Implications for the 8P12-11 Genomic Locus in Breast Cancer Diagnostics and Therapy: Eukaryotic Initiation Factor 4E-Binding Protein, EIF4EBP1, As an Oncogene

Medical University of South Carolina MEDICA

MUSC Theses and Dissertations

2018

Implications for the 8p12-11 Genomic Locus in Breast Cancer Diagnostics and Therapy: Eukaryotic Initiation Factor 4E-Binding Protein, EIF4EBP1, as an Oncogene

Alexandria Colett Rutkovsky Medical University of South Carolina

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Implications for the 8p12-11 Genomic Locus in Breast Cancer Diagnostics and Therapy:

Eukaryotic Initiation Factor 4E-Binding Protein, EIF4EBP1, as an Oncogene

Alexandria Colett Rutkovsky

A dissertation submitted to the faculty of the Medical University of South Carolina in fulfillment of the requirements for the degree of Doctor of Philosophy in the College of Graduate Studies Biomedical Sciences

Department of Pathology and Laboratory Medicine 2018

Approved by:

Chairman, Advisory Committee Stephen P. Ethier

Co-Chairman Robin C. Muise-Helmericks

Amanda C. LaRue

Victoria J. Findlay

Elizabeth S. Yeh

This work is dedicated to the fight against cancer.

ACKNOWLEDGEMENTS

I would like to acknowledge the Hollings Cancer Center and many departments at the

Medical University of South Carolina for investing the resources and training required to complete the research presented. This research was also supported in part by the

Department of Pathology and Laboratory Medicine at the Medical University of South

Carolina and grants from the National Institute of Health awarded to Dr. Stephen Ethier. I would like to thank the Ethier laboratory, both past and present, which have been instrumental in the presented work. I stand on the shoulder of giants, thankful for the many brilliant and dedicated scientists before me and alongside me, especially collaborative, combined, and applied knowledge which allows the mining of big data presented. I am truly grateful for the many faculty and staff at the Medical University of

South Carolina and Appalachian State University who have dedicated much effort towards my scientific training, passions, and procurement of resources detrimental to the work presented. Special thanks to my committee members for their wealth of knowledge, advice, help, and ideas, especially Dr. Stephen Ethier and Dr. Stephen Guest who were instrumental in teaching me the skills necessary to complete this work. Materials relied on the unwavering support of Dr. Robin C. Muise-Helmericks and Dr. Elizabeth Yeh.

This research would not have been possible without the support from my family, friends, and the community.

TABLE OF CONTENTS List of Tables viii

List of Figures ix-xii

Abstract xiii

Chapter I: Background 1

Human mammary tissue and breast cancer 2-12

Gene amplification in cancer 13-15

Driving oncogenes on amplicons 16-19

Amplification of the 8p12-11 genomic locus 20-22

Eukaryotic Initiation Factor 4E-Binding Protein (4EBP1, EIF4EBP1) 23-34

4EBP1 and mTOR in mRNA translation 34-36

Genomic location of 4EBP1 36

Conservation among species demonstrates 4EBP1 necessity 37-38

The behavior and expression of 4EBP1 in normal cells 39-40

4EBP1 is highly overexpressed in many malignant tumor types 41-42

4EBP1 transcripts are rarely mutated 43-44

What causes the increase in 4EBP1 transcript levels? 45

Viruses 45-46

Transcription Factors 47-49

Gene amplification 50-52

4EBP1 in cell signaling in breast cancer 53-57

Chapter II: Materials and Methods 58-74

Chapter III: Results 75

Evidence suggests 4EBP1 is important to many cancer types 76-77

EIF4EBP1 expression levels correlate with reduced relapse free survival 78-79 in human breast cancer

4EBP1 is not required for normal mammary cell proliferation 80-81

4EBP1 is highly expressed and phosphorylated in 8p11-12 breast cancer 82-84 cells

Phosphorylated AKT levels and phosphorylated S6 levels across the 85-88 models

4EBP1 knockdown is detrimental to ER+ 8p11-12 breast cancer cells 89-90

Downregulation of 4EBP1 in ER+ 8p11-12 breast cancer cells causes 91-92 cell cycle arrest

4EBP1 knockdown inhibits proliferation of ER- 8p11-12 amplified breast 93-94 cancer cells

The effects of 4EBP1 knockdown on non-amplicon bearing cancer cell 95-97 models

Loss of 4EBP1 does not promote changes in apoptosis associated 98-99 proteins

The decrease in ERα with 4EBP1 loss does not alter protein levels of 100-102 NSD3 or ASH2L

4EBP1 knockdown decreases MYC levels 103-105

Chapter IV: Discussion 106-116

Chapter V: Future Directions 117

Future directions related to 4EBP1 in cell dynamics 118-121

A role for 4EBP1 in transformation 121-122

A role for 4EBP1 in growth factor independence 122-123

A role for 4EBP1 in anchorage-independent growth 123

A role for 4EBP1 to bypass senescence 124-125

A role for 4EBP1 to influence cancer stemness 125-126

A role for 4EBP1 in apoptosis 126

A role for 4EBP1 in adipogenesis and lipid metabolism 127-128

A role for wild-type 4EBP1 transcripts in malignant neoplasms 129

A role for 4EBP1 as an antiviral strategy 129-130

A role for 4EBP1 in translation 130-131

A role for 4EBP1 in precision medicine 131-135

Chapter VI: Appendix 136

The 8p11-12 amplicon candidate oncogenes in breast cancer 137

Data mining results of 8p11-12 oncogenes in cancer 138-181

Summarized gene ontologies for 8p11-12 gene involvement in cancer 182-183 transformation processes

Available lentiviral 8p11-12 gene expression plasmids for future studies 183-184

Discussion of 8p11-12 data mined results 185-186

References 187-214

Biographical Sketch 215

vii

LIST OF TABLES

Table 1. Highlights of genomic, clinical and proteomic features of molecular 7 breast cancer subtypes from The Cancer Genome Atlas Network (2012)

Table 2. Breast cell line requirements and growth conditions 60

Table 3. Required additions to medium for growth of cell lines in culture 61

Table 4. Short-hairpin RNA plasmids from The RNAi Consortium’s (TRC) 63-64 genome-wide lentiviral human libraries

Table 5. The human ORFeome Collaboration entry clone cDNA plasmid 67 information

Table 6. Lentiviral cDNA expression plasmids 68

Table 7. Recruiting clinical trials for FGFR inhibition in breast cancer 165

Table 8. Available lentiviral cDNA expression plasmids 184

viii

LIST OF FIGURES

Figure 1. The incidence of clinical breast cancer subtypes in the United States 4 of America

Figure 2. PAM50 intrinsic breast cancer molecular subtypes 6

Figure 3. Transcript expression of the estrogen receptor (ESR1) in breast 10 tumors based on the patient’s age

Figure 4. ESR1 mRNA expression increases with age with no increase in ESR1 11 copy number in breast cancer patients

Figure 5. ESR1 gene essentiality across cancer cell lines 12

Figure 6. Candidate oncogenes within the 8p11-12 genomic locus in breast 18-19 cancer

Figure 7. Amplification of the 8p11-12 genomic region frequently occurs in 21 cancer

Figure 8. The 8p11-12 amplicon genes can predict the survival of breast cancer 22 patients

Figure 9. Eukaryotic Initiation Factor 4E-Binding protein (4EBP1) is defined as a 24 translational repressor

Figure 10. Important interactions between 4EBP1 and other proteins 27

Figure 11. 4EBP1 acts as a convergence factor for multiple pathways 28

Figure 12. The demonstrated dynamic post-translational modification sites of 30 4EBP1

Figure 13. GeneTree for ENSGT00390000013843 (EIF4EBP1, 4EBP1) 38

Figure 14. 4EBP1 gene expression across 53 normal human tissues 40

Figure 15. TCGA data show many types of cancer overexpress 4EBP1 42 transcripts at high levels

Figure 16. Frequency of 4EBP1 mutations in the TCGA cohorts 44

Figure 17. Eukaryotic Translation Initiation Factor 4E Binding Protein 49 (EIF4EBP1) transcription factor binding profiles

Figure 18. EIF4EBP1 is amplified in human breast cancer 52

Figure 19. cBioPortal TCGA provisional breast cancer data results (2017) 56 showing the top mutational burdens and 4EBP1 alterations in breast cancer

Figure 20. Eukaryotic Initiation Factor 4E-Binding Protein 1 (EIF4EBP1) is 57 overexpressed in malignant breast tumors when compared to normal tissue samples in The Cancer Genome Atlas breast cancer cohort

Figure 21. EIF4EBP1 is a predicted driver of many cancer types 77

Figure 22. High 4EBP1 expression can predict the prognosis of breast tumor 79 patients

Figure 23. 4EBP1 knockdown in normal mammary cells does not inhibit 81 proliferation and levels are comparable to normal primary isolated mammary epithelial cells

Figure 24. 4EBP1 is highly expressed and phosphorylated in 8p11-12 breast 84 cancer cells

Figure 25. Phosphorylated AKT levels and phosphorylated S6 levels across the 86 models

Figure 26. Loss of 4EBP1 does not substantially alter phosphorylated S6 or 88 phosphorylated AKT levels

Figure 27. 4EBP1 knockdown inhibits proliferation of ER+ 8p11-12 breast 90 cancer cells and decreases ER levels

Figure 28. 4EBP1 knockdown leads to G0/G1 arrest in ER+ 8p11-12 breast 92 cancer cells

Figure 29. 4EBP1 knockdown inhibits proliferation of ER- 8p11-12 breast cancer 94 cells

Figure 30. 4EBP1 knockdown inhibits proliferation of MCF7 and T47D breast 96 cancer cells

Figure 31. 4EBP1 knockdown inhibits proliferation of SUM-229 and SUM-149 97 breast cancer cells

Figure 32. Knockdown of 4EBP1 does not drastically alter levels of apoptosis 99 associated proteins

Figure 33. Loss of ER by 4EBP1 knockdown does not alter the levels of NSD3 102 or ASH2L

Figure 34. The influence of 4EBP1 on MYC protein levels and varying gene 104 expression levels of MYC family members

Figure 35. Expression of EGR1 in TCGA breast cancer cohort 114

Figure 36. Unc-5 netrin receptor D (UNC5D) 139

Figure 37. Zinc Finger Protein 703 (ZNF703) 141

Figure 38. ER lipid raft associated 2 (ERLIN2) 143

Figure 39. Pyridoxal phosphate binding protein (PLPBP) 145

Figure 40. RNA polymerase III transcription initiation factor subunit (BRF2) 147

Figure 41. RAB11 family interacting protein 1 (RAB11FIP1) 149

Figure 42. Eukaryotic Initiation Factor 4E-Binding Protein (EIF4EBP1) 151

Figure 43. ASH2 like histone lysine methyltransferase complex subunit (ASH2L) 153

Figure 44. LSM1 homolog, mRNA degradation associated (LSM1) 155

Figure 45. BCL2 associated athanogene 4 (BAG4) 147

Figure 46. DDHD domain containing 2 (DDHD2) 159

Figure 47. Phospholipid phosphatase 5 (PLPP5) 161

Figure 48. Nuclear receptor binding SET domain protein (NSD3) 163

Figure 49. Fibroblast growth factor receptor 1 (FGFR1) 166

Figure 50. Transforming acidic coiled-coil containing protein 1 (TACC1) 168

Figure 51. ADAM metallopeptidase domain 9 (ADAM9) 170

Figure 52. ADAM metallopeptidase domain 32 (ADAM32) 172

Figure 53. IDO2 gene expression in the TCGA-BRCA cohort 174

Figure 54. Indoleamine 2,3-dioxygenase 1 (IDO1) 175

Figure 55. Chromosome 8 open reading frame 4 (TCIM) 177

Figure 56. Lysine acetyltransferase 6A (KAT6A) 179 xi

Figure 57. Inhibitor of nuclear factor kappa B kinase subunit beta (IKBKB) 181

xii

ALEXANDRIA COLETT RUTKOVSKY. Understanding 8p11-12 Candidate Oncogenes to Influence Breast Cancer Pathology. (Under the direction of STEPHEN P. ETHIER and ROBIN C. MUISE-HELMERICKS).

Abstract

The goal of this research was to elucidate the individual and cooperative role of oncogenes within the frequently amplified 8p12-11 genomic locus which is known to predict poor prognosis and resistance to endocrine therapy in breast cancer. The role of specific oncogenes from the 8p11-12 amplicon in breast tumors is highlighted and the construction of expression constructs for future studies is described. Further exploration of the oncogenic role of Eukaryotic Initiation Factor 4E-Binding Protein (4EBP1,

EIF4EBP1) showed 4EBP1 is often highly overexpressed in malignant tumors and is predicted as an essential driving gene in many cancer cell lines in vitro, so we hypothesized 4EBP1 was a driving oncogene in breast cancers. High 4EBP1 gene expression was associated with reduced relapse free patient survival across all breast tumor subtypes, including post treatment tumors. We found 4EBP1 was crucial to the proliferation of all breast cancer cell lines tested, but not normal cells, making it a prime therapeutic target. This is the first report showing 4EBP1 levels influence malignant estrogen receptor α (ER, ESR1) levels, and we also explored the levels of other influential cancer related genes including oncogenes within 8p11-12 which are known to influence ER. Applied data mining and a current clinical trial implied importance for

4EBP1 as a biomarker for therapeutic efficacy, so we explored mTOR inhibition in multiple cell culture models. It is likely 4EBP1 mediates upstream signaling from many known tumorigenic signaling cascades in adaptive and different ways thus it should be further explored and has much potential as a biomarker and target for breast cancer, especially endocrine resistant tumors.

xiii

Chapter I

BACKGROUND

Human mammary tissue, breast cancer patient subtypes, and frequent amplification of the 8p12-11 locus in breast cancer

In the United States last year, it was estimated that 249,260 new cases of breast cancer were diagnosed which will result in 40,890 estimated deaths. Breast cancer remains second to lung cancer as a leading cause of cancer deaths among USA women1. Breast cancer is the most commonly diagnosed female malignancy in most countries worldwide; in the US about 12% (1 in 8 women) will be diagnosed at some point in their lifetime2. Breast cancer diagnosis at early stages dramatically improves 5- year survival rates3, albeit tumors can recur even after 5 years, and these are often resistant to previous effective therapies. The most common symptom of breast cancer is an abnormal mass in the breast, highlighting the need for continued self-examination and thorough adherence to recommended guidelines associated to risk and early detection.

The human breast is a glandular milk-producing system held together by connective tissue, containing a large supply of nerves, blood vessels, and lymphatic vessels. The epithelium is organized into lobes radiating outward from the nipple, each branching into a ductal network leading to terminal duct lobular units (TDLUs). TDLUs regulation is closely tied to endocrine system function and relies on an organized, hierarchical pattern of differentiation with continued maturation through menstruation and full differentiation for lactation during pregnancy4.

Breast tumors are heterogeneous and classified by origin: as lobular or ductal, size, grade, lymph node invasion, and the expression of various proteins such as hormone receptors5-16. Clinically breast cancer subtypes are classified by

immunohistochemistry for the expression of estrogen receptor α (ER), progesterone receptor (PR), and the epidermal growth factor 2 (HER2/neu/ErbB2). The USA incidence of each of these subtypes from 1975-2011 is shown in Figure 1.

Figure 1. The incidence of clinical breast cancer subtypes in the United States of America. These values were taken from the Annual Report to the Nation on the Status of Cancer, 1975-2011. Featuring Incidence of Breast Cancer Subtypes by Race/Ethnicity, Poverty, and State. Luminal A (dark blue, 73%), Luminal B (light blue, 10%), Triple-negative (red, 12%), and HER2-enriched (pink, 5%) are represented as the percentage of each subtypes in the United States over 36 years.

Gene expression profiling, such as the PAM50 assay, can provide complementary information of molecular features to aid in treatment decisions.

Molecular breast cancer subtypes can be grouped by gene expression into ERBB2 overexpressing, luminal A, luminal B, basal-like, and normal-like breast tumors; with gene signatures showing promise for further classification9,13,17-22. These loosely correlate to the defined immunohistochemical subtypes (Figure 2); additional diagnostics are often adopted and molecular profiles are used in clinical practice9,13,14,23,24 to incorporate genetic signatures with the hope of improving patient outcome9. Classification is important to treatment response, prognosis, and survival. The highlights of genomic, clinical, and proteomic features of molecular subtypes as published in 2012 by the Cancer Genome Atlas Network are shown in Table 1 and suggest a role for the 8p11-12 amplification among multiple molecular subtypes.

Figure 2. PAM50 intrinsic breast cancer molecular subtypes. Molecular subtypes are grouped by immunohistochemical molecular subtypes as studied by Jenkins and colleagues. Oncologist 2014. Normal-like (green), HER2-Enriched (pink), Luminal B (light blue), Luminal A (dark blue) and basal-like (red) are represented by percentage of cases from the cohort studied by Jenkins and colleagues in 2014.

Table 1. Highlights of genomic, clinical and proteomic features of molecular breast cancer subtypes from The Cancer Genome Atlas Network (2012).

Luminal breast tumors, which express the estrogen receptor (ESR1, ER), are the most common subtype, and represent most patients age 50 and up. Luminal tumors are further subdivided into luminal A and luminal B tumors. The Cancer Genome Atlas

(TCGA) breast cancer cohort shows on average that luminal tumors express far more

ESR1 than other subtypes or normal samples (UALCAN25 mined 06-09-2018 transcript per million values: normal samples 46.98, luminal samples 156.13, Her2 samples 2.09, triple-negative samples 0.97) and that the expression of ESR1 increases with age of presentation (Figure 3). This is specific to estrogen receptor α (ESR1) rather than estrogen receptor β (ESR2) in breast tumors (Figure 3). Interestingly, the high levels of

ESR1 are rarely caused by increased copy number of ESR1. This is witnessed in multiple large breast cancer patient cohorts like METABRIC and TCGA-BRCA, which show ESR1 mRNA expression increases with age yet ESR1 copy number levels trend mostly the same (Figure 4), suggesting other mechanisms are causing the increase in

ER. Luminal tumors are often reliant on ER, and ER function is influenced by hormones like estrogen and related compounds, allowing for the success of ER-targeted therapies such as tamoxifen, aromatase inhibitors, and selective estrogen degraders. Circulating hormones regulate cell and tissue function, so it is not surprising that ER is predicted as an essential gene in many cancer cell lines in vitro which arise from different tissues, as suggested by data found within the DepMap portal (Figure 5). If luminal tumors recur, they are often resistant to the ER-targeted therapy that was once effective. Many studies aim to uncover mechanisms that can influence ER expression and resistance to therapy. The RNA-sequencing data from the metastatic breast cancer (BRCA-MET500) dataset (UALCAN25 06-19-2018) shows differences among ESR1 expression in different backgrounds such as with (median Reads Per Kilobase per Million (RPKM) value at

28.28) and without CCND1 amplification (median RPKM value at 1.02), with (median

RPKM value at 0.78) or without ERRB2 (HER2) amplification (median RPKM value at

1.57), and with (median RPKM value at 3.21) or without MYC amplification (median

RPKM value at 1.33). Although these are small sample sizes, it suggests that increased

ESR1 transcript levels are correlated to specific amplification events such as 11q and 8p amplifications which often correlate with ER levels and endocrine resistance.

Figure 3. Transcript expression of the estrogen receptor (ESR1) in breast tumors based on the patient’s age. The gene expression values of ESR1 in transcript per million reported from the TCGA-BRCA data set by UALCAN which are grouped by normal (blue), then increasing age. Breast tumor patients age 21-40 years old (orange), 41-60 years old (brown), 61-80 years old (green), and 81-100 years old (red) are shown. Significance is calculated by UALCAN and displayed. UALCAN data seen here shows ESR1 mRNA levels increase with age of breast cancer presentation, while ESR2 transcript levels are significantly decreased in all breast tumor patients compared to normal samples. The international ICGC Data Portal shows that the frequency (# Donors affected) of ESR1 and ESR2 mutations are not consistent across international efforts (Project). ESR1 and ESR2 gene-coding mutations across international efforts and independent data sets are not consistent and highlights a continued need for high quality standards across all efforts to relay significant findings.

Figure 4. ESR1 mRNA expression increases with age with no increase in ESR1 copy number in breast cancer patients. Oncomine data from the Curtis Breast cohort (published data set Nature 2012) featuring 2,136 mRNA samples and the Curtis Breast 2 cohort featuring 1,992 DNA samples all grouped by age. The TCGA data cohort mimics the pattern seen.

Figure 5. ESR1 gene essentiality across cancer cell lines. The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood ESR1 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes). Seen here, ESR1 acts as a pan-essential gene in multiple breast cancer cell lines including HCC1428 and EFM19 (with high expression and CERES score or -1 or less). The four cell lines with high expression and CERES scores less than -0.5 are ZR751, KPL1, Cama-1, and HCC1419, respectively. (https://depmap.org/portal/gene/ESR1?tab=dependency&characterization=expression_r pkm).

Gene amplification in cancer oncogenesis and amplifications in breast cancer

Since the mid 1970’s, genomic mechanisms that activate oncogenes have been extensively studied. Importantly, oncogenes were first discovered because they caused cancer in animals when introduced by a retrovirus that had a normal form of the gene, a proto-oncogene. Proto-oncogene activation to an oncogene is one step in oncogenesis.

Tumor virus studies led to the discovery of many of the most well-known proto- oncogenes like MYC and RAS, albeit some oncogenes are thought to have no relationship to oncogenic viruses.

In humans, proto-oncogenes can become activated oncogenes in multiple ways.

A deletion or point mutation in the coding sequence can make a hyperactive protein that is made in normal amounts. A regulatory mutation can make the normal protein over- produced. Chromosome rearrangement can cause fusion of the gene so that it is actively transcribed or nearby regulatory sequences that then increase gene expression.

Gene amplification can cause overexpression of the gene and protein product26.

Alternative translational initiation mechanisms may operate to more efficiently translate transcripts such as the case with certain viral mRNA molecules and some eukaryotic transcripts under stress27.

The human genome is usually diploid, but some cells are normally non-diploid, which automatically increases the availability of genomic content. Allelic frequency in normal cell populations is understudied but a non-diploid phenotype is known to normally occur in some cells of tissues like salivary, bone, and liver. Regulation of the polyploid genome is crucial to cytoplasmic fragmentation of megakaryocytes to produce platelets28-31. Evolution can select higher copies of certain genes, such as the case with higher copy number of amylase genes in certain human populations32-34. Thus, 13

polyploidy is crucial to some normal human cells, and aneuploidy often presents in cancer. Chromosomal changes in cancer cells resulting from gene amplification can produce amplicons35,36. There are two general karyotypic cytogenetic patterns of chromosomal alterations observed with gene amplifications: additional pairs of miniature chromosomes as double minute chromosomes (DMs) or amplifications within the banding pattern of a chromosome as homogenously staining regions (HSRs)26,37,38.

There are five models that explain the generation of amplicons: extrareplication and recombination, breakage-fusion-bridge cycle, double rolling-circle replication, replication fork stalling and template switching39, and chromothripsis40-42. The mechanisms governing amplicon generation, maintenance, and retention during cell division are not understood. Turner and colleagues highlighted a role for circular extrachromosomal DNA

(ecDNA) amplification to increase oncogene copy number and intratumoural heterogeneity43. This idea may more readily explain the term “transplication” coined by

Birnbaum and colleagues where fusions like 8p12-11q13 may occur to explain the striking frequency of coamplification in cancer44. Chromothripsis-like patterns were also recently highlighted for the 8p11-12 amplicon45.

