outcome.

Since ATF3 plays a central role in cellular stress response and in cell-cell communication, it is highly plausible that stromal ATF3 expression is functionally significant for cancer development. Indeed, ATF3 has been studied in cultured cells using cell lines that are the types of cells found in the stroma, such as endothelial cells [85-87], fibroblasts [88], and macrophages [50, 89, 90]. In some studies, ATF3 was shown to regulate gene expression or modulate biological processes known to be important in cancer biology, such as up-regulating erythropoietin [88]. However, those studies did not directly address whether ATF3 expression in those stromal cells modulates their ability to affect cancer cell progression. Thus far, only two reports have addressed this issue and I briefly describe them here.

(1) Macrophages/Myeloid Cells: Our laboratory showed that ATF3 expression in the tumor-associated macrophages (TAMs) is functionally important for breast cancer metastases [70]. Using myeloid-selective conditional knock-out of Atf3 (ATF3f/f/LysM-

Cre mice), we demonstrated that cancer cells injected into mice lacking Atf3 in the myeloid compartment had reduced lung metastases compared to that injected into the wild-type mice. Additionally, we demonstrated MMP9 to be a functionally important

ATF3 target gene in macrophages and identified an ATF3-regulated gene signature from mouse TAMs that distinguishes human tumor stroma from distant stroma. Intriguingly, this mouse-derived gene signature can predict clinical outcomes, lending credence to the relevance of our mouse models. Significantly, expression of ATF3 itself in the

mononuclear cells within the human breast tumor stroma is an independent predictor for breast cancer-related death.

(2) Fibroblasts: Buganim et.al. reported that ectopic-expression of ATF3 in cancer- associated fibroblasts (CAFs) promoted tumor growth of co-injected sarcoma cancer cells in mice [91]. Importantly, they identified three potential ATF3 downstream genes—

CLDN1, CXCL12, and RGS4. While ectopic expression of ATF3 repressed CLDN1, it induced the expression of CXCL12 and RSG4 in the CAF cell lines. Moreover, they showed that knock-down of CXCL12 and RSG4 in CAFs reduced the effect of ectopic

ATF3 expression.

I note that the above two studies differ in three important aspects: (a) the cell types

(macrophage versus fibroblast), (b) loss-of-function (in vivo conditional knockout of

ATF3 in macrophage/myeloid cells) versus gain-of-function (ectopic expression of ATF3 in CAF cell lines), and (c) correlation of Atf3 expression with outcome versus no clinical correlate. Nevertheless, these two studies together support a pro-cancer function of stromal Atf3.

Additionally, in the beginning of my dissertation work, I examined ATF3 expression in two human tumor specimens: melanoma and breast cancer. As shown in the Fig. 1.4A and B, ATF3 expression is seen in both the melanoma cells as well as the CD45-positive immune cells. Similarly, in the human breast cancer samples, ATF3 expression is seen in both the cancer epithelial cells (by size) and the CSF1R-positive macrophages (Fig. 1C)

and CD68-positive macrophages [70]. A portion of my data here contributed to our publication in 2013 [70] and earned me a co-authorship.

Taken together, both cancer cells and the host stromal cells express ATF3 during tumor development. In both these cellular compartments, ATF3 plays a critical role in determining disease outcome. When expressed within the cancer cells, ATF3 may function as an oncogene or a tumor suppressor depending on cellular contexts, where the degree of malignancy is at least one key factor. In stromal cells, current literature supports a pro-tumor function of ATF3. It should be noted that the function of ATF3 in cancer cells has been examined more extensively than that in stromal cells. I have analyzed publicly available cRNA array data and found that ATF3 expression correlates with worse clinical outcome in many different cancers. I will include those data in

Chapter 2. However, since the RNAs for array analyses were isolate from the total tumors, those data do no distinguish the expression of ATF3 in cancer versus stroma cells. In addition, they do not indicate whether the protein data would recapitulate the

RNA data.

A.3 Summary

The Atf3 gene encodes a member of the ATF/CREB family of transcription factors. The protein can either repress or activate genes in a context dependent manner.

Overwhelming evidence indicates that Atf3 is a stress-inducible gene. Based on our work and the work of others, we put forth the idea that ATF3 is a “hub” in the cellular adaptive-response network and that a unifying theme of ATF3 function is to modulate immune response. Since maladaptive immune response plays an important role in the 19

development of many diseases, it is not surprising that ATF3 has been demonstrated to play a role in various diseases using mouse models. In cancer, ATF3 plays an important role in both cancer cells and the non-cancer host cells. My dissertation has two main parts: (1) the role of Atf3 in breast cancer, particularly in the context of chemotherapy stress (Chapter 2), and (b) the generation of a transgenic mouse line to study the fate of stressed cells using the ROSA tracing method (Chapter 3). In the rest of this chapter, I will briefly review several cancer biology concepts that are relevant to my work (section

B), the ROSA tracing concept (section C), and then the rationales and hypotheses for my dissertation work (section D).

B. A background on breast cancer metastasis: issues related to the dissertation

B.1 Breast cancer

B.1.1 Breast cancer: General background

In the United States breast cancer is the second most common type of cancer in women after skin cancer and is second to lung cancer in causing cancer-related mortality. In the last few decades, greater awareness, early detection and improved treatment options have significantly decreased breast-cancer related mortality. Statistics from 2015 indicated that the 5-year survival for localized female breast cancer in above 98%. However, the 5-year survival of patients with distant or metastatic breast cancer is only about 25%. Thus, breast cancer metastases continue to be a significant hurdle and several agencies are invested in exploring preventive and curative strategies against it.

B.1.2 Breast cancer: Pathology and heterogeneity

Physiologically the breast or the “mammary gland” is a specialized organ for milk production (lactation) in mammals. It mainly consists of a network of glands that undergo dramatic changes in females during puberty, gestation and menopause, along with connective tissue, fat cells, lymph nodes and blood vessels. The glands are made up of a) milk-producing lobules and b) ducts (thin tubules) that carry the milk from the lobules to the nipples (Fig. 1.5A). The lobules and the ducts are lined with epithelial cells (Fig.

1.5B) that can undergo oncogenic transformation, thereby resulting in carcinomas— cancers of the epithelial cells. A majority of breast cancers consist of carcinomas originating from the epithelial cells of the ducts (called ductal carcinoma) or carcinomas originating from the lobules (called lobular carcinoma). Some women present a mixture of ductal and lobular carcinoma. A very small fraction (about 1%) of breast cancers are sarcomas or cancers that originate in the non-epithelial cells, such as fibroblasts, that are also present within the mammary gland. Another rare but aggressive form of breast cancer found in about 1% of the women is inflammatory breast cancer, in which the cancer cells block the breast lymph nodes, causing the tissue to become swollen and red or “inflamed.”. The focus of my research is on breast carcinoma, which accounts for roughly 90% of the cases. Thus, all my discussion below will be limited to that topic.

Breast carcinomas, both ductal and lobular, can be in situ (localized) or invasive (Fig.

1.5C). Ductal Carcinoma In Situ (DCIS) or Lobular Carcinoma In Situ (LCIS) is the early, pre-invasive stage of disease, in which the proliferating cancer cells are restricted locally within the duct or lobule enclosed by the basement membrane. As the cancer 21

advances, DCIS and LCIS may progress to Invasive Ductal Carcinoma (IDC) or Invasive

Lobular Carcinoma (ILC) respectively. In invasive cancers, the cancer cells breakdown the basement membrane, invade into the surrounding ECM within the tissue, and then spread systemically to distant metastatic sites via the lymphatic or blood circulation.

About 80% of the invasive breast cancers are IDCs.

In addition to the morphological classifications described above, breast cancer cells display marked molecular heterogeneity that is used to distinguish disease subtypes and determine appropriate treatment options. Biopsies from breast tumors are routinely tested for the presence of three markers, namely i) Estrogen Receptor (ER), ii) Progesterone

Receptor (PR), and iii) Human epidermal growth factor receptor 2 (Her2). ER and PR are hormone receptors and are collectively referred to as HR. HR and Her2 are differentially up-regulated in breast carcinomas and have been used as therapeutic targets, since tumors with elevated ER, PR and/or Her2 levels are amenable to hormone therapy or anti-Her2 therapy. Based on the HR and Her2 expression, there are four main molecular subtypes of breast cancer: (1) Luminal A (HR+ Her2-), (2) Luminal B (HR+ Her2+), (3) Her2 enriched (HR- Her2+), and (4) Triple negative (HR- Her2-). Approximately 74% of breast cancers are luminal type A. These are slow growing cancers with mostly a favorable prognosis. About 10% of cancers are luminal type B and these are more aggressive than luminal A. Only about 4% of breast cancers overexpress Her2 and do not express either ER or PR. These Her2 enriched, HR-negative tumors are more aggressive than either luminal A or B. Triple negative breast cancers (TNBCs) constitute roughly

12% of the breast cancer cases. TNBCs express neither HR nor Her2 and typically 22

represent aggressive cancers. Patients with TNBCs are treated with a rigorous chemotherapy regimen, since no targeted therapies for TNBCs exist at present.

The HR and Her2 status is a useful and feasible approach to classify breast carcinomas.

However, it does not capture the molecular complexity of the tumors. Advancement in cDNA microarray and proteomics has greatly impacted our understanding of breast cancer heterogeneity. Those global studies, although not routinely used in the clinics, revealed that the make-up of tumor tissues varies significantly within different sub-types of cancer, with specific gene expression patterns in tumors correlate with cellular origin, outcome and prognosis of the disease [92-103]. Thus, breast cancer is not a single disease and each patient may present unique cancer features [104].

B.1.3 Breast cancer: Cancer versus non-cancer cells in tumor

In addition to the heterogeneous molecular profile of the transformed breast cancer cells themselves, the solid tumors are characterized by striking cellular heterogeneity. The tumor mass is made up of epithelial cancer cells and non-cancer cells from the host— organism carrying the tumor [105]. The non-cancer host cells include immune cells, endothelial cells, fat cells to name just a few. Together, the non-cancer host cells and proteins produced by the tumor cells form the “tumor microenvironment” or the “stroma”

(See section B.2.4.1 for details). In the beginning, the scientific community believed that it is the property of the cancer cells themselves that determines tumor aggressiveness and outcome. However, it has rapidly become obvious that cancer cells do not function in isolation. In fact, they get a lot of their survival cues from their surroundings—the

stroma. Indeed, the cancer cells and the host cells of the stroma communicate bi- directionally and this cross-talk eventually determines disease outcome [105]. For instance, in the breast cancer context, infiltrating leukocytes (white blood cells) often correlate with a poor prognosis and subsets of leukocytes have been demonstrated to be functionally important for breast cancer metastases (See section B.2.4.2).

Our laboratory studies breast cancer metastasis and my dissertation research focuses on the host-dependent, stromal mechanisms at the primary tumor sites that allow cancer cells to escape. I investigated two issues: (a) the blood vessels, which represent a “highway” for cancer cells to spread to the rest of the body (lymphatic system is the other

“highway”); (b) the cancer cells, whose ability to invade the surrounding environment, migrate toward the blood vessels, and exit the vessels are key determinants for cancer cell escape. In the rest of this Chapter, I will provide some background on metastasis and then discuss the above two issues (a and b), with a special emphasis on how macrophages affect blood vessel and cancer cells in the context of cancer cell escape (See section B.2.4 for relevance of macrophage in cancer). Although other stromal cells also affect blood vessels and cancer cells, I did not investigate them in my dissertation work and will thus not discuss them below.

B.2 Metastasis: Mechanisms of cancer cell escape from the primary tumors

B.2.1 The hallmarks of cancer (defined by Hanahan and Weinberg)

In 2000, Hanahan and Weinberg listed six distinct “acquired capabilities” shared by solid tumors that are essential for neoplastic progression [106]: a) self-sufficiency in growth signals, b) insensitivity to growth-inhibition, c) resistance to cell death, d) limitless replicative potential, e) induction of sustained angiogenesis, and f) tissue invasion and metastasis. They called these as the six “hallmarks of cancer.” More recently, they incorporated two additional hallmarks: g) reprogramming energy metabolism, and h) evading immune destruction [107]. Taken together, these eight traits are widely accepted as the rules that govern the transition of normal cells into malignant metastatic cancers.

B.2.2 The invasion-metastatic cascade

Metastasis is the spread of cancer cells from the primary tumor (site of origin) to a secondary distant site in the body. Although tumor cells that escape the primary tumor are disseminated throughout the body, predominantly via the blood circulation, they tend to colonize select organs depending on tumor type or subtype [108-110]. This tropism of cancer cells is partly explained by Steven Paget’s century-old ‘seed and soil’ hypothesis, which states that cancer cells are analogous to the ‘seeds’ that can only grow successfully in ‘soils’ with proper tissue microenvironments. In the case of breast cancers, they preferentially metastasize to the lungs, bone, brain and liver. Importantly, while primary tumors can often be removed surgically, metastatic tumors are frequently inoperable and resistant to chemotherapy. Consequently, a majority of breast cancer associated deaths are due to metastases. 25

Thus, to combat breast cancer, it is essential to inhibit metastasis—a culmination of multiple sequential cellular processes that are collectively referred to as the invasion- metastatic cascade [110-115]. This cascade includes (1) local breakthrough of basement membrane by cancer cells, (2) invasion of cancer cells into the ECM and their migration toward the blood vessels, (3) intravasation or entering of cancer cells into the blood vessels, (4) survival in blood circulation while being transported to distant sites, (5) arrest at distant sites, (6) extravasation or exiting the blood vessel into the parenchyma of the distant tissue, (7) survival and adaptation in the foreign environment to form micrometastases, and (8) proliferation to form macroscopic, clinically detectable metastatic nodules (Fig. 1.6). Each step in the metastatic cascade needs to be completed successfully to achieve metastases and can thus potentially be rate-limiting [114].

The events of the metastatic cascade are regulated by multiple cellular and molecular processes orchestrated by both cancer-cell intrinsic events (such as genetic mutations)

[109, 116-124] and non-cancer host cell events (such as recruitment of immune cells)

[125, 126]; these events work together to form intricate cancer-host interaction loops. For instance, to invade locally into the ECM, single cancer cells have to undergo an epithelial-to-mesenchymal transition whereby the epithelial cancer cells acquire a mesenchymal phenotype: less adherent to each other (thus dissociated from the cancer cell cluster or the cancer nest), and more migratory [127]. However, local cancer cell invasion is assisted by the degradation of the ECM by enzymes such as the matrix metalloproteinases (MMPs) and cathepsins, which are secreted not just by cancer cells but also by various non-cancer host cells (such as macrophages and fibroblasts) [128]. In 26

fact, the non-cancer host cells that form part of the tumor microenvironment play a critical role in the metastatic cascade [126, 129]. Below, I will discuss two issues that my dissertation work focuses on: (a) the blood vessel, and (b) cancer cell invasion, migration, and egress.

B.2.3 The role of blood vessel in cancer cell escape

B.2.3.1 Angiogenesis: A historical perspective

In 1945 Glenn Algire reported the following findings from in vivo transplantable tumor models. (1) The rapid growth of tumor transplants is preceded by and dependent upon the development of a rich vascular supply; (2) the formation of vessels is initiated by the transplanted tumor cells that elicit new blood capillaries from existing vessels of the host.

He also observed that normal tissue transplants did not cause a similar increase in vascularity [130]. This process of blood vessel sprouting from pre-existing vessels (as opposed to de novo vessel synthesis) in rapidly growing solid tumors is termed “tumor angiogenesis” [131] and is essential for adequate supply of nutrients and oxygen to the growing tumor. Supporting this idea, Ian Tannock reported that in a transplanted mammary tumor the mitotic index of cancer cells decreases with increased distance from endothelial cells (ECs) [132]. At that time, the prevailing idea was that tumors secrete a diffusible substance capable of stimulating endothelial proliferation [131]. However, it was not until the 1970s that the pioneering work by Judah Folkman (popularly called the father of angiogenesis research) firmly established this concept and provided a substantial impetus to angiogenesis research [133]. Folkman made three key contributions: (1) He demonstrated that tumors could not grow beyond a 1-2 millimeters in diameter without 27

recruiting new capillaries [134]. (2) He successfully isolated a tumor-derived soluble factor with mitogenic activity for capillary endothelium and termed it Tumor

Angiogenesis Factor (TAF) [135]. (3) He proposed the “anti-angiogenesis” idea, which states that the tumor growth might be arrested at a very small size if the angiogenesis activity of TAF was blocked [136] [137]. Subsequently, several research groups entered the quest to further characterize the angiogenesis factor [138], leading to the discovery of the basic fibroblast growth factor (b-FGF) [139] and the vascular endothelial growth factor (VEGF) [140]—two potent pro-angiogenic proteins [141] [142]. Notable, early clinical trials to block VEGF activity yielded promising results by limiting solid tumor growth in patients [143]. Here, I will briefly review the current mechanistic understanding of tumor angiogenesis.

B.2.3.2 Vessel morphogenesis and molecular signaling

The angiogenic switch: The “angiogenic switch” or initiation of angiogenesis is triggered when the pro-angiogenic factors within the tumor outweigh the anti-angiogenic factors (Table 1.3) [144-147]. This is especially true in rapidly growing tumors with pockets of inadequate vasculature. Such avascular tumor areas become hypoxic due to poor oxygen supply [148] and cannot grow further unless they are vascularized.

Significantly, tumor hypoxia or low oxygen tension is a critical driver of angiogenesis

[149] as it induces the expression of pro-angiogenic factors, such as VEGF, to stimulate

EC migration and proliferation [150]. In addition to hypoxia, other tumor stimuli such as low pH, hypoglycemia, mechanical stress due to proliferating cancer cells, and inflammation can also trigger the angiogenic switch [144]. 28

Angiogenic sprouting process: The typical architecture of a mature blood vessel consists of a lumen surrounded by a triple-layered vessel wall (Fig. 1.7). The innermost layer of a vessel, exposed to the lumen and the blood flow, is made up of the ECs that interact with each other laterally via tight-junctions. The second layer immediately outside the EC is the collagen-rich layer of the basement membrane. The basement membrane in mature vessels is covered by a third layer of supporting perivascular cells such as the pericytes, fibroblasts and vascular smooth muscle cells. Angiogenic sprouting from a mature, dormant vessel is a carefully orchestrated, multistep process involving discrete events such as (1) vascular sprouting, (2) vessel elongation, and (3) vessel maturation [151-153].

Vascular sprouting is initiated in the presence of pro-angiogenic factors and involves degradation of the underlining basement membrane, detachment of perivascular support cells and loosening of the EC junctions to allow EC proliferation and migration. During vessel elongation, the newly budded endothelial sprouts continue to invade into the surrounding tissue along the pro-angiogenic gradient. Subsequently, the ECs trailing the leading front cease to proliferate, become quiescent and adhere tightly to each other to form the lumen of a new capillary tube. Finally, the extending sprouts fuse with each other to form a continuous lumen, resulting in a network of capillaries. The newly formed capillaries are stabilized during vessel maturation by the deposition of a fresh basement membrane and the recruitment of perivascular cells such as pericytes that interact firmly with the ECs [151-153].

Cellular basis of angiogenesis and selection of tip cells: In a dormant capillary, the onset of angiogenesis requires the selection of the endothelial “tip” cell [151]. A tip cell is defined as the specific EC that initiates the pro-angiogenic response and leads the migration front of the sprout. Tip-cell formation needs the degradation of the basement membrane, detachment of perivascular cells and loosening of endothelial cell junctions; together they make the tip cells more accessible and therefore more responsive to the angiogenic gradient. The ECs immediately adjacent to the tip cell in the capillary are called the “stalk” cells. In addition to being spatially distinct, the endothelial tip cells and the stalk cells respond differently to the angiogenic stimuli. Tip cells are non- proliferative cells that extend filopodia in response to the angiogenic gradient and steer through the extra-cellular matrix, while the trailing stalk cells proliferate to enable vessel elongation and the formation of a lumen. Finally, the stalk cells become quiescent, attract pericytes and deposit basement membranes to become stabilized mature vessels.

Molecular basis of angiogenesis: The processes of angiogenic sprouting are coordinated by multiple receptor-ligand interactions [151-153] of which three are particularly essential:

(a) The VEGF/VEGFR2 pathway: Vascular endothelial growth factor-A (VEGFA) is the chief soluble ligand that interacts with VEGFR2 (or Flk1), a receptor tyrosine kinase selectively expressed on ECs [140, 141, 154, 155]. Among multiple angiogenic factors,

VEGFA is the most widely studied. It is a potent mitogenic, chemotactic and pro-survival factor for ECs. Engagement of VEGFR2 (a receptor) by VEGFA causes its dimerization

followed by phosphorylation of the intracellular domain of VEGFR2. This triggers EC proliferation via activation of the intra-cellular ERK/MAPK pathway [156].

(b) The Angiopoietin/Tie (ANG/TIE) pathway: This pathway is required for vascular remodeling and maturation after VEGF-induced angiogenic sprouting since it mediates complex interactions between the ECs and the perivascular cells [155, 157]. Two functionally important angiopoietin ligands are widely studied in both humans and mice:

ANG1 and ANG2. A third ligand, ANG4 in humans or ANG3 in mice, has not been well studied. ANG1 and ANG2 are secreted factors that bind to the receptor tyrosine kinase

TIE2 (or TEK in humans) [158-160]. TIE2 is primarily but not exclusively expressed on the ECs (see section B.2.2.2.2). ANG1, which is constitutively expressed in perivascular cells such as fibroblasts, pericytes, and others, activates TIE2 in the ECs to promote blood vessel maturation and stabilization by enhancing interactions between perivascular cells and endothelium, thereby leading to a more stable vasculature with decreased permeability [158]. In contrast, ANG2, which is highly expressed by tumor ECs, is thought to inhibit TIE2 activity and destabilize blood vessels by causing impaired pericyte coverage and increasing vascular permeability [159]. Thus, the balance between

ANG1 and ANG2 plays a critical role in dictating vascular properties: stabilized and non- permeable vs. destabilized and permeable [161].

ECs in a vessel via trans-membrane ligand and the NOTCH receptor [162, 163].

Engagement of the delta/notch pathway is essential for endothelial tip cell selection

[164]. In response to VEGFA, the tip cells activate VEGFR2 and consequently up- regulate delta-like4 (DLL4) expression. DLL4, a transmembrane NOTCH ligand, then activates NOTCH in neighboring endothelial stalk cells. In a negative feedback loop,

NOTCH activation inhibits VEGFR2 transcription in the stalk cells. Thus, the stalk cells down-regulate VEGFR2 and become less responsive to the sprouting activity of VEGFA.

JAGGED1, another NOTCH ligand with higher expression in the stalk cells, also promotes tip-cell selection by interfering with the reciprocal DLL4 and NOTCH signaling from the stalk cell to the tip cell. As a result, the tip cells continue to express higher levels of VEGFR2 than the stalk cells. It is suggested that following VEGFA exposure, all cells up-regulate DLL4. However, ECs that stochastically express DLL4 more quickly or at higher levels have a competitive advantage to become a tip cell as they activate inhibitory Notch signaling in neighboring cells more effectively. Given the dynamic shuffling of tip-stalk position of ECs during sprouting and the regular exchange of the leading tip cell, DLL4 expression must be dynamically regulated. Thus, the delta/Notch pathways control vessel sprouting and branching by regulating the formation of appropriate numbers of tip cells.

For the sake of brevity, in the above section I presented an accurate but simplistic overview of the molecules directly involved in tumor angiogenesis, with fairly universal agreement in the literature. I did not discuss the contribution of other VEGF ligands, 32

isoforms and receptors (such as VEFGB, VEGFC, VEGFD, VEGFR1, VEGFR3) that are involved in non-endothelial cell types or other processes such as lymphangiogenesis.

Neither did I discuss the FGF family of pro-angiogenic factors, because they are considered less potent than VEGF and are less widely studied in angiogenesis, despite being important factors in cancer biology. It is worth noting that several VEGF pathway inhibitors have been developed as a strategy to target tumor angiogenesis but the development of specific FGF or FGFR inhibitors is not as advanced. This is in part due to significant functional redundancy between the members of the FGF superfamily.

B.2.3.3 Characteristics of tumor vasculature: “Leaky”

Tumor vessels differ significantly from normal, non-pathological blood vessels and are considered to be abnormal [165] (Fig. 1.7) with the following characteristics. (a) Tumor vessels are heterogeneous, serpentine, and tortuous. (b) They are highly branched unlike normal vasculature, which has dichotomous branching. (c) Tumor vessels are often dilated and poorly perfused. (d) They have thin walls characterized by irregular or low pericyte coverage and an absence of basement membrane, which makes the tumor vessels

“leaky” [166]. Owing to their leakiness, fluid escapes and raises the interstitial fluid pressure within the tumor. As a result, blood flow is heterogeneous, and oxygen, nutrients, immune cells and drugs are distributed unevenly. The leaky points of the tumor vessels also act as the “gateways” for cancer cells to escape from the primary tumor.

B.2.3.4 Blood vessels: A “highway” for cancer cell escape

Since the newly formed “abnormal” vessels provide the principle route by which cancer cells exit primary tumor, it is not surprising that angiogenesis is now well appreciated as a critical driver for metastasis. Weidner provided the first piece of correlative evidence supporting this: by immunohistochemical (IHC) analyses of the microvessel densities in

49 invasive breast cancer patient samples, he demonstrated that the microvessel densities within the primary tumors significantly correlated with distant metastases [167, 168].

Subsequently, data from various mouse cancer models supported the concept [169].

