Elucidating the Molecular Mechanism of FZD5 Essentiality in RNF43 Mutant Pancreatic Cancer

Elucidating the Molecular Mechanism of FZD5 Essentiality in RNF43 mutant Pancreatic Cancer

by Meng Zhang

A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Pharmaceutical Sciences University of Toronto

© Copyright 2020 by Meng Zhang Abstract Elucidating the Molecular Basis of RNF43 Mutant Pancreatic Cancer, Master of Science, 2020, Meng Zhang, Department of Pharmaceutical Sciences, University of Toronto

Our laboratory recently identified FZD5 as the sole essential FZD, out of ten human FZD receptors, in RNF43 mutant pancreatic cancers. My work explores two hypotheses behind this finding. First, we hypothesized that FZD5 is the preferential RNF43 substrate. Alternatively, FZD5 may preferentially recognize the Wnt ligand involved in PDAC, and as such RNF43 mutations lead to increased activity of this Wnt-Frizzled signalling circuit. I quantified FZD levels in control cells and in cells harbouring a RNF43 mutation and and observed a broad negative regulation role for RNF43 towards FZD receptors. Then I examined the role of FZD5 as the main FZD responsible of transmitting endogenous Wnt signalling in pancreatic cancer. There, I obtained evidence that the N-terminus ligand binding domain of FZD5 was required for the growth of PDAC cells. Together, my work highlights that the FZD5 essentiality can be attributed to preferential FZD receptor-Wnt ligand selectivity.

Acknowledgements

I would like to thank my research group, without whom all of this wouldn’t have been possible. Thank you to my supervisor, Dr. Stephane Angers. Thank you to my past and current labmates for making these three years an adventure I will never forget.

I would also like to thank my committee members Dr. Carolyn Cummins and Dr. Thomas Kislinger for their scientific guidance as well as all staff and members of the graduate department of Pharmaceutical Sciences.

Lastly, to my family and friends who supported me through the ups and downs of my master’s degree, thank you!

iii

Table of Contents Abstract ii Acknowledgements iii List of Figures vii List of Tables viii Abbreviations ix Chapter 1: Introduction 1 1.1 Pancreatic cancer 1 1.1.1 Epidemiology of pancreatic cancer 1 1.1.2 Major risk factors of pancreatic cancer 1 1.1.3 Pathogenesis 2 1.1.4 Genetic background of pancreatic cancer 4 1.1.5 Current treatment options 5 1.2. Wnt signalling 6 1.2.1 Historical origins 6 1.2.2 Beta-catenin dependent pathway 6 1.2.3 Wnt in development, stem cell differentiation and tissue homeostasis 9 1.2.4 Wnt in cancer 10 1.2.5 Pharmacological Inhibitors in Wnt dependent cancers 12 1.3. The regulation of the canonical Wnt signalling pathway 14 1.3.1 Wnt ligands 14 1.3.2 Frizzled receptors 15 1.3.3 RNF43 and ZRNF3 16 1.3.4 R-spondin 17 1.3.5 Dishevelled 17 1.4. Genetic vulnerabilities of RNF43 mutant pancreatic cancer 19 1.4.1 FZD5 essentiality 19 1.4.2 Wnt7B, Wnt10A and Wnt3 essentiality 19 1.5. Mass spectrometry 20 1.5.1 Mass spectrometry and proteomics 20 1.5.2 Targeted mass spectrometry 21 1.5.3 Quantitative proteomics 22

1.5.3.1 Absolute and relative quantification 22 1.5.3.2 Stable isotope labelling and label-free quantification 22 1.5.4 Parallel reaction monitoring 23 1.5.5 Steps of a PRM experiment 24 1.5.5.1 Selection of target peptides 24 1.5.5.2 Building a PRM data acquisition method 24 1.5.5.3 Using labelled peptides as internal standards 26 1.5.6 Common applications of PRM 26 Chapter 2: Project Rationale 28 Chapter 3: Materials and methods 29 3.1 Plasmids 29 3.2 Cell lines 29 3.3 Cell culture 30 3.4 Sample preparation for PRM mass spectrometry 30 3.5 Liquid chromatography 30 3.6 PRM mass spectrometry 31 3.7 PRM data processing 31 3.8 Lentivirus production and infection 31 3.9 Immunofluorescence 32 3.10 TIDE analysis 32 3.11 Crystal violet staining proliferation assay 32 3.12 RSPO media preparation 33 3.13 Statistics 33 3.14 Heavy labelled peptide calibration curve creation 33 3.15 Peptide Cleanup 34 Chapter 4: Results 35 4.1 Development of immunoprecipitation coupled parallel reaction monitoring mass spectrometry for FZD quantification. 35 4.2 Determination of the lower limit of detection, lower limit of quantification and linear range of each signature peptide. 37 4.3 Validation of immunoprecipitation by PRM-MS 39 4.4 Effect of RNF43 knockout and RSPO treatment on FZD1, 2, 5, 7 and 8 levels in wild-type YAPC by PRM-MS 41 4.5 Effect of DVL knockout on FZD1, 2, 5, 7 and 8 in wild-type HEK 293T by PRM-MS 43

4.6 Wnt-FZD subtype specificity determines Wnt signalling in HPAF-II cells 44 Chapter 5: Discussion 48 5.1 Development of a parallel reaction monitoring mass spectrometry technique to quantify FZD receptors 48 5.2 Elucidating the role of RNF43 and RSPO on FZD expression 49 5.3 Elucidating DVL’s role in FZD regulation 49 5.4 Validating the Wnt-FZD signalling circuit required in RNF43 mutated PDAC cells. 50 Chapter 6: Perspectives 52

List of Figures

Figure 1: The Wnt Signalling Pathway

Figure 2: Development of Immunoprecipitation-Coupled Parallel Reaction Monitoring Mass Spectrometry for FZD Quantification.

Figure 3: Determination of Lower Limit of Detection, Lower Limit of Quantification and Linear Range of Each Signature Peptide.

Figure 4: Validation of Immunoprecipitation by PRM-MS

Figure 5: Effect of RNF43 -/- and RSPO on FZD1, 2, 5, 7 and 8 Levels in Wild-Type YAPC by PRM-MS

Figure 6: Effect of DVL Knockout on FZD1, 2, 5, 7 and 8 Levels in Wild-Type HEK 293T by PRM-MS

Figure 7: Role of FZD5 and FZD7 CRD in Transducing Wnt Signalling in HPAF-II

Figure 8: Representative PRM-MS Traces

Figure 9: Representative TIDE Chromatograms

vii List of Tables

Table 1: List of CRISPR Guides

Table 2: List of PCR Primers

Table 3: PRM-MS Signature Peptides

Table 4: LLOD and LLOQ of Signature Peptides

viii Abbreviations

APC: Adenomatous polyposis coli BSD: Blasticidin CK1α: Casein kinase-1 alpha CRC: Colorectal Cancer CRD: Cysteine rich domain DAPI: 4’, 6-diamidino-2-phenylindole DMEM: Dulbeccos modified eagle medium DOX: Doxycycline DVL: Dishevelled DTT: Dithiothreitol EDTA: Ethylenediaminetetraacetic Acid EGTA: Ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid ESC: Embryonic stem cell FBS: Fetal bovine serum FZD: Frizzled Gla: Glazed GSK3β: Glycogen synthase kinase-3 beta HEK: Human embryonic kidney HCD: High collision dissociation IPMNs: Intraductal papillary mucinous neoplasms KRAS: Kirsten rat sarcoma LEF: Lymphoid enhancer factor LIF: Leukemia inhibitory factor LGR: Leucine-rich repeat containing G-protein coupled receptor 5 LLOD: Lower limit of detection LLOQ: Lower limit of quantification LRP: Lipoprotein receptor-related protein MCNs: Mucinous cystic neoplasm MS: Mass spectrometry

ix MRM: Multiple reaction monitoring m/z: Mass to charge PanINs: Pancreatic intraepithelial neoplasia PBS: Phosphate buffered saline PCP: Planar cell polarity PCR: Polymerase chain reaction PDAC: Pancreatic ductal adenocarcinoma PORCN: Porcupine PRM: Parallel reaction monitoring PTM: Post-translational modification QQQ: Triple quadrupole Q-OT: Quadrupole orbitrap Q-TOF: Quadrupole time of flight RSPO: R-spondin RNF43: Ring finger protein 43 SRM: Selected reaction monitoring TCF: T-cell factor TIDE: Tracking of indels by decomposition TLE: Transducin-like enhancer of split 1 TP53: Tumor protein 53 Wg: Wingless ZNRF3: Zinc and ring finger 3

x Chapter 1: Introduction

1.1 Pancreatic cancer

1.1.1 Epidemiology of pancreatic cancer

Globally, pancreatic cancer is considered a rare cancer, ranking 14th in its incidence rates (Bray et al., 2018). However, it accounts for 4.5% of total cancer deaths, making it the 7th cause of cancer death. In Canada, pancreatic cancer is the 4th leading cause of cancer death and its five-year survival rate is at an astonishing low of 8%. The low incidence rate combined with the high mortality rate of pancreatic cancer highlights the deadly nature of the disease. Due to the lack of symptoms and the proximity of blood vessels allowing easy spread of the tumor, diagnosis of pancreatic cancer most often occurs in patients that already present with either locally advanced or metastatic disease (McGuigan et al., 2018). Therefore, 80%-85% of patients tumours are deemed unresectable and with surgical tumor removal being the sole potential “cure” for pancreatic cancer, the outcome for patients with pancreatic cancer remains grim.

Pancreatic carcinoma can be separated into two distinct categories based on the origin of the tumor tissue: exocrine pancreatic neoplasms and endocrine pancreatic neoplasm. Exocrine pancreatic neoplasm constitutes upwards of 95% of pancreatic cancer cases and include all tumors related to the pancreatic duct, the acinar cells and their stem cells. Among these tumors, pancreatic ductal adenocarcinoma is by far the most prevalent with over 95% of pancreatic cancers falling in this category (Stark and Eibl). Therefore, for the purpose of this work, pancreatic ductal adenocarcinoma and pancreatic cancer will be used interchangeably.

1.1.2 Major risk factors of pancreatic cancer

Numerous risk factors have previously been studied for their link with pancreatic cancer and they can be separated into non-modifiable and modifiable risk factors. The biggest risk factor in the modifiable risk factor group is smoking. Smokers have a 74% increase

1 in risk of developing pancreatic cancer when compared to nonsmokers (Iodice et al., 2008). Other risk factors commonly linked to pancreatic cancer include excessive alcohol consumption, chronic pancreatitis, obesity and dietary habits (McGuigan et al., 2018).

Amongst non-modifiable risk factors, age plays the biggest influence on the risk of developing pancreatic cancer. Pancreatic cancer is first and foremost a disease of the elderly, with nearly 90% of diagnoses in patients above the age of 55 (McGuigan et al., 2018). Other big risk factors in this category are sex (pancreatic cancer has a higher prevalence amongst men), ethnicity (risks are higher in African-Americans and lower in Asian-Americans) and family history and genetics (patients with familial risk factors have nine times higher risk of developing the disease than patients without a family history in the disease). In familial pancreatic cancer, studies have demonstrated the recurrence of certain mutations including BRCA2 and CDKN2A predisposes families to develop pancreatic cancer (Maitra and Hruban, 2008). These genetic mutations will be discussed in further detail in a later section.

1.1.3 Pathogenesis

Pancreatic cancer, like many other cancer types, arises from the incremental accumulation of genetic mutations. These mutations transform a normal pancreatic mucosa to specific precursor lesions and finally to invasive malignancies. Three stages are said to guide the transformation of a healthy tissue to a carcinogenic one. First, during the initiation stage, a normal cell of the pancreas acquires an initiating driver gene mutation, such as KRAS, leading to uncontrolled multiplication of the said cell (Makohon-Moore and Iacobuzio-Donahue, 2016). Second, during the clonal expansion stage, the mutated cell multiplies to give rise to a clonal population that in turn continue to gather more mutations in driver genes such as CDKN2A and TP53. Lastly, in the introduction to foreign microenvironment stage, mutated clonal populations will break through the basement membrane of the organ and into the surrounding stroma. This surrounding stroma will be home to a complete different set of external signalling cues to tumor cells leading to their acquisition of new mutations or properties benefiting the

2 cancer cells. Ultimately, the tumor cells will be transformed into subclonal tumor populations with different overall fitness.

Pancreatic cancer has three distinct histologically recognized precursor lesions: pancreatic intraepithelial neoplasia (PanINs), intraductal papillary mucinous neoplasm (IPMNS), and mucinous cystic neoplasms (MCNs) (Maitra et al., 2005). PanINs are microscopic noninvasive neoplasms that arise in the small pancreatic ducts (Hong et al., 2012). These lesions have characteristic mucin-containing cuboidal to columnar cells (Maitra et al., 2005). This group is further divided into high grade and low grade PanIN (PanIN 1a/1b or PanIN 2) (Basturk et al., 2015). IPMNS are another type of precursor lesion arising from the main pancreatic duct or side branches. Pathologically, they represent a broad group with a gradient in risk of malignancy (McGuigan et al., 2018). Lastly, MCNs is the third recognized premalignant lesion of pancreatic cancer accounting for 25% of pancreatic cysts undergoing resection. These lesions are also more frequent in women vs men and have no connection with the pancreatic duct (Hruban et al., 2007; Mohammed et al., 2014).

Molecular mutations play a huge role in pancreatic adenocarcinoma pathogenesis. PanIN is the most common form of precursor lesions and demonstrate a clear molecular evolution in terms of genetic mutations and histological progression. Lower grade of PanIN lesions have been found to contain mutations in the KRAS oncogene, the most frequently mutated gene in pancreatic cancer. Concurrently, the rate of KRAS mutation is higher amongst higher grades of PanIN lesions (Hruban et al., 2008; Löhr et al., 2005). Higher grades of PanIN lesions will then accumulate mutations in CDKN2A, TP53 and SMAD4, indicative of waves of clonal expansion and accumulation of driver gene alterations. Overall, an average of 11.7 years are needed from the initiation of tumorigenesis until the birth of the cell that will give rise to the primary parental tumor cell (Matthaios et al., 2011). It will take another approximate 5 years for a tumor to gain metastatic capabilities and another 2.7 years between metastasis and patient death.

3 1.1.4 Genetic background of pancreatic cancer

Research into the molecular basis of PDAC show that the cancer has roots in both genetics and epigenetic changes. On a genetic standpoint, KRAS2, CDKN2A/p16, and TP53 are the most frequent mutations with KRAS2 being mutated in >95% of pancreatic cancer. KRAS2 is an oncogene which codes for membrane bound guanosine tri- phosphate (GTP) binding protein mediating functions such as proliferation, cell survival and therefore, KRAS2 activation results in constitutive signalling and uncontrolled proliferation of cells (Hingorani and Tuveson, 2003). Conversely, CDKN2A and TP53 are commonly inactivated tumor suppressor genes in PDAC (Jones et al., 2008). CDKN2A, mutated in >90% of pancreatic cancers, encodes both for p16INK41 and p19ARF (two proteins sharing exon 2 and 3 but with different exon 1). Both proteins play roles in inhibition of cell cycle progression. On the other hand, TP53 is a transcription factor activated by DNA damage or stress. Upon activation, TP53 play key roles in cell arrest and cell metabolism and apoptosis regulation (Vogelstein and Kinzler, 2004). Loss of function of p53, observed in 50% to 75% of PDAC, enables cellular division in the presence of DNA damage, enhancing the accumulation of genetic abnormalities.

Historically, genetic lesions in members of the β-catenin dependent Wnt signalling pathway were considered relatively rare in PDAC. However, studies in the early 2000s began to establish a role of Wnt signalling in pancreatic cancer. One major tell-tale sign of the implication of Wnt signalling in PDAC is the presence of elevated levels of stabilized β -catenin in both the cytoplasm and the nucleus. For instance, in PanIN lesions and PDAC, nuclear accumulation is observed in 10-60% of cases while 25% - 65% of cases display elevated cytoplasmic levels (Al-Aynati et al., 2004; Zeng et al., 2006). Furthermore, in these cases, the increased β -catenin accumulation correlates with the degree of severity of PanIN lesions.. Among all Wnt signalling modules, the R- spondin/Lgr5/RNF43 module has the highest prevalence in pancreatic cancer with mutations rates around 2.1% (Zhan et al., 2017). These tumor types are reliant on Wnt ligand stimulation. Treatment with porcupine inhibitors that block Wnt ligand secretion, Wnt antagonist and anti-frizzled antibodies delays the formation of tumors (Jiang et al., 2013; Morris et al., 2010; Steinhart et al., 2017; Zhang et al., 2013).

