the role of phip in t-cell acute lymphoblastic leukemia

The role of PHIP in T-cell acute lymphoblastic leukemia Word count: 17.544

Françoise Sanderse Student number: 01301209

Supervisor(s): Prof. Dr. Pieter Van Vlierberghe

Mentor: Dr. Julie Morscio

A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of Master of Science in the Biomedical Sciences Academic year: 2018 – 2019

PREFACE This master thesis was made to complete the Master of Oncology in Biomedical Sciences and was one of the amazing challenges I had to face these past years. When I read the subject two years ago, I have to admit that it did not instantly excite me. But then I thought, “wouldn’t it be great to discover the function of an unknown protein, let alone in T-cell leukemia?”. I envisioned this whole success story in my head, and I couldn’t wait to start and discover the lab. I have learned so much these past two years and I wouldn’t trade it for the world. I want to thank Professor Dr. Pieter Van Vlierberghe for not only giving me the opportunity to complete my research internship in his laboratory but also for giving me the chance to go abroad. I am very grateful for your feedback, your kindness and trust, and for letting me grow independently. I also want to thank the entire lab of Professor Dr. João T. Barata in the Institute of Molecular Medicine in Lisbon for teaching me the first small steps and showing me how to plan my experiments. It truly was an amazing experience. In addition, a big shout-out to my supervisor, the one and only Dr. Julie Morscio for teaching me all the practical work, answering all my questions and guiding me through every step. Thank you for making me forget all the failed experiments and for teaching me to be patient. I could not have been supported any better. Furthermore, I would like to thank my colleagues of team PVV for being so open and warm. You immediately included the students in all of the lab activities and life at MRBII was so much more fun when you guys were around. I really appreciate you shared your projects with me and helped me with both my practical work, the interpretation of my experiments and for answering all of my questions regarding my project with a smile. I wish you all the best. Finally, I would like to thank my parents and sister for reviewing my thesis and putting up with my endless whining. You stood by my side and pushed me to become a better version of myself. In addition, special thanks to Vic Loccufier and my friends for their support and helping me take my mind of my thesis. The cozy drinks and dinners really made a difference. Last but not least, a big thank you to my fellow students from the Master Oncology. We were there for each other and I really hope you all find your first dream job and have an awesome career. Break a leg!

TABLE OF CONTENTS

SUMMARY ...................................................................................................................... 1

Chapter 1. Introduction ............................................................................................. 2

1.1. Normal T-cell development .............................................................................. 2

1.2. T-cell acute lymphoblastic leukemia ................................................................ 4

1.1.1. Driver mutations in T-cell acute lymphoblastic leukemia ........................ 5 1.2. Pleckstrin Homology domain Interacting Protein (PHIP) ................................ 7

1.2.1. Discovery and characterization ................................................................ 7 1.2.2. Structure.................................................................................................... 9 1.2.3. PHIP associated with cancer.................................................................... 9

1.3. Research objectives ....................................................................................... 10

Chapter 2. Materials and methods......................................................................... 11

2.1. Cell lines and patient samples ....................................................................... 11

2.1.1. Suspension cell lines .............................................................................. 11 2.1.2. Adherent cell lines .................................................................................. 11 2.1.3. Patient samples ...................................................................................... 11

2.2. Intracellular stainings ..................................................................................... 11

2.2.1. Intracellular PHIP staining ...................................................................... 11 a) Live/dead stain ........................................................................................ 11 b) Fixation and permeabilization................................................................. 11 c) Intracellular stain with a secondary antibody ......................................... 11 2.2.2. Intracellular pAKT staining ..................................................................... 12 2.2.3. Gating strategies ..................................................................................... 12

2.3. Starvation experiment .................................................................................... 14

2.4. Short hairpin (sh)RNA mediated knockdown of PHIP .................................. 14

2.4.1. Transformation ........................................................................................ 14 a) LB agar plates ......................................................................................... 14 b) Transformation of DH5 bacteria ........................................................... 14 c) Midiprep plasmid purification .................................................................. 14 2.4.2. Virus production ...................................................................................... 14 a) Seeding HEK293TN cells – day one ...................................................... 14 b) Transfection of the HEK293T cells – day two or three .......................... 14 c) Virus collection – day four ...................................................................... 15 d) Concentrated virus collection – day five ................................................ 15 2.4.3. Gating strategies ..................................................................................... 15

2.5. Small interfering (si)RNA mediated knockdown of PHIP .............................. 16

2.5.1. Preparation of the plate .......................................................................... 16 2.5.2. Preparation of the cells ........................................................................... 16 2.5.3. NEON transfection .................................................................................. 16

2.6. Preparation of cytoplasmic and nuclear extract for western blot .................. 16

2.6.1. Nuclear extract preparation .................................................................... 16 2.6.2. BCA protein quantification (optional step) ............................................. 17

2.7. Cell cycle analysis .......................................................................................... 17

2.7.1. Gating strategies ..................................................................................... 17 2.8. Western blotting ............................................................................................. 17

2.8.1. Cell lysis .................................................................................................. 17 2.8.2. Loading, running and transferring the gel .............................................. 17 2.8.3. Blotting with antibodies ........................................................................... 17 2.8.4. Detection ................................................................................................. 18

2.9. RNA sequencing ............................................................................................ 18

Chapter 3. Results ................................................................................................... 19

3.1. Expression, localization and characterization of PHIP in leukemia cell lines and patient-derived xenograft T-ALL samples .......................................................... 19

3.1.1. PHIP is ubiquitously expressed in leukemia cell lines ........................... 19 3.1.2. PHIP expression in patient derived xenograft T-ALL samples .............. 20 3.1.3. Cellular localization of PHIP ................................................................... 22

3.2. The exploration of a potential link between PHIP and the insulin/IGF1 signaling in T-ALL ...................................................................................................... 24

3.2.1. Baseline responsiveness to insulin/IGF1 in different T-ALL cell lines .. 24 3.2.2. Responsiveness of patient derived xenograft T-ALL samples to insulin/IGF1 ............................................................................................................. 28 3.2.3. PHIP knock down by shPHIP – The impact on pAKT levels and insulin/IGF1 responsiveness .................................................................................. 30 3.2.4. PHIP knock down by siPHIP - The impact on pAKT levels and insulin/IGF1 responsiveness .................................................................................. 33

3.3. PHIP knock down - The impact on the cell cycle .......................................... 37

3.4. RNA sequencing results from shPHIP Jurkat ............................................... 38

Chapter 4. Discussion ............................................................................................. 40

4.1. PHIP is overall expressed in various leukemia cell lines .............................. 40

4.2. PHIP is present in both the cytoplasm and the nucleus ............................... 40

4.3. PHIP and its effect on the insulin/IGF1 pathway........................................... 41

4.4. PHIP associated gene network ..................................................................... 43

4.5. Conclusion and future perspectives .............................................................. 44

REFERENCES ............................................................................................................. 46

SUPPLEMENTARY DATA .......................................................................................... 48

SUMMARY T-cell acute lymphoblastic leukemia is a hematologic cancer, characterized by the diffuse infiltration of malignant immature T-cells. It affects both adults and children and has a poor prognosis in relapse patients. Our group pinpointed 6q deletions targeting the PHIP/BRWD2 gene, particularly in TLX1/3 cases. PHIP was identified as a modulator of the insulin/IGF1 signaling. However, it was also shown to interact with histones, suggesting a potential role in both the cytoplasm and the nucleus. By performing western blots, intracellular pAKT/pERK stainings and RNA sequencing on PHIP knock down cells, the function of the protein was assessed. PHIP seems to be expressed in several leukemia cell lines and is both present in cytoplasmic and nuclear extracts. Validation of the baseline pAKT/pERK levels of wild type cell lines showed that Jurkat contained the highest levels, whereas TALL-1 had the lowest. Insulin/IGF1 treatment in these WT cell lines did not lead to a strong effect. In addition, the impact of PHIP loss on the pAKT/PERK levels was analyzed. pAKT levels seemed to shift in some cases when PHIP was knocked down. Also, the RNA sequencing results revealed association with the MAPK, Rap1 and FOXO signaling pathways. Suggesting that, indeed, the protein has something to do within the insulin/IGF1 pathway. In the future, the nuclear function of PHIP needs to be evaluated. Especially, since the RNA sequencing results showed that genes in the nucleus were also modulated by loss of PHIP. The interaction of PHIP with the COMPASS complex may confirm this role.

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Chapter 1. Introduction T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy that can arise from thymocytes at different stages of the T-cell development. T-ALL affects both adults and children and has a threefold higher incidence in males [1]. It is characterized by the diffuse infiltration of bone marrow, peripheral blood and frequently the central nervous system by malignant immature T-cells. The disease is a result of a multistep transformation process of key oncogenic, tumor suppressor and developmental pathways [2]. The current standard of care for this tumor entity is high-dose multiagent chemotherapy. It is very effective in the majority of childhood leukemia patients, with an overall survival of 85%. Nevertheless, these aggressive treatment regimens are often associated with severe acute toxicities and long-term side effects. These long-term side effects can include secondary tumors later in life. Unfortunately, the situation for older leukemia patients is less favorable. At least 40% of adult T-ALL patients fail the current therapy. Hematopoietic stem cell transplantations can be a solution for refractory leukemias, but still, the clinical outcomes of these high-risk primary tumors remain extremely poor [3]. Therapy-related toxicity and resistance underscore the need for the identification of new therapeutic targets for targeted therapy.

1.1. Normal T-cell development Thymic seeding progenitors (TSP), which originate from common lymphoid progenitors in the bone marrow, leave the bone marrow and seed the thymus where they gradually develop into fully mature and functional T lymphocytes. In the thymus, TSPs progress to the early thymic progenitor (ETP) stage, which are uncommitted progenitor cells [4]. Two distinct subsets of progenitor cells can be distinguished within the pool of intrathymic CD34+CD1a

- uncommitted T-cell progenitors. This is based on the expression of CD7. The CD34+CD1a

-CD7- subset is the most immature subset and has the potential to generate lymphoid, myeloid and even erythroid cells. In contrast, CD34+CD1a

-CD7int cells have lost myeloid and erythroid potential and thus resemble lymphoid primed progenitors. Both subsets express CD10 but only CD7int cells can colonize the thymus. Furthermore, it is not yet known whether both CD7- and CD7int cells enter as separate entities or if one subset leads to the development of the other. CD7- cells could lead to CD7int, especially since NOTCH activation results in induction of CD7 expression and suppresses B-cell development [5]. NOTCH1 signaling gets stimulated through interleukin 7 (IL-7) and stem cell factor (SCF) while circulating along the thymic cortex [6]. Further differentiation induces T-cell commitment which is complete when the immature T-cell marker CD1a is expressed (Figure 1) [5].

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Figure 1 Schematic overview of the different developmental stages that characterize human T cell development. Based on figure 1 [5].

NOTCH signaling is required throughout T-cell development and is also thought to help

regulate other T-cell lineage choices, including the : versus : choice (Figure 1). First, NOTCH signaling induces the expression of T-cell factor-1 (TCF-1) and GATA3. Together they initiate expression of several T-lineage-specific genes, such as those encoding for the CD3 component and Rag1, which is required for T-cell receptor (TCR) rearrangements [7]. Beside NOTCH, a second crucial element is the cortex of the thymus. Only mature single positive thymocytes are found in the medulla. First progenitors from the bone marrow enter the thymus from the blood and then they migrate to the outer cortex. Deeper in the cortex, most of the thymocytes develop into small double-positive cells (Figure 2). TCR rearrangements are initiated during these specification and commitment processes. In-frame rearrangements that

yield a TCR- and TCR- chain will result

in the generation of TCR+CD3+ T-cells,

while a functional TCR- chain will pair

with TCR-, to induce the process of -selection (Figure 1) [5]. The pre-T-cell receptor rearrangements are guided by the presence or absence of the RAG-1 and RAG-2 genes [7].

Afterwards, -selection is characterized

by CD28 expression, temporarily NOTCH-dependent proliferation and rapid

differentiation into CD4+CD8+ double

positive thymocytes. Finally, positive and negative selection via MHC class I and MHC class II molecules determines which cells mature into CD4+ or CD8+ single positive T-cells [5]. After positive selection, developing T-cells migrate from the cortex to the medulla, where the matured single-positive T-cells will eventually leave the thymus. In addition, specialized medullary epithelial cells present peripheral antigens for the negative selection of T-cells reactive for these self-antigens [7].

Figure 2 Thymocytes at different developmental stages are found in distinct parts of the thymus. (Murphy K, Weaver C (2017) Janeway’s Immunbiology).