Angiogenesis enhances tumor cells intravasation by providing an increased density of immature, highly permeable or “leaky” blood vessels that have little basement membrane and irregular pericyte coverage. Consequently, in theory, treatments to decrease angiogenesis should produce a decrease in the number of tumor cells intravasating into the circulation and a concomitant decrease in metastases. However, anti-angiogenic therapy has not been successful in clinical trials. This apparent paradox turned out to be due to alternative mechanisms that are activated in response to the anti-angiogenic therapy, resulting in angiogenic rebound (among other things) and rendering the treatment ineffective. One stromal cell type that plays an important role in this angiogenic rebound is macrophage [170-177]. Below, I will discuss how macrophage affects angiogenesis and cancer cell behaviors, and then the current literature that explains the above paradox of anti-angiogenic therapy.

B.2.4 Tumor-associated macrophages (TAMs) in cancer cell escape

B.2.4.1 The tumor microenvironment

Thirty years ago Dvorak described primary tumors as “wounds that do not heal” and stated that solid tumors are composed of two distinct but interdependent compartments: the transformed cancer cells themselves and the stroma, in which the cancer cells are dispersed [178]. Specifically, he proposed that cancer cells hijack the wound- healing program of the host organism and co-opt the stroma to become “reactive” and provide supportive environment for cancer maintenance and growth. The tumor stroma is also referred to as the tumor microenvironment and is a dynamic milieu initiated by the cancer cells. It is comprised of all the non-cancer host cells, including the angiogenic vascular cells (endothelial cells, pericytes, and vascular smooth muscle cells), infiltrating immune cells (myeloid cells, lymphocytes), fibroblasts, and others. Additionally, the tumor stroma includes the ECM and proteins produced by the cells within the tumor

[175, 179-182]. Many independent lines of evidence indicate that the primary source for the host cells in the “reactive stroma” is the bone marrow [181]. The cancer cells communicate with the host by secreting soluble factors that educate the bone marrow and recruit bone-marrow derived precursors to the tumor. At the tumor, these precursor cells differentiate into functionally defined cells that cross-talk with the cancer cells and promote their activity, thereby forming a cancer-host feedback loop.

It is now well accepted that the reactive tumor stroma is an integral, cancer-cell extrinsic modulator of neoplastic progression, contributing to the success of the metastatic cascade

[125, 180, 181]. Indeed, the host cells recruited to the tumor have been described as 35

“accessories to the crime” [175]. Amongst the multiple stromal components, the TAM— a key immune cell type in the primary tumor—is particularly relevant to my thesis.

Below, I will discuss our current understanding on how TAM helps cancer cell escape, by focusing on its effect on blood vessels and three cancer cell behaviors: invasion, migration, and intravasation [172]. Although TAMs are known to affect other processes within the primary tumor, such as ECM degradation [183] and immune suppression

[184], I did not investigate them in my dissertation work and will thus not discuss them here.

B.2.4.2 TAMs: Relevance to cancer

TAMs link inflammation to cancer: In 1863, Rudolf Virchow recognized that inflammation is a risk factor for tumorigenesis [185]. Inflammation is characterized with a rapid influx of immune cells at the site of injury/insult accompanied by systemic changes in immune-mediators such as chemokines and cytokines. Virchow noticed immune cell infiltrates in neoplastic tissue and suggested that “lymphoreticular infiltrate” was indicative of the origin of cancer at sites of chronic inflammation. Research over the last two decades has firmly established a link between inflammation and cancer [60-63,

185-187]; it is proposed that inflammatory immune cells including myeloid cells

(macrophages, neutrophils, dendritic cells, granulocytes) and lymphocytes, along with cytokines found within the TME are more likely to contribute to tumor growth and progression than to inhibit it [182, 188-193]. Macrophages are particularly relevant to the work presented in this thesis. They are versatile, mononuclear phagocytes of the myeloid

lineage, and play a central role in innate immunity, host defense and inflammation [170,

194].

Origin of TAMs: TAMs are a major component of the immune infiltrates in most tumors.

In breast cancer patients, high TAM influx into the tumor stroma is primarily associated with poor prognosis [172, 195-197]. TAMs are derived from circulating monocytes, which originate in the bone-marrow and exit the bone-marrow [172] in response to tumor-derived chemokines (such as CCL2, CXCL12 and others) and survival factors such as colony-stimulating factor-1 (CSF-1) [170]. Upon recruitment, monocytes differentiate into macrophages in the TME and make up a population of highly plastic cells that assume distinct functional phenotypes (see polarization below) in response to environmental cues, such as hypoxia, tissue damage and cytokine profiles.

B.2.4.2 TAM polarization and location

Macrophage polarization: The “macrophage polarization” hypothesis posits that depending on the stimuli, macrophages can undergo either classical M1 activation or alternative M2 activation [198, 199]. On the one hand, M1-macrophages are activated by the type-1 cytokines such as interferons and TLR ligands and are considered to be antitumor. These macrophages typically participate in acute inflammation and in the host defense mechanism to clear infection. On the other hand, the alternatively activated M2- macrophages are stimulated by the type-2 cytokines such as IL4 and IL13 and display pro-tumor functions such as tissue remodeling and promote tumor angiogenesis. M2- macrophages are more abundant in tissues with chronic inflammation such as solid

tumors. However, this M1-M2 polarization is an overly simplified view; most TAMs in the tumors exist in intermediate states. Mantovani and colleagues proposed that TAMs are a heterogeneous population of cells in a 2-dimensional continuum flanked by the M1 and M2 phenotype [198, 199]. We suspect that it is likely to be more nuanced than that.

Data in the literature support the notion that different TAM subtypes express their signature genes and display unique functions; thus they can be viewed from a multi- dimensional, rather than a 2-dimensional, manner.

Location of TAMs: Although various key cell-surface markers have been used to delineate subtypes of macrophages or myeloid cells (Table 1.4), it has been challenging to distinguish TAM sub-types due to the significant overlaps in the markers they express.

However, TAMs can be distinguished based on their location within the TME and it is suggested that TAMs in different tumor locations have distinct functions [171, 172, 196,

200]. Studies of mouse and human mammary cancers have shown at least three distinct populations based on location [196] (Fig. 1.8): (1) Stromal TAMs present within the tumor stroma, embedded in the ECM and usually enriched at the invasive front. These

TAMs produce ECM-degrading enzymes, thereby promoting cancer cell invasion and release of growth/survival factors sequestered by the ECM. (2) TAMs in hypoxic/necrotic tumor areas, where they tend to accumulate around the edge of the dying cells. These

TAMs promote tumor neoangiogenesis and immunosuppression. Casazza and colleagues, through studies using various genetically modified mouse lines, reported that exposure of

TAMs to hypoxia is a pre-requisite for acquisition of their pro-tumor phenotype [201].

(3) Perivascular TAMs aligned along the abluminal side of the vessels. These TAMs are implicated to promote cancer cell intravasation.

B.2.4.3 Pro-metastatic functions of TAMs in breast cancer

Evidence for pro-metastatic role of TAMs: Genetic studies in mice have shown that decreased rates of tumor growth and metastasis are associated with decreased TAM numbers. Lin and colleagues crossed a mouse line prone to mammary tumor—the

MMTV-PyMT transgenic tumor model—with mice containing a recessive null mutation in the CSF-1 gene (Csf1op) [202]. CSF1 is an important survival factor for macrophages and thus, lack of CSF1 results in the depletion of TAMs. They observed that the absence of CSF-1 significantly reduced lung metastases. This phenotype can be rescued by ectopically expressing CSF-1 in the mammary epithelium, with concomitant rescue of

TAM infiltration into the primary tumors. Importantly, they also found that TAMs play a crucial role in the ‘‘angiogenic switch” [173, 174] and I will discuss it in more details below. These studies provide strong evidence that TAMs promote malignant tumor progression [197, 203, 204].

TAMs promote tumor angiogenesis: The driving force for TAMs to turn on the

“angiogenic switch” appears to be the lack of oxygen [171, 201]. Upon infiltration

(recruitment) into the tumors, TAMs accumulate preferentially in the poorly vascularized hypoxic regions where they promote angiogenesis [125, 171, 172, 174, 181, 184, 196,

201]. Expression of hypoxia-inducible factor-2 (HIF-2) and HIF-1, observed in TAMs from human breast carcinoma, induce pro-angiogenic factors such as VEGF and bFGF,

thereby stimulating angiogenesis [172, 203, 204]. Additionally, TAMs produce enzymes that regulate the digestion of the ECM—such as MMPs, cathepsins, and urokinase-type plasminogen activator (uPA) [172, 183, 203, 204]—and enhance EC sprouting and migration.

Significantly, a distinct subset of circulating and infiltrating monocytes, known as the

TIE2 expressing macrophages/monocytes (TEMs), was demonstrated to have pro- angiogenic activity [177, 205-210]. They were identified in both human and mouse tumors [206, 209], and share with TAMs in not only some cell-surface markers (like

CD11b, F480 in mouse) but also some type-2 genes such as MMP9, VEGFA and others.

Thus, TEMs can be viewed as a subtype of TAMs. However, several features distinguish them from other TAMs. (a) TEMS express the angiopoietin receptor TIE2, which was previously thought to be endothelial-specific. (b) TEMs preferentially localize around angiogenic tumor blood vessels, are reportedly absent in necrotic regions and have stronger pro-angiogenic activity than the other TAMs. [207, 210] (c) Interestingly,

DePalma and colleagues used TEMs isolated from peripheral blood and found that circulating TEMs induced robust angiogenesis in a mouse Matrigel-plug assay [206].

Thus, unlike TAMs that are believed to be educated by the cancer cells and gain pro- angiogenic capabilities within the tumor environment, the pro-angiogenic activity of

TEMs is inherently present in the circulating TEMs and is not newly acquired after infiltration into the tumor. (d) Systematic gene expression profiling indicates that tumor- derived TEMs express overlapping but distinct genes from TIE2-negative TAMs [211].

For instance, some of the genes up-regulated in TEMs as compared to TAMs are cd163, 40

lyve-1, mrc1, tlr4, while genes down-regulated in TEMs include il1b, nos2, tnf, Il13, ccl5, vegfa and cxcl10. Additionally, the TIE2 ligand, ANG2 further stimulates the pro- angiogenic activity of the TEMs by inducing the expression of the pro-angiogenic lysosomal enzyme cathepsin B (CTSB) [212]. However, since these data are correlative in nature, more investigation is required to elucidate the comprehensive molecular basis for TEM-mediated angiogenesis.

Taken together, overwhelming evidence supports a pro-angiogenic role for TAMs, particularly a subtype of TAMs called TEMs. Below, I briefly discuss the effects of

TAMs on cancer cells in the context of their escape from the primary tumors.

TAMs promote cancer cell invasion, migration and intravasation: In order for cancer cells to escape, having higher density of blood vessel and more permeable vessel is not enough. Another pre-requisite is for the cancer cells to go to the vessel and exit. This entails the ability of cancer cells to dissociate from the other cancer cells, to breakdown the basement membrane, to migrate through the ECM toward the vessel, and to egress the vessel (intravasation). TAMs can promote all these processes. It is not surprising that

TAMs have been called the “jack-of all trades” [195, 213] and the “Swiss army knife”

(reference 5 in [214]) among the stromal cells within the tumor microenvironment. Taken together, TAMs can affect both the blood vessels and cancer cells to promote multiple steps of the metastatic cascade. I will discuss the ability of TAMs to promote cancer cell invasion, migration, and intravasation, because they are directly related to my dissertation work (see Chapter 2).

Invasion and migration—the CSF-EGF loop: TAMs produce a wide range of proteases including matrix metalloproteases (MMP2, MMP7, MMP9) and cathepsins that degrade the ECM, thereby facilitating local tumor cell invasion [197, 203, 204]. TAMs also produce a variety of growth factors that stimulate cancer cell survival, growth and movement (migration); these factors include fibroblast growth factor (FGF), hepatocyte growth factor (HGF), epidermal growth factor (EGF) and others [197, 203, 204]. EGF production by TAMs is particularly important for tumor metastases, especially in the breast cancer context. Wycoff and colleague identified a CSF-EGF paracrine loop between TAMs and cancer cells that enables migration of cancer cells into TAM-rich tumor regions [215]. Upon CSF1 stimulation, TAMs produce EGF that creates a chemotactic gradient for cancer cells that express the EGF receptor, EGFR. These EGFR- positive cancer cells themselves produce CSF1 to cross-talk with TAMs [215]. It is postulated that in mammary tumors the EGF-producing TAMs are situated adjacent to tumor vessels and this paracrine loop is involved in the recruitment of cancer cells to the vessel for their subsequent egress into the blood stream.

Intravasation—the TMEM structure: In an elegant experiment using intravital 2-photon confocal imaging of mouse mammary tumors, Wycoff et al. directly visualized cancer cell intravasation occurring preferentially at the place of blood vessels with macrophages within a one-cell diameter (defined as 20 microns) [216]. They called these macrophages as perivascular. They proposed for the first time that the interactions between macrophages and blood vessels create a microenvironment that favors cancer cell intravasation. Subsequently, Robinson and colleagues defined a tripartite anatomical 42

structure consisting of the endothelium, perivascular macrophages, and Mena- overexpressing cancer cells [217]. Mena is an Ena/VASP family of protein and is selectively up-regulated in invasive cancer cells [218-225]. They called this structure as the “tumor microenvironment of metastasis” (TMEM). Significantly, the higher

TMEM structure in human breast cancers is positively associated with the metastatic risk: when patients with matched tumor grades are analyzed, the higher TMEM density correlates with higher metastasis [217, 226]. Thus, TMEM is a better diagnostic marker than the histological criteria to predict metastasis. Recently, Harney et.al. (a collaborative work from the leading TAM laboratories of Condeelis, Jones, Pollard, and colleagues) published an intriguing paper showing cancer cell intravasation exclusively at TMEM

[227]. Their data support the model that TMEM acts as a landmark for a continuous stream of cancer cells to exit the vessel. Remarkably, they captured transient vascular permeability as evidenced by dextran leakage at the moment of cancer cell intravasation.

Taken together, TAMs affect both the blood vessels and cancer cells; thus, they play a critical role in cancer cell escape from the primary tumors. I studied TAMs in our cancer models, and will describe in Chapter 2 my data for the effect of paclitaxel, a chemotherapeutic agent, on vessel density, permeability, and TMEM structure in primary tumors either with or without the host-ATF3.

B.3 Breast cancer therapies and challenges

The treatment plan for breast cancer depends on the cancer stage at detection and the cancer sub-type. Non-invasive, localized breast cancer is surgically removed followed by radiation therapy, hormone therapy, and/or chemotherapy. Locally invasive breast cancer is usually pre-treated with radiation therapy, chemotherapy, hormone therapy and/or targeted therapy prior to surgery. However, at present there are no therapies in the clinics that can completely cure metastatic breast cancer.

B.3.1 Breast cancer chemotherapy

In the clinics, chemotherapy remains the predominant treatment to manage TNBC and metastatic cancer [228-230]. Chemotherapeutic (anti-neoplastic) drugs preferentially kill rapidly dividing cells and therefore maximally affect the rapidly proliferating cancer cells, leading to tumor reduction. However, chemotherapy also destroys the dividing host cells thus, causing significant toxicity in patients as manifested by dramatic weight loss, hair loss, nausea, fatigue, loss of appetite, and weakened immunity. The commonly used chemotherapeutic drugs for breast cancer include taxanes (paclitaxel, docetaxel) or their derivatives, anthracyclines (doxorubicin, epirubicin), platinum agents (cisplatin, carboplatin), 5-fluorouracil, methotrexate, and gemcitabine. These drugs are administered either as monotherapy or in combination.

Chemotherapy regimens can be classified by the time of their administration relative to the main therapy (surgery): adjuvant versus neoadjuvant. Adjuvant chemotherapy is given after surgical removal of the primary tumor and is used for early stage cancers. It is

believed to lower the risk of cancer recurrence by killing any residual cancer cells remained after surgery. In contrast, neoadjuvant therapy is given prior to surgery and is used as the first line of treatment in the following scenarios: i) inoperable, locally advanced primary tumors, ii) TNBCs, and iii) large, early-stage tumors that need to be down-sized before surgery in order to avoid mastectomy [229]. Although neoadjuvant therapy is initially effective, in many cases the disease continues to progress [231-239].

For instance, Pierga et.al. detected circulating tumor cells (CTCs) after neoadjuvant therapy in ~15% of the breast cancer patients who did not have detectable levels of CTCs in their blood before neoadjuvant therapy [240]. In that study, CTC detection was an independent and reliable prognostic factor for early metastatic relapse. Thus, neoadjuvant therapy was not an effective treatment in that study; in fact, it may have exacerbated the disease progression, since the CTCs were not detected before neoadjuvant therapy. Overwhelming clinical data indicates that neoadjuvant therapy benefits TNBC over non-TNBC patients [236]. However, more than 50% of the neoadjuvant therapy -treated TNBC patients do not achieve pathological complete response and have poor prognosis. To improve the response of TNBC patients to neoadjuvant therapy, combination therapies using anti-angiogenic drugs such as the

VEGF inhibitor, bevacizumab, have been tested. However, the results showed conflicting and variable efficacies, with increased toxicity in some instances. Thus, there is need to identify additional treatment for use in conjunction with neoadjuvant therapy to improve therapy [232-235, 237, 239].

B.3.2 Chemoresistance and the role of TAMs

A significant challenge to the outcome of chemotherapy in clinics is chemoresistance, manifested as primary tumor recurrence [231-237, 239]. Traditionally, it was thought that cancer cell-intrinsic properties allow them to be resistant to the therapy [241]. These include acquired genetic or epigenetic mutations, activation of alternative intra-cellular pathways, expression of multiple drug efflux pumps, and others. However, more and more evidence indicates that cancer cell-extrinsic properties also contribute to chemoresistance [242-259]. That is, the tumor microenvironment plays an important role in the efficacy of cancer chemotherapy. Although an emerging concept, this notion is supported by strong data from preclinical mouse models of cancer. Among the various host cells in the tumor microenvironment, TAMs have emerged as a key cell type that contributes to breast cancer chemoresistance [245, 247-250, 254, 255, 260-262]. In 2006,

Paulus et. al. demonstrated that depletion of TAMs by anti-CSF1 antibody enhanced the efficacy of combination chemotherapy (cyclophosphamide, methotrexate, and 5-fluorouracil) in xenograft models, where human breast cancer cells were injected into immune compromised mice [245]. This is one of the first few reports that connected TAMs to chemoresistance. Since then, more papers confirmed this finding and provided some mechanistic explanations. Below, I describe three potential mechanisms.

(a) DeNardo and colleagues identified a distinct “immune signature” in patient derived breast cancer sections that predicted survival: breast cancer patient with high TAMs, high

CD4 T cells and low CD8 T cells in the primary tumor had greater risk of tumor recurrence and thus lower survival than those with low TAMs, low CD4 T cells and high

CD8 T cells [249]. They further established a causal link between TAMs and poor response to chemotherapy: depletion of TAMs in a transgenic mouse model of breast cancer improved efficacy of paclitaxel. They also observed an inverse correlation between TAM numbers and the number of cytotoxic CD8 T cells in this mouse model, similar to their observations in human tumors. Hence, they proposed that TAMs may limit the therapeutic benefit of paclitaxel in breast cancer, in part, by suppressing the antitumor T cell response (high CD8 T cells). (b) A second proposed mechanism by which

TAMs limit chemotherapy is by releasing lysosomal enzymes, cathepsin B and S, which act as “chemo-protective factors” by inhibiting paclitaxel-induced breast cancer cell death

[250]. (c) A recent paper provided a third mechanism by which a subset of TAMs limits chemotherapy and increases tumor recurrence. Hughes and colleagues observed an increase in the perivascular TIE2hi/CXCR4hi TAMs in mouse mammary tumors in response to paclitaxel; importantly this increase correlated with increased tumor vascularization and re-growth [263]. Pharmacological blockade of CXCR4 reduced the numbers of this subset of TAM, and reduced tumor revascularization and re-growth. One caveat of the CXCR4 blockade experiment is that the pharmacological agent may affect other unintended targets; furthermore, even if it is “specific” to CXCR4, it would inhibit all cells expressing CXCR4, not just the TIE2hi/CXCR4hi TAMs. This is a limitation recognized and accepted by the field. With these caveats, the data support a potential causal relationship between this subset of TAM and chemoresistance. Taken together, a growing body of literature supports the notion that TAMs modulate chemotherapy

efficacy. However, the effect of TAMs is likely to be dependent on the drug as well as cancer types or stage.

B.3.3 Anti-angiogenesis therapy

Folkman’s original “anti-angiogenesis” hypothesis states that inhibition of tumor angiogenesis can limit tumor growth [134, 136, 137, 143]. This, combined with the initially promising pre-clinical results in mouse cancer models, prompted the development of numerous anti-angiogenic drugs and their clinical trials [152, 264, 265].

Most of these targeted strategies impinge upon the VEGFA/VEGFR2 pathway, including monoclonal antibodies against VEGFA (bevacizumab, trade name Avastin), monoclonal antibodies against VEGFR2 (IMC-1C11, IMC-1121B or ramucirumab, CDP791), and inhibitors that block intracellular signaling downstream of VEGFR2, such as the receptor tyrosine kinase inhibitors sunitinib and sorafenib. These drugs are currently undergoing clinical trials not just for breast cancer but other solid tumors such as lung cancer and renal cancer as well. However, these therapeutic drugs have only yielded short-term benefits in breast cancer patients without significantly enhancing overall patient survival beyond a few months [145, 151, 152, 176, 243, 264-280]. It turned out that the

VEGF/VEGFR targeting drugs inhibit primary tumor growth, but paradoxically also promote metastases, thereby shortening survival [265, 270-272, 275, 276, 279].

These disappointing results led to mouse models looking for mechanistic explanation for the paradox. In general, the results suggest that potent angiogenic inhibitors, by excessive vascular pruning to reduce tumor blood supply, promote hypoxia [271, 275, 276, 279].

Tumor hypoxia has many detrimental effects: (1) It is a critical driver of cancer cell invasion; (2) It selects for more malignant metastatic cancer cells that are resistant to hypoxia; (3) It drives neoangiogenesis via recruitment of bone marrow derived pro- angiogenic myeloid cells that are not inhibited by the VEGF-targeted therapy. The following study provided a mechanism by which mice may develop resistance to anti-

VEGF therapies. In healthy mice, the VEGF inhibitors induce chronic inflammation, resulting in elevated levels of G-CSF, SDF1a, IL-6, erythropoietin, osteopontin, and other cytokines that stimulate metastasis and angiogenesis in a VEGF-independent manner

[281]. Thus, under the pressure of anti-VEGF therapies, alternate pathways are selected, thus allowing the tumors to become refractory. This underscores the need to identify strategies to inhibit angiogenesis in a manner other than targeting the VEGF/VEGFR axis. One such strategy is to target the ANG/TIE2 axis [161, 282-286]. These drugs can then be used in combination with the current chemotherapy agents and anti-angiogenic drugs. In addition to developing new drugs, other factors should be considered. Reducing blood supply is the goal of anti-angiogenic therapy. However, too much reduction will lead to hypoxia, which has many deleterious effects as discussed above. Thus, to avoid the unintended consequences, a fine balance must be achieved. Careful modulations of the dose, duration, and time of administration—tailored for the individual patients—will likely to improve the efficacy of any anti-angiogenic drugs.

B.3.4 Other therapy options

Other standard breast cancer treatment options include radiation therapy and hormone therapy. Radiation therapy is treatment with high-energy rays or particles that destroy cancer cells. Radiation to the breast is often given as adjuvant therapy after surgery to remove residual cancer cells and to treat metastatic cancer that has spread throughout the body. Although radiation therapy is effective, it is an aggressive and toxic form of treatment that causes severe side effects, including weakened bones, weight loss, skin changes, and others. Hormone therapy is another form of systemic therapy most often used as an adjuvant therapy to help reduce the risk of the cancer recurrence after surgery.

It can be used as neoadjuvant treatment as well as treatment for recurrent or metastatic cancer. Commonly used hormone therapy (tamoxifen) specifically targets the ER-positive breast cancer cells that require estrogen to grow and survive. Tamoxifen blocks estrogen activity on breast cancer cell by binding to ER, thereby disabling them. Thus, it is used to treat ER+ patients, but not the ER-negative or TNBC patients.

B.4 Summary

Breast cancer is a heterogeneous disease affecting about 30% of women in the US.

Advancement in prevention, early detection, and therapies has dramatically reduced breast cancer death in the country. However, an ongoing challenge is the treatment of metastatic breast cancer—the major cause of cancer-related mortalities. In order to metastasize, cancer cells need to escape from the primary tumors. Two broadly defined features determine the likelihood of this event. (a) Blood vessel: Blood vessel is the major route for cancer cells to escape. Thus, higher vessel density and higher vessel leakiness increase the chance of cancer cell escape. (b) Cancer cell: Cancer cells need to breakdown the basement membrane, invade the surrounding environment (ECM), migrate toward the blood vessels, and exit the vessels. Current literature indicates that

TAMs influence both angiogenesis and cancer cell escape by enhancing all of the above.

Significantly, recent studies in mouse breast cancer models indicate that TAMs also enhance the ability of breast cancer cells to resist chemotherapy. Three potential mechanisms have been identified: (i) TAMs reduce or inhibit the cytotoxic T cells in the tumor (immune suppression); (ii) TAMs inhibit paclitaxel-induced cell death in breast cancer cells (chemoprotection); (iii) TAMs promote angiogenesis in response to chemotherapy, leading to angiogenic rebound and tumor re-growth.