Copy number gains and losses have long been known to predict poor prognosis in many cancers. Aneuploidy is common in solid tumors as well as blood cancers, with about 25% of the cancer genome affected by whole-arm or whole-chromosome somatic copy number alterations (SCNAs) and 10% affected by focal SCNAs46. A recent TCGA comprehensive pan-cancer molecular study of gynecologic and breast cancers determined 5 subtypes across the Pan-Gyn tumors and showed high somatic copy number alterations grouped into 3 clusters, immune high(C3), AR or PR low (C4), and

AR or PR high (C5) with C4 and C5 having the poorest survival probabilities47. Davoli and colleagues examined 5255 TCGA tumor and normal samples across cancer types 14

and found that assessing tumor SCNA level alone or together with neoantigen burden may help predict patient benefit from immune checkpoint blockade48. This suggests a dynamic interplay between aneuploidy and immune surveillance. Bilal and colleagues showed that the presence of amplicons, including 8p11-12, in breast cancer as detected by multiplex ligation-dependent probe amplification (n=268 ER+ breast cancers treated with tamoxifen) could significantly predict distant metastasis (HR=2.53, p=0.0067) more than tumor size (HR=1.38, p=0.0180), histological grade (HR=0.44, p=0.0959), or ODx

RS (HR=1.08, p=0.3838) using Cox analysis49. Additionally, amplicons can be individually or simultaneously amplified in breast cancer. The most commonly amplified regions are 1q, 8p, 8q, 11q, 12q, 17q, and 20q18,50-59. The 8p11-12 amplicon often coamplifies with the 11q1360 amplicon to promote aggressiveness18,50,51,53,57,58 in addition to 8q2461, 12p13-15, 20q13, others18,50,51,53. This highlights that genomic analysis should be routinely assessed, especially since these regions contain driver oncogenes whose overexpression can potentiate transformation.

Driving oncogenes on amplicons

Evidence suggests that amplicons are non-randomly selected during cancer development62. It is possible amplicons encode multiple oncogenes that interact to drive transformation, but there are important, individual driving genes within these regions, like

EIF4EBP1. Driving genes provide a necessary growth advantage that is essential for tumor survival, making them prime therapeutic targets63 whereas, passenger genes are amplified with the driving gene and are not good therapeutic targets. Additionally, loss of a prime therapeutic target should not be adverse to normal cells. Increasing evidence suggests that more than one driver exists within chromosomal amplifications supporting our hypothesis that amplicons function as transforming units, nonetheless they carry discreet oncogenes like EIF4EBP1.

The 8p11-12 amplicon contains both driver and passenger genes however different studies have proposed different driving genes within this amplicon57,64-80. Most researchers agree that focal amplifications contain cooperative oncogenes that drive malignancy. This paradigm shift is largely because techniques used to detect amplicon oncogenes are continually improving. The 1970’s and 1980’s relied on karyotype/G- banding to detect numerical chromosomal aberrations as aneuploidy. The 1990’s brought fluorescence in situ hybridization (FISH) based technologies with higher sensitivity but low resolution (> several Mb). This allowed measurement of the fluorescence ratio between the chromosome region and diploid reference DNA to distinguish between relative loss and gain. In the early 2000’s, far better resolution and high sensitivity was conferred by microarray-based technologies. Initially, the detection of amplicons was based upon large genomic clones with an average resolution (>1 Mb)

which shaped the idea that each amplicon had one driving oncogene. As array-based technologies (custom array >400 bp) and next-generation-sequencing improved, each amplicon region could be further resolved, suggesting multiple oncogenes were present in one region81. In 2005, our lab contributed to the identification of four 8p11-12 amplicon regions as defined by array comparative genomic hybridization, gene expression profiling, and FISH analysis80. The regions of gains, which can occur individually or simultaneously, were designated amplicons A1 to A4 from telomere to centromere and were used to define candidate oncogenes within the region. We previously defined approximately 70 breast cancer oncogenes and then narrowed the list to 22 candidate oncogenes based on prior data, curated literature, and patient tumor data. Figure 6 shows copy number alterations of the candidate genes from 8p11-12 in the breast cancer patients form The Cancer Genome Atlas (TCGA) data which are copy number altered in the cohort analyzed Summer 2018 from the cBioPortal82,83. Each is matched to chromosome location, orientation, region of sub-amplification and frequency of amplification as determined from the Genome Identification of Significant Targets

(GISTIC)84-86 in cancer algorithm.

Figure 6. Candidate oncogenes within the 8p11-12 genomic locus in breast cancer. Copy number alterations of the candidate genes from 8p11-12 in breast cancer patients from TCGA data which are copy number altered as analyzed Summer 2018 from the cBioPortal.82,83 The candidate genes are grouped into different sub-regions (A1- A4) based on individual and simultaneous occurrence of amplified genes. Copy number amplification is shown in red and copy number deletion is shown in blue. This is organized by chromosomal location. GISTIC uses copy number alterations and chance to predict drivers of the 8p11-12 region from TCGA data. We compiled the GISTIC breast cancer subset reports for each gene by frequency of amplification events, and prediction values by GISTIC84-86 based on peak presence and q-value. Strand orientation is also reported.

Amplification of 8p12-11 (8p region 1 band 2 – region 1 band 1)

The 8p11-12 amplicon presents itself in many cancer types20,23,24,40,114-135,100-121 as shown in Figure 7 with amplification frequencies across cancer types. Our laboratory and others have demonstrated the importance of the 8p11-12 amplicon and many of its genes in the development and pathogenesis of breast cancer6245,49,54,57,64-76,78-80,87-99108,109, including its role in endocrine resistance. Amplification of the 8p11-p12 region of the human genome, which occurs in ~20-30% of metastatic ER+ breast cancers, is associated with resistance to endocrine therapy and poor prognosis100. Recently, a pre- surgical clinical genomics platform was used for the unbiased discovery of endocrine resistant mechanisms and highlighted 8p11-12 amplification with a high proliferative index and independence from PIK3CA mutations94. We have shown using copy number changes and expression analysis that the approximately 70 gene amplicon often expresses distinct sets of genes which can be divided80 into minimal regions which have been identified in breast cancer69,79,80,101. These regions can be amplified together or independently80 to confer poor prognosis (Figure 8). Our lab was one of the first to show oncogene interactions within an amplicon66,102 and it is likely that many of these genes do cooperate, further diluting a picture of driving genes within 8p11-12. Many studies have focused on the in vitro transformative abilities of these genes to induce growth factor independence, provide anchorage independent growth, improve invasive capacity, and alter morphogenesis in 3D culture. As sequencing efforts improve and the availability of genomic data increases, there is increased evidence for the importance of

8p11-12 genes in many malignancies. This work has focused to understand which 8p11-

12 oncogenes are most important to the transformed phenotype and we highlight

EIF4EBP1. 20

Figure 7. Amplification of the 8p11-12 genomic region frequently occurs in cancer. TCGA alteration frequency for genes from the A2 region visualized using the cBioPortal 82,83. Amplification is shown in red. Breast cancer cohorts are indicated with a star and amplification in these cohorts occurs in approximately 15% of patients across the data sets.

Figure 8. The 8p11-12 amplicon genes can predict the survival of breast cancer patients. Expression analysis using TCGA provisional data shows that high expression of A2 genes: BRF2, RAB11FIP1, EIF4EBP1, ASH2L, LSM1, BAG4, DDHD2, PLPP5, NSD3, FGFR1, TACC1, ADAM9, and ADAM32 correlates with reduced overall survival. Cases with alterations is shown in red and cases without alternations are shown in blue. The results are significant with a p-value of 0.00523 as reported by the cBioPortal.

Eukaryotic Initiation Factor 4E-Binding Protein (EIF4EBP1, BP-1, 4EBP1, 4E-BP1, and PHAS-I)

Many human cancers103,104, including breast cancers with the 8p11-12 amplicon, overexpress Eukaryotic Initiation Factor 4E-Binding Protein (EIF4EBP1, BP-1, 4EBP1,

4E-BP1, and PHAS-I)105 95,106. This is especially evident by high 4EBP1 transcript levels compared to normal tissue (Rutkovsky, DOI: 10.5281/zenodo.1186444) along with frequent amplification of the 8p11-12 genomic locus. We have sought to distinguish the functional driving oncogenes from passenger oncogenes in this amplicon and herein we highlight 4EBP1 as a driver in all breast cancer subtypes studied with or without the

8p11-12 amplicon.

4EBP1 is canonically regarded as a translational repressor protein that interacts with eukaryotic initiation factor 4E (EIF4E) and represses translation by inhibiting EIF4E from recruiting 40S ribosomal subunits during translation107,108-111. Upon phosphorylation112-115, 4EBP1 dissociates from EIF4E causing active cap-dependent translation as seen in Figure 9, where the 4EBP1 structure is modeled with EIF4E from previously defined 4EBP1 structures116 using MMDB and VAST+ for tracking structural similarities between macromolecular complexes117.

Figure 9. 4EBP1 is defined as a translational repressor. The 4UED structure is depicted with solvent accessible surface outlined over ribbon diagram. 4EBP1 (blue) bound to EIF4E (pink) is known to prevent translation, while phosphorylated (green) 4EBP1 (blue) is known to promote translation.

4EBP1 is most known for an involvement in the mTOR signaling cascade prompted by growth factor stimulation, whereby it is phosphorylated to promote release of Eukaryotic translation initiation factor 4E (EIF4E) and subsequent translation. Since non-phosphorylated 4EBP1 inhibits translation, it is expected that active 4EBP1 acts as a tumor suppressor. Consistent with active 4EBP1 having this role, high expression of

4EBP1 coincides with the protein being maintained in a phosphorylated state to contribute to breast cancer105,11895,106,118-121. Indeed, proteins that can regulate 4EBP1 phosphorylation, like Casein kinase 1ᵋ122,123, glycogen synthase kinase (GSK)-3β124, G1

To S Phase Transition 2 (eRF3b)125,126, mammalian target of rapamycin complex 1

(mTORC1)114,115,127-134, Polo Like Kinase 1 (PLK1)135-137, Family With Sequence Similarity

129 Member A (Niban)138, PI3-Kinase isoforms139,140, cyclin-dependent kinase 1

(CDK1)141-144, ATM Serine/Threonine Kinase (ATM)145,146, Mitogen Activated Protein

Kinase (MAPK)147,148, Protein Kinase B (AKT)149, and others142,148,150 have been suggested as therapeutic targets for cancer, in part to decrease 4EBP1 phosphorylation and inhibit translation. Importantly, many kinases can regulate and stabilize 4EBP1150.

The Expression2Kinases151,152 interface shows some of the protein-protein interactions between 4EBP1 and HSP90AB1, MAPK10, mTOR, ATM, CSKK2A2, PPP1CA,

PPP2CA, PRKDC, MAPK3, AKT1, MAPK1, MAPK14, CSNK2A1, CDK1, or GSK3B.

Some important interactions between 4EBP1 and other regulators can be visualized using Pathway Commons (http://www.pathwaycommons.org/), as shown using the PCViz network visualizer in Figure 10. The Gene Ontology (GO)

Consortium148,149 (http://geneontology.org/) summarizes known function and processes for 4EBP1 seen at the GO database, PHAROS (https://pharos.nih.gov/idg/index), or

Enrichr (http://amp.pharm.mssm.edu/Enrichr/). Predicted biological processes and 25

pathways have been summarized in Figure 11 which was generated using the ARCHS4 portal (https://amp.pharm.mssm.edu/archs4/)153.

Figure 10. Important interactions between 4EBP1 and other proteins. Pathway Commons PCViz (http://www.pathwaycommons.org/pcviz/#neighborhood/EIF4EBP1) was used to visualize nodes of interaction between 4EBP1 and other proteins. Interaction types are listed per protein with the estimated number of Pubmed references and co-citations reported for each interaction. The ability of mTOR to alter the state of 4EBP1 remains the most referenced interaction.

Figure 11. 4EBP1 acts as a convergence factor for multiple pathways. The role for 4EBP1 in cell signaling is extensive and there are many known and predicted KEGG pathways involving 4EBP1. Warburg showed over half a century ago that altered metabolism is crucial to cancer cells, so a role for 4EBP1 among these types of pathways is not surprising, especially nutrient sensing pathways. 4EBP1 is potentially mediating upstream signaling from many known tumorigenic signaling cascades, likely in adaptive and different ways. Some known KEGG pathways include: EGFR tyrosine kinase inhibitor resistance (hsa01521), RNA transport (hsa03013), ErbB signaling pathway (hsa04012), HIF-1 signaling pathway (hsa04066), mTOR signaling pathway (hsa04150), PI3K-Akt signaling pathway (hsa04151), AMPK signaling pathway (hsa04152), Longevity regulating pathway (hsa04211), Cellular senescence (hsa04218), Insulin signaling pathway (hsa04910), Human papillomavirus infection (hsa05165), Acute myeloid leukemia (hsa05221), and Choline metabolism in cancer (hsa05231).

Studies show many tumor types phosphorylate 4EBP1 (please refer to Table 1 in a recent review of 4EBP1103 , Uniprot154, or PhosphoSitePlus155) but it is not accurate to assume that this is always related to mTOR or EIF4E, as highlighted in recent reviews by Qin and colleagues104 or Batool and colleagues156. Phosphorylation may not be necessary for oncogenesis, especially as related to hierarchical phosphorylation of

4EBP1 by mTOR which was shown necessary for subsequent dissociation from

EIF4E,115 particularly in response to exogenous stimuli114,115. Additionally, post- translational modification is often dynamic and there are many demonstrated protein modifications besides phosphorylation, like acetylation, lipid modification, and ubiquitination which can influence 4EBP1 regulation (Figure 12). It is hypothesized that the multiple phosphorylation sites on 4EBP1 may set a higher threshold for ubiquitination112,157 and degradation. There is no doubt that nutrients alter 4EBP1 function by post-translational modification, and that 4EBP1 is closely tied to adipogenesis regulation and insulin signaling. Many studies show that molecules stimulate phosphorylation of 4EBP1, like lipopolysaccharides (Endotoxin / LPS) can impair skeletal muscle protein synthesis while stimulating 4EBP1 phosphorylation in macrophages158. There is yet a thorough study on the dynamic regulation of 4EBP1 post-translational modifications, especially in breast cancer.

Figure 12. The demonstrated dynamic post-translational modification sites of 4EBP1. 4EBP1 (Q13541, NP_004086.1, NM_004095.3) with 118 amino acids is depicted with the EIF4E binding site (pink), YXXXXL phi motif (blue), and TOS motif (purple). Known phosphorylation (yellow and turquoise circles), acetylation (red circles), lipid modification (phosphoglycerol at Lysine69 circle), and ubiquitination (green circles) modifications can influence 4EBP1 regulation. Phosphorylation by mTOR is known to regulate Threonine37/46/70 and Serine65 (turquoise). This figure was generated with known Uniprot modifications along with the addition of other published findings using the IBS illustrator159.

RNA species are dynamically regulated, and cells rely on availability of transcripts for regulation at many levels especially in response to exogenous substances, circumstances, and epigenetic programming. The mammalian transcriptome is rich in diversity and different tissues harbor unique transcript libraries, making it intriguing that cancers arising from different cell types up-regulate 4EBP1 transcript levels so frequently. 4EBP1 may be so frequently expressed because it affects translation efficiency of both protein-coding RNA species as well as other RNA species.

4EBP1 may be critical to the regulation of transcripts that are not capped, such as those transcribed by RNA polymerase I and RNA polymerase III. Since about half of the total

RNA synthesized is ribosomal RNA and ribosome composition changes with different differentiation states and cell types160-162, it is easy to speculate that 4EBP1 may so frequently be overexpressed in tumors because it is playing a role in regulating ribosome heterogeneity which in turn regulates translating the genetic code. This remains widely underexplored in different types of cells, differentiation states and transformative processes. In addition to altered components within a ribosome there exists regulation of the ribosome itself which is also not well explored.

Translation is a complex and regulated cell process. It has been known for more than 40 years that increased translation is necessary for entry into and transit through the G1 phase of cell cycle163. Cells regulate growth and proliferation by regulating translation. A key step in translational regulation is recruiting ribosomes to transcription start sites to initiate the translational process. The canonical “scanning model’ of eukaryotic translation is a well validated explanation for the translational initiation of most protein-coding mRNA species. In this model, the ribosomal subunits are recruited to the

5’ end of the mRNA, scanning in a 5’ to 3’ direction until the start codon is encountered 31

and protein synthesis is initiated. Other translation initiation mechanisms also operate to ensure efficient translation, such as the ability to distinguish the proper start codon when many are present; altered transcriptional start sites provide a viable explanation for alternative, heterogenous, and tissue-specific isoforms which help explain the presence of genes known to express mRNAs with alternative first exons164. Cap-dependent

(protein-coding) mRNA translation is a well-studied and tightly regulated process in eukaryotes so it is not surprising that deregulation at this level is associated with cancer.

It is important to remember that mRNA made by RNA polymerase I, RNA polymerase III, or viral polymerases are not capped and underrepresented in translational studies.

Polymerase II has a unique carboxy-terminal domain which allows interaction with capping enzymes and subsequent capped transcripts165.

Other mechanisms include the capacity for ribosomal initiation complexes to bind specific internal ribosome entry sites (IRES) of which no consensus sequence has been defined but can occur independently of the 5’ 7-methylguanylate (5’ m7G) cap structure present on protein-coding transcripts transcribed by RNA polymerase II166. IRES translation is the strategy that some viruses use for translational initiation, allowing them to overcome host antiviral responses167. Evidence has since suggested that some cellular mRNAs also use IRES-mediated translation under stress, and the IRES structure is present in at least 50 viruses and 70 cellular mRNAs168,169. The manually annotated IRESite database is helpful for identifying Internal Ribosome Entry Site (IRES) elements in RNA species169. Tools available to study eukaryotic IRES-mediated translation remain limited and thus controversy persists. This is especially evidenced by the fact that cap-trapped transcript library processing selects against non-capped

RNAs170 and an idea that uncapped transcripts are unstable. RNA secondary structure is critical to function and the 5’ UTR of mRNAs play important roles in translation efficiency, 32

especially ribosome recruitment171 and cap-independent pre-initiation complex binding172 in eukaryotic translational activity164. Many have thought it difficult to truly characterize the 5’ ends of transcripts, determine transcription start sites, the regulation of capping, and the quantity of uncapped transcripts. The ENCODE project suggests that the vast majority of the genome is transcribed, and efforts are underway to more accurately assess entire transcriptional contents, with noted efforts dedicated to optimizing 5’- capped, 5’-monophosphorylated, or 5’-triphosphorylated ends of transcripts.

Many studies have implicated 4EBP1 as a critical regulatory node in both normal and cancerous cell translation, we and others have hypothesized this is because 4EBP1 is regulating cap-independent translation and somehow contributes in recognition and initiation. This could be different from facilitating translation through hyperphosphorylated 4EBP1 which can increase the interaction of EIF4E and EIF4G; this defined the role for 4EBP1 as a negative regulator of cell growth173. This role for 4EBP1 or EIF4E in translation is understudied. Evidence showed EIF4E knockdown in HeLa cells caused only a minor reduction of translation157 which may support regulation of particular transcripts but it does not seem to be a limiting factor in translational efforts.

Well known cell regulators like CCND1 and MYC can use cap-independent174 and cap- dependent translation. Other studies and our results show 4EBP1 levels regulate

CCND1 and MYC levels, but we cannot comment on translational preference of these transcripts in our models.

We hypothesize the role for 4EBP1 in cell cycle regulation is closely tied to temporal and spatial regulation of translation, similarly to what is necessary in oocyte maturation133,175. This would better explain any preferential translation of uncapped mRNAs during mitotic cell cycle176 as well as phosphorylation of 4EBP1 at Serine65 and

Threonine70 which has been shown to increase during mitosis142; mitosis and the cell 33

division cycle has known implications in cancer development. This might also suggest a role for 4EBP1 in regulating mRNA transcripts with alternative fates and could support a role in stress granules which contain RNA-binding proteins and mRNAs177. 4EBP1 has been implicated in stress granule formation178 and this could be detrimental to its oncogenic role. We cannot exclude a capacity for 4EBP1 to interact within the many transcriptional and post-transcriptional regulatory layers which prime processes prior to translation, especially since 4EBP1 is found within many cell compartments. It is possible 4EBP1 might act as a RNA binding protein. At the post-transcriptional level,

RNA-binding proteins coordinate transcriptional efforts, please refer to the review by

Morris and colleagues179 for a more detailed table of RNA binding proteins of which most are able to associate with multiple mRNA species but are also eukaryotic lineage specific179, implying the only way to understand this possible function would be to study it in homo sapiens.

4EBP1 and mTOR in mRNA translation

mTOR has an established role in mRNA translation180-182 and metabolism183. For an overview on mTOR phosphorylation of 4EBP1 please refer to the reviews by Orth and colleagues103 or Qin and colleagues104. Briefly, the Mammalian Target of Rapamycin

(mTOR) (FRAP) complexes, either mTORC1 or mTORC2, are major players in cell signaling response184-186. Signaling through mTORC1 can phosphorylate 4EBP1 which is thought to activate protein synthesis, so phosphorylated 4EBP1 is generally accepted as a marker of active mTOR signaling. mTOR complexes phosphorylate many things and often converge with other pathways in regulation. A good example of this is the convergence of the MAPK and mTOR pathway on protein phosphorylation of Serine167 on ER187 or the phosphorylation of 4EBP1. mTORC1 also phosphorylates Ribosomal

Protein S6 Kinase B1 (RPS6KB1, S6K1) and Ribosomal Protein S6 Kinase B2

(RPS6KB2, S6K2). It has been shown S6K2 and 4EBP1 gene expression profiles share significant overlap, particularly in genes involved in cell cycle progression188, and 4EBP1 and S6K2 are frequently co-expressed to confer poor prognosis and endocrine resistance in breast cancer105. Importantly, both downstream regulators in the KEGG pathway have been shown to regulate mTOR complexes, implying feedback loops that are not understood. S6K1 and S6K2 gene silencing can upregulate genes in the mTOR complexes188 and 4EBP1 can stabilize mTORC1189; these authors postulated that

4EBP1 might facilitate translation by recruiting mTORC1 and eukaryotic initiation factors to the 5’ end of certain mRNAs189. This coincides with other reports showing mTORC1 can regulate translation of a subset of mRNAs with special motifs (5’TOP motifs) and this does not require active S6K1 or phosphorylated Ribosomal Protein S6 (RPS6,

S6)190. This may be related to regulating both ribosomal biogenesis and mRNA translation because 4EBP1 can partake in both cap-dependent and cap-independent translation. 5’TOP sequences are usually found in mRNAs for peptide elongation factors and ribosomal proteins and consist of a starting Cytidine followed by a stretch of 5 to 15

Pyrimidine nucleotides. It was once thought that the phosphorylation of S6 was a gatekeeper of 5’TOP mRNA translation, but this is no longer conclusive. Actually, mTOR dependent translational regulation of 5’TOP mRNAs has long been known to not require active S6K1 or phosphorylated S6190. Dowling and colleagues showed 4EBP proteins could control proliferation downstream of mTORC1 but independently of S6Ks191.

Thoreen and colleagues support a model showing the subset of mRNAs specifically regulated by mTORC1 have established 5’TOP motifs or related TOP-like motifs with

4EBP1 mediating a large part of mTOR dependent control of gene translation; they also suggest the translation of mRNAs with IRES may be translationally initiated independent 35

of mTOR activity and/or the selective recognition of mRNAs with complex 5’ UTRs may require prolonged mTOR inhibition192. The notion that 4EBP1 and S6Ks act independently within the mTOR pathway continues to be demonstrated; regardless these downstream markers are continuously used as predictors of active mTOR signaling. Interestingly, it was recently shown in mouse embryonic fibroblasts that mTORC1 balances amino acid supply with protein synthesis demands through post- transcriptional control of ATF4; in this study, 4EBPs were necessary for repression of some transcripts but not all with mTOR inhibition193. We do not exclude a role for mTORC2 in 4EBP1 modification, especially considering an involved role for mTORC2 in cytoskeleton regulation and AKT phosphorylation194,195. Evidence for 4EBP1 to influence microtubule stabilization and tubulin assembly by influencing Discodermolide resistance exists in lung cancer cells196,197. Indeed, all these regulators have distinct biological features that remain elusive.

Genomic location of 4EBP1

The EIF4EBP1 (4EBP1) sequence is located on the short arm of chromosome 8:

38030502 – 38060365 (GRCh38.p7; current assembly) which is encoded within the

8p11-12 genomic region. It covers 29,863 base pairs within the genome and is processed into 3 exons from the sense strand, ultimately producing one predominant mature mRNA isoform that is transcribed into 357 nucleotides and translated into 118 amino acids not including the initiating methionine. Two mRNA isoforms have been identified. Two human psuedogenes exist at 14q11.2 (LOC768328) and 22q12

(EIF4EBP1P), implying its importance over the course of Homo sapien evolution198.