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1.2. T-cell acute lymphoblastic leukemia T-ALL development is a multistep process that results from the cooperation of genetic alterations that deregulate T-cell growth, proliferation, survival and differentiation, leading to developmental arrest and uncontrolled clonal expansion. Furthermore, it is marked by an aberrant expression of particular transcription factors (TF) frequently caused by chromosomal rearrangements to enhancer regions of T-cell receptor genes. These TFs can function as oncogenes and define four T-ALL subtypes: TLX1/HOX11, TLX3/HOX11L2, homeobox A (HOXA) and TAL/LMO T-ALL [8].

These subtypes have thus unique gene expression signatures and are thought to reflect distinct stages of the normal T-cell development (Figure 3). TLX1 rearranged T-ALL typically has a cortical phenotype (CD1a+) [8] and has been associated with excellent outcome in various studies, which is different from the TLX3 subgroup. TLX3-rearranged T-ALLs reflect an arrest at earlier stages of T-lymphopoiesis than TLX1-rearranged cases [9]. The early cortical thymocyte leukemias are primarily associated with translocations resulting in aberrant expression of TLX1, TLX3 and related homeobox transcription factor oncogenes. Mutations in BCL1, WT1, PHF6 or rearrangements of the ABL1 oncogene with NUP214 are characteristically enriched in this group [10].

In HOXA T-ALLs, the T-cell differentiation mainly blocks just before -selection, with arrest that

can occur both early, at the CD4-CD8- double negative stages or later, during T-cell

differentiation along the T-cell receptor lineage. This also applies to the TLX3 subtype (De Smedt et al. (2019) Targeting steroid resistance in T-cell acute lymphoblastic leukemia, submitted to Blood Reviews). HOXA subtypes usually have IL7R/JAK/STAT mutations and a strong enrichment of RAS/MEK/ERK signaling defects [11]. The late cortical leukemias or the TAL1- and LMO2- rearranged cases have very similar gene expression signatures, with the highest frequency of mutations in PTEN [10] but the two

genotypes may be associated with arrest at different maturational stages within the -lineage.

Further differentiation suggested that the TAL/LMO subgroup comprised 2 entities, the TAL_RA and TAL_RB sub-clusters. The first one has high TCR expression with positivity for TCRαβ. This subset, therefore, appeared more mature than the TAL_RB subset that was associated with high expression of the pre-TCRα gene and NOTCH3 [9]. To date, it is unclear whether TAL_RA or TAL_RB characteristics within the TAL/LMO subgroup predict for differences in outcome. Nevertheless, TAL1-rearranged T-ALLs demonstrated a trend to a better outcome in various studies. In contrast, LMO2-rearranged leukemias, that are more frequent in the TAL_RB sub-cluster, predicted for a poorer outcome [9].

A fifth subgroup was defined more recently: early T-lineage Progenitor (ETP) T-ALL which is defined by the expression of one or more myeloid markers, e.g. CD33 and the lack of CD1a. This type of ALL reflects very early immigrants from the bone marrow to the thymus that share a conserved pathogenic mechanism, but no specific chromosomal abnormality and has been associated with a poor outcome [9]. This early T-cell precursor T-ALL is characterized by activating mutations in genes that regulate the cytokine receptor RAS signaling, including FLT3, inactivating lesions in GATA3, and RUNX1, which disrupt hematopoietic development. Also mutations in histone modifying genes, such as EZH2, SUZ12 and EED frequently occur. In addition, ETP-ALL has a lower frequency of mutations in NOTCH1 and loss of the short arm of chromosome 9 [10].

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Figure 3 Genetic subtypes of human T-ALL are associated with normal T cell differentiation and display variable frequencies of oncogenic signaling mutations. Schematic representation of normal T-cell differentiation in humans and stages of T-cell differentiation arrest which are observed in different molecular genetic subtypes of human T-ALL. The triangles reflect the unequal distribution of mutations that activate specific oncogenic signaling pathways between the major subtypes of human T-ALL (De Smedt et al. (2019) Targeting steroid resistance in T-cell acute lymphoblastic leukemia, submitted to Blood Reviews).

1.1.1. Driver mutations in T-cell acute lymphoblastic leukemia The five distinct subgroups mostly overlap with the presence of specific mutations, considered as driving oncogenes [9]. The most prevalent mutations are constitutive activation of NOTCH1 signaling (60%), and loss of the CDNK2A/p16INK4a locus (70%). NOTCH1 is key T-cell fate specification and thymocyte developmental factor and is usually activated, co-occurring with the loss of CDNK2A [1]. In most T-ALLs NOTCH1 is not activated by chromosomal translocation, but as a result of activating mutations that lead to the disruption of specific domains responsible for controlling the initiation and termination of NOTCH1 signaling [2]. Loss of function mutations in FBXW7 are also frequent in T-ALL (15%) and contribute to sustain NOTCH1 activation by preventing its proteasomal degradation in the nucleus. Activating mutations in genes regulating cytokine and RAS signaling, inactivating mutations in hematopoietic development genes and histone-modifying genes were identified in the aggressive T-ALL subtype, defined by very early arrest in T-cell development (ETP-ALLs) [1]. PHF6 is an example of an epigenetic regulator and is inactivated by mutations and deletions in approximately 16% of pediatric T-ALLs and 38% of adult T-ALLs. Genetically, ETP T-ALL has a lower prevalence of NOTCH1 mutations, rarely has CDNK2A deletions and is characterized by mutations in genes encoding for signaling factors and epigenetic regulators [2].

NOTCH promotes leukemia cell growth through direct transcriptional upregulation of anabolic pathways, including ribosome biosynthesis, protein translation and nucleotide and amino acid metabolism. The upregulation of the MYC oncogene, a direct target of NOTCH1, enhances these growth-promoting effects of the NOTCH1 transcriptional program [6].

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Apart from recurrent lesions, several coding and noncoding alterations associated with distinct T-ALL subtypes have been identified. For example, as mentioned above PHF6 [12] and PTPTN2 mutations [11], are enriched in TLX1/3 T-ALL. This suggests that different signaling pathways have distinct roles according to T-cell maturational stage. In addition, broad deletions of chromosome 6q14-q23 were enriched in TAL1 cases [11]. Understanding of the interaction between particular genetic lesions in the context of specific T-ALL subtypes will provide novel opportunities for targeted treatment.

Next to these mutations three pathways are often affected in T-ALL (Figure 3). The PI3K-AKT pathway is crucial for cell growth, proliferation and survival of the T-cells. Most frequently, T-ALLs show loss of the PTEN tumor suppressor gene as a result of loss-of-function mutations or deletions [2]. In addition, PI3K/AKT/mTOR can be activated directly by mutations in AKT1, PI3KCA, PI3KR1, and IL7R, or indirectly from abnormalities in JAK/STAT, NOTCH, or MAPK [13]. IL7R/JAK/STAT signaling is one of the most studied pathways in T-ALL. In healthy early T-cell progenitors, activation of the JAK-signal transducer and activator of STAT pathways by IL-7R is required to support growth, proliferation and survival. Translocations, leading to a constitutively active kinase fusion oncoprotein and gain-of-function mutations in IL-7R, resulting in a constitutive JAK-STAT signaling are also common in T-ALL, particularly in TLX1/3, HOXA and ETP subtypes [2]. Less common than the other pathways mentioned, a reasonable percentage (60% in ETP-ALL) of patients with T-ALL have MAPK alterations, including KRAS, NRAS, FLT3, and BRAF mutations. Similar to JAK/STAT alterations, mutations of MAPK are more common in ETP ALL than non-ETP ALL [13]. Although many of the most frequent mutations in T-ALL have been described previously, there are still a large number of unrecognized targets of mutations and stage- and subtype-specific associations among genes, cellular pathways and outcomes [11]. In a series of T-ALL cases our group recently pinpointed small 6q deletions targeting the PHIP/BRWD2 gene, suggesting a tumor suppressor function of this gene in T-ALL. Notably, PHIP deletions were enriched in TLX1/3 T-ALL cases.

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1.2. Pleckstrin Homology domain Interacting Protein (PHIP) 1.2.1. Discovery and characterization Pleckstrin Homology domain Interacting Protein (PHIP) was initially identified as a novel interaction partner of the insulin receptor substrate 1 (IRS-1), a key hub in insulin/insulin-like growth factor 1 (IGF1) signaling [14] (Figure 4). Farhang-Fallah et al. found that PHIP was selectively bound to the IRS-1 PH (Pleckstrin Homology) domain, but not to PH domains from unrelated proteins [15]. PHIP was found stably associated with IRS-1 in several mammalian cell types, including fibroblasts and insulin responsive tissues such as myoblasts and adipocytes. This suggests that PHIP may be important for recruitment of IRS proteins to activated insulin/IGF1 receptor complexes. The IRS-1 plays a central role in transducing insulin-dependent signals that regulate biological processes such as cell growth and cellular uptake of glucose. One of the mitogen-signaling events initiated downstream of the insulin/IGF1-receptor is activation of the MAPK [14]. The insulin-like growth factor 1 receptor (IGF1R) is a αβ-heterodimer transmembrane receptor tyrosine kinase [16], closely related to the insulin receptor (IR). The IGF1R can form homodimers or heterodimers with the IR to recognize its ligands, IGF-1 and IGF-2. Two of the most prominent downstream signaling pathways of the IGF1R are the PI3K/AKT and ERK pathway [17] (Figure 4). NOTCH1, a prominent oncogene in T-ALL is able to upregulate IGF1R, leading to a higher growth/survival rate and also to a higher leukemia-initiating activity [18]. Inhibition of IGF signaling may in this way have a therapeutic role in T-ALL [17]. There are over 70 potential serine-phosphorylation sites in IRS1, and in general, serine phosphorylation seems to negatively regulate IRS signaling [19]. Unlike other receptor tyrosine kinases (RTK), that bind directly to the cytoplasmic tails of downstream effectors, the IR and IGF1R satellite proteins (IRS proteins) mediate the binding of intracellular effectors. The shorter isoform, called IGF1R-a, binds to and responds to both insulin and IGF2, whereas the longer form, IGF1R-b is selective for insulin only. In addition, there are at least 11 intracellular substrates of the IR and IGF1R kinases that have been identified. On top of that, there are 6 different isoforms of the IR substrate, of which IRS1 and IRS1 are the most widely distributed [20].

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One of the challenges in understanding ligand-receptor interactions, is the identification of the components within the network, that are essential mediators or modifiers of the ligand’s signal. Although most pathways look sufficient to explain the insulin signaling, there are a lot of gene and protein isoforms that are involved in the activation of AKT. For example, the IR has two splice isoforms, which are usually co-expressed in cells that also express the IGF1R, that can also be activated by insulin (Figure 4) [20].

Figure 4 Involvement of PHIP in the IGFR1/insulin pathway [21]. PHIP was also found to bind histones, suggesting it can shuttle between the cytoplasm and the nucleus [22,23].

Further on, PHIP produces at least three proteins (PHIP1, PHIP and NDRP) through alternative splicing. PHIP1, also known as DCAF14, acts as a substrate receptor in a ubiquitin ligase pathway and mediates substrate-specific proteolysis [24]. Next to its interaction with the IR/IGF1R pathway, PHIP was also found to colocalize with histone H3K4 methylation, genome-wide in human cells. This happens through a recently identified chromatin-binding module related to the Royal Family Tudor domains, the CryptoTudor domain. Methylation of histone 3 on lysin 4 is a chromatin modification associated with promotors and transcriptional cis-regulatory elements. H3K4me3 occurs mostly at gene promotors near their transcription start sites. In contrast, H3K4me1 levels are low at promotors but occur nearby enhancers, those can be further categorized as active or non-active on their levels of H3K27 acetylation. Interestingly, PHIP binding to the genome was shown to be regulated by COMPASS H3K4 methyltransferases, which include the MLL proteins. MLL1 fusion proteins are key drivers of mixed lineage leukemia which is characterized by both lymphoid and myeloid features. In conclusion, BRWD2/PHIP binds to the chromatin of both enhancers and promotors [23].

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To date, the full physiological role of PHIP remains completely unknown. Analysis of PHIP knock out mice suggests that PHIP plays a non-essential role during embryogenesis but is critically required for postnatal growth and survival of mice. PHIP could be an important regulator of somatic cell growth and cell size in mice [25]. Because PHIP seems to be exclusively localized in the nucleus in pancreatic β-cells [26], it still needs to be determined how PHIP also functions as a downstream target of the IGF-1/RAS/MAPK and IGF-1/PDK1/p70S6K pathways [25].