Clearly, further investigation is required to elucidate the mechanisms by which TAMs enhance chemoresistance. These insights will help the development of strategies to improve chemotherapy and address the mechanisms evolved by the cancer cells to counteract treatments. 51

C. A background on genetic cell tracing

C.1 Cell fate tracing

C.1.1 Historical perspective

Robert Hooke, an English physicist, first coined the term “cell” in 1665 to describe the regular, honeycomb-like structures in thin slices of cork that he observed under a simple compound microscope [287]. However, it took another two centuries before Theodor

Schwann and Matthias Schleiden recognized the significance of the cell and formulated

“the cell theory” that i) all living things are made up of cells, and ii) the cell is the basic structural and functional unit of life [288]. Subsequently, Rudolf Virchow added a third crucial tenet to the cell theory: all cells arise from pre-existing cells ("Omnis cellula e cellula") [289]. Since then, scientists have recognized that cells divide into daughter cells, which differentiate into diverse specialized cells with distinct functions in a spatially and temporally regulated manner throughout development to form different tissues.

The developmental biologists employ the strategy of “cell fate tracing” (also referred to as “cell lineage tracing”) to determine which cell(s) give rise to particular structures and map out the cellular origins of tissue. Lineage tracing, thus, is the identification of all progeny of a single cell [290]. Charles Whitman, Edmund Wilson, E.G. Conklin and colleagues, pioneered lineage tracing in the early 1900s through their studies of nematode embryonic development [291]. They tracked the fate of cells after early cleavage, by direct observation under a light microscope, and made the discovery that each cell is developmentally distinct and produces specific cells with pre-defined function. However, direct observation is not always possible, especially in higher organisms. Hence, 52

biologists are continuously developing new tools to allow cell tracing. In most contemporary methods of lineage tracing a single cell is marked by a tracer (such as a dye or fluorescent protein) such that the tracer is passed on to the daughter cell without changing its property, resulting in a population of labeled progeny that are traceable over time. Ideally, the tracer is stable, retained within the cell, and selectively passed onto all progeny (but not the adjacent cells). The first investigator to develop and apply this technique to study cell fate was the embryologist, Walter Vogt in 1929. He loaded a vital dye into small agar pieces and implanted these into gastrulating amphibian embryos to label groups of cells. He thus constructed the fate maps of vertebrate embryos by tracking the labeled cells over time. Subsequently, David Weisblat enabled single cell labeling by microinjection of horseradish peroxidase enzyme [292] or fluorescent peptides [293] as tracer into leech embryonic cells. Presently, fluorescent proteins and their derivatives are the most commonly used tracers, since their discovery by Shimomura in 1962 [294].

As described above, lineage tracing was initially developed as a method to study embryonic development in invertebrates. However, advances in molecular genetics, discovery of fluorescent tracers and innovative imaging capabilities permits the adaptation of this method to investigate more diverse and intriguing biological areas.

Overwhelming evidence in literature indicates that cell fate tracing is a constantly evolving yet proven methodology that has greatly enhanced our knowledge of developmental biology and various pathologies in both invertebrates and vertebrates. In the following section, I will describe the key design considerations that enable the study of cell fates. 53

C.1.2 Key experimental strategies

Several factors need consideration when designing a tracing experiment. These include:

1) the cell(s) of interest, 2) the choice of the tracer/label, 3) the method of incorporation of the tracer, 4) the time of labeling, and 5) the duration of the tracing experiment.

Currently, there are multitudes of tracers and labeling methods available to label most cells of interest. Selection of the appropriate approach depends upon the overall goal of the experiment. Broadly, the goals can be for i) prospective, or ii) retrospective lineage analysis [295].

Prospective lineage analysis: To achieve this goal, it requires a prior knowledge of the cell(s) to be labeled. As an example, specific cells, such as progenitor cells, are labeled at a fixed time and a fixed location in the tissue and subsequently followed over time, often by grafting (transplant of the labeled cell). The cells can be labeled by microinjecting them with various labels, such as radioactive material, lyophilic vital dyes, dextran conjugated dyes, fluorescent dyes, horseradish peroxidase enzyme, fluorescent proteins and their derivatives (such as fusion proteins, photomodulatable fluorescent proteins and others). However, these methods are invasive, and are more useful for short-term than long-term experiments due to the limitation of dye dilution with every cell division. In the current era of tracing, the preferred method is to express stable fluorescent protein encoded by a transgene integrated into the target cell genome for long-term expression of the tracer, a method referred to as genetic tracing.

Retrospective lineage analysis: To achieve this goal, it entails random and spontaneous labeling of the cells, without pre-selecting any particular cell(s) to label at the start of the experiment. In retrospective lineage tracing, the analysis of labeled cells occurs at the end of the experiment and deductions about their interrelationships are made based on statistical observations. Thus, this systematic approach requires a large number of observations for statistical analysis. Random introduction of labels into cells can occur either at a fixed time (spatially random) or at an unspecified time (spatially and temporally random) during the experiment. For instance, in spatially random retrospective analysis, introduction of the labels occurs at a specific time determined by the researcher via inducible systems such as Cre-mediated DNA recombination, X- radiation induced DNA recombination in drosophila, temperature sensitive or hormone responsive controls, or by retroviral infection of cells with DNA tags. These methods allow the researcher to control the time at which the label is introduced, but does not allow accurate selection of specific cell(s) to label. By contrast, an example of spatially and temporally random retrospective analysis is the analysis of a cell population that contains similar somatic mutations acquired sequentially over time, which can be used as endogenous marks to trace back their cellular origin. The accumulation of these mutations over time is a random event not controlled by the researcher. Yet, sequencing allows the identification of these mutations, which serve as tracers for retrospective analysis. Based on statistical analysis of a cell population carrying genetic mutations, researchers can predict when, where and in what order a set of mutations originate.

Taken together, the basic idea behind cell tracing, as the term suggests, is observing a cell or population of cells over time to record the changes occurring within them. Prospective lineage analysis allows the tracing of pre-selected candidate cells to study their outcome or fate. Retrospective lineage analysis involves the systematic analysis of a cell population to determine their origin(s). The simplest method of cell tracing is by direct observation of the cells under the microscope without incorporation of an artificial label, and commonly finds application in studying the development of lower organisms. When direct observation is not feasible, the introduction of an exogenous marker within the cells facilitates their tracing. There are several ways to introduce an exogenous marker — either by genetic manipulations or by non-genetic means (such as by using vital dyes).

Genetic manipulation is preferred to label cells, especially in long-term experiments, as they allow stable incorporation of the label into the cells that remains undiluted over time. Additionally, using genetic methods permits the expression of the label to be constitutive, cell-type specific, or inducible, thus providing greater flexibility to the researcher and allowing both spatial and temporal control over labeling. Thus, “genetic tracing” refers to cell tracing via genetic introduction of labels. In the following section, I will elaborate on the ROSA genetic tracing approach, a commonly used genetic tracing approach in mice.

C.2 ROSA genetic tracing

C.2.1 Concept and origin

The ROSA genetic tracing method utilizes the Cre-lox mediated genetic recombination to introduce a permanent change in the genome and thus label the cells [290, 295, 296]. It works for any murine cell that can be engineered to express the Cre recombinase, and is a commonly used method of genetic tracing. Briefly, there are two main aspects to ROSA genetic tracing in mice: i) The Cre-lox mediated genetic recombination: The discovery of the site-specific DNA recombinase Cre has found great utility in genetic tracing applications, specifically to incorporate permanent labels into cells. The Cre enzyme (named after

"Causes recombination") catalyzes DNA recombination between the loxP sites—a 34bp consensus DNA sequences. Consequently, the Cre activity enables the removal of any

DNA sequence flanked by lox sites (referred to as “floxed”). Of note, Cre-mediated removal of the floxed DNA sequence is an irreversible genetic change, which permanently alters the DNA. In genetic tracing applications, a floxed transcriptional stop signal (lox-STOP-lox) ahead of a reporter gene R (such as -gal or fluorescent protein, which acts as the label) is introduced into the mouse genome, where the STOP prevents the expression of the reporter R, until the removal of the STOP by Cre (Fig. 1.9).

Because the Cre-mediated removal of STOP is irreversible, once effected, the Cre- mediated reporter expression is permanent. The integration of the reporter into the genome allows the reporter to be passed on to all progeny cells, thus labeling them for tracing purpose. Currently, there are at least three frequently used versions of Cre that

improve the applicability of the Cre-lox system: (a) improved Cre or iCre, for better codon usage in mammals, (b) CreER, for an inducible Cre dependent on tamoxifen [297], and (c) CreERT2, for a tighter, tamoxifen control of Cre and reduced basal enzyme activity. Given the broad utility of the Cre-lox system, mouse lines that permit tissue- selective, temporally restricted or inducible Cre expression are important reagents and have been generated using standard transgenic or knock-in strategies. ii) The ROSA26 reporter mice: To monitor (or reflect) the Cre activity and expression pattern, the system requires a reporter (containing lox-STOP-lox-R) that has potential to be expressed in all cell types. That is, the reporter cassette must be under the control of a constitutively active promoter in the mouse. The endogenous mouse locus ROSA26 on chromosome 6 is ideal for this purpose because it is a) ubiquitously active in all cell types since early development, and b) it is non-essential for normal mouse development and amenable to knock-in of exogenous genes. The discovery of the ROSA26 locus by

Friedrich and Soriano in 1991 was a serendipitous discovery of their attempts to identify novel mouse developmental genes by promoter entrapment [298]. They performed retroviral transduction of mouse embryonic stem (ES) cells with a plasmid vector coined as Reverse Orientation Splice Acceptor β-gal-neo (ROSA βgeo), and screened for strains resistant to neomycin and expressing β-gal. The retroviral vector lacked promoter elements to drive expression of β-gal; thus, any ES cells expressing β-gal would indicate an in-frame integration of the gene at an endogenous locus driven by 5’ promoter and enhancer elements, thereby identifying and tagging a mouse gene. Using this approach,

Friedrich and Soriano successfully generated several genetrap mouse strains from the β- 58

gal positive ES cells (naming them ROSA 1-29). Based on further characterization, they identified that the ROSA26 mice had a strong and ubiquitous expression of β-gal, starting at an early embryonic development stage (Embryonic day 7). Additionally, this locus appeared non-essential for normal mice development, as homozygous mice were fertile and developed normally [299]. Indeed, subsequent studies from Soriano’s laboratory characterized the endogenous ROSA26 locus and noted that it encodes three transcripts; however, the function of these transcripts remains unknown. The Soriano group called the ROSA26-β-gal mouse line as the R26R mice [300]. This was the first report introducing the utility of the ROSA26 locus to generate reporter mice. Since then, numerous ROSA26 mouse lines expressing different reporters have been developed in the past 15 years (over 200 strains in the Jackson laboratory mice repository alone), greatly underscoring the usefulness of this locus.

C.2.2 ROSA genetic tracing: Applications

The ROSA genetic tracing method has key advantages over other genetic tracing approaches. For instance, knock-in or transgenic mice expressing a reporter like green fluorescent protein (GFP) under the of an endogenous, cell-type specific locus facilitates the tracking of cells in which the promoter is active. However, in this approach, the reporter expression is entirely dependent on the promoter activity and thus, is transient.

This disallows tracking of the cells after the promoter is silenced. In addition, reporter knock-ins into functionally important endogenous loci often results in heterozygous mice, which may affect the biology of the cells under study. The ROSA genetic tracing method overcomes these limitations by a) introducing a permanent label, and b) by using a non- 59

essential, constitutively active endogenous locus for reporter knock-in. Moreover, ROSA tracing enables both prospective cell tracing (determine cell’s fate) and retrospective cell tracing (determine cell’s origin). Finally, it enables studying cells within their natural milieu without further manipulations and thus yields physiologically relevant insights; as opposed to tracing experiments that involve explant cultures or adoptive transplant of labeled cells.

Elegant reports in literature demonstrate the utility of ROSA tracing in mice [290, 295,

296, 301-305]. Pioneering work conducted by the Hans Clevers group employed the

ROSA genetic tracing to track the fate of Lgr5+ cells within the rapidly cycling murine intestinal crypts [301], a study that led to the fundamentally crucial identification of

Lgr5+ crypt basal cells as the stem cells of the small intestines and the colon. Subsequent lineage tracing experiments by them identified that intestinal Lgr5+ crypt cells also mark a subpopulation of intestinal adenoma cells that promote tumor growth [304]. Another interesting study applied ROSA genetic tracing to label cells of the dermis in order to determine the origin and mode of growth of squamous skin tumors in their native environment, without transplantation of tumor cells [303]. The main finding from this study is that the majority of labeled cancer cells in benign papilloma have only limited proliferative potential, and only a fraction of cells persist long-term, giving rise to progeny that populate most of the tumor. This insight provided an early support for the hypothesis of tumor initiating cells (or the so-called cancer stem cells). Thus, the ROSA genetic tracing approach has contributed towards landmark discoveries related to normal murine development as well as pathologies. 60

C.2.3 ROSA genetic tracing: Limitations and experimental controls

The efficacy of ROSA genetic tracing is dependent on the balance between two main factors—the specificity and the penetrance of Cre activity. The Cre allele can either be a transgene driving by a partial or complete promoter or a knock-in into an endogenous promoter. The specificity of Cre is dictated by the tissue-specificity of the promoter element driving Cre expression. Transgene, by definition, is not the endogenous gene and is often compromised by various factors, including the lack of all regulatory elements in the transgene (such as the complete promoter, splicing, and UTR elements), the non- native chromatin environment surrounding the transgene (except in the case of knock-in), and the repetitive nature of transgenes (usually inserted as head-to-tail repeats). Thus, even with designs to recapitulate the endogenous gene expression pattern, the transgene may not function as planned. Because of this caveat, I will refer to transgene expression as selective—instead of specific—in the rest of my dissertation. Addiotionally, the use of the inducible derivatives of Cre such as CreER or CreERT2 can improve Cre selectivity.

On the other hand, the penetrance of Cre activity is the fraction of cells labeled by the removal of lox-STOP-lox among the total cell population, and is therefore reflective of the amount of active Cre. Low penetrance of Cre activity results in labeling an insufficient fraction of the cells of interest. Increasing the copy number of Cre or the amount of tamoxifen in the case of CreER and CreERT2 may improve the penetrance of

Cre. However, high copy number may result in higher leaky expression of Cre and higher concentration of tamoxifen may be toxic. High toxicity is stressful and is thus not compatible with my project to generate transgenic mice expressing Cre in a manner

mimicking the expression of ATF3, a stress-inducible gene. Thus, the balance between selectivity and penetrance dictates how meaningful the results may be from the ROSA tracing experiments.

C.3 Summary

The ROSA genetic tracing is an elegant in vivo method to trace the cell fate and has been used to address a wide range of biological questions without the need for transplantation of exogenously labeled cells. It utilizes the Cre-lox system of DNA recombination to incorporate a permanent “tracer” into the murine cells of interest. Typically, the mice used for ROSA genetic tracing have two alleles: i) the Cre allele, and ii) the ROSA reporter allele (ROSA26-lox-STOP-lox-R, ROSA26-R, where R is the reporter). By using a tissue-selective inducible Cre, one can determine when and where to produce active Cre and thus remove the STOP from the ROSA reporter allele. This results in the expression of the reporter gene (usually a fluorescent protein) and permanently labeling the cell and its progeny for cell fate tracing. Advances in microscopy facilitate the visualization of the labeled cells in situ. Importantly, the fluorescent labels also permit the isolation of cells for further biochemical analysis. Consequently, observations from

ROSA tracing mice have yielded insights to normal mouse physiology and pathology, as evidenced in literature.

We propose to use the ROSA genetic tracing approach to determine the in vivo fates of murine cells that experience “stress” due to perturbations in their extra-cellular milieu.

Briefly, since ATF3 in a stress-inducible gene, we posit that Cre driven by the ATF3

genomic region will “mark” cells experiencing stress. These cells can then be traced over time to study their behavior, in both acute and chronic stress paradigms. In Chapter 3, I will describe my effort of generating a transgenic mouse line expressing an inducible Cre under the control of the ATF3 locus contained within a BAC clone (~ 55 kb). Thus far, there are no murine models designed for this purpose. Therefore, the mouse line I generated is new to the field and will be useful for addressing various questions in stress biology.

D. An overview of Chapters 2-4

The unifying theme of my thesis is the investigation of the adaptive-response gene Atf3 that encodes the ATF3 transcription factor. As discussed in section (A), Atf3 is a stress- inducible gene and may be viewed as a hub of the cellular adaptive-network to respond to external signals and determine cell fates. A central theme of ATF3 functions appears to be its ability to modulate immune response. In the cancer context, Atf3 skews TAMs to an M2 pro-tumor bioactivity, a feature that likely contributes to the requirement of Atf3 in TAMs to promote breast cancer metastases. My dissertation work focuses on two aspects of Atf3 and I will present them separately in Chapters 2 and 3.

Chapter 2 describes my data on the role of host-Atf3; specifically, its role in modulating the efficacy of breast cancer chemotherapy. This is an extension of our previous work investigating the role of host-Atf3 in breast cancer. I carried out the project as collaboration with another graduate student in Dr. Hai’s laboratory, Yi Seok

Chang. Excitingly, we found that paclitaxel—a commonly used chemotherapeutic drug— significantly exacerbated lung metastases, despite its ability to reduce primary tumor size.

Importantly, this exacerbation depends on the presence of ATF3 in the non-cancer host cells. To our knowledge, this is the first pre-clinical report that explores chemotherapy- exacerbated breast cancer metastases; earlier reports have focused on chemoresistance and primary tumor recurrence/regrowth. I have focused on the mechanisms at the primary tumor that govern cancer cell escape, thereby promoting metastases. Yi Seok has focused on the mechanisms at the metastatic site (lung in our models). In Chapter 2, I will present my data and our current model explaining how paclitaxel may promote cancer 64

cell escape in a host-ATF3 dependent manner. At the end of Chapter 2, I will include a figure and a brief description of our model at the metastatic site (from Yi Seok’s work).

Chapter 3 describes my data on the generation of a novel transgenic mouse model to study the fate of cells that are under either acute or chronic stress, or was stressed previously in the past. As detailed in section (A.2), overwhelming evidence indicates that ATF3 is induced in all cell types examined thus far, when the cells are under stress; furthermore, the spectrum of the stress signals that can induce Atf3 is not limited to any specific categories. This near universality of ATF3 induction by stress signals prompted us to propose that expression of Atf3 is an indicator of cellular stress. Thus, I generated a transgenic mouse line expressing an inducible Cre under the control of the ATF3 genomic region (~50kb). In Chapter 3 I will provide details about the rationale, the proposed utilities of the mouse model, experimental strategies used to generate the transgenic cassette, and the preliminary characterization of the mouse line.

Finally, in Chapter 4, I will discuss the future perspectives for my dissertation work, with an emphasis on the functional consequence of Atf3 expression in stress and disease.

Table 1.1. ATF3 modulates the expression of inflammatory mediators. A partial list of immune-related genes that are direct targets of ATF3 in immune cells and other cell types is shown. These genes are either up-regulated or down-regulated in a context- dependent manner. Adapted from [3]

Table 1.2. ATF3 function in cancer cells: Oncogene versus tumor suppressor. A partial list indicating the context-dependent role of ATF3 in different cancer types is shown; only studies that report causality of ATF3 action either by loss-of-function (LOF) or gain-of-function (GOF) are indicated. 67

Table 1.3. A partial list of known regulators of angiogenesis (proteins).

Table 1.4. Sub-classifications of the monocyte/macrophage cells of the myeloid lineage. This table lists commonly used markers to identify different monocyte/macrophage cell types in humans and mouse. * are unique cell surface markers of the monocyte/macrophage lineage and include CD14, CD16, CD11b, CD68, CSF1R, F480. ^ are chemokine receptors that identify the indicated sub-types but are also found on other cells in the body; these include CCR2, CX3CR1, CXCR4, TEK/TIE2.

Figure 1.1. A dendrogram of ATF/CREB proteins based on their amino acid sequences in the bZip region. Adapted from Fig. 20.1 in [2]

Figure 1.2. The genomic organization of mouse Atf3 gene. The mouse Atf3 gene has two alternate promoters at A1 and A2 (indicated by the arrows) and contains 5 exons that give rise to the full length ATF3 transcript: A1/A2, B, C, and E (size is indicated beneath each exon). Intervening introns are indicated by the black line connecting the exons. A1 and A2 are non-coding exons (indicated by the white box) which give rise to most of the 5’ untranslated region (UTR) of ATF3. The open reading frame of ATF3 spans exon B, C and part of E (indicated by the black box). The ATF3 methionine start codon (indicated by Met) is in exon B and the stop codon is in E (indicated by the asterix). The total Atf3 gene is ~50kb. Adapted from Fig. 2 in [3]

Figure 1.3. ATF3 as a hub of cellular adaptive-response network. A non- comprehensive summary of the extracellular signals reported in literature to induce Atf3 gene transcription via activation of diverse intracellular signaling pathways (some of them are shown here). Adapted from Fig. 1 in [3]

Figure 1.4. ATF3 expression in the stromal immune cells of human melanoma and breast cancer. (A) Representative image of adjacent sections from human melanoma, single-stained for Melanoma antigens or ATF3. Brown stain corresponds to the antigen signal (melanoma on the left, ATF3 on the right). C = cancer (margin indicated by dotted black line), S = Stroma. (B) Representative high magnification image of human melanoma co-stained for CD45 (green), an immune cell marker, and ATF3 (red). Nuclii are stained for Topro-3 and are indicated in blue. CD45-positive cells in the human melanoma tissue are positive for ATF3 (indicated by green triangular arrow-head). Not all CD45 cells are ATF3 positive (indicated by the green arrow). Scale bar = 10. (C) Representative high magnification image of human breast cancer co-stained for CSF1R (yellow), a macrophage marker, and ATF3 (red). Nuclii are stained for Topro-3 and are indicated in blue. CSF1R-positive cells in the human breast cancer tissue are positive for ATF3 (indicated by green triangular arrow-head). Not all CSF1R cells are ATF3 positive (indicated by the green arrow). The dashed line indicates the cell boundary. Scale bar = 10.

Figure 1.5. Architecture of the mammary glands and breast cancer progression. (A) The anatomy of a typical mammary gland. (B) A schematic of the longitudinal section of the mammary duct and lobule, indicating the different cell types. (C) Breast cancer progression. Normal epithelial cells (indicated by the innermost monolayer of purple cells) transform into hyperplastic cells that proliferate and give rise to localized mass of cells (ductal carcinoma in situ, DCIS) that remains bound by the myoepithelial layer (in orange) and the basement membrane (in grey). DCIS progresses to invasive ductal carcinoma (IDC) characterized by degradation of the basement membrane and invasion of cancer cells into the surrounding extracellular matrix (ECM).

Figure 1.6. The invasion-metastatic cascade. Metastasis is a culmination of multiple processes, starting with (1) the degradation of the basement membrane, (2) local cancer cell invasion through the primary tumor stroma, (3) intravasation through the blood vessel walls, and (4) survival of cancer cells in circulation. Systemic circulation results in the dissemination of cancer cells to distant secondary sites where the cancer cells (5) arrest, (6) extravasate into the parenchyma and (7) survive while remaining dormant. Under favorable conditions, the dormant cancer cells at the secondary site (8) proliferate to form micrometastases and avoid death to continue to growth into clinically detectable metastatic nodules.

Figure 1.7. Architecture of normal blood vessel versus tumor blood vessel. The tumor blood vessels are “leaky” and have reduced barrier function due to actively proliferating, non-adherent endothelial cells (ECs), low and heterogeneous basement membrane deposition, and detaching pericytes (ANG1 expression by the ECs enhances pericyte coverage of vessels). Also, compared to normal vessels the blood flow through the tumor vessels is irregular. Tumor vessel characteristics have important implications in cancer biology as the “leaky” vessels create hypoxic regions within the tumor due to insufficient oxygen supply (poor blood flow), allow infiltration of stromal cells, enhance intravasation of cancer cells and limit drug delivery to the tumors.

Figure 1.8. An overview of the different TAM locations within the primary tumor. The location of TAMs affects their bioactivity. (1) TAMs in the tumor stroma at the invasive front promote local cancer cell invasion. (2) TAMs in the hypoxic regions of the tumor promote tumor angiogenesis. (3) Perivascular TAMs promote cancer cell intravasation. Intra-tumoral TAMs are TAMs within the cancer nest and are believed to be non-tumor promoting.

Figure 1.9. ROSA genetic tracing method. ROSA genetic tracing enables permanent labeling of cells by incorporating Cre-lox mediated genetic change in the cells of interest. Two genetic elements are required to achieve this: (i) a cell-type or tissue-type specific promoter driven Cre (allele 1) that determines which cells are labeled, and (ii) a lox- STOP-lox-reporter cassette knocked into the mouse ROSA26 locus. Activation of Cre facilitates the permanent removal of STOP, thereby activating the transcription of the reporter (in this example -gal). This genetic alteration labels the cell and all its progeny that can then be traced over time.

Chapter 2

ATF3 in the host contributes to PTX-aggravated breast cancer metastases: the role

of ATF3 at the primary tumor site

2.1 Summary

Chemo-resistance, disease-relapse, and metastases severely limit the efficacy of current cancer chemotherapy. Recently, tumor associated macrophages (TAMs) have emerged as a critical cell-type from the host— organism carrying the tumor—to limit the efficacy of a common clinical drug, paclitaxel (PTX) in breast cancer. Most of the literature has focused on the roles of TAM in PTX efficacy by examining primary tumor recurrence.

However, it is unclear whether TAMs may limit PTX efficacy in terms of metastases.