Conservation among species demonstrates 4EBP1 necessity

The necessity of 4EBP1 in mammals is corroborated by high sequence conservation. The generation of a comparative genomics gene tree for EIF4EBP1 using

Ensembl (ENSGT00390000013843) shows 4EBP1 is ubiquitously expressed and defines 11 closely related homologs in primates (Figure 13). Pairwise alignment scores using HomoloGene:3021 shows the identity of Homo sapiens versus Pan troglodytes is

100% protein and 99.4% DNA, H. sapiens versus Macaca mulatta is 97.3% protein and

95.8% DNA, H. sapiens versus Canis lupus is 94.9% protein and 92.9% DNA, H. sapiens versus Bos taurus is 92.4% protein and 89.9% DNA, H. sapiens versus Mus musculus is 91.5% protein and 90.6% DNA, H. sapiens versus Rattus norvegicus is

93.2% protein and 90.9% DNA, H. sapiens versus Gallus gallus is 67% protein and 67%

DNA, and H. sapiens versus Danio rerio is 62.6% protein and 60.9% DNA198.

Figure 13. GeneTree for ENSGT00390000013843 (EIF4EBP1, 4EBP1). The Ensembl GeneTree is sorted by class, with nodes collapsed at the taxonomic rank Order. Primates have 11 homologs. (http://may2017.archive.ensembl.org/Homo_sapiens/Gene/Compara_Tree?collapse=14 29549%2C1429488%2C1429452%2C1429462%2C1429473%2C1429502%2C1429567 %2C1429520%2C1429517%2C1429545%2C1429499%2C1429480%2C1429550%2C1 429441%2C1429580%2C1429534%2C1429576%2C1429554%2C1429465%2C142956 6%2C1429449;db=core;g=ENSG00000187840;gtr=order;r=8:38030341-38060365)

The behavior and expression of 4EBP1 in normal cells

4EBP1 is normally expressed across most cell types, as seen from the GTEx portal (https://gtexportal.org/home/) in Figure 14 or with the IDG tool using PHAROS

(https://pharos.nih.gov/idg/index). 4EBP1 expression is not essential to the organism, as double knockout of both 4EBP1 and 4EBP2 is not embryonic lethal but results in increased adipogenesis tied to increased CCAAT/enhancer-binding protein delta

(CEBPD), CCAAT/enhancer-binding protein alpha (CEBPA), and Peroxisome proliferator-activated receptor gamma (PPAR-γ or PPARG) with reduced lipolysis and increased fatty acid re-esterification; these mice had decrease energy expenditure and insulin resistance with more Ribosomal Protein S6 Kinase B1 (S6K or RPS6KB1) activity and impaired Protein Kinase B (AKT) signaling in muscle, liver, and adipose tissue199.

PPARG has also been linked to modifying 4EBP1 in the regression of vascular remodeling200. Additionally, gender-specific 4EBP1 expression was more recently linked to obesity suppression, insulin sensitivity, and energy metabolism in mice201, but for over two decades 4EBP1 has been thought to be required for normal body weight in male mice but not female mice202; any gender specific differences should be further elucidated.

Figure 14. 4EBP1 gene expression across 53 normal human tissues. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal in 2017. Gene expression is represented by boxplots in Log10(TPM) values across tissue types. Outliers are represented outside of the boxplot. Exact median transcript per million (TPM) values can be obtained online (https://gtexportal.org/home/gene/EIF4EBP1). Mammary tissue is shown in turquoise and the average TPM from 290 samples is 74.345. The highest expressing tissue is transformed fibroblasts (median TPM = 174.240, n = 343).

4EBP1 is highly overexpressed in many malignant tumor types

4EBP1 is most known by increased post-translational phosphorylation that releases EIF4E to promote translation, so it is logical to think that this protein would be a tumor suppressor, suggesting a decrease in its expression in carcinomas. However, many tumors overexpress 4EBP1 compared to normal tissue (Figure 15). 4EBP1 is small, rarely mutated, can undergo extensive post-transcriptional modification, and is often overexpressed in malignant tumors. Extensive reports highlight an importance to cancer, but studies should define when excess 4EBP1 transcripts are indeed translated, if post-translational modifications affect stabilization along with transcriptional and translational rates, and how different settings might affect this. If higher mRNA expression is producing functional 4EBP1 protein, other reports predict that it is often stable112,203 with a half-life of more than 16 hours112. 4EBP1 shows diffuse staining patterns in tumor samples as well as localization within both the cytoplasm and nucleus of cells. Others have hypothesized that localization may play a role in oncogenesis105; this study showed a sizable fraction of total 4EBP1 localized to the nucleus (30%) under stress, whereby it influenced EIF4E204. Cell compartmentalization encoded by gene sequence does not yet define localization, because 4EBP1 lacks a known nuclear localization and export motif. 4EBP1 may then play different roles in different compartments.

Figure 15. TCGA data show many types of cancer overexpress 4EBP1 transcripts at high levels. The Broad Firehose Browser was used to visualize this data by RPKM (log2) expression. Tumor populations are shown in red and normal populations are shown in blue. The breast cancer cohort (BRCA) is highlighted by the green arrow.

4EBP1 transcripts are rarely mutated

The National Cancer Institute Genomic Data Commons shows the distribution of

EIF4EBP1 mutations across ten projects (Figure 16). It can be concluded that

EIF4EBP1 is rarely mutated (Rutkovsky, DOI: 10.13140/rg.2.2.27198.38729). The

TCGA-BRCA data shows 1 out of 986 breast tumors have mutated 4EBP1. It is interesting to speculate a protective mechanism is in place to ensure somatic mutations in the nucleic acid sequence do not occur. Recent research has shown that the true number of somatic variants from many large-scale sequencing efforts cannot be accurately accessed due to DNA damage prior to sequence determination, including the

1000 Genomes Project and The Cancer Genome Atlas205. Although 4EBP1 remains rarely mutated across these efforts (USA efforts), there are 4EBP1 mutations reported in other international cohorts which can be witnessed using the ICGC Data Portal

(https://dcc.icgc.org/genes/ENSG00000187840/mutations). If these are true reported mutations, deeper sequencing efforts or geographic specific occurrences may cause this difference. Whatever protective feature is occurring to ensure 4EBP1 does not undergo gene coding mutations opposes the thought that somatic small-scale mutational burdens randomly increase with age in tissues predominately driven by self-renewing cells206,207.

Rather if this is occurring there are some genes like EIF4EBP1 that are better protected.

Note that overexpression of 4EBP1 in breast tumors is not exclusive of age or mutated at the coding level. If TCGA data are a valid model, it would suggest that wild-type

4EBP1 is critical to cancer cells and the oncogenic properties of 4EBP1 may be exclusive to alterations in the total levels of gene expression and post-transcriptional modification/stability.

Figure 16. Frequency of 4EBP1 mutations in the TCGA cohorts. The National Cancer Institute Genomic Data Commons shows distribution of 4EBP1 sequence mutations across ten projects. This includes the TCGA project identification (project ID), disease type, site, number of affected cases (# affected cases), and number of mutations in the project data (# mutations). 4EBP1 is rarely mutated in US efforts.

What might cause the increase in 4EBP1 mRNA expression?

Cancer is an individual disease, yet many tumors express 4EBP1 at high levels regardless of the tissue of origin. This is unusual, interesting, and suggestive of common adaptive themes for cancerous cells to use 4EBP1 regardless of background. The mechanisms governing increased mRNA levels of 4EBP1 in cancer are not fully understood but are not limited to virus utilization, upstream or coordinating transcription factors, and gene amplification events as highlighted below. Other mechanisms exist such as epigenetic regulation. Additionally, growth factors can alter EIF4EBP1189,208, because 4EBPs can control the expression of key proteins to influence proliferation191.

Compounds also affect the transcription of 4EBP1, like polyamines which have been shown to directly interact with the 4EBP1 gene209.

Viruses use 4EBP1

It is possible that cancer cells use oncogenic 4EBP1 to maintain translational efficiency similarly to how viruses take advantage of 4EBP1119,210-213. Many viruses use

4EBP1 in translation, especially under stressful conditions like amino acid starvation211.

This can involve post-translational modifications of 4EBP1 or promoting internal ribosomal entry site (IRES) mediated translation167-169, among other mechanisms167,214 many which have been highlighted in a recent review on viral subversion of the mTOR signaling pathway187. Although viruses can use 4EBP1 to promote cancer it remains understudied. Asimomytis and colleagues recently showed high expression levels of

4EBP1 in all uterine cervical carcinomas (n=73) and this correlated with the degree of dysplasia and high-risk human papilomavirus (HPV) assessment. All of these cases also tested positive for phosphorylated 4EBP1215. Many HPV strains use E6 and E7

oncoproteins to activate the PI3K/AKT/mTOR (PAM) signaling pathway216. Some viruses such as respiratory syncytial virus or HIV1 decrease the phosphorylation of 4EBP1 to favor replication119,217. There is evidence that 4EBP1 interacts with transcription factors

(TFs) that can influence viral latency (see section on TFs). Different types of cancer may utilize 4EBP1 in different ways, similar to viruses, or the cells may be infected with an unidentified virus. Evidence has been presented outlining viral presence in breast cancer such as herpesviridae, retroviridae, parapoxviridae, polyomaviridae, and papillomaviridae families detected in breast cancer218 219.

Some studies have suggested targeting 4EBP1 as an antiviral strategy considering it potentiates innate antiviral immunity220. It was shown that knockout of

4EBP1 and 4EBP2 in mouse embryonic fibroblasts results in about 700 fold reduction in virus titer production from infection with Sindbis virus, encephalomyocarditis virus, and influenza virus, suggesting 4EBP loss markedly impairs viral replication and protected against VSV infection; this study also showed 4EBP1 loss promoted an upregulation of

Type-I IFN response and many genes involved in inflammation and immune response, including IRF7221. This is opposite from the behavior of other gene knockout mice like

Stat1222,223, Stat2224, and RnaseL225 knockouts, which show increased sensitivity to infection; this highlights the importance of understanding these fundamental behaviors in the promotion of targeted gene-therapy. This evidence makes 4EBP1 a promising therapeutic target against virus infection and promotes 4EBP1 as a proto-oncogene which may be activated with or without viral influence.

Transcription factors

Changes to the genomic region close to or around 4EBP1 could enhance its expression, for example the introduction of de novo transcription factor recognition sites could promote overexpression. Additionally, the 4EBP1 sequence that is transcribed is crucial for further regulation because transcript modifications influence other epigenetic functions like microRNA-mediated regulation.

Transcription factors (TFs) can control gene expression and usually have distinct regulatory processes to initiate and regulate gene transcription. 4EBP1 regulates and coordinates function with TFs and TFs govern 4EBP1 expression. Transcription factors play an important role in regulating 4EBP1 mRNA levels. The Embryonic Stem Cell Atlas from Pluripotency Evidence (ESCAPE) integrates data from high content studies that profile mouse and human embryonic stem cells. ESCAPE shows the interactions extracted from ChIP-X studies (mostly ChiP-chip or ChIP-seq) experiments of 4EBP1 with many transcription factors involved in development, viral latency, and self-renewal

(Rutkovsky DOI:10.5281/zenodo.1186367). These include ChIP interactions with MYC and MAX which are key oncogenes in cancers, TIP60 involved in viral and oncogene induction, TRIM28 associated with viral latency, TBX3 involved in development, SMAD1 mediating multiple signaling pathways such as the signals of bone morphogenetic proteins, POU5F1 (OCT4) and NANOG in self-renewal, and E2F4 and SOX2 which are tied to the RB-E2F pathway and tamoxifen resistance in breast tumors (Figure 17)226,227.

Additional analysis reveals consensus TFs, from the Encyclopedia of DNA Elements

(ENCODE) project and ChIP-x Enrichment Analysis (ChEA) databases, which may influence 4EBP1 expression228. This includes MYC, MAX, and BRCA1 (Figure 17). It is interesting to speculate that BRCA1 may coordinate with EIF4EBP1 in genetic

regulation. Others include hormone or steroidogenic related TFs like N2F1, NR5A1, and

RORB. While some show a high indication for symmetry and might then contribute to metastasis such as PITX1 and FOXJ1. GlI2 is also known to regulate CCND1. We note that the potential of MYC, OCT4, and SOX2 to interact with 4EBP1 is likely of high importance, considering tumor initiating cells (TICs) also known as cancer stem cells

(CSCs) highly express these transcription factors. This was recently confirmed by machine learning methods which identified stemness features associated with oncogenic dedifferentiation; this study showed high variation of cancer stemness markers among the patient glioma and breast cancers but involved the core expression of MYC, OCT4, and SOX2229.

Figure 17. Eukaryotic Translation Initiation Factor 4E Binding Protein (EIF4EBP1) transcription factor binding profiles. Compilation from large-scale efforts such as ENCODE, TRANSFAC, JASPAR, Enrichr, and X2Kweb which can be visualized online. Note that not all transcription factors are profiled within these efforts. The majority of transcription factors binding to EIF4EBP1 are different. Many transcription factors involved in embryonic development, viral latency, and self-renewal can interact with the EIF4EBP1 DNA sequence to potentially influence transcription.

Gene amplification

4EBP1 is important by itself, but it is also critical to cancers reliant on the amplification of the 8p11-12 region that bears EIF4EBP1. Malignant tumors frequently amplify the 8p11-

12 locus where 4EBP1 is located; about 15% of breast tumors amplify the 8p11-12 region (Figure 18A & Figure 18B ). There is much evidence to support this, including the mutational profiles of metastatic breast cancer of whole-exome sequencing of 216 tumor-normal blood pairs from metastatic breast cancer patients who underwent a biopsy during the SAFIR01/SAFIR02, SHIVA, or MOSCATO trials in France in 2016230 which confirm the population of breast tumors that can amplify 8p11-12 (as seen from the cBioPortal). Loss of 4EBP1 rarely occurs, as only 5 of 2051 breast cancer samples in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset show deletion of EIF4EBP1231, and there have been no reported deletions in breast cancer patient xenografts (BCCRC Xenograft, British Columbia)232 as visualized using the cBioPortal. There is no doubt that this genomic region harbors critical genes. It is likely that copy number amplification of these genes is significantly enriched by selective pressures in breast tumors. It is true that random copy number increases and genomic changes occur across the whole cancer genome50,51,233-243, but evidence suggests that specific amplified regions53, like focal regions 11q and 8p11-12 are selected over the course of cancer progression57,58,244-246 to confer a growth advantage and encourage relapse43,49. Birnbaum and colleagues coined the term “transplication” when they proposed chromosome fusions of 8p12-11q13 may occur to explain the striking frequency of coamplifcation44 which occurs in many cancers, especially in the breast. This co-amplification can be visualized using the Broad firebrowse database for the TCGA-BRCA data, and may be associated with results herein that show 4EBP1 50

levels can dictate Cyclin D1 levels. The mechanisms governing amplicon generation are still not understood, and it is less understood how amplicons are maintained within the cell or passed to progeny. 4EBP1 may be crucial to maintenance and function of 8p11-

12 amplification. Here we have demonstrated that it is an especially critical target in the setting of 8p11-12 amplification.

Figure 18. EIF4EBP1 is amplified in human breast cancer (A) Amplification data from The Cancer Genome Atlas (TCGA) and (B) the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC).

The extensive role of 4EBP1 in cell signaling in breast cancer

4EBP1 is cell signaling hallmark in breast cancer118. Breast cancers overexpress

4EBP1105 95,106 and the overexpression of 4EBP1 in large, advanced breast cancers has been consistently demonstrated. Braunstein and colleagues quantified immunohistochemistry levels of 4EBP1 to show overexpression (63%) in large tumors compared to (22%) in small tumors; EIF4G behaved similarly, but EIF4E levels were moderately elevated (60%) and strongly elevated in (20%) small tumors. They also showed EIF4E levels were unchanged between normal and tumor breast tissues. An extensive characterization of the role of 4EBP1 and EIF4G under hypoxic conditions revealed that 4EBP1 was important to controlling a cap-dependent to cap-independent translational switch. Importantly, they demonstrated overexpression of 4EBP1 in cancer but not MCF10A cells increased vascularization, irregularity, and leakiness as assessed by chicken chorioallantoic membrane model; they also showed knockdown could decrease tumor angiogenesis. MCF10A 4EBP1 expressing cells did not form tumors in mice while BT474 tumors did, and mimicked results seen in other models106. This is different from ectopic expression of tagged-4EBP1 in other breast cancer cells which showed different 4EBP1 phosphorylation site mutants suppressed tumorigenicity although cell proliferation quantified by BrdU incorporation does not display drastic changes among different 4EBP1 isoforms247. The findings in this study, including mouse models, were reproduced248 but may not be reliable considering the assays chosen to measure proliferation and the use of tagged-expression vectors.

The 8p11-12 copy number amplified (CNA) breast cancer cell lines studied here had the highest levels of phosphorylated 4EBP1 across backgrounds and high malignant expression of 4EBP1 coincides with the protein being maintained in a phosphorylated 53

state to contribute to breast cancer105,11895,106,118-121,249. Phosphorylated 4EBP1 has been shown important to breast cancer prognosis and pathological grade. In a study of 103 breast tumors, Rojo and colleagues showed phosphorylated 4EBP1 at Threonine70 in

81.5% of the tumors. From all markers tested, phosphorylated 4EBP1 was the only statistically significant marker in all parameters: tumor size, histological grade, lymph node metastasis, and locoregional recurrence118. Others have corroborated that 4EBP1 is important to breast cancer95,105,106,119,120,250. Karlsson and colleagues found similar results and showed phosphorylated 4EBP1 at Serine65 in the cytoplasm could predict poor survival and recurrence but not nuclear phosphorylated 4EBP1 staining. They went on to show high cytoplasmic but not nuclear 4EBP1 expression predicted less benefit from tamoxifen which was significant for total 4EBP1 but not for phosphorylated 4EBP1.

The predictive value of 4EBP1 was especially evident in the ER/PR subgroup105. This confirmed prior studies showing increased 4EBP1 and phosphorylated 4EBP1 could predict poor prognosis in advanced breast cancers, particularly hormone receptor positive breast cancer. 4EBP1 also has a role in intra-tumoral breast tumor heterogeneity where phosphorylated 4EBP1 and phosphorylated eiF4E were shown homogenously expressed throughout most tumors and the authors highlight them as real potential targets in cancer therapy249. Another study suggested tamoxifen resistant cells are associated with increased hyperphosphorylated 4EBP1251 which parallels our findings showing SUM-44 cells have hyperphosphorylated 4EBP1 and are tamoxifen resistant. 4-hydroxytamoxifen (but not tamoxifen) was shown to decrease 4EBP1 phosphorylation at Serine65252.

Interestingly, 4EBP1 genomic copy number amplification within the 8p11-12 locus, is often independent of PIK3CA mutations (Rutkovsky, DOI:

10.13140/rg.2.2.20034.07361 or Figure 19), and high mRNA expression levels of 54

4EBP1 can present regardless of 8p11-12 amplification (Figure 20). Overexpression of

4EBP1 transcript levels frequently occur within the TCGA-BRCA, showing 4EBP1 mRNA is highly expressed across breast cancer subtypes when compared to normal tissue

(Rutkovsky, DOI: 10.5281/zenodo.1186445 or Figure 20); 4EBP1 levels are significantly higher in luminal tumors compared to normal tissue, and significantly higher in HER2 and triple-negative tumors compared to luminal tumors (Figure 20A). Transcript levels trend higher with increased stage (Figure 20C), are highest in African-American samples (Figure 20B), and are independent of age (Figure 20D). The high levels of

4EBP1 which are found in patient tumors of various backgrounds suggests 4EBP1 is important in cell dynamics and signaling events of cancerous cells.

Figure 19. cBioPortal TCGA provisional breast cancer data results (2017) showing the top mutational burdens and 4EBP1 alterations in breast cancer. The National Cancer Institute Genomic Data Commons confirms known players whose mutant forms contribute to cancer such as TP53 (36% of breast tumors) and PIK3CA (34% of breast tumors)253. Assessing expression patterns for 4EBP1 alongside the top mutational burdens across breast tumors in The Cancer Genome Atlas data for breast tumors (TCGA-BRCA) indicate that PIK3CA mutated cancers are a predominantly different population than 4EBP1 amplified cancers, which a recent study also confimed94. PIK3CA mutations, one of the most frequent mutations in breast cancer254, are mostly exclusive of AKT1 and PTEN mutations255, but do correlate with hormone receptors, node metastasis, and ERBB2256.

Figure 20. Eukaryotic Initiation Factor 4E-Binding Protein 1 (EIF4EBP1) is overexpressed in malignant breast tumors when compared to normal tissue samples in The Cancer Genome Atlas breast cancer cohort (BRCA). The UALCAN portal was used to compare EIF4EBP1 transcript expression across different breast cancer subgroups; box plot data are represented as transcript per million with n representing sample size and stringent significant thresholds reported. (A) There is a significant increase in EIF4EBP1 expression across all breast cancer subtypes compared to normal, and higher expression in the HER2 and Triple-negative subtype compared to the luminal subtype. (B) There are expression differences based on race. (C) There are higher levels of EIF4EBP1 in stage 2, 3, and 4 compared to stage 1 breast cancers. Post-menopausal women have significantly more EIF4EBP1 mRNA compared to pre-menopausal women. (D) There are no significant changes based on patient age or gender.

Chapter II

MATERIALS AND METHODS

Cell culture models and maintenance

Breast cell culture models, characteristics, and maintenance requirements are listed in

Table 2 and 3. All cell lines were maintained at 37°C with 10% CO2. SUM cell lines and culture requirements for maintenance with Hams F12 cell culture medium (Hyclone

SH30026FS, Thermo Fisher Scientific) with supplementation have been previously described257-259 (please refer to the SLKBase (https://sumlineknowledgebase.com/) for additional information about these cell lines). The Cama-1 cell line (obtained from ATCC) and MCF7 cell line (obtained from the Michigan Cancer Foundation) were grown in

Dulbecco’s Modified Eagle’s (DMEM) medium (obtained from Thermo Fisher Scientific) containing 10% Fetal Bovine Serum (FBS) purchased from Gemini Bioproducts (900-

108) or Atlanta Biologicals (S11050). The T47D cell line (obtained from ATCC) was grown in Roswell Park Memorial Institute (RPMI) medium (Thermo Fisher Scientific) containing 10% FBS. The MCF10A cells were obtained from Dr. Herb Soule at the

Michigan Cancer Foundation260 and were maintained in serum-free Hams F12 supplemented with Bovine serum albumin (BSA) (126579, Millipore), 5 ug/mL Insulin

(700-112P, Gemini Bioproducts), 1 ug/mL Hydrocortisone (H-4001, Sigma-Aldrich), and

10 ng/mL Epidermal Growth Factor (E9644 Sigma-Aldrich) (SFIHE medium). H16N2 cells 261,262 were also grown in SFIHE medium. When trypsinizing cells grown in serum- free medium, 2% FBS was added for the first 24 hours.

Table 2. Breast cell line requirements and growth conditions.

Split Trypsin/EDT Extra Cell Line Medium Ratio A Time Incubation Split Frequency Comments SUM44PE SFIH 1:3 2 min no 7-14 days Maintain high density SUM52PE SFIH or 5%IH 1:6 2 min no 7-14 days Grow in clumps SUM102PT SFIHE 1:6 5 min yes/5 min 7-14 days Attach strongly SUM1315MO2 5%IE 1:3 2 min no 7-14 days Detach easily SUM149PT SFIH or 5%IH 1:6 5 min yes/10 min 7 days Attach strongly SUM159PT 5%IH 1:10 2 min no 7 days Resemble fibroblasts SUM185PE 5%IH 1:3 2 min no 7-14 days Grow in clumps SUM190PT SFIH 1:3 2 min no 7-14 days Grow in clumps SUM225CWN 5%IH 1:3 3 min no 7-14 days Grow in patches SUM229PE 5%IH 1:3 3 min yes/5 min 7-14 days Grow in a monolayer Cama-1 10% DMEM 1:10 2 min no 7 days MCF7 10% DMEM 1:20 2 min no 7 days Fast growing T47D 10% RPMI 1:5 2 min yes/2 min 7 days Fast growing MCF10A SFIHE 1:10 5 min yes/ 5 min 7 days Attach strongly H16N2 SFIHE 1:10 4 min yes/ 5 min 7 days Attach strongly HCC1500 10% RPMI 1:2 2 min yes/3 min 7 days ZR75-1 10% RPMI 1:10 3 min no 7 days Cal-120 10% DMEM 1:10 3 min no 7 days

Abbreviations: SF=Serum Free, 5%=medium containing 5% FBS, H=hydrocortisone, I=Insulin, E=Epidermal Growth Factor, PT=Primary Tumor, PE=Pleural Effusion, CWN=Chest Wall Nodule. Table adapted from the Ethier laboratory protocols.