1.2.2. Structure PHIP is located on chromosome 6q14.1 and consists of 42 exon regions encoding a large protein of 207 kDa. Several functional domains have been identified in PHIP: two bromodomains, BD1 and BD2, located in tandem in the center of the molecule as well as WD40 repeats [15] (Figure 5). Notably, the PHIP protein does not share any sequence homology with any other known proteins [15]. Bromodomains are conserved sequences of ~100 AA that have been proposed to bind acetylated lysine residues, e.g. in histones [22] and mediate protein-protein interactions [15]. The WD40 domain, consisting of tryptophan-aspartic acid repeats, is one of the most abundant domains in eukaryotic genomes. The repeats bind to methylated lysins and function as an adaptor in many different protein complexes or protein-DNA complexes. WD40 domain-containing proteins are in that way implicated in a variety of functions: signal transduction, regulation of transcription, cell cycle control, autophagy, apoptosis [22]. PHIP has also been shown to stimulate cell proliferation [26] and is found to be required for the initiation of DNA replication in melanoma cells [27]. PHIP could be a sequence-specific DNA-binding protein that contributes to replication initiation at a subset of replication origins [27]. The two nuclear localization signals suggest a nuclear function of PHIP.

Figure 5 Protein structure of PHIP with its different domains.

1.2.3. PHIP associated with cancer So far only a few studies have associated PHIP with a potential role in cancer development. In a study using a genetic mouse model of Burkitt Lymphoma (BL), PHIP inactivation significantly accelerated BL development compared to the control cohort, strongly supporting a tumor suppressor role of PHIP in this model [28]. In contrast, studies of solid tumors suggest an oncogenic role of PHIP. Bezrookove et al. found that suppression of PHIP in human melanoma cells significantly reduces glycolytic activity in vitro and is accompanied by reduced micro vessel density in vivo [29]. In melanoma, PHIP promotes glycolysis and angiogenesis by regulating HIF1a, LDH-4 and VEGF (Figure 4) [21]. PHIP was also identified as the gene that was most highly expressed in metastatic melanoma, compared with primary tumors. A direct relationship was noted between the level of PHIP expression and the metastatic potential of melanoma. By performing cDNA microarray analysis, it was shown that PHIP seemed to regulate the expression of upstream mediators of the IGF axis and downstream mediators of tumor cell invasion in melanoma. One-third of the melanomas has high levels of PHIP expression and around 80% of these PHIP-positive

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melanomas had three or more copies of the PHIP locus. This supports its role as a melanoma progression gene. Furthermore, AKT, potentially directed by PHIP, plays an important role in melanoma progression [30]. Notably, mice injected with melanoma cells in which PHIP was knocked down survived longer than animals injected with wild type cells [21]. In triple negative breast cancer, suppression of PHIP led to a reduction in cell colony formation, invasive capacity and a lower metastatic potential. The knock down of PHIP was not only accompanied by reduction of phosphorylated AKT and Talin1 but it also resulted in a reduced expression of Cyclin D [31].

Together, these studies indicate that the function of PHIP is likely to depend on the cellular context and that PHIP may represent a rational therapeutic target against a range of different, lethal solid and non-solid tumor types. Nevertheless, a major outstanding issue concerns whether the PHIP protein is druggable. PHIP does not have enzymatic activity that can be readily targeted. It does have two bromodomain motifs, one of which was recently shown to be targetable [32].

1.3. Research objectives In this master thesis, we will try to investigate the role of PHIP in T-cell acute lymphoblastic leukemia. We hypothesize that PHIP plays a tumor suppressor role and is particularly important in TLX1/3 T-ALL, in which the gene encoding PHIP is frequently deleted. Although there are already a few studies that have associated PHIP with a potential role in cancer development (1.2.3), its function in blood cancer remains unknown. The protein was shown to interact in both cellular compartments, in the cytoplasm via the insulin receptor substrate 1 [14,15] and in the nucleus by interacting with different nuclear proteins among which histones [22,23]. First, we will assess the expression of PHIP in different leukemia cell lines and we will try to locate the protein via cytoplasmic and nuclear extractions. The interaction with the insulin/IGF1 pathway will be measured by intracellular staining and western blot of pAKT Ser473, pAKT Thr308, pERK and AKT. Further on, we will evaluate the impact of PHIP loss in cellular responsiveness to IGF1 and Insulin via short hairpins or small interfering RNA targeting PHIP. Patient derived xenograft T-ALL samples with different PHIP levels will also be used in these experiments. Finally, we will also check the effect of PHIP loss in the cell cycle and we will try to assess the associated gene network of PHIP by performing RNA sequencing in a PHIP knock down cell line. The understanding of the downstream networks perturbed by the loss of PHIP in T-ALL could lead to the identification of new therapeutic targets [28].

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Chapter 2. Materials and methods

2.1. Cell lines and patient samples 2.1.1. Suspension cell lines Human T-ALL cell lines (obtained from DSMZ) were kept in culture in RPMI 1640 medium (Gibco, 52400025) supplemented with 10% (Jurkat, DND41, MOLT16) or 20% (HPB-ALL, TALL1) fetal bovine serum (Biochrome, 8589841)) and antibiotics (1% penicillin/streptavidin) and L-glutamine (1%) at 37°C under 5% CO2.

2.1.2. Adherent cell lines HEK-293 (DSMZ) cells used for lentivirus production were kept in RPMI 1640 medium (Gibco, 52400025) supplemented with 10% fetal bovine serum (Biochrome, 8589841), antibiotics (1% pencillin/streptavidin) and L-glutamine (1%) at 37°C under 5% CO2.

2.1.3. Patient samples Primary T-ALL cells for xenografting in NSG mice were acquired by informed consent from the Department of Pediatric Hematology-Oncology at Ghent University Hospital. Patient-derived xenograft T-ALL cells were isolated from the spleens of leukemic mice and used for in vitro IGF1 and insulin treatment.

2.2. Intracellular stainings 2.2.1. Intracellular PHIP staining 100.000 cells were used for the intracellular staining per condition. They were resuspended in

100 l human wash buffer (hWB) (PBS (Gibco, 10010023), supplemented with 2% FCS (Biochrome, 8589841)).

a) Live/dead stain

After the addition of 2 l Live/Dead staining (1/4 diluted, Invitrogen, 65-0866-14), the cells were

incubated for 30 minutes (4°C) in the dark. The cells were washed once with 1 ml of hWB (1500 rpm, 5min, 4°C).

b) Fixation and permeabilization The cells were resuspended in 0,5 ml Fix/Perm (1/4 Fixation/permeabilization (Invitrogen, 00512342) diluted in eBioscience™ Fixation/Permeabilization Diluent (Invitrogen, 00522356)), vortexed and incubated for 30 minutes at RT in the dark. After the incubation period the cells were washed twice with 1 ml Perm/Wash (Permeabilization buffer 10x (Invitrogen, 00833356)) (1700 rpm, 5min, 4°C). The primary antibody against PHIP (Sigma Aldrich, HPA019140) was

added after discarding the supernatants (1 l). The FACS tubes were then again placed for 30min at RT in the dark.

c) Intracellular stain with a secondary antibody The cells were washed once with 1 ml Perm/Wash (1700 rpm, 5min, 4°C) and the secondary (1/20.000), fluorescent antibody (Alexa Fluor ® 488, Life technologies, A11034 or Alexa Fluor ® 594, Life technologies, A21207) was added. After the last incubation period of 30 minutes at RT in the dark, the cells were washed with 1 ml Perm/Wash (1700 rpm, 5min, 4°C) and 1 ml hWB (PBS (Gibco, 10010023), 2% FCS (Biochrome, 8589841)) (1700 rpm, 5min, 4°C). The intracellular staining was measured on the BD LSRII flow cytometer and analyzed with the FACS Diva and FlowJo software.

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2.2.2. Intracellular pAKT staining 300.000 cells were used per condition and washed with PBS. After centrifugation (5 min, 1200

rpm, RT), the cells were resuspended in 100 l human washing buffer (hWB) (PBS (Gibco,

10010023), 2% FCS (Biochrome, 8589841)). 2 l Live/dead staining (Invitrogen, 65-0866-14)

was added and the FACS tubes were put in the dark for 30 min (4°C). After the incubation

period the cells were again centrifuged (5min, 1200 rpm, RT) and 100 l of reagent A (Fix&Perm cell permeabilization kit, Invitrogen, GAS003) was added. The cells incubated for 2-3 min at RT before 1 ml of pre-cooled MeOH was added. After vortexing the cells were again incubated for 30 min at 4°C. The cells were centrifuged (5 min, 1500 rpm) and washed with

500 l hWB. 1 l of the pAKT Ser473 antibody (Miltenyi Biotec, 130-119-570) or 5 l of the

pERK antibody (Invitrogen, 17-9109-42) was added afterwards. The cells were vortexed at low

speed and put in the dark for 30 min, RT. Finally, the cells were washed once with 500 l hWB

(5 min, 1500 rpm) and resuspended in 100 l hWB for analysis. The intracellular staining was checked on the BD LSRII flow cytometer and analyzed with the FACS Diva and FlowJo software.

2.2.3. Gating strategies For the intracellular PHIP staining, the following gating strategies were applied. Every time there were three different conditions: live/dead staining only (Figure 6), live/dead staining + secondary antibody (Figure 7) and live/dead staining + primary antibody + secondary antibody (Figure 8).

Figure 6 Gating strategies for the intracellular PHIP staining, live/dead staining only. The Alexa Fluor ® 488 secondary antibody can be visualized in the FITCH channel via the blue laser of the BD LSRII flow cytometer and the Alexa Fluor ® 594 secondary antibody with the PE-Texas Red channel via the yellow laser.

Figure 7 Gating strategies for the intracellular PHIP staining, live/dead staining + secondary antibody. The Alexa Fluor ® 488 secondary antibody can be visualized in the FITCH channel via the blue laser of the BD LSRII flow cytometer and the Alexa Fluor ® 594 secondary antibody with the PE-Texas Red channel via the yellow laser.

13

Figure 8 Gating strategies for the intracellular PHIP staining, live/dead staining + primary antibody + secondary antibody. The Alexa Fluor ® 488 secondary antibody can be visualized in the FITCH channel via the blue laser of the BD LSRII flow cytometer and the Alexa Fluor ® 594 secondary antibody with the PE-Texas Red channel via the yellow laser. Histogram plots were made to compare the PHIP levels in different samples. The gating for the intracellular pAKT staining goes as follows (Figure 9, Figure 10).

Figure 9 Gating strategies for the intracellular pAKT staining, live/dead staining only. The pAKT antibody can be visualized in the APC channel via the red laser of the BD LSRII flow cytometer.

Figure 10 Gating strategies for the intracellular pAKT staining, live/dead staining + the fluorescent pAKT antibody from Bioké. The pAKT antibody can be visualized in the APC channel via the red laser of the BD LSRII flow cytometer. Here as well, pAKT levels in different samples were compared by making histograms.

Lymphocytes

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2.3. Starvation experiment A minimum of 300.000 cells per condition was used. First, the cells were washed in PBS and put into serum free RPMI 1640 medium (Gibco, 52400025) (1% penicillin/streptavidin and L-glutamine). The cells were starved for 4 hours straight. After the starvation either insulin was added (1/100) (Gibco, 41400045) or IGF (20 ng/ml) (Sigma Aldrich, I3769) for 15 minutes. Afterwards the cells were again washed in PBS and the intracellular pAKT staining (2.2.2) was performed.

2.4. Short hairpin (sh)RNA mediated knockdown of PHIP The efficiency of five human PHIP shRNA Mission plasmids (TRCN0000127738, 130419, 130643, 147009, 147136; Sigma Aldrich) was tested. The puromycin cassette, used for selection of successfully transduced cells, was previously successfully exchanged for a GFP cassette in four plasmids (130419, 130643, 147009, 147136 further referred to as shPHIP 2, 3, 4 and 5). As a control, a Mission plasmid encoding a scrambled short hairpin, SHC002 plasmid, was used.

2.4.1. Transformation

a) LB agar plates The agar is made out of LB agar powder (Thermofisher Scientific, 22700-041) and is kept in a cold room at 4°C. To be able to poor the agar in plates, it is heated up to 56°C and supplemented with ampicillin (Sigma-Aldrich, A9518) 1/1000.

b) Transformation of DH5 bacteria

To 50 l of bacteria, 100 ng DNA is added. A negative, as well as a positive control (E. coli +

pUC19) were included. The bacteria were kept on ice for 30 minutes without resuspending. Afterwards a heat-shock was carried out in a warm water bath at 42°C for 45 seconds. After a 5-minute incubation on ice, Stable Outgrowth Medium (Biolabs, B9035) was added. The samples were put in a shake table for 1 hour, 200 rpm at 37°C. With a sterile inoculation needle

300 l of the E-coli solution was gently being rubbed open on the LB agar plates and kept

overnight in a 37°C/7% CO2 humidified incubator. Individual colonies were picked and cultured overnight in liquid LB medium supplemented with ampicillin.

c) Midiprep plasmid purification The plasmid purification was carried out using the Qiagen Plasmid Plus Kit; midi (12943), according to the manufacturer’s instructions. Purified plasmid DNA was stored at -20°C.