Our laboratory previously reported that Atf3, an adaptive-response gene, is necessary for the ability of TAMs to promote breast cancer metastases. Since the Atf3 gene is induced by chemotherapeutic drugs, including PTX, we hypothesized that, by “switching on”

Atf3, PTX would promote metastasis, limiting its therapeutic efficacy. Mr. Yi Seok

Chang, another graduate student in Dr. Hai’s laboratory, and I collaborated on testing this hypothesis using a spontaneous metastasis model, which allowed us to examine the escape of cancer cells from the primary tumor and the colonization of the distant site by cancer cells. We injected breast cancer cells into wild type (WT) or Atf3 knock-out (KO)

mice and treated them with either PTX or saline. Excitingly, we found that PTX aggravated cancer metastases: PTX is not just ineffective in treating metastases, but in fact enhances it—at a dose that is therapeutic in reducing primary tumor size.

Significantly, we did not observe this effect in the Atf3 KO mice, indicating that PTX aggravated metastasis in a host-ATF3 dependent manner.

To elucidate the mechanisms underlying this aggravation, Yi Seok analyzed the lung and

I analyzed the primary tumor. I focused my investigation on the following features: vessel density, vessel integrity, cancer cell invasion and the tripartite structure around the blood vessel called “tumor microenvironment for metastasis” (TMEM) (see Chapter 1,

B.2.4.4, page 42). Although not comprehensive, these are important features affecting cancer cells escape from the primary tumors. Among them, blood vessel density, cancer cell invasion and TMEM structure are known to be regulated by TAMs—the cell type described previously to limits PTX efficacy and also important for ATF3 action.

My data can be summarized as follows. (a) The host-ATF3 promotes an overall tumor microenvironment that is more conducive to cancer cell escape, as indicated by the higher blood vessel density and higher TMEM abundance in tumors from WT mice than from

Atf3 KO mice. (b) PTX enhances this permissible microenvironment by reducing pericyte coverage and increasing TMEM abundance—in a host-ATF3 dependent manner.

(c) The above differences are corroborated by the higher circulating tumor cells (CTCs) in WT than Atf3 KO mice and by the ability of PTX to further increase CTCs in WT but not KO mice. (d) Analyses of TAMs and a subset of TAMs called TIE2 expressing

macrophages (TEMs) indicate that the host-ATF3 increases the abundance of TEMs; however, PTX does not further increase the abundance of TEMs. Instead it appears that

PTX affects the property of WT TEMs (e) Finally, analyses of publicly available cRNA microarray data from human clinical samples indicate that the combined expression level of ATF3 and TEK (the human ortholog of TIE2) is a better predictor for outcome than their individual level: higher ATF3 and TEK correlates with reduced overall survival in human patients. This lends credence to our data derived from a mouse model.

2.2 Introduction

Chemotherapy remains the predominant treatment for breast cancer, especially in the case of TNBC and metastatic breast cancer [228, 229]. However, chemo-resistance, tumor relapse and accelerated metastatic spread limit treatment efficacy in some instances [231-

237, 239]. The tumor response to chemotherapy is dictated by both cancer-cell intrinsic factors such as genetic mutations [241], and cancer-cell extrinsic factors such as the tumor stroma [242]. Indeed, chemotherapy promotes a general tissue-damage response in the host that triggers an influx of inflammatory cells into the tumor microenvironment, which can alter therapeutic efficacy [262]. The notion that the tumor stroma is a key determinant of disease outcome in response to chemotherapy is an emerging concept in the field, strongly supported by literature [242, 244, 247-252, 256-258, 261, 262, 306]. In my dissertation, I have focused on modulation of chemotherapy by cancer-cell extrinsic, stromal factors.

Pro-tumor roles of TAMs: As described in chapter B.2.4.1, the tumor stroma refers to the reactive tumor microenvironment that supports the cancer cells. It consists of host derived cells (such as immune cells, endothelial cells and others), the extra-cellular matrix and soluble proteins in the milieu. An important cell type within the tumor stroma is TAM [174, 181, 184, 196, 203, 204, 217, 307-309]. As described in chapter B.2.4,

TAMs are a heterogeneous population of innate immune cells of myeloid origin that promote numerous activities necessary for breast cancer growth and metastases [172,

193, 197, 200, 203, 308]. TAMs are necessary to trigger the angiogenic switch prior to transformation of pre-malignant breast cancer to malignant disease [173, 174] [266].

TAMs also promote cancer cell survival, proliferation, migration and invasion within the tumor [196, 204, 215, 216]. An important function of TAMs is to direct migration of

EGFR-expressing cancer cells towards blood vessels via secretion of the EGFR-ligand,

EGF [215]. Significantly, in vivo two-photon microscopy has revealed that TAMs physically interact with cancer cells near the tumor blood vessels and enable cancer cells escape into the blood—a process known as intravasation [216]. Intravasation is a key early event within the primary tumor that enables the cancer cells to disseminate systemically, leading to metastases. The three-cell micro-anatomical structure within tumors comprising of TAMs, endothelial cells and Mena-expressing cancer cells in close proximity is called the “tumor microenvironment of metastases” or TMEM and is a risk factor for metastases [217]. The number of TMEMs in human patients with invasive breast cancer significantly correlates with metastatic index [217, 226].

TEMs, a subset of TAMs: A subset of TAMs, called the TIE2-expressing macrophages, or

TEMs, have been identified in both mouse and human breast cancer [206, 209]. The

TEMs have an inherent pro-angiogenic capacity and a distinct gene expression profile that separates them from other TAMs [207, 208, 210-212]. The TEMs are located in the perivascular region on the abluminal side of blood vessels and the main function attributed to these cells is tumor angiogenesis [207, 208]. However, the underlying molecular mechanisms are not clearly defined. Other functions of TEMs had not been reported until recently when Harney et.al., through real-time intravital two-photon imaging, demonstrated that intravasation of cancer cells occurs exclusively at the

TMEMs that acquire transient vascular permeability in tandem with cancer cell egress into the blood circulation [227]. Moreover, they showed that TIE2hi macrophages are a part of TMEMs and secrete VEGFA to induce vascular permeability and promote cancer cell intravasation. Thus, this seminal report identifies TMEM as a functional unit within the breast cancer stroma, in addition to discovering that TIE2hi macrophages are a part of

TMEMs that participate in promoting cancer cell intravasation at the TMEMs [227].

TAMs limit chemotherapy efficacy in breast cancer: In addition to numerous pro-tumor functions of TAMs, TAMs also modulate efficacy of cancer chemotherapy [247, 248,

254]. As discussed in Chapter B.3.2, in the context of breast cancer, TAMs limit the efficacy of PTX-based chemotherapy as demonstrated by recent studies in mouse models of breast cancer. The proposed mechanisms by which TAMs do so include (i) limiting tumor infiltration of cytotoxic T cells (immune suppression) [249], and (ii) inhibiting paclitaxel-induced breast cancer cell death (chemoprotection) [250]. Additionally, 83

perivascular TAMs have been implicated to limit chemotherapy by promoting tumor neoangiogenesis leading to primary tumor regrowth (via tumor re-vascularization) [263].

Significantly, these studies using mouse models of breast cancer have focused on primary tumor growth and recurrence. Not much is known regarding how the interaction of chemotherapy and stroma affects distant metastases. This is the main question that we have addressed through the work presented in this thesis.

ATF3 in TAMs: As described in chapter 1A, ATF3 is a bZip transcription factor belonging to the ATF/CREB family [27]. ATF3 is undetected in normal cells but is induced by stress and other stimuli present within the tumor microenvironment such as chemokines, cytokines, hypoxia and others [1, 3, 30]. Notably, we have demonstrated that ATF3 is expressed in stromal cells of both mouse and human breast tumors (see Fig.

1.4) [70]. We have further identified a correlation between ATF3 expression in

“mononuclear” immune cells and worse outcome in a cohort of breast cancer patients

[70]. Surprisingly, ATF3 expression in the cancer cells themselves did not correlate with worse outcome. Mononuclear cells include myeloid cells/macrophages, T and B lymphocytes and granulocytes [70]. Specifically, we demonstrated that ATF3 expression in the myeloid/macrophage cells is functionally important to promote breast cancer metastases to lungs: genetic ablation of ATF3 in the myeloid/macrophage lineage reduces breast cancer metastases in a conditional knock-out (CKO) mouse model [70]. We reported that ATF3 expression in mouse TAMs makes them more pro-tumor and increases their ability to promote cancer cell invasion in vitro [70]. Further, we identified

an ATF3-regulated gene expression profile from mouse TAMs that can distinguish cancer stroma from distant stroma in human patients and also has prognostic value.

Finally, we estimate that about 15% of TAMs in human patient samples express ATF3

[70]. Taken together, our studies prove that ATF3 is a clinically relevant protein that enhances breast cancer metastases, in part via affecting TAM biology to make them more pro-tumor.

Knowledge gap and hypothesis: As mentioned earlier, one important question that has not been addressed in depth is how the interactions of tumor stroma with chemotherapy affect breast cancer metastases. To model this in mice is not trivial because in most mouse models the progression to metastases from an orthotopically implanted tumor is slow. However, since metastatic disease is incurable and the leading cause of breast- cancer related deaths, it is significant to address this issue. Since ATF3 is induced by many different chemotherapy drugs, including PTX, and because ATF3 expression in

TAMs promotes breast cancer metastases, we hypothesized that ATF3 expression in the tumor stroma, and especially in the TAMs, determines chemotherapy efficacy and disease outcome in breast cancer. We tested the hypothesis using an aggressive mouse breast cancer cell line, MVT-1, that progresses to metastases in a month after injecting it into the fat pad of immunocompetent mice. We injected the same cancer cells into WT and ATF3 KO mice (mice lacking ATF3 in all cells; referred to as KO), and treated them with PTX, to determine if presence of ATF3 in the host affects chemotherapy outcome.

The detailed methodology and results are presented below.

2.3 Methods and materials

Mouse model for orthotopic breast cancer: All animal experiments were carried out in accordance with the Institutional Animal Care and Use Committee (IACUC) at the Ohio

State University. Age-matched, 6-8 week old female FVB/N mice (WT or ATF3 KO) were used for all experiments. A syngeneic, aggressive murine breast cancer cell line,

MVT-1, was used in our studies. MVT-1 cells were cultured in DMEM with 10% FBS and 1% P/S. 200, 000 MVT-1 cells were injected into the fourth fat pad of the mouse.

When the tumors were palpable on day 7, mice were injected with 20mg/kg PTX

(dissolved in 1:1 Cremophore-EL:PBS) three times a week for a total of eight times.

Mice were sacrificed on day 26 and the primary tumors and lungs removed. The tumors and lungs were either fixed in 10% normal buffered formalin and analyzed or digested enzymatically for other assays.

Rationale for drug dosage: We determined the drug dose and regimen based on literature

(to determine a starting point) and titration experiments that we conducted in the FVB/N mice. Our goal was to use a PTX dose that is non-toxic and shows a significant primary tumor reduction in the WT mice. Reduction in primary tumor was desired as a control for drug activity. Toxicity was gauged by (i) monitoring mice body weight—more than 5% reduction in body weight was considered as toxic, (ii) signs of discomfort in the animal such as labored breathing, inhibited movement, raised fur and others, (iii) significantly enlarged intestines—chemotherapy toxicity affects bowel movement causing enlargement of the bowel due to retention of stool, (iv) smaller than normal spleen sizes

(<80 gm) due to chemotherapy mediated ablation of cells. The last two anomalies (iii and 86

iv) are signs of extreme toxicity and were rarely seen in our study. To determine the appropriate drug treatment plan, we tried three different regimens: (i) 20mg/kg, once a week, a total of three times—this regimen was non-toxic and we saw enhanced lung metastases in the PTX-treated WT mice. However, we did not see a reduction in primary tumor and thus did not use this regimen. (ii) 10mgs/kg, three times a week, a total of eight times—again, this regimen was not toxic, enhanced lung metastases but did not reduce primary tumor. Therefore, we did not use it. (iii) 20mg/kg, three times a week, a total of eight times—since the mice appeared to be tolerating the drug well without toxicity we increased the amount and frequency of drug usage. At this dose and frequency, we saw significant primary tumor reduction. Thus, we decided to use this regimen for our experiments.

Analysis of lung micrometastases: Formalin-fixed paraffin embedded lung sections were stained with hematoxylin and eosin. The micrometastases were counted or analyzed by

ImageJ-Fiji for pixel counts of hematoxylin stained area. The pixel numbers of the micrometastatic nodules were divided by that of the total lung area to obtain percent metastatic area per lung.

Analysis of circulating tumor cells: Total blood (600-800 l) was collected from mice by cardiac puncture and put into EDTA-coated tubes (lavender). The blood was spun for 10 minutes at 4000 rpm in a microcentrifuge in the cold room. The supernatant plasma was separated and the cell pellet was resuspended in RBC lysis buffer (~10 ml) for 5 minutes at room temperature. The cells were spun in the clinical centrifuge in the cold, the

supernatant discarded and the cell pellet was stored in trizol for RNA extraction. cDNA was made from the RNA and analyzed by quantitative PCR using primers for cMyc, an oncogene expressed by the MVT-1 cancer cells. Actin was used as the internal control.

Immunofluorescent staining and analysis of microvessel density in tumors: Briefly, formalin-fixed paraffin embedded tumor sections (4 thickness) were rehydrated, followed by heat-mediated antigen retrieval in citrate buffer pH 6. The slides were cooled and blocked in 0.3% hydrogen peroxide in methanol, rinsed and blocked in 5% normal goat serum. The tissues were incubated with anti-CD31 primary antibody (raised in rabbit, from abcam; 1:50 dilution) overnight at 4C to stain blood vessels. Next day, the primary antibody was washed off in TBST, and the slides were incubated in HRP- conjugated anti-rabbit secondary (polymer system from Vector labs.) for half hour at room temperature. The slides were washed and developed with the TSA fluorescent substrate from Perkin and Elmer. Slides were mounted in Vectashield.

The slides were masked prior to imaging and all imaging was done blind. Representative images (for tumors harvested on day 26) or all fields (for tumors harvested on day 15), excluding the tumor edges were captured at 200X magnification using Olympus confocal microscope. Images were analyzed for CD31 area by counting red pixels by ImageJ-Fiji

(Fig. 2.3A) using the following algorithm: function processImage() { processImageTitle = getTitle(); run("Split Channels"); selectImage(processImageTitle + " (blue)"); close(); 88

selectImage(processImageTitle + " (green)"); close(); selectImage(processImageTitle + " (red)"); setThreshold(30, 255); setOption("BlackBackground", false); run("Convert to Mask"); run("Despeckle"); windowTitle = processImageTitle + " (red)"; run("Set Measurements...", "area redirect=[windowTitle] decimal=3"); run("Analyze Particles...", "summarize"); selectImage(processImageTitle + " (red)"); close(); return processImageTitle;

Immunofluorescent staining and analysis of pericyte coverage in tumors: Briefly, formalin-fixed paraffin embedded tumor sections (8 thickness) were rehydrated, and blocked in 0.3% hydrogen peroxide in methanol, rinsed and blocked in 5% normal goat serum. Slides were incubated with anti-smooth muscle actin antibody (raised in rabbit,

Thermofisher Scientific, 1:50) for half an hour at room temperature. The slides were washed, and incubated with HRP-conjugated anti-rabbit secondary (polymer system from

Vector labs.) for half hour at room temperature to stain the pericytes. The slides were washed and developed with the TSA fluorescent substrate (fluorescein) from Perkin and

Elmer. Next, the slides were heated in the microwave for 3-4 minutes (without boiling) in antigen retrieval buffer (citrate, pH 6) followed by incubation in a steamer for one hour.

The slides were cooled, rinsed, blocked in hydrogen peroxide, and normal goat serum.

The slides were then incubated with anti-CD31 primary antibody (raised in rabbit, from abcam; 1:50 dilution) overnight at 4C to stain blood vessels. Next day, the primary 89

antibody was washed off in TBST, and the slides were incubated in HRP-conjugated anti- rabbit secondary (polymer system from Vector labs.) for half hour at room temperature.

The slides were washed and developed with the TSA fluorescent substrate (cyanine3) from Perkin and Elmer. Slides were mounted in Vectashield.

The slides were masked prior to imaging and all imaging was done blind. Five fields (or more) with relatively high blood vessels were captured per tumor at 200X magnification, to compare pericyte coverage in areas of tumor with similar microvessel densities. Tumor edges were excluded. Imaging was done with the Olympus confocal microscope. Pericyte coverage (% green pixel in areas of red pixel) was calculated using ImageJ-Fiji (Fig.

2.3B) and the following algorithm: function processImage() { processImageTitle = getTitle(); run("Split Channels"); selectImage(processImageTitle + " (blue)"); close(); selectImage(processImageTitle + " (green)"); setAutoThreshold("Default"); setThreshold(40, 255); setOption("BlackBackground", false); run("Convert to Mask"); selectImage(processImageTitle + " (red)"); //setAutoThreshold("Default"); //run("Threshold..."); setThreshold(40, 255); //setOption("BlackBackground", false); run("Convert to Mask"); run("Despeckle"); run("Create Selection"); 90

selectImage(processImageTitle + " (green)"); run("Restore Selection"); windowTitle = processImageTitle + " (green)"; run("Set Measurements...", "area redirect=[windowTitle] decimal=3"); run("Analyze Particles...", "summarize"); selectImage(processImageTitle + " (green)"); close(); selectImage(processImageTitle + " (red)"); close(); return processImageTitle;

Immunofluorescent staining and analysis of TMEM density in tumors: Briefly, formalin- fixed paraffin embedded tumor sections (4 thickness) were rehydrated, followed by heat-mediated antigen retrieval in citrate buffer pH 6. The slides were cooled and blocked in 0.3% hydrogen peroxide in methanol, rinsed and blocked in 5% normal goat serum.

The tissues were incubated simultaneously with anti-CD31 primary antibody (raised in rabbit, from abcam; 1:50 dilution) and anti-Mena primary antibody (raised in mouse, from santa cruz, 1:50) overnight at 4C to stain blood vessels. Next day, the primary antibody was washed off in TBST, and the slides were incubated in HRP-conjugated anti- rabbit secondary (polymer system from Vector labs.) for half hour at room temperature.

The slides were washed and developed with the TSA fluorescent substrate (cyanine 3) from Perkin and Elmer. Next, they were incubated with HRP-conjugated anti-mouse secondary (polymer system from Vector labs.) for half hour at room temperature. The slides were washed and developed with the TSA fluorescent substrate (fluorescein) from

Perkin and Elmer. Slides were washed and transferred into the antigen retrieval buffer

(citrate, pH 6). Next, the slides were heated in the microwave for 3-4 minutes (without boiling) followed by incubation in a steamer for one hour. The slides were cooled, rinsed, blocked in hydrogen peroxide, and normal goat serum. The slides were then incubated with anti-F480 primary antibody (raised in rat, from Molecular Probes; 1:50 dilution) overnight at 4C to stain macrophages. Next day, the primary antibody was washed off in

TBST, and the slides were incubated in HRP-conjugated anti-rat secondary (polymer system from Vector labs.) for half hour at room temperature. The slides were washed and developed with the TSA fluorescent substrate (coumarine) from Perkin and Elmer. Slides were mounted in Vectashield.

The slides were masked prior to imaging and all imaging was done blind. Five random images per tumor were taken at 400X magnification using the Olympus confocal microscope. TMEMs were scored by manually counting the three cell structures containing Mena-positive cells, F480 positive cells and CD31 positive cells in close proximity (20), but not overlapping, to each other.

Primary Tumor digestion: Freshly removed primary tumors were digested using a combination of enzymatic and mechanical digestion following the Milteyni mouse tumor digestion protocol with the gentleMAC dissociator, followed by RBC lysis and sequentially filtered through first the 100 filter and then the 40 filter to get a single cell suspension.

Flow cytometry and isolation of TAMs and TEMs by flow sorting: Single cell tumor suspensions were incubated with Fc receptor blocking antibody and then stained with

APC-conjugated anti-CD11b antibody, FITC-conjugated anti-F480 antibody and PE- conjugated TIE2 antibody and data was acquired using the LSR machine. Unstained cells, and single stained cells were used as controls for gating. Analysis was done using the FlowJo software. All flow antibodies were from ebioscience, against mouse epitopes.

Flow sorting: Briefly, tumor suspensions from three tumors per group were pooled and then stained with antibodies as above. Sorting was done for TAMs (CD11b+ F480+ double positive cells) or TEMs (CD11b+ F480+ TIE2+ triple positive cells) using either the Aria or AriaIII sorter. Cells were sorted under cold conditions and collected cells were put on ice. Sorting was completed between 6-8 hours after sacrificing the mice.

Post-sort purities were about 85%.

Magnetic isolation of F480 positive cells from tumor digests using Dynabeads: Single cell tumor suspension in ice-cold buffer (PBS with 2% FBS and 2mM EDTA) was passed through 30 filter to remove cell clumps and debris. The cells were blocked with goat anti-rat IgG for 10 minutes at 4C and then incubated with anti-F480 antibody (Molecular probe, 20l/10 million cells) for 15 minutes in the cold. The cells were washed and incubated with Dynabeads anti-rat IgG (25l/10 million cells) as per the recommended protocol for 20 minutes in the cold. The unbound cells were separated from the beads- bound (positively selected) cells by using a magnetic separator. Unbound cells were discarded; bound cells were rinsed at least three times and stored in Trizol for RNA extraction. 93

In vivo Matrigel plug assay: Matrigel was thawed overnight on ice. 50l of DMEM (FBS

10%, P/S 1%) containing 75,000 sorted TAMs (CD11b+F480+) cells – or 50l PBS as a control - were added to 300l of Matrigel. Male, 2-3 month old FVB/N WT mice were anesthetized and injected with 350l of the Matrigel mixture into the left lower abdomen in the region between the fourth and fifth fat pad in the ventral side. The injection was done slowly to allow the Matrigel plug to form a circular dome. After 6 days, the mice were sacrificed and Matrigel plugs collected, fixed in 10% normal buffered formalin, sectioned and analyzed for CD31 as described above.

In vitro Boyden chamber invasion assay: Corning BioCoat Tumor Invasion 24 well plate was used for the invasion assay. Each time, the assay was set up in triplicate wells and the experiment was repeated three independent times. Briefly, GFP expressing MVT-1 cells were seeded with flow sorted tumor TEMs (CD11b+ F480+ TIE2+) in the Matrigel coated insert in DMEM with 1% FBS media. DMEM with 10% FBS was used in the bottom chamber to create a chemotactic gradient. After 16 hours, nine images per insert were captured of the lower surface of the insert using an inverted fluorescent microscope at 100X magnification. The images were masked and green cells were counted manually.

Statistics: Data were analyzed for significance by two-way ANOVA and post-hoc

Bonferroni test using the SigmaPlot statistical software. Students’t-test was used to analyze in vitro invasion assay data. A P-value less than or equal to 0.05 was considered as significant. A p-value less than 0.3 was considered as a trend.

2.4 Results

ATF3 in the host contributes to PTX-aggravated breast cancer metastases and PTX increased circulating tumor cells: We compared four groups of mice—WT and KO tumor bearing mice treated with PTX or saline as control (Fig. 2.1A). PTX treatment significantly reduced primary tumor growth in both WT and KO mice (Fig. 2.1B).

However, in the WT mice PTX treatment enhanced metastases to the lungs, as shown in

Fig. 2.1C, D. Additionally, we saw a significant increase in circulating tumor cells in the

PTX-treated WT mice four days after the last PTX injection, assayed by reverse transcription coupled with quantitative polymerase chain reaction (RT-qPCR) of total blood cells (minus the red blood cells) for cMyc mRNA—an oncogene expressed by the

MVT-1 cancer cells used in this study (Fig. 2.1E). A plausible explanation for the increase in tumor cells within blood is enhanced escape of cancer cells from the primary tumor upon PTX treatment; hence we investigated potential mechanisms at the primary tumor that promote cancer cell escape. Another explanation is enhanced survival of circulating cancer cells but we did not test this.

Metastasis is a carefully orchestrated multistep process involving degradation of the basement membrane, local invasion of cancer cells within the primary tumor, intravasation of cancer cells into the blood circulation, survival of circulating cancer cells, arrest and extravasation of cancer cells at distant metastatic sites, and out-growth of cancer cells at the metastatic sites [68]. Each step needs to succeed for metastases to occur. Significantly, the microenvironment provided by the host plays a critical role in regulating multiple steps of the metastatic cascade, both in the primary tumor and at the 95

metastatic site, where the environment in distinct from the one in the primary tumor [110-

115, 126]. Thus, we investigated which step of metastases is affected by host ATF3 in the context of chemotherapy. While I focused on mechanisms in the primary tumor that enable cancer cell escape, my colleague in the laboratory, Mr. Yi Seok Chang, investigated processes at the secondary site, which in this case is the lung. In the remainder of this chapter, I will describe results obtained from testing factors that influence the early steps of metastases in the primary tumor—my area of focus. In the discussion, I will summarize the findings from Yi Seok’s studies.

Host-ATF3 promotes tumor angiogenesis and PTX tends to reduce pericyte coverage in

WT but not KO mice: Tumor angiogenesis is critical for primary tumor growth and is also a key driver of metastases [144, 147, 155, 167, 310, 311]. The blood vessels act as

“highways” for cancer cells to escape the primary tumor and enter systemic circulation.