Table 3. Required additions to medium for growth of cell lines in culture.

Stock Final add to 500 Additions Concentration Concentration mL Growth Factors Insulin (I) 1 mg/ml 5 μg/ml 2.5 mL Hydrocortisone (H) 1 mg/ml 1 μg/ml 500 μL EGF (E) 10 μg/ml 10 ng/ml 500 μL Cholera Toxin 100 μg/ml 100 ng/ml 500 μL Progesterone 20 μg/ml 31.4 ng/ml (10-7 M) 786 μL Prolactin 1 mg/ml 5 μg/ml 2.5 mL Estradiol 20 μg/ml 2.7 ng/ml (10-8 M) 68 μL Lysophosphatidic Acid 10 mM 10 μM 500 μL Anti-fungal Fungizone (Amphotericin B) 250 μg/ml 2.5 μg/ml 5 mL Bacteria Gentamicin 50 mg/ml 25 μg/ml 250 μL Anti-mycoplasma Plasmocin (maintenance) 25 mg/ml 2.5 μg/ml 50 μL Serum Replacement Factors Ethanolamine 16.6 M 5 mM 155 μL HEPES 1 M 10 mM 5 mL Transferrin 2.5 mg/ml 5 μg/ml 1 mL Triiodo Thyronine (T3) 20 μg/ml 10 nM (6.6 ng/ml) 167 μL Sodium Selenite (Se) 20 μg/ml 50 nM (8.7 ng/ml) 217 μL Bovine Serum Albumin 20 g/L 1 g/L 25 ml Serum Fetal Bovine Serum 5% 25 mL Media components for preparation and use in cell culture medium. Table adapted from the Ethier laboratory protocols.

Reagents

The antibodies against 4EBP1 (9644), phosphor-4EBP1 Ser65 (9451), phosphor-4EBP1

Thr37/46 (2855), phosphor-4EBP1 Thr70 (9455) were purchased from Cell Signaling.

The PathScan® antibody cocktail containing antibodies against phospho-AKT, phospho-

S6 ribosomal protein, and Rab11 was also purchased from Cell Signaling (5301).

Antibody against β-actin (A5441) was purchased from Sigma-Aldrich. The ASH2 antibody (A300-489A) was purchased from Bethyl Laboratories. The NSD3 (11345-1-

AP) antibody was purchased from Proteintech. The KAT6A (2162.00.02) antibody was purchased from sdix. The DDHD2 (NBP1-82965) was purchased from Novus

Biologicals. The p21 Waf1/Cip1 (2947), CyclinD1 (2978), p27 Kip1 (3686), Bcl-2 (2872),

Bcl-xl (2764), Caspase-3 (9665), and c-MYC (5605) antibodies were purchased from

Cell Signaling. A rabbit (DA1E) IgG Isotype Control (3900) was purchased from Cell

Signaling. The ERα antibody (sc-543) was purchased from Santa Cruz Biotechnology.

The mTOR inhibitor, Everolimus (RAD001, Affinitor), was purchased from Selleckchem

(S1120, A112024).

PrimeTime qPCR Primers were used for GAPDH (Hs.PT.39a.22214836), CCND1

(Hs.PT.56a.4930170), MYC (Hs.PT.58.26770695), and DDHD2 (Hs.PT.58.39837542) as synthesized at Integrated DNA Technologies (IDT, Coralville, IA). ESR1 forward

(GCTTCGATGATGGGCTTACT) and reverse (CCTGATCATGGAGGGTCAAATC) primers were synthesized at IDT. All forward and reverse primer pairs were combined and diluted to a 10X working stock concentration.

Short-hairpin RNA plasmids from The RNAi Consortium’s (TRC) genome-wide lentiviral human libraries were provided from the Sigma Mission Library at the Medical University of South Carolina Hollings Cancer Center shRNA Technology Resource Technology

(Hollings Cancer Center, the Medical University of South Carolina). Table 4 shows the shRNA plasmid, its genotype, and the targeted region.

Table 4. Short-hairpin RNA plasmids from The RNAi Consortium’s (TRC) genome- wide lentiviral human libraries. The name of the shRNA plasmid, its genotype, and if it targets the CDA or 3’UTR of the gene is listed. The sequence (5’-3’) is listed or can be found using the CloneID at Sigma-Aldrich (St. Louis, MA).

Plasmid Genotype Region Sequence

shLACZ pLKO.1-puro::LACZ n/a CGCTAAATACTGGCAGGCGTT shE4BP1 pLKO.1- CDS CCGGCGGTGAAGAGTCACAGT #1 puro::EIF4EBP1 TTGACTCGAGTCAAACTGTGA TRCN0000040206 CTCTTCACCGTTTTTG shE4BP1 pLKO.1- 3UTR CCGGGCCAGGCCTTATGAAA #2 puro::EIF4EBP1 GTGATCTCGAGATCACTTTC TRCN0000040203 ATAAGGCCTGGCTTTTTG shE4BP1 pLKO.1- 3UTR CCGGGCCAGGCCTTATGAAA #3 puro::EIF4EBP1 GTGATCTCGAGATCACTTTC TRCN0000310343 ATAAGGCCTGGCTTTTTG shE4BP1 pLKO.1- CDS CCGGCGGTGAAGAGTCACAG #4 puro::EIF4EBP1 TTTGACTCGAGTCAAACTGT TRCN0000298904 GACTCTTCACCGTTTTTG shDDHD2 pLKO.1-puro::DDHD2 CDS CCGGGCAGCTTGTATGAACC #1 TRCN0000040233 AGTTTCTCGAGAAACTGGTT CATACAAGCTGCTTTTTG shDDHD2 pLKO.1-puro::DDHD2 CDS CCGGGCTTCTGAAATGAACC #2 TRCN0000040234 GAATACTCGAGTATTCGGTT CATTTCAGAAGCTTTTTG shDDHD2 pLKO.1-puro::DDHD2 CDS CCGGCCAGGATGAGTATGGA #3 TRCN0000040235 CCTTACTCGAGTAAGGTCCA TACTCATCCTGGTTTTTG shDDHD2 pLKO.1-puro::DDHD2 CDS CCGGCCAATGCTGATCCCAC #4 TRCN0000040236 ATCATCTCGAGATGATGTGG GATCAGCATTGGTTTTTG shDDHD2 pLKO.1-puro::DDHD2 CDS CCGGCGAAGCATTGTACAGT #5 TRCN0000040237 GTGTTCTCGAGAACACACTG TACAATGCTTCGTTTTTG shBRCA1 pLKO.1-puro::BRCA1 3UTR CCGGTATAAGACCTCTGGCA #1 TRCN0000009823 TGAATCTCGAGATTCATGCC AGAGGTCTTATATTTTTG shBRCA1 pLKO.1-puro::BRCA1 CDS CCGGAGAATCCTAGAGATAC #5 TRCN0000010305 TGAACTCGAGTTCAGTATCT CTAGGATTCTCTTTTTG shLSD1 pLKO.1-puro::KDM1A CDS CCGGGCTACATCTTACCTTA #71 TRCN0000046071 GTCATCTCGAGATGACTAAG GTAAGATGTAGCTTTTTG shLSD1 pLKO.1-puro::KDM1A CDS CCGGCCACGAGTCAAACCTTTA #72 TRCN0000046072 TTTCTCGAGAAATAAAGGTT TGACTCGTGGTTTTTG shLSD1 pLKO.1-puro::KDM1A CDS CCGGCCAACAATTAGAAGCA 63

#70 TRCN0000046070 CCTTACTCGAGTAAGGTGCT TCTAATTGTTGGTTTTTG shLSD1 pLKO.1-puro::KDM1A CDS CCGGGCCTAGACATTAAACT #68 TRCN0000046068 GAATACTCGAGTATTCAGTTT AATGTCTAGGCTTTTTG shLSD1 pLKO.1-puro::KDM1A CDS CCGGGCTCCAATACTGTTGG #69 TRCN0000046069 CACTACTCGAGTAGTGCCAA CAGTATTGGAGCTTTTTG shp21 pLKO.1-puro::CDKN1A 3UTR CCGGCGCTCTACATCTTCTG #1 TRCN0000287021 CCTTACTCGAGTAAGGCAGA AGATGTAGAGCGTTTTTG shp21 pLKO.1-puro::CDKN1A CDS CCGGGACAGATTTCTACCAC #2 TRCN0000287091 TCCAACTCGAGTTGGAGTGG TAGAAATCTGTCTTTTTG shp21 pLKO.1-puro::CDKN1A 3UTR CCGGCGCTCTACATCTTCTG #3 TRCN0000040123 CCTTACTCGAGTAAGGCAGA AGATGTAGAGCGTTTTTG

Cloning the open reading frame expression library constructs

The Invitrogen Gateway system (Thermo Fisher Scientific, Waltham, MA) was used to clone cDNA of the respective genes. Briefly, an entry vector containing the cDNA of interest was obtained from the United States of America distributors of the Human

ORFEOME library (ORFeome Collaboration) which include: DF/HCC DNA Resource

Core (Boston, MA), GE Healthcare: Dharmacon RNAi and Gene Expression (Lafayette,

CO), Arizona State University: DNASU Plasmid Repository (Tempe, AZ), and

GeneCopoeia (Rockville, MD). Addgene (Cambridge, MA) was also used. The LR clonase enzyme (Thermo Fisher Scientific) was used to clone Gateway entry ORFs

(pENTR223 backbone, pDONR223 backbone, pDONR221 backbone, or pShuttle backbone) listed in Table 5 into Gateway compatible lentiviral destination backbones

(pLenti6.3/V5-DEST backbone (Thermo Fisher Scientific), pLX304 backbone (gift from

David Root, Addgene plasmid #25890), pLEX_307 backbone (gift from David Root,

Addgene plasmid #41392), and pLenti CMV HygroDest (w117-1) (gift from Eric

Campeau, Addgene plasmid #17454)) encoding selection markers for either blasticidin, puromycin, or hygromycin, respectively (Table 6). Reactions were stopped using proteinase K solution (Thermo Fisher Scientific), promptly transformed into chemically competent One Shot Stbl3 cells (Thermo Fisher Scientific) or DH5α cells (Thermo Fisher

Scientific), incubated in SOC medium at 37° C for 1 hour at 200 rpm, and then plated at two densities on LB agar plates containing the appropriate selection marker. The empty destination vector negative control was confirmed to have <10 colonies. Individual colonies were simultaneously grown, stored, and miniprepped using a Qiaprep Spin

Miniprep kit (Qiagen (Valencia, CA)). Plasmids were enzymatically digested with either

the restriction enzyme BsrG1 which cuts in Gateway sequences flanking the ORF or double digested with equal parts of Af1II and XhoI (New England Biolabs (Beverly, MA)).

Reactions were stopped by the addition of 6X Bromophenol Blue loading dye. The reactions were separated using ethidium bromide DNA gel electrophoresis to confirm insert presence. The inserted plasmid DNA was sequenced by Eurofins MWG Operon

(Louisville, KY) using universal flanking primers and compared to NCBI blast and multiple sequence alignment tools for orientation and sequence confirmation. Confirmed positives were grown and harvested using a PureYield Plasmid Midiprep System

(Promega, Madison, WI). The pLX304KAT6A construct was constructed and gifted from the laboratory of Dr. Hui Wing Cheung (Medical University of South Carolina,

Charleston, SC).

Table 5. The human ORFeome Collaboration entry clone cDNA plasmid information. There are gene repeats because multiple gene isoforms are available. The vector indicates the plasmid backbone. The length of the cDNA insert is indicated by the number of base pairs (bp). The format of the insert carries either a stop codon (Closed) or altered stop codon for read through to a potential antigenic V5 tag (Fusion). The percent that the insert matches any available NCBI transcript is presented under CDS and if the sequence is a perfect match the accession is listed (perfect match Fusion constructs represent 99%). IMAGE indicates cDNA clone identity from the human ORFeome collection and this is not available for all clones.

Gene Vector Insert Size Format CDS IMAGE (bp) DDHD2 pENTR223 1155 Fusion 54% 3451888 ERLIN2 pENTR223 618 Fusion 92% 5296776 ASH2L pENTR223 1605 Fusion NM_001105214.2 3921999 ASH2L pShuttle 1887 Closed NM_004674.4 n/a NSD3 pDONR223 1935 Fusion NM_017778.2 100073288 NSD3 pShuttle 4314 Closed NM_023034 n/a NSD3 pShuttle 1938 Closed NM_017778.2 n/a EIF4EBP1 pDONR221 357 Fusion NM_004095.3 2820216 EIF4EBP1 pDONR221 357 Closed NM_004095.3 n/a BAG4 pENTR223 1374 Fusion NM_004874.3 5259771 C8ORF4 pENTR223 318 Fusion NM_020130.4 3682270 RAB11FIP1 pENTR223 1950 Fusion 42% 6537110 LACZ pDONR223 3207 Fusion n/a n/a hrGFP pDONR223 700 n/a n/a

Table 6. Lentiviral cDNA expression plasmids. All plasmids, except plasmids carrying the GFP insert, have been sequence verified. The pLenti6.3LACZ plasmid was obtained from Thermo Fisher Scientific and all other backbones obtained from Addgene. The pLX304KAT6A plasmid was received in collaboration with Dr. Cheung at the Medical University of South Carolina.

Name Plasmid Genotype Stop Codon pACR1 pLenti6.3NSD3short pLenti6.3/V5-DEST:: NSD3 No (NM_017778) pACR2 pHygroNSD3short pLentiCMVHygroDEST:: NSD3 Yes (NM_017778.2) pACR3 pLenti6.3ASH2L pLenti6.3/V5-DEST:: ASH2L No TV2 (NM_001105214.2) pACR4 pLX304ASH2L TV1 pLX304:: ASH2L (NM_004674.4) Yes pACR5 pLEX307ASH2L TV1 pLEX_307:: ASH2L (NM_004674.4) Yes pLX304KAT6A pLX304:: KAT6A (NM_001099412.1) Yes pACR6 pHygro4EBP1 pLentiCMVHygroDEST:: EIF4EBP1 Yes (NM_004095.3) pACR7 pLEX3074EBP1 pLEX_307:: EIF4EBP1 (NM_004095.3) Yes pACR8 pLEX3074EBP1 pLEX_307:: EIF4EBP1 (NM_004095.3) No pACR9 pLEX307DDHD2 pLEX_307:: DDHD2 (IMAGE: 3451888) No pACR10 pLX304GFP pLX304:: hrGFP pACR11 pHygroGFP pLentiCMVHygroDEST:: hrGFP pACR12 pLEX307GFP pLEX_307:: hrGFP pACR13 pLenti6.3GFP pLenti6.3/V5-DEST:: hrGFP pACR14 pLEX307LACZ pLEX_307:: LACZ No pACR15 pLX304LACZ pLX304:: LACZ No pACR16 pHygroLACZ pLentiCMVHygroDEST:: LACZ No pLenti6.3LACZ pLenti6.3/V5-DEST:: LACZ No

Production of lentivirus for ORF-encoding vectors and RNAi-encoding vectors

Lentivirus was produced in 293FT cells transfected in Opti-MEM with Lipofectamine

2000, plasmid, and Mission packaging mix (Sigma-Aldrich, St. Louis, MO) under optimal conditions. Collected virus was filtered through a 0.2 um filter before storage at -80° C.

Efficient viral titer production was confirmed by a Lenti-X p24 Rapid Titer Kit (Takara) and 4EBP1 western blot. All BSL-2 safety protocols were performed during production, storage, and continued use.

Lentivirus transduction of human cells in culture

Cells were transduced with lentivirus, with appropriate growth medium, and polybrene.

Virus was removed 24 hours later and cells were fed with media. Cells began selection with appropriate concentration of antibiotics 48 hours following transfection. Antibiotic concentration at 2 ug/ml Puromycin (invivoGen) was sufficient to ensure selection. The

SUM-44 cell line requires 3 ug/ml Puromycin selection. Control cells without the addition of lentivirus were plated alongside lentivirus infected cells to ensure the appropriate concentration of antibiotic was used. Cells were continuously maintained in the resistance marker. All further parameters were tested after four days of selection in

Puromycin.

Cell counts:

Cells were plated in 12-well plates at 1E5 cells/well, washed with 1X PBS, then 0.5 mL

HEPES/MgCl2 buffer (Isoton) was added to each dish and agitated for 5 minutes. Cell swelling was confirmed by visualization then 50 uL ZAP (Bretol Solution) was added and incubated for 10 minutes with agitation, followed by the addition of 10 mL NaCl-Formalin to prevent deterioration, and read using a Coulter Acuvette. The Coulter Counter was set 69

to count nuclei between 4 and 8 um diameter through a 100 um aperature. Each sample was counted twice and then averaged. The counts were multiplied by 20 to obtain the total number of nuclei, and background counts with NaCl-Formalin were performed with analysis.

Cell growth in situ

A Celigo (Nexcelom Bioscience) was used to determine changes in cell growth over a 4 day time period. Briefly, cells were plated in Corning 12-well dishes at 1E5 cells/well density. Cells were imaged daily by bright-field scanning each whole well using a 1 um/pixel resolution. Analysis settings for confluence were optimized to each individual cell line.

Immunoblotting

Cells were continuously maintained on ice and harvested using RIPPA buffer (Sigma-

Aldrich) supplemented with a proteinase inhibitor cocktail and PhosSTOP (Roche). A

Bradford Protein Assay was used to fit samples to a standard curve and determine protein concentrations. Samples were loaded on a denaturing gel (pre-cast 4-20%

Gradient BIO-RAD gel, pre-cast 7.5% BIO-RAD gel, or freshly prepared 15% resolving gel with a 4% stacking gel) and run at 125 V for 1 hour. The gel and blotting paper were equilibrated in 1X BIO-RAD Trans-Blot Turbo Transfer Buffer (#10026938, BIO-RAD). A

PVDF membrane was quickly exposed to methanol and then incubated in 1X transfer buffer. The gel was transferred to the membrane using a BIO-RAD Trans Blot Turbo.

The membrane was blocked 1 hour with 5% skim milk in 1X TBST at room temperature, and incubated overnight with antibody manufacturer’s directions. The following day, the membrane was washed three times in 1X TBST, incubated in a secondary antibody at

1:10000 dilution in 1X TBST for 1 hour at room temperature, and then washed three times in 1X TBST. The membrane was visualized calorimetrically using SuperSignal

West Pico Chemiluminescent Substrate (#34080, Thermo Scientific). The membrane was developed using the Li-COR Odyssey Fc.

Flow cytometry of live cells

Cells were trypsonized, counted, and analyzed at [1E6 cells/mL]. Vybrant DyeCycle

Orange (V35005, Thermo Scientific) was used according to the manufacturer’s protocol for live cell-cycle analysis. Conditions were optimized to a final stain concentration of 5 uM in 1X PBS in all cell lines tested. Cells were promptly analyzed using a BSL2

FACSAria Cell Sorter. Verity ModFit LT 4.1 was used to analyze and visualize the generated data.

Modfit program analysis

ModFit LT 4.1 was used to analyze data generated by flow cytometry. Briefly, the data was gated to exclude outliers and an automatic analysis was performed with no auto linearity. This allowed the program to attempt to determine the number of cell cycles present in the sample and the best representative model. The ploidy mode included first cycle is diploid. Ensuring the Dip G1 peak was positioned over the first peak (G0/G1 phase), the subsequent Dip G2 peak (G2/M phase) is represented by about twice the position of the Dip G1 range, and all results between these two peaks indicate the Dip S

(S phase). These data are plotted as Number versus Alexa Fluor 488-A wavelength (the appropriate detection wavelength for Vybrant Cell Cycle Orange dye).

Isolation of primary mammary cells from reduction mammoplasty specimens

A fresh reduction mammoplasty specimen, obtained from a non-malignant patient undergoing breast reduction surgery with no history of breast disease, was worked up immediately after being received from the Hollings Cancer Center Biorepository at the

Medical University of South Carolina. Using sterile practices, specimen MUSC-HMEC1 was minced and dissociated with freshly prepared 0.05% collagenase in M199 medium and incubated at 37°C shaking at 122 rpm overnight in an erylymer flask. The following day, fat was pipetted away, the specimen was spun down at 1000 rpm for 5 minutes. All supernatant was removed and the pellet was washed three times with warm M199 medium supplemented with Gentamycin. An aliquot was removed, visualized, and large aggregates indicated additional necessary sedimentation. The sample was further dissociated with 0.05% collagenase for 2 hours at 37°C, the pellet was washed twice and subjected to differential sedimentation twice, again discarding the supernatant with resuspension in M199 medium. The total number of cells was counted using either a

LUNA cell counter (Logos) or Beckman Coulter Counter, then plated into a 6-well plate at ~1E6 cells/well or stored at ~5E6 cells/ampule in freezing medium (M199 medium supplemented with FBS and dimethyl sulfoxide (DMSO)) in liquid nitrogen. Cells were maintained in Hams F12 supplemented with insulin, hydrocortisone, epidermal growth factor, and cholera toxin.

KM plotter database

The KM plotter for breast cancer (http://kmplot.com)263 was used on all releases available from the database. Restricted analyses of different populations are indicated and altered the number of breast cancer patients with available survival data as shown by the number at risk. The determined and represented prognostic values by recurrence

free survival (RFS) of EIF4EBP1 in all analyses were more than 500 samples, indicating highly reliable analysis using all parameters presented. The JetSet best probe set for

EIF4EBP1 (probe ID: 221539_at) was used for all analyses. Patients were divided into a high and low expression group by median mRNA expression values, all possible cutoff values between the lower and upper quartiles were computed and the best performing threshold was determined by using auto select the best cutoff. Relapse free survival

(RFS) was plotted using suggested quality controls. This excluded biased arrays, removed redundant samples, and checked proportional hazards assumptions. The cutoff values, probe expression range, false discovery rate (FDR), and P-value were extracted from the KM plotter webpage and each analysis is represented.

cBioPortal database

The cBioPortal (http://www.cbioportal.org/)82,83 TCGA provisional data was used to generate the overall survival curve for breast tumors with and without A2 8p11-12 region alterations. The Amplification frequency of 4EBP1 in the TCGA or METABRIC breast data cohorts was also determined using the cBioPortal.

UALCAN database

The UALCAN25 database (http://ualcan.path.uab.edu/index.html) uses The Cancer

Genome Atlas level 3 RNA-seq and clinical data (processed data ready for high level analyses) from more than 30 cancer types. This can be used for in silico validation of target genes and the identification of tumor biomarkers. Box and whisker plots with generated LogRank p-values for comparisons are reported here for genes of interest.

DepMap (Cancer Dependency Map) database

The Depmap database (https://depmap.org/portal/) is an ongoing resource towards the building of a comprehensive reference map to accelerate precision medicine. The portal is home to continually updated data related to genetic screens, cellular models, and drug sensitivity. The BROAD Institute CRISPR (Avana) public data (18Q3)264 is reported for genes of interest and represents CRISPR-Cas9 genome-scale knockout libraries (17634 genes) in approximately 485 cell lines representing different diseases (additional cancer dependency data is released on a quarterly basis). The descriptions of methods and

CERES algorithm inferred gene effect matrix scores were previously reported by Meyers and colleagues265. Briefly, CERES is a computational method that estimates gene- dependency levels from CRISP-Cas9 gene essentiality screens while accounting for copy number specific effects to enable the unbiased interpretation of gene dependency at all levels of copy number. The CERES outcome (reported here the Summer of 2018) represents a dependency score, wherein a lower score means that a gene is more likely to be dependent. A score of 0 indicates the gene is not essential. On the contrary, a score of -1 indicates the median of all common essential genes. A common essential

(pan-essential) gene ranks in the top X depleted genes in at least 90% of cell lines in large, pan-cancer screens (where X is chosen empirically using the minimum of the distribution of gene ranks in their 90th percentile least depleting lines). Crudely interpreted, a score less than 0 but greater than -1 indicates a potential driving gene that is not common essential for life but potentially essential to the cell line. Quality control for the Avana dataset includes dropping cell lines if they show insufficient separation between positive and negative controls and dropping replicates if they show insufficient separation. The portal is also home to mutation, indel, copy number, and gene expression data for genes in the Avana dataset. 74

Chapter III

RESULTS

Evidence suggests 4EBP1 is important to many cancer types

4EBP1 is found at high levels across tissues, yet tumors often express higher mRNA levels than normal tissue (Figure 15). 4EBP1 is frequently found in the top 1% or

5% of genes overexpressed in cancer compared to normal (in more than 30 Oncomine data sets) wherein the gene coding sequence is rarely mutated (Rutkovsky, DOI:

10.13140/rg.2.2.27198.38729). 4EBP1 is a predicted driver of many cancer types by

GISTIC (Figure 21) and has been predicted as an essential cancer gene in our genome wide RNA-interference screens (SUM Knowledge Base: https://sumlineknowledgebase.com/) as well as large-scale efforts by others (DepMap portal: https://depmap.org/portal/).