4 1.1.5 Current treatment options

Surgical resection is the only effective treatment for pancreatic cancer currently offered in clinics. When practiced together with adjuvant chemotherapy, surgical resection in patients improves survival rates. Current surgical options consist of pancreaticoduodenectomy (removal of the head of the pancreas, an initial portion of the small intestine, gallbladder and bile duct), distal (removal of a portion of the pancreas not including the head) or total pancreatectomy (removal of the entire pancreas, gallbladder, bile duct and portions of the small intestine and stomach) (McGuigan et al., 2018). The choice of surgical procedure depends on tumor location.

In terms of neoadjuvant chemotherapy, the current standard of care is FOLFIRONOX for very fit patients undergoing post-surgery therapy and dual therapy composed of gemcitabine and capecitabine in less fit patients (Ghosn et al., 2016). The treatment combination used for each patient is largely dependent on the spread of the tumor at diagnosis. In patients with resectable pancreatic cancers, surgery combined with adjuvant chemotherapy is the standard of care. These patients have the highest rate of survival, with a median survival rate of 26 months and a 30% 5-year survival rate (Neoptolemos et al., 2018). In borderline resectable and/or locally advanced unresectable pancreatic cancer, patients have a median survival rate between 6 and 18 months. In these cancers, neoadjuvant chemotherapy such as chemotherapy with FOLFIRINOX is used. Lastly, metastatic pancreatic cancers have the worst survival rates of 11 months or less with the standard first line therapy for these patients being a combination of chemotherapeutic agents. In these cases, treatment focuses on symptom control, management of jaundice and palliative chemotherapy. Therefore, the current limitations in pancreatic cancer therapies highlight the urgency of new novel therapy development.

5 1.2. Wnt signalling

1.2.1 Historical origins

Hunt Morgan and his colleagues pioneered the field of Wnt signalling in the early 1930s through the discovery of a dominant mutation in drosophila melanogaster that conveyed a glazed eye phenotype. This phenotype, which they named Glazed (Gla), subsequently became known as the result of a gain of function of the wingless allele (Wg). The subsequent discovery in the Wnt field came approximately 40 years later when Sharma and his colleagues described a drosophila mutant that exhibited absent or reduced wings and halters that he named wingless (wg). In the 1980s, Nusslein-Volhard and Wieschaus tied together these two previous findings through the discovery of an entire network of genes regulating embryonic patterning in Drosophila and among the genes discovered was wg (Nüsslein-Volhard and Wieschaus, 1980). The years following this discovery were particularly prolific for the field of Wnt signalling. Key members of the Wnt signalling pathway were discovered one after the others including armadillo (the vertebrate homologue of β-catenin), dishevelled and APC (Riggleman et al., 1989; Rothbächer et al., 2000; Rubinfeld et al., 1993).

In parallel with the work done in drosophila, Nusse and Varmus identified, using forward genetic screens, an oncogene named Int-1 that became activated upon the insertion of mouse mammary tumour virus (Nusse et al., 1984). This Int-1 gene also had a proven role in polarity, with overexpression of Int-1 in xenopus embryos leading to the formation of an ectopic axis (McMahon and Moon, 1989). Later, comparative genomic studies revealed that wg and Int-1 were homologous and this finding led to the combination of their names (Wg + Int1) into Wnt (Nusse et al., 1991).

1.2.2 Beta-catenin dependent pathway

Wnt signalling is separated into two distinct pathways: the β-catenin dependent pathway and the β-catenin independent pathway. The β-catenin independent pathway can further be divided into 2 branches according to their function: the planar cell

6 polarity pathway (PCP) that regulates, as the name suggests, cell polarity and the Wnt/calcium pathway that regulates intracellular levels of calcium. The β-catenin dependent pathway, also known as the canonical pathway, is primarily responsible for regulating stem cell differentiation and tissue homeostasis through targeting the transcription of a series of Wnt target genes. Both the canonical and the non-canonical Wnt signalling pathways are known to signal through the binding of Wnt ligands to Frizzled (FZD) receptors.

Figure 1

Figure 1: The Wnt Signalling Pathway: (A) Schematic of the Wnt pathway. The binding of Wnt ligands to FZDs recruits DVL to the plasma membrane which acts as a scaffold for the recruitment of the destruction complex (Axin, GSK3β and CK1α). This frees up β-catenin in the cytoplasm and allows its translocation to the nucleus where it leads to the transcription of hundreds of Wnt target genes including RNF43 and ZNRF3. As two membrane E3 ubiquitin ligases, RNF43 and ZNRF3 control the level of FZD receptors at the plasma membrane through ubiquitin mediated endocytosis and degradation. RNF43 and ZNRF3 levels are in turn controlled through its binding of R-spondin (RSPO) in conjunction with LGR5/6.

In the absence of Wnt ligands, the β-catenin dependent pathway remains in an off state and the transcription of Wnt target genes is inhibited. Cytoplasmic β-catenin is associated with the destruction complex which is composed of scaffold proteins APC and Axin as well as kinases GSK3β and CK1α. When bound to the destruction complex,

7 constitutively active kinases CK1α and GSK3β sequentially phosphorylate β-catenin on ser45 (CK1α) and thr41, ser37 and ser33 (GSK3β) (MacDonald et al., 2009). This phospho “degron”-motif acts as a docking site for βTrCP, which induces ubiquitination and subsequent degradation of β-catenin (Aberle et al., 1997). With the degradation of cytoplasmic β-catenin, no free β-catenin can translocate to the nucleus. In the nucleus, the TCF/LEF family of transcription factors controls the transcription of Wnt target genes and in the absence of nuclear β-catenin, TCF associates with Groucho (TLE in humans) to repress transcription through histone deacetylation and chromatin compaction (Arce et al., 2006; Hoppler and Kavanagh, 2007).

Production of Wnt ligands in Wnt secreting cells starts in the endoplasmic reticulum (ER). Once translated, Wnt ligands are subsequently appended with a palmitoic acid by Porcupine, an acyl transferase residing at the ER (Kadowaki et al., 1996). This modification of Wnt ligands will enable not only their release into the extracellular space but also their binding to FZD receptors. Modified Wnt ligands then make their way to the Golgi apparatus where they are bound to Wntless/Evi (WIs), enabling them to be shuttled to the plasma membrane via endosomes (Bänziger et al., 2006). Evidence suggests that Wnt ligands are secreted through secretory vesicles or exosomes in a manner that exposes Wnt ligands on the outer surface of the vesicle, allowing their binding to FZD receptors (Korkut et al., 2009).

The β-catenin dependent pathway turns on upon the binding of Wnt ligands to its co- receptors: FZD and LDL receptor related protein 5 or 6 (LRP5/6). Binding occurs through the extracellular cysteine rich domain (CRD) of FZD receptors (Janda et al., 2012). The binding of Wnt elicits a series of signalling events cascading into the expression of Wnt target genes. Upon binding of Wnt, Dishevelled (DVL) -1, -2 and -3, a family of cytoplasmic scaffolding proteins, are recruited to the FZD and LRP complex (Bilic et al., 2007; Cong et al., 2004). Once recruited, DVL, in turns, recruits Axin1 and GSK3β, to the plasma membrane thereby alleviating the negative regulation the destruction complex has on β-catenin (Cliffe et al., 2003). With its degradation halted, cytoplasmic levels of β-catenin accumulate and eventually translocate to the nucleus. Once in the nucleus, β-catenin displaces Groucho/TLE and forms a complex with T-cell

8 factor (TCF) and recruits histone modifying co-activators (Behrens et al., 1996). This transcriptional switch results in the transcription of hundreds of Wnt target genes and leads to changes in multiple cell processes (Bilic et al., 2007; van de Wetering et al., 1997). Wnt ligands also play a role in inactivating the destruction complex. Phosphorylated LRP5/6 act as direct inhibitors of GSK3β’s catalytic abilities (Stamos et al. 2014).

1.2.3 Wnt in development, stem cell differentiation and tissue homeostasis

The Wnt β-catenin signalling pathway is a highly evolutionarily conserved pathway that plays an important role in regulating embryonic development, stem cell self-renewal and differentiation and adult tissue homeostasis.

In embryonic development, the Wnt β-catenin signalling pathway plays an important role in defining the dorsoventral and anterior-posterior body axes of vertebrates. In fertilized Xenopus eggs, the expression of a β-catenin gradient across dorsal and ventral cells leads to the creation of the initial dorsoventral polarity (Moon and Kimelman). Specifically, β-catenin is expressed at higher levels in dorsal cells nuclei, leading to its interaction with TCF to facilitate the expression of siamois and twin, two genes essential in the formation of the Spemann organizer. In addition, injection of Wnt mRNA or other Wnt β-catenin signalling eliciting factors into future ventral blastomeres was shown to lead to dorsal axis duplication and the famous phenotype of the two headed frog (McMahon and Moon, 1989).

Wnt signalling plays two contradicting roles in stem cells. In mice, stem cells help the maintenance of stem cell pluripotency while in humans, stem cells promote differentiation of naïve stem cells. In mice, to maintain embryonic stem cell (ESC) pluripotency, β-catenin dependent Wnt signalling acts interchangeably alongside leukemia inhibitory factor (LIF) signalling. β-catenin knockout embryonic stem cells were shown to maintain their pluripotency when cultured with LIF. Alternatively, in cells with normal Wnt signalling, the use of 6-bromoindirubin-3'-oxime (BIO), a GSK3 inhibitor in the absence of LIF signalling, was sufficient in maintaining the pluripotency

9 state of ESC (Lyashenko et al., 2011; Sato et al., 2004). These two findings together demonstrate that β-catenin dependent Wnt signalling plays a role, albeit not essential, in the maintenance of the pluripotency in embryonic stem cells. Conversely, in humans, Wnt signalling is known to promote the differentiation of ESCs into multiple lineages. For instance, it plays an important role in the generation of mDA neurons through promoting the transition from hESC to a floor plate intermediate (Tabar and Studer, 2014). Alternatively, active Wnt signalling also promotes the differentiation into embryonic stem cell derived mesoderm (Lindsley, R. et al., 2006).

Lastly, Wnt signalling plays an important role in maintaining the homeostasis of adult tissues such as the intestine, the bone and the skin. However, the best studied and described tissue remains the human intestine. The adult intestinal epithelium is composed of two distinct structures: intestinal crypts and intestinal villi. The intestinal villi are responsible for the absorptive and secretory functions cover the entire surface of the intestinal cavity and is exposed to harsher conditions (e.g. low pH). Therefore, the villi are composed of a layer of rapidly shearing endothelial cells that renew every 4-5 days. The intestinal crypt, on the other hand, is located at the base of the intestinal villi, nested away from the harsh environment. It is the area housing intestinal progenitor stem cells, Paneth cells and transit amplifying cells that ultimately go on to repopulate the villi (Fevr et al., 2007). Many genetic studies have linked the role of Wnt signalling in the repopulation of the epithelial layer of the intestinal villi. Both disruption of β- catenin or overexpression of Dkk1, an antagonist of the Wnt signalling pathway, leads to loss of transit amplifying cells and the intestinal crypt-villus structure (Korinek et al., 1997). These studies are further strengthened by the fact that APC loss of function mutations lead to abnormal intestinal adenoma formation. Together, these findings highlight the role of Wnt signalling in normal intestinal homoeostasis.

1.2.4 Wnt in cancer

The first connection made between Wnt signalling and cancer was in the 1980s with the discovery that activation of Int-1 (Wnt1) through proviral insertion, resulted in mammary hyperplasia and tumors in mice (Nusse and Varmus, 1982). A decade later,

10 the first link was made between human cancer patients and Wnt signalling. A mutation in the adenomatous polyposis coli (APC) gene was discovered as the underlying root cause of a hereditary colorectal cancer termed familial adenomatous polyposis (Kinzler et al., 1991). APC was subsequently found to interact with β-catenin to promotes its degradation and its loss of function resulted in overactive β-catenin signalling (Korinek et al., 1997; Rubinfeld et al., 1993). This discovery ultimately established a direct link between Wnt signalling and colorectal cancer. In recent years, advances in genomics and sequencing technologies have tremendously improved our understanding of cancer genomes and more and more mutations in components of the Wnt signalling pathway have shed clarity on the role of Wnt signalling in cancer.

Cancer driving mutations occur at different nodes of the Wnt signalling pathway. In colorectal adenocarcinomas, nearly 50% of mutations occur within APC (Zhan et al., 2017). However, pancreatic ductal adenocarcinomas (PDAC) have mutations most often at the level of RNF43, a transmembrane E3 ligase at the plasma membrane (Zhan et al., 2017).

In colorectal cancer (CRC), aside from the well characterized APC mutation, mutations in the R-spondin/Lgr5/RNF43 module were also demonstrated to be drivers of Wnt induced tumor growth. Deleterious mutations in RNF43 have been described in ~19% of CRC cases and are found to be mutually exclusive with downstream APC mutations (Giannakis et al., 2014). Additionally, 3% of CRC cases express gene fusions involving R- spondin3 leading to overexpression of the RSPO3 protein and a hyperactive Wnt signalling pathway (Seshagiri et al., 2012). In both of these cases, Wnt secretion therapy and Wnt antagonizing therapies were proven to be effective in alleviating tumor burden in pre-clinical models (van de Wetering et al., 2015).

In pancreatic cancer, Wnt signalling is involved in both initiation of cancer development and cancer progression. In the model of KRAS-mutant pancreatic cancer, inhibition of Wnt signalling can significantly delay the formation of PanIN lesions demonstrated through the use of Dkk1 or OMP-18R5, a broad FZD antagonist, and a β-catenin null mouse model, respectively (Zhang et al., 2013; Brault et al., 2001). This taken together

11 suggests the existence of a minimal threshold of ligand mediated canonical Wnt signalling required for PanIN formation. Furthermore, a survey of established PDAC cell lines to identify lines that are sensitive to the Porcupine inhibitor (LGK974) led to the identification of a subset of lines, mutated for RNF43, exhibiting exquisite sensitivity (Jiang et al., 2013). The growth of these cell lines was also found to be very sensitive to anti-FZD antibodies OMP-18R5 or Ab2919 (Morris et al., 2010; Steinhart et al., 2017).

Recent advances in Wnt signalling driven cancers have highlighted the role of the tumor microenvironment and the immune system in driving growth of the tumor carrying Wnt aberrations. A good example of this is the role of myofibroblast-secreted factors in increasing Wnt activity and restoring stemness in colorectal cancer cells (Vermeulen et al., 2010).

1.2.5 Pharmacological Inhibitors in Wnt dependent cancers

Wnt signalling inhibition remains one of the well talked about treatment strategies for cancers due to the positive correlation of active Wnt signalling and poor disease prognosis. However, as the Wnt signalling pathway is also responsible for stem cell renewal and normal tissue homeostasis, targeting of the Wnt signalling pathway in cancer becomes a fine balancing act. This therefore exemplifies the need for therapies targeting the tumor cells.

Many small molecules and antibodies have been developed to block Wnt signalling and these molecules mainly act at two nodes due to the ease of access: Wnt secretion and Wnt-FZD binding. Wnt secretion can be blocked through the action of LGK974, one of many porcupine inhibitors that selectively inhibits the acyl-transferase responsible of Wnt palmitoylation. Clinical studies with LGK974 have demonstrated a decrease in viability of Wnt addicted tumor cells (Jiang et al., 2013; Seshagiri et al., 2012). While clinical trials for LGK974 have started, mechanistic side effects on bone density reduction have been observed in patients and mouse models (Madan et al. 2018).