Moreover, the tumor vessels are abnormal: they have poor basement membrane deposition, and irregular or low pericyte coverage, which makes the tumor vessels

“leaky” [150, 153, 274, 312-315]. This heterogeneous blood vessel architecture provides less resistance to cancer cells entering the blood stream. In fact, evidence in literature demonstrates a causal link between reduced pericyte coverage and increased metastases in several cancer models, including breast cancer [316-319]. Also, targeting tumor vasculature by improving the pericyte coverage is suggested as a therapeutic approach to limit metastases [320]. Given the overall importance of tumor angiogenesis in metastases,

I tested by immunohistochemical analysis if the microvessel density is altered in tumors from the four groups of mice. 96

I found that tumors from PTX-treated and saline-treated WT mice had higher microvessel density compared to tumors from either of the KO groups at the endpoint on day 26, assayed by CD31 staining of the tumor sections (Fig. 2.2A, B). This genotype difference in tumor microvessel density also existed at an earlier time point, day 15, when the tumors from all four groups were small and similar in size (Fig. 2.2B). However, PTX treatment did not affect the microvessel density at either time point (Figs. 2.2A, B and

C). Thus, the presence of ATF3 in the host significantly promotes tumor vessel density, but PTX does not further alter it.

I next examined whether the property of tumor vasculature was different among the four groups, by assaying pericyte coverage using immunohistochemical co-staining for CD31 and alpha smooth muscle actin (SMA) for pericytes. Interestingly, in WT mice the PTX treatment reduced pericyte coverage (Fig. 2.2D, E; ANOVA interaction = 0.15; WT PBS versus WT PTX Bonferroni post hoc test; WT+PTX versus WT+PBS P= 0.036). This reduction in pericyte coverage by PTX is not seen in the tumors from KO mice

(interaction between PTX treatment and genotype P=.15). Reduced pericyte coverage in tumors from PTX-treated WT mice suggests that the blood vessels are leakier than in untreated tumors and correlates well with increased circulating cancer cells (Fig. 2.1D).

Gene expression analysis of total tumor cells for pro-angiogenic and anti-angiogenic candidate genes (Fig. 2.2F) revealed that pro-angiogenic genes such as Ang1, Notch1, and CX3CL1 were higher in the WT-derived tumors. Notch1 is significantly higher in

PTX-treated tumors from WT mice than in PTX-treated tumors from KO mice.

Interestingly, Notch1 in bone-marrow derived cells is necessary for these cells to exit the bone marrow in response to PTX chemotherapy and for chemotherapy-enhanced tumor angiogenesis [321]. This, in addition to other pro-angiogenic roles of Notch1 [162, 164,

322], makes it a relevant candidate that needs further characterization to determine its potential function in our model. CX3CL1, a pro-angiogenic cytokine [323-325], is further up-regulated by PTX treatment in the WT but not in the KO. Also, VEGFR2 mRNA was significantly higher in tumors from PTX-treated WT mice than in tumors from PTX- treated KO mice. VEGFR2 is an endothelial specific receptor that mediates VEGFA- mediated angiogenesis. By contrast, anti-angiogenic genes CXCL9, 10, 11 and 14 [266] were up-regulated several folds in the KO-derived tumors. Another gene of interest is

TNFA, which is reported to have a dose-dependent effect on angiogenesis: at low concentration it promotes angiogenesis and at high concentration it inhibits angiogenesis

[326, 327]. Our data indicate that tumors from KO animals have two-fold higher TNFA mRNA than the tumors from the WT mice. Taken together, the gene expression data support the higher tumor vesssel density in WT than KO mice shown above.

ATF3 in the host increased the abundance of TEM, a pro-angiogenic subset of TAMs:

TAMs promote tumor angiogenesis [173-175, 177, 207, 210, 308], and recently, TAMs have been found to limit chemotherapy efficacy in breast cancer [247, 248, 254, 262]. In mouse models of breast cancer (PyMT transgenic mice, or orthotopic injection of PyMT or 4T1 cells), PTX treatment increases the number of tumor infiltrating TAMs [249, 250].

Thus, I carried out immunophenotyping analysis of tumors from the four groups using flow cytometry. As reported by us previously and shown in Fig. 2.4A, there was no 98

difference in the percentage of CD11b+ F480+ TAMs in tumors from WT or KO animals

(saline control group). Although we expected PTX to increase the percentage of CD11b+

F480+ TAMs (based on literature), we did not observe it in our model in either the WT or the KO mice (Fig. 2.4A). One potential explanation is that we have used a different cancer cell line (MVT-1), which may result in tumors different in their responsiveness to

PTX in terms of TAM recruitment/expansion. Although surprising, this result is consistent with our gene expression data from the total tumor that indicates that there is no difference in the mRNA levels of CSF1—the main recruitment and pro-survival factor for TAMs [202, 328, 329]—across the four groups (Fig. 2.2F).

I next examined the TIE2-expressing macrophages (TEMs), because they are the pro- angiogenic subset of TAMs [207, 210, 211]. Excitingly, the tumors from WT mice have strikingly higher TEMs than those from KO (Fig. 2.4B). Since the same cancer cells

(MVT-1) were injected into the mice—WT or ATF3 KO—we interpret that ATF3 in the non-cancer host cells determined (increased) TEM abundance. PTX treatment however, did not further alter TEM abundance. Consistently, gene expression data showed that the mRNA level of CX3CL1—the chief ligand for the recruitment of TEM precursors to the tumor [330, 331]—was significantly higher in WT mice than in the KO (Fig. 2.2F). Thus, although the total TAMs are not different between WT and KO mice, the pro-angiogenic subset is significantly higher in the tumors formed in WT host, consistent with the genotype difference in vascular density described above (Fig. 2.2A, B, C). During the course of my investigation, TEMs have been shown to be a part of functional TMEMs

[227], and to limit chemotherapy efficacy and cause primary tumor relapse by promoting 99

angiogenesis [263]. Therefore, the genotype effect on TEM abundance is likely to have functional consequences beyond microvessel density.

In addition to analyzing the mRNAs from the total tumor, I also analyzed gene expression in F480 positive cells, isolated from the primary tumors using magnetic beads.

Interestingly, WT TAMs expressed lower amounts of anti-angiogenic genes (CXCL9, 10,

11) than the KO TAMs (Fig. 2.4C); supporting the notion that ATF3 expression in the

TAMS skews the TAMs to be more angiogenic. The effect of PTX was less pronounced in either the WT or KO derived TAMs for the candidate genes tested, except for VEGFB that was up-regulated in WT PTX-treated TAMs but not in the KO TAMs. Recently,

VEGFB was identified to promote leaky vasculature and metastases in a mouse model of melanoma [319]. Thus, VEFGB may be a potentially important candidate to test further.

Although PTX did not alter the number of TAMs (as described above), it may alter their properties. We did a Matrigel plug assay (schematic in Fig. 2.4D), using CD11B+ F480+ cells from tumors isolated by fluorescent activated cell sorting (FACS). Single cell suspensions from three tumors per group were pooled prior to sorting, and the resulting

TAMs were injected with Matrigel into one recipient mouse (WT). The Matrigel plug was removed six days later and assayed for the percent (%) of CD31+ area, using immunofluorescence followed by image analysis that captured all the fields of view

(FOVs) from each plug (~ 2-6 FOVs). This experiment was repeated total three times, and the % of CD31+ area per FOV are shown in Fig. 2.4E, F. Interestingly, TAMs from

PTX treated WT tumors had significantly higher angiogenic potential than TAMs from

100

untreated WT mice. Thus, it appears that PTX alters the property of WT TAMs and promotes their pro-tumor activity.

At first glance, this result appears inconsistent with the in vivo analysis of microvessel density (Fig. 2.2A and 2.2B) where PTX did not increase CD31 area in the WT mice. I propose two plausible explanations for this inconsistency. (i) For the in vivo analysis of microvessel density, the mice were systemically treated with PTX. In that case, not just

TAMs but all cells, including cancer cells, endothelial cells, fibroblasts, and other immune cells, are exposed to PTX. This may obfuscate the PTX-enhanced pro- angiogenic activity of TAMs observed in the Matrigel plug assay, where the TAMs were isolated from mice receiving PTX or saline but the recipient mice are naïve (untreated).

In addition, no cancer cells were included in the assay. Thus, the milieu in the Matrigel plug is very different from that in the primary tumor, providing a potential explanation for the apparent discrepancy. (ii) Another explanation is that the angiogenic program in the WT tumor was already running at a high capacity, thus failed to respond to further stimulation by PTX. The cancer cells used in the model express a transgene encoding

VEGF. Since VEGF is highly pro-angiogenic, it is possible that the pro-angiogenic program in the primary tumor is “saturated,” thus obscuring the effect of PTX. In the

Matrigel plug, however, this potential “saturation” is not an issue.

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PTX affects the property of WT TEMs; enhances their ability to promote cancer cell invasion in vitro: As described above, we uncovered a novel and significant genotype difference in TEM levels higher TEMs in tumors from WT than ATF3 KO host (Fig.

2.4B). However, PTX did not alter the number of TEMs. We asked if the property of

TEMs is altered by PTX treatment. I isolated TEMs by sorting after triple staining for

CD11b+/F480+/TIE2+ (Fig. 2.6A) and tested their ability to influence cancer cell invasion in vitro. Due to the low abundance of TEMs in the KO tumors (<1%), I only isolated TEMs from the two WT groups: PTX-treated and saline-treated. The invasion assay was carried out in Boyden chamber with Matrigel-embedded membrane as schematized in Fig. 2.6B. Briefly, GFP-positive cancer cells were co-cultured together with TEMs in the upper chamber (referred to as direct co-culture) and monitored 16-18 hours later for their invasion by counting the number of GFP-positive cells on the underside of the membrane. As shown in Fig. 2.6C, TEMs from PTX-treated tumors significantly enhanced cancer cell invasion compared to control TEMs from PSB-treated tumors. This was reproduced in three independent experiments. Interestingly, this PTX effect was not observed in an indirect co-culture assay (Fig. 2.6E), where TEMs were placed in the bottom chamber separated from cancer cells in the upper chamber (Fig.

2.6D). Thus, direct contact between TEM and cancer cells is required to see the effect of

PTX; we conclude that TEMs from PTX-treated tumors promote invasion of proximal cancer cells. This is especially relevant because TEMs are found in the perivascular region, and in functional TMEMs. Their ability to induce invasion in proximal cancer cells suggests that they may similarly enhance invasion of cancer cells locally near blood

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vessels and TMEMs, thereby promoting their intravasation. This is only a speculation and further study is required.

Tumors from WT mice have higher number of TMEMs than tumors from KO mice: As described in the introduction, the TMEM is a microanatomical, functional unit consisting of a TAM, endothelial cell and Mena-overexpressing aggressive cancer cells in physical proximity (Fig. 2.8A) [217, 226]. Mena is an actin binding protein that marks invasive breast cancer cells [218-225]. TMEMs are sites of active cancer cell intravasation and have predictive value for metastatic risk in human patients [227]. Therefore, we tested whether the abundance of TMEMs is different in the tumors from the four groups of mice in our model. Primary tumors from six mice per group were stained for F480, CD31 and

Mena, followed by image analysis. To be counted as a TMEM, the distance between any two cells (within this tripartite structure) needs to be equal or shorter than 20 micrometer.

Fig. 2.8C shows representative images of structures identified as TMEMs in our tumor sections. Fig. 2.8D shows an example excluded from TMEM counts; this structure is consisting of Mena-positive cells (green) next to a CD31 positive vessel (red), without a near-by macrophage (white). Excitingly, the host genotype made a significant difference in TMEM abundance: WT tumors had higher TMEM than ATF3 KO tumors on average

(for both PTX- and PBS-treated groups) (Fig. 2.8B). Intriguingly, PTX further increased

TMEM abundance in the WT but not ATF3 KO tumors, indicating a dependence of PTX on the host-ATF3 to exert its action. At this point, the effect of PTX is only a trend

(p=0.3), not yet statistically significant (p< 0.05 as the commonly accepted standard).

This is likely due to the small sample size (n=6) and biological variation (see Fig. 2.8B); 103

I am repeating the experiment to test it further. If this phenomenon is true, it would be a significant and novel finding (see Discussion below).

ATF3 is expressed in TIE2 positive macrophages in human patient samples: In support of our hypothesis for the role of ATF3 in the host in breast cancer chemotherapy, analysis of an online dataset using Oncomine (Boersma et. al. [332]) revealed that ATF3 expression is further enhanced significantly in the stroma of breast cancer patients treated with chemotherapy (Fig. 2.9A). In our mouse model, ATF3 in the host correlates with a higher level of TEMs—a subset of TAMs (Fig. 2.4B). To test the potential relationship between

ATF3, TAM, and TEM in human cancers, I analyzed ten breast cancer patient samples

(ductal adenocarcinoma and invasive ductal carcinoma) by co-immunofluorescence (co-

IF) for CD68 (macrophage marker), TEK (the human ortholog of TIE2), and ATF3. I detected TEMs in 5 out of ten patients. Fig. 2.9B is a representative image of ATF3 expressing TEM in human breast tumors. In this small samples of 5 patients

(chemotherapy status unknown), only about 5% of the CD68-positive macrophages were positive for TEK (Fig. 2.9C, D), based on the counting of 70-200 CD68-positive cells per patient. Interestingly, although very few in number, I detected high ATF3 expression in more than 75% of the TEMs (Fig. 2.9C histogram, 2.9E); this is in contrast to the 50% of

TAMs expressing ATF3 in this small cohort of patients. I note that, previously, from a different cohort of patients (3 patients, on average ~500 CSF1R-positive cells analyzed per patient) we reported that ATF3 is expressed in about 13-17% of TAMs [70]. Thus, the 50% data in the current small samples may be an overestimation. Further investigation is needed to test whether ATF3 is expressed preferentially in the TEM 104

subset of TAMs in humans. In addition, it would be interesting to test whether ATF3 expression in TEMs segregates patients into chemo-responsive versus non-responsive, or segregates patients into good or bad prognosis. To address these issues properly, we need samples from a large cohort of patients before and after chemotherapy, and with outcome data. However, these samples are not available. As an alternative, I used an online tool

(PROGgeneV2; [333]) and analyzed publicly available microarray datasets. Interestingly, high co-expression of ATF3 and TEK correlates with reduced overall survival in breast cancer, lung cancer, ovarian cancer and colon cancer (Fig. 2.9F). This co-expression is a more significant predictor than either gene expression alone. A caveat of this analysis is that it does not distinguish if ATF3 expression is in TEMs or other cells, since TEK is well known to be expressed in the endothelial cells. However, the co-IF data above showed that ATF3 is indeed expressed in the TEK-positive macrophages in human breast tumors (Fig. 2.9B). Thus, we are encouraged that the link between ATF3 and TIE2 identified in our mouse model may have clinical significance in predicting outcome.

2.5 Discussion

In summary, we found that the stromal ATF3 status exacerbates PTX-enhanced breast cancer metastases to the lungs. At the primary tumor, ATF3 in the host promotes tumor angiogenesis. Particularly in the context of PTX, stromal ATF3 reduces pericyte coverage of blood vessels, presumably making them leakier than untreated vessels.

Additionally, tumors from the WT mice have a greater abundance of the pro-angiogenic

TEMs than tumors from the KO mice. Notably, tumor-derived TEMs from PTX-treated

WT mice increase the invasion of proximal cancer cells in vitro. Finally, tumors from 105

WT mice have significantly more TMEMs, which according to literature are the sites of cancer cell intravasation. In short, PTX and the host-ATF3 lead to the following: (i) greater vascular density, (ii) reduced pericyte coverage, (iii) increased ability of TEMs to mediate cancer cell invasion in response to PTX treatment, and (iv) higher TMEM abundance in WT mice correlates with (v) increased circulating tumor cells found in the blood of PTX-treated WT mice. Taken together, we interpret that PTX and ATF3 in the host create a conducive environment within the primary tumor for cancer cell escape—an early step in metastases.

In the metastatic lungs, Yi Seok found that PTX increases metastatic seeding of cancer cells in WT mice using a lung colonization assay. In this assay, the cancer cells are injected directly into the blood intravenously, thus by-passing the early steps of cancer cell escape from the primary tumor. This assay tells us that even when a similar number of cancer cells are present in the blood stream, PTX-treatment of the host creates a favorable lung environment for the cancer cells to colonize and form metastatic nodules in a host-ATF3 dependent manner. He further observed that CD11b+ myeloid cells from lungs of PTX-treated tumor bearing, WT mice are significantly more immunosuppressive in an in vitro T-cell suppression assay than untreated or KO-mice derived myeloid cells.

Thus, in response to PTX, host ATF3 promotes metastatic seeding in the lungs; in addition, ATF3 in the myeloid cells creates a favorable environment for cancer cell outgrowth by immune-suppression of cancer-killing CD8+ T cells. Together, we find that

ATF3 in the host plays a role in both the primary tumor and the metastatic site, which collectively aggravates PTX-enhanced breast cancer metastases (Fig. 2.10). 106

Novelty and significance: (i) ATF3 in the host limits chemotherapy efficacy and contributes to PTX-aggravated breast cancer metastases. In the past, research has focused on the effect of PTX on cancer cells, in order to investigate the deleterious effects of PTX treatment on breast cancer metastases. For instance, PTX is an agonist of

TLR4 receptor [334]— a key mediator of inflammation—that is expressed by certain experimental breast cancer cell lines and in clinical samples ([335] and references therein). In fact, TLR4 expression in breast cancer patients correlates with metastases. In mouse models, activation of TLR4 signaling induces highly permissible intra-tumoral lymphatic vessels and an influx of inflammatory immune cells into the tumor, leading to enhanced breast cancer metastases by PTX treatment [335, 336]. This is one of the cancer-cell intrinsic mechanisms by which PTX exacerbates breast cancer metastases.

However, in this dissertation, we have uncovered a cancer-cell extrinsic mechanism dependent on host-ATF3 that mediates the deleterious effect of PTX to aggravate breast cancer metastases. This deleterious effect of PTX is despite its apparent therapeutic benefit of reducing the primary tumor size. Thus, PTX is a double-edged sword.

Inhibiting ATF3 is a potential way to dampen its deleterious effect and may be translated in the future to improve chemotherapy.

(ii) ATF3 in the host enhances circulating cancer cells in response to PTX.

Chemotherapeutic drugs are designed to kill cancer cells. Hence, at first glance the enhanced circulating tumor cells in PTX-treated WT mice may seem surprising.

However, not all cancer cells are alike. For instance, it is well documented that the tumor initiating cells are resistant to chemotherapy and persist after chemotherapy [337-341]. 107

Further, it is proposed that cancer cells that can metastasize acquire additional capabilities, such as resistance to hypoxia and anoikis, which gives them a metastatic advantage. Similarly, it is possible that chemotherapy treatments selects for more aggressive cancer cells that are resistant to the drug (hence not killed by it) and subsequently enter circulation through the permissible tumor environment created by

PTX. However, at this point this is speculative as we did not test the properties of the cancer cells themselves in response to PTX (by assays such as in vitro colony formation or in vivo tumorigenesis by serial dilution of cancer cells derived from PTX-treated tumors). Although interesting, how chemotherapy affects the intrinsic properties of cancer cells is not the focus of the research described here. What we showed is that PTX, through affecting the non-cancer cells, increases the abundance of cancer cells in circulation.

(iii) ATF3 in host promotes tumor angiogenesis. Not much is known about transcriptional regulation of tumor angiogenesis, except for Sox17, which when expressed by tumor ECs has been shown to promote tumor angiogenesis in breast cancer and melanoma mouse models [342]. Interestingly, SOX17 is significantly down-regulated in ATF3-KO TAMs isolated by FACS from a PyMT spontaneous tumor model (from microarray data in [70]).

Also, literature is limited on the role of ATF3 in tumor angiogenesis with reports indicating that ATF3 in ECs promotes angiogenesis in vitro [343]. Dr. Xin Yin, a previous graduate student from Dr. Hai’s laboratory, had observed that human breast cancer cells ectopically expressing ATF3 formed more vascularized tumors in nude mice than control cancer cells. In that study she investigated the role of ATF3 in cancer cells 108

and not within the host. Thus, this is the first in vivo study that investigates the role of host ATF3 in regulating tumor angiogenesis. However, in the present study the underlying molecular mechanisms or host cell types that are necessary for ATF3 action are not fully uncovered; we speculate that ATF3 in TAMs is important. Interestingly, presence of ATF3 in the host is necessary for PTX to affect the tumor vasculature: ATF3 in the host reduces vessel pericyte coverage in response to PTX, presumably making them leakier. Improving pericyte coverage is suggested as a rationalized strategy to limit breast cancer metastases. Thus, figuring out the ATF3-dependent mechanisms for regulating pericyte coverage has important therapeutic implications.

(iv) ATF3 in host promotes TEMs in tumors—the importance of TEMs in the context of solid tumors has emerged in recent years. The molecular mechanisms underlying their biology and function are unclear. We report a novel link between ATF3—a key immune modulator that influences macrophage gene expression profiles and is induced by a spectrum of chemokines and cytokines in the tumor microenvironment—and TEM abundance in tumors. However, more studies are required to understand the functional implication of ATF3 in TEM biology.

(v) ATF3 in the host promotes TMEM numbers in the tumor—our preliminary data shows that tumors in PTX-treated WT mice have significantly higher TMEMs than the tumors in the PTX-treated KO mice. TMEMs have prognostic value in determining the metastatic risk in human patients [217, 226]. Our pilot study showed a trend that PTX increases TMEM abundance in a manner dependent on host ATF3. Thus, ATF3 and

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TMEM may potentially be useful to stratify patients. However, this needs to be tested in a larger cohort of patients with outcome before coming to any conclusion. The functional importance of TMEM in promoting cancer cell intravasation was only recently demonstrated [227], about 6 months ago. Our finding (if true) to link chemotherapy to

TMEMs is not only novel but also biological significant. It is novel, because no one has described the link. It is significant, because it would provide a mechanistic insight for the ability of PTX to enhance cancer cells intravasation (as demonstrated by the increased

CTCs, Fig. 2.1E).

(vi) It has been shown that PTX chemotherapy efficacy can be improved by concurrent anti-angiogenesis therapy. Immune-compromised mice orthotopically injected with human breast cancer cells and treated with Nab-PTX and anti-VEGFA therapy had reduced metastases compared to either treatment alone [344]. The proposed mechanism is that anti-angiogenesis therapy inhibits the neoangiogenic response mediated by bone marrow-derived cells mobilized to the tumor after chemotherapy. In the present study, we observe that in the absence of stromal ATF3, tumor angiogenesis is significantly inhibited and not enhanced by PTX chemotherapy, thereby potentially identifying an underlying ATF3-dependent mechanism of achieving an enhanced efficacy of combined chemotherapy and anti-angiogenesis therapy.

Taken the above points (i-vi) together, we identified ATF3 as a key determinant of several processes within the primary tumor that promote cancer cell escape. By contrast, the absence of ATF3 in the host inhibits angiogenesis and mitigates the deleterious

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effects of PTX such as reduced pericyte coverage of vasculature and increased metastases. Thus, ATF3 coordinates multiple pro-tumor processes and may be a promising target for future therapeutic intervention.

Limitations and future studies: During the course of this project, we developed a mouse model to mimic the deleterious effects of PTX in breast cancer metastases. Using this model, we were able to test what are the deleterious phenotypic differences dependent on host-ATF3 in response to PTX. Although this study takes us a step forward in addressing a crucial and clinically relevant issue, it did not answer all the questions. Some limitations of the study are listed below.

(i) In this study we used a single cancer cell line, MVT-1, and a single chemotherapeutic drug, PTX, to test our hypothesis. To address how broadly applicable our results are, we need to test if the observed mechanisms are applicable to other cancer cell lines and/or other chemotherapeutic drugs. These experiments are on-going in the lab. In fact, using the lung colonization assay, Yi Seok found that cyclophosphamide, another commonly used chemotherapeutic agent, showed similar results as PTX: It promotes lung colonization in a host-ATF3 dependent manner. On the other hand, I tested another chemotherapeutic drug, cis-platin, which did not aggravate the lung metastases in a spontaneous breast cancer model. In a pilot experiment with another breast cancer cell line, PyMT, I found that PTX treatment of WT mice causes high incidence of tumor recurrence (described in detail in chapter 3, Fig. 3.10). Thus, in collaboration with Justin

Middleton in the lab, I am testing the effect of PTX using a different mouse strain

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(C57BL/6, versus FVB/N mice used in this study) injected with PyMT breast cancer cells. These cells are derived from a different initial oncogenic event (polyoma middle T antigen, versus VEGF and c-Myc in the MVT-1 cells), therefore we can test if the chemotherapy effect on aggravated disease is dependent of specific cancer type.

(ii) The use of the whole body Atf3 KO mice allowed us to test the necessity of ATF3 in the host in modulating the effect of chemotherapy. Our results indicate that ATF3 in the host regulates numerous processes within the primary tumor that enhance cancer cell escape. Many of these processes such as tumor angiogenesis, cancer cell invasion and

TMEMs are regulated by the myeloid/macrophage lineage. However, we cannot rule out the contribution of ATF3 in other cell types such as endothelial cells, pericytes, and other immune cells, which also lack ATF3. Evidence in literature indicates that ATF3 can be induced by stimuli present within the tumor microenvironment in many host cell-types

(vascular cells, fibroblasts, and others) and influence their biology (as described previously in chapter 1.A.2.6.2). For instance, ATF3 is induced by hypoxia in endothelial cells (ECs) [87]. Hypoxia is prevalent in most solid tumors; thus it is likely that tumor

ECs also express ATF3. Additionally, ATF3 expression in ECs protects them from

TNFA mediated death in an in vitro assay by transcriptional down-regulation of p53 [85].

Since TNFA is often present in the tumor milieu, ATF3 in the tumor ECs may have a protective role. Another study reports that ATF3 promotes diabetic angiopathy [86].