Figure 21. EIF4EBP1 is a predicted driver of many cancer types. GISTIC (which uses copy number alterations and chance) predicts 4EBP1 as a driver of many cancers as seen here where low q-values indicate significance, regardless of peak presence. The frequency of alterations are reported as focal events (less than ½ chromosome arm), high-level frequency (amplification/deletion > 1 copy), and the overall fraction of cancers that exhibit any amplification/deletion of the gene. GISTIC shows 4EBP1 is significantly focally amplified (12/33) but not deleted (1/33) across cancer types and is a predicted driver of breast cancer (Breast invasive adenocarcinoma).

EIF4EBP1 expression levels correlate with reduced relapse free survival in human breast cancer

To determine the overall impact of EIF4EBP1 on survival and to assess whether treatment affects the outcomes, we used the online Kaplan-Meier plotter database tool

(kmplot.com) to assess the relationship between EIF4EBP1 gene expression and relapse free survival. This tool uses gene expression data from Gene Expression

Omnibus (GEO), the European Genome-phenome Archive (EGA), and The Cancer

Genome Atlas (TCGA)263.The JetSet probe set for EIF4EBP1 (probe ID: 221539_at) was used for all analyses. We found that high EIF4EBP1 gene expression significantly corresponded to reduced relapse free survival not only in ER+ populations (Figure 22A), including when separated by luminal A (Figure 22B) and luminal B (Figure 22C) subtypes, but also across all subtypes (Figure 22D). Furthermore, this was also true post treatment with chemotherapy (Figure 22E) and either tamoxifen (Figure 22F) or endocrine therapy (Figure 22G). 4EBP1 is overexpressed with or without amplification and high expression culminates in a lower probability for recurrence free survival across breast cancer subtypes including post-treatment tumors. Because 4EBP1 is a known convergence factor for multiple pathways governing cell regulation, a hit in many breast cancer cell line genome-wide viability screens, and often highly overexpressed where it correlates with prognosis, the role of 4EBP1 in normal mammary and breast cancer cell lines was assessed.

Figure 22. High 4EBP1 expression can predict the prognosis of breast tumor patients. KM plotter analysis of EIF4EBP1 (probe ID: 221539_at) gene expression and overall survival in (A) ER+ populations (B) separated by luminal A (C) luminal B subtypes (D) all subtypes (no parameters selected) (E) post treatment with chemotherapy (F) tamoxifen or (G) endocrine therapy.

4EBP1 is not required for normal mammary cell proliferation

Although 4EBP1 is not amplified in normal mammary epithelial cells, it is an important protein for translation. Published findings do show 4EBP1 is not essential to normal cells or mouse embryonic development but is important to adipogenesis and energy homeostasis199,266. Consequently, we wanted to evaluate the potential effects of

4EBP1 targeting in non-transformed cells. 4EBP1 was knocked down in two non- transformed cell lines, MCF10A (Figure 23A) and H16N2 (Figure 23B) with two short- hairpin 5’UTR-gene-targets to EIF4EBP1 and one targeting LACZ for control knockdown. Efficient knockdown of 4EBP1 was confirmed for all analyses and we noted that short-hairpins targeting the 3’UTR of 4EBP1 were not as efficient as the 5’UTR short-hairpins and not used in this study (data not shown). Cell proliferation was then measured by counting the total number of cell nuclei present at day 1 and day 4 after plating. All populations increased in number significantly over four days and no differences were observed between control and EIF4EBP1 knockdown in MCF10A cells

(Figure 23C) or H16N2 cells (Figure 23D). These results indicate that downregulation of

4EBP1 in non-transformed breast epithelial cell lines does not influence cell proliferation, which suggests that targeting 4EBP1 in normal cells will not be detrimental from a therapeutic standpoint.

To contextualize our cell culture models to normal breast, we compared the steady-state protein levels of 4EBP1 in H16N2 cells, a pure population of human papillomavirus (HPV) E6 and E7 oncoprotein immortalized cells, to primary human mammary cells from a healthy reduction mammoplasty specimen cultured to 2 passages

(MUSC HMEC1) which showed 4EBP1 protein levels were comparable (Figure 23E).

This evidence indicates that loss of 4EBP1 in normal breast cell lines does not influence proliferation and suggests that 4EBP1 is not vital for mammary cells to escape senescence using only E6/E7 oncoproteins.

Figure 23. 4EBP1 knockdown in normal mammary cells does not inhibit proliferation and levels are comparable to normal primary isolated mammary epithelial cells. (A) Western blot of 4EBP1 in MCF10A cells and (B) H16N2 cells engineered with either control shRNA to lacZ or two individual shRNAs to EIF4EBP1 (4EBP sh_1 or sh_2). (C) Cell proliferation was assessed in MCF10A and (D) H16N2 control and EIF4EBP1 knockdown cells on day 1 and day 4 in culture. (E) Western blot of 4EBP1 levels in MUSC_HMEC1 and H16N2 cells.

4EBP1 is highly expressed and phosphorylated in 8p11-12 breast cancer cells

To investigate the significance of 4EBP1 overexpression in breast cancer, we employed a set of human breast cancer cell lines representing ER+ and ER- cohorts.

The SUM-44 (ER+), Cama-1 (ER+), and SUM-52 (ER-) cells are luminal and have amplification of the 8p11-12 genomic locus. The T47D (ER+), HCC1500 (ER+), and

MCF7 (ER+) cells are luminal. SUM-229 cells are triple-negative. Normal breast cells are represented by MCF10A and H16N2 cells. Amplification of the EIF4EBP1 gene product in SUM-44, Cama-1, and SUM-52 resulted in high total levels of 4EBP1, and robust levels of 4EBP1 phosphorylation compared to the other cell lines tested (Figure 24A).

Furthermore, the non-transformed cell lines, MCF10A and H16N2 did not express any more or less 4EBP1 protein than the T47D, HCC1500, MCF7, or SUM-229 cell lines.

4EBP1 is phosphorylated by mTORC1 in a hierarchical fashion114,115. Our findings that 4EBP1 expression and phosphorylation levels are high on multiple residues targeted by mTORC1 in SUM-44, Cama-1, and SUM-52 cells suggest active mTORC1 signaling in these 8p11-12 models. Considering an active role for mTOR in the 8p11-12 models, a predicted role for 4EBP1 in the CTD2 database as a biomarker for response to many therapies including mTOR inhibitors and FGFR inhibitors (DOI:

10.5281/zenodo.1095849), a demonstrated role for 4EBP1 as a biomarker to cancer therapies, and a current clinical trial to determine if phosphorylated 4EBP1 can be used to predict mTOR inhibitor response in breast tumors, the 8p11-12 CNA lines were tested for increased sensitivity to everolimus (Affinitor) compared to MCF10A, MCF7 and T47D lines. Cells were exposed to varying concentrations of everolimus for 72 hours and the total number of cell nuclei was counted at day 1 and day 4. Cells were also profiled in the same way using live cell imaging and confluence quantification by a Celigo, where

images were taken each day over the 4 day period (data not shown) and trends in growth from day 1 to day 4 were similar to findings in Figure 24. All the 8p11-12 models tested were sensitive at 1 M everolimus, but the SUM-52 cells were far less sensitive to everolimus treatment (Figure 24B).

Figure 24. 4EBP1 is highly expressed and phosphorylated in 8p11-12 breast cancer cells. (A) Western blot of 4EBP1 and phospho-4EBP1 on residues Thr 37/37, Thr 70, and Ser 65 in SUM-44 (ER+), Cama-1 (ER+), and SUM-52 (ER-) cells with amplification of the 8p11-p12 genomic locus as well as T47D (ER+), HCC1500 (ER+), MCF7 (ER+), and SUM-229 cells (TN) without amplification of the 8p11-p12 genomic locus. Normal breast cells are represented by MCF10A and H16N2 cells. (B) Cell proliferation was assessed in SUM-44, Cama-1, and SUM-52 cells in the presence or absence of 1 M everolimus treatment for 72 hours.

Phosphorylated AKT levels and phosphorylated S6 levels across the models

Phosphorylated 4EBP1 levels have been shown to correlate with phosphorylated AKT but not phosphorylated S6 levels in breast tumors118. Additionally, 4EBP1 is a key regulator of oncogenic AKT as well as ERK signaling pathways267. All models here showed detectable phosphorylated AKT at Serine473 except HCC1500 cells and some models express far more than others (Figure 25) but does not correlate with phosphorylated 4EBP1 levels. Similarly, phosphorylated S6 at Serine235/236 was present across the models, including all 8p11-12 copy number amplified (CNA) lines

(Figure 25). Phosphorylated S6 and phosphorylated 4EBP1 suggest active mTORC1 signaling. Phosphorylation of 4EBP1 by mTORC1 has been shown to occur in a hierarchical fashion115. Some models like the Cama-1 and SUM-52 cells expressed more phosphorylated 4EBP1 at Threonine70 or Serine65 than Threonine37/46, but similar levels of phosphorylated S6 (Figures 24A & 25). Together, these results suggest hierarchical phosphorylation of 4EBP1 by mTORC1 is not always necessary or other kinases are responsible in this setting.

Figure 25. Phosphorylated AKT levels and phosphorylated S6 levels across the models. Western blot of phosphorylated Akt and phosphorylated S6 levels.

To determine if 4EBP1 could influence the protein levels of phosphorylated S6 or phosphorylated AKT, the SUM-44 and Cama-1 knockdown models were assessed and while SUM-44 knockdown cells did lose phosphorylated AKT the Cama-1 knockdown cells with more phosphorylated AKT did not (Figure 26A & 26B) Substantial changes in phosphorylated S6 were also not observed with 4EBP1 loss in either of these models

(Figure 26A & 26B).

Figure 26. Loss of 4EBP1 does not substantially alter phosphorylated S6 or phosphorylated AKT levels. (A) A western blot of SUM-44 4EBP1 knockdown cells compared to control (LACZ KD) cells of phosphorylated AKT (Phospho-AKT) and phosphorylated S6 (Phospho-S6). (B) A western blot of Cama-1 4EBP1 knockdown cells compared to control (LACZ KD) cells of phosphorylated AKT (Phospho-AKT) and phosphorylated S6 (Phospho-S6).

4EBP1 knockdown is detrimental to ER+ 8p11-12 breast cancer cells

Despite the amplification of the 8p11-12 amplicon in SUM-52 cells, this cell line is

ER- rather than ER+ like the SUM-44 and Cama-1 cell lines. mTORC1 was previously shown to activate ER187,214,268-271. Therefore, the difference in ER status could in part explain why the SUM-52 cells responded less well to mTORC1 inhibition by everolimus.

Consequently, we next asked if targeting 4EBP1 directly would have a similar or better effect than targeting 4EBP1 indirectly via mTORC1 inhibition.

We first tested the two ER+ 8p11-12 cell lines. To determine the effect of directly targeting 4EBP1, we used two individual shRNA to target EIF4EBP1 in the SUM-44

(Figure 27A) and Cama-1 cells (Figure 27B). shRNA targeting lacZ was used as a control. In addition to total protein knockdown of 4EBP1, there was a concomitant decrease in 4EBP1 phosphorylation (Figure 27A & 27B). We then profiled the proliferation rate of cells expressing EIF4EBP1 shRNA compared to control cells. Cells were evaluated by counting the number of nuclei at day 1 and day 4 after plating. We found that cells containing knockdown of 4EBP1 significantly inhibited cell doubling compared to control cells in both cell lines (Figure 27C & 27D).

Prior studies demonstrate a co-regulatory effect on ER by genes associated with the 8p11-p12 amplicon96-100,272. Therefore, we next evaluated ER expression in the

SUM-44 and Cama-1 EIF4EBP1 knockdown cell lines and found that ER levels were reduced (Figure 27A & 27B) compared to control cells expressing LACZ shRNA. Taken together, these findings suggest that reducing 4EBP1 levels or phosphorylation impairs tumor cell proliferation of the ER+ 8p11-12 breast cancer cells potentially through downregulation of ER.

Figure 27. 4EBP1 knockdown inhibits proliferation of ER+ 8p11-12 breast cancer cells and decreases ER levels. (A) Western blot of 4EBP1, phospho-4EBP1 on residues Thr 37/37, and ER in SUM-44 cells engineered with either control shRNA to lacZ or two individual shRNAs to EIF4EBP1 (4EBP sh_1 or sh_2). (B) Western blot of 4EBP1, phospho-4EBP1 on residues Threonine37/46, and ER in Cama-1 cells engineered with either control shRNA to lacZ or two individual shRNAs to EIF4EBP1 (4EBP sh_1 or sh_2). (C) Cell proliferation was assessed in SUM-44 and (D) Cama-1 control and EIF4EBP1 knockdown cells on day 1 and day 4 in culture.

Downregulation of 4EBP1 in ER+ 8p11-12 breast cancer cells causes cell cycle arrest

Previous studies suggest that 4EBP1 regulates cell cycle progression133,135,142,273-

276. To better understand the cellular effects of 4EBP1 knockdown, SUM-44 and Cama-1 cells were assessed by flow cytometry to evaluate cell cycle progression. An increase in the number of cells in G1 cell-cycle in both SUM-44 (Figure 28A) and Cama-1 cells

(Figure 28B) was observed with EIF4EBP1 knockdown when compared to control cells.

These results show that knockdown of 4EBP1 promotes G1 cell cycle arrest.

To determine why SUM-44 and Cama-1 cells were reliant on 4EBP1 expression, we evaluated the protein expression levels of key cell cycle regulators. Prior studies demonstrated that 4EBP1 is required for coupling of mTORC1 signaling to Cyclin D1 expression, where inhibition of mTORC1 with rapamycin led to a decrease in Cyclin D1 expression273. As follows, we tested the effects of 4EBP1 on Cyclin D1 expression and found that Cyclin D1 protein levels were decreased in SUM-44 and Cama-1 cells following EIF4EBP1 knockdown (Figure 28C & 28D). Additionally, we observed a slight increase in p27 levels in the EIF4EBP1 knockdown cells compared to control cells

(Figure 28C & 28D). The alterations of Cyclin D1 and p27 expression that we find is consistent with the cell cycle arrest phenotype that we observe. This suggests the non- proliferating populations are in G1 cell cycle arrest and supports the notion that highly proliferative cells need to traverse this cell cycle checkpoint to replicate then ultimately support cell cycle division and growth.

Figure 28. 4EBP1 knockdown leads to G0/G1 arrest in ER+ 8p11-12 breast cancer cells. (A) Cell cycle analysis of SUM-44 and (B) Cama-1 cells shows that 4EBP1 knockdown results in an accumulation of cells in G0/G1 with an associated decrease in cells in S-phase. (C) Western blot of cyclin D1 and p27 in SUM-44 and (D) Cama-1 cells engineered with either control shRNA to lacZ or two individual shRNAs to EIF4EBP1 (4EBP sh_1 or sh_2).

4EBP1 knockdown inhibits proliferation of ER- 8p11-12 amplified breast cancer cells

Although we saw minimal effect of everolimus on the proliferation of the ER-

8p11-12 SUM-52 breast cancer cell line, we also wanted to test the effect of EIF4EBP1 knockdown on these cells to determine whether we would see the same effect given that direct targeting of 4EBP1 would bypass mTORC1. Using the same two shRNAs targeted to EIF4EBP1 as we used on the previous cell lines described, we downregulated 4EBP1 protein in the SUM-52 cells and likewise, saw a reduction in 4EBP1 phosphorylation

(Figure 29A). We also probed these control and knockdown cells for cyclin D1 (CCND1) and Cyclin Dependent Kinase Inhibitor (p27, CDKN1B) protein expression. We saw a similar effect on these two proteins as we did in the SUM-44 and Cama-1 cells where

Cyclin D1 levels were decreased and p27 levels were increased (Figure 29A). When we assessed the control and EIF4EBP1 knockdown SUM-52 cells for proliferation, we saw a similar reduction in proliferation in the cells where 4EBP1 was downregulated (Figure

29B), similar to what we observed with the two ER+ 8p11-12 cell lines. These results suggest that bypassing mTORC1 to target 4EBP1 directly overcomes the absence of

ER expression in the ER- SUM-52 8p11-12 breast cancer cell line. Additionally, loss of

Cyclin D1 induced by 4EBP1 loss in SUM-52 cells may be detrimental to this cell line, because Cyclin D1 is suggested as an essential gene in our genome-wide functional shRNA SUM-52 screen (SLKBase).

Figure 29. 4EBP1 knockdown inhibits proliferation of ER- 8p11-12 breast cancer cells. (A) Western blot of 4EBP1, phospho-4EBP1 on residues Thr 37/37, ER cyclin D1, and p27 in SUM-52 cells engineered with either control shRNA to lacZ or two individual shRNAs to EIF4EBP1 (4EBP sh_1 or sh_2). (B) Cell proliferation was assessed in SUM-52 control and EIF4EBP1 knockdown cells on day 1 and day 4 in culture.

The effects of 4EBP1 knockdown on non-amplicon bearing cancer cell models

We also evaluated the effect of 4EBP1 downregulation on the non-amplicon bearing models; MCF7 (ER+) (Figure 30A), T47D (ER+) (Figure 30B), SUM-229 (ER-)

(Figure 31A), and SUM-149 (ER-) (Figure 31B) and found that 4EBP1 targeting in these cells impaired proliferation (Figures 30C, 30D, 31C, & 31D). Altogether, these results suggest that targeting 4EBP1 could benefit breast cancers of all subtypes regardless of 8p11-12 amplicon status.

Figure 30. 4EBP1 knockdown inhibits proliferation of MCF7 and T47D breast cancer cells. (A) Western blot of 4EBP1 in MCF7 cells and (B) T47D cells engineered with either control shRNA to lacZ or two individual shRNAs to EIF4EBP1 (4EBP sh_1 or sh_2). (C) Cell proliferation was assessed in MCF7 and (D) T47D control and EIF4EBP1 knockdown cells on day 1 and day 4 in culture.

Figure 31. 4EBP1 knockdown inhibits proliferation of SUM-229 and SUM-149 breast cancer cells. (A) Western blot of 4EBP1 in SUM-229 cells and (B) SUM-149 cells engineered with either control shRNA to lacZ or two individual shRNAs to EIF4EBP1 (4EBP sh_1 or sh_2). (C) Cell proliferation was assessed in SUM-229 and (D) SUM-149 control and EIF4EBP1 knockdown cells on day 1 and day 4 in culture.

Loss of 4EBP1 does not promote changes in apoptosis associated proteins

Cells were also profiled for apoptosis by flow cytometry with Propidium Iodide and Annexin V stain; varied apoptotic rates among technical and biological replicates were observed in all samples, including non-engineered, no selection, and control samples (data not shown) which suggested assessment of fixed then stained populations would be needed and optimized in this assay. Resources were used elsewhere, and protein lysates were profiled for apoptosis associated processes. There were no significant changes in SUM-44, Cama-1, SUM-52, SUM-229, and T47D total

Parp and Caspase-3 protein levels or cleavage products when compared to control cells, except for cleaved-parp in SUM-52 cells (Figure 32A). 4EBP1 has been associated with regulating cap-independent translation which is suggested for some apoptosis associated proteins such as BCLXL and would result in altered protein levels. BCLXL along with BCL2 have implicated roles and varying expression profiles in breast cancer.

The protein levels of BCLXL in SUM-44, SUM-52, and SUM-229, as well as BCL2 in

SUM-44 remained similar in 4EBP1 knockdown populations compared to control (Figure

32B). These results showed knockdown of 4EBP1 did not drastically alter the expression levels of apoptosis associated proteins.

Figure 32. Knockdown of 4EBP1 does not drastically alter levels of apoptosis associated proteins. Control (lacZ) cells represented as LACZ KD. 4EBP1 knockdown cells represented by two short-hairpin targets (sh1 & sh4). (A) No detected changes in apoptotic Parp or Caspase-3 total protein levels or cleavage products as quantified by western blot across cell lines. (B) No substantial changes witness in BCLXL or BCL2 protein levels by western blot when expressed in model cell lines.

The decrease in ERα with 4EBP1 loss does not alter protein levels of NSD3 or ASH2L

Luminal tumors express and rely on estrogen receptor alpha (ERα/ESR1) transcription factor (Figures 3, 4, & 5). ERα and mTOR have a close signaling relationship214,269,277-280 and the 8p11-12 amplification is known to potentiate endocrine resistance and cell proliferation. Knockdown of 4EBP1 decreased the total protein levels of ERα when present in SUM-44, Cama-1, MCF7, and T47D luminal models (Figures 27

& 30). The field is still trying to understand which 8p11-12 amplicon oncogenes are most important to ERα expression and therapy; we and others have invested efforts towards the role of NSD3 and ASH2L in this capacity. Our previously published results showed knockdown of NSD3 decreased ERα levels and promoted tamoxifen resistance97 like

4EBP1 knockdown which shown here decreased ERα levels in both tamoxifen sensitive and resistant cell lines. So, the protein levels of ASH2L and NSD3 in 4EBP1 knockdown cells were determined. NSD3 (WHs) knockdown samples were also profiled and target the short isoform of NSD3 (obtained from Dr. Brittany Turner-Ivey). The use of control

LacZ targeting knockdown remained the same for both 4EBP1 knockdown and NSD3 knockdown. Although NSD3 and 4EBP1 knockdown in SUM44 both decreased ERα protein levels independently, the NSD3 levels remained unchanged in 4EBP1 knockdown cells and the levels of 4EBP1 remained unchanged in NSD3 knockdown cells (Figure 33A), while NSD3 was barely detected in MCF7 cells. Degradation was witnessed with frozen control LacZ and NSD3 knockdown samples. Additionally, the protein levels of ASH2L remained unchanged in SUM-44, Cama-1, and SUM-52 cells with 4EBP1 knockdown (Figure 33B). These data show 4EBP1 can influence ERα levels in breast tumors of different backgrounds, and this is independent of protein levels

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of other 8p11-12 amplicon genes NSD3 and ASH2L. Studies should further address a role for 4EBP1 to influence the transcription of ASH2L and NSD3, as we had thought

8p11-12 amplicon genes worked together in the 8p11-12 amplified setting. It is unlikely that 4EBP1 protein levels alone promote ER expression, as MCF7 cells have more ERα than SUM-52 or Cama-1 but less 4EBP1 protein.

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Figure 33. Loss of ER by 4EBP1 knockdown does not alter the levels of NSD3 or ASH2L. Control (lacZ) cells represented as LACZ KD. Two different 4EBP1 short-hairpin targets represented. Western blot of 4EBP1, the short isoform of NSD3, 4EBP1, and ERα in 4EBP1 knockdown or NSD3 knockdown (WHs) cells.

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4EBP1 knockdown decreases MYC levels

MYC is well known proto-oncogene across cancers that has been used to immortalize mammary epithelial cells281. Frequent amplification of the 8q24 region harboring MYC (cMYC) occurs in breast tumors. It has been shown that increasing MYC levels can increase 4EBP1 expression in prostate tumors282. Indeed, mouse embryonic stem cells show the EIF4EBP1 DNA sequence can be regulated by MYC (Figure 17) and MYC also has a role in mTOR resistance in luminal breast cancers251. So, the influence of 4EBP1 knockdown on cMYC protein levels was tested and a similar trend occurred across the breast cancer cell models. SUM-44 cells do not express cMYC

(data not shown) but cMYC protein levels were reduced in other luminal Cama-1, MCF7, and T47D cells in 4EBP1 knockdown cells compared to control cells. Additionally, triple- negative SUM-229 and SUM-149 cells also reduced cMYC levels upon 4EBP1 knockdown. Contrary, 4EBP1 knockdown in SUM-52 cells did not alter cMYC levels

(Figure 34A). A drastic reduction in MYC transcript levels with 4EBP1 knockdown in

Cama-1 and MCF7 cells was also observed (data not shown). These results are not surprising given a role for 4EBP1 to influence the translation of cap-dependent and cap- independent transcripts whereby MYC is known to be regulated in both ways.