12 Aside from anti-Wnt secretion therapeutics, an array of drugs targeting the binding of Wnt ligands to their receptors are in the development process. OMP-54F28 and OMP18R5 are respectively a fusion protein (FZD8 and a human IgG1 Fc domain) and a monoclonal antibody targeting a broad range of FZD receptors developed to treat Wnt addicted cancers (Gurney et al., 2012). OMP18R5, currently distributed by OncoMed as Vantictumab, is being used for treatment of pancreatic cancer, non-small cell lung cancer and breast cancer. Despite being available on the market, side effects have been observed for OMP18R5 from clinical study data on skeletal constitution as Wnt signalling plays a key role in bone tissue homeostasis (Baron and Kneissel, 2013). Some pharmaceutical agents have been able to bypass the numerous side effects by exploiting the specific expression patterns of FZD receptors in cancer cells. For instance, FZD10 is overexpressed in synovial sarcomas, a cancer affecting soft tissues (Nagayama et al., 2002). OTSA101 is an anti-FZD 10 antibody currently in clinical trials that harnesses the differential expression of FZD10 to target this cancer subtype (Giraudet et al., 2014).

Targeting downstream members of the Wnt signalling pathway is more difficult as ligands need to be equipped with the ability to cross cell membranes. However, PRI-724 is one of the developed small molecules capable of crossing the cellular membrane that specifically targets the assembly of β-catenin and CBP thereby leading to a decrease in tumor burden (Gang et al., 2014; Rebel et al., 2002). Other small molecules created include Tankyrase inhibitors such as XAV939 that lead to stabilization of Axin. However, Tankyrase inhibitors have never been able to make it to clinical trials due to their toxicity in preclinical models (Lau et al., 2013).

Overall, the current existing therapeutics targeting the Wnt signalling pathway warrant for the need of more targeted therapies to reduce potential side effects across several stem cell niches in the organism.

13 1.3. The regulation of the canonical Wnt signalling pathway

1.3.1 Wnt ligands

The Wnt ligand family is composed of 19 different secreted glycoproteins that control cell proliferation, cell polarity, cell fate determination and tissue homeostasis. Wnt proteins activate several downstream intracellular signaling pathways, of which the β- catenin-dependent pathway is the best characterized. For this pathway, Wnt ligands signal through the FZD family and the LRP family of co-receptors to elicit the transcription of Wnt target genes. The existence of 19 human Wnts and 10 FZD receptors create hundreds of possible different ligand-receptor interaction combinations, adding further complexity to the signalling pathway. Although not much work has been done on mapping the selectivity of interaction between Wnt ligands and FZD receptors, certain Wnt ligands (e.g. Wnt3a) show broad FDZ binding patterns while others (e.g. Wnt8a) bind preferentially to one specific FZD (Voloshanenko et al., 2017).

Wnt ligands, roughly 40 kDa in size, are cysteine rich proteins that undergo multiple modifications prior to their secretion into the extracellular space. After their translation, Wnt ligands undergo a series of modifications that enable them to be shuttled, secreted and abled to engage in active Wnt signalling. First, they are N-terminally glycosylated at the Asn87 and Asn298 residues (Komekado et al., 2007). Following glycosylation, Wnts receive the addition of a palmitoleic acid, a lipid group, at Ser209 (Willert et al., 2003; Janda et al., 2012). This essential lipid modification occurs in the endoplasmic reticulum (ER) closer to the C-terminal of Wnt ligands and is performed by porcupine (PORCN), a multipass transmembrane ER protein containing an O-acyltransferase domain (Kadowaki et al., 1996). This in turn enables the secretion of Wnts and renders the ligand hydrophobic, enabling its binding to the hydrophobic motif of FZD receptors and the activation of a Wnt signal (Janda et al., 2012). Modified Wnt ligands are then able to move to the Golgi apparatus where Wntless/Evi (WIs), a transmembrane protein, binds to the modified Wnt ligands and transfers them to the plasma membrane

14 for secretion (Yu et al., 2014). Evidence suggests that Wnt secretion occurs through incorporation into secretory vesicles or exosomes alongside Wls (Gross et al., 2012). It is believed that in most tissues, Wnt signalling occurs between neighboring cells. However, Wnt ligands also play the role of a morphogen in development, signalling across an entire organism. In Xenopus embryos, an activity gradient of Wnt-beta- catenin signalling driven by Wnt ligands drive the patterning of the Xenopus central nervous system (Kiecker, C et al. 2001).

1.3.2 Frizzled receptors

The discovery of the FZD protein traces back to the discovery of the fly FZD orthologue fz in 1944 by Calvin Bridges. In humans, ten FZD receptors exist that fall into five phylogenetic groups: FZD1/2/7, FZD3/6, FZD5/8, FZD9/10 and FZD4 (MacDonald and He, 2012). These FZD receptors share a highly conserved 120-amino acid extracellular cysteine rich domain (CRD) followed by seven transmembrane segments. The CRD is located at the amino acid terminus and bind Wnt ligands (Vinson et al., 1989; Wu and Nusse, 2002).

The mode of interaction between a Wnt ligand and a FZD receptor was revealed through a co-crystal structure of FZD8CRD and Wnt8 (Janda et al., 2012). Wnt8 forms a structure reminiscent of a human hand, with a “thumb” and an “index finger” extending from the central “palm”. Interaction between the FZD receptor and the Wnt ligand occurs at two sites. Site one is located between the palmitoic acid of Wnt, located on the tip of the thumb, and the hydrophobic groove of the FZD8 receptor. Site two is between the “index finger” and the depression of FZD8CRD, two hydrophobic regions as well. Both these regions are conserved in certain FZD but altered in others, suggesting possible opportunities for selectively between Wnt ligands and FZD receptors.

With the existence of 19 Wnt ligands and 10 FZD receptors with various spatiotemporal expression patterns, elucidating the specific role of each FZD receptor becomes extremely convoluted. Studies regarding FZD-Wnt pairing have been conducted in an attempt to elucidate insights into the affinities of the 19 different Wnt ligands for the 10

15 FZD receptors (Dijksterhuis et al., 2015). However, most of the work has been performed in a cell free environment with purified proteins, making it difficult to make any conclusions about the physiological relevance of these data.

1.3.3 RNF43 and ZRNF3

Ring finger protein 43 (RNF43) and Zinc and Ring Finger 3 (ZNRF3) are two highly homologous E3 ligases that function as negative regulators of the Wnt signalling pathway. The two homologues are composed of a signal peptide, an extracellular domain, a transmembrane domain and an intracellular RING domain. RNF43 and ZRNF3 are two transmembrane E3 ubiquitin ligases of the RING family of E3 ubiquitin ligases (Hao et al., 2012; Koo et al., 2012). They exert their regulatory activity through promoting the ubiquitination of FZD receptors on the lysine residues of their intracellular loop. Mechanistically, RNF43 and ZRNF3 were found to associate with Dishevelled to ubiquitinate the intracellular loops of FZD receptors, targeting them for internalization (MacDonald et al., 2009). This, in turn, leads to the rapid endocytosis of FZD receptors and their destruction in lysosomes. Interestingly, RNF43 and ZNRF3 are Wnt target genes transcribed during active Wnt signalling. Therefore, they act as negative feedback regulators of signaling by downregulating expression of Wnt receptor at the cell surface. Loss of function mutations in either or both E3 ligases leads to upregulation of FZDs at the cell surface and hyperresponsiveness to Wnt signals.

Mouse studies have demonstrated that double knockout of Znfr3 and Rnf43 in intestine induces rapid formation of intestinal adenomas, suggestive of hyperactive Wnt signalling (Koo et al., 2012). Furthermore, mutations in RNF43 and ZNRF3 have been observed in multiple cancers including colorectal, gastric, ovarian and endometrial cancers. Inactivating mutations in RNF43 have been observed in cancers such as colon cancer, bile duct cancer and pancreatic cancer. On the flip side, mutations in ZNRF3 have only been found in adrenocortical carcinoma (Assié et al., 2014). Mutations in RNF43 and ZNRF3 tend to be mutually exclusive with other downstream Wnt signalling mutations. Biochemical experiments conducted by Hao et al. suggest that the RNF43

16 and ZNRF3 mediated membrane clearance of FZD receptors can be reversed with the addition of R-spondin (Hao et al., 2012).

1.3.4 R-spondin

R-spondin proteins (RSPO1-4) are a family of 4 secreted proteins capable of potentiating both the canonical and non-canonical Wnt pathway (de Lau et al., 2012). The four family members harbor two N-terminal Furin domains and a C-terminal TSR domain. Both Furin domains are essential for the activation of the canonical Wnt signalling pathway, with the Furin domain 1 binding to LGR4/5, two members of the rhodopsin G-protein coupled receptor (GPCR) family, and the Furin domain 2 binding to RNF43/ZNRF3 (Chen et al., 2013). Through their binding to LGR4/5 and RNF43/ZNRF3, the RSPO family of proteins potentiate Wnt signalling by promoting the degradation of RNF43 and ZNRF3 by targeting both E3 ligases for self-ubiquitination leading to membrane clearance and degradation (Hao et al., 2012; Zebisch et al., 2013). Consequently, RSPO proteins play a role in potentiating Wnt signalling. Amongst the family of RSPO proteins, RSPO2 and RSPO3 have the highest efficacy in activating Wnt signalling while RSPO4 has the lowest potency.

Physiologically, RSPO proteins play numerous roles in development through potentiation of both canonical and non-canonical Wnt signalling pathway. The role of RSPO has been particularly well studied in the context of self-renewal of the digestive epithelium where gut renewal is driven in large part by canonical Wnt signalling. Consistent with the fact that RNF43/ZNRF3 knockout leads to unrestricted growth of intestinal stem cells, overexpression of RSPO also induces strong intestinal crypt expansion (Kim et al., 2005). Conversely, knockout of either RSPO’s receptor, LGR4/5, or depletion of RSPO using neutralizing antibodies lead to a rapid demise of intestinal crypts (Kinzel et al., 2014; de Lau et al., 2011; Storm et al., 2016).

1.3.5 Dishevelled

The Dishevelled (DVL) family of scaffolding proteins is composed of three members in humans (DVL1, DVL2 and DVL3) and the entire family of DVL proteins play a large role

17 in integrating Wnt signals in both a biological and pathological settings. Although DVL has been associated with a multitude of signalling pathways, our focus will remain on its role in the beta-catenin dependent Wnt signalling pathway.

DVL1, DVL2 and DVL3 are composed of three conserved domains and two variable domains. In the conserved domains, the amino-terminal DIX domain functions to relay signal to downstream members of the Wnt signalling pathway such as Axin, enabling their recruitment to DVL (Schwarz-Romond et al., 2007). The PDZ domain on the other hand, was initially thought to be involved in protein-protein interaction to mediate their recruitment to FZD receptors (Wong et al., 2003). However, this was later disproved using rescue experiments in loss of function cell lines, in which the DEP domain was identified to be critical for Wnt β-catenin signalling (Gammons et al., 2016). This finding is consistent with the DEP domain’s role to target DVL to the plasma membrane (Pan et al., 2004).

In the β-catenin dependent pathway alone, DVL proteins play many different scaffolding roles. First, in the Wnt active state, DVL proteins are recruited to the plasma membrane upon Wnt binding to FZD receptors through its DEP domain (MacDonald and He, 2012). This provides a platform for recruitment of Axin and GSK3β via DVL’s DIX domain and enables the subsequent phosphorylation of LRP5/6 ultimately preventing the degradation of β-catenin. Second, DVL also plays a scaffolding role in dampening Wnt signal through bridging RNF43/ZNRF3 and FZD receptors. DVL knockout cells were found to display increased levels of FZD and LRP6 co-receptors (Jiang et al., 2015). Taken together, DVL proteins, in the context of the β-catenin dependent Wnt signalling pathway, play an important role in mediating the interaction of different pathway components in either initiating or turning off Wnt signalling.

18 1.4. Genetic vulnerabilities of RNF43 mutant pancreatic cancer

1.4.1 FZD5 essentiality

A 2016 study by Steinhart et al. uncovered the essentiality of FZD5 vs all other FZD receptors for the growth of RNF43 mutant pancreatic cancer lines HPAF-II, AsPC-1 and PaTu8988S (Steinhart et al., 2017). FZD5 was one of the most essential genes revealed with several others in a series of genome-wide CRISPR genetic screens, performed in RNF43 mutated cell lines. FZD5 essentiality was validated through a series of viability assay where FZD5 knockout but not FZD7 led to cell cycle arrest and reduced proliferation in multiple RNF43 mutated PDAC cell lines. As RNF43 was known to play a role in targeting FZD for degradation, one natural hypothesis behind the FZD5 essentiality in RNF43 mutant pancreatic cancer is a potential substrate specificity of RNF43 for FZD5 vs the nine other FZD receptors leading to its specific degradation.

1.4.2 Wnt7B, Wnt10A and Wnt3 essentiality

Examining the CRISPR screens, out of the 19 Wnt ligands, only a select few came out as essential in the different PDAC cell lines. These were respectively WNT7B and WNT10A in HPAF-II, WNT7B in AsPC-1 and WNT3 in PaTu8988S. While the essentiality of WNT3 in PaTu8988S may be attributed to its high expression level in these cells, WNT7B essentiality is consistent with previous studies that reported a role for this Wnt in PDAC development and cell growth (Afelik et al., 2015; Arensman et al., 2014). Taken together with the essentiality of FZD5 in the RNF43 mutant context, the essentiality of a small subset of Wnt ligands could potentially point to the evidence of preferential Wnt ligand - FZD receptor signaling circuit. This hypothesis is supported by the rescue of the cell cycle arrest observed upon FZD5 knockout when cultures are supplemented with exogenous WNT3A conditioned media (Steinhart et al., 2017).

19 1.5. Mass spectrometry

1.5.1 Mass spectrometry and proteomics

Mass spectrometry is an analytical tool used in multiple research and development fields to measure the mass to charge (M/Z) of ions in a sample. Results are often represented in the form of a mass spectrum: a two-dimensional readout plotting the signal intensity of each mass to charge detected. Using the comprehensive mass spectra generated from a sample, one is able to identify the analytes present in the sample and in some cases, their abundance.

In a typical mass spectrometry experiment, the sample of interest is ionized and injected into the mass spectrometer. As the sample is injected, analytes in the samples undergo ionization at the source. Then, ionized molecules travel into a collision chamber where they fragment. This charged state, alongside with the molecular weight of the fragments, is detected by the mass spectrometer to generate an ion’s M/Z that is recorded in the mass spectrum. The intensity of each M/Z peak depends on the observed abundance of the ion generated. Using a series of mass spectra containing peptide fingerprints, generated in a single sample, one is able to identify the peptidesand subsequently the parent analytes in the sample through searching against databases..

The coupling of mass spectrometry to a chromatography technique has always been a popular practice. This coupling enables the separation of a complex sample across a long elution window, largely reducing sample complexity for the mass analyzer. The coupling of gas chromatography to a mass spectrometer started as early as in the 1950s and became more and more of a commercially practice in the 1970s. With the advances in ion source capable of ionizing liquids in the 1980s, liquid chromatography coupled mass spectrometers became the new norm (Fenn et al., 1989).

The field of proteomics resulted from the desire of scientists to study the human proteome as an entity. Scientists wanted to use it as a tool to understand the comprehensive phenotype of an organism as it would be a much stronger predictor of the current state of an organism than its genotype. Therefore, proteomics refers to the

20 use of technologies to study the overall proteins present in a cell, tissue or organism (Cox and Mann, 2011). While it involves a series of different techniques, the study of proteomics has been largely advanced by the development of Mass Spectrometry.

Mass Spectrometry proteomics can be further divided into the study of expression proteomics, the study of the modification state of proteins and the study of protein- protein interactions. Expression proteomics focuses on the elucidation of the relative or absolute expression of different proteins in a sample and shall be the focus of this thesis.

Mass-spectrometry based proteomics is also siloed into two categories based on the state of the sample: top down and bottom up proteomics. In top down proteomics, a sample with intact proteins is injected into the mass spectrometry for ionization. In a bottom up workflow, the sample’s proteome is first digested into peptides by an enzyme such as trypsin prior to being injected into the mass spectrometer. Because peptides are easier to separate by liquid chromatography and mass spectrometers are much more sensitive to low molecular weight molecules, bottom up proteomics is the preferred method for the majority of mass spectrometry proteomics.