ATF3 in ECs has also been shown to promote angiogenesis in vitro [343]. These studies support the functional importance of ATF3 in ECs, which may also be relevant to tumor angiogenesis and the phenotype observed here. Cancer associated fibroblasts express 112

ATF3 and have been shown to promote tumor progression in mice, when co-injected with cancer cells [91]. Thus, to delineate which host cell type(s) is/are necessary for ATF3 action to exacerbate PTX-aggravated breast cancer metastases, more experiments are needed. For instance, genetic ablation of Atf3 in vivo in the myeloid/macrophage lineage using the LysM-Cre/Atf3 floxed mice is required to test if ATF3 in TAMs is necessary for the PTX-aggravated breast cancer metastases.

(iii) Finally, in this study we have uncovered ATF3-dependent primary tumor phenotypes that correlate with enhanced metastases in the context of chemotherapy. However, the underlying molecular mechanism is not fully elucidated. Additional studies are required to uncover how ATF3 regulates processes such as angiogenesis, pericyte coverage, recruitment of TEMs, and TMEM abundance at the molecular level.

2.6 Acknowledgement

I thank Mr. Yi Seok Chang, another member of the team, for collaborating with me on this project. As described in other parts of the dissertation, Yi Seok and I carried out the mouse experiments together but divided the work for the analyses of primary tumors (by me) and metastatic lungs (by Yi Seok). Together, we were able to address diverse and interesting questions that opened up the project to exciting possibilities. I also thank

Justin Middleton, who recently joined Dr. Hai’s laboratory as a graduate student, for his assistance on some experiments.

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Figure 2.1. Host-ATF3 contributes to PTX-aggravated breast cancer metastases. (A) A schematic for the spontaneous metastases model. Female mice were injected with syngeneic breast cancer cells at the fat pad; when tumors became palpable (day 7 post cancer cell injection), mice were treated repeatedly with PTX (20mgs/kg, 3 times/week, 8 times total) or saline and analyzed at the end point (day 26). (B) Primary tumor weight at the end point. The recorded weights are shown on a scatter plot; each circle represents a single mouse with the bars indicating the Mean (n=12 from two independent experiments). (C) Representative H&E images of the lungs showing micrometastases. (D) Quantification of lung micrometastases. H&E lung images were quantified using ImageJ-Fiji and shown on a scatter plot; each circle represents a single mouse with the bars indicating the Mean±SEM, (n=12 from two independent experiments). (E) Estimation of circulating tumor cells (CTCs). CTC level in RBC-depleted blood cells was estimated by RT-qPCR for cMyc, a transgene in the cancer cells. Actin was used as an internal control and the standardized cMyc signal in the WT+PBS group was arbitrarily defined as one. Results from two independent experiments is shown (Mean±SEM, n=5). All quantitative data were analyzed by two-way ANOVA with post- hoc Bonferroni test. * P<0.05, ** P <0.1, # P <0.005, ## P <0.001.

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Figure 2.2. ATF3 in the host promotes tumor angiogenesis and PTX reduces vessel pericyte coverage in a host-ATF3 dependent manner. (A) Representative immunofluorescent images of day 26 tumor sections stained for CD31, an endothelial cell marker, to indicate blood vessels (microvessels). Scale bar = 100. (B) Quantification of microvessel density of day 26 tumors. Immunofluorescent images of day 26 tumors stained for CD31 were analyzed using ImageJ-Fiji as detailed in methods and materials (also see Fig. 2.3A)). The percent (%) of CD31-positive area was determined per field of view (FOV). A minimal of five representative FOVs (under 200 magnification) were analyzed per mouse. The average percent CD31-positive area per mouse was calculated and is shown on a scatter plot; each circle represents a single mouse with the bars indicating the Mean±SEM, (n=9 from two independent experiments). (C) Quantification of microvessel density of day 15 tumors. ImageJ-Fiji analysis was done as in (B). A minimal of fifteen FOVs (under 200 magnification) were analyzed per mouse to comprehensively capture the majority of the tumor section. The average percent CD31-positive area per mouse was calculated and is shown on a scatter plot; each circle represents a single mouse with the bars indicating the Mean±SEM, (n=8 WT+PBS, n=10 WT+PTX, n=6 KO+PBS, n=10 KO+PTX; from three independent experiments). (D) Representative immunofluorescence images of day 22 tumor sections stained for CD31 (red) and SMA (green), to indicate pericytes. Images from four different mice per group are shown. Inset: area within the white box. Scale bar = 100. (E) Quantification of pericyte coverage. Immunofluorescent images were analyzed using ImageJ-Fiji as detailed in the methods and materials (also see Fig. 2.3B). A minimal of five random FOVs (under 200 magnification) were analyzed per mouse. The percent (%) of area positive for SMA (green) within the CD31-positive area (red) in each FOV was determined. The values for all FOVs examined in each mouse were averaged and is shown on a scatter plot; each circle represents a single mouse with the bars indicating the Mean±SEM, (n=5 WT+PBS, n=8 WT+PTX, n=5 KO+PBS, n=6 KO+PTX from two independent experiments). (F) Gene expression analysis by RT- qPCR of total tumor. RNA from total tumor was analyzed by RT-qPCR for the indicated genes with a focus on pro-angiogenic and anti-angiogenic genes. Actin was used as an internal control, and the standardized signal in the WT+PBS group was arbitrarily defined as one. Results from four independent experiments is shown (Mean±SEM, n=12). The horizontal bars indicate statistical significance (P<0.05). Blue: between the two WT groups; Black: between the two PBS groups; Red: between the two PTX groups; Green: between the two KO groups. All quantitative data were analyzed by two-way ANOVA with post-hoc Bonferroni test. * P<0.05, ** P <0.1, # P <0.005, ## P <0.001.

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Figure 2.2. ATF3 in the host promotes tumor angiogenesis and PTX reduces vessel pericyte coverage in a host-ATF3 dependent manner.

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Figure 2.3. Analysis of immunofluorescent images for microvessel density and pericyte coverage using ImageJ-Fiji. (A) An example for analysis of microvessel density. Immunofluorescent images captured at 200 magnification of tissues stained for CD31 (Red) were analyzed, as detailed in methods and materials. Briefly, the image was split into independent channels (red, blue and green) and the red channel was used for further analysis. The red channel (8 bit) image was adjusted by using the threshold function, to create a mask for the areas positive for CD31. This mask was then measured to determine percent CD31 area per FOV. (B) An example for analysis of pericyte coverage. Immunofluorescent images captured at 200 magnification of tissues stained for CD31 (endothelial cell marker, red) and SMA (pericyte marker, green) were analyzed using sequential ImageJ-Fiji functions, as detailed in methods and materials. Briefly, the image was split into independent channels (red, blue and green) and the red and green channel were used for further analysis. The red channel (8 bit) image was adjusted by using the threshold function, to create a mask for the areas positive for CD31. This mask was used to create a selection—the region of interest that indicates all the blood vessels (outlined by yellow). This selection (identifying the areas of blood vessels) was then applied to the green channel image, which was also first adjusted for threshold. Applying the selection created in the red channel to the green image allows the quantification of green pixels restricted within the selection. This pixel number is then divided by the pixel number of the selected region to obtain percent (%) of pericyte coverage.

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Original image Red channel, 8 bit Masked image Image measured: Area (%)

Tool bar à Image à Tool bar à Image à Tool bar à Process à Color à Split channel Adjust Threshold à Noiseà Despeckle

Original image

Tool bar à Image à Color à Split channel

S 1

M 3

D C

Red channel, 8 bit Green channel, 8 bit Image à Adjust Threshold à Image à Adjust Threshold à

Masked image, CD31 Area of interest Selected region Masked image, SMA Image measured: SMA Area (% of Tool bar à Edit à Tool bar à Edit à selected region) Tool bar à Process à Selectionà Create Selectionà Restore Noiseà Despeckle selection selection

Figure 2.3. Analysis of immunofluorescent images for microvessel density and pericyte coverage using ImageJ-Fiji.

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Figure 2.4. ATF3 in the host promotes a pro-angiogenic subset of TAMs. (A) An estimation of total TAMs. Single cell suspension from tumor digests was stained with APC-conjugated anti-CD11b and FITC-conjugated anti-F480 antibody, followed by flow cytometry. The data were analyzed using the FlowJo software to estimate the CD11b+ F480+ double positive cell: TAMs. These immunophenotype results are represented as a scatter plot; each circle represents a single mouse with the bars indicating the Mean±SEM, (n=18, from six independent experiments). (B) An estimation of total TEMs. TEMs, a subset of TAMs, were analyzed by immunophenotyping. Single cell suspension from tumor digests was stained with APC-conjugated anti-CD11b, FITC- conjugated anti-F480 and PE-conjugated anti-TIE2 antibody, followed by flow cytometry. The data were analyzed using the FlowJo software to estimate the CD11b+ F480+ TIE2 triple positive cell: TEMs (see Fig. 2.5 for gating strategy). The results are represented as a scatter plot; each circle represents a single mouse with the bars indicating the Mean±SEM, (n=21, from seven independent experiments). (C) Gene expression analysis by RT-qPCR of F480+ cells isolated from primary tumors. F480+ cells were enriched from total tumor digests by magnetic bead (Dynabeads), as detailed in the methods and materials. Briefly, single-cell suspensions from primary tumor were incubated with anti-F480 antibody (raised in rat) and selectively enriched using magnetic beads coated with anti-Rat IgG antibody. RNA extracted from the F480- enriched cells was analyzed by RT-qPCR for the indicated genes. Actin was used as an internal control and the standardized signal in the WT+PBS group was arbitrarily defined as one. Results from two independent experiments are shown (Mean±SEM, n=6). (D) A schematic for the experimental design of Matrigel plug assay. TAMs were isolated from tumors in the spontaneous metastases model on day 26 from WT and KO mice treated with either PTX or saline. Single cell suspension from tumor digests was stained with APC-conjugated anti-CD11b and FITC-conjugated anti-F480; double positive cells (TAMs, CD11b+ F480+) were then isolated by fluorescent activated cell sorting (FACS) (Aria/AriaIII). ~70,000 freshly isolated TAMs were resuspended in 350 l of Matrigel and injected into the ventral side between the fourth and the fifth fat pad of FVB/N mice. In each experiment, TAMs were sorted from three tumors combined (per group) and injected into a single WT recipient. The Matrigel plugs were removed 6 days later, fixed, and analyzed for CD31+ area as in Fig. 2.2B,C. (E) Representative immunofluorescent images of Matrigel plug sections stained for CD31 (microvessel density). Scale bar = 100. (F) Quantification of CD31+ area in Matrigel plug. ImageJ-Fiji analysis was done as in Fig. 2.2B. For each plug, all FOVs (under 200 magnification) were captured for comprehensive analysis. The percent CD31-positive area for each FOV is shown on a scatter plot, with the bars indicating the Mean±SEM (n=4, for WT+PTX, n=3 for all other groups from three independent experiments). Note that in this plot, each point on the scatter plot represents one FOV, while n refers to the number of plugs; thus, multiple points correspond to one plug. All quantitative data were analyzed by two-way ANOVA with post-hoc Bonferroni test. * P<0.05, # P <0.005, ## P <0.001.

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Figure 2.4. ATF3 in the host promotes a pro-angiogenic subset of TAMs.

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Figure 2.5. Gating strategy for the analyses of TEM numbers in primary tumors (immunophenotyping). Data were acquired on the LSRII and analyzed using the FlowJo software. Unstained cells (top row) and single stained samples (not shown) were used as gating controls. Stained cells were first gated for CD11b versus side scatter (SSC). CD11b+ cells from this gate were then gated for F480 versus SSC. Among these, the F480+ cells (that is CD11b+ F480+ cells) were gated for TIE2+ versus SSC. The TIE2+ cells in this final gate were CD11b+ F480+ TIE2+ cells: TEMs. Representative analyses of one sample from each group are shown as examples for the gating applied. The red rectangle highlights the TEM cell population across the four groups of mice.

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Figure 2.5. Gating strategy for the analyses of TEM numbers in primary tumors (immunophenotyping). 122

Figure 2.6. PTX treatment affects the property of WT TEMs. (A) A schematic for isolation of TEMs from primary tumors. TEMs were isolated from tumors in the spontaneous metastases model on day 26 from WT mice treated with either PTX or saline. In each experiment, TEMs were sorted from three tumors combined (per group). Single cell suspension from tumor digests was stained with APC-conjugated anti-CD11b, FITC-conjugated anti-F480 and PE-conjugated anti-TIE2 antibody. Triple positive cells (TEMs, CD11b+ F480+ TIE2+) were sorted using Aria/AriaIII (see Fig. 2.7 for gating strategy). (B) A schematic for the in vitro cancer cell invasion assay: direct co-culture of TEMs with GFP-expressing MVT-1 cancer cells. Boyden chamber with Matrigel- coated membrane was used and freshly isolated TEMs were co-cultured with GFP- expressing cancer cells on the top. Chemotactic gradient was created using DMEM media with 1% FBS in the top chamber, but DMEM with 10% FBS in the bottom. (C) Quantification of cancer cell invasion in direct co-culture. GFP-positive cancer cells were incubated alone or with TEMs isolated from the indicated mice (WT+PBS or WT+PTX, three donor mice per group). Triplicate inserts were set up for each group, and after 16 hours of co-culture, cancer cell invasion was assayed by imaging the bottom surface of the insert. Nine FOVs were captured at 100X magnification for each insert (thus, 27 FOVs for each group). 81 images from all three groups (no TEM, WT+PBS TEMs, WT+PTX TEMs) were scrambled and GFP-positive cancer cells counted manually in a blind fashion to minimize bias. Bars represent Mean±SEM derived from 27 FOVs for each group. Data from one representative experiment are shown; reproduced in three independent experiments. (D) Representative images of invaded GFP positive cancer cells. GFP-positive cells that have invaded through the Matrigel and across the insert membrane are shown. Images taken at 100X magnification. (E) A schematic for the in vitro cancer cell invasion assay: indirect co-culture of TEMs with GFP- expressing MVT-1 cancer cells. Same as panel (B), except TEMs and cancer cells were placed in different chambers as indicated. (F) Quantification of cancer cell invasion in indirect co-culture. Same as in (C). Bars represent Mean±SEM from 27 FOVs for each group; the data were reproduced in two independent experiments. All quantitative data were analyzed by one-way ANOVA with post hoc Students’ t test. ns=not significant, ** p<0.1, ## p<0.001.

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Figure 2.6. PTX treatment affects the property of WT TEMs. 124

Figure 2.7. Gating strategy and post-sort analysis for tumor-derived TEMs. (A) Sequential gating for cell sorting. (a) SSC (for granularity) and forward scatter (FSC, for size) were used to exclude smaller cells and debris (low on both SSC and FSC). (b) These cells were then preferentially selected for single cells by width and height: around the diagonal in the SSC-H versus SSC-W plot, and around the diagonal in the FSC-H versus FSC-W plot (not shown). (c) Unstained cells (top row) and single stained cells (not shown) were used as gating controls. Triple stained cells were selected for CD11b positivity, then for F480 and TIE2 positivity to enrich CD11b+ F480+ TIE2+ cells (TEM). Representative images from WT+PBS group and WT+PTX group are shown as examples for the gating applied. (B) Post-sort purity. Cells after sorting were analyzed and showed enrichment (~80% or more).

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Figure 2.7. Gating strategy and post-sort analysis for tumor-derived TEMs.

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Figure 2.8. ATF3 in the host increases the abundance of the TMEM structures in the mouse primary tumors. (A) A schematic representing the tripartite TMEM structure. As defined by Condeelis and colleagues, TMEM contains F480-positive macrophage and Mena-positive cancer cells adjacent to each other and to the blood vessel (discussed in Chapter 1, B.2.4.3). (B) Estimation of TMEM number. Tumor sections (day 26) were stained for F480, Mena and CD31. Five random high power fields (400X magnification) per tumor were captured and analyzed in a blind fashion. TMEM structures were defined as F480 positive cells, Mena positive cells and CD31 vessels in close proximity (within 20 but not overlapping with each other. The average number of TMEMs for each mouse was shown on a scatter plot, with the bars indicating the Mean±SEM (n=5 KO+PBS, n=6 for all other groups from two independent experiments). Data were analyzed by two-way ANOVA with post-hoc Bonferroni tet. PTX treatment showed a trend to increase TMEM abundance in the WT mice (P=0.3) but not in the KO groups, indicating that the effect of PTX—if true—is dependent on the host-ATF3. A genotype effect of host-ATF3 to increase TMEM was observed: a trend between the two PBS groups (P=0.12) and statistically significant between the two PTX groups (P=0.008). (C) Representative images of TMEMs in day 26 mouse tumors. Tumor sections were stained for F480 (grey), Mena (green) and CD31 (red). Nuclear stain is indicated by Topro 3 (blue). Images were captured at 400X magnification. Scale bar = 25. The arrows indicate the three cell types that contribute to one TMEM structure: red for the blood vessel, grey for the macrophage, and green for Mena-positive cell. The yellow lines in the top images indicate the plane that gives rise to the signals shown in the histogram. The black bar below the histogram represents 20, indicating that the three cell types are close-by. (D) A representative image of the structures not counted as TMEMs. Many structures commonly found in the tumor sections were not TMEMs. In fact, such non-TMEM structures were more abundant than TMEM. As an example, the image shows a green cell (Mena-positive) next to the vessel (red), with no macrophage (grey) in the vicinity. Such structures were not counted as a TMEM. In addition, if any signals overlapped with each other completely, they were interpreted as the same cells. To be counted as a TMEM, all three signals had to be separated from each other, yet within close proximity (the edges of the participating cells are within 20 in our analyses. Scale bar = 25.

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Figure 2.8. ATF3 in the host increases the abundance of the TMEM structures in the mouse primary tumors.

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Figure 2.9. Clinical relevance of ATF3 and TEK (human TIE2) in breast cancer. (A) ATF3 induction by chemotherapy in the stroma of human breast tumors. A publicly available dataset, which contains gene expression information in the stroma of patients with or without chemotherapy, was analyzed for ATF3 at the Oncomine website (n=27 for patients without chemotherapy, n=20 for patients with chemotherapy, Boersma et. al.). * P<0.05, Students’ one-tail t test. (B) ATF3 expression in TEK-positive macrophages. Shown is a representative image from human breast tumors stained by co- immunofluorescence for ATF3 (red), TEK (grey), and CD68 (green, for macrophage). Nuclei were stained by Topro 3 (blue). The white arrow indicates an ATF3-expressing TEM: a cell positive for all three markers. Note that some macrophages are TEK- negative. Scale bar = 20. (C) A comparison of the ATF3 level in TEK-positive macrophages (TEMs) and TEK-negative macrophages (TAMs). Both TEMs and TAMs express ATF3. The yellow line indicates the plane that gives rise to the signals shown in the histogram, which provides information regarding the intensity (amount) and overlap (co-localization) of the signals. Note the higher red peak in TEMs (white arrow) than that in TAMs (yellow arrow), indicating higher ATF3 level in TEK-positive macrophages. However, the sample size is too small to do statistical analysis at this point. Scale bar = 20. (D) The estimated percent of macrophages positive for TEK in human breast tumors. 70-200 CD68+ cells were examined for each patient sample depending upon the amount of macrophage infiltrate. The percent of TEK-positive macrophages within the FOV was determined and the average percent for each patient calculated (from 2-6 fields per patient). Each circle on the scatter plot represents one patient; bars indicate Mean±SEM. (E) The estimated percent of ATF3 expressing TEMs and ATF3 expressing TAMs in human breast tumors. ATF3 positive cells within total TEMs (TIE2+ CD68+) and total TAMs (TIE2- CD68+) were counted in 6 patients (70-200 CD68+ cells counted per patient) and the average percentage was plotted; bars indicate Mean±SEM. (F) Co-expression of ATF3 and TEK predicts worse outcome in human cancers. Using the online tool (PROGgeneV2), I found that high co-expression of ATF3 and TEK (red line) significantly correlated with reduced overall survival in breast cancer, lung cancer, ovarian cancer and colon cancer.

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Figure 2.9. Clinical relevance of ATF3 and TEK (human TIE2) in breast cancer.

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Figure 2.10. A working model for the mechanisms by which the host-ATF3 contributes to PTX-aggravated breast cancer metastases. This figure is shared by Mr. Yi Seok Chang and me. Each of us made the part of the figure relevant to our research. Collectively, our data indicate that the host-ATF3 regulates multiple processes that can contribute towards PTX-aggravated breast cancer metastases. Dotted line indicates the part based on my data. Systemic PTX treatment affects host cells in the primary tumor and at the secondary site (lung in our model), thereby influencing key steps of the metastatic cascade. (A) In the primary tumor, ATF3 in the host promotes tumor angiogenesis (microvessel density) and TEM abundance as evidenced by their higher values in WT than KO host; however, these effects are not further enhanced by PTX. Importantly, PTX reduces pericyte coverage and tends to increase TMEM, both in a host-ATF3 dependent manner. PTX also affects the property of TEMs in the WT host (as indicated by their increased ability to induce cancer cell invasion in a Boyden chamber assay). These differences culminate in higher circulating tumor cells (CTCs) in WT host than ATF3 KO host, and an increase in CTCs by PTX in WT but not ATF3 KO host. Thus, PTX—despite its therapeutic benefit as indicated by primary tumor reduction—creates an environment conducive for cancer cells to escape in a host-ATF3 dependent manner. (B) In the lung, Yi Seok found that PTX promotes in vivo cancer cell seeding and enhances immune suppression in the lungs of tumor bearing mice, in a host-ATF3 dependent manner. In addition, his results indicate that PTX-treated WT mice produce the highest amount of CCL2 in the lung amongst the four groups compared. CCL2 is an important chemokine for the recruitment of inflammatory monocytes (IMs) that are known in the literature to drive the metastatic outgrowth of cancer cells in the lungs. Interestingly, Yi Seok found a concomitant increase in IMs in the lungs of tumor-bearing, PTX-treated WT mice, which was not observed in the KO mice.

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Chapter 3

Tracing “stressed cells” in vivo: The ATF3-Cre* mouse

3.1 Summary

The overall goal of this project is to generate a novel transgenic mouse model to genetically trace stressed cells by integrating our knowledge of ATF3 biology with the

ROSA-genetic tracing methodology. The proposed mouse will carry double transgenic alleles: (i) an ATF3-Cre allele expressing Cre under the control of the ~50kb Atf3 locus, and (ii) the ROSA26 reporter allele (ROSA26-lox-STOP-lox-reporter). The rationale is as follows. Since Atf3 transcription is rapidly induced by stress, the ATF3-Cre allele will express Cre in a stress-inducible manner and functions as an indicator of stress. The Atf3-

Cre mice can then be crossed with the commercially available ROSA26 reporter mice to generate the double transgenic mice; upon stress these mice will produce Cre to remove the STOP and express the reporter gene, thus marking the stressed cells for tracing applications. As a part of my dissertation work, I generated a transgenic mouse line Atf3-

Cre*, which uses the Atf3 genomic locus to drive the expression of a double-coding mRNA that produces a fluorescent protein and an inducible Cre (CreERt2). In this chapter, I describe the experimental strategies and my work generating the transgenic construct (Atf3-Cre*) by bacterial artificial chromosomal (BAC) recombineeering. I also describe the preliminary characterization of the Atf3-Cre* mouse lines using an acute

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stress paradigm and a chronic stress paradigm. This work is continued by another graduate student in the laboratory to make homozygous Atf3-Cre* mice for future crossing with the ROSA reporter.

3.2 Introduction

There are many environmental factors that act as stressors and negatively affect our lives, such as pollution, hazardous chemicals, toxins, socio-economic stresses, infectious agents, wound/injury, dietary imbalance and lack of nutrition. Over time, these stressors can lead to disease. At the cellular level, stress—defined as an event/stimulus that disturbs homeostasis—can have diverse effects on the cell fate. For instance, a stressed cell may arrest during cell cycle, undergo cell death, proliferate, differentiate, change properties (for instance, adherent or migratory), or influence other neighboring cells.

These reactions of cells to stress are collectively called the “cellular stress responses” and constitute the cells’ coping mechanisms to homeostatic disturbances. The cellular stress responses are gene-directed active processes. Thus, to better understand the gene- environment interactions and design potential future interventions for stress-induced diseases, it is important to understand the cellular responses at the genetic and molecular levels.

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The ROSA genetic tracing method: As detailed in Chapter 1.C, the ROSA tracing technique uses the Cre-lox system to permanently mark cells of interest. The two genetic elements necessary for ROSA tracing are: (1) a tissue/cell-selective Cre, and (2) the

ROSA26-lox-STOP-lox-reporter cassette knocked into the ubiquitous and non-essential mouse ROSA26 genomic locus (ROSA26-lox-STOP-lox-Reporter). The reporter allele allows marking the cells that had the Cre activity at any point of their lifetime: Cre catalyzes site-specific DNA recombination at the lox sites and removes the transcriptional STOP sequence preceding the reporter gene. Once the STOP sequence is removed, the reporter gene is permanently and irreversibly “switched on” and transcribed. In other words, the removal of STOP by Cre permanently activates the reporter gene expression and “marks” the cell and all its progeny. Consequently, these

“marked” cells can be “traced” over time. Hence, the presence of both Cre and the reporter gene are essential for tracing purposes.

In our design, we plan to use the commercially available ROSA26-lox-STOP-lox-Yellow

Fluorescent Protein (YFP) mice to fluorescently label cells. However, we need to generate the Cre mouse lines to selectively mark stressed cells, since the mice are not available in the field. I will refer to these mice generically as the “stress-driver mice.”

Below, I describe the rationale that the Atf3 locus is an excellent tool for this purpose.

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ATF3 as an indicator of cellular stress: The cellular stress response is as varied as the stressors existing in Nature. To fully understand the stress response to a particular stressor, we need to investigate what happens to the cell after it has experienced stress.