Interestingly, MYC transcript levels are highest in the triple-negative breast tumor subtype but significantly decreased compared to matched normal tissue (Figure 34B) which suggests other MYC isoforms are important to breast cancers, like MYCL1 or

MYCN which is significantly increased across breast tumor subtypes compared to normal (Figure 34C & 34D).

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Figure 34. The influence of 4EBP1 on MYC protein levels and varying gene expression levels of MYC family members. (A) Control (lacZ) cells represented as LACZ KD. 4EBP sh_1 cells represented as 4EBP1 KD sh1. 4EBP sh_2 cells represented as 4EBP1 KD sh4. Western blot analysis of MYC (cMYC, MYCC, bHLHe39) protein levels in breast cancer models. (B) UALCAN shows Transcript Per Million (TPM) values for MYC in both normal and breast tumor samples grouped by breast cancer subtype from The Cancer Genome Atlas (TCGA) data. (C) UALCAN shows Transcript Per Million (TPM) values for MYCL (bHLHe38, LMYC, MYCL1) in both normal and breast tumor samples grouped by breast cancer subtype from The Cancer Genome Atlas (TCGA) data. (D) UALCAN shows Transcript Per Million (TPM) values for MYCN (bHLHe37, N-myc, MYCNOT, NMYC) in both normal and breast tumor samples grouped by breast cancer subtype from The Cancer Genome Atlas (TCGA) data.

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Increased MYC levels have been tied to an increased association with

Bromodomain Containing 4 (BRD4) which can drive everolimus resistance in ER+ breast cancers. This supports a role for BRD4 inhibitors combined with everolimus in breast cancer therapy251; this study showed that 8p11-12 amplicon (ZR75, SUM52, Cama-1) bearing everolimus resistant cell lines did not decrease phosphorylated 4EBP1 at

Serine65 contrary to MCF7 resistant cells. Additionally, all everolimus resistant cell lines showed a decrease in phosphorylated S6 levels. It is possible that total 4EBP1 levels might increase in the everolimus resistant cells, but total 4EBP1 levels were not assessed by RT-PCR or western blot251. We have preliminary evidence showing 4EBP1 can alter BRD4 levels and the results presented here show 4EBP1 levels influence MYC transcript (data not shown) and protein levels (Figure 34). It should be noted that I previously tested a panel of cell lines with the BRD4 inhibitor, JQ1, and in parallel to published findings showed there was increased effectiveness of this drug with increased

MYC protein levels (multiple isoforms; data not shown). The ability of 4EBP1 to alter

MYC suggests it could also be tied to mediating antitumor immune response, since MYC can regulate many different genes including the immune checkpoint proteins CD47 molecule (CD47) and CD274 molecule (PD-L1, CD274) in tumors283.

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Chapter IV

4EBP1 DISCUSSION

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Several oncogenes are positioned within the 8p11-12 region in human breast cancer. Of significance, the 8p11-12 amplicon is implicated in endocrine resistance. This study aimed to determine whether 4EBP1 could influence ER to potentiate proliferation in ER+ 8p11-12 amplicon positive breast cancer cells by testing a panel of breast cancer cell lines that included ER+ and ER- 8p11-12 breast cancer cell lines as well as ER+ and

ER- non-amplicon bearing cell lines. Our current findings suggest that 4EBP1 is a critical protein for breast cancer cell proliferation regardless of amplicon and/or ER status.

Interestingly, it is highly overexpressed but rarely mutated in tumors, regardless of amplification, and is predicted as an essential driving gene in many cancer cell lines in vitro which we (https://sumlineknowledgebase.com/) and others have witnessed

(https://depmap.org/portal/) using genome-wide gene essentiality screens. However, targeting 4EBP1 in non-transformed “normal” mammary epithelial cells does not affect proliferation. This finding is significant because it suggests that 4EBP1 has roles outside of ER specific regulation across cancers and targeting it in breast cancer would likely have minimal negative effects on normal cells.

Consistent with the idea that 4EBP1 has a potential role in regulating ER expression due to the localization of EIF4EBP1 on the 8p11-12 amplicon, as well as a potential role outside of ER regulation, downregulation of 4EBP1 reduces not only ER expression but also affects Cyclin D1 expression and p27 expression. These observations are consistent with the reduced proliferation and cell cycle arrest phenotypes that are reported here. This also substantiates a relationship between Cyclin

D1 and 4EBP1 levels besides the known59 and consistently demonstrated occurrence between co-amplification of genomic loci harboring these genes. Yet, this common co-

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occurrence might be central to the transition of cell cycle states, meiotic progression, and the regulation of aneuploidy. Studies have shown that blocking interaction of EIF4E and EIF4G with 4EGI promotes aneuploidy175, similarly indirect targeting 4EBP1, through translational inhibition, is associated with the loss of many important regulators, like D- cyclins267. Indeed, Cyclin D1 (CCND1) is frequently overexpressed and a critical gene in luminal tumors which works with cyclin-dependent kinases (CDKs) like Cyclin

Dependent Kinase 4 (CDK4) which is a common essential gene in breast tissue284 that regulates appropriate cell cycle stages285,286. The field has known for over two decades that Cyclin D1 is a proto-oncogene287 wherein overexpression can be maintained by many mechanisms including gene amplification and post-transcriptional mechanisms174,288-293. Although, it is only in the past few years that clinical trials have demonstrated potential for CDK4/6 inhibition in the treatment of luminal breast tumors. It should be noted that 4EBP1 was required for coupling mTORC1 signaling to Cyclin D1 expression273 and it has been shown that CCND1 translation depends on both cap- dependent294,295 and cap-independent initiation174, both of which 4EBP1 can influence.

Our results support 4EBP1 influences Cyclin D1 expression. So, future studies should address the predictive value of 4EBP1 levels to CDK inhibition in breast tumors, especially in the context of dual inhibition with PI3K/AKT/mTOR inhibitors which has been studied286,296-299 and is being explored in clinical trials around the world.

Amplification of EIF4EBP1 leads to increased 4EBP1 expression and phosphorylation suggesting that mechanisms are in place to promote 4EBP1 mediated translation and post-translational regulation during breast cancer initiation and progression. Consequently, targeting of 4EBP1 either directly or via inhibition of mTOR could relieve repressive effects of phosphorylated 4EBP1 on translation as well as any capacity of 4EBP1 to stabilize mTORC1189 or other proteins like Cyclin Dependent 108

Kinase Inhibitor 1A (p21, CDKN1A)300. Research suggests 4EBP1 phosphorylation is associated with resistance to inhibitors, as has been implicated in mTOR kinase inhibitor resistance273,301-303, likely due to regulation by kinases independent from mTOR. It has long been known that mTOR effector phosphorylation of 4EBP1 and S6K1 are not always associated304,305. Additionally, preliminary evidence from the CTD2 database predicts 4EBP1 as a biomarker for response to many types of inhibitors, including mTOR inhibitors (Rutkovsky, DOI: 10.5281/zenodo.1095849). Several Phase II clinical trials have evaluated use of mTOR inhibitors for ER+ breast cancer306-308. While promising, results from trials in patients with ER+ breast cancer who experience aromatase inhibitor failure were only somewhat successful306. However, a current clinical trial is underway to determine if the phosphorylation status of 4EBP1 can be used to predict everolimus response in breast tumors (NCT00855114, ClinicalTrials.gov).

Direct targeting of 4EBP1 or targeting of multiple upstream kinases that target

4EBP1, like mTOR and AKT may provide additional benefit. Several kinases have been identified that phosphorylate 4EBP1 in both mTOR dependent as well as independent manners103,104 and many are druggable such as mTOR and AKT. Furthermore, phosphorylated 4EBP1 acts as a biomarker for other targets in the PI3K/AKT/mTOR pathway like the phosphatidylinositol 3’ kinase inhibitor GDC-0941 in breast cancer cell lines and xenograft models140 so further studies should address 4EBP1 behavior with combine therapies. The PI3K/AKT/mTOR and RAS/RAF/MEK/ERK (MAPK/ERK) signaling pathways use 4EBP1 to integrate their function267 and combined inhibition of both PI3K/AKT/mTOR and MAPK/ERK signaling pathways may be necessary to maximally inhibit 4EBP1 phosphorylation309. Receptor tyrosine kinases, such as HER2, influence upstream signals that then effect 4EBP1. Preliminary observations of HER2 protein levels in the SUM-44 and Cama-1 cells showed no changes with 4EBP1 loss 109

(data not shown), but this is not the HER2 enriched setting, so further studies should explore the HER2 enriched setting along with the impact of HER2-targeted therapies on

4EBP1 levels, because 4EBP1 has been shown to be important in HER2 enriched tumors121. An ideal strategy would decrease total and phosphorylated 4EBP1 levels, producing a parallel phenotype to our reported findings. Whether the distinct effects of the different phosphorylation states of 4EBP1, determined by distinct phosphorylation events driven by individual kinases, affects 4EBP1’s ability to drive breast cancer progression or endocrine resistance and how this relates to total levels of 4EBP1 would be of significant interest for future studies particular in the context of therapeutic interventions.

The influence of 4EBP1 loss on the protein levels of phosphorylated S6 or phosphorylated AKT in the 8p11-12 ER+ setting showed no substantial capacity for

4EBP1 to influence phosphorylated levels of these regulators, supporting independence from the PI3K/AKT/mTOR pathway in this setting. Additionally, although phosphorylation of 4EBP1 is thought to drive EIF4E cap-dependent translation and promote growth173, residual 4EBP1 protein after 4EBP1 knockdown in the highest expressing SUM-44 model remained phosphorylated at Threonine37 and Threonine46, yet the cells did not proliferate. These results suggest breast cancers are more reliant on total 4EBP1 levels than phosphorylated 4EBP1 levels to influence proliferation and the expression of certain proteins. Studies should further examine this through gene knockout and rescued complementation with wild-type 4EBP1 and phosphorylation mutant 4EBP1 cDNA expression vectors.

EIF4E is proposed to have oncogenic properties in cancer, and 4EBP1 is known to regulate EIF4E. Most studies relate 4EBP1 findings to this pathway or the

PI3K/AKT/mTOR pathway. Activated EIF4E is correlated with high phosphorylated 110

4EBP1 in breast tumors. Aggressive breast tumors have high rates of phosphorylated

4EBP1 which supports our findings regarding the 8p11-12 amplicon bearing cell lines which have the highest amount of phosphorylated 4EBP1 among the models studied here. The importance of the 4EBP1/EIF4E protein ratio and colocalization has been demonstrated, but breast tumor gene expression patterns do not support this finding.

Breast tumors do not usually lose EIF4EBP1 mRNA expression, but they do lose EIF4E expression. Additionally, EIF4EBP1 is expressed highest in late stage breast tumors, whereas EIF4E is lowest expressed in stage IV breast tumors. EIF4EBP1 is higher expressed post-menopause whereas EIF4E is higher expressed in pre-menopause tumors. Thus, the same gene expression trends do not hold true for EIF4EBP1 and

EIF4E, so determining the half-life of all products, including mRNA and protein, is necessary to accurately draw conclusions regarding a role for 4EBP1 in the signaling pathway that is EIF4E regulation. It would be interesting to study the role of 4EBP1 in tumors that lose EIF4E gene expression.

Many studies do not quantify total levels of 4EBP1 and instead quantify phosphorylated 4EBP1 protein, nonetheless they still highlight that 4EBP1 predicts outcome such as poor prognosis and differentiation, tumor size, progression, and metastasis. Breast tumors highly express 4EBP1 mRNA, so if this mRNA product is indeed translated, we would predict that the 4EBP1 protein is stable112,203 as the half-life of 4EBP1 was reported to be more than 16 hours, but how modifications influence this period is inconclusive. This should be determined. No studies have yet determined if localization plays a role in 4EBP1 stability. 4EBP1 is present in the cytoplasm and nucleus. Localization may play a role in oncogenesis105 and a sizable fraction of total

4EBP1 was found localized to the nucleus (30%) where it regulated EIF4E localization under stress204. How 4EBP1 is localized to different cell compartments is not 111

understood, but it does not contain known nuclear localization or export motifs, so studies should elucidate yet unknown mechanisms such as important regulatory motifs, lipid modifications, or other modifications that allow 4EBP1 to traverse cell compartments. Future studies should highlight any important differences in stability, especially among normal and cancerous populations, particularly in relation to mTOR signaling in breast cells.

The regulation of 4EBP1 transcription and translation rates should be further explored, but the high amounts of 4EBP1 mRNA are suggestive of an advantage at both levels: high transcriptional rates would allow for immediate translational availability.

There are many possible explanations for high 4EBP1 levels found in breast tumors.

One such explanation is that pathogens, like viruses, increase the levels of 4EBP1, such as what was recently seen in HPV positive cervical carcinomas215. Here we noticed that

E6/E7 oncogene-induced bypass of senescence (a commonality in HPV tumors) in a normal cell line had no effect on the proliferative capacity with loss of 4EBP1. Mining the

TCGA data using UALCAN also showed that although there is an increase 4EBP1 gene expression in head and neck squamous cancers compared to normal, there are no significant changes between HPV positive or HPV negative samples; this is the same for

EIF4EBP2. So further studies should better define the background and potential for viral oncogenic induction for 4EBP1 in malignant neoplasms.

Transcription factors can also regulate 4EBP1 levels. Many of the TFs governing other family members like EIF4EBP2 and EIF4EBP3 are different and fewer than the results for 4EBP1. Further exploration of EIF4EBP1, EIF4EBP2, and EIF4EBP3 transcript levels in normal and cancerous samples from TCGA data show that EIF4EBP1 is frequently overexpressed in cancer and rarely displays lower transcript levels than its comparable normal samples. On the contrary, EIF4EBP2 mostly shows no significant 112

change or a decrease in mRNA expression compared to normal (BRCA loss, HNSC no change, STAD no change, COAD loss, PRAD loss, LIHC increase), and EIF4EBP3 also behaves differently from its family members.

4EBP members have been shown to control fat storage by regulating the expression of other transcription factors such as EGR1310, and it is not far-fetched to speculate 4EBP1 regulates expression of the tumor suppressor EGR1 in breast cells. A more thorough investigation between 4EBP1 and EGR1 is warranted considering most breast tumors lose expression of this transcription factor (Figure 35), EGR1 contributes to endocrine resistance in breast cancer cells311, and EGR1 is a regulator of other important tumor suppressors like TGFB1, PTEN, TP53, and FN1312 all of which have their own implicated roles in breast cancer. Studies should focus on individual and coordinated efforts among the 4EBP family members and how this relates to other important oncogenes and tumor suppressors. We used data mining tools to further explore transcription factors that could regulate these family members and the individual profiles do vary, so studies should also profile transcription factor changes in gene expression studies of models using sequencing or transcription factor activating profiling plate arrays.

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Figure 35. Expression of EGR1 in TCGA breast cancer cohort. Transcript per million (TPM) expression of EGR1 is grouped by normal (blue), luminal (orange), HER2 positive (green) and triple-negative (brown). Breast tumors significantly decrease EGR1 mRNA compared to normal samples. The Normal median TPM is 684.995 and maximum TPM is 2348.376 while the luminal median TPM is 71.703 and maximum TPM is 412.2. EGR1 transcript levels in normal cells can be expressed to levels greater than 2500 TPM, while oncogenic EIF4EBP1 levels in breast tumors are not usually higher than 500 TPM.

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Epigenetic regulation is dynamic, varies in different cell types, and reprogramming is critical to cancer cells313 within all areas of the human genome, so it could affect 4EBP1 levels. Within the area of epigenetics, studies continue to show the regulation of chromatin at many levels and are starting to elucidate proteins that can play important roles. Enhancer regions can promote the transcription of both protein-coding genes and noncoding RNAs with nucleosome occupancy making a difference at these levels. Chromatin modifications influence nucleosome occupancy and the accessibility of genomic regions. Although I have not come across a specific study that thoroughly evaluates the detailed chromatin modifications influencing 4EBP1 gene expression (or how total loss or gain of 4EBP1 mRNA could affect other genomic modifications especially the rest of the 8p11-12 region), it is straightforward to assume the modification of H3K4 through acetylation or methylation (me) are activating gene expression, particularly H3K4me3 at transcriptional start sites. Although, these assumptions are not always accurate because chromatin modification is thought to be dynamic and can be complicated by co-occurrence of bivalent domains. Bernstein and colleagues showed over ten years ago that sometimes generally repressive marks like H3K27me also harbor smaller regions of H3K4me and these bivalent domains coincided with genes poised but not yet translationally active314. It is likely that alterations in chromatin modifications governing increased 4EBP1 expression are finely regulated, particularly in different cell types which show varying expression thresholds. Our lab previously performed H3K4me3 ChIP-seq and RNA-seq in the SUM-44 cell line which harbors the highest amount of 4EBP1 in any of our model cell lines. So, the behavior of SUM-44 or

SUM-44 with loss of 8p11-12 genes can be compared to the available ENCODE data which harbors at least 4 normal mammary gland data sets. This comparison may be 115

able to highlight changes in the chromatin regulation of the 4EBP1 gene in cancer and normal cells of the breast. Our other studies aimed to elucidate roles for chromatin modifiers present within the 8p11-12 region in breast cancer, like NSD3, ASH2L, and

KAT6A. The results I generated using bromodomain inhibitor, JQ1, demonstrated effectiveness across various breast cancer cell types, including 8p11-12 amplicon cells, with higher effectiveness when combined with broad inhibition of KDM1A (LSD1) and related family members using the 2d315-317 compound (data not shown). Thus, targeting

BRD4 in the 8p11-12 setting may be a promising therapeutic strategy and is in accordance with preliminary results that showed 4EBP1 knockdown could decrease

BRD4 levels as well (data not shown).

Other 8p11-12 genes likely coordinate function with 4EBP1 to promote neoplastic transformation. This is not limited to other known growth factor responsive regulators, like FGFR1, BAG4, TCIM, and ADAM9. The lab previously showed 8p11-12 genes helped mammary cells escape anoikis67, and genes involved in cell migration, like

BAG4, ZNF703, and ADAM9, may coordinate with 4EBP1 towards this capacity. Genes that promote self-renewal contribute to relapse, and FGFR1, KAT6A, and ZNF703 are already implicated in stem cell processes and might coordinate with 4EBP1 to promote aggressiveness. Additionally, ASH2L and C8ORF4 are implicated in WNT regulation which is often a critical in regulation of self-renewal, and NSD3 like 4EBP1 can influence cell cycle fate. Other genes involved in differentiation processes, like ADAM9 and

ZNF703, or genes involved in epigenetic processes, like NSD3, ASH2L, and KAT6A, may also act in consort with 4EBP1.

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Chapter V

FUTURE DIRECTIONS

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Future directions related to 4EBP1 in cell dynamics

This work has high impact for better understanding and leveraging therapy for

8p11-12 amplicon bearing tumors, as well as tumors reliant on 4EBP1 without amplification of the 8p11-12 locus. This includes an application in broad settings as

4EBP1 and chromosome 8 abnormalities are involved in many cancers. This is especially applicable to breast cancer treatment and diagnostics. Oncogenes on the amplicon are potential and current therapeutic targets, such as IDO and FGFR inhibitors in the clinic, and this research highlights 4EBP1 as an ideal gene-targeting strategy for exploration as well as use as a biomarker for therapy prediction, especially for inhibition of other 8p11-12 genes like FGFR1. Additionally, advancements in determining allelic frequency and 8p11-12 detection could improve breast cancer patient survival by predicting prognosis and more appropriate therapies for patients.

The future of cancer therapy must be less toxic to the health of normal cells and more potent towards the elimination of cancer cells. This includes a more practical approach in pharmacogenomic efforts and highlights the need for further preclinical exploration of combinatorial strategies and how this relates to patient tolerability. This includes extensive cooperation among current initiatives to build the resources required to explore and explain trends and effective strategies. The National clinical trials network

(NCTN), specimen-resource locator (SRL) within the national catalog of biospecimens

(CHTN), and patient-derived xenograft models (PDMR) alongside the PDX development and trials network consortium (PDXNet) with primary and metastatic matched samples, matched tumor and fibroblast cell lines, conditionally programmed cell (CRC) lines, organoid models, and 3D Tissue-Chips can all be applied for 4EBP1 applications.

Others have been trying to better understand the structure of the 4EBP1 protein for 118

inhibitor design, but this has not yet elucidated allowable biochemical design. Thus, we have suggested the use of gene-targeting strategies for 4EBP1, but further preclinical efforts must address multiple options towards this goal as well as study the potential in multiple cancer types and influence on normal cells.

Different cell types and tumors harbor unique transcript libraries making it very intriguing that cancers arising from different cell types up-regulate 4EBP1 transcript levels so frequently. A more detailed understanding of how and why 4EBP1 is overexpressed in cancers is needed, especially in relation to upstream signals that dictate 4EBP1 function. An inventive new technology, GLoPro203, uses a blunted version of the CRISP-Cas9 engineering system and would provide a more comprehensive view of proteins able to manage 4EBP1 gene expression. More thorough evaluation of post- translation modifications, the responsible mechanisms, and how these influence behaviors, particularly localization to influence stability or skeletal dynamics, such as microtubule stabilization and tubulin assembly, are warranted.

Most normal human cells have diploid genomes, albeit this is not true in all tissues such as some bone, liver, and salivary cells. This is not yet well understood especially in relation to allelic frequency within the population. Which if any specific copy number locations remain understudied as well as any variance in frequency among these cell types (hopefully the All of Us Research Program and Genomic Ascertainment cohort (TGAC) will promote this effort). Although not yet widely explored, these copy number amplifications, polyploidy, may have profound implications for megakaryocyte platelet production, particularly during therapy for cancer patients and the development of thrombocytopenia. Although the role of polyploidy in self-renewal is not known, it is known that aneuploidy can offer advantages to cancer through the amplification of certain genomic regions. This is true for chromosome 8, which when initially sequenced 119

was hypothesized to contribute to the evolution of homo sapiens from primates and has since demonstrated significant importance in many studies tied to the nervous system and cancer. Chromosome 8 harbors many important genes and specific genomic loci on the short and long arm that undergo frequent amplification events in cancer. Herein we have highlighted significance for 4EBP1 in many different large-scale data driven initiatives, including across The Cancer Genome Atlas (TCGA) pan-cancer analysis showing overexpression compared to normal. We hypothesize 4EBP1 overexpression has a role in amplicon generation of chromosome 8 loci along with other chromosome loci, perhaps to influence production, maintenance, and passage to progeny.

Microsatellite instability (impaired DNA mismatch repair) and karyotype changes should be explored in models, especially in relation to 4EBP1 levels and cell cycle events.

4EBP1 has extensive orthologs in other species, highlighting a role for this gene to eukaryotic function. It also shares homology and overlapping expression with other family members, implying the role of EIF4EBP2 and EIF4EBP3 should also be explored in relation to the oncogenic properties of EIF4EBP1. It is likely that these family members are regulated in a different manner, but this remains inconclusive. Thorough evaluation of 4EBP1 expression, such as alternative transcript expression should also be explored to confirm that 4EBP1 transcripts are indeed one isoform.

Additionally, EIF4EBP1, EIF4EBP2, and EIF4EBP3 do not follow similar trends in a meta-analysis of the TCGA cohort. For example, in the breast cancer cohort (BRCA)

EIF4EBP1 is frequently overexpressed, EIF4EBP2 is frequently lost, and EIF4EBP3 has no significant changes compared to normal tissue. This trend holds true in other TCGA cohorts like the colon adenocarcinoma cohort (COAD). In the prostate adenocarcinoma cohort (PRAD) EIF4EBP1 is overexpressed, EIF4EBP2 is lost, and EIF4EBP3 is increased compared to normal samples. Importantly, the trend between 4EBP1 mRNA 120

expression and copy number is partially linear in the TCGA-BRCA, which suggests that copy number amplification of 4EBP1 is not necessary for high expression. Our data also suggest that though protein levels of 4EBP1 can be similarly expressed in cancer and normal cell lines, 4EBP1 is only detrimental to the proliferative capacity of cancer cells.

We did not profile the transcript levels of 4EBP1 in any models, but patient tumor gene expression data suggests that breast tumors overexpress 4EBP1 mRNA to higher levels than what is usually expressed in normal breast samples, so further studies should address a role for gene knockout and any possible defects in cells resulting from gene- targeting of 4EBP1 in the breast.