1.5.2 Targeted mass spectrometry

Bottom up mass spectrometry-based proteomics is further divided into two main categories: discovery proteomics and targeted proteomics. In discovery based proteomic experiments, proteins of interest are identified in an unbiased manner to identify peptides and associated proteins present in a given sample. However, this becomes less effective when the goal is for consistent detection of proteins of interest across multiple samples due to the stochastic nature of data acquisition. Therefore, in quantitative mass spectrometry, where consistent identification of ions, peptides and proteins of interest across all samples is needed, results from discovery-based proteomics cannot provide enough statistical power to establish any quantitative conclusions.

Targeted proteomics, on the other hand, provide a method to reliably identify and quantify ions of interest. In the targeted workflow, surrogate ions (peptides) are selected for the proteins of interest and these surrogates are monitored across their specific

21 retention time and predefined m/z ranges. This type of targeted experiments is predominantly performed on a triple-quadrupole (QQQ) using a data acquisition method called multiple reaction monitoring (MRM) (Stahl-Zeng et al., 2007). However, with the new generation of fusion mass spectrometers, a new type of targeted experiment termed parallel reaction monitoring (PRM) has been developed (Gallien and Domon, 2015; Peterson et al., 2012).

Comparison between MRM and PRM have demonstrated similar linearity, dynamic range and precision in protein quantification (Schiffmann et al., 2014). However, PRM methods are relatively easier to build and provide a higher specificity as full MS/MS spectra are monitored instead of a handful of transitions only in the case of MRM.

1.5.3 Quantitative proteomics

1.5.3.1 Absolute and relative quantification Quantitative proteomics is a field aimed at determining the amount of proteins in a sample. Quantification of proteins can be either absolute (quantification of the absolute concentration of a given protein in a sample) or relative (quantification of the relative ratio of the amounts of a given protein in a sample). Absolute quantification relies on the addition of a spiked-in heavy labelled standard with known absolute concentrations. While it is not 100% correlative, the total ion signal can be related back to the concentration of a protein in vivo. Therefore, using a heavy labelled internal standard, the absolute abundance of proteins can therefore be inferred (Bondarenko et al. 2002). Relative quantification measures the intensity of the same peptide in samples having undergone different treatments. It is worthwhile to note that relative quantification can only be performed when quantifying the sample peptide. This is because the ionization efficiency varies from peptide to peptide, therefore making it impossible to compare the MS signal of different peptides (Pappireddi et al. 2019).

1.5.3.2 Stable isotope labelling and label-free quantification

Quantitative proteomics can be performed using the addition of stable isotopes or mass tags or in a label-free environment. Popular methods of stable isotope labelled mass

22 spectrometry include isotope-coded affinity tags (ICAT), isobaric tags for relative and absolute quantification (iTRAQ) and amino acids in cell culture (SILAC). Label free quantification, on the other hand, is performed by calculating the area under the peak of a given peptide spectrum in a LC-MS run. Parallel reaction monitoring and multiple reaction monitoring are two examples that employ the said methodology. Another approach to label free quantification consists of spectral counting. Spectral counting involves counting the spectra of an identified protein (Lundgren et al, 2014). This is typically done with an abundant peptide mass selection that is then fragmented into MS/MS that are counted.

1.5.4 Parallel reaction monitoring

Parallel reaction monitoring (PRM) is a targeted quantification method that harnesses the resolution of hybrid mass spectrometers such as quadrupole-Orbitrap (Q-OT) and quadrupole time of flight (Q-TOF).

When performing PRM quantification in a Q-OT, predefined precursor ions are selected in the quadrupole and enter the high collision dissociation (HCD) cell via the C-Trap (Gallien et al., 2012; Peterson et al., 2012). The C-trap spends a predetermined amount of time, called fill time, collecting ions from the quadrupole. This fill time can be optimized to increase signal-to-noise ratio. Ions are then shuttled through to the HCD and fragmented by a beam type collisional dissociation. The resulting fragments are subsequently shuttled via the C-trap into the Orbitrap mass analyzer where the ions’ intensities are collected over their elution time. Since PRM generates high resolution and high mass accuracy MS/MS spectra of target peptides, the PRM method is both highly specific and sensitive. Isotope labeled internal standards can be spiked-in to obtain absolute quantification of endogenous targeted peptides.

When processing data, all MS/MS fragments are used for identification of the peptide but only fragments ions of the highest intensity are used for quantification. Peak areas of those fragments are extracted using <10ppm mass window and integrated across the

23 elution profile. Final processing and quantification are performed on analysis programs such as Skyline (MacLean et al., 2010).

1.5.5 Steps of a PRM experiment

1.5.5.1 Selection of target peptides

Since PRM experiments are used for monitoring of select endogenous proteins through surrogate peptides, endogenous peptides quantified must be specific and stoichiometric to the protein of interest (Worboys et al., 2014). In early stages of selecting surrogate peptides, it is important to have multiple peptides per protein to find the correct set of surrogate peptides. This set can subsequently be narrowed down to one or two peptides for identification and quantification. These surrogate peptides are also known as signature peptides. Signature peptide selection can be done experimentally through discovery-based experiments or through searching publicly available databases such as PeptideAtlas or the Global Proteome Machine (Beavis, 2006; Deutsch et al., 2008).

Selected surrogate peptides must be between 8 to 25 amino acids with a m/z value of the peptide within the detection range of the instrument (Rauniyar, 2015). Furthermore, the peptide must be unique to the protein of interest and deprived of any non-reproducible mis-cleavage sites. Peptides containing post-translational modifications should be avoided unless specific controls are included during sample preparation (e.g. cysteine alkylation). Lastly, peptides with a symmetrical chromatographic peak and strong signal intensity should be favored.

1.5.5.2 Building a PRM data acquisition method

PRM methods are a lot less labor intensive to build than their SRM counterparts as full MS/MS spectrum is acquired for each peptide avoiding the need to select specific peptide fragments to monitor. Method development can be accelerated with the use of existing data from publicly available shotgun proteomic experiments with information such as precursor ions, charge state, collision energy and elution time available (Prakash

24 et al., 2009). Aside from these, a couple of other steps are needed in method development.

First, the fill time of ions must be customized for each PRM method. In order to achieve reproducible quantification, 8 to 10 MS/MS scans per fragment across the chromatographic peak is required (Gallien and Domon, 2015). Assuming a hypothetical average peak width of 30 seconds and a target of 10 MS/MS scans per fragment across the peak, the hypothetical cycle time (time spent cycling through each target peptide’s detection) should be no longer than 3s. Since cycle time is determined by the number of peptides multiplied by their individual fill time, an adequate fill time must be set for each peptide. With our hypothetical cycle time of 3s and assuming a total of 100 peptides being monitored, the individual fill time of each peptide should be set to 30ms. Higher fill time are synonymous with better resolution as the machine dwells longer on the capture and fragmentation of the ions of interest. Therefore, the balance becomes to achieve the highest fill time per signature peptide while monitoring many peptides and ensuring ample coverage points of each chromatographic peak.

One way of alleviating this is through scheduling of a peptide’s elution window. Peptide scheduling refers to the scheduling of the elution time frame during which the mass spectrometer is ionizing the signature peptides and subsequently fragmenting them for signal detection. This practice becomes especially valuable when many signature peptides are monitored in a single method because it frees up the machine from ionizing and fragmenting confounding ions with identical m/z in timeframes where the signature peptide is not expected to elute. Altogether, this reduces the noise and enables more MS/MS scans of the signature peptides monitored during their elution time (Gallien and Domon, 2015).

Lastly, chromatographic gradients should be optimized to achieve high separation of peptides of interest while maintaining strong and sharp chromatographic peaks (sharp peaks have better signal to noise ratio).

25 1.5.5.3 Using labelled peptides as internal standards

The last step in developing a PRM method would be the selection and use of heavy labelled peptides as internal standards for both identification and quantification purposes.

Heavy labelled synthetic versions of the signature peptides are commonly spiked into samples prior to their injection. These heavy peptides typically have an arginine or lysine with 13C and 15N isotope at the C-terminal end. This modification allows them to retain the same physicochemical properties of their endogenous counterparts (retention time, ionization efficiency, fragmentation pattern) but enable them to be distinguished based on the mass difference of their precursor and fragment ions. During sample processing, these labelled peptides can be spiked in after digestion of the endogenous proteins at an ideal 1:1 ratio to avoid detector saturation (Abbatiello et al., 2010).

In validating a heavy labelled peptide, one must first confirm the signature peptide’s identity by comparing fragmentation patterns and intensity between the synthetic and endogenous peptides. Subsequently, they must experimentally determine the amount of endogenous peptide present in the sample in order to enable a 1:1 ratio of endogenous to heavy peptide. Lastly, using the heavy peptide as a reference for normalization, one can then adequately quantify the levels of each endogenous signature peptide and their protein.

1.5.6 Common applications of PRM

Parallel reaction monitoring is used for two main applications: the quantification of the relative abundance of proteins and their posttranslational modifications (PTMs). PRM- based targeted methods are especially useful in the analysis of samples with limited amounts as the method is highly specific and sensitive.

In protein quantification, PRM is widely used in quantification of proteins in biological fluids such as serum, plasma or urine at a higher sensitivity and specificity than traditional approaches including SRM. In detection of lung cancer, plasma levels of the

26 serum amyloid A (SAA) family of proteins were quantified by PRM at a superior analytical outcome than previous assays (Kim et al., 2015). Furthermore, in a study of serum level of Hepcidin upon small interfering RNA (siRNA) treatment, PRM quantification demonstrated at least a 10-fold improvement in specificity and sensitivity than SRM (Li et al., 2015).

Alternatively, PRM-MS is also commonly employed in PTM validation as PRM-MS can differentiate and quantify peptides with isobaric PTMs. For instance, in prostate cancer biopsy patients, PRM was used to quantify the relative abundance of N-linked glycosides-containing peptides in serum (Thomas et al., 2015). PRM quantification showed significant difference between 4 of the 41 glycosite-containing peptides monitored in the non-aggressive versus aggressive prostate patient group.

Taken together, PRM-MS enables not only identification of analytes at very low levels but also permits a highly reproducible quantification. This technique is therefore a great tool to study the role of RNF43 loss of function mutations in altering FZD expression.

27 Chapter 2: Project Rationale

Previous work by our group revealed the requirement of a single FZD receptor, FZD5, for the growth of RNF43 mutant pancreatic cancer cells. The goal of my thesis set out to investigate the molecular basis of FZD5 essentiality in RNF43 mutated pancreatic cancers.

Then, using two FZD overexpression cell lines, we set out to study the existing of a preferential Wnt signalling circuit through FZD5 in RNF43 mutant pancreatic cancer. We asked whether Wnt ligands secreted in the pancreatic cancer niche of these cells exhibited preferential signalling through FZD5 vs other FZD receptors.

28 Chapter 3: Materials and methods

3.1 Plasmids

To generate the HPAF-II FZD7 plasmids, Cas9 in the Lenti Cas9-2A-BsdR vector (Addgene #83481) was replaced with ssFLAG-FZD7 (Lentipuro_ssFLAG-FZD7-huBira, unpublished, Angers lab) using Gibson cloning. The HPAF-II FZD7/FZD5 plasmid was generated by replacing the FZD7 CRD with that of FZD5 using Gibson cloning. RNF43 guides were cloned into Px459 (Addgene #62988) and FZD5, FZD7 and LacZ guides were cloned into LentiCRISPRv2 (Addgene #52961).

Table 1: List of CRISPR Guides Target Gene Guide

RNF43 TTACCCCAGATCAACACCAC

FZD5 AGGCCACCACAATGCTGGCG

FZD7 TGGTACGGCTGCGCCCCGGC

LacZ CCCGAATCTCTATCGTGCGG

3.2 Cell lines

YAPC RNF43 knockout lines were generated by CRISPR knockout using RNF43 guide mentioned above. Briefly, WT YAPC cells were transfected with the said guide using LIPO3000 (ThermoFisher) as per manufacturer’s instructions. Media was changed 24h post transfection and cells were selected using 2 µg/mL of puromycin for 48h post transfection. Individual clones containing the RNF43 knockout were then generated by plating the transfected cells at a density of 0.3 cells per well into 2 96-well plates. Plates were monitored daily and 24h after plating, wells containing a single cell were identified, propagated and sequenced using TIDE to confirm the presence of a RNF43 mutation. Clones 29 and 38 were used for the experiment. HEK293T DVL triple knockout cells previously described by Gammons et al were used (Gammons et al., 2016).

29 3.3 Cell culture

HEK293T and HPAF-II cell lines were cultured in DMEM (ThermoFisher) supplemented with 10% fetal bovine serum (FBS, ThermoFisher) and 1% Penicillin/Streptomycin (ThermoFisher). YAPC cell lines were cultured in RPMI supplemented with 10% FBS (ThermoFisher) and 1% Penicillin/Streptomycin (ThermoFisher). Cells were selected for expression of plasmids using 2 µg/mL puromycin dihydrochloride (BioShop Canada and/or 8 µg/mL blasticidin (BioShop Canada). Doxycycline (0 ng/mL-1 µg/mL; BioShop Canada) was used to induce expression.

3.4 Sample preparation for PRM mass spectrometry

Confluent 10 cm plates were lysed in 5 mL RIPA buffer (1% NP40, 5 mM Tris pH 7.4, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 0.1% SDS, protease inhibitor, 0.5% sodium deoxycholate and 1 µl benzonase) while rotating at 4°C for 1h. Lysates were then sonicated three times for ten seconds and cleared via a 30 minutes centrifugation at 10 000 g at 4°C. 5 mg of cleared lysate, unless otherwise indicated, was incubated overnight with 6 µg of IgG 5020 while rotating at 4°C. 50 µl of protein G-sepharose beads slurry (Roche) was added to each sample and the samples were left rotating for 4h at 4°C. After the capture, beads were washed 5 times with 1 mL 0.05 M ammonium bicarbonate, reduced with dithiothreitol for 40 minutes at 50°C and alkylated with Iodoacetamide in the dark at room temperature for 20 minutes. 2 µg of mass spec grade trypsin was added to each sample for overnight on beads digestion at 37°C. Digested samples were desalted using C-18 ziptips and spiked with 500 fmol heavy labelled synthetic peptides (JPT peptides). For PRM mass spectrometry peptide linearity studies, heavy labelled peptides were spiked into digested and desalted YAPC cell lysates at the indicated concentrations.

3.5 Liquid chromatography

EASY-nLC 1200 (Thermo Scientific) was coupled to the Q-Exactive HF mass spectrometer (Thermo Scientific) for peptide separation and detection. An EASY-Spray

30 column (2 μm, 100A, 75 μm x 50 cm; Thermo Scientific) was employed for compound separation. Mobile phase A (0.1% formic acid in H2O) and mobile phase B (0.1% formic acid in 80% acetonitrile, 20% H2O) were used with the following gradient: 0 min, 95% A and 5% B; 50 min, 65% A and 35% B; 55 min, 0% A and 100% B; 60 min, 0% A and 100% B at a flow rate of 225 nl/min. 10 μL of sample was injected into the liquid chromatograph in technical duplicates.

3.6 PRM mass spectrometry

Q-Exactive HF (Thermo Fisher Scientific) was used with one full MS scan followed by 10 PRM scan of heavy and light precursor ions (table 3). Full MS mode was used with the following parameters: positive polarity, r = 60,000, AGC target 1e6, maximum IT 120 ms, scan range = 425 to 2000 m/z. PRM mode was used with the following parameters: positive polarity, default charge =2, r = 60,000, AGC target 1e5, maximum IT 450 ms, MSX count 1, isolation window 1.0 m/z, NCE 30%. Cell lysate samples were loaded at 2.5 µg per injection and injected in technical duplicates.

3.7 PRM data processing

Skyline (v.4) was used for data processing. PRM data acquired by LC-MS/MS was imported into Skyline for transition peak area extraction and calculation for each peptide. Quantification was performed using the four peptides ion fragments with the highest intensity (top four peptides).

3.8 Lentivirus production and infection

For lentiviral production, HEK293T cells were seeded in 10 cm plates 24h before transfection. 6 μg of lentiviral delivery vector, 4.5 μg psPAX2 and 1.5 μg pMD2.G were transfected per plate usingcalcium phosphate. Medium was changed after 24h and viral media was harvested 48h post transfection and filtered through a 0.45 μm syringe filter. HPAF-II were transduced with 0.3 mL virus in the presence of 0.8 µg/mL polybrene in a total volume of 1 mL. Viral medium was replaced with fresh medium 24h post transduction and selection occurred 48h post transfection if applicable.