2.4.2. Virus production

a) Seeding HEK293TN cells – day one The seeding density of the HEK293 cells is 0,65.106 cells/dish and in each 6 cm dish the cells need to be suspended in 6ml medium (RPMI 10%) (Gibco, 21980032).

b) Transfection of the HEK293T cells – day two or three The medium was refreshed (6 ml, 10% RMPI) 1 hour before the transfection. The next steps were carried out in a biosafety level 3 laboratory.

In an Eppendorf tube, a mixture was made of lentiviral helper plasmids 2,7 g psPAX2

(Addgene, 12260), 0,3 g MD2.G (Addgene 12259) and 3 g of the target vector. NaCl was

added until a total volume of 250 l was obtained.

Next, a solution with jet-PEI (VWR, 101-10N) was made in NaCl. Per transfection 10 l jet-PEI

plus 240 l NaCl was vortexed gently and spun down. 250 l of this jet-PEI stock solution was added to each Eppendorf tube with DNA. After resuspending, the mix was spun down and incubated for 30 minutes at RT.

15

The jet-PEI/DNA mix was added drop by drop to the HEK cells. The day after transfection, the medium was again refreshed.

c) Virus collection – day four The supernatant that contains the viral particles was collected in a falcon and was immediately put on ice.

d) Concentrated virus collection – day five For virus concentration cold PEG-it virus precipitation solution (5x stock) (System Biosciences, LV810A-1) was added to the supernatant of the HEK cells. This was kept at 4°C for at least 12 hours. The supernatant was centrifuged at 1500 g for 30 minutes at 4°C. The supernatant was transferred to a new tube and the residual PEG-it solution was spun again at 1500 g for 5 minutes. The viral particles, present in the pellet, were resuspended 1/10 using cold medium (RPMI 10% FCS). Aliquots were put into cryogenic vials and were stored at -80°C until ready for use. e) Lentiviral transduction

The same amount of virus was added together with the cells (500 l of each) and the plate was centrifuged for 90 minutes, 3200 rpm, 32°C. Afterwards the microplate was put inside the incubator for 2 days. GFP expression was assessed using the BD LSRII flow cytometer. The plots were analyzed with the FACS Diva and FlowJo software.

2.4.3. Gating strategies To find out the efficiency of the transduction, the GFP positivity was checked out via the following gating strategy (Figure 11, Figure 12).

Figure 11 Gating strategies for GFP positivity after transduction. Non transduced Jurkat serving as the negative control group. The GFP positivity can be visualized in the FITCH channel via the blue laser of the BD LSRII flow cytometer.

Figure 12 Gating strategies for GFP positivity after transduction. The GFP positivity can be visualized in the FITCH channel via the blue laser of the BD LSRII flow cytometer.

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2.5. Small interfering (si)RNA mediated knockdown of PHIP A human PHIP siRNA SMARTpool mix (5 nM, L-019291-00-0005, Dharmacon), consisting of 4 siRNA targeting human PHIP was resuspended in nuclease-free water. The ON-TARGETplus Non-targeting control siRNA was used as a control (D-001810-01-05, Dharmacon).

2.5.1. Preparation of the plate RPMI 20% (Life Technologies Europe, 11879-020) was prepared without antibiotics. A 6-well plate was filled with 2 ml medium per well (for 2.106 cells) and put in the incubator (37°C).

2.5.2. Preparation of the cells 2 million cells were used per nucleofection. After counting, the required number of cells was

centrifuged at 1600 rpm, 5min, RT. The cells were washed and resuspended in 110l R buffer

(Neon Invitrogen 100l kit, MPK10096) per 2.106 cells. 100l of the cell suspension was put

into eps and in each epp, the respective siRNA was added to obtain a final concentration of 400 nM.

2.5.3. NEON transfection A new Neon tube was used, every time other cells/constructs were used. The Neon tube was

filled with 3ml Electroporation Buffer (Neon Invitrogen 100l kit, MPK10096). 100l cell/plasmid suspension was taken up with the Neon pipet (Invitrogen, MPP100) + Neon tip. Every time the presence of air bubbles was checked carefully. Cells were nucleofected using the following settings: 1325 V, 10ms pulses, 3 pulses. After nucleofection the cells were transferred into the 24-well plate with medium. The plate was then again put in the incubator. PHIP knockdown was checked via intracellular PHIP staining (2.2.1), on the BD LSRII flow cytometer and analyzed with the FACS Diva and FlowJo software 48 hours or 72 hours after electroporation.

2.6. Preparation of cytoplasmic and nuclear extract for western blot All used buffers (including PBS) were kept at 4°C, on ice

2.6.1. Nuclear extract preparation For each condition, 100.106 cells were used. The pellet was resuspended in 3 ml Buffer A (10 mM Hepes 7.6, 1,5 mM MgCl2, 10 mM KCl, 0,5 mM DTT + protease inhibitor), by mixing it gently by shaking and inversion. This was left on ice for 10 minutes and then centrifuged for 10 minutes, 3000 rpm, 4°C. The cells were again resuspended in 3 ml Buffer A and added to the Dounce Homogenizer (10 strokes with pestle A). After another centrifugation step (10 min, 3000 rpm) a cytoplasmic fraction was received and the pellet was resuspended in 3 ml Buffer C (20 mM Hepes pH 7,6, 20% glycerol, 420 mM NaCl, 1,5 mM MgCl2, 0,2 mM EDTA, 0,5 DTT and a protease inhibitor). The nuclei suspension was put into the homogenizer (10 strokes with pestle B) and then placed into a rotator for 30 minutes in a cold room. The insoluble particles were spun out by centrifugation for 15 min, 13000 rpm in Eppendorf tubes. The supernatant was taken off and kept aside (nuclear extract) and the pellet was kept as a control for the nuclear extract (NXT). For dialysis Slide-A-Lyzer cassettes were used (Thermoscientific, 66370) after a pre-incubation for 10 minutes in Buffer D (20 mM Hepes pH 7.6, 20% glycerol, 100 mM KCl, 1,5 mM MgCl2, 0,2 mM EDTA, 0,5 mM DTT, 0,2 mM PMSF and sodium meta bisulfite (1:1000 of freshly made 0,95 gr/10 ml stock)). The nuclear extract was injected into a cassette and dialyzed for 2x 1 hour in 1 l Buffer D at 4°C. After the dialysis, the extract was taken out, put into Eppendorf tubes and spun out for 15 min, 13000 rpm.

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2.6.2. BCA protein quantification (optional step) Protein quantification test were conducted using the BCA Protein Assay Reagent kit (Fisher Scientific, 23227).

2.7. Cell cycle analysis For each condition, 500.000 cells were needed. First, the cells were spun down (5min, 1200 rpm) and washed with 1 ml PBS. Afterwards the cells were resuspended in 1 ml of 70% ethanol (dropwise) and incubated at -20°C for minimum one hour (could be stored for days as well). After the incubation, the cells were again centrifuged and washed in PBS.

The cells were resuspended in 500 l PBS and 2,5 l of RNAse A (Biorad, 8040-0106) was

added (final concentration of 0,2-0,5 mg/ml). The cells were incubated for one hour at 37°C.

Finally, 20 l of PI (Propidium Iodide) was added (final concentration of 40 l/ml) and the cells

were checked on the BD LSRII flow cytometer and analyzed with the FACS Diva and FlowJo software.

2.7.1. Gating strategies For the cell cycle analysis, the following gating strategies were applied (Figure 13).

Figure 13 Gating strategies for cell cycle analyis. PI can be visualized in the PE channel via the red laser of the BD LSRII flow cytometer.

2.8. Western blotting

2.8.1. Cell lysis A total lysis was conducted using a 1/40 dilution of -mercaptoethanol (Sigma-Aldrich, M6250) in Laemli buffer (Serva, 42556.01). The samples are then denatured for 10min. at 95°C.

2.8.2. Loading, running and transferring the gel Gels from Biorad (456-1035) were used, as well as a 10% running buffer (Biorad, 161-0772).

A visible ladder, page ruler (Life Technologies Europe, 26619), was used (3l) and 15 l of each sample was loaded. The gel ran at 100V for 1 hour. After running a sandwich was made and put in the container with transfer buffer (10% TGS buffer (Biorad, 161-0772) in 20% MethOH) at 100V for also 1 hour.

2.8.3. Blotting with antibodies 5% milk (BD Benelux NV, 232100) or 5% BSA (Sigma Aldrich, A7030-100G) in TBST (TBS (Sigma Aldrich, T5912) + 0,1% Tween (Sigma Aldrich, P1379)) was used to block for 1 hour before adding the antibody. The blotted nitrocellulose membrane was kept overnight in TBST 5% milk (or BSA) with the primary antibody at 4°C. The next day the secondary antibody was used after three washing steps with TBST, for one hour. The secondary antibody was also diluted in milk or BSA.

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2.8.4. Detection Visualization was conducted using Vision works and the dura (Thermo Scientific, 34076) or femto (Thermo Scientific, 34096) substrate. After detection a loading control was carried out using either actin, vinculin or GAPDH. For all the specification of the used antibodies, see Addendum 2.

2.9. RNA sequencing To investigate differential gene expression following PHIP knockdown (2.4), RNA sequencing was performed on Jurkat cells transduced with shPHIP (encoding shPHIP2, 3, 4 or 5) or SHC002 control lentivirus. Three biological replicates were made. RNA was purified using the RNeasy Plus Micro Kit from Qiagen (74034). After the isolation of RNA, the samples were sent to NXTGNT for 3’ UTR RNA sequencing. Afterwards, the RNA concentration was measured using NanoDrop™ ND-1000 (Thermo Fisher). The RNA samples where then sent for QuantSeq™ 3’ mRNA seq library preparation (Lexogen) and sequencing. The sequencing results were analyzed by an RNA sequencing specialist in the lab, using the following pipeline: Reads were aligned to the reference genome GRCh38 using STAR-2.4.2a with default settings. STAR was also used for gene expression quantification on the Ensembl GTF file version 84. Normalization of gene level counts and differential expression analysis was performed using DESeq2 in R; a two-condition design was implemented (shPHIP versus SHC002 control). To find the top up and top down differentially expressed genes (DEGs), two lists were created: The top upregulated genes (positive Log2 Fold Change) and the top downregulated genes (negative Log2 Fold Change) after PHIP knockdown, both lists were ranked from lowest to highest p adjusted value. To biologically interpret the results, gene set enrichment String analysis was performed on the dataset.

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Chapter 3. Results

3.1. Expression, localization and characterization of PHIP in leukemia cell lines and patient-derived xenograft T-ALL samples

3.1.1. PHIP is ubiquitously expressed in leukemia cell lines PHIP expression was assessed via western blot in a panel of leukemia cell lines: DND4.1, HPB-ALL, HSB-2, Jurkat, Karpas-45, KE-37, Loucy, MOLM-13, MOLT-16, MONO MAC-6, PF-382, PGER, RAMI-8402, SEM and TALL-1 (Figure 14). DND4.1 and HPB-ALL cells are characterized by the [t(5;14)(q35.1;q32.2)] BCL11B-TLX3 and represent the TLX oncogene group. Jurkat and TALL-1 are both characterized by TAL1 oncogene rearrangements and are therefore considered as a model for non-TLX T-ALLs. For the visualization of PHIP the Novus antibody was used at a 1/2000 dilution.

Figure 14 PHIP expression in different cell lines. Western blot analysis of T-ALL cell lines to detect

the expression of the PHIP protein. Fifteen l of protein was blotted to each lane. The Novus antibody

against PHIP was used as well as an antibody to vinculin to serve as loading control.

Both HPB-ALL and Jurkat express the highest concentration of PHIP whereas Karpas-45, MOLM-13 and MOLT-16 almost completely lack PHIP (Figure 14). Karpas-45 is CDKN2A, NOTCH1, PTEN, TP53 mutant. Notably, both Karpas-45 (T-cell leukemia cell line) and MOLM-13 (acute myeloid leukemia cell line) harbor an MLL fusion. MOLT-16 also belongs to the TAL1 oncogene group and has no mutation of NOTCH1. By analyzing the area under the curve (AUC) via the Fiji Software, a histogram was made showing a more representative way of the expression levels of the PHIP protein (Figure 15).

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Figure 15 Quantification of PHIP in the different cell lines. The western blot (Figure 14) was analyzed via Fiji.

DND41/HPB-ALL and Jurkat/TALL1 cells representing TLX and non-TLX T-ALL respectively, were selected for further experiments (Figure 14).

3.1.2. PHIP expression in patient derived xenograft T-ALL samples To assess the expression of the PHIP protein in primary T-ALL, a second western blot was performed on patient derived T-ALL xenograft samples isolated from the spleens of leukemic mice (Table 1). xT-ALL G1 and xT-ALL C1 served as controls and both harbored wild type PHIP. The xT-ALL 47 sample was derived from a patient with a mutation in the PHIP gene while the xT-ALL D1 sample was lacking the PHIP gene completely (due to a bi-allelic deletion). The immunophenotype of all four patient derived xenograft T-ALL samples was determined by means of flow cytometry (Table 1).