Thus, it is necessary to identify and track cells after they are stressed. As described in

Chapter 1.A, the Atf3 gene encodes a member of the ATF/CREB family of transcription factors. Overwhelming evidence indicates that Atf3 is normally not expressed in the cells, but is induced by a broad spectrum of stress signals and in many different cell types

(see Chapter 1, A.2.1). Importantly, transcriptional activation is a key mechanism by which stress signals induce Atf3 expression, as evidenced by reporter assays and the presence of numerous DNA binding sites on the Atf3 promoter that are recognized by transcription factor known to mediate inducible gene expression [3]. These features— broad induction by stress signals at the transcriptional level—indicate that the Atf3 locus can be used as a “handle” to drive the expression of Cre recombinase for labeling or marking the stressed cells.

Design of the ATF3-Cre* transgenic construct: In our design, we decided to generate a transgenic mouse model, where the Cre recombinase is driven by the Atf3 locus as an extra allele—in addition to the two endogenous Atf3 alleles (see below). This is in contrast to a knock-in model, where the Cre is inserted into the endogenous Atf3 locus.

The reason for our design is to keep the endogenous Atf3 alleles intact, thus not disturbing the cellular adaptive-response network. We included the following design features in order to not produce extra Atf3 gene product, but optimize the Cre expression pattern mimicking the endogenous Atf3 gene as close as possible. 136

First, use the entire ~50kb Atf3 genomic locus to drive the expression of Cre. Thus, the entire ~35kb region upstream from the transcriptional start site (TSS) is included; it contains the proximal promoter and the putative distant promoter, with numerous transcription factor binding sites. We believe that this is significantly better than using just partial promoter regions as in the traditional transgenic design. Second, replace the

Atf3 coding region between the Start codon in exon B and the Stop codon in exon E with the Cre coding sequence. This will leave the following regions in the transgenic construct: Atf3 exon A, the first intron-exon junction, and the region downstream from the Stop codon; together they give rise to the 5’ and 3’ untranslated regions (UTRs) in the transcript. This was done for two reasons: (i) to ensure that the transgene does not make extra ATF3 protein, and (ii) to terminate Cre translation near the endogenous Stop codon

(in exon E), and thus prevent non-sense mediated decay, an event that occurs when translation is pre-maturely terminated [345]. We recognize that our design omitted some introns and intron/exon junctions, which may have regulatory roles in Atf3 gene expression. Since no such regulation has been demonstrated thus far, we decided that the benefits (i and ii above) outweigh the potential risk. Together, the above two designs allow us to express Cre under the control of the entire Atf3 locus, except from the Start to the Stop codon, optimizing the chance of mimicking Cre expression as close as possible to the endogenous Atf3 gene.

We also added the following features to enhance the utility of our “stress-driver.” (a) Use the tamoxifen-inducible derivative of Cre: CreERT2. In the absence of tamoxifen,

CreERT2 remains sequestered in the cytoplasm and cannot recombine nuclear DNA. 137

When tamoxifen is added, the CreERT2 is able to translocate into the nucleus and recombine the DNA to remove STOP, thereby switching on the expression of the reporter and labeling the cell. Thus, in our model to produce active Cre, two treatments are required: stress stimulation to express CreERT2 and then tamoxifen to activate it. This design provides an additional level of control for the time point of initiating tracing via tamoxifen administration. (b) Add the fluorescent protein mCherry upstream of CreERT2 with P2A sequence separating them: mCherry-P2A-CreERT2. The P2A sequence causes

“ribosome skipping,” resulting in the production of mCherry and CreERT2 as two separate proteins [346]. The addition of mCherry will enable us to identify the cells that are “actively” stressed and currently expressing CreERT2. For convenience, the final transgenic construct is denoted as Atf3-Cre*. Fig. 3.1 shows a schematic comparing the endogenous Atf3 gene and the Atf3-Cre* transgene. I used the BAC recombineering

[347] technique to generate the transgenic cassette Atf3-Cre*, and carried out preliminary characterization of the resulting mice. The data are presented below.

3.3 Materials and Methods

Traditional cloning of mCherry-P2A-CreERT2-Frt-Neo-Frt construct: This construct

(Cre*-NF for short) was generated by piecing together two parts: mCherry-P2A-

CreERT2 and Frt-Neo-Frt (FNF). First, the mCherry-P2A-CreERT2 construct (~3kb) was generated by four-primer PCR with high fidelity DNA polymerase PfuUltra

(Agilent). The primers used for this cloning are: TH1561, TH1569, TH1570 and Th1715

(for sequences see Table 3.1). The 5’ primer of mCherry has a Sal1 site (GTCGAC) and the 3’ primer of CreERT2 has a Pac1 (TTAATTAA) and an EcoR1 site (GAATTC). This 138

enabled directional cloning of mCherry-P2A-CreERT2 into the 5’-Sal1-EcoR1-3’ sites upstream of FNF in the Addgene vector, pL451 (~5kb). Briefly, the mCherry-P2A-

CreERT2 fragment was PCR-amplified, digested overnight with EcoR1 and Sal1, gel purified and ligated into the EcoR1-Sal1 digested pL451 vector. The ligation product was transformed into chemically competent DH5 E.coli strain by heat shock. The pL451vector contains the antibiotic resistance gene for ampicillin, in addition to Neo that codes for neomycin/kanamycin resistance. Thus, positive clones were selected by plating the transformed bacteria on agar plates with both ampicillin and kanamycin. Plasmid

DNA isolated from the antibiotic-selected clones was analyzed by EcoR1-Sal1 digestion to detect the presence of two bands corresponding to the vector (~5kb) and insert

(~2.7kb) on 1% agarose gels. One positive clone was sent for DNA sequencing to determine sequence integrity of mCherry, P2A and CreERT2 using sequencing primers

TH1577, TH1594 and TH1595 (Table 3.1).

BAC Recombineering to swap Cre*-NF into Atf3-BAC: BAC recombineering is defined as recombination-mediated genetic engineering. It utilizes specialized bacterial strains that carry phage DNA recombination genes—exo, bet, and gam. These encode for enzymes that allow the manipulation of large target DNA fragments present within these bacteria —such as Atf3-BAC—with exogenous, linear DNA inserts, provided the insert has at least 50bps of homology with the target DNA at both its ends. The homologous sequence determines the site of integration of the insert with the target DNA [348, 349].

Advantageously, this method permits manipulation of large DNA molecules with

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minimal introduction of extra, undesired sequences, such as restriction enzyme sites, which are required for traditional cloning.

Here, BAC recombineering was used to introduce the Cre*-NF into the open reading frame of Atf3 between exon B and exon E at specific sites. To achieve this, 5’ and 3’ homology arms (5’Ho and 3’Ho) were attached to the Cre*-NF insert. Briefly, we designed long PCR primers with 65bps homology to specific Atf3 genomic regions and used these to PCR amplify the entire Cre*-NF insert (~5kb) from the previously cloned and sequenced plasmid template. We used high fidelity PfuUltra II DNA polymerase enzyme (Agilent) for PCR-based cloning. The 5’ primer, TH1571, has 65bp homology to the Atf3 intron-1/exon B junction. The 3’ primer, TH1592, has 65bp homology to the

Atf3 region after the stop codon in exon E (for sequence see Table 3.1). The PCR conditions are as as follows—initial denaturation: 95C, 3 min; Cycling: 95C, 30 sec; 60C,

30 sec; 72C, 3 min; for 30 cycles; Final extension: 10 mins.

The PCR reaction was then digested with DpnI to remove the plasmid template followed by gel purification to isolate high purity linear PCR product (insert). Finally, the purified

PCR product (100-200ng/l) was electroporated into freshly prepared electrocompetent and recombinogenic SW102 bacterial strain that already contained the Atf3-BAC. The

SW102 cells were made recombinogenic by a brief 15 minutes’ heat-shock at 42C to derepress the phage recombination genes—exo, bet and gam (detailed protocol ref), which then catalyze recombination between the PCR insert and the Atf3-BAC. Note that

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Dpn1 treatment and purification of the PCR reaction is crucial in order to minimize false positive results during colony screening (discussed in a section below).

Positive antibiotics selection of recombined BAC clones: After electroporation with the insert, the transformed bacteria were allowed to grow in 1ml liquid culture for an hour at

32C to allow recombination to occur, before plating them onto agar plates with chloramphenicol and kanamycin antibiotics. The Atf3-BAC has chloramphenicol resistance gene but lacks the kanamycin resistance gene. The insert has kanamycin resistance gene (Neo) but lacks the chloramphenicol resistance gene. Thus, only successfully recombined clones containing the insert-derived Neo selection marker on the

Atf3-BAC backbone will grow on chloramphenicol+kanamycin double selection plates, thereby allowing their positive selection.

Negative antibiotic selection of false positive clones: As mentioned earlier, it is crucial to remove the entire plasmid template used for PCR amplification of Cre*-NF insert by

Dpn1 digestion and gel purification (see above) prior to electroporation. However, despite these treatments, it is possible to carry over trace, undetectable amounts of the plasmid template to the electroporation step. The plasmid template is electroporated more efficiently into bacteria. If electroporated with the insert, the plasmid DNA can repopulate the bacteria leading to kanamycin resistance due to the Neo. The Atf3-BAC clones are already chloramphenicol resistant. Thus, even without proper recombination, the co-existence of the template plasmid and Atf3-BAC can result in bacterial clones that survive double antibiotic selection. To exclude this possibility and distinguish between

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the recombined clone and unrecombined plasmid, colonies that grew on the chloramphenicol + kanamycin plate were replica plated onto an ampicillin plate. Only the plasmid vector contains an ampicillin resistance gene. Therefore, BAC clones that grew on ampicillin plates were identified and rejected as false positives.

PCR based assay to determine accuracy of recombination: Three sets of primers were designed to test if the insert recombination was at the desired location within the Atf3-

BAC. Set-1primers flanked the expected 5’ junction (site of integration)—at the Atf3 intron1-exon B area (TH822 and TH1598; expected amplicon size equals ~1.2kb). Set-2 primers flanked the 3’ junction at exon E (TH1570 or any Cre forward primer and

TH1507; expected amplicon size equals ~5kb). Set-3 amplified part of Atf3 exon B

(TH822 and TH823; expected amplicon size ~1.5kb). The product sizes of these PCR reactions are indicative of recombination precision. Set-3 primers amplify unrecombined, or improperly recombined DNA and act as a negative control. The primer sequences are as listed in Table 3.1.

To screen the antibiotic selected clones, colony PCR was done with Set-1 primer. BAC

DNA was isolated from the colonies that tested positive for the Set-1 amplicon, and re- tested with Set-1, Set-2 and Set-3 primers. Clone positive for Set-1 and Set-2 but negative for Set-3 was used for BAC segregation and generating the final BAC transgene.

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Excision of the selectable marker, Neo, from Atf3- Cre*-NF: The final Atf3-Cre* BAC transgene does not contain the Neo gene at the 3’ end. The Neo gene was removed by employing another BAC recombineering strain, SW105 that contains arabinose-inducible flippase gene (Flp). The Flp gene encodes an enzyme that mediates site-directed recombination of DNA at the Frt sequence, thereby removing any DNA in between two

Frt sites. Therefore, we isolated the recombined BAC DNA (from antibiotic selected and

PCR tested clone) and transformed it into SW105 by electroporation. One of the transformed SW105 colonies was grown in liquid culture at 32C, with continuous shaking. At OD600=0.5, L-arabinose was added to the culture at a final concentration of

0.5% to induce Flp. After another hour at 32C to allow Flp-mediated recombination, the culture was diluted and plated on (a) chloramphenicol plates, and (b) kanamycin plates

(control). This system is highly efficient in removing Neo. Thus, the control kanamycin plate showed no colonies compared to more than 100 colonies on the chloramphenicol plate. Ten colonies that grew on the chloramphenicol plate were further tested for kanamycin resistance by inoculation into liquid culture with kanamycin. None of the colonies grew in the presence of kanamycin, thereby attesting to successful excision of

Neo. Three of these colonies were also tested by PCR with primers TH1596 and TH1597

(Table 3.1), designed flanking Neo. The amplicon size of the excised DNA (~1.4kb) is smaller than the unrecombined DNA (~3.3kb). This is the final Atf3-Cre* BAC transgenic construct. The insert part of the transgene was PCR amplified and send for

DNA sequencing.

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Restriction enzyme digestion and field-inversion gel electrophoresis to test integrity of

Atf3-Cre* BAC transgene: The Atf3-Cre* BAC is nearly ~180kb. To test that the large

DNA molecule is not altered unexpectedly (deletions, insertions in other part of the Atf3-

BAC), and to further test that the insert has integrated properly we used restriction enzyme digestion. The Cre*-NF insert has two unique restriction enzyme sites: (i) Nru1 within CreERT2, and (ii) Pac1 that we introduced just after CreERT2—by designing it into the reverse primer used to PCR amplify mCherry-P2A-CreERT2 (TH1715). The digestion pattern for Nru1 and for Pac1 can distinguish between unrecombined Atf3-

BAC and recombined Atf-Cre*.

The restriction enzyme digests of the BAC with either Nru1 or Pac1 result in large sized

DNA fragments (>30kb). To resolve these fragments on an agarose gel, field inversion gel electrophoresis was used. Briefly, the digest was run on a pre-cooled 0.8% agarose gel, in 0.5X TBE buffer in the cold room using the Hoefer PC 500 SwitchBack Pulse

Controller at the following settings: 60V, Run in 10 min, 0.4 to 20 sec, 50 hours, Rev 3:1.

The gel was pre-stained with ethidium bromide before casting to allow visualization of the bands. The running buffer also contained ethidium bromide due to the long gel running time.

Animal breeding: The mice housing, breeding and experiments were conducted in accordance with the rules established by the Institutional Animal Care and Use

Committee (IACUC) at the Ohio State University. Polygamous breeding cages were established with one male C57BL/6 transgenic mice and two C57BL/6 non-transgenic

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females. To breed transgenic females, monogamous breeding was set up with one male non-transgenic mice. Mice were weaned at day 28.

Genotyping PCR for the transgene: Mice were ear tagged and tail clipped. The tail DNA was isolated by overnight proteinase K digestion of the tail, followed by ethanol precipitation with sodium salt. Two sets of primers were used to test for the transgene:

Set A amplified part of mCherry (TH1577 and TH1598; expected amplicon size ~350bp), and Set B amplified part of CreERT2 (TH1595 and TH1609; expected amplicon size

~450bp). The sequences are listed in Table 3.1). I used Tm=62C for PCR amplification.

Acute stress model: We used the lipopolysaccharide (LPS) induced fever response as our acute stress model. 6-8 week old mice were injected intraperitoneal with 1.5mg/kg LPS.

Five hours later the mice were sacrificed, their livers collected and fixed with 10% normal buffered formalin.

Chronic stress model: We used orthotopic mouse breast cancer with or without chemotherapy to test transgene expression in the chronic stress model. We used the previously described Pymt breast cancer cells derived from C57BL/6 mice () and paclitaxel as the chemotherapeutic drug. Two million Pymt cells were suspended in 30l

DMEM (low glucose) : Matrigel (1:1), and injected into the fourth mouse fat pad. The tumors were allowed to grow and then treated with either saline or 20mg/kg paclitaxel, starting on day 7 post cancer cell injection. When the primary tumor reached ~ 0.4 cm3 in the untreated group, we removed the tumors from both groups and fixed them in 10% normal buffered formalin for immunofluorescent analysis of the tissue.

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Immunofluorescent detection of mCherry and ATF3 in formalin-fixed paraffin-embedded tissue: Briefly, the tissue was hydrated, followed by heat-mediated antigen retrieval using citrate buffer pH6. The slides were cooled to room temperature, blocked in 0.3% hydrogen peroxide for 10 minutes and rinsed. The slides were then blocked with 5% normal goat serum for an hour before addition of the primary antibody (anti-mCherry or anti-ATF3). The slides were incubated with the primary antibody overnight at 4C, then washed with TBST the next day and incubated with horseradish peroxidase (HRP)- conjugated secondary antibody (polymer system from Vector) for half an hour at room temperature. The secondary antibody was washed off and signal developed using the fluorescent substrate from TSA amplification system (Perkin and Elmer). Topro-3 was used to stain the nucleus. The substrate was washed off and the slides were mounted in

Vectashield. Imaging was done using the Leica confocal microscope.

For co-immunofluorescent staining of ATF3 and mCherry, a similar protocol was followed. Slides were hydrated, followed by heat-mediated antigen retrieval, and blocking. The slides were first incubated with the anti-ATF3 primary overnight at 4C.

These were developed the next day, re-blocked by incubating in hydrogen peroxide followed by normal goat serum, and then incubated with the anti-mCherry primary again overnight at 4C. The slides were developed the next day using a different fluorescent substrate and mounted.

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3.4 Results

Generation and characterization of the ATF3-Cre* BAC transgenic construct: The generation of the final ATF3-Cre* BAC transgenic DNA construct was a multistep process involving both traditional cloning and BAC recombineering to piece together three separate DNA elements: (i) mCherry-P2A-CreERT, (ii) Frt-Neo-Frt (FNF for short), and (iii) the Atf3 locus in a BAC plasmid. The mCherry-P2A-CreERT2 element was first generated by four-primer PCR, and then inserted into a plasmid (obtained from

Copeland and colleagues) containing the FNF sequence (Fig. 3.2) by traditional cloning method to generate the “template plasmid” containing the following region of interest: mCherry-P2A-CreERT2-FNF. I will refer to this region as Cre*-NF, which will be then inserted into the Atf3 locus by recombination. This region of interest was PCR amplified using long primers with homology to specific Atf3 regions as indicated in Fig. 3.3. Thus, the final PCR product is a DNA fragment containing Cre*-NF flanked by the Atf3 homologous sequences (~50 base pairs at each side); I will refer to it as (5’Ho)-Cre*-NF-

(3’Ho). These ends allow the fragment to be recombined into the homologous regions within the Atf3 BAC clone. The BAC clone we used is xxxx, which contains the Atf3 locus (~ 65kb) within a 178 kb mouse BAC clone. I introduced the (5’Ho)-Cre*-FNF-

(3’Ho) fragment into recombinogenic SW102 bacteria by electroporation and positively selected for kanamycin resistance (KanR) and chloramphenicol resistance (ChlR). KanR is provided by the Neo gene in the FNF region and ChlR by the BAC vector. Since the

(5’Ho)-Cre*-FNF-(3’Ho) fragment does not contain the replication origin, it is not maintained in the bacteria. Thus, any clone with double resistance is due to the successful

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recombination of the fragment into the BAC vector. I also negatively selected the clones for ampicillin resistance (AmpR). This is necessary, because the original “template plasmid” contains AmpR gene and may be present as a trace contaminant in the (5’Ho)-

Cre*-NF-(3’Ho) fragment, even after gel isolation of the PCR product. As a plasmid

DNA, the “template plasmid” enters into the SW102 cells with higher efficiency than the fragment and will be maintained due to its replication origin.

I then tested whether the insert was recombined into the BAC clone at the desired homologous regions. I designed three sets of PCR primers as shown in Fig. 3.4A: (1) Set-

1 primers flanking the 5’ junction of recombined DNA, (2) Set-2 primer flanking the 3’ junction of recombined DNA, and (3) Set-3 primers recognizing the Atf3 region removed by recombineering; will generate PCR product only if the recombination occurred at non- homologous regions. Thus, Set-3 will be used to rule out the undesired clone. The results for the PCR screening for selected clones are shown in Fig. 3.4B. Out of 8 colonies screened by PCR, two were positive for Set-1 and 2 and negative for Set-3 (Clone# 2 and

3), as expected from correctly recombined clone. One of these clones, Clone# 3, was sent for DNA sequencing and used for subsequent steps (see below). Interestingly, among the

8 clones screened, all the clones had correct amplicon sizes for the 5’ junction (Set-1).

However, some of the clones (numbers 1, 4, 7) did not have accurate amplicon size for the 3’ junctions, which is suggestive of improper recombination. Other clones (numbers

5, 6, 8) had 3’ junctions but were also positive for Atf3 exon B, suggesting that these bacteria may have more than one copy of Atf3-BAC and only one copy was successfully recombined, thereby creating a mixed population. 148

Next, I removed the Neo from the recombined clone (#3) using SW105—a different recombinogenic strain of bacteria—with arabinose-inducible Flp gene (Fig. 3.5A).

Successful excision of Neo was screened by the loss of KanR (while maintaining ChlR). I then examined the candidate colonies by PCR. As shown in Fig. 3.5, after excision of

Neo, the PCR amplicon size is reduced to ~1.4kb from ~3.3kb prior to removal. Note that this final BAC transgenic construct contains an Frt site immediately after mCherry-P2A-

CreERT2. This construct was sent out for sequencing for the region spanning exon B- mCherry-P2A-CreERT2-Frt-exon E to test whether there were any mutations and none was detected. In addition, restriction enzyme digestion with Nru1 and Pac1 showed expected digestion patters, indicating that this final BAC transgenic construct does not have major rearrangement. Fig. 3.6 shows the digestion patterns after field inversion gel electrophoresis: 32kb and 76kb bands after Pac1 digest and 48kb band after Nru1 digest.

Identification of transgenic founders and screening for germline transmission: The Atf3-

Cre* BAC construct was sent to the Transgenic Animal Model Core at the University of

Michigan for pro-nuclear injections. I screened all 41 live births by PCR genotyping using two sets of primers: one for the mCherry region and the other for the CreERT2 region. Among them, five were transgenic founders (Table 3.2). I then tested the founders for germline transmission by breeding (Table 3.3). Founder #2 failed the test; thus only the remaining four founders (#1, #3, #4 and #5) were analyzed further. I estimated the copy number of the transgene by quantitative PCR analysis of their genomic DNAs; as controls, I added specified amounts of the Atf3-Cre* BAC plasmid

DNA to non-transgenic genomic DNAs to provide approximate signals for 1, 2, and 3 149

copies of transgene. As shown in Fig. 3.7, the copy numbers are estimated to be between

4 and 8.

As shown in Table 3.3, lines #1, #3, and #4 followed approximately the Mendelian inheritance ratio of the transgene (~ 50%). However, line #5 is almost 75% positive for the transgene, suggesting that there are 2 transgenic alleles. Since it takes considerable amount of effort to segregate the alleles (with follow up characterization) and since this line showed considerable background of transgene expression without stress treatment

(see below), we decided not to maintain this line.

Preliminary characterization of transgenic mice with acute stress: While carrying out breeding for Mendelian analysis, I analyzed #1, #3, #4, #5 lines for their transgene expression upon stress induction using the low dose lipopolysaccharide (LPS, 1.5mg/kg intraperitoneal) paradigm referred to as LPS-induced fever response in literature. At 5 hours after treatment, a time point known to induce the endogenous Atf3 gene, I examined the transgene by immunofluorescence analysis for mCherry. As comparison, I examined the endogenous ATF3 protein. Line #1 showed the lowest basal expression of the transgene but a robust induction by LPS (Fig. 3.8); furthermore, it had about 50% overlap between the transgene (mCherry, red) and the endogenous ATF3 (green) (Fig.

3.9). This is the best among the 4 transgenic lines and we selected line#1 to characterize further (see below). For storage, I sent lines #1, #3, and #4 to the OSU xxxx Core Facility to make frozen sperms.

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Preliminary characterization of transgenic mice with chronic stress: We named line #1

Atf3-Cre* mice as the Atf3 Inducible Reporter Indicating Stress mice, or the Atf3-IRIS mice for short. I analyzed the mice using a chronic stress model of mouse breast cancer.

As discussed in Chapter 1, tumor is “a wound that never heals.” Thus, it is a chronic inflammation model. I also added chemotherapy to the cancer model, since chemotherapeutic agents induce various stress singling pathways and responses. The goal of this experiment is two-fold: (i) to find out how the transgene mCherry responds to chronic stress as compared to the endogenous Atf3 gene; (ii) to find out whether paclitaxel exacerbates cancer progression in this cancer model as in the cancer model described in Chapter 2. The Atf3-IRIS mice are C57BL/6 strain; thus, it requires the injection of the syngeneic cancer cells derived from this strain of mice. As described before (citation), we developed a PyMT breast cancer cells from C57BL/6. Therefore, to test the Atf3-IRIS mice, I used PyMT breast cancer cells in C57BL/6 mice, in contrast to

MVT-1 breast cancer cells in FVB/N mice described in Chapter 2. PyMT is less aggressive than MVT-1 and does not metastasize to the lung efficiently. Thus, to investigate whether chemotherapy exacerbates cancer progression in this model, it is necessary to wait for a long time—at least 3 months from our experience. Since the primary tumors will be oversized by then, this experiment requires the removal of primary tumor. Fig. 3.10A shows our experimental design: Inject the cancer cells into 6-8 weeks old mice; remove primary tumor when the tumor reaches ~ 0.3 cubic centimeters and then inject paclitaxel (20mg/kg, three times a week, seven times total). At 2 months

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after tumor removal, euthanize the mice and examine for tumor recurrence and potential lung metastasis.

As shown in Fig. 3.10B, in the transgenic mice mCherry (red) was expressed in the cells surrounding the cancer nest and these cells were also green, indicating that they co- expressed endogenous ATF3. Significantly, in all the fields captured I did not find any mCherry positive cells that did not express the endogenous ATF3. However, not all

ATF3 positive cells express the transgene. That is, mCherry-positive cells appear to be a sub-set of ATF3-positive cells. Therefore, mCherry is an indicator for stressed cells but not all stressed cells (ATF3-positive) express mCherry. This flaw of mCherry to miss some positive cells (false-negative identification) is better than false-positive identification, which would lead the investigation astray. Clearly, a more extensive study is required to address this issue rigorously. We note that the cancer cells expressed the endogenous ATF3 (green) but not the transgene. This is because the transgene is only expressed in the host (the mice), not the breast cancer cells injected into the mice.