31 3.9 Immunofluorescence

Cells were seeded on glass coverslips and treated with respective concentration of doxycycline 24h after seeding and left to grow in the presence of doxycycline for 60h. Cells were blocked for 1h using 5% donkey serum in PBS. Primary staining was performed with 1:1000 dilutions of antibodies for 30 minutes on ice. Antibodies used in this study include Flag (M2, Sigma) and IgG 5020 (in house). Cells were then washed and fixed with 4% PFA for 20 minutes at room temperature. Secondary staining was performed with 1:1000 dilutions of antibodies for 1h at room temperature in the dark. Cells were mounted in Vectashield (Vectors labs) plus DAPI and images are captured using a 40x numerical aperture oil immersion objective on a confocal microscope (LSM 700, Carl Zeiss) operated with ZEN software black edition.

3.10 TIDE analysis

Genomic DNA was harvested from cells and areas around editing were amplified using PCR (table 2) and PCR products were sequenced. All sequencing was completed at the Centre for Applied Genomics (Toronto, ON) and sequencing was analyzed using TIDE (https://tide.deskgen.com/). Primers used for PCR are listed below:

Table 2: List of PCR Primers for TIDE Analysis

Target Gene Forward PCR Primer Reverse PCR Primer Sequencing Primer

TTGCCCGACCAGAT TCTGTCTGCCCGAC CCAGACGCCCGACG FZD5 CCAGAC TACCAC TGATG

TGAGGACTCTCATG AGCCGTCCGACGTG ACGGGCGCATACAT FZD7 CGTCGG TTCT GGAGC

3.11 Crystal violet staining proliferation assay HPAF-II WT, HPAF-II FZD7 OE and HPAF-II FZD5/7 OE cells were infected with indicated lentivirus. 24h after infection, cells were treated with 2 µg/mL puromycin. 48h after puromycin selection, wells were washed with 1 mL PBS, dissociated with 0.2

32 mL trypsin, neutralized with 500 µL media and counted. Cells were re-seeded at 10,000 cells per well in a 6-well plate. When applicable, doxycycline was added 24h following seeding and replenished every 60h with respective concentrations. Plates were collected 10 days post plating or upon confluency of the LacZ knockout well, washed with 1 mL PBS and fixed overnight with 1 mL 100% ice cold methanol at -20°C. After fixation, the cells were stained with 500 µL 0.1% crystal violet solution for 30 minutes at room temperature, after which the staining solution was removed. Plates were washed several times in dH2O and dried at room temperature. Crystal violet staining were dissolved in 10% acetic acid for quantification.

3.12 RSPO media preparation

HEK293T RSPO1 producing cells were grown to confluence in 10 cm plates. Cells were subsequently split 1:2 and fresh media was allowed to condition for 10 days after which conditioned media was collected, spun down for cell debris and filtered using a 44 µM filter. RSPO media was aliquoted and stored at -20°.

3.13 Statistics

Statistical analysis was completed using Graphpad Prism 6 (Graphpad Software, California, USA). For gRNA based crystal violet survival assay, each experiment was normalized to LacZ control values. Statistical analysis of multiple independent experiments was completed by a one-way ANOVA. For parallel reaction monitoring mass spectrometry assays, each experiment was normalized to RNF43 WT control values. Statistical analysis of multiple independent experiments was completed by a one-way ANOVA.

3.14 Heavy labelled peptide calibration curve creation

For PRM mass spectrometry peptide linearity studies, 2.5 µg of lysate was desalted using a C-18 tip. Then, heavy labelled peptides for each FZD (table 3) were spiked into the 2.5 µg of cell lysate at concentrations indicated in figure 3. Samples were run in technical duplicates and biological triplicates and quantification of heavy labelled

33 peptides was performed by liquid chromatography coupled parallel reaction monitoring mass spectrometry.

3.15 Peptide Cleanup

Peptides for PRM mass spectrometry samples were cleaned up using C-18 desalting tips. Briefly, the pH of samples were brought down to below pH 3 using formic acid. C-18 desalting tips were washed in 1% formic acid in acetonitrile twice followed by 1% formic acid in H2O twice using centrifugation (1 minute, 10,000g). Samples were then passed through tips 5 times using centrifugation (1 minute, 10,000g). Tips loaded with samples were washed twice using 1% formic acid in H2O and eluted twice using 50 µL elution buffer (1% formic acid, 49.5% H2O, 49.5% acetonitrile).

34 Chapter 4: Results

4.1 Development of immunoprecipitation coupled parallel reaction monitoring mass spectrometry for FZD quantification.

We developed an immunoprecipitation coupled PRM-MS workflow using a pan-FZD antibody IgG 5020 to detect endogenous levels of FZD receptors. IgG 5020 targets a broad range of FZD receptors (FZD1, 2, 4, 5, 7 and 8). In this workflow, wild-type or RNF43 -/- PDAC cells were harvested, lysed, immunoprecipitated with IgG 5020, reduced, alkylated and digested on beads (Fig 2a). Samples were then desalted using C- 18 ziptips, spiked with heavy labelled peptides for peptide validation and quantification and loaded onto the LC-MS where transitions were be isolated, fragmented and quantified (Fig 2a-b).

Figure 2

Figure 2 Development of Immunoprecipitation Coupled Parallel Reaction Monitoring Mass Spectrometry for FZD quantification. (A) A pan-Frizzled antibody (IgG 5020) with sepharose G-beads was used to immunoprecipitate FZDs (1,2,4,5,7,8) from cell

35 lysates. Enriched samples were then denatured, alkylated and digested on beads. Peptides were subsequently collected and desalted using C-18 ziptips. Lastly, heavy labelled peptides were spiked into samples for quantification and samples were analyzed by liquid chromatography mass spectrometry (B) PRM-MS employs a hybrid Quadrupole-Orbitrap Mass spectrometer. First, the quadrupole isolates precursor ions from selected signature peptides. These ions are fragmented in a high-energy collision dissociation cell and ultimately detected by an Orbitrap mass analyzer. Quantification is carried out by extracting the area under the curve of the highest quality fragments. (C-D) Chromatograms of representative endogenous and heavy labelled synthetic signature peptide respectively. Each colored curve indicates a different fragment generated and their respective intensities across time while dotted lines delineate signal peak from noise.

Table 3 Receptor Signature Peptide M/Z Retention Time (min)

FZD1 VPSYLNYHFLGEK 783.90119 ++ 31.5 - 33.5 FZD2 VPSYLSYK 478.75802 ++ 25.5 - 27.5 FZD5 QYGFAWPER 577.27490 ++ 33.5 - 35.5 FZD7 VPPYLGYR 482.76618 ++ 27 - 29 FZD8 TDLTTAAPSPPR 613.82241 ++ 23.5 - 25.5

Table 3: PRM-MS Signature Peptides. Signature for peptides, their charge state and retention times were derived theoretically and tested experimentally. The best signature peptide for each targeted FZD receptor was selected for use.

In order to carry out the receptor quantification, we used a list of signature peptides as surrogate for each receptor (Table 3). Using Skyline, we first generated a list of theoretical signature peptides for the FZDs targeted by IgG 5020. This list was narrowed down by removing peptides that did not fit within criteria for quantitative mass spectrometry (length, post translational modifications, uniqueness and possibilities for miscleavage). This left us with anywhere between 5 and 8 peptides per FZD receptor. Using a broad unscheduled PRM-MS assay, we then tested all potential signature peptides for their fragmentation pattern, reproducibility from run to run and signal intensity. While we found peptides for FZD1, 2, 5, 7 and 8, we were unable to find an adequate peptide for FZD4 and therefore will not be monitoring levels of FZD4 for our study (Table 3). Retention times were determined for each peptide using retention

36 window of 2 minutes while ensuring minimal overlap in retention time between different peptides (Table 3). The final panel of signature peptides was then validated using heavy labelled synthetic peptides as both light and heavy peptides will display identical fragmentation patterns and retention time (Fig 2d-e).

Figure 3

Figure 3 Linear gradient of Signature Peptides for FZD1, 2, 5, 7 and 8 by PRM-MS (A) Linear gradient of signature for FZD1, FZD2, FZD5, FZD7 and FZD8 respectively as determined by PRM-MS. Heavy labelled synthetic peptides for respective FZDs were spiked into a cell lysate background and quantification of heavy peptides was performed using light normalized peaks for R2 and regression equation are shown for each FZD and error bars represent SEM, n=3.

4.2 Determination of the lower limit of detection, lower limit of quantification and linear range of each signature peptide.

Table 4 Receptor Peptide M/Z LLOQ (fmol) LLOD (fmol) FZD1 VPSYLNYHFLGEK 787.90829 ++ 8.33 2.50 FZD2 VPSYLSYK 482.76512 ++ 2.33 0.69 FZD5 QYGFAWPER 582.27903 ++ 4.63 1.39 FZD7 VPPYLGYR 487.77031 ++ 2.50 0.75

37 FZD8 TDLTTAAPSPPR 618.82654 ++ 1.40 0.42

Table 4: LLOD and LLOQ of Signature Peptides. Lower limit of quantification and lower limit of detection were calculated for each FZD of interest using the linear gradient generated in A.

In order to quantify endogenous levels of each peptide, we must test the lower limit of detection (LLOD) and the lower limit of quantification (LLOQ) of each peptide. LLOD refers to the lowest quantity of peptides that can be distinguished from the absence of that substance with a stated confidence level of 99%. On the other hand, LLOQ refers to lowest peptide concentration for which the peptide can be quantitatively detected with a stated accuracy and precision.

LLOD = 3 x 휎 lowest concentration / slope of the calibration curve LLOQ = 10 x 휎 lowest concentration / slope of the calibration curve

In terms of LLOD and LLOQ, FZD8 (TDLTTAAPSPPR) displayed the lowest value (LLOD = 1.40 fmol, LLOQ = 0.42 fmol), followed by FZD2 (VPSYLSYK) (LLOD = 0.69 fmol, LLOQ = 2.33 and FZD7 (VPPYLGYR) (LLOD = 0.75, LLOQ = 2.50) (Fig 3b). Both FZD1 (VPSYLNYHFLGEK) and FZD5 (QYGFAWPER) had significantly higher LLOD and LLOQ making detection and quantification of these FZDs challenging at quantities below 8.33 fmol and 4.63 fmol (Table 4). However, our immunoprecipitation coupled sample preparation methodology that enriches for FZD receptors should provide proteins above these limits.

Peptide linearity across a wide range of concentrations is essential for quantification as quantification can only be carried out within the linear range of the peptide. All peptides used in our method displayed strong peptide linearity across two to three orders of magnitude (Fig 3a). Concurrently with LLOD and LLOQ, FZD1 and FZD5 both display peptide linearity at much higher concentrations than the other FZD receptors (Fig 3a). Overall, LLOD, LLOQ and linearity of our signature peptides indicate the minimum peptide concentration required in the sample for detection and quantification by PRM- MS.

38 4.3 Validation of immunoprecipitation by PRM-MS

In order to be able to capture changes in FZD5 vs other FZDs in various conditions, we first needed to make sure that the immunoprecipitation conditions were fully optimized. We tested the dynamic range of IgG 5020 using PRM-MS. We chose to perform test for the dynamic range of IgG 5020 using a peptide from FZD5 (QYGFAWPER) seeing that the focus of our PRM-MS method is to gain further insight into the upregulation of FZD5 vs other FZDs. Using 0 mg to 40 mg of lysate, we titrated for the amount of lysate that can be used without saturating 6 µg of IgG 5020. We observed a saturation of IgG 5020 by FZD5 (QYGFAWPER) between 10 mg and 20 mg of lysate, therefore prompting us to use 5 mg of lysate in our immunoprecipitation coupled PRM-MS workflow (Fig 4a).

Figure 4

Figure 4 Validation of Immunoprecipitation by PRM-MS (A) Dynamic range of IgG 5020 as determined by PRM-MS of FZD5. Different amounts of protein lysate were immunoprecipitated with 6µg IgG 5020. Quantification was performed using heavy normalized top 3 fragments for FZD5. Error bars represent SEM, n=3. (B) Representative chromatograms of control (beads only) and 6 µg IgG 5020 for FZD5. (C) Quantification of the chromatograms in (C), n=3.

Once the immunoprecipitation workflow was optimized, we proceeded to validation via PRM-MS. Using a control IgG and IgG 5020, we quantified the levels of FZD5 in YAPC cells, a PDAC cell line with wt RNF43. While control IgG picked up no fragments of FZD5, 6 y fragments of FZD5 (QYGFAWPER) were detected in samples immunoprecipitated with IgG 5020 (Fig 4b). Quantification of signal intensity further validated the immunoprecipitation workflow using IgG 5020 (Fig 4c).

40 4.4 Effect of RNF43 knockout and RSPO treatment on FZD1, 2, 5, 7 and 8 levels in wild-type YAPC by PRM-MS

RNF43 is an E3 ubiquitin ligase transcribed upon activation of the Wnt signalling pathway. Together with ZNRF3, it plays the role of a negative regulator of Wnt signalling by ubiquitinating and targeting FZD receptors for internalization and degradation. Using PRM-mass spectrometry, we were able to study the specificity of RNF43 towards a panel of FZD receptors. This will not only help understand the potential mechanisms underlying FZD5 essentiality in RNF43 mutated PDAC cells but also shed light into its specificity towards different FZD receptors.

Using the FZD targeting PRM-MS we developed, we set out to study two thing. First, we wanted to understand the role of RNF43 behind the observed essentiality of FZD5 for the growth of RNF43 mutated PDAC cells . The hypothesis behind this work stream is that RNF43 preferentially targets FZD5 for degradation in pancreatic cancer therefore leading to its essentiality. Then, at a higher level, we also aim to gather further insights into the specificity of RNF43 towards various FZD receptors.

Figure 5

Figure 5 Effect of RNF43 knockout and RSPO treatment on FZD1, 2, 5, 7 and 8 levels in Wild-Type YAPC by PRM-MS (A) Quantification of FZD1, FZD2, FZD5, FZD7 and FZD8 in wild-type and RNF43 -/- YAPC cells using PRM-MS. Quantification was performed using heavy normalized top four fragments for each FZD. Error bars represent SEM, n=3. (B) Quantification of FZD2, FZD5, FZD7 and FZD8 in wild-type YAPC cells and 72h following RSPO treatment using PRM-MS. Quantification was performed using heavy normalized top four fragments for each FZD. Error bars represent SEM, n=3

We performed PRM-MS on both a wild-type and two RNF43-/- clones of YAPC (cl.29 and cl.38), a pancreatic cancer cell with a wild-type RNF43. We observed the strongest significant increase in FZD2 (3.7-fold) upon RNF43 loss of function (Fig 5a). This increase was observed over both clones suggesting that the effect was not clonal (Fig 5a). The second strongest significant increase in FZD receptors was observed in FZD7 (2.2- fold) and this effect was seen once again in both clones (Fig 5a). Lastly, FZD5 also expressed increases in expression albeit not as pronounced nor significant (1.7-fold) (Fig 5a). The lack of statistical significance in FZD5’s increase could be explained by the combination of a small fold increase and a large standard deviation between biological replicates (Fig 5a). Taken together, these experiments indicate that multiple FZD are upregulated upon RNF43 knockout, and therefore suggest that the FZD5 essentiality

42 observed in RNF43 mutated PDAC cells does not result from a substrate preference of RNF43 for this FZD isoform.

With a quantitative tool to measure FZD levels, we sought to gather further insights on the role of RSPO in membrane FZD regulation. Membrane levels of RNF43 and ZNRF3 are controlled by RSPOs. Indeed, binding of RSPO to its receptors, RNF43/ZNRF3 and LGR4/5, leads to endocytosis of the two E3 ligases. This results in the ultimate degradation of RNF43/ZNRF3 and an increase of membrane FZD levels. We were able to quantify this effect in vitro by treating wild-type YAPC cells with RSPO conditioned media for 72 h (Fig 5b). In wild-type YPAC cells, we saw a 8-fold increase of FZD2, a 2- fold increase in FZD5 and a 5-fold increase for FZD7 upon RSPO treatment (Fig 5b). As for FZD8, endogenous expression of FZD8 is below the LLOD and therefore, no conclusion can be made regarding the effect of FZD8 in YAPC cells (Fig 5b). Taken together, our findings support previous work on RSPO showing that it leads to increased FZD membrane expression. As RSPO regulates RNF43/ZNRF3 expression, it is not surprising that the increase in FZD observed upon RSPO treatment mirrors the FZD increase pattern in RNF43 -/- cells with FZD2 having the largest increase, followed by FZD7 and FZD5 (Fig 5a-b).