Sample Subtype CD1a mCD3 CD4 CD7 CD8b

xT-ALL G1 TLX3 + + +/- + +/-

xT-ALL C1 TLX1 + - - - +/-

xT-ALL 47 NKX2-1 + + +/- + +/-

xT-ALL D1 TLX1 + - +/- + - Table 1 The immunophenotype of the patient derived xenograft T-ALL samples, based on previously obtained qPCR experiments. Because of the potential link between PHIP and the PI3K/AKT signaling (Figure 4), the expression of PTEN (Phosphatase and tensin homologue) was also assessed (Figure 16). PTEN is inactivated in a subset of T-ALL (most frequently in TAL1 rearranged cases) [17]. PI3-kinase and PTEN are major positive and negative regulators of the PI3K pathway. PTEN directly opposes the activity of PI3Ks by dephosphorylating PIP3 into PIP2, thus acting as the central negative regulator of PI3K [33]. For the visualization of PHIP, the Novus antibody was used at a 1/2000 dilution. For PTEN the antibody was diluted 1/1000. All antibodies can be found in Addendum 2.

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Figure 16 PHIP and PTEN expression in patient derived xenograft T-ALL samples and T-ALL cell lines. Western blot analysis of different patient derived xenograft samples together with T-ALL cell line samples to detect the expression of both PHIP and PTEN. The Novus anti-PHIP antibody as well as the anti-PTEN antibody were used to visualize the expression. An anti-GAPDH antibody was used to serve as a loading control. PHIP is completely absent in the xT-ALL D1 xenograft sample. Despite the monoallelic PHIP mutation in xT-ALL 47 sample, PHIP expression does not seem altered compared to the wild type samples (xT-ALL G1 and C1). However, it is not clear if the PHIP protein is still functional or not in this specific sample (Figure 16). Regarding PTEN expression, Jurkat and HPB-ALL harbor a PTEN mutation and therefor show no or a lower amount of PTEN. Also, the patient derived xenograft samples show a low expression of PTEN, especially the C1 sample (Figure 16). Although for all samples 2 million cells were lysed, the GAPDH loading control is not equally clear for the cell lines versus the xenograft samples. Actin and vinculin were also tested as loading controls (not shown), but the expression of these proteins was even more variable in the xenograft samples. The reason for this is unclear. Complementary to the western blot, an intracellular PHIP staining was carried out to check the PHIP expression in the patient derived xenograft T-ALL samples. Jurkat was taken along as a positive control and MOLM-13 as a negative control (Figure 17). The staining was done by using the Sigma antibody against PHIP and the secondary Alexa Fluor ® 488 antibody.

Figure 17 PHIP expression in different patient derived xenograft T-ALL samples.

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Looking at cell lines and xenograft samples separately, the expression pattern is as expected: On the one hand, PHIP expression is higher in Jurkat than in MOLM-13 and on the other hand the PHIP expression is higher in xT-ALL G1 and C1 compared to xT-ALL D1, with xT-ALL 47 falling in between. Even though on western blot there is no band for PHIP in MOLM-13, it still contains a higher amount of the protein comparing to the xT-ALL C1 sample, which was included as a positive control.

3.1.3. Cellular localization of PHIP Previous studies suggest a cytoplasmic as well as a nuclear role for PHIP, either as a mediator of the insulin/IGF1 signaling or as an interaction partner of several nuclear proteins [22,23]. This implies that PHIP could shuttle between both cellular compartments. To explore the cellular localization of PHIP several western blots were carried out of different cytoplasmic and nuclear extracts of DND4.1, HPB-ALL and Jurkat (Figure 18, Figure 19, Figure 20). For T-ALL no pure cytoplasmic and nuclear extract could be obtained (not shown).

Figure 18 Localization of PHIP via cytoplasmic and nuclear extracts of Jurkat. Three western blots were made of three different cytoplasmic and nuclear extract preparations of Jurkat. The Novus antibody was used for PHIP detection, BRD4 and actin served as positive controls for the nuclear and cytoplasmic extracts respectively.

Cell line/sample Count Geometric mean: FITC-A

Jurkat 10311 24338

MOLM-13 11167 10983

xT-ALL G1 4780 12336

xT-ALL C1 7322 5970

xT-ALL D1 9011 3183

xT-ALL 47 7221 3675

Table 2 PHIP expression in different patient derived xenograft T-ALL samples.

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Figure 19 Localization of PHIP via cytoplasmic and nuclear extracts of HPB-ALL. Two western blots were made of two different cytoplasmic and nuclear extract preparations of HPB-ALL. The Novus antibody was used for PHIP detection, BRD4 and actin served as positive controls for the nuclear and cytoplasmic extracts respectively.

Figure 20 Localization of PHIP via cytoplasmic and nuclear extracts of DND4.1. Two western blots were made of two different cytoplasmic and nuclear extract preparations of DND4.1. The Novus antibody was used for PHIP detection, BRD4 and actin served as positive controls for the nuclear and cytoplasmic extracts respectively.

Repeated assessment of PHIP expression in cytoplasmic and nuclear extracts of Jurkat (Figure 18), HPB ALL (Figure 19) and DND4.1 (Figure 20) suggest that PHIP is not exclusively expressed in the nucleus but can also be found in the cytoplasm. The relative abundance of PHIP in the nucleus or cytoplasm seems to vary. For reasons that are unclear, e.g. in Jurkat, the PHIP protein can sometimes be found mainly in the cytoplasm, the nucleus or both (Figure 18). In the right western blot of Figure 18, BRD4 is not present in the total lysis extract. This is probably due to a mistake during pipetting. The presence of PHIP in the cytoplasm could point to a link with insulin/IGF1 signaling as reported for melanoma or breast cancer [21].

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3.2. The exploration of a potential link between PHIP and the insulin/IGF1 signaling in T-ALL

3.2.1. Baseline responsiveness to insulin/IGF1 in different T-ALL cell lines By means of western blot and intracellular staining, baseline expression levels of several components of the insulin/IGF1 pathway, being IGF1Rβ, PTEN (Figure 16), pAKT Ser473 and pERK were assessed in Jurkat, DND41, HPB ALL, TALL1. First, a western blot was carried out to look at the expression levels of the IGF1Rβ and pAKT Ser473 (Figure 21).

Figure 21 The expression of the IGF1R and pAKT Ser473 in Jurkat, HPB-ALL, DND4.1 and TALL-1. Antibodies against the IGF1Rβ and pAKT Ser473 with a 1/2000 dilution respectively. An anti-GAPDH antibody was used to serve as a loading control. Jurkat, HPB-ALL and DND4.1 have the highest expression of the IGF1Rβ, suggesting that these cell lines could be more sensitive to active IGF1 signaling (Figure 23, Figure 24). As shown above in Figure 16, Jurkat and HPB-ALL had no or low expression of PTEN. As a result, Jurkat has high levels of phosphorylated AKT Ser473. HBP-ALL shows little pAKT Ser473. In DND4.1 and TALL-1, the very high expression of PTEN is correlated with the absence of pAKT Ser473 (Figure 21). Because all four cell lines expressed the IGF1Rβ to some extent, they were further used for starvation experiments and their responsiveness to insulin and IGF1 was assessed via an intracellular staining of pAKT Ser473 and pERK. Our aim was to verify whether the response to IGF1/insulin could be correlated to the levels of IGF1Rβ and PTEN expression. Because insulin and IGF1 are positive modulators of PI3K/pAKT signaling [17], we expect that insulin/IGF1 treatment results in increased levels of pAKT and pERK in responsive cells. First, the baseline pAKT and pERK levels were validated by performing the intracellular stainings on serum starved Jurkat, HPB-ALL, DND4.1 and TALL-1 cells (Figure 22).

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Figure 22 Intracellular pAKT Ser473 and pERK staining of Jurkat, HPB-ALL, DND4.1 and TALL-1 in serum free conditions. The pattern seems to match with the western blot on Figure 21. Jurkat expresses the highest amount of pAKT Ser473, while the other three cell lines have lower pAKT levels. The same accounts for the pERK levels (Figure 22).

Figure 23 Intracellular pAKT Ser473 and pERK staining of Jurkat. The staining was performed in cells cultured in normal medium (serum positive) and serum free medium (serum negative) to determine the effect of starvation on baseline pAKT Ser473 levels. negative conditions and in the addition of Insulin or IGF1 for 15 minutes.

Jurkat does not show any changes in pAKT Ser473 levels after the addition of insulin or IGF1, but a small increase is visible when you look at the ratio of the pERK levels (Figure 23, Table 3).

Jurkat GM pAKT Ratio pAKT GM pERK Ratio pERK

Serum + 7939 0,9941 3140 1,1377

Serum - 7986 1 2760 1

Insulin 7635 0,9560 3287 1,1909

IGF1 8264 1,0348 3218 1,1659

Table 3 The geometric mean (GM) of the pAKT Ser473 intracellular staining (APC-A) and the ratio when comparing to the serum negative condition in Jurkat.

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Figure 24 Intracellular pAKT Ser473 and pERK staining of HPB-ALL. The staining was performed in cells cultured in normal medium (serum positive) and serum free medium (serum negative) to determine the effect of starvation on baseline pAKT Ser473 levels. negative conditions and in the addition of Insulin or IGF1 for 15 minutes.

In HPB-ALL, pAKT Ser473 levels decrease after the addition of insulin (ratio pAKT 0,7974) or IGF1 (ratio pAKT 0,7545) (Table 4). The same accounts for the pERK levels. Because of the fact that DND4.1 expresses the lowest IGF1R (Figure 21), we did not expect a big effect of the insulin/IGF1 addition (Figure 25). When comparing to DND4.1 cells cultured in serum-free conditions (ratio pAKT: 1), the pAKT levels are slightly decreased after addition of IGF1 (ratio pAKT: 0.8922) (Table 5). The measurement of DND4.1 serum negative + insulin was not saved correctly when using the BD LSRII flow cytometer. In comparison, incubation with insulin or IGF1 does not affect pERK levels.

Figure 25 Intracellular pAKT Ser473 and pERK staining of DND4.1. The staining was performed in cells cultured in normal medium (serum positive) and serum free medium (serum negative) to determine the effect of starvation on baseline pAKT Ser473 levels. negative conditions and in the addition of Insulin or IGF1 for 15 minutes.

HPB-ALL GM pAKT Ratio pAKT GM pERK Ratio pERK

Serum + 4062 0,6379 1257 0,8292

Serum - 6368 1 1516 1

Insulin 5078 0,7974 1266 0,8351

IGF1 4805 0,7545 1290 0,8509

Table 4 The geometric mean (GM) of the pAKT/pERK intracellular staining (APC-A) and the ratio when comparing to the serum negative condition in HPB-ALL.

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In contrast to the other cell lines, there is a big increase in pAKT Ser472 levels in the TALL-1 cell line after the addition of insulin and especially IGF1 (ratio pAKT, Table 6) (Figure 26). The pERK levels on the other hand remained unchanged.

Figure 26 Intracellular pAKT Ser473 and pERK staining of TALL-1. The staining was performed in cells cultured in normal medium (serum positive) and serum free medium (serum negative) to determine the effect of starvation on baseline pAKT Ser473 levels. negative conditions and in the addition of Insulin or IGF1 for 15 minutes.

Taken together, we can say that incubation with insulin or IGF1 does not have a consistent impact on pAKT Ser473 and pERK levels in the four T-ALL wild type cell lines tested here. In DND4.1 and HPB ALL, we do see a small decrease in pAKT levels after administration of IGF1. In contrast, insulin and particularly IGF1 treatment induces a notable increase in pAKT levels of TALL-1. Jurkat seems to be the most invariable to the addition of both insulin and IGF1, which could be due to the already high levels of pAKT at baseline

DND4.1 GM pAKT Ratio pAKT GM pERK Ratio pERK

Serum + 4666 1,1328 1762 1,0999

Serum - 4119 1 1602 1

Insulin / / 1496 0,9338

IGF1 3675 0,8922 1553 0,9694

Table 5 The geometric mean (GM) of the pAKT/pERK intracellular staining (APC-A) and the ratio when comparing to the serum negative condition in DND4.1.

TALL-1 GM pAKT Ratio pAKT GM pERK Ratio pERK

Serum + 3116 1,1356 1304 1,1727

Serum - 2744 1 1112 1

Insulin 3327 1,2125 1075 0,9667

IGF1 4603 1,6775 1273 1,1448

Table 6 The geometric mean (GM) of the pAKT Ser473 intracellular staining (APC-A) and the ratio when comparing to the serum negative condition in TALL-1.