We are not the first to report on the importance of 4EBP1 to breast cancer, others have promoted 4EBP1 as a cell signaling hallmark in breast cancer and algorithmic predictions also suggest critical association to cancer, especially breast tumors. Many reports highlight a role in other cancer types and even our and others prior breast cancer studies undervalued the importance of 4EBP1 presence within the 8p11-

12 amplicon. Our newfound understanding promotes an adaptive role whereby cancers perhaps evolve many mechanisms to maintain the oncogenic processes afforded by

4EBP1.

A role for 4EBP1 in transformation

A role for 4EBP1 in transformation should be further explored, especially since

4EBP1 was identified in an up-regulated genetic signature, in primary human embryonic lung fibroblasts, associated with accelerated growth of premalignant and malignant cells318. Some transformative processes can be readily studied in the laboratory because transformed cells need fewer extracellular growth factors, are not affected by lack of cell- to-cell contact, and are immortal. The effects of wild-type 4EBP1 overexpression to elicit

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transformation in normal mammary cells has not yet been thoroughly explored. 4EBP1 should be overexpressed in MCF10A cells as well as primary human mammary epithelial cells (HMECs). The MCF10A karyotype is stable and near-diploid but they are still considered cytogenetically abnormal319. Because MCF10A contain locus and genes that may cooperate with the introduction of additional oncogenes, other “normal” immortal breast cell lines and patient mammary epithelial cells should also be used.

These can be readily obtained through NIH resources or the Hollings Cancer

Biorepository affiliation at the Medical University of South Carolina. Normal human mammary epithelial cells (HMECs) are likely to harbor less genetic abnormalities commonly associated with immortal cell lines.

The influence of 4EBP1 loss in normal cells is paramount towards the use of targeting 4EBP1 in the clinic. Towards this goal, normal cell behavior with loss of 4EBP1 should be further explored, especially the levels of key regulatory protein changes that are witnessed in cancer cells and how this relates to normal cell livelihood. It may be possible to induce the oncogenic and evolutionary processes afforded by 4EBP1 in normal cells under certain conditions such as infection, then determine changes to

4EBP1 levels and modifications. Also engineering the overexpression of wild-type, non- tagged 4EBP1 in normal cells might elicit transformation especially combined with selection of populations in various settings to induce a perhaps necessary oncogenic background. This will require thorough evaluation of model development with various conditions, including exposure to current clinical therapies.

A role for 4EBP1 in growth factor independence

Normal human mammary epithelial cells depend on growth factors and hormones for survival and proliferation, but not malignant cells. 4EBP1 has a known role

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in response to growth factors, like insulin [GO:0032869], and may be crucial to growth factor independent growth. This can be determined by infecting normal mammary cells with a 4EBP1 expression vector then quantifying any capacity to proliferate without growth factors. The serum-free culture system was developed in our lab and is important to demonstrating that the acquisition of insulin-independent and EGF-independent growth is associated with neoplastic transformation. Additionally, nutrients alter 4EBP1 function and many studies show molecules can stimulate phosphorylation of 4EBP1, like

L-threonine which stimulates phosphorylation of 4EBP1 in mouse embryonic stem cells promoting G1/S transition. These authors went on to show increased stimulation was blocked by the addition of LY294002 (PI3K), Wortmannin (PI3K), AKT inhibitor,

PD98059 (ERK), SB203580 (p38), and SP600125 (JNK)320. Bioactive sphingolipids, like

Sphingosylphosphorylcholine (SPC), have also been shown to decrease phosphorylated but not total levels of 4EBP1 in cardiomyocytes which suppressed apoptosis and induced autophagy321. So, the role of growth factors, mitogens, and other molecules to influence 4EBP1 should be further explored.

A role for 4EBP1 in anchorage-independent growth

Anchorage-independent growth confers neoplastic transformation by a series of changes that promote growth and proliferation independent of attachment to a surface.

We have shown that genes from the 8p11-12 region help mammary cells escape anoikis67. 4EBP1 overexpression may inhibit the need for cell-to-cell contact through anchorage-independence in soft agar. So, normal cells overexpressing 4EBP1 or cancer cells with loss of 4EBP1 could be profiled for loss of anoikis. Additionally, variations of this assay can show changes in branching morphology and 4EBP1 may contribute to morphogenesis and polarity in branched epithelial organs. 123

A role for 4EBP1 to bypass senescence

Irreversible cell senescence restricts the number of cellular divisions while maintaining cells in a viable state322-327; this state can sequentially progress from cell cycle arrest to permanent arrest to cell death234,328,329. Malignant cells must bypass senescence during tumorigenicity. The Tumor Protein P53 (TP53, p53) has a defined role in senescence and there is an established relationship between p53 and 4EBP1

188,197,253,330-333. Resistance to senescence has been shown dependent on 4EBP1 and may be independent of mTORC1196,197. Dazard and colleagues showed that 4EBP1 was part of a network of down-regulated genes in quiescent cells after E1A expression334.

Thus, 4EBP1 has a role in bypassing senescence. Senescent cells have an increased cell size, flattened shape, and may be multinucleated335. Monitoring senescent hallmarks in cells engineered with 4EBP1 loss and gain (in normal human mammary epithelial cells that are not immortal), such as senescent-associated beta-galactosidase accumulation336-339, and cell cycle markers such as CDKN2A, RB1 and TP53 expression340-345 can determine if 4EBP1 can help to bypass different phases of cell cycle and overcome senescence. We would expect changes in cell cycle populations as

4EBP1 has a known role to influence cell cycle fate [GO:0045931]346 which we have also demonstrated here. Additionally, monitoring the proliferative lifespan of cells engineered to overexpress 4EBP1 will allow determination of changes in population doubling, which might promote an extended or immortal lifespan.

To better define a role for 4EBP1 in immortality, HMECs could be temporarily immortalized, then changes in 4EBP1 levels determined. One method for TERT reactivation increases cell proliferation in culture, while recapitulating the telomeric state of normal cells. It uses a modified, non-immunogenic mRNA of TERT that extends telomeres up 10% in length to increase lifespan, dissipates after 48 hours, and allows 124

telomeres to progressively begin to shorten again347. In this manner, we could induce immortality in HMECs then measure changes to 4EBP1 mRNA and protein levels, while also monitoring any phenotypic changes.

A role for 4EBP1 to influence cancer stemness

4EBP1 may promote the tumor initiating cell (TIC or cancer stem cell) phenotype to contribute to relapse. Stem-cell marker identification, clonogenicity, and tumor generation are used to identify tumor initiating cells (TICs). Assessment of colony formation capacity, quantification of spheroid production, and profiling cancer gene transcripts implicated in breast cancer stemness can help determine if 4EBP1 overexpression can maintain the TIC phenotype. This would include assessment of stemness markers: CD133, ALDH, CD44, and CD24, although these markers remain controversial4,348-351. Evaluating marker performance could be performed through gene expression profiling and fluorescence-activated cell sorting (FACS) by flow cytometry.

A mammosphere assay could be used to quantify contribution to spheroid generation in normal cells that have been engineered to overexpress 4EBP1. This clonogenic assay is frequently used to monitor stemness of cells by spheroid formation in suspension culture although clonogenic assays of cells affixed to substrates are also used to similarly measure proliferative potential. In mammosphere assays, cells are seeded at single colony density in ultra-low attachment 6-well plates at 5000-10000 cells/well and fed with stem cell media. If mammosphere formation is possible, it usually occurs by day 4. Colonies are quantified by staining with 500 ug/mL para- iodontrotetrazolium violet overnight and then counted. There are caveats to the generally accepted methods to select for transformed, stem-like cells. For example, sphere forming assays may not select for TICs that do not form spheres, isolation using cell

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surface markers is controversial while their relevance as tumor repopulating cells is not clear352, and alternative substrates (collagen gels, fibrin gels352, and three-dimensional nanofiber scaffolds) could select populations with higher TIC potential. Determining growth on alternative substrates would also allow for profiling of changes to apical-basal polarity353,354,355 and planar-cell polarity356. Profiling the expression of polarity regulators may be important in this setting, like laminin and desmosomes which can be critical to mammary cell polarity357. Polarity is critical to metastasis and less differentiated, aggressive cells are more likely to metastasize, making this an important avenue for exploration as related to 4EBP1.

A role for 4EBP1 in apoptosis

A role for 4EBP1 in apoptosis remains complicated considering the role of

4EBP1 in cell cycle. We could not accurately assess apoptosis stages in our models because of death associated with the selection process during engineering of 4EBP1 knockdown. Nonetheless, we did profile apoptotic associated proteins in knockdown cells after selection, which were not influenced by loss of 4EBP1 contrary to cell cycle associated proteins. It was shown in fibroblasts that ectopic expression of wild-type

4EBP1 did not activate apoptosis but mutant-phosphorylated 4EBP1 expression did have apoptotic effects, which varied depending on which residue was altered358.

Additionally, checking acini formation and polarization with 4EBP1 overexpression or underexpression might indicate changes in apoptosis since only polarized acini are resistant to apoptosis359. It is likely membrane potential influences polarization and apoptosis so it should also be further explored.

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A role for 4EBP1 in adipogenesis and lipid metabolism

Lipid metabolism is critical to cell regulatory function. Cancerous processes are influenced by mitogenic signals and lipid species, this especially true in metastasis where lipid dynamics have a role in cell regulation, phase separation, and membrane potential. 4EBP1 does not contain known localization sequences but can traverse multiple cell compartments, indicating other mechanisms like lipid regulation and phase separation are promoting this ability. Phase separation is important and some cell components promote phase separation while others do not360. Phase separation can be an adaptive response361 and any capacity of 4EBP1 to be linked to pathological aggregation and physiological phase transitions should be explored362, especially since compartmentalization is a dynamic, adaptive state that is tied to availability and mitogenic stimulation. There are 4EBP1 knockout mice available

(http://www.informatics.jax.org/marker/MGI:103267) and many studies show altered phenotypes associated with lipid dynamics, including decreased adipose tissue and weight. Recently, gender-specific 4EBP1 expression was linked to obesity suppression, insulin sensitivity, and energy metabolism in mice201 but it has been known for over two decades that 4EBP1 is required for normal body weight in male mice but not female mice202. The ways that 4EBP1 responds to various signaling events should be further explored, especially since 4EBP1 is involved with pathways responding to lipid soluble

(e.g. estrogen) or water soluble (e.g. insulin) hormones. Steroid, peptide, and amino-acid derived thyroid hormones interact with cells in different ways, so it is important to determine if 4EBP1 is behaving in specific ways, a universal way, or merely as a bystander to altered systemic regulation, especially related to altered endocrine system dynamics which are tied to endocrine disruptors and related molecules as well as obesity signaling events. This includes the role of 4EBP1 function in relation to diabetes 127

and insulin signaling alongside mTOR inhibition or related molecules, polyamine- mediated cell regulation alongside polyamine analogues or inhibitors targeting the polyamine synthesis pathway as cancer therapy, and lipid dynamics alongside clinical inhibitors targeting lipid species or enzymes responsible in lipid generation and breakdown; all strategies with demonstrated profound implications in potential cancer therapy. I believe it is important to account for current therapies in future studies and this also includes the understudied endocannabinoid signaling system considering the adoption of cannabidiol and related molecules in cancer treatment, or therapies adopted prior to malignancy such as Bexarotene, a retinoic-acid derived prevention for high risk breast cancer currently being explored clinically.

The role for 4EBP1 to alter adipogenesis may suggest a role for vesicle

(exosome) formation, stress granule formation, and polarization to invade organs, as highlighted in a review on metastasis that accounts for varying possible scenarios related to metastatic progression363. Because 4EBP1 regulates the proliferation of cells, it is not far-fetched to suppose it regulates tumor cell clusters or individual tumor cells and then metastasis. Importantly, metastasis do not always share characteristics of the primary tumor and metastatic spread often uses multiple routes186 so defining the role for

4EBP1 in metastasis might be difficult but is supported with aggressive tumors having more 4EBP1 especially at later tumor stage. To better understand the role of 4EBP1 in adipogenesis and metastasis, invasion potential can be studied using invasion assays, exosome formation and content can be quantified by harvesting exosomes, stress granules can be isolated and counted, and lipid species alterations can be profiled under different settings.

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A role for wild-type 4EBP1 transcripts in malignant neoplasms

Many tumors, likely reliant on 4EBP1, have high 4EBP1 transcript levels, yet most do not harbor transcript coding mutations, suggesting wild-type 4EBP1 is important to cell function. This includes colorectal tumors, which are known to harbor increased mutational burden with age. The protective mechanisms in place that ensure the 4EBP1 gene sequence is not mutated are not defined. This is difficult to study, but clues may be found by inducing mutational burden in various models, then sequencing populations.

Using various methods of induction, such as chemical and radiation, then comparing changes may yield insights into how various carcinogens affect changes in 4EBP1 as well as how other co-expressed overlapping or different transcripts, especially genes involved in DNA repair and maintenance, influence 4EBP1. Additionally, gene transcription in wild-type 4EBP1 colorectal tumors of different ages, may help narrow a functional list. Further comparison to models in international cohorts with mutated

4EBP1, such as some melanomas, may also provide additional insight into imperative regulators for the protection of the 4EBP1 gene-coding sequence. Normal cells do not seemingly mutate 4EBP1, so it would be interesting to also compare these libraries.

A role for 4EBP1 as an antiviral strategy

Targeting 4EBP1 has been touted as an antiviral strategy, because of a role for

4EBP1 in infection. It was once thought that cancers arise from viral infection defining a role for proto-oncogenes and it has since been shown this is often the case especially in cervical or head and neck cancers. With improved efforts to identify viral presence, it is possible that higher rates of infection will be correlated with cancer occurrence, making vaccination a high priority. It is possible that tumors overexpressing 4EBP1 could be caused by unidentified infections and is probable, that like viral infected cells, tumors are

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using 4EBP1 in an adaptive way. The role of 4EBP1 in HPV associated tumors should be established considering current contradictory evidence. Studies should address a defined role for 4EBP1 in infection, especially with different pathogens and how this relates to immune function. This should be addressed alongside or independently from a role in anti-tumor response by the immune system, as 4EBP1 may have a critical role in immunotherapy escape and subsequent effectiveness of therapies targeting immune response mechanisms. These ideas should be explored in detail, and 4EBP1 is a prime target in all avenues here; it is especially interesting to note that developing a 4EBP1- targeted therapy could have potential benefit as an anti-viral, anti-cancer, and anti- metabolic-disorder considering other key roles in obesity, diabetes, and cardiovascular function.

A role for 4EBP1 in translation

Previous research highlights an extensive role for 4EBP1 in translational processes although much of this evidence can be regarded as preliminary considering most studies are not accounting for total translational changes. This is evidenced by the potential of 4EBP1 to influence protein coding species and other RNA species such as ribosome biogenesis which accounts for about half the total RNA synthesized and regulates the genetic code. This is a widely underexplored area in cell function, different cell types, differentiation states, and transformative processes. This is particularly true in relation to the temporal and spatial regulation of translation which is especially important in oocyte maturation where 4EBP1 has an implicated role. Advancements in isolation and extensive characterization for sequencing efforts can further elucidate these processes. Determining alterations in the regulation of mRNA species, such as cap- dependent and independent translational processes will also help us understand how

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modifications to transcripts can alter cell function, particularly under stress. Moreover,

4EBP1 might act like a RNA-binding protein in this capacity and many RNA-modifying proteins are also anti-cancer targets364. 4EBP1 is known to stabilize mTORC1189 and p21300. The influence on p21 stability may correlate with a role in stress granule formation (disassembly of processing bodies and mRNA decay entities)178 because stress granule formation has been shown to promote p21 recruitment upon assembly365 and p21 levels can indicate stabilization under stress366. I did knockdown p21 and explore 4EBP1 levels in some of our models, but results did not warrant further pursuit.

Any further studies addressing the role of 4EBP1 as a RNA-binding protein must be performed in homo sapien systems because of known eukaryotic lineage specific differences in the function of RNA-binding proteins.

Indeed, many of these hypotheses align with predicted KEGG processes generated by ARCHS4 for 4EBP1 and show the highest scoring processes are associated with DNA replication, ribosome, mismatch repair, proteasome, ribosome biogenesis, RNA polymerase, one carbon pool by folate, spliceosome, aminoacyl-tRNA biosynthesis, RNA transport, homologous recombination, nucleotide excision repair, pyrimidine metabolism, cell cycle, p53 signaling pathway, basal transcription factors,

RNA degradation, amino sugar and nucleotide sugar, and fatty acid elongation.

A role for 4EBP1 in precision medicine

The field is still fundamentally struggling to identify true variation and significance in related settings, but true potential exists for 4EBP1 inhibition breast cancers. It is likely that 4EBP1 plays different roles in different settings, but our data importantly show that

4EBP1 is important to different types of breast cancer but not normal breast, setting it apart from other targets especially in the gene-targeting sector and the promise of

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precision medicine. Inhibition of 4EBP1 has importance regarding most of its functions including roles in translation, infection, obesity, and cardiovascular disease. The role in cardiovascular function was shown with mouse studies which suggested that 4EBP1 inhibition could treat heart failure, as seen in cardiac-specific mTOR knockout mice wherein cardiac pressure overload was accompanied with increased levels of 4EBP1 and decreased heart function. In mTOR-4EBP1 double knockout mice, increased survival and improved cardiac function was observed compared to mTOR knockout or wild-type mice367. We propose gene-targeting of 4EBP1 in future studies that demonstrate both the potential and setbacks of various gene silencing systems like RNA interference (used here) or other systems like crispr-based technologies; advancements approachable in the clinical setting.

Much literature highlights a role for 4EBP1 as a biomarker and potential pharmacological target, especially for a less toxic future of cancer therapy, as highlighted in a recent review by Orth and colleagues103. Indeed, there is a clinical trial regarding 4EBP1 phosphorylation as a biomarker in breast cancer (NCT02444390,

SAFIR-TOR) which aims to identify alterations associated with resistance to endocrine therapy and impact for mTOR inhibition. We must be more practical in our pharmacogenomic efforts368. Understanding patient tolerability to drug exposure is paramount, especially the effects on normal cells for patients of young age. Clinical evidence suggests that patients can tolerate three or more drugs in parallel, promoting the use of combination strategies that are necessary to confer anticancer drug resistance. Our data suggests that targeting total 4EBP1 levels inhibits different types of cancer cells but not normal cells. Profiling 4EBP1 changes, especially total 4EBP1 levels, in relation to various individual and combinatorial strategies for the clinic will

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prove useful in determining the predicted role for 4EBP1 in drug resistance and effectiveness in the pre-clinical setting. Strategies able to reduce total 4EBP1 levels, as we have demonstrated, may prove most effective. It is also important to note the behavior of normal cells to effective strategies, because we have not studied 4EBP1 loss for an extended period time. Preclinical studies should better address tumorigenic potential of therapies in normal cells which is especially important for patients being treated at a young age.

Many have hypothesized 4EBP1 is prime therapeutic target. Dr. Gene Budger may be used to help identify drugs that regulate the expression of 4EBP1

(http://amp.pharm.mssm.edu/DGB/)369-371. Indeed, compounds can affect the transcription of 4EBP1, like polyamines which have been shown to directly interact with the 4EBP1 gene209. The crystal structure of 4EBP1 bound to EIF4E is available

(https://www.ncbi.nlm.nih.gov/Structure/pdb/4UED) and may serve as a template for efficient drug design for 4EBP1 inhibitors116. There is invested effort in understanding the finer details of the 4EBP1 protein such as regions with high propensity to form ordered, secondary structures372-374. The 4EBP1 C-terminal extension (motif 3) was shown to be critical to mediated 4EBP1 cell cycle arrest and it partially overlaps with the binding site of the EIF4E/EIF4G interaction inhibitor 1 (4EGI-1)375. 4EGI-1 and 4E1RCat (blocks the interaction of EIF4E:4EBP1) target cap-dependent protein translation initiation and are being explored in cancer; studies should test for promotion of further aneuploidy in cells because this was demonstrated in oocytes using 4EGI-1175. Rotterlin was used to inhibit protein synthesis and promote cell death in melanoma cells, where it was tied to 4EBP1 regulation376. Pathenolide and inhibitors targeted to PI3K have been shown to affect

4EBP1 expression and are thought to be effective in multiple cancer types377,378. Rosen and colleagues highlighted combined inhibition by AKT and MEK pathways to inhibit 133

4EBP1 phosphorylation and tumor growth, but noted knockdown of MAP kinase interacting kinases (MNKs) did not alter 4EBP1 function267. Oftentimes, compounds can decrease phosphorylated 4EBP1, but rarely total 4EBP1 levels. Compounds that can safely decrease total 4EBP1 levels could give much extra time to suffering patients.

Many studies have suggested 4EBP1 to be a predictive factor of clinical response to inhibitors. This is usually the role of phosphorylated 4EBP1 as a biomarker of general response in patient tumors. 4EBP1 is also associated with response to chemotherapy and radiotherapy178,379-381, and expression is important to other tumor types like prostate cancer382. Hsieh, Ruggero, and colleagues further showed 4EBP1 primed sensitivity or resistance to inhibitors of the PI3K/AKT/mTOR pathway in mouse model of prostate cancer with PTEN loss. They showed 4EBP1 abundance was different in different types of normal prostate epithelial cells, and cancerous luminal epithelial cells demonstrated the highest transcript and protein levels of 4EBP1 despite the lowest protein synthesis rates which mediated resistance to the PAM pathway inhibitor

MLN0128. They went on to show that decreasing 4EBP1 abundance could reverse drug resistance in drug sensitive cells and that high 4EBP1 was commonly found in prostate cancer patients treated with the PAM inhibitor BKM120, further supporting that elevated

4EBP1 expression is necessary for drug resistance. It should be noted that this study highlighted that other factors besides 4EBP1 dictate protein synthesis rates and that the phosphorylation of 4EBP1 was similar in both basal and luminal epithelial models used383. A recent report in patient xenograft models of triple-negative breast cancer exposed to the pan-PI3K inhibitor showed that all models used 4EBP1 (This can be visualized using the Omics Data Browser: http://prot-shiny- vm.broadinstitute.org:3838/BKM120/ with password: BKM120viewer!)384. Kinases that

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modify 4EBP1 are current drug targets, but we do not thoroughly understand post- translational modifications of 4EBP1, especially in cancer.

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Chapter VI

APPENDIX

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The 8p11-12 amplicon candidate oncogenes in breast cancer

Data mining results of 8p11-12 oncogenes in cancer

Many prior and current efforts aim to understand critical oncogene targets within the 8p11-12 genomic locus. In 2006, Nusbaum and colleagues proposed the distal 8p locus was crucial to homo sapien evolution385 and since then, much evidence has been presented supporting a role for 8p11-12 in the nervous system and cancer. Malignant tumors frequently amplify and/or overexpress genes on the 8p11-12 locus, especially breast cancers with poor prognosis. Although there is no doubt that the 8p11-12 genomic region harbors critical genes, the essential genes and pathogenic mechanism of this region remain undefined. We previously defined about 70 breast cancer candidate oncogenes and then narrowed the list to 22 candidate oncogenes based on previous data, curated literature, and patient tumor data (Figure 6). This bias method yielded interested findings as displayed through mined tumor copy number amplification differences, gene expression differences, and gene essentiality predictions across breast cancer cell lines and subtypes among these genes. These results are shown here and discussed in parallel with gene function. Druggable targets and potential applications in clinical diagnostics are highlighted. In correlation with our experimental data, we find EIF4EBP1 (4EBP1) highly performs in comparison to other tested genes.

Innovative, parallel, and broader findings should be presented to optimize what is presented here.

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Results of candidate oncogenes explored by data mining

Although UNC5D is reported in a peak by GISTIC, UALCAN confirmed that transcript per million (TPM) values for UNC5D are extremely low in breast tissue, so further analysis of this gene was eliminated (Figure 36A), but we did report the recurrence free survival plot across breast tumors as predicted by KM plotter (Figure

36B), and the gene essentially results reported by the DepMap portal (latest release of

436 cell lines screened with the Avana 1.0 library, source: Broad, release: 2018Q2) which showed the UNC5D CERES dependency score was often greater than 0 indicating UNC5D is not an essential gene in most of the cancer cell lines present in the cohort (Figure 36C). There are 7 alternatively spliced transcript variants for this gene, but no substantial evidence exists to suggest it is a driver of the 8p11-12 amplicon.