4.5 Effect of DVL knockout on FZD1, 2, 5, 7 and 8 in wild-type HEK 293T by PRM-MS

We then proceeded to study the requirement of DVL in mediating FZD expression levels through its scaffolding role. DVL proteins are known to play an important scaffolding role in targeting FZD receptor endocytosis and degradation. Indeed previous results have demonstrated that DVL mediates the interaction of RNF43 and ZNRF3 with FZD receptors to enable their ubiquitination of FZD receptors, a step necessary for their internalization and degradation. We were therefore interested to apply our mass spectrometry methods to validate these previous findings and determine the extent of FZD stabilization in the absence or presence of DVL proteins. Since three DVL genes exist (DVL1,2,3) we previously generated a triple knockout cell line that we can use in these assays.

Figure 6

Figure 6 Effect of DVL knockout on FZD 2, 5, 7 and 8 in wild-type HEK 293T by PRM-MS (A-E) Quantification of FZD2, FZD5, FZD7 and FZD8 in wild-type and DVL 1, 2, 3 triple knockout. HEK293T cells using PRM-MS. Quantification was performed using heavy normalized top four fragments for each FZD. Error bars represent SEM, n=3.

Using HEK293T wild-type and HEK293T DVL1,2,3 TKO, we observed a 2.5-fold significant upregulation in FZD5 expression but no significant changes in levels of FZD2, FZD7 and FZD8 (Fig 6). Although this finding suggests the existence of a preferential specificity towards FZD5, quantification on the other 6 FZD receptors must be executed before concluding a specificity in regulation of FZD5.

4.6 Wnt-FZD subtype specificity determines Wnt signalling in HPAF-II cells

Our results indicate that expression of multiple FZD is increased in the absence of RNF43. These results rule out our hypothesis that RNF43 had a substrate preference for FZD5 that would underlie its requirement for growth of RNF43 deficient PDAC cells. We therefore proceeded to explore the specificity of interaction between Wnt and FZD5 in this subset of Wnt addicted pancreatic cancer cells. Interestingly, out of a total of 19 Wnt ligands, WNT7B came out as the sole essential Wnt ligand in the CRISPR screens performed in HPAF-II, mirroring the preferential essentiality observed for FZD5. These results suggest that previously under-appreciated Wnt-FZD circuits may be

44 relevant at endogenous levels of expression that are masked in conditions of overexpression.

We hypothesized that the FZD5 cysteine rich domain (CRD) is specifically recognizing WNT7B in HPAF-II cells. To test this possibility, we generated a receptor chimera in which we replaced the FZD7 CRD with the CRD of FZD5. HPAF-II cell stably expressing FZD7 and the FZD5/7 chimera were then derived enabling the inducible expression of these proteins (Fig 7a). An N-terminal FLAG tag was engineered before the receptors to monitor the level of expression. In the absence of Doxycycline induction, a very faint membrane staining can be picked up by the pan-FZD antibody reflecting endogenous expression levels (Fig 7b). However, upon doxycycline induction with 1µg/ml, an increase in pan-FZD staining and a presence of FLAG staining can be detected in both lines suggesting the overexpression of the FZD constructs (Fig 7b).

Our lab previously established that FZD5 knockout in HPAF-II cells leads to cell cycle arrest and inhibition of cell proliferation whereas FZD7 had no effect (Steinhart et al, 2016). We tested both the effect of FZD5 and FZD7 knockout using FZD5g1 and FZD7g2 (gRNAs targeting FZD5 and FZD7) in both FZD7 OE and FZD5/7 OE HPAF-II cells. While expression of FZD7 did not rescue the decrease in viability observed upon FZD5 knockout, expression of the FZD5/7 chimera significantly rescued the growth impairment caused by FZD5 knockout (Fig 7c). A partial rescue can also be observed, albeit to a much lesser degree, in the uninduced HPAF-II FZD5/7 cells (Fig 7c). This could be attributed to a couple of different reasons, such as lower efficiency of knockout in the FZD5/7 cells as described by TIDE (Fig 7d) or possible leakiness of the inducible construct.

Figure 7

Fig 7 Requirement of the FZD5 CRD in Transducing Wnt Signalling HPAF-II (A) Schematic of HPAF-II cells expressing FZD7 and FZD5/7 chimeric constructs respectively. Expression of the FZDs is controlled by a doxycycline inducible system. (B) Immunofluorescence of HPAF-II cells lines respectively infected with the FZD7 or FZD5/7 constructs respectively. Cells were treated with varying concentrations of doxycycline for 72 hours and stained with anti-FZD IgG 5020 (green), FLAG (Red) and DAPI (Blue). (C) Cell viability assays in wild-type, FZD7 or FZD5/7 expressing HPAF-II cells. Each cell line was

46 knocked out for FZD5, FZD7 or LacZ as a control using CRISPR-Cas9. 10 000 cells from each knockout population were re-plated to assess for cell line viability using crystal violet staining. Quantification is performed by normalizing each knockout to LacZ knockout. Error bars are representative of SEM, n=5. (D) Representative tracking of indels by decomposition (TIDE) analysis was used to quantify proportions of indel types generated with FZD5 and FZD7 gRNAs in HPAF WT, HPAF FZD7 and HPAF FZD5/7.

All together, these findings suggest that FZD5 specifically recognized WNT7B in HPAF- II cells and that this specific signaling circuit underlies the essentiality revealed in the CRISPR screen. These results also support preferential interactions between Wnt growth factors and Frizzled receptors.

47 Chapter 5: Discussion

5.1 Development of a parallel reaction monitoring mass spectrometry technique to quantify FZD receptors

“Electrophoretic Transfer of Proteins from Polyacrylamide Gels to Nitrocellulose Sheets: Procedure and Some Applications” has been cited, as of July 2019, over 58 000 times, putting it in the elite group of papers having revolutionized life science research. This goes to exemplify the revolutionary role that Western blotting has played in the field of proteomics since the paper’s publication in 1979. However, as the authors caveated in their publication, “for sodium dodecyl sulfate gels, the original band pattern was obtained with no loss of resolution, but the transfer was not quantitative” (Towbin et al., 1979). The advance of mass spectrometry technologies in the past decades is providing new powerful solutions to quantify proteins in complex samples. The development of targeted mass spectrometry methods, specifically methods such as parallel reaction monitoring mass spectrometry and multiple reaction monitoring mass spectrometry, is allowing for precise, sensitive and reproducible mass spectrometry-based protein quantification.

Because of the lack of robust FZD antibodies, detection and quantification of FZD receptors have historically been a challenging task. Furthermore, the need for selective antibodies specific for each FZD receptor makes systematic FZD quantification a rather expensive endeavor. In our study, we developed a parallel reaction monitoring mass spectrometry technique that enables the simultaneous quantification of a panel of FZD receptors using a pan-FZD antibody specific for 6 FZD that we recently developed (Pavlovic et al., 2018). This method is useful in that it bypasses the need for selective antibodies for the individual FZD receptors since it uses proteolytic signature peptides as surrogates for each FZD, making simultaneous FZD quantification significantly more accessible. One limitation of our method is that the current pan-FZD antibody recognizes six of the ten FZD receptors (1,2,4,5,7,8) hence four FZD still cannot be quantified. New antibodies with extended cross-reactivity or selectivity for the other FZDs could be used in the future to quantify all FZD receptors.

48 5.2 Elucidating the role of RNF43 and RSPO on FZD expression

Using our PRM-MS method, we first wanted to understand the role of RNF43 in regulating FZD receptors. More specifically, we wanted to understand whether RNF43 played a preferential role in regulating FZD5 expression vs the expression of all other FZD receptors. Using wild-type and RNF43 mutant pancreatic cancer lines, we observed that the presence of RNF43 loss of function mutations leads to upregulation, although not significant in all FZD, of not only FZD5 (not significant) but also FZD1 (not significant), FZD2 and FZD7. Upregulation was most pronounced in FZD2, followed by FZD7, FZD1 and then FZD5. FZD8 expression levels in our pancreatic cancer model cell line (YAPC) was below the lower limit of detection. Altogether, we show that RNF43 does not exhibit substrate preference towards FZD5 but rather acts on the entire population of FZD receptors, albeit with different efficiency for each FZD. This finding raises new questions to understand the specificity and selectivity of RNF43 in degrading FZD receptors.

RSPO has previously been demonstrated to act as a potentiator of the Wnt signalling pathway (de Lau et al., 2012). Using our PRM-MS method, we set out to quantify the upregulation of FZD following RSPO treatment in pancreatic cancer cells. Since RSPO acts through RNF43 our results consistently showed that levels of multiple FZD were induced by RSPO. FZD2 has the highest upregulation upon RSPO treatment, followed by FZD7 and FZD5. These findings go to further confirm the role of RSPO in regulating FZD expression while highlighting a potential preference for specific FZD receptors over others. Seeing the involvement of RNF43 and ZNRF3 in the mediation of FZD expression by RSPO, a study of the effect of RSPO in RNF43 mutant, ZNRF43 mutant and RNF43/ZNRF3 double mutant lines on FZD expression would reveal further insights into RSPO’s role.

5.3 Elucidating DVL’s role in FZD regulation

Using our quantitative PRM-mass spectrometry tool, we were able to study the effect of a DVL triple knockout on FZD receptor levels in HEK293T cells. DVL is known to be required for the RNF43-dependent regulation of FZD (Gammons et al., 2016; Jiang et

49 al., 2015). While many studies have been performed on the mechanism behind this upregulation, no work has been done on evaluating systematically the upregulation of each FZD receptor upon DVL loss of function mutations.

Using PRM-MS, we quantified the increase in FZD receptors in DVL triple knockout (DVL1, DVL2 and DVL3 knockout) cells and found that DVL leads to a significant increase in FZD5 but has no significant effect on the level of FZD2, FZD7 and FZD8. While this finding points to a potential specificity of DVL towards FZD5, a systematic study of the effect of DVL on all ten FZD receptors must be conducted before making any further conclusions. Our findings support other groups’ previous work on the regulation of DVL on FZD5 (Gammons et al., 2016). However, it is contradictory to some previous findings that DVL knockout increases membrane levels of FZD4 in an overexpression setting (Jiang et al., 2015). However, this could also be caveated by the use of an FZD overexpression system in their study vs an endogenous FZD expression study in ours.

Overall, our findings on DVL’s specificity towards FZD5 vs FZD2, FZD4 or FZD8 warrants further studies into DVL’s specificity towards other FZD receptors and also reason behind DVL’s selectivity towards FZD5.

5.4 Validating the Wnt-FZD signalling circuit required in RNF43 mutated PDAC cells.

Previous work done in pancreatic cancer cells has illustrated the involvement of specific Wnt ligands in pancreatic cancer development. Wnt7B was shown to be expressed in the pancreatic epithelium and required to be expressed within a specific range as its loss led to a reduction in pancreatic mass while its overexpression causes cystic epithelial metaplasia (Afelik et al., 2015). Further studies in HPAF-II pancreatic cancer cells has also demonstrated a higher level of Wnt7B expression, correlating with a high level of Wnt signalling present in the cells, when compared to healthy pancreatic cells (Arensman et al., 2014). These two studies, taken together, highlight the specific role of Wnt7B in pancreatic cancer development.

The discovery of FZD5 being the only essential FZD receptor out of a panel of ten and Wnt7B, Wnt10A and Wnt3A being the only essential ligand essential amongst all 19 Wnts suggest the existence of preferential Wnts and FZD circuits in different pancreatic cancer cells (Steinhart et al., 2017). With Wnt signalling shown to be required for the growth of RNF43 mutant pancreatic cancer cells, we set out to investigate the requirement for specificity of interactions between Wnt growth factors and Frizzled receptors.

We hypothesized that secreted WNT7B preferentially interacts with FZD5 in RNF43 mutated PDAC cells thereby underlying their essentiality phenotypes. To directly test this, we built chimeric receptors between FZD5 and FZD7 (fig 4a), in which we swapped the CRD domain of FZD7 for the CRD of FZD5. Knowing that FZD7 expression is not sufficient to rescue the knockout of FZD5 in these cells, we rationalized that introduction of the FZD5 CRD would enable recognition of WNT7B that perhaps interacts preferentially with FZD5 and lead to downstream β-catenin signaling. Our results demonstrated that the overexpression of FZD7 was not sufficient to rescue the growth arrest induced upon FZD5 knockout but that expression of the chimera could rescue the growth. Together with previous findings showing that Wnt conditioned media could also rescue the growth arrest in FZD5 knockout (Steinhart et al., 2017), our results suggest that at endogenous levels of expression, a previously underappreciated level of specificity exist between Wnt and Frizzled receptors .

51 Chapter 6: Perspectives

Study of FZD expression has traditionally been through methods, such as flow cytometry, that rely heavily on FZD specific antibodies for quantification, making concurrent quantification of multiple FZD receptors difficult and expensive. The development of a parallel reaction monitoring mass spectrometry method enabling simultaneous quantification of multiple FZD receptors in the same sample opens the door for quicker and easier study of the Wnt signalling pathway. The use of a single pan- FZD antibody coupled with the power of targeted proteomics not only reduces the cost associated with multiple antibodies but also enables accurate FZD detection and quantification at a much lower magnitude, proving itself to be a significant improvement from traditional quantification methods. Although our current method demonstrates improvement from those traditionally used, it is currently limited in terms of which FZD receptor it is able to quantify. As the antibody used, IgG2919, is only specific for 6 of the 10 FZD receptors (1,2,4,5,7,8), our approach could be borrowed and re-applied to pan-FZD antibodies selective for a higher number of FZD receptors.

Using our PRM-MS method, we demonstrated the absence of specificity of the E3 ligase RNF43: we observed increases in all FZD 1, 2, 5, 7 and 8 in YAPC immortalized pancreatic cancer cells harboring a loss of function mutation in RNF43. This finding opens up a couple of questions for further research. First, is this lack of RNF43 specificity context dependent? One interesting avenue to explore this hypothesis would be to re-introduce RNF43 at an endogenous level into HPAF-II and test FZD5 essentiality in that engineered cell line. Concurrently, it would be worthwhile to test the effect of a RNF43 mutation in other cell lines, cancerous or non-cancerous. This exploration will allow us to gather further insights on RNF43’s specificity and its context specificity.

Previous work on ZNRF3, an E3 ligase homologue to RNF43, has demonstrated its role in regulating membrane FZD expression as well. Its depletion has shown to increase the membrane FZD expression level of all FZDs but also specifically the membrane level of FZD8 in an overexpression system in HEK293T cells (Hao et al., 2012). With our

52 discoveries in RNF43’s regulation of FZD receptors, it would be interesting to investigate the role of ZNRF3 specifically in mediating FZD levels. By performing a systematic study of the regulation of RNF43, ZNRF3 and RNF43 and ZNRF3 on all FZD receptors, we would gain further insight on the membrane regulation of FZD receptors, the functional redundancy of the two E3 ligases and their role and essentiality in different biological contexts.

Furthermore, our studies highlighted an interesting and novel substrate specificity of DVL towards FZD5. Using a DVL triple knockout model in HEK293T cells, we observed a selective increase in FZD5 only amongst a panel of four receptors (FZD2, 5, 7 and 8). Although intriguing, conclusions on DVL specificity cannot be made prior to determining whether this is exclusive to FZD5 or observed amongst other non-studied FZD receptors. Therefore, the immediate next step would be to systematically quantify the effect of a DVL triple knockout on all 10 FZD receptors. Concurrently, in order to determine whether this effect is context specific, it would be valuable to study the effect of DVL knockout in other cell lines as well. Lastly, seeing the essentiality of FZD5 in HPAF-II cell lines, it would be of interest to examine whether DVL is mutated or not in HPAF-II. One possible hypothesis explaining FZD5 essentiality in HPAF-II is that HPAF-II have a mutation in all three DVL and FZD5 expression in HPAF-II is mediated by ZNRF3. These two scenarios, when combined together, would lead to higher expression level of FZD5 which in turn could explain the FZD5 essentiality.