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3.2.2. Responsiveness of patient derived xenograft T-ALL samples to insulin/IGF1 Frozen T-ALL xenograft spleen cells (>95% infiltrated by human T-ALL cells) were incubated in serum free RPMI 1640 medium (Gibco, 52400025) for four hours followed by a 15-minute incubation with 1/100 insulin or 20 ng IGF1. Afterwards, the cells were fixed for intracellular pAKT Ser473 and pERK staining (Figure 27).

Figure 27 Intracellular pAKT/pERK staining of four patient derived xenograft T-ALL samples G1, C1, 47 and D1 in serum free conditions.

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For none of the samples a difference in pAKT or pERK levels was observed in control vs insulin/IGF1-treated cells (Figure 27). The geometric means and the ratios are shown in Addendum 8. A potential explanation is that thawed xenograft T-ALL cells are suboptimal for this experiment. This experiment was there for repeated using fresh xT-ALL D1 xenograft cells from the spleen (99% infiltrated with hCD45+ T-ALL cells, data not shown). Only the pAKT levels were checked because repeated intracellular pERK stainings did not show any changes after the addition of insulin/IGF1 (data also not shown). In contrast to the experiment with thawed cells, treatment of fresh xT-ALL D1 cells with IGF1 or insulin resulted in a decrease in phosphorylated AKT levels, induced by IGF1 (Figure 28, Table 7). As this was not seen in the thawed xenograft samples, we must conclude that it is better to use fresh cells. As this T-ALL completely lacks PHIP, this experiment suggests that loss of PHIP is not associated with increased PI3K/pAKT signaling induced by IGF1.

Figure 28 Intracellular pAKT staining of fresh spleen cells of the xT-ALL D1 sample in serum free conditions, with or without insulin or IGF1.

Fresh xT-ALL D1 GM pAKT Ratio pAKT

Serum - 2028 1

Insulin 2249 1,1090

IGF 1570 0,7742 Table 7 The geometric mean (GM) of the pAKT Ser473 intracellular staining (APC-A) and the ratio when comparing to the serum negative condition in the xT-ALL D1 sample.

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3.2.3. PHIP knock down by shPHIP – The impact on pAKT levels and insulin/IGF1 responsiveness

To assess the effect of PHIP loss on response to insulin or IGF1, Jurkat cells were transduced with lentivirus harboring one of three shRNA plasmids (shPHIP2-5), targeting the human PHIP gene or a control plasmid (Mission Sigma-Aldrich, #SHCLNG). Seventy-two hours after transduction, the cells were collected for western blot for PHIP, pAKT Ser473, pAKT Thr308 and total AKT (Figure 29).

Figure 29 The expression of PHIP, pAKT Ser473/Thr308 and total AKT. The western blot shows a clear knock down of PHIP (Novus Ab). No decreases or increases are seen in the pAKT or AKT levels. Loss of PHIP does not directly affect the levels of pAKT Ser473, pAKT Thr308 and total AKT. To assess whether loss of PHIP affects the response to insulin or IGF1, these transduced Jurkat cells were starved for four hours with afterwards a 15-minute incubation with insulin. Intracellular stainings for pAKT Ser473 and pERK were performed. The validation of the knock down of these experiments can be found in the Addendum 9. This experiment was repeated three times (Figure 30). The geometric means of pAKT Ser473 levels were obtained by analysis via the FlowJo Software and were used to make boxplots. During these transductions of short hairpins against PHIP, we were not able to add IGF1 in triplicate. To make a clearer image, the averages of the three repeats with insulin are shown in Figure 31.

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Figure 30 Box and whisker plot showing the difference in pAKT levels between the knock down cells (shPHIP) and the control cells (WT and shC002). The pAKT levels in serum starved (serum-) and in insulin treated (insulin) wild type and transduced Jurkat cells are shown. In PHIP knock down in Jurkat, after insulin treatment, seems to boost pAKT Ser743 compared to control SHC002 cells.

Figure 31 Line graph showing the changes in pAKT ser473 levels after the addition of insulin, comparing WT/control cell samples with shPHIP cell samples. In Figure 30 as well as in Figure 31 we can clearly see that the levels of pAKT Ser473 do not change in wild type cells or cells transduced with the control vector (shC002), after the administration of insulin. Between the WT and shC002 we do see an increase when comparing the serum – conditions or the insulin conditions. Notably, when PHIP is knocked down, pAKT is increased after the addition of insulin. To see if these pAKT Ser473 levels were significantly different in shPHIP cells in comparison to the WT and control cells, a Kruskal-Wallis test was conducted (Figure 32). This is a non-parametric method for comparing two or more independent samples of equal or different sample sizes. The Kruskal-Wallis method does not assume a normal distribution. First, we conducted the test on the entire data set. We received a p-value of the insulin group of 0,04 (< 0,05) (Addendum 10), which means that the null hypothesis of no differences between the mean ranks can be rejected. Afterwards, we conducted some post-up tests or specific comparison tests between two groups at a time (Group 1 = WT, Group 2 = shC002, Group 3 = shPHIP3, Group 4 = shPHIP4 and Group 5 = shPHIP5). This was done in both the serum starved group and the insulin group.

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Figure 32 The test statistics of the Kruskal-Wallis test of the intracellular pAKT measurement of shPHIP Jurkat cells. Group 1 stands for the WT Jurkat cells, 2 represent the control vector shC002, whereas group numbers 3, 4 and 5 stand for the shPHIP3-5. Kruskal-Wallis H represents the chi square value. The WT Jurkat cells and the control vector shC002 should represent the same pAKT levels. Unfortunately, this is not the case for the insulin group (P-value = 5%). If we do the comparisons (shC002 vs shPHIPs), we can only reject the null hypothesis in the insulin group where shC002 is compared to shPHIP5. When we look at WT versus shPHIPs, we have statistically significance in all three shPHIPs regarding the insulin group and, in shPHIP4 and shPHIP5 in the serum starved group as well (data not shown).

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3.2.4. PHIP knock down by siPHIP - The impact on pAKT levels and insulin/IGF1 responsiveness

Because we did not succeed in stably transducing other cell lines than Jurkat, PHIP short interfering RNA was used to knock down PHIP in Jurkat, DND4.1, HPB-ALL and TALL-1. By means of electroporation, either a pool of 4 human siPHIP or a non-targeting control siRNA was transferred into the cell lines. 48 or 72 hours after electroporation, the knock down was checked by doing intracellular PHIP stainings.

All the intracellular PHIP stainings, used as confirmation for the knock down, can be found in Addendum 11. For the boxplots and line graphs (Figure 33, Figure 34, Figure 36, Figure 37), the geometric means of the intracellular pAKT Ser273 stainings were used. We were able to make duplicates and triplicates of the starvation experiment with electroporated DND4.1 and Jurkat, respectively. So far, one experiment was successfully performed for HPB-ALL and TALL-1 (Table 8, Table 9).

Figure 33 Box and whisker plot showing the difference in pAKT levels between the knock down Jurkat cells (siPHIP) and the control Jurkat cells. Both the serum starved cells and the ones with insulin or IGF1 added are shown.

Figure 34 Line graph showing the changes in pAKT ser473 levels in Jurkat after the addition of insulin or IGF1, comparing control cell samples with siPHIP cell samples.

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The control Jurkat cells react the same after insulin is added, but the addition of IGF1 results in lower pAKT levels. the reaction to insulin in the siPHIP cell samples is somewhat different and results in lower pAKT levels. In contrast, IGF1 induces higher pAKT Ser473 in siPHIP cells compared to control Jurkat. Overall, loss of PHIP sensitizes Jurkat to IGF1 but not to insulin. This result contradicts the shPHIP knockdown experiment in Jurkat, where shPHIP mediated loss of PHIP sensitized Jurkat cells to insulin. Here as well, a Kruskal-Wallis test was conducted to determine whether the pAKT Ser473 levels of the siPHIP Jurkat cells were significantly different in comparison to the control cells. First, we conducted the Kruskal-Wallis test on the entire data set. We received a p-value bigger than 5% for the serum negative group, the insulin group and the IGF1 group (Figure 35). This means that the null hypothesis of no differences between the mean ranks cannot be rejected. In other words, there are no statistically significant differences detected in pAKT levels between the cells that lack PHIP and the control ones.

Figure 35 The test statistics of the Kruskal-Wallis test of the intracellular pAKT measurement of siPHIP Jurkat cells. Group 1 stands for the control Jurkat cells, whereas 2 represent siPHIP cells. Kruskal-Wallis H represents the chi square value. In DND4.1 there is a clear difference between the control group and the siPHIP group (Figure 36, Figure 37). After the addition of insulin, the pAKT Ser473 levels in the cells nucleofected with siRNA against PHIP increase notably. The control cells on the other hand, show a small decrease. This is comparable to the IGF1 addition in DND4.1 in 3.2.1. Overall, knock down of PHIP sensitizes DND4.1 to insulin, but not IGF1.

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Figure 36 Box and whisker plot showing the difference in pAKT levels between the knock down DND4.1 cells (siPHIP) and the control DND4.1 cells. Both the serum starved cells and the ones with insulin or IGF added are shown. During the measurements on the BD LSRII flow cytometer one measurement (siPHIP + insulin) was not correctly saved. The data was therefore lost.

Figure 37 Line graph showing the changes in pAKT ser473 levels in DND4.1 after the addition of insulin or IGF, comparing control cell samples with siPHIP cell samples. So far, in HPB-ALL and TALL-1 siPHIP mediated knockdown was successfully performed once. The results are summarized in Table 8 and Table 9.

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siPHIP HPB-ALL

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Serum - 6113 4320 1 1

Insulin 4840 4850 0,7918 1,1227

IGF1 4033 6668 0,6597 1,5435 Table 8 Geometric means of the intracellular pAKT staining after the addition of Insulin/IGF in siPHIP electroporated HPB-ALL cells. The ratio was calculated by taking serum starved cells as a control.

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The ratios of the geometric means of pAKT Ser473 in Table 8 show us that in the control HPB-ALL cells, the levels of pAKT decrease slightly after the addition of both Insulin and IGF1. When PHIP is knocked down, the levels increase, especially in the IGF1 condition. Altogether, loss of PHIP in HPB-ALL sensitizes the cells to IGF1.

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Serum - 5801 5349 1 1

Insulin 5884 3980 1,0143 0,7441

IGF1 4907 3540 0,8459 0,6618 Table 9 Geometric means of the intracellular pAKT staining after the addition of Insulin/IGF in siPHIP electroporated TALL-1 cells. The ratio was calculated by taking serum starved cells as a control. In the TALL-1 cell line loss of PHIP is accompanied by a decrease in phosphorylated AKT in particular when IGF1 is added to the cells. Overall, loss of PHIP in TALL-1 sensitizes the cells to insulin and IGF1, however p-AKT Ser473 levels are reduced. Table 10 shows a small summary of the influence of both insulin and IGF1 on the four cell lines, electroporated with siPHIP.

Jurkat HPB-ALL DND4.1 TALL-1

Insulin IGF1 Insulin IGF1 Insulin IGF1 Insulin IGF1

Baseline +/- - - - - - = -

siPHIP - + + + + +/- - - Table 10 Summary of the influence of both insulin and IGF1 on Jurkat, HPB-ALL, DND4.1 and TALL-1, electroporated with siPHIP. The ‘=’ means that the levels of pAKT Ser473 did not change after the administration of insulin or IGF1. ‘+’ means a higher geometric mean of pAKT, ‘-‘ a lower geometric mean. The ‘+/-‘ indicates that the levels more or less stayed the same.

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3.3. PHIP knock down - The impact on the cell cycle Because PHIP was also found to interact with members of the cell cycle [27], a cell cycle analysis was performed on Jurkat, HPB-ALL and DND4.1, electroporated with siPHIP. Either a pool of 4 human siPHIP or a non-targeting control siRNA was transferred into the cell lines. The knock down was checked by doing an intracellular PHIP staining (See Figure 12, Addendum 11). TALL-1 was not included because there was no efficient knock down. No differences were seen in all three cell lines between control groups and the siPHIP ones when looking at the G1, S and G2-M phase distribution (Figure 38).

Figure 38 The cell cycle analysis of Jurkat, HPB-ALL and DND4.1 with and without PHIP. The four different cell cycle phases are indicated as well as the distribution (%).

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3.4. RNA sequencing results from shPHIP Jurkat RNA sequencing was performed to find differentially expressed genes in control Jurkat cells compared to Jurkat cells in which PHIP was knocked down by means of lentivirus transduction and delivery of shPHIP. After arranging the genes as described in section 2.9, the top down- and upregulated genes were obtained (Table 11 and Table 12). Overall, 350 of the 20.461 genes were significantly differentially expressed (adjusted p-value < 0,05), of which 254 were up- or downregulated in cells where PHIP was knocked down (log2FoldChange > 1 or < -1 respectively).