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Figure 36. Unc-5 netrin receptor D (UNC5D). Also known as KIAA1777 and Unc5h4. (A) UALCAN shows Transcript Per Million (TPM) values for this gene are considered extremely low (TPM<1) in both normal (blue) and breast tumor samples (red) from The Cancer Genome Atlas (TCGA) data. (B) Recurrence Free Survival (RFS) plots generated using KM plotter are shown for low (black) and high (red) expression tumors. (C) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood UNC5D is essential in that cell line (0 = not essential / - 1 = median of all pan-essential genes).

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ZNF703 is also in a GISTIC peak and amplified as part of the A1 sub-region of

8p11-12 amplification. ZNF703 is a zinc-finger binding protein that is known to regulate cell adhesion, migration, and proliferation. Evidence shows ZNF703 behaves as an oncogene in many cancer types, including breast cancer65,67,68,73,386-390 where targeted overexpression has been used to mimic 8p11-12 amplification391. There are no reported alternatively spliced transcript variants for this gene. UALCAN gene expression analysis highlights overexpression frequently occurs in luminal tumors (Figure 37A). No results were found using the KM plotter tool. The DepMap portal shows a dependency score less than 0 indicating ZNF703 is an essential gene in many cancer cell lines (Figure

37B). Additionally, the DepMap portal shows many translocations in breast cancer cell lines.

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Figure 37. Zinc Finger Protein 703 (ZNF703). Also known as ZNF503L, ZEPPO1, NLZ1, Zpo1, and FLJ14299. Also known as ZNF503L, ZEPPO1, NLZ1, Zpo1, and FLJ14299. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple- negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. Recurrence Free Survival (RFS) plots could not be generated using KM plotter. (B) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood ZNF703 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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The ERLIN2 profile is similar to ZNF703 albeit not as pronounced. ERLIN2 is a member of the SPFH domain-containing family of lipid raft-associated proteins and is often localized to lipid rafts within the endoplasmic reticulum where it functions in inositol

1,4,5-trisphosphate (IP3) signaling. There are reports of ERLIN2 as an important gene in breast cancer67,71,392,393. There are 11 alternatively spliced transcript variants encoding multiple isoforms for this gene. Lower median gene expression levels are observed in luminal tumors and triple-negative tumors (Figure 38A). Low gene expression also shows poor recurrence free survival across breast tumor subtypes (Figure 38B) as well as luminal tumors (Figure 38C). DepMap portal results show some cancer cell lines are potentially dependent on ERLIN2 (Figure 38D).

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Figure 38. ER lipid raft associated 2 (ERLIN2). Also known as C8orf2, SPG18, NET32, Erlin-2, and SPFH2. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood ERLIN2 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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The PLPBP (PROSC) profile varies, whereby significant loss of expression is also witnessed in breast cancers compared to normal samples. This pyridoxal 5'- phosphate binding protein is involved in homeostatic regulation of intracellular pyridoxal

5'-phosphate. It has a demonstrated tumor suppressor role in cancer394 yet remains a predictor of 8p11-12 amplification395. There are 9 alternatively spliced transcript variants for this gene. Median gene expression levels are significantly lower in luminal and triple- negative breast tumors (Figure 39A). Varying recurrence free survival curves between all tumors (Figure 39B) and luminal tumors (Figure 39C) show high expression can confer poor prognosis in luminal tumors but not combined with other subtypes. The

DepMap portal predicts PLPBP may be essential in multiple types of cancer, so the dual tumor suppressor and oncogene roles should be further elucidated.

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Figure 39. Pyridoxal phosphate binding protein (PLPBP). Also known as PROSC. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood PROSC is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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A varied gene expression rate is also observed across breast cancers for BRF2

(Figure 40A). BRF2 is a subunit within the RNA polymerase III transcription factor complex with reports highlighting significance in breast cancer396-399. The probability of

Recurrence Free Survival (RFS) over time using KM plotter could not be found. The

DepMap portal shows BRF2 is a pan-essential (housekeeping) gene within most cancer cell lines (Figure 40B), making it a poor therapeutic target. BRF2 has 4 alternative transcript variants.

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Figure 40. RNA polymerase III transcription initiation factor subunit (BRF2). Also known as TFIIIB50, FLJ11052, BRFU. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood BRF2 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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Luminal breast tumors have significant overexpression of RAB11FIP1 when breast cancers are grouped by clinicopathologic subtype (Figure 41A). RAB11FIP1 has a role in vesicle recycling, sorting, and trafficking. It has 6 alternatively spliced transcript variants. The prognostic potential for high RAB11FIP1 expression is different among different breast cancer subtypes (Figure 41B & 41C). The DepMap portal finds few cancer cell lines where RAB11FIP1 is essential (Figure 41D) regardless of primary findings showing importance to breast cancer70,77,400,401.

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Figure 41. RAB11 family interacting protein 1 (RAB11FIP1). Also known as FLJ22524, Rab11-FIP1, RCP, FLJ22622. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood RAB11FIP1 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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Although we have studied many genes within the 8p11-12 region in detail,

EIF4EBP1 (4EBP1) is an extremely important gene to cells with 8p11-12 amplification and as well as any other breast cancer cell model we have studied, but not important to normal breast cell proliferation. We have defined 4EBP1 according to clinicopathological parameters available in UALCAN (Figure 20), by patient prognostic values in KM plotter

(Figure 22), and through cancer cell lines in the DepMap portal (Figure 42). Exploring gene essentiality screens using the DepMap portal, shows that 4EBP1 is essential in almost every cancer cell line available in the portal, but is not considered a pan-essential gene. This supports our findings reported here, which show that 4EBP1 is only essential to the proliferation of breast cancer cells, but not normal mammary cells. 4EBP1 has 2 potential transcript variants, but 1 isoform is thought to be predominately expressed.

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Figure 42. Eukaryotic Initiation Factor 4E-Binding Protein (EIF4EBP1). Also known as 4EBP1, PHAS-I, and 4E-BP1. The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood 4EBP1 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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We have also more closely studied the adjacent gene ASH2L, which has extremely varied expression rates and is significantly altered in only luminal tumors

(Figure 43A); this was specific to women, not men. This may be due to highly fluctuating expression levels which are witnessed and may also contribute to less pronounced recurrence free survival prediction rates (Figure 43B & 43C). Albeit, the DepMap portal results do show that ASH2L behaves similarly to 4EBP1, with both having optimal dependency scores across cancer cell line models (Figure 43D). Inconstancies in gene expression may also be partially explained by the 12 reported alternative transcript variants. We witness high expression of 2 predominate isoforms in some of our models, whereby we have constructed both of these cDNA clones for further experimentation. As an essential epigenetic regulator, ASH2L influences the expression of many genes within cells, including the estrogen receptor272.

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Figure 43. ASH2 like histone lysine methyltransferase complex subunit (ASH2L). Also known as ASH2L1, Bre2, ASH2L2, ASH2. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood ASH2L is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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LSM1 is a member of the LSm RNA-binding protein family which are known to interact with U6 snRNA. The gene has 5 alternative transcript variants, and high LSM1 expression is known to contribute to transformation and malignant progression in multiple types of cancer, including breast66,72,402. Similar to 4EBP1, LSM1 is significantly overexpressed across breast cancer cell lines compared to normal samples (Figure

44A) and high expression confers poorer survival rates in breast tumors (Figure 44B &

44C). Although the DepMap portal results show LSM1 is essential to many cancer cell lines, the profile is not as striking across cancer cell types as 4EBP1 (Figure 44D).

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Figure 44. LSM1 homolog, mRNA degradation associated (LSM1). Also known as CASM, YJL124C. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple- negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood LSM1 is essential in that cell line (0 = not essential / -1 = median of all pan- essential genes).

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LSM1 and BAG4 share overlapping sequence orientation and our lab previously showed they worked together with TCIM66, but these all have very different transcriptional rates in patient presentation. With 4 alternative transcript variants, BAG4 seems to have a highly variable gene expression profile, with significance in only luminal cells (Figure 45A). This may be tied to poor prognostic significance (Figure 45B &

45C). The known capacity of BAG4 (a BAG1 related family member) to influence apoptosis may be context dependent. Regardless, the DepMap portal shows BAG4 is essential is some cancer cell lines (Figure 45D).

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Figure 45. BCL2 associated athanogene 4 (BAG4). Also known as SODD. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood BAG4 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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DDHD2 is a phospholipase known to influence membrane trafficking and lipid levels. DDHD2 has 24 transcript variants, and our lab previously showed that DDHD2 was a potent transforming oncogene67. I explored DDHD2 knockdown in the SUM-52 and SUM-44 breast cancer cell lines and found that knockdown could inhibit proliferation of these cell lines (data not shown) but noted that DDHD2 knockdown did not noticeably reduce proliferation as efficiently as 4EBP1 knockdown. We were interested in DDHD2 because it shares overlapping gene sequence orientation with NSD3, and the overlapping portion is approximately the sequence difference between the long and short isoforms of NSD3. Interestingly, DDHD2 gene expression is significantly lost in

HER2 and triple-negative tumors (Figure 46A), while KM plotter results show little prognostic potential with differing expression thresholds (Figure 46B & 46C).

Additionally, DepMap dependency scores on average hover around 0 across cancer cell lines (Figure 46D).

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Figure 46. DDHD domain containing 2 (DDHD2). Also known as SPG54, KIAA0725, SAMWD1. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple- negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood DDHD2 is essential in that cell line (0 = not essential / -1 = median of all pan- essential genes).

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PLPP5 (PPAPDC1B) harbors 15 transcript variants and is a phospholipase with known implications in breast cancer64,403. It is over expressed in all subtypes (Figure

47A), but high expression only acts a prognostic indicator in luminal tumors (Figure 47B

& 47C). PLPP5 is an essential gene in about half of the cancer cell lines in the DepMap portal (Figure 47D).

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Figure 47. Phospholipid phosphatase 5 (PLPP5). Also known as DPPL1, HTPAP, or PPAPDC1B. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple- negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood PPAPDC1B is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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We are very interested in NSD3 (WHSC1L1), have previously shown it influences the estrogen receptor97, and can promote mammary hyperplasia, dysplasia, and invasive ductal carcinoma in transgenic mice98. Others have also shown NSD3 is important in breast cancer403-405. NSD3 is methyltransferase with known histone modifying function and 12 alternative transcripts. UALCAN shows luminal tumors significantly alter NSD3 gene levels (Figure 48A), while high gene expression culminates in higher probability of recurrence free survival across all tumor subtypes (Figure 48B & 48C). DepMap portal dependency scores on average are greater than 0 for most cell lines, indicating NSD3 may not always be essential (Figure 48D).

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Figure 48. Nuclear receptor binding SET domain protein (NSD3). Also known as WHISTLE, WHSC1L1, FLJ20353, KMT3F. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood NSD3 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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FGFR1, a receptor tyrosine kinase, is a well studied gene and often the clinical biomarker used for 8p11-12 detection. The data presented here calls into question why this gene was chosen for this setting, especially with 41 alternative gene transcripts.

FGFR1 is a druggable target but evidence indicates it is most effective if other FGFR family members are present. Current clinical trials for FGFR inhibition in breast cancer are shown in Table 7. We mined the other members and only FGFR3 or FGFR4 showed significant overexpression compared to normal using UALCAN (data not shown). Figure

49A shows that FGFR1 gene expression is signifincantly lower across breast cancer cell lines compared to normal. The recurrance free survival plots for FGFR1 also do not indicate poor prognosis for high versus low expression (Figure 49B & 49C). The

DepMap portal results show FGFR1 is pan-essential in some cancer cell lines and on average it is an essential gene in cancer cell lines (Figure 49D). FGFR1 remains a promising drug target406 but not for detection of 8p11-12 amplification as related here.

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Table 7. Recruiting clinical trials for FGFR inhibition in breast cancer.

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Figure 49. Fibroblast growth factor receptor 1 (FGFR1). Also known as BFGFR, CD331, H3, H4, N-SAM, H2, FLG, CEK, FLT2, KAL2, H5. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood FGFR1 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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TACC1 is located close to FGFR1, has 29 alternative transcripts, and expression is significantly decreased in each subtype compared to normal (Figure 50A). Low expression does confer poor prognosis compared to high expression in breast tumors

(Figure 50B & 50C), and some cancer cell lines are dependent on TACC1 (Figure

50D). TACC1 forms many fusions and translocations, especially with the rest of the

8p11-12 amplicon genes including UNC5D, DDHD2, and FGFR1. TACC1 is implicated in breast cancer407-412, estrogen regulation413, and PI3K inhibition414.

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Figure 50. Transforming acidic coiled-coil containing protein 1 (TACC1). (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood TACC1 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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ADAM9 levels are significantly altered in luminal and triple negative tumors but not extensively (Figure 51A). ADAM9 has 9 alternative transcript variants and is a member of the ADAM family which have a disintegrin and metalloprotease domain.

ADAM9 is implicated in cancer, including breast cancer415-422 and drug resistant breast cancer cells423. High expression does confer poor prognosis in all breast cancer subtypes (Figure 51B) including luminal tumors (Figure 51C). ADAM9 is also essential in about half of the cancer cell lines currently profiled in the DepMap portal (Figure 51D).

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Figure 51. ADAM metallopeptidase domain 9 (ADAM9). Also known as KIAA0021, CORD9, MDC9, MCMP, Mltng. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood ADAM9 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes.

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ADAM32 has significant loss in expression in the HER2 subtype (Figure 52A).

With 11 alternative transcript isoforms, ADAM32 is predominately expressed in testes424 but I am not aware of published studies focused on ADAM32 in cancer. ADAM32 is not a good prognostic indicator (Figure 52B & 52C), and it is not often essential in cancer cells (Figure 52D).

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Figure 52. ADAM metallopeptidase domain 32 (ADAM32). (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood ADAM32 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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IDO1 (9 transcript variants) and IDO2 (4 transcript variants) represent major drug targets in the immunotherapy field which are involved in tryptophan metabolism. IDO2 is not expressed at high levels in breast cancer (Figure 53) whereas IDO1 is significantly overexpressed in breast tumors, especially triple-negative breast tumors (Figure 54A).

Reports do suggest IDO1 is important to breast cancer425-437 including ER- tumors, yet

KM plotter results do not show any significant impact on prognosis in ER- tumors (data not shown) or across all subtypes (Figure 54B). On the contrary, low IDO1 expression does confer poor prognosis in ER+ tumors (Figure 54C). Additionally, the DepMap portal suggests IDO1 is not essential to most cancer cell lines with a dependency score higher than 0 (Figure 54D).

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Figure 53. IDO2 gene expression in the TCGA-BRCA cohort. IDO2 is not highly expressed in breast tumors.

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Figure 54. Indoleamine 2,3-dioxygenase 1 (IDO1). Also known as INDO or IDO. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood IDO1 is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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TCIM (C8ORF4) levels are significantly decreased across subtypes (Figure 55A) and, with 1 transcript, C8ORF4 has a role in breast cancer66,438 especially to influence the Wnt/beta-catenin signaling pathway. C8ORF4 does not drastically alter survival prognosis in breast tumors (Figure 55B & 55C), but some cancer cell lines in the

DepMap portal are reliant on C8ORF4 (Figure 55D).

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Figure 55. Chromosome 8 open reading frame 4 (TCIM). Also known as C8ORF4, TC-1, hTC-1, and TC1. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood TCIM is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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KAT6A (MYST3) levels are not significantly altered in breast cancer compared to normal samples (Figure 56A) yet altered expression levels do influence breast tumor prognosis (Figure B & C) and many breast cancer cell lines from the DepMap portal are reliant on KAT6A (Figure 56D). KAT6A is an acetyltransferase with 8 alternative transcript variants and a known role in breast cancer75,96,439. We overexpressed KAT6A in mouse mammary glands, but did not witness an adverse phenotype, contrary to

NSD3.

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Figure 56. Lysine acetyltransferase 6A (KAT6A). Also known as ZNF220, RUNXBP2, MOZ, ZC2HC6A, and MYST3. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from all of the breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood KAT6A is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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Inhibitors are available for IKBKB and it represents a significant oncogenic target in luminal tumors (Figure 57A), but low expression differences confer poor prognosis in all breast tumors (Figure 57B), including ER+ tumors (Figure 57C). IKBKB, harboring

30 alternative transcript variants, is often found in a complex that is known to phosphorylate the inhibitor in the inhibitor/NF-kappa-B complex which causes dissociation of the inhibitor and activation of NF-kappa-B. IKBKB has a role in breast cancer and is essential in many cancer cell lines, including about half of the breast cancer cell lines in the DepMap portal (Figure 57D).

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Figure 57. Inhibitor of nuclear factor kappa B kinase subunit beta (IKBKB). Also known as NFKBIKB, IKK-beta, IKKB, and IKK2. (A) UALCAN shows Transcript Per Million (TPM) values for this gene in normal samples (blue) and breast tumor samples (Luminal = orange, Her2+= green, and Triple-negative = brown) from The Cancer Genome Atlas (TCGA) data. Significance is reported and was generated by UALCAN. (B) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from breast tumor samples. High expression is shown in red and low expression is shown in black. (C) The probability of Recurrence Free Survival (RFS) over time were generated using KM plotter from only ER+ tumors as defined by either gene expression or immunohistochemistry. (D) The DepMap portal shows the latest Broad release of 436 cell lines screened with the Avana 1.0 library. The larger the circle the higher the expression, damaging mutations are shown in red, and missense mutations are shown in orange. The Dependency Score (CERES) is based on data from the depletion assay where a low score suggests higher likelihood IKBKB is essential in that cell line (0 = not essential / -1 = median of all pan-essential genes).

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Summarized gene ontologies for 8p11-12 gene involvement in cancer transformation processes

Summarized gene ontology findings for 8p11-12 genes tie the 8p11-12 amplicon to key processes in cancer transformation, such as growth factor independence, anchorage independence, the tumor initiating cell (TIC or stem cell) phenotype, and bypassing senescence.

Genes from the amplicon are involved in growth factor response (FGFR1 and

EIF4EBP1 gene ontology in insulin response [GO:0032868;GO:0032869] and BAG4 in

EGF response [GO:0070849]346). Oftentimes members can influence proliferation through MAPK signaling by growth factors and other cytokines (C8ORF4346,440 and

BAG4441 to influence Raf kinase; ADAM9 and FGFR1 gene ontologies in MAPK signaling [GO:0000186; GO:0043410;GO:0000165]346).

Anchorage-independent growth confers neoplastic transformation by a series of changes that promote growth and proliferation independent of attachment to a surface.

We have shown that genes from the 8p11-12 region help mammary cells escape anoikis67. Genes involved in cell migration (BAG4, ZNF703, and ADAM9

[GO:2000147;GO:0030335; GO:0051272]346) may play important roles towards this capacity.

The 8p11-p12 amplicon may promote the TIC phenotype to contribute to relapse.

Many pathways involved in DNA damage response and senescence also contribute to

TIC stemness, such as p21, TP53, and CyclinD2442,443. There are many regulators of breast cell self-renewal153,154,444,445 Evidence exists in primary HMECs that implicate

ZNF703 in CTNNB1 up-regulation and E2F3 overexpression.346,446 Similarly,

ASH2L346,447 and C8ORF4438 are implicated in WNT regulation and signaling, and many 182

8p gene ontologies include stem-like processes (FGFR1 in stem cell division

[GO:0048103; GO:0017145] and mesenchymal cell differentiation [GO:0048762],346

KAT6A in stem cell maintenance [GO:0035019], and ZNF703 in stem cell differentiation

[GO:2000736;GO:2000738]346).

The 8p11-p12 amplicon likely influences senescence. Most cells have a limited proliferative lifespan. Irreversible cell senescence restricts the number of cellular divisions while maintaining cells in a viable state322-327; this state can sequentially progress from cell cycle arrest to permanent arrest to cell death234,328,329. It must be overcome to promote tumorigenesis and confer immortality. Monitoring senescent hallmarks, such as senescent-associated beta-galactosidase accumulation336-339, alongside cell cycle markers340-345 are used to assess any ability to bypass phases of cell cycle and overcome senescence. Some 8p11-12 genes are known to influence cell cycle fate (such as NSD3404,448 and EIF4EBP1 [GO:0000082;GO:0045931],346 genes implicated in epithelial cell differentiation like ADAM9, ZNF703 [GO:0060644;

GO:0030855],346 and the other epigenetic modifiers: NSD3, ASH2L, and KAT6A;

(KAT6A is also important to cellular senescence and aging [GO:0090398;GO:0007569;

GO:0007568]346)).

Available lentiviral 8p11-12 gene expression plasmids for future studies

To engineer breast cancer cells that overexpress 8p11-12 genes, the lentiviral gene expression plasmids in Table 8 were constructed. This includes various cDNA constructs with or without a stop codon that are present in different plasmid backbones for co-expression studies. The different backbones bear different antibiotic selection markers for individual, dual, or triple selection of engineered cells. In our experience, very high titer virus is necessary for co-expression studies, albeit cells were efficiently 183

engineered to overexpress individual genes. We noted that constructs without a stop codon were not always detected with the antibody used for detection of the native protein.

Table 8. Available lentiviral cDNA expression plasmids

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Discussion of 8p11-12 data mined results

Here we have attempted to better understand hypothesized drivers of this region by studying trends in primary breast tumor samples, relating this to the behavior in cancer gene essentiality screens across cancer types, and translated these findings to application. This curation has identified profound inconsistencies between the behavior of these individual genes in a clinical application and provide an enhanced understanding of significant biomarkers and targets for translational benefit. There is much individual and coordinating gene laboratory evidence that implicates many genes from this region, but it is important to remember these efforts are specific and complicates the picture. We ourselves have focused on individual genes within 8p11-12, even in broader efforts to understand many genes from the region. Here we have consolidated 8p11-12 candidate oncogenes and applied data mining of patient tumors and cancer cell lines to contribute towards a foundation for future efforts. The innovative data mining resources used can be easily adopted by the research community to relate pre-clinical design to translational genomics in many settings.

This region includes many genes that are co-amplified together, but the trends in transcript expression levels are not the same for these genes in breast tumors. As seen here, some genes are not significantly amplified compared to normal tissue or expression is even lost compared normal tissue. The genes from this region display striking dissimilarities in their expression profiles. This may be due to the amplification event and many hypotheses are available to explain mechanisms governing the generation of amplicons, but this remains an inconclusive area. Regardless, these expression differences must be accounted in the diagnostic setting.

185

Limitation exists regarding our capacity to draw definitive conclusions from pieced together resources. Bias is a limiting factor in much of this inconsistency. If we are going to make strides in this patient population we need uniform collaborative efforts.

Consistency is not a common theme among the primary literature for each of these genes which dilutes the picture. Here we have attempted to spearhead this initiative in a crude way with the hope that future studies regarding these genes have a broader working knowledge base and can fill in any lack of evidence for further applications.

Dedicated efforts continue to demonstrate the importance of the 8p11-12 amplicon in breast cancer presentation, particularly endocrine resistant tumors.

Innovative technologies in various settings continue to build upon 8p11-12 importance in cancer, and efforts are underway to understand allelic frequency under normal settings.

We have confirmed 4EBP1 oncogenic capacity in multiple breast cancer cell types with or without the 8p11-12 amplicon and showed 4EBP1 is not essential to normal cell models, highlighting 4EBP1 use in translational settings.

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BIOGRAPHICAL SKETCH

Alexandria Colett Rutkovsky was born in Des Plaines, IL on September 21, 1988.

After attending high school at Mooresville Senior High School in Mooresville, NC, she obtained a Bachelors of Science in Chemistry from Appalachian State University with a minor in biology. She began working in Dr. Ece Karatan’s laboratory in collaboration with Dr. Claudia C. Cartaya-Marin in 2008, where she realized her passion for research.

Upon completing a Masters of Science in Molecular and Cell Biology at Appalachian

State University in 2012, she pursued her doctoral degree in Biomedical Medicine at the

Medical University of South Carolina. After rotating through the labs of Dr. Stephen

Ethier and Dr. Patrick Woster for a working collaboration, she officially joined the

Pathology and Laboratory Science Department in the laboratory of Dr. Stephen Ethier in

2013 and has also studied under the direction of Dr. Robin Muise-Helmericks since

2016.

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