Before primary tumor removal, I measured the tumor size and found that paclitaxel showed clear therapeutic benefit: the primary tumors from PBS treated group continued to grow to 0.3 cubic centimeters (tumor weight >0.3 gm) at the time of removal whereas the tumors in the PTX-treated group remained palpable (not measurable with the caliper; tumor weight <0.1 gm) but did not grow any further. Thus, the tumors in the saline treated group were significantly larger at the time of removal compared to tumors from

PTX treated group (Fig. 3.10C). However, at 2 months after removing the tumors, 4 out

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of 4 mice with PTX treatment had recurring tumors (weight > 0.35gm) at the site of removal, while none (n=3) in the control group had any recurrent tumor (Fig. 3.10D).

Thus, this preliminary result indicates that PTX, despite its therapeutic benefit, had deleterious effect by increasing tumor recurrence. This supports the findings described in

Chapter 2 that, despite its therapeutic benefit, paclitaxel exacerbates cancer metastasis in a more aggressive cancer model.

3.5 Discussion

Through work presented in this chapter I was able to generate a transgenic mouse encoding an inducible Cre under the control of Atf3 genomic region. The transgenic mouse line shows stress-inducible expression of the transgenic protein in an acute stress model. The transgene is also expressed in the context of chronic stress within mouse mammary tumors. This is the first step in our overall goal of generating a mouse model for ROSA tracing that can be used to investigate cells under stress. We are currently breeding heterozygous transgenic mice to generate homozygotes, before continuing with further characterization and crossing with the ROSA reporter mice.

More studies are required to determine the extent of penetration of the transgene: is it expressed in all ATF3 positive cells? The preliminary characterization suggests that the transgene is expressed in a fraction of the ATF3-positive (stressed) cells. It is encouraging, however, that mCherry expressing cells are always positive for ATF3, especially in the tumor context. This suggests that mCherry expression is selective and is regulated in a similar fashion to Atf3. In the LPS-induced acute stress model, about 20%

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mCherry positive cells were negative for ATF3 (Fig. 3.9). This observation can have several reasons: 1) transcription is a stochastic event, hence the mCherry allele and the endogenous Atf3 alleles are not transcribed equally, 2) mCherry protein is much more stable than ATF3, thus can be detected for a longer duration than ATF3, or 3) mCherry expression is not tightly regulated in this stress paradigm. All these reasons can account for non-overlapping expression of the transgene and ATF3. However, more in vivo experiments are required to address this in a conclusive manner.

Another important consideration moving forward is what kind of stress paradigms can be investigated using this model? We saw a higher percentage of cells co-expressing mCherry and ATF3 in the chronic stress context. This leads us to question if the magnitude and duration of stressful stimuli will determine the applicability of this line.

My efforts were focused on designing and generating a version of Atf3-Cre* most likely to succeed in vivo, while being applicable to study a wide variety of acute and chronic stress paradigms. I cloned and tested several Cre* variants (data not shown) including (i) mCherry-linker-CreERT2 (fusion protein), (ii) CreERT2-P2A-mCherryCAAX

(membrane-targeted mCherry), and (iii) CreERT2-P2A-mCherry (opposite orientation from current Cre*). However, each of these variants had issues when tested in cell lines.

For instance, the CreERT2-P2A-mCherryCAAX had weak mCherry expression and was not clearly discernable at the membrane in transient transfection assays. CreERT2-P2A- mCherry had leaky, tamoxifen-independent activity of Cre, which we suspect was due to the P2A tail interfering with ERT2. We also tried several strategies of BAC

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recombineering (data not shown) without much success before finally using a PCR based strategy to knock-in the insert into the BAC, which worked. In addition, we changed the site of insertion from the initial plan: we were initially planning to replace only exon B with the insert instead of removing the entire Atf3 open reading frame. However, we decided against it to reduce the probability of non-sense mediated decay of the mRNA.

We were lucky to get five BAC transgenic founders to screen. Multiple rounds of breeding enabled us to determine that four out of the five founders had germ-line transmission. However, the presence of a transgene does not necessarily mean that it transcriptionally active. Depending on the integration site of the transgene in the mouse genome, the transgene may be silenced or constitutively expressed, both of which are undesirable. Additionally, the transgene may not faithfully mimic the stress-inducibility of Atf3. Therefore, we used a relatively quick, LPS induced fever response model to test transgene expression and stress-inducibility. This also enabled us to select a line for further analysis.

3.6 Acknowledgement

I want to especially acknowledge Dr. James Jontes for his help on this project. He very kindly shared his expertise, laboratory protocols, reagents and equipment with me for the

BAC recombineering aspect of this work. Also, I appreciate the assistance that I received from Chinami Ikeda, who takes care of our mouse colony. I had multiple breeding cages from different mouse lines at one point and Chinami helped by ear-tagging and tail- clipping the mice.

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Table 3.1. List of primers used with sequences. The primers are in order of appearance in the text.

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Table 3.2. PCR screening of live births from pro-nuclear injection to identify transgenic founders. PCR genotyping with two different sets of primers was used to determine transgene positivity: primers for mCherry and primers for CreERT2. Out of 41 live births, five males were positive for the transgene by both the primers. These were used to set up breeding for F1. The lines were called #1, #2, #3, #4 and #5.

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Table 3.3. Identification of mouse line(s) with germline transgene transmission. Data was collected from several rounds of breeding (up to 10 litters) to determine lines with a Mendelian ratio of transgene transmission. Line 1, 3 and 4 had approximately 50% pups that were positive for the transgene. Line 2 did not produce any off-springs with the transgene and was discontinued. Line 5 had ~75% transgene positive pups indicating that the transgene had integrated in more than one genomic location.

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Figure 3.1. Design of the transgenic construct. (A) The endogenous mouse Atf3 locus. The mouse Atf3 gene is nearly 50kb and consists of 5 exons (A1, A2, B, C, E) and two alternate promoters at A1 and A2 (indicated by the arrows). The ATF3 protein is coded by exon B, C and part of exon E (black rectangles). The Atf3 start codon for methionine is in exon B (denoted by Met) and the stop codon in exon E (denoted by asterix). A1 and A2 are non-coding exons (white rectangle) that give rise to the 5’ UTR, while the region after the stop codon in exon E gives rise to the 3’ UTR. (B) Atf3-Cre* transgene. To generate the ~50 kb Atf3-Cre* BAC transgenic cassette, the insert Cre* (mCherry-P2A-CreERT2-Frt; denoted by the blue rectangle) is inserted into the Atf3 open reading frame by swapping out exon B (after the start codon), C and E (till the stop codon). All other regions of the endogenous Atf3 gene are retained in the transgene including the two promoters, A1, A2 and part of exon E after the stop codon.

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Figure 3.2. Traditional cloning of mCherry-P2A-CreERT2 into the FNF vector. (A) The Cre*-NF construct. Cre*-NF is short for mCherry-P2A-CreERT2-Frt-Neo-Frt. To generate Cre*-NF the mCherry-P2A-CreERT2 was PCR-amplified and cloned into the PGK/EM7-FNF vector (pL451 from Addgene) at the Sal1- EcoR1 site by directional cloning as detailed in the methods and materials section. In this construct the Neo gene for kanamycin resistance is driven by the PGK/Em7 promoter for expression in both eukaryotes and prokaryotes, and is flanked by the Frt sites (denoted by the red rectangles). The pL451 vector backbone has an Ampr gene (denoted by the yellow rectangle) for ampicillin resistance. Therefore, the clones were selected for both kanamycin and ampicillin resistance on agar plates. Note that a Pac1 site was added after the CreERT2 in this step by introducing it into the CreERT2 reverse primer. The utility of the Pac1 site will be discussed later (see Fig. 3.6). (B) Agarose gel image for the Sal1- EcoR1 digested Cre*-NF plasmid clones. After directional cloning to generate the Cre*-NF (by ligation and transformation), the resulting bacterial clones were tested for the presence of the ligated product by restriction enzyme digestion. 6 clones were selected for testing. Plasmid DNA was isolated, digested with Sal1 and EcoR1 and subsequently run on a 1% agarose gel. The bands were visualized by ethidium bromide staining of the gel. All 6 clones gave the expected two bands for vector (V) and insert (I) for Sal1-EcoR1 double digest.

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Figure 3.3. A schematic for BAC recombineering method to swap Cre*-NF into the Atf3-BAC. (A) Homologous recombination in SW102. As detailed in the methods and materials, Cre*-NF was inserted into the Atf3-BAC by homologous BAC recombineering using a specialized bacterial strain—SW102—that carries the phage recombination genes to mediate homologous recombination between double stranded DNA molecules. The SW102 strain used in our experiment housed the mouse Atf3-BAC. The Cre*-NF was PCR amplified using long PCR primers with homology to specific Atf3 genomic regions (depicted by purple rectangles) to generate the double stranded insert (mCherry-P2A- CreERT2-Frt-Neo-Frt with Atf3 homology arms: 5’Ho-Cre*-NF-3’Ho). This double stranded insert was then electroporated into recombinogenic SW102, which mediate homologous recombination between the PCR insert and the Atf3-BAC to yield the recombined product: Atf3-Cre*-NF. The Atf3-BAC carries a gene for chloramphenicol resistance (denoted by green rectangle) for antibiotic selection. The recombined colonies were thus, positively selected for both kanamycin resistance (from the inserted Neo gene) and chloramphenicol resistance. (B) Agarose gel image of the gel purified, PCR amplified insert. The insert (5’Ho-Cre*-NF-3’Ho) generated by PCR is 5kb. The PCR reaction was digested by Dpn1 after completion to remove the plasmid template and then gel-purified. The purified insert was run on a gel alongside a DNA estimation ladder (shown here; E=estimation ladder) to determine DNA concentration, size and purity. The bands were visualized by ethidium bromide staining of the gel. M=DNA size marker, P=PCR insert. The concentration of the purified insert was ~150ng/l.

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Figure 3.4. Screening of recombined Atf3-Cre*-NF BAC clones by PCR. (A) Three sets of primers for PCR screening of recombined DNA. I designed three sets of primers that can differentiate the properly recombined DNA product from unrecombined or improperly recombined DNA. Set-1 (denoted in pink) primers flank the 5’ homologous region, Set-2 (denoted in green) flanks the 3’ homologous region; Set 1 and Set 2 primers amplify accurately recombined DNA. Set-3 (denoted in black) binds in the Atf3 region removed by recombineering and thus, detects unrecombined Atf3-BAC or improperly recombined DNA. (B) Agarose gel images of PCR screening results. 8 SW102 clones (positively selected for kanamycin and chloramphenicol resistance and negatively selected for ampicillin resistance) were tested for the presence of recombined Atf3-Cre*-NF BAC using the three sets of primers. The PCR reaction products were run on 1% agarose gel. The bands were visualized by ethidium bromide staining of the gel. Clone 2 and 3 show the expected PCR amplification results for proper recombination. Clone 3 (shown in the red rectangle) was selected and sent for sequencing. M=DNA size marker, 1-8=clones, C=control plasmid, Atf3-BAC.

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Figure 3.5. Removal of Neo to derive the final Atf3-Cre* BAC transgene. (A) A schematic for Flp-mediated removal of Neo in SW105 bacteria. As detailed in the methods and materials, Atf3-Cre*-NF BAC (clone# 3 from Fig. 3.4) was electroporated into a specialized recombineering bacterial strain—SW105—that encodes the arabinose- inducible Flp, to remove the Neo gene flanked by Frt sites and derive the Atf3-Cre* BAC. Addition of L-arabinose to the media triggers Flp activity in the SW105 bacteria resulting in the Flp-mediated excision of Neo. The DNA after excision of Neo— mCherry-P2A-CreERT2-Frt, or Cre* in short—in the transgene of interest. Successful excision was assayed by antibiotic selection for chloramphenicol resistance and kanamycin sensitivity. In the clones screened by antibiotic selection the excision of Neo was assayed by PCR with primers designed flanking the Neo cassette (shown here in red). The product size after excision is smaller (1.4kb versus 3.3kb). (B) Agarose gel image of PCR screening. Three clones positively selected for chloramphenicol resistance and negatively selected for kanamycin resistance were screened by PCR using the primers shown in (A). The PCR reaction products were run on 1% agarose gel. The bands were visualized by ethidium bromide staining of the gel. All three clones tested gave the 1.4kb amplicon and not the 3.3kb amplicon, attesting to successful removal of Neo. 1-3=clones, C=control DNA before removal of Neo, M=DNA size marker.

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Figure 3.6. Restriction enzyme digestion and field inversion electrophoresis of Atf3- Cre*. To test the integrity of the Atf3-Cre* BAC transgene I performed two separate restriction enzyme digestions: Pac1 and Nru1. The Pac1 site was introduced into the transgene after CreERT2 at the time of cloning mCherry-P2A-CreERT2 into the FNF vector, by designing CreERT2 reverse primers containing the Pac1 site, as detailed in the methods and materials (also see Fig. 3.2). The Nru1 site is a pre-existing unique site in CreERT2. The restriction enzyme digests were resolved on a 0.8% agarose gel by field inversion electrophoresis that resolves high molecular weight DNA fragments. The DNA bands on the gel were visualized by ethidium bromide staining of the gel. As shown here, Pac1 digestion of the insert yields the characteristic 76kb and 32kb band; Nru1 digest yields the 60kb and 48kb band. The higher molecular weight band sizes were extrapolated by calculating the relative migration distance of the the standards (M). R=ruler, M=DNA size ladder, B=Atf3-Cre* BAC.

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Figure 3.7. Estimation of transgene copy number by qualitative PCR. Genomic DNA from non-transgenic mice was spiked with appropriate amounts of Atf3-Cre* BAC plasmid to create copy number standards. Highly purified genomic DNA was isolated from heart of mice from line #1, 3, 4 and 5. CreERT2 primers were used to assay for transgene by qPCR.

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Figure 3.8. Qualitative analysis of transgene expression in LPS-treated mice liver: acute stress paradigm. (A) Detection of mCherry (transgene) by immunofluorescence. F1 transgenic mice from Line #1, 3, 4, 5 were treated with 1.5mg/kg LPS, i.p. and compared with saline treated mice to assay both the stress- inducibility of the transgene and the basal transgene expression in unstressed mice. Livers were harvested 5 hours (hrs) after LPS treatment, fixed in normal buffered formalin and assayed for transgene presence by immunofluorescence using antibody against mCherry (red). Non-transgenic mouse treated with LPS in a similar fashion was used as negative control for mCherry. Images were captured at 200X magnification. The saline-treated livers were imaged as controls for basal transgene expression. (B) Detection of endogenous ATF3 by immunofluorescence. Livers from LPS-treated and saline treated (unstressed control) transgenic mice was assayed for endogenous ATF3 expression by immunofluorescence using anti-ATF3 antibody (green signal). Non- transgenic, LPS-treated mouse was used as a positive control for ATF3 expression. Images were captured at 200X magnification.

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Figure 3.9. Co-immunofluorescence for ATF3 and mCherry in LPS-treated liver of line#1. Livers were harvested 5 hours (hrs) after LPS treatment as before, fixed in normal buffered formalin and assayed by immunofluorescence for mCherry and ATF3. Representative images of liver stained for ATF3 (green) and mCherry (red). Nuclii are in blue, stained with Topro-3. ATF3 expression was seen in both the cytoplasm and the nuclii. mCherry expression was restricted to the cytoplasm. Three kinds of staining patterns were noticed: (i) White solid arrows indicate cells with co-expression of ATF3 and mCherry. (ii) White line arrows indicate mCherry single positive cells. (iii) Green line arrows indicate cells positive for ATF3 but negative for mCherry. Scale bar = 50.

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Figure 3.10. Co-immunofluorescence for ATF3 and mCherry in PyMT tumors in line#1: chronic stress paradigm. (A) A schematic for the PyMT orthotopic breast cancer model in C57BL/6 mice. Briefly, 2 million PyMT cells were injected into the fat pad of syngeneic 6-8 week old female mice from line# 1. When the tumors became palpable on day 7 post injection, the mice were treated with PTX (20mg/kg, three times a week, a total of seven times) or saline. Tumors were removed from both groups of mice on day 27, when the tumors in the saline treated group were ~0.3 cm3. These tumors were used for the immunofluorescent staining of the transgene (mCherry) and ATF3 in (B). The mice were followed for another 2 months after the removal of primary tumor to assay for tumor recurrence and progression to metastases. (B) Representative images of PyMT tumors (day 27) stained for mCherry and ATF3. Primary tumors were analyzed by co-immunofluorescence to test overlap between endogenous ATF3 expression and transgene expression in a chronic stress model. Red = mCherry, Green = ATF3, Blue = Topro-3 nuclear dye. Solid white arrows indicate cells with co-expression of ATF3 and mCherry (yellow). Scale bar = 50. (C) Primary tumor weight at the day 27. The tumors removed from the mice were weighed before fixing in 10% buffered formalin. The recorded weights are shown on a scatter plot; each circle represents a single mouse with the bars indicating the Mean±SEM (n=3 WT+PBS, n=4 WT+PTX). P<0.04 by Student ttest. (D) Frequency of tumor recurrence. Two months after primary tumor recurrence, none of the tumors in the saline treated group reappeared (even though they were significantly larger at the time of removal, (C)). 4 out of 4 mice in the PTX treated group had large recurrent tumors at the original site weighing more than 0.35 gm.

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Figure 3.10. Co-immunofluorescence for ATF3 and mCherry in PyMT tumors in line#1: chronic stress paradigm.

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Chapter 4

Future perspectives

Over the last three decades, research in Dr. Hai’s laboratory has uncovered much about the physiological role of the Atf3 gene and protein. Atf3, an adaptive-response gene, encodes a bZip transcription factor that can both activate and repress target genes [1-3,

26, 27, 34]. ATF3 is a key immune modulator and is implicated in many chronic inflammation-related diseases including cancer [3, 38]. In breast cancer, ATF3 is expressed by both the cancer epithelial cells themselves and the surrounding host cells within the tumor [70, 80, 81]. In the cancer epithelial cells ATF3 has a dichotomous role: in untransformed epithelial cells (early disease stage) ATF3 is anti-tumor, whereas in transformed cancer epithelial cells (late stage disease) ATF3 is pro-tumor [80, 81].

However, in a work that I participated in when I first joined the laboratory, we found that

ATF3 expression in cancer epithelial cells does not correlate with worse outcome in patients (Wolford et al., [70]). Instead, ATF3 expression in the mononuclear immune cells within the tumor stroma has significant prognostic value. This intriguing observation greatly affected the direction of research in Dr. Hai’s laboratory. Subsequent research has focused on dissecting out the role of ATF3 in the tumor stroma and its implications in disease outcome. Given its overall significance, the tumor stroma

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provides an exciting new therapeutic target for rationalized drug design [242, 254, 258,

350-355]. The tumor stroma is a particularly attractive target because the stromal cells exhibit greater genetic stability as compared to the rapidly evolving cancer cells that are prone to genetic mutations and develop drug-resistance over time.

In Wolford et.al., we demonstrated that ATF3 expression in TAMs, a subset of mononuclear cell in the stroma, is necessary for breast cancer metastases [70]. Part of my thesis, described in chapter 2, is a continuation of this research but aimed at furthering our understanding of the role of host ATF3 in the context of cancer chemotherapy. In chapter 3, I describe the development of a novel mouse model based on our understanding of ATF3 biology that can be used to study the fate of stressed cells. Thus, there are two aspects to my dissertation research that converge on ATF3. In this chapter I will summarize the results from my dissertation research and discuss potential future directions in which the projects can be advanced.

In chapter 2, I describe my data from a collaborative project, in which we discovered that

ATF3 in the host contributes to chemotherapy-aggravated breast cancer metastases to the lungs. This is a significant finding because ATF3 is induced by several chemotherapy drugs, including PTX, in experimental systems as well as in human clinical samples of breast cancer. ATF3 is a hub of cells’ adaptive-response network and mediates cell-cell communication within the tumor. Thus, it is important to elucidate the mechanism behind host ATF3-mediated exacerbation of breast cancer metastases in response to chemotherapy. I focused on mechanisms within the primary tumor that potentially

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promote cancer cell escape from the primary tumor. I found that ATF3 in the host promotes tumor angiogenesis and the pro-angiogenic subset of TAMs, called the TEMs.

Additionally, in the presence of ATF3 in the host, the PTX treatment influences the tumor vasculature by reducing its pericyte coverage, presumably making them leakier.

Also, PTX treatment influences the property of ATF3-expressing TEMs by enhancing their ability to promote cancer cell invasion in vitro. Excitingly, ATF3 in the host significantly increases the abundance of TMEMs in the breast tumors. TMEMs are functional units that act as sites for cancer cell intravasation in the primary tumor. Thus, we made key observations that correlate well with increased circulating tumor cells and metastases in PTX treated WT mice. These finding are novel and raise several interesting

(but as yet unanswered) questions about the role of stromal ATF3 in limiting the efficacy of chemotherapy: (1) Do these observations apply to other cancer types/sub-types and other chemotherapy drugs or are these true for highly aggressive breast cancers and PTX chemotherapy alone? (2) What is the molecular mechanism by which ATF3 in the host promotes tumor angiogenesis? (3) How does ATF3 in the host reduce pericyte coverage in response to PTX? (4) How does ATF3 in the host enhance TEM in the primary tumor

(recruitment versus survival)? (5) What are the molecules involved in mediating the effect of TEMs on cancer cells? (6) What is the role of ATF3 in the endothelial cells and other vascular cells such as pericytes, in the context of increased tumor angiogenesis? (7)

Does the TMEM number differ in a larger cohort, and does PTX treatment enhance their abundance in the WT? (8) What is the clinical relevance of our findings in terms of patient stratification? These questions should be addressed for a deeper molecular

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understanding about ATF3 function and how it influences chemotherapy efficacy and disease outcome.

Moving forward, two questions seem particularly important to resolve: the role of ATF3 in TAMs versus the role of ATF3 in the endothelial cells, and investigating the link between ATF3 and TMEMs. To test the function of ATF3 in TAMs, we would need to selectively ablate ATF3 in the myeloid/macrophage cell lineage in vivo by using the conditional KO (CKO) mice (LysM-Cre with homozygous Atf3-flox alleles) and compare breast cancer progression in response to PTX in the CKO versus the control mice (LysM-Cre with homozygous WT Atf3 alleles). Not much is known about the role of ATF3 in the murine endothelial cells. Thus, initially we can test if deletion of ATF3 affects the property of endothelial cells in vitro. We can test if ATF3 affects endothelial sprouting in the mouse aortic ring assay using WT and KO mice derived aortas. We can test their response to pro-angiogenic stimuli and co-culture with TAMs. Some of these experiments are currently being performed by another graduate student in the laboratory.

To test the link between ATF3 and TMEMs, I propose the following experiment. We can score ATF3 expression in human breast cancer samples by immunohistochemistry.

Simultaneously, in serial sections we can stain for macrophages, Mena-positive cancer cells and endothelial cells to detect TMEM. We can then test if there is a correlation between increasing ATF3 expression and TMEM numbers in clinical samples.

In chapter 3, I have described the methodology and results to generate a novel mouse model that can potentially be used to investigate cellular stress in diverse acute and

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chronic stress paradigms including but not limited to cancer. I used BAC recombineering

[347] to generate a transgenic cassette comprising of mCherry-P2A-CreERT2 (referred to as Cre*, encoding a fluorescent protein marker and an inducible Cre) under the control of the Atf3 genomic locus. The rationale is that in these mice the cells experiencing stress will express mCherry and CreERT2, much like ATF3 is expressed by cells in response to stress. When these Atf3-Cre* mice are crossed with the ROSA26 reporter mice, the resulting double transgenic mice can be utilized to trace cells under stress. Thus far, we have successfully generated the Atf3-Cre* mice and are currently deriving homozygous mice for this line. In the future, the homozygous mice will be crossed with the commercially available ROSA reporter mice, such as ROSA26-lox-STOP-lox-YFP mice.

The double transgenic mice will be tested for Cre activity to determine if there is leaky activity of Cre and if the Cre activity is inducible by tamoxifen in vivo. The double transgenic mice can then be tested to determine their utility in tracing cells in different stress paradigms.

We propose that the double transgenic mice can be used to study the following: (i) cells that are actively being stressed and appear red due to mCherry expression. (ii) Cells that have been stressed in the past. These cells will be positive for YFP but negative for mCherry. (iii) Cells that are actively being stressed but have also experienced stress in the past (chronically stressed cells). Such cells will be positive for both mCherry and YFP, provided they were treated with tamoxifen after the past insult. In other words, after a cell experiences stress they can be labeled with YFP by triggering CreERT2 activity with tamoxifen. The cells can be followed over time. If they receive additional stress after 174

being labeled with YFP, they will express mCherry and be positive for both YFP and mCherry. Thus, using this model we can study both acute and chronic stress responses.

(iv) The Atf3-Cre* mice can be crossed with any floxed mice to determine the function of a gene-of-interest in the context of stress. For instance, we can cross Atf3-Cre* mice with p53-floxed mice and determine the role of p53 in stressed cells, since the stressed cells in these mice express Cre* and thus ablate p53 selectively in the stressed cells.

In summary, the two projects that I worked on from the time of their inception are at a promising stage that can initiate other impactful projects. It is my hope that through work described in this dissertation I have contributed towards advancing the field of ATF3 biology and made meaningful contributions to the wider field of breast cancer biology and stress.

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