Gene Symbol Adjusted P-value Log2FoldChange

ENSG00000171530 TBCA 8,74009E-18 -1,676069559

ENSG00000139350 NEDD1 1,35278E-16 -1,648225127

ENSG00000171720 HDAC3 8,3279E-13 -1,554539141

ENSG00000146247 PHIP 3,72927E-12 -2,049270688

ENSG00000175352 NRIP3 3,74487E-11 -2,058297944

ENSG00000164323 CFAP97 5,08486E-11 -1,395418143

ENSG00000120805 ARL1 4,58224E-10 -1,667524207

ENSG00000147526 TACC1 2,78081E-09 -1,319045586

ENSG00000128951 DUT 6,47921E-09 -1,536765487

ENSG00000133706 LARS 1,4644E-08 -1,240762263

ENSG00000111445 RFC5 2,2129E-08 -1,322544716

ENSG00000092201 SUPT16H 2,95277E-08 -1,180304389

ENSG00000114738 MAPKAPK3 5,45235E-08 -1,287731173

ENSG00000168092 PAFAH1B2 5,48115E-08 -1,221097317

ENSG00000177879 AP3S1 5,6058E-08 -1,247476197

ENSG00000082074 FYB1 6,11016E-08 -1,524560282

ENSG00000213064 SFT2D2 1,53611E-07 -1,340292073

ENSG00000064886 CHI3L2 1,61814E-07 -1,510185499

ENSG00000170017 ALCAM 1,7013E-07 -1,738752408

ENSG00000198791 CNOT7 1,7459E-07 -0,999308181 Table 11 The top 20 downregulated genes (140 in total), listed from smallest to highest p-value, after RNA sequencing of shPHIP Jurkat cells. The downregulation of PHIP serves as a control.

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Gene Symbol Adjusted P-value Log2FoldChange

ENSG00000163597 SNHG16 1,23824E-21 2,831048656

ENSG00000159388 BTG2 2,59818E-09 2,477007432

ENSG00000151893 CACUL1 1,602E-08 1,55795675

ENSG00000175097 RAG2 9,75789E-07 2,456433867

ENSG00000111536 IL26 4,9665E-06 2,705837384

ENSG00000118402 ELOVL4 5,28722E-06 1,808694253

ENSG00000166349 RAG1 3,09083E-05 1,82600911

ENSG00000106244 PDAP1 5,89719E-05 1,209053906

ENSG00000163508 EOMES 6,93858E-05 2,110000868

ENSG00000143319 ISG20L2 6,93858E-05 1,376339387

ENSG00000243927 MRPS6 7,11877E-05 1,259112066

ENSG00000158006 PAFAH2 7,28772E-05 1,632465154

ENSG00000135677 GNS 7,48737E-05 1,192698339

ENSG00000139998 RAB15 7,90788E-05 1,656147172

ENSG00000144455 SUMF1 9,02728E-05 1,470342483

ENSG00000198963 RORB 0,000148102 1,302204704

ENSG00000135930 EIF4E2 0,000153615 1,400236905

ENSG00000078246 TULP3 0,000287108 1,642871764

ENSG00000124635 HIST1H2BJ 0,000414325 2,014313269

ENSG00000214960 ISPD 0,0004352 1,94627765 Table 12 The top 20 upregulated genes (114 in total), listed from smallest to highest p-value, after RNA sequencing of shPHIP Jurkat cells.

By performing String analysis of the gene lists, relevant pathways of these down- and upregulated genes were found. One of the pathways that gets downregulated by loss of PHIP is the MAPK signaling pathway (due to, among others, the reduced expression of the MAPK14 gene). The Rap1 (Ras-proximate-1) pathway is another example (e.g. MAPK14, RASGRP2, FYB). On activation, Rap1 undergoes conformational changes that facilitate recruitment of a variety of effectors, triggering its participation in integrin signaling, ERK activation, and others. Also, the cell cycle (e.g. CDKN1C) and the signaling pathways regulating pluripotency of stem cells (e.g. MAPK14, SMAD1) are downregulated. TRAF5 and STAT5A also have a lower expression after PHIP knock down. These genes are often mutated in several types of cancer. Upregulated genes were linked to the FoxO signaling pathway (e.g. Rag1/2, CDKN2D) and multiple pathways regarding gene promotor sites. FoxO1 is a transcription factor that plays important roles in the regulation of gluconeogenesis and glycogenolysis by insulin signaling. The core MLL complex was also upregulated. This complex methylates H3K4Me2-Nucleosomes at the MIR27A, GP1BA and THBS1 gene promotor. The following biochemical reactions were found regarding the MLL complex: RUNX1 and GATA1 which bind the promotor of the MIR27A gene, the GP1BA gene and the THBS1 gene.

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Chapter 4. Discussion T-cell acute lymphoblastic leukemia is an aggressive hematologic cancer that affects both adults and children. Multiple genetic deviations work together to transform the normal T-cell precursor to a leukemic T-cell [2]. The harmful side-effects of chemotherapeutics and the extremely poor prognosis of refractory cases emphasize the need for a better insight in the biology of this disease. Recently, our group identified small 6q deletions, targeting the BRWD2/PHIP gene particularly in tumors that show overexpression of the transcription factor oncogenes TLX1 or TLX3. These genetic findings suggest that PHIP might act as a novel tumor suppressor gene in human T-ALL, similarly to what was shown in a mouse study on Burtkitt lymphoma [28]. PHIP was initially discovered as an interaction partner of the insulin receptor substrate 1 [14], which plays a central role in transducing insulin/IGF-dependent signals that activate the PI3K/AKT and ERK pathways [17]. However, PHIP was also shown to interact with methylated histones, suggesting a potential role of this protein in epigenetic regulation [23]. Altogether, the function of PHIP is very obscure. In this master thesis, we aimed to provide some initial insight in the role of PHIP in the context of T-ALL. In particular we focused on the potential link with insulin/IGF1 signaling.

4.1. PHIP is overall expressed in various leukemia cell lines Western blot on different leukemia cell line lysates revealed that PHIP was prominently present

in most samples containing all sorts of oncogenes and activated pathways.

In HPB-ALL (TLX3), Jurkat (TAL1) and SEM (B-cell leukemia with MLL fusion) PHIP was most

abundantly expressed, whereas Karpas-45 (T-ALL with MLL fusion), MOLM-13 (acute myeloid

leukemia with MLL fusion) and MOLT-13 (non-TLX) almost completely lacked the protein. This

could suggest that, depending on the type of T-ALL, the role of PHIP could differ.

4.2. PHIP is present in both the cytoplasm and the nucleus PHIP western blot on nuclear and cytoplasmic extractions showed us that the location of the PHIP protein differs from one T-ALL cell line to another and that in repeated experiments using the same cell line. This could suggest that PHIP could shuttle between the cytoplasm and the nucleus and maybe perform different functions in each cellular compartment. There are studies suggesting a role of PHIP as regulator of the mediators of the IGF1 pathway [14,15,30], whereas the study of Morgan et al. showed a nuclear confinement of PHIP, binding H3K4 methylated histones [23]. Next to this, preliminary ChIP sequencing data shows that, also in the T-ALL cell lines Jurkat and HPB-ALL, PHIP binds to a large number of genomic sites.Because of this, the protein could both be present in the cytoplasm or nucleus respectively. The results of the cytoplasmic and nuclear extracts can conclude that depending on the type of cancer the location of PHIP can vary. This could suggest that PHIP harbors different kind of functions in different diseases.

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4.3. PHIP and its effect on the insulin/IGF1 pathway In this master thesis we tried to assess the function of PHIP regarding the insulin and IGF1 pathway. Metabolic abnormalities have an adverse impact on outcomes of cancer patients. However, the exact role that elevated glucose and insulin exposure play in cancer progression remains unclear [34]. The promotion of tumor cell growth by increased glucose consumption is commonly known as the Warburg effect [35]. This same effect could also be present in T-cell acute lymphoblastic leukemia. Glycolysis in tumor cells is driven by AKT, and PHIP is a potent activator of AKT in melanoma [29]. In addition, could the increased glucose metabolism itself drive tumor angiogenesis, invasion, and metastasis [30]. Furthermore, a study on glioblastoma shows that blockade of the IR/IGF1R pathway by small molecules or knock down leads to a compromised glioblastoma xenograft tumor growth in mice [34]. Because the genomic region encoding PHIP is deleted in a subset of T-ALL we hypothesized that it could play a role as a tumor suppressor. If that were the case, loss of PHIP in T-ALL cells would be associated with a stronger response to insulin/IGF1, resulting in increased levels of p-AKT Ser473 and/or p-ERK. Our data however suggest that the impact of PHIP loss varies in different T-ALL cell lines. Four cell lines were selected: HPB-ALL, DND4.1, Jurkat and TALL-1. Two TLX and two non-TLX cell lines, respectively. Initially we looked at the expression of the IGF1Rβ, the intracellular component of the IGF1R, and found out that the DND4.1 cell line expressed the lowest amount of the IGF-receptor. Next to this, pAKT Ser473 was only present in high amount in the Jurkat cell line which is most likely a result of its lack of PTEN. Neither DND4.1, HPB-ALL nor TALL-1 showed any expression. Because all four cell lines did express the IGF1Rβ, they were all used in the following experiments. To assess the effect of PHIP on the IGF1 and insulin pathway in T-cell acute lymphoblastic leukemia, we conducted several starvation experiments with wild type or PHIP knock down cells, followed by intracellular pAKT/pERK staining. The baseline pAKT/pERK levels of the wild type cell lines in serum free conditions differ from each other. Jurkat contained the highest pAKT Ser473 and pERK levels, whereas the TALL-1 cell line expressed the lowest amount of pAKT and pERK. The insulin/IGF1 treatment in the wild type cells did not lead to a strong effect of the pAKT/pERK levels. Surprisingly, there was a trend for decrease of phosphorylated AKT after IGF1 in HPB-ALL and DND4.1. This was in contrast with a previous report, which shows a clear induction of pAKT in HPB-ALL after IGF1 was used at the same concentration as we did [17]. As for the pERK levels, there was a small increase after the administration of insulin/IGF1 in Jurkat and a small decrease in HPB-ALL. The reason why there is no change observed in pERK levels in DND4.1 may have to do with its NRAS mutation. This causes constitutive activation of pERK. We hypothesized that loss of PHIP would have not made a difference. The same accounts for the TALL-1 cell line.

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The question that arose was whether these levels would change if PHIP was knocked down. We first tried to attempt this by using short hairpins against PHIP. Via western blot we did not see any differences in pAKT Ser/Thr, and total AKT levels in Jurkat after knock down of PHIP with short hairpins via lentiviral transduction. This was not the case with the intracellular pAKT Ser473 staining after starvation, and insulin or IGF1 administration. In Jurkat, the levels showed an upwards shift after the addition of insulin, but only in the samples with PHIP knock down. The Kruskal-Wallis test on the entire data set of the three repeats in the different short hairpin samples concluded that the null hypothesis could be rejected. There was a P-value of 0,04 which led to the assumption that there was a statistically significant difference somewhere within the data set that was not based on coincidence. Further comparisons between the control vector and the shPHIP samples showed that only the insulin group within shC002-shPHIP5 had a P-value of 0,05. No changes were visualized after the administration of IGF1. Because we were only able to efficiently transduce Jurkat, we further used small interfering RNA to acquire a stable knock down of PHIP via nucleofection. Depending on the T-ALL cell line, there are variable pAKT Ser472 levels. We see a big increase in the pAKT levels of Jurkat siPHIP after the addition of IGF1. This is not the case when insulin is added. The levels of the control cells are staying more or less the same. Although there was a visual shift, the Kruskal-Wallis test did not show a significance in the entire data set (P-value > 5%). In contrast, DND4.1 showed an upwards shift after the administration of insulin in the siPHIP samples. The addition of IGF1 there is no clear alteration. The pAKT levels of the control siRNA samples are higher than the ones where PHIP is knocked down. The lower levels of pAKT after IGF1 administration could be due to the fact that DND4.1 does not express the IGF1Rβ as much as Jurkat does. Further on, HPB-ALL shows a decrease in the control cells after insulin/IGF1 administration but an increase when PHIP is knocked down (mainly after IGF1). siPHIP in TALL-1 shows the complete opposite of the intracellular staining that was conducted to look at the baseline responsiveness. When the protein of interest is knocked down there is a clear decrease in the levels of pAKT Ser473. The differences in response in these four cell lines could partly be due to mutations in PTEN (Jurkat and HPB-ALL), mTOR, PDK1 or maybe other genes involving the PI3K/AKT pathway. Because pAKT Ser473 levels seem to shift in some of the cases when PHIP is knocked down, we might suggest that, indeed, the protein has something to do within the insulin and/or IGF1 pathway. Because there is no clear path in the T-ALL samples it is difficult to say whether PHIP promotes the phosphorylation of AKT or not.