Lastly, our work unveiled a preferential signalling circuit in the HPAF-II between two Wnt ligands and one FZD receptor. In RNF43 mutant HPAF-II pancreatic cancer cells, endogenous Wnt ligands were found to signal preferentially through FZD5’s CRD vs FZD7’s CRD. This finding is extremely novel as very little is known as to which Wnt ligands signal through which FZD receptors. This is in large part due to the complexity of the Wnt signalling pathway, with a total of 17 possible Wnt ligands signalling through 10 possible FZD receptors. Our discovery raises the question of why, in the context of an RNF43 dependency, do pancreatic cancer cells signal through FZD5 with Wnt10a and Wnt7b? Is there even a link between the RNF43 mutation and the new uncovered signalling axis? In addition, the understanding of preferential signalling in cancer

53 specific context can be extremely beneficial for drug development as it enables researchers to target specific Wnt-FZD axis driving cancerous growth but not normal tissue homeostasis. This is turn will significantly decrease side effects from targeting the Wnt pathway.

54 Appendix A: Representative PRM-MS Chromatograms Figure 8

Figure 8 Representative PRM-MS chromatograms PRM-MS traces as plotted in Skyline for heavy labeled, endogenous and quantified peptides of FZD1. Chromatographs representative of all FZDs’ PRM-MS.

Appendix B: Representative TIDE Chromatograms Figure 9

Figure 9 Representative TIDE chromatograms Representative tracking of indels by decomposition (TIDE) analysis was used to quantify proportions of indel types generated with FZD5 and FZD7 gRNAs in HPAF WT, HPAF FZD7 and HPAF FZD5/7.

55 References

Abbatiello, S.E., Mani, D.R., Keshishian, H., and Carr, S.A. (2010). Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry. Clin. Chem. 56, 291–305.

Aberle, H., Bauer, A., Stappert, J., Kispert, A., and Kemler, R. (1997). β‐catenin is a target for the ubiquitin–proteasome pathway. EMBO J. 16, 3797–3804.

Afelik, S., Pool, B., Schmerr, M., Penton, C., and Jensen, J. (2015). Wnt7b is required for epithelial progenitor growth and operates during epithelial-to-mesenchymal signalling in pancreatic development. Dev. Biol. 399, 204–217.

Al-Aynati, M.M., Radulovich, N., Riddell, R.H., and Tsao, M.-S. (2004). Epithelial-cadherin and beta-catenin expression changes in pancreatic intraepithelial neoplasia. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 10, 1235–1240.

Arce, L., Yokoyama, N.N., and Waterman, M.L. (2006). Diversity of LEF/TCF action in development and disease. Oncogene 25, 7492.

Arensman, M.D., Kovochich, A.N., Kulikauskas, R.M., Lay, A.R., Yang, P.-T., Li, X., Donahue, T., Major, M.B., Moon, R.T., Chien, A.J., et al. (2014). WNT7B mediates autocrine Wnt/β- catenin signalling and anchorage-independent growth in pancreatic adenocarcinoma. Oncogene 33, 899–908.

Assié, G., Letouzé, E., Fassnacht, M., Jouinot, A., Luscap, W., Barreau, O., Omeiri, H., Rodriguez, S., Perlemoine, K., René-Corail, F., et al. (2014). Integrated genomic characterization of adrenocortical carcinoma. Nat. Genet. 46, 607–612.

Bantcheff, M., Schirle, M., Sweetman, G., Rick, J., and Kuster, B. (2007) Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017-1031.

Bänziger, C., Soldini, D., Schütt, C., Zipperlen, P., Hausmann, G., and Basler, K. (2006). Wntless, a conserved membrane protein dedicated to the secretion of Wnt proteins from signalling cells. Cell 125, 509–522.

56 Baron, R., and Kneissel, M. (2013). WNT signalling in bone homeostasis and disease: from human mutations to treatments. Nat. Med. 19, 179–192.

Basturk, O., Hong, S.-M., Wood, L.D., Adsay, N.V., Albores-Saavedra, J., Biankin, A.V., Brosens, L.A.A., Fukushima, N., Goggins, M., Hruban, R.H., et al. (2015). A Revised Classification System and Recommendations From the Baltimore Consensus Meeting for Neoplastic Precursor Lesions in the Pancreas. Am. J. Surg. Pathol. 39, 1730–1741.

Beavis, R.C. (2006). Using the global proteome machine for protein identification. Methods Mol. Biol. Clifton NJ 328, 217–228.

Behrens, J., Kries, J.P. von, Kühl, M., Bruhn, L., Wedlich, D., Grosschedl, R., and Birchmeier, W. (1996). Functional interaction of β-catenin with the transcription factor LEF-1. Nature 382, 638.

Bilic, J., Huang, Y.-L., Davidson, G., Zimmermann, T., Cruciat, C.-M., Bienz, M., and Niehrs, C. (2007). Wnt induces LRP6 signalosomes and promotes dishevelled-dependent LRP6 phosphorylation. Science 316, 1619–1622.

Bondarenko, PV., Chelius, D., and Shaler, TA. (2002). Identification and relative quantification of protein mixtures by enzymatic digestion followed by capillary reversed-phase liquid chromatography-tandem mass spectrometry. Anal. Chem. 74, 4741-4749.

Brault,V., Moore, R. and Kemler, R. (2001). Inactivation of the beta-catenin gene by Wnt1-Cre- mediated deletion results in dramatic brain malformation and failure of craniofacial development. Development 128, 1253-1265.

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., and Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. Cancer J. Clin. 68, 394–424.

Chen, P.-H., Chen, X., Lin, Z., Fang, D., and He, X. (2013). The structural basis of R-spondin recognition by LGR5 and RNF43. Genes Dev. 27, 1345–1350.

Cliffe, A., Hamada, F., and Bienz, M. (2003). A role of Dishevelled in relocating Axin to the plasma membrane during wingless signalling. Curr. Biol. CB 13, 960–966.

57 Cong, F., Schweizer, L., and Varmus, H. (2004). Wnt signals across the plasma membrane to activate the β-catenin pathway by forming oligomers containing its receptors, Frizzled and LRP. Development 131, 5103–5115.

Cosman, F., Crittenden, D.B., Adachi, J.D., Binkley, N., Czerwinski, E., Ferrari, S., Hofbauer, L.C., Lau, E., Lewiecki, E.M., Miyauchi, A., et al. (2016). Romosozumab Treatment in Postmenopausal Women with Osteoporosis.

Cox, J., and Mann, M. (2011). Quantitative, High-Resolution Proteomics for Data-Driven Systems Biology. Annu. Rev. Biochem. 80, 273–299.

Deutsch, E.W., Lam, H., and Aebersold, R. (2008). PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 9, 429–434.

Dijksterhuis, J.P., Baljinnyam, B., Stanger, K., Sercan, H.O., Ji, Y., Andres, O., Rubin, J.S., Hannoush, R.N., and Schulte, G. (2015). Systematic Mapping of WNT-FZD Protein Interactions Reveals Functional Selectivity by Distinct WNT-FZD Pairs. J. Biol. Chem. 290, 6789–6798.

Farin, H.F., Jordens, I., Mosa, M.H., Basak, O., Korving, J., Tauriello, D.V.F., de Punder, K., Angers, S., Peters, P.J., Maurice, M.M., et al. (2016). Visualization of a short-range Wnt gradient in the intestinal stem-cell niche. Nature 530, 340–343.

Fenn, J.B., Mann, M., Meng, C.K., Wong, S.F., and Whitehouse, C.M. (1989). Electrospray ionization for mass spectrometry of large biomolecules. Science 246, 64–71.

Fevr, T., Robine, S., Louvard, D., and Huelsken, J. (2007). Wnt/ -Catenin Is Essential for Intestinal Homeostasis and Maintenance of Intestinal Stem Cells. Mol. Cell. Biol. 27, 7551– 7559.

Gallien, S., and Domon, B. (2015). Detection and quantification of proteins in clinical samples using high resolution mass spectrometry. Methods San Diego Calif 81, 15–23.

Gallien, S., Duriez, E., Crone, C., Kellmann, M., Moehring, T., and Domon, B. (2012). Targeted proteomic quantification on quadrupole-orbitrap mass spectrometer. Mol. Cell. Proteomics MCP 11, 1709–1723.

58 Gammons, M.V., Rutherford, T.J., Steinhart, Z., Angers, S., and Bienz, M. (2016). Essential role of the Dishevelled DEP domain in a Wnt-dependent human-cell-based complementation assay. J. Cell Sci. 129, 3892–3902.

Gang, E.J., Hsieh, Y.-T., Pham, J., Zhao, Y., Nguyen, C., Huantes, S., Park, E., Naing, K., Klemm, L., Swaminathan, S., et al. (2014). Small-molecule inhibition of CBP/catenin interactions eliminates drug-resistant clones in acute lymphoblastic leukemia. Oncogene 33, 2169–2178.

Ghosn, M., Kourie, H.R., El Rassy, E., Haddad, F.G., Hanna, C., El Karak, F., and Nasr, D. (2016). Where does chemotherapy stands in the treatment of ampullary carcinoma? A review of literature. World J. Gastrointest. Oncol. 8, 745–750.

Giannakis, M., Hodis, E., Jasmine Mu, X., Yamauchi, M., Rosenbluh, J., Cibulskis, K., Saksena, G., Lawrence, M.S., Qian, Z.R., Nishihara, R., et al. (2014). RNF43 is frequently mutated in colorectal and endometrial cancers. Nat. Genet. 46, 1264–1266.

Giraudet, A.-L., Badel, J.-N., Cassier, P., Desuzinges, C., Kriza, D., Perol, D., and Blay, J.-Y. (2014). SYNFRIZZ-A phase Ia/Ib of a radiolabelled monoclonal AB for the treatment of relapsing synovial sarcoma. J. Nucl. Med. 55, 223–223.

Gross, J.C., Chaudhary, V., Bartscherer, K., and Boutros, M. (2012). Active Wnt proteins are secreted on exosomes. Nat. Cell Biol. 14, 1036–1045.

Gurney, A., Axelrod, F., Bond, C.J., Cain, J., Chartier, C., Donigan, L., Fischer, M., Chaudhari, A., Ji, M., Kapoun, A.M., et al. (2012). Wnt pathway inhibition via the targeting of Frizzled receptors results in decreased growth and tumorigenicity of human tumors. Proc. Natl. Acad. Sci. U. S. A. 109, 11717–11722.

Hao, H.-X., Xie, Y., Zhang, Y., Charlat, O., Oster, E., Avello, M., Lei, H., Mickanin, C., Liu, D., Ruffner, H., et al. (2012). ZNRF3 promotes Wnt receptor turnover in an R-spondin-sensitive manner. Nature 485, 195–200.

Hingorani, S.R., and Tuveson, D.A. (2003). Ras redux: rethinking how and where Ras acts. Curr. Opin. Genet. Dev. 13, 6–13.

Hong, D.S.-M., Park, D.J.Y., Hruban, D.R.H., and Goggins, D.M. (2012). Molecular Signatures of Pancreatic Cancer. 24.

59 Hoppler, S., and Kavanagh, C.L. (2007). Wnt signalling: variety at the core. J. Cell Sci. 120, 385–393.

Hruban, R., Pitman, M., and Klimstra, D. (2007). Tumors of the Pancreas (Washington, DC: Armed Forces Institue of Pathology).

Hruban, R.H., Maitra, A., and Goggins, M. (2008). Update on pancreatic intraepithelial neoplasia. Int. J. Clin. Exp. Pathol. 1, 306–316.

Iodice, S., Gandini, S., Maisonneuve, P., and Lowenfels, A.B. (2008). Tobacco and the risk of pancreatic cancer: a review and meta-analysis. Langenbecks Arch. Surg. 393, 535–545.

Janda, C.Y., Waghray, D., Levin, A.M., Thomas, C., and Garcia, K.C. (2012). Structural Basis of Wnt Recognition by Frizzled. Science 337, 59–64.

Jawad, M.U., Fritton, K.E., Ma, T., Ren, P.-G., Goodman, S.B., Ke, H.Z., Babij, P., and Genovese, M.C. (2013). Effects of sclerostin antibody on healing of a non-critical size femoral bone defect. J. Orthop. Res. 31, 155–163.

Jiang, X., Hao, H.-X., Growney, J.D., Woolfenden, S., Bottiglio, C., Ng, N., Lu, B., Hsieh, M.H., Bagdasarian, L., Meyer, R., et al. (2013). Inactivating mutations of RNF43 confer Wnt dependency in pancreatic ductal adenocarcinoma. Proc. Natl. Acad. Sci. 110, 12649–12654.

Jiang, X., Charlat, O., Zamponi, R., Yang, Y., and Cong, F. (2015). Dishevelled promotes Wnt receptor degradation through recruitment of ZNRF3/RNF43 E3 ubiquitin ligases. Mol. Cell 58, 522–533.

Jones, S., Zhang, X., Parsons, D.W., Lin, J.C.-H., Leary, R.J., Angenendt, P., Mankoo, P., Carter, H., Kamiyama, H., Jimeno, A., et al. (2008). Core signalling pathways in human pancreatic cancers revealed by global genomic analyses. Science 321, 1801–1806.

Kadowaki, T., Wilder, E., Klingensmith, J., Zachary, K., and Perrimon, N. (1996). The segment polarity gene porcupine encodes a putative multitransmembrane protein involved in Wingless processing. Genes Dev. 10, 3116–3128.

Kiecker, C. and Nierhs, C. A morphogen gradient of Wnt/beta-catenin signalling regulates anteroposterior neural patterning in Xenopus. Development 128, 4189-4201

60 Kim, K.-A., Kakitani, M., Zhao, J., Oshima, T., Tang, T., Binnerts, M., Liu, Y., Boyle, B., Park, E., Emtage, P., et al. (2005). Mitogenic influence of human R-spondin1 on the intestinal epithelium. Science 309, 1256–1259.

Kim, Y.J., Gallien, S., El-Khoury, V., Goswami, P., Sertamo, K., Schlesser, M., Berchem, G., and Domon, B. (2015). Quantification of SAA1 and SAA2 in lung cancer plasma using the isotype-specific PRM assays. Proteomics 15, 3116–3125.

Kinzel, B., Pikiolek, M., Orsini, V., Sprunger, J., Isken, A., Zietzling, S., Desplanches, M., Dubost, V., Breustedt, D., Valdez, R., et al. (2014). Functional roles of Lgr4 and Lgr5 in embryonic gut, kidney and skin development in mice. Dev. Biol. 390, 181–190.

Kinzler, K.W., Nilbert, M.C., Su, L.K., Vogelstein, B., Bryan, T.M., Levy, D.B., Smith, K.J., Preisinger, A.C., Hedge, P., and McKechnie, D. (1991). Identification of FAP locus genes from chromosome 5q21. Science 253, 661–665.

Komekado, H., Yamamoto, H., Chiba, T., and Kikuchi, A. (2007). Glycosylation and palmitoylation of Wnt-3a are coupled to produce an active form of Wnt-3a. Genes Cells Devoted Mol. Cell. Mech. 12, 521–534.

Koo, B.-K., Spit, M., Jordens, I., Low, T.Y., Stange, D.E., van de Wetering, M., van Es, J.H., Mohammed, S., Heck, A.J.R., Maurice, M.M., et al. (2012). Tumour suppressor RNF43 is a stem-cell E3 ligase that induces endocytosis of Wnt receptors. Nature 488, 665–669.

Korinek, V., Barker, N., Morin, P.J., van Wichen, D., de Weger, R., Kinzler, K.W., Vogelstein, B., and Clevers, H. (1997). Constitutive transcriptional activation by a beta-catenin-Tcf complex in APC-/- colon carcinoma. Science 275, 1784–1787.

Korkut, C., Ataman, B., Ramachandran, P., Ashley, J., Barria, R., Gherbesi, N., and Budnik, V. (2009). Trans-Synaptic Transmission of Vesicular Wnt Signals through Evi/Wntless. Cell 139, 393–404.

Lau, T., Chan, E., Callow, M., Waaler, J., Boggs, J., Blake, R.A., Magnuson, S., Sambrone, A., Schutten, M., Firestein, R., et al. (2013). A novel tankyrase small-molecule inhibitor suppresses APC mutation-driven colorectal tumor growth. Cancer Res. 73, 3132–3144.

61 de Lau, W., Barker, N., Low, T.Y., Koo, B.-K., Li, V.S.W., Teunissen, H., Kujala, P., Haegebarth, A., Peters, P.J., van de Wetering, M., et al. (2011). Lgr5 homologues associate with Wnt receptors and mediate R-spondin signalling. Nature 476, 293–297. de Lau, W.B.M., Snel, B., and Clevers, H.C. (2012). The R-spondin protein family. Genome Biol. 13, 242.

Li, S., Nakayama, T., Akinc, A., Wu, S.-L., and Karger, B.L. (2015). Development of LC-MS methods for quantitation of hepcidin and demonstration of siRNA-mediated hepcidin suppression in serum. J. Pharmacol. Toxicol. Methods 71, 110–119.

Lindsley, R., Gill, J., Kyba, M., Murphy, T., and Murphy, K. (2006). Canonical Wnt signaling is required for development of embryonic stem cell-derived mesoderm. Development 113, 3787- 3796.

Löhr, M., Klöppel, G., Maisonneuve, P., Lowenfels, A.B., and Lüttges, J. (2005). Frequency of K-ras mutations in pancreatic intraductal neoplasias associated with pancreatic ductal adenocarcinoma and chronic pancreatitis: a meta-analysis. Neoplasia N. Y. N 7, 17–23.

Lundgren, D., Hwang, S., Wu, L., and Han, D. (2014) Role of spectral counting in quantitative proteomics. Expert Review of Proteomics. 7, 39-53

Lyashenko, N., Winter, M., Migliorini, D., Biechele, T., Moon, R.T., and Hartmann, C. (2011). Differential requirement for the dual functions of β-catenin in embryonic stem cell self-renewal and germ layer formation. Nat. Cell Biol. 13, 753–761.

MacDonald, B.T., and He, X. (2012). Frizzled and LRP5/6 Receptors for Wnt/β-Catenin Signalling. Cold Spring Harb. Perspect. Biol. 4.

MacDonald, B.T., Tamai, K., and He, X. (2009). Wnt/beta-catenin signalling: components, mechanisms, and diseases. Dev. Cell 17, 9–26.

MacLean, B., Tomazela, D., and MacCoss, M. (2010). Skyline: an open source document editor for creating and analyzing proteomics experiments. Bioinformatics 26, 966-968.

Maitra, A., and Hruban, R.H. (2008). Pancreatic cancer. Annu. Rev. Pathol. 3, 157–188.

62 Maitra, A., Fukushima, N., Takaori, K., and Hruban, R.H. (2005). Precursors to invasive pancreatic cancer. Adv. Anat. Pathol. 12, 81–91.

Makohon-Moore, A., and Iacobuzio-Donahue, C.A. (2016). Pancreatic cancer biology and genetics from an evolutionary perspective. Nat. Rev. Cancer 16, 553–565.

Madan, B., Mitchell, M., Foxa, G., Diegel, C., Williams, B., Virshup, D. (2018) Bone loss from Wnt inhibition mitigated by concurrent alendronate therapy. Bone Research 6.

Matthaios, D., Zarogoulidis, P., Balgouranidou, I., Chatzaki, E., and Kakolyris, S. (2011). Molecular Pathogenesis of Pancreatic Cancer and Clinical Perspectives. Oncology 81, 259– 272.

McGuigan, A., Kelly, P., Turkington, R.C., Jones, C., Coleman, H.G., and McCain, R.S. (2018). Pancreatic cancer: A review of clinical diagnosis, epidemiology, treatment and outcomes. World J. Gastroenterol. 24, 4846–4861.

McMahon, A.P., and Moon, R.T. (1989). Ectopic expression of the proto-oncogene int-1 in Xenopus embryos leads to duplication of the embryonic axis. Cell 58, 1075–1084.

Mohammed, S., Van Buren, G., and Fisher, W.E. (2014). Pancreatic cancer: advances in treatment. World J. Gastroenterol. 20, 9354–9360.

Moon, R.T., and Kimelman, D. From cortical rotation to organizer gene expression: toward a molecular explanation of axis specification in Xenopus. 10.

Morris, J.P., Cano, D.A., Sekine, S., Wang, S.C., and Hebrok, M. (2010). Beta-catenin blocks Kras-dependent reprogramming of acini into pancreatic cancer precursor lesions in mice. J. Clin. Invest. 120, 508–520.

Nagayama, S., Katagiri, T., Tsunoda, T., Hosaka, T., Nakashima, Y., Araki, N., Kusuzaki, K., Nakayama, T., Tsuboyama, T., Nakamura, T., et al. (2002). Genome-wide analysis of gene expression in synovial sarcomas using a cDNA microarray. Cancer Res. 62, 5859–5866.

Neoptolemos, J.P., Kleeff, J., Michl, P., Costello, E., Greenhalf, W., and Palmer, D.H. (2018). Therapeutic developments in pancreatic cancer: current and future perspectives. Nat. Rev. Gastroenterol. Hepatol. 15, 333.

63 Nusse, R., and Varmus, H.E. (1982). Many tumors induced by the mouse mammary tumor virus contain a provirus integrated in the same region of the host genome. Cell 31, 99–109.

Nusse, R., van Ooyen, A., Cox, D., Fung, Y.K., and Varmus, H. (1984). Mode of proviral activation of a putative mammary oncogene (int-1) on mouse chromosome 15. Nature 307, 131–136.

Nusse, R., Brown, A., Papkoff, J., Scambler, P., Shackleford, G., McMahon, A., Moon, R., and Varmus, H. (1991). A new nomenclature for int-1 and related genes: the Wnt gene family. Cell 64, 231.

Nüsslein-Volhard, C., and Wieschaus, E. (1980). Mutations affecting segment number and polarity in Drosophila. Nature 287, 795–801.

Pan, W.J., Pang, S.Z., Huang, T., Guo, H.Y., Wu, D., and Li, L. (2004). Characterization of function of three domains in dishevelled-1: DEP domain is responsible for membrane translocation of dishevelled-1. Cell Res. 14, 324–330.

Pavlovic, Z., Adams, J.J., Blazer, L.L., Gakhal, A.K., Jarvik, N., Steinhart, Z., Robitaille, M., Mascall, K., Pan, J., Angers, S., et al. (2018). A synthetic anti-Frizzled antibody engineered for broadened specificity exhibits enhanced anti-tumor properties. MAbs 10, 1157–1167.

Peterson, A.C., Russell, J.D., Bailey, D.J., Westphall, M.S., and Coon, J.J. (2012). Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol. Cell. Proteomics MCP 11, 1475–1488.

Prakash, A., Tomazela, D.M., Frewen, B., Maclean, B., Merrihew, G., Peterman, S., and Maccoss, M.J. (2009). Expediting the development of targeted SRM assays: using data from shotgun proteomics to automate method development. J. Proteome Res. 8, 2733–2739.

Rauniyar, N. (2015). Parallel Reaction Monitoring: A Targeted Experiment Performed Using High Resolution and High Mass Accuracy Mass Spectrometry. Int. J. Mol. Sci. 16, 28566– 28581.

Rebel, V.I., Kung, A.L., Tanner, E.A., Yang, H., Bronson, R.T., and Livingston, D.M. (2002). Distinct roles for CREB-binding protein and p300 in hematopoietic stem cell self-renewal. Proc. Natl. Acad. Sci. U. S. A. 99, 14789–14794.

64 Riggleman, B., Wieschaus, E., and Schedl, P. (1989). Molecular analysis of the armadillo locus: uniformly distributed transcripts and a protein with novel internal repeats are associated with a Drosophila segment polarity gene. Genes Dev. 3, 96–113.

Rothbächer, U., Laurent, M.N., Deardorff, M.A., Klein, P.S., Cho, K.W., and Fraser, S.E. (2000). Dishevelled phosphorylation, subcellular localization and multimerization regulate its role in early embryogenesis. EMBO J. 19, 1010–1022.

Rubinfeld, B., Souza, B., Albert, I., Müller, O., Chamberlain, S.H., Masiarz, F.R., Munemitsu, S., and Polakis, P. (1993). Association of the APC gene product with beta-catenin. Science 262, 1731–1734.

Sato, N., Meijer, L., Skaltsounis, L., Greengard, P., and Brivanlou, A.H. (2004). Maintenance of pluripotency in human and mouse embryonic stem cells through activation of Wnt signalling by a pharmacological GSK-3-specific inhibitor. Nat. Med. 10, 55–63.

Schiffmann, C., Hansen, R., Baumann, S., Kublik, A., Nielsen, P.H., Adrian, L., von Bergen, M., Jehmlich, N., and Seifert, J. (2014). Comparison of targeted peptide quantification assays for reductive dehalogenases by selective reaction monitoring (SRM) and precursor reaction monitoring (PRM). Anal. Bioanal. Chem. 406, 283–291.

Schwarz-Romond, T., Fiedler, M., Shibata, N., Butler, P.J.G., Kikuchi, A., Higuchi, Y., and Bienz, M. (2007). The DIX domain of Dishevelled confers Wnt signalling by dynamic polymerization. Nat. Struct. Mol. Biol. 14, 484–492.

Seshagiri, S., Stawiski, E.W., Durinck, S., Modrusan, Z., Storm, E.E., Conboy, C.B., Chaudhuri, S., Guan, Y., Janakiraman, V., Jaiswal, B.S., et al. (2012). Recurrent R-spondin fusions in colon cancer. Nature 488, 660–664.

Stahl-Zeng, J., Lange, V., Ossola, R., Eckhardt, K., Krek, W., Aebersold, R., and Domon, B. (2007). High sensitivity detection of plasma proteins by multiple reaction monitoring of N- glycosites. Mol. Cell. Proteomics MCP 6, 1809–1817.

Stamos, L., Chu, M., and Weis, W. (2014). Structural basis of GSK-3 inhibition by N-terminal phosphorylation and by the Wnt receptor LRP6. eLife. 3

Stark, A., and Eibl, G. Pancreatic Ductal Adenocarcinoma (The University of Michigan Library).

65 Steinhart, Z., Pavlovic, Z., Chandrashekhar, M., Hart, T., Wang, X., Zhang, X., Robitaille, M., Brown, K.R., Jaksani, S., Overmeer, R., et al. (2017). Genome-wide CRISPR screens reveal a Wnt–FZD5 signalling circuit as a druggable vulnerability of RNF43-mutant pancreatic tumors. Nat. Med. 23, 60–68.

Storm, E.E., Durinck, S., de Sousa e Melo, F., Tremayne, J., Kljavin, N., Tan, C., Ye, X., Chiu, C., Pham, T., Hongo, J.-A., et al. (2016). Targeting PTPRK-RSPO3 colon tumours promotes differentiation and loss of stem-cell function. Nature 529, 97–100.

Tabar, V., and Studer, L. (2014). Pluripotent stem cells in regenerative medicine: challenges and recent progress. Nat. Rev. Genet. 15, 82–92.

Taelman, V.F., Dobrowolski, R., Plouhinec, J.-L., Fuentealba, L.C., Vorwald, P.P., Gumper, I., Sabatini, D.D., and De Robertis, E.M. (2010). Wnt Signalling Requires the Sequestration of Glycogen Synthase Kinase 3 inside Multivesicular Endosomes. Cell 143, 1136–1148.

Thomas, S.N., Harlan, R., Chen, J., Aiyetan, P., Liu, Y., Sokoll, L.J., Aebersold, R., Chan, D.W., and Zhang, H. (2015). Multiplexed Targeted Mass Spectrometry-Based Assays for the Quantification of N-Linked Glycosite-Containing Peptides in Serum. Anal. Chem. 87, 10830– 10838.

Towbin, H., Staehelin, T., and Gordon, J. (1979). Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications. Proc. Natl. Acad. Sci. U. S. A. 76, 4350–4354.

Vermeulen, L., De Sousa E Melo, F., van der Heijden, M., Cameron, K., de Jong, J.H., Borovski, T., Tuynman, J.B., Todaro, M., Merz, C., Rodermond, H., et al. (2010). Wnt activity defines colon cancer stem cells and is regulated by the microenvironment. Nat. Cell Biol. 12, 468–476.

Vinson, C.R., Conover, S., and Adler, P.N. (1989). A Drosophila tissue polarity locus encodes a protein containing seven potential transmembrane domains. Nature 338, 263–264.

Vogelstein, B., and Kinzler, K.W. (2004). Cancer genes and the pathways they control. Nat. Med. 10, 789–799.

66 Voloshanenko, O., Gmach, P., Winter, J., Kranz, D., and Boutros, M. (2017). Mapping of Wnt- Frizzled interactions by multiplex CRISPR targeting of receptor gene families. FASEB J. 31, 4832–4844. van de Wetering, M., Cavallo, R., Dooijes, D., van Beest, M., van Es, J., Loureiro, J., Ypma, A., Hursh, D., Jones, T., Bejsovec, A., et al. (1997). Armadillo Coactivates Transcription Driven by the Product of the Drosophila Segment Polarity Gene dTCF. Cell 88, 789–799. van de Wetering, M., Francies, H.E., Francis, J.M., Bounova, G., Iorio, F., Pronk, A., van Houdt, W., van Gorp, J., Taylor-Weiner, A., Kester, L., et al. (2015). Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945.

Willert, K., Brown, J.D., Danenberg, E., Duncan, A.W., Weissman, I.L., Reya, T., Yates, J.R., and Nusse, R. (2003). Wnt proteins are lipid-modified and can act as stem cell growth factors. Nature 423, 448–452.

Wong, H.-C., Bourdelas, A., Krauss, A., Lee, H.-J., Shao, Y., Wu, D., Mlodzik, M., Shi, D.-L., and Zheng, J. (2003). Direct binding of the PDZ domain of Dishevelled to a conserved internal sequence in the C-terminal region of Frizzled. Mol. Cell 12, 1251–1260.

Worboys, J.D., Sinclair, J., Yuan, Y., and Jørgensen, C. (2014). Systematic evaluation of quantotypic peptides for targeted analysis of the human kinome. Nat. Methods 11, 1041–1044.

Wu, C., and Nusse, R. (2002). Ligand receptor interactions in the Wnt signalling pathway in Drosophila. J. Biol. Chem. 277, 41762–41769.

Yu, J., Chia, J., Canning, C.A., Jones, C.M., Bard, F.A., and Virshup, D.M. (2014). WLS Retrograde Transport to the Endoplasmic Reticulum during Wnt Secretion. Dev. Cell 29, 277– 291.

Zebisch, M., Xu, Y., Krastev, C., MacDonald, B.T., Chen, M., Gilbert, R.J.C., He, X., and Jones, E.Y. (2013). Structural and molecular basis of ZNRF3/RNF43 transmembrane ubiquitin ligase inhibition by the Wnt agonist R-spondin. Nat. Commun. 4, 2787.

Zeng, G., Germinaro, M., Micsenyi, A., Monga, N.K., Bell, A., Sood, A., Malhotra, V., Sood, N., Midda, V., Monga, D.K., et al. (2006). Aberrant Wnt/beta-catenin signalling in pancreatic adenocarcinoma. Neoplasia N. Y. N 8, 279–289.

67 Zhan, T., Rindtorff, N., and Boutros, M. (2017). Wnt signalling in cancer. Oncogene 36, 1461– 1473.

Zhang, Y., Morris, J.P., Yan, W., Schofield, H.K., Gurney, A., Simeone, D.M., Millar, S.E., Hoey, T., Hebrok, M., and Pasca di Magliano, M. (2013). Canonical wnt signalling is required for pancreatic carcinogenesis. Cancer Res. 73, 4909–4922.

Morgan: Constitution of the germinal material in... - Google Scholar.

Sharma: Wingless, a new mutant in D. melanogaster - Google Scholar.

Yeung: Wnt pathway antagonist OMP-54F28 (FZD8-Fc)... - Google Scholar.

Bridges: The mutants of Drosophila melanogaster - Google Scholar.