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4.4. PHIP associated gene network RNA sequencing on Jurkat cells, lacking PHIP led to 499 down- and upregulated genes that were significantly differentially expressed. One of the significantly different downregulated genes was the MAPK14 gene. This gene is part of the MAPK signaling pathway and is therefore less effective due to loss of PHIP. Further on, a couple of genes regulating the Rap1 pathway were also downregulated. Rap1 is a GTPase and is a link between cadherins and integrins. Consequently, there is a lower activity of the integrin αβ-activation and the ERK signaling, which is required for their activation [29]. However, we did not see any changes in pERK levels when PHIP was knocked down. Also, the cell cycle (e.g. CDKN1C) and the signaling pathways regulating pluripotency of stem cells (MAPK14, SMAD1) are downregulated. Here as well, no changes in the cell cycle were analyzed. As for the upregulated genes, there were a couple of them linked to the FOXO signaling pathways, regarding gene promotor sites. Forkhead box (FOX) proteins are conserved transcriptional factors which play a role in a variety of cellular processes. FOXOs regulate cell cycle arrest, apoptosis, DNA damage repair, stress response, angiogenesis and play a role in metabolism. AKT and ERK are two oncogenic kinases that target FOXO3 by phosphorylation, resulting in degradation of the protein [29]. This is not in line with the insulin/IGF1 experiments in HPB-ALL and DND4.1, where administration of Insulin in siPHIP cells led to an increase in phosphorylated AKT levels, which was not seen in the control cells. In TALL-1 on the other hand, the addition of insulin/IGF1 led to a decrease in pAKT Ser473. In this way the FOXO proteins would be less inhibited. Genes involving the core MLL or COMPASS complex were also upregulated. Recently, Morgan et al. found that COMPASS H3K4 methyltransferase family members differentially regulate PHIP chromatin occupancy in Drosophila by ChIP. Mammalian MLL1 and MLL2 catalyze H3K4me3 at promoters of developmentally regulated genes, whereas MLL3 and MLL4 mediate H3K4me1 at intergenic and intragenic enhancer regions. Deletion of MLL4 in a colon cancer cell line was accompanied by the loss of PHIP binding [23]. This could support the role of PHIP in the nucleus. Altogether, we can conclude that PHIP does have a role in both cellular compartments. At least in Jurkat cells.

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4.5. Conclusion and future perspectives Precision medicine has enabled the selection of drug therapies based on the molecular analysis of individual patient tumors. In this way, patients that most likely will benefit from the targeted intervention can be selected for treatment, while patients whose tumors lack these targets will be spared from the therapy and its adverse events. But, despite of the increased response rates in matched patient populations, challenges still remain. There is the development of acquired resistance against the specific drug and many identified molecular targets are currently not yet druggable. In addition, the biomarker screening tests do not always contain the highest sensitivity or specificity. This is why it is important to look for new cancer biomarkers. One of them could be the Pleckstrin Homology-domain Interacting Protein. Genetic data suggested that PHIP might act as a novel tumor suppressor gene in human T-ALL because it was frequently deleted in a couple of TLX1/3 cases. But deletion is not its sole mechanism in cancer. In melanoma for example, the protein is often amplif ied [21,23,29–31]. Both in the deleted state and the amplified one, adjacent genes might provide advantage, because co-deletion or co-amplification might decrease or increase the expression of PHIP respectively. Further on, we do not yet know what the downstream signaling pathways are of PHIP (deleted or amplified). In this master thesis we mainly tried to assess the function of PHIP regarding the insulin and IGF1 pathway. We did not investigate its potential role in the nuclear compartment. Because pAKT Ser473 levels seem to shift in some of the cases when PHIP is knocked down, we might suggest that, indeed, the protein has something to do within the insulin and/or IGF1 pathway. Because there is no clear path in the T-ALL samples it is difficult to say whether PHIP promotes the phosphorylation of AKT or not. The RNA sequencing results suggests as well a function of PHIP in the cytoplasm, through the Rap1 pathway and the FOXO proteins. Nevertheless, this is still in contradiction to the results of the insulin/IGF1 experiment after PHIP loss. In the future there must also be looked into the nuclear function of PHIP in T-ALL. Especially, since the RNA sequencing results showed that genes in the in the nucleus also get down- or upregulated by loss of PHIP. The interaction of PHIP with the MLL or COMPASS complex [36] may confirm its role inside the nucleus. In addition, another study showed that PHIP has an important role in DNA replication, with the observation of significant overexpression of the proliferation markers PCNA, Ki67, and phosphor-Histone 3 (pHH3) on PHIP overexpression [27]. However, we do not see any changes in the cell cycle progression when PHIP is knocked down in different leukemia cell lines (Jurkat, HPB-ALL and DND4.1). Apart from the association with insulin/IGF1 signaling, PHIP very likely functions in the nucleus. Unfortunately, PHIP does not have any enzymatic activity that can be readily targeted. The PHIP protein does contain two bromodomain motifs but all of this is pointless if we are dealing with a loss of function. This does not take away the essence to study the function of this protein and its interaction partners in both healthy and leukemic cells. With this knowledge we could understand the molecular biology of T-cell acute lymphoblastic leukemia better.

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In summary, we could say that PHIP is a highly multifaceted protein with diverse cellular functions in different cancer subtypes. Additional research will definitely be required to realize the promise of PHIP both as a biomarker and as a target for cancer therapy. More knock down and RNA sequencing experiments in different cell lines are required and ongoing. Co-Immunoprecipitation (Co-IP) would be very interesting to identify direct interaction partners of PHIP. As PHIP may be present in both the cytoplasm and the nucleus, it is interesting to do the Co-IP on both extracts. Separately, bone marrow transplant experiments could be carried out by using overexpression of ICN (Intracellular domains of NOTCH), mimicking NOTCH hyperactivation in T-ALL, with or without knock down of PHIP. The injection of these cells in mice could lead to the assessment whether PHIP knock down alters tumor development or not.

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SUPPLEMENTARY DATA ADDENDUM 1: Abbreviation list.

Table A1: Abbreviations

Abbreviation Explanation

AA Amino Acid

BD Bromo Domain

BL Burkitt Lymphoma

CD Cluster of Differentiation

Co-IP Co-Immunoprecipitation

DMSO Dimethyl Sulfoxide

DP Double positive

DPBS Dulbecco’s Phosphate-Buffered Saline

eGFP Enhanced Green Fluorescent Protein

ETP Early Thymic Progenitor

FCS Fetal Calf Serum

FOX Forkhead Box protein

HSC Human Stem Cell

hWB Human Washing Buffer

ICN Intracellular domains of NOTCH

IGF Insulin Growth Factor

IL Interleukin

IR Insulin Receptor

IRS Insulin Receptor Substrate

KO Knockout

MHC Major Histocompatibility Complex

MOI Multiplicity of Detection

NF Nuclease Free

NLS Nuclear Localization Signal

NSG Nod Scid Gamma

NXT Nuclear extract

ON Overnight

PBS Phosphate-Buffered Saline

PHIP Pleckstrin Homology-domain Interacting Protein

PI Propidium Iodide

RT Room Temperature

RTK Receptor Tyrosine Kinase

SCF Stem Cell Factor

SDS Sodium Dodecyl Sulfate

S.O.C. Super Optimal broth with Catabolite repression

SP Single Positive

SN Supernatants

T-ALL T-cell Acute Lymphoblastic Leukemia

TB Trypan Blue

TCF-1 T-cell factor-1

TCR T-Cell Receptor

TF Transcription Factor

TSP Thymic Seeding Progenitor

TU Transducing Unit

WD Tryptophan Aspartic acid

WT Wild Type Table 1 Abbreviation list.

ADDENDUM 2: Antibodies list.

Table A2: Monoclonal antibodies for flow cytometry

Laser Antibody Producer

APC pAKT Ser473, human mouse Ab

Miltenyi Biotec, 130-119-570

APC pERK, mouse mAb eBioscience, 17-9109-42

Alexa Fluor ® 488, FITCH Anti-IgG (H+L), rabbit, anti-Goat

Life technologies, A11034

Alexa Fluor ® 594, PE-Texas red

Anti-IgG (H+L), donkey, anti-rabbit

Life technologies, A21207

Table 2 Monoclonal antibodies for flow cytometry.

Table A3: Monoclonal and polyclonal antibodies for western blotting

Antibody Host Dilution Producer

Anti-AKT Rabbit 1/1000 Cell Signaling Technology, 9272S

Anti-β-Actin Mouse 1/10000 Sigma Aldrich, A2228

Anti-BRD4 Rabbit 1/2000 Bethyl, A301-985A50

Anti-GAPDH Rabbit 1/25000 Sigma Aldrich, G8795

Anti-IGF1Rβ Rabbit 1/1000 Cell Signaling Technology, 9750S

Anti-pAKT Ser473 Rabbit 1/2000 Cell Signaling Technology, 4060S

Anti-pAKT Thr308 Rabbit 1/1000 Cell Signaling Technology, 4056S

Anti-PHIP Rabbit 1/2000 Novus, NBP1-19096

Anti-PHIP Rabbit 1/1000 Sigma Aldrich, HPA019140

Anti-PTEN Rabbit 1/1000 Cell Signaling Technology, 9188

Anti-Vinculin Mouse 1/5000 Sigma Aldrich, V9131 Table 3 Monoclonal and polyclonal antibodies for western blotting.

ADDENDUM 3: Figure 14 PHIP expression in different cell lines. Western blot analysis

of T-ALL cell lines to detect the expression of the PHIP protein. Fifteen l of protein was blotted to each lane. The Novus antibody against PHIP was used as well as an antibody to vinculin to serve as loading control.

Figure 1 The expression of PHIP and PHF6 in different T-ALL cell lines.

ADDENDUM 4: Figure 16 PHIP and PTEN expression in patient derived xenograft T-ALL samples and T-ALL cell lines. Western blot analysis of different patient derived

xenograft samples together with T-ALL cell line samples to detect the expression of both PHIP and PTEN. The Novus anti-PHIP antibody as well as the anti-PTEN antibody were used to visualize the expression. An anti-GAPDH antibody was used to serve as a loading control.

Figure 2 The expression of PHIP and PTEN via western blot in T-ALL cell lines and patient derived xenograft samples.

ADDENDUM 5: Figure 20 Localization of PHIP via cytoplasmic and nuclear extracts of DND4.1. Two western blots were made of two different cytoplasmic and nuclear extract

preparations of DND4.1.

Figure 3 The localization of PHIP via cytoplasmic and nuclear extract of two independent time points in DND4.1

ADDENDUM 6: Figure 19 Localization of PHIP via cytoplasmic and nuclear extracts of HPB-ALL. Two western blots were made of two different cytoplasmic and nuclear extract

preparations of HPB-ALL.

Figure 4 The localization of PHIP via cytoplasmic and nuclear extracts of two independent time points in HPB-ALL

ADDENDUM 7: Figure 18 Localization of PHIP via cytoplasmic and nuclear extracts of Jurkat. Three western blots were made of three different cytoplasmic and nuclear extract

preparations of Jurkat.

Figure 5 The localization of PHIP in cytoplasmic and nuclear extracts of three independent timepoints in Jurkat.

ADDENDUM 8: Geometric means and ratios of Figure 27 Intracellular pAKT/pERK staining of four patient derived xenograft T-ALL samples G1, C1, 47 and D1 in serum free conditions.

Figure 6 The geometric means and ratios of the intracellular pAKT/pERK staining of four patient derived xenograft T-ALL samples G1, C1, 47 and D1 in serum free conditions.

ADDENDUM 9: Knock down validation of shPHIP transduced Jurkat (Figure 30, Figure 31).

Figure 7 The knock down validation via GFP measurements of the first experiment regarding the intracellular pAKT staining of shPHIP Jurkat.

Figure 8 The knock down validation via GFP measurements of the second experiment regarding the intracellular pAKT staining of shPHIP Jurkat.

Figure 9 The intracellular PHIP staining of the third experiment regarding the intracellular pAKT staining of shPHIP Jurkat cells. The expression of PHIP in sh4 is a little higher in comparison to the other short hairpins.

ADDENDUM 10: Kruskal-Wallis test on the entire data set (Figure 32).

Figure 10 The test statistics of the Kruskal-Wallis test in the entire data set. The P-value of the insulin group = 0,04 (< 5%), which means that in that subgroup there is a statistically significant difference.

ADDENDUM 11: Intracellular PHIP staining of siPHIP electroporated cell lines (Figure 36, Figure 37, Figure 33, Figure 34) (Table 8, Table 9).

Figure 11 The intracellular PHIP staining of the siPHIP cells of the first experiment. HPB-ALL is not shown because there was no knock down.

Figure 12 The intracellular PHIP staining of the siPHIP cells of the second experiment. The nucleofection of siPHIP failed on TALL-1

Figure 13 The intracellular PHIP staining of the siPHIP cells of the third experiment. Only DND4.1 and Jurkat had an efficient knock down.

the role of phip in t-cell acute lymphoblastic leukemia

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