Understanding the Role of SLFN12 in Selective Cancer Cell Killing Induced by PDE3A Modulating Small Molecules

Understanding the Role of SLFN12 in Selective Cancer Cell Killing Induced by PDE3A Modulating Small Molecules.

For the attainment of the academic degree

Master of Science

From the University of Applied Sciences FH Campus Wien

Submitted by: Marcus Tötzl, B.Sc.

Personal identity code 1510544049

Supervisor: Xiaoyun Wu, Ph.D. Broad Institute of MIT and Harvard Cambridge USA

Submitted on: 21. 12. 2017

Abstract:

Cancer, essentially a disease of the genome, is considered one of the most dangerous medical conditions and the leading cause of death worldwide in 2012. In the past few decades novel discoveries and innovative ideas have led to promising treatment options for several cancer types. This is due to vast improvements in preventative care as well as emerging research fields such as nanotechnology, epigenetic drugs and immune therapies. The notion of broad one- size-fit-all treatments has gradually yielded to tailored therapies, which target specific dependencies in different types of cancer based on the genomic landscape of mutations in each individual patient. In 2015 de Waal et al. have discovered a novel compound, 6-(4- (diethylamino)-3-nitrophenyl)-5-methyl-4,5-dihydropyridazin-3(2H)-one (DNMDP) that selectively killed a subset of cancer cell lines. Further investigations revealed that this small molecule binds to phosphodiesterase 3A (PDE3A), which is a common target for inhibition in several cardiovascular diseases, and enables engagement with another protein, Schlafen 12 (SLFN12), in order to form a compound-dependent complex. This PDE3A-SLFN12 protein interaction further induces a yet unknown pathway that ultimately leads to apoptosis. Notably, DNMDP-induced cell killing represents a novel cellular mechanism in contrast to typical cancer dependencies. In this study, I focused on understanding the role of SLFN12 in DNMDP-induced cell death. Specifically, I generated knock-outs of endogenous SLFN12 using CRISPR/Cas9 technology in several cancer cell lines, which verified that SLFN12, like PDE3A, is a required factor in the mode of action of PDE3 modulating agents. I also performed deletion analysis of SLFN12 in order to elucidate the domain(s) required for DNMDP-induced cell death. This study gave new insights into the biology of SLFN12 that is leveraged by DNMDP to achieve selective cancer cell killing. The new information gained in this project may contribute to further improvements of DNMDP as a promising antitumor therapy.

Abstract (German):

Krebs, im Wesentlichen eine Erkrankung des Genoms, wird als eine der gefährlichsten medizinischen Fälle angesehen und war darüber hinaus die führende Todesursache im globalen Vergleich im Jahr 2012. In den letzten Jahrzehnten haben neue Entdeckungen und innovative Ideen zu vielversprechenden Behandlungsmöglichkeiten für mehrere Krebsarten geführt. Dies ist mit den enormen Verbesserungen in der Prävention sowie mit neu entstehenden Forschungsbereichen zu begründen, wie beispielsweise Nanotechnologie, epigenetische Wirkstoffe und Immuntherapien. Das Konzept einer allgemeinen Breitband-Behandlung hat allmählich den Weg für maßgeschneiderte Therapien freigegeben bei denen spezifische Abhängigkeiten in unterschiedlichen Krebsarten auf Basis der genetischen Landschaft von Mutationen in individuellen Patienten in Angriff genommen werden. Im Jahr 2015 haben de Waal et al. einen Wirkstoff namens 6-(4-(diethylamino)-3-nitrophenyl)-5-methyl-4,5- dihydropyridazin-3(2H)-one (DNMDP) entdeckt, welcher selektiv Krebszellen zerstört hat. Weitere Untersuchungen haben aufgedeckt dass dieses kleine Molekül an Phosphodiesterase 3A (PDE3A) bindet, welche des Öfteren ein Ziel von Inhibierung in bestimmten kardiovaskulären Erkrankungen ist, und die Interaktion mit einem weiteren Protein, Schlafen 12 (SLFN12), ermöglicht um einen Wirkstoff-abhängigen Komplex zu bilden. Diese PDE3A-SLFN12 Protein- Interaktion induziert im weiteren Verlauf einen bisher unbekannten Signalweg, welcher im Endeffekt zu Apoptose führt. DNMDP nützt hierbei einen neuen Mechanismus aus, welcher im Kontrast zu typischen Krebs-Abhängigkeiten steht. Der Fokus dieser Studie lag auf dem Sammeln neuer Erkenntnisse um die Rolle von SLFN12 beim DNMDP-induzierten Zelltod besser zu verstehen. Ich generierte genetische Knock-Outs von endogenem SLFN12 mittels CRISPR/Cas9 Technology in unterschiedlichen Zelllinien hergestellt, welche SLFN12, wie PDE3A, als essentiellen Faktor im Mechanismus von PDE3-modulierenden Wirkstoffen bestätigte. Des Weiteren habe ich eine SLFN12 Deletions-Analyse durchgeführt um herauszufinden welche Domäne(n) essentiell für einen DNMDP-induzierten Krebszelltod ist/sind. Diese Studie ermöglichte einen tieferen Einblick in die biologische Funktion von SLFN12, welche durch DNMDP ausgenützt wird um selektiv Krebszellen zu zerstören. Die neuen Erkenntnisse die durch dieses Projekt erlangt werden konnten, leisten einen unterstützenden Beitrag zur Verbesserung von DNMDP auf dem Weg zu einer Anti-Tumor Therapie.

Table of Contents:

List of Abbreviations...... 6

Introduction ...... 8

Current state on cancer ...... 8

DNMDP is a PDE3 modulating small molecule with selective cancer cell killing activity ...... 9

PDE3A in the context of cardiovascular diseases and cancer ...... 11

The Schlafen gene family ...... 13

SLFN12, the uncharacterized partner of PDE3A ...... 15

Anagrelide is a potent inhibitor of PDE3A and platelet formation ...... 17

Melanoma: Subtypes, current medical options and setbacks ...... 17

CRISPR is a powerful tool for generating site-specific knock-outs ...... 21

Lentiviral transduction enables efficient gene delivery...... 25

Summary of aims and study design...... 25

Materials and Methods ...... 26

Cell culture maintenance ...... 26

Monitoring of cell proliferation by population doubling ...... 26

Generation of truncated SLFN12 mutants by site-directed mutagenesis ...... 26

Generation of SLFN12 knock-out cell lines by CRISPR/Cas9 ...... 27

Viral transduction...... 27

Compound sensitivity testing ...... 28

Detection of mRNA expression levels ...... 28

Validation of SLFN12-antibodies ...... 29

Evaluation of protein-protein interaction using Co-immunoprecipitation ...... 29

Verifying SLFN12 knock-out efficiency by CRISPRseq ...... 30

Results ...... 35

SLFN12 is a key protein and the limiting factor to confer sensitivity to PDE3 modulating agents...... 35

Design of distinct sgRNAs by CHOPCHOP to attain genetic knockout of SLFN12 ...... 35

Evaluation of α-SLFN12 antibodies ...... 36

Validation of SLFN12 knock-out in HeLa and A2058 ...... 38

CRISPRseq reveals on-target efficacy on endogenous SLFN12 in HeLa and A2058 ...... 40

Genetic perturbation of SLFN12 by CRISPR resulted in loss of sensitivity to PDE3 modulating agents...... 47

Restoration of DNMDP-sensitivity phenotype in SLFN12-KO cancer cell lines by integrating non-CRISPR-targeted rescue constructs ...... 50

RVH421 is a PDE3A-lacking melanoma cell line that requires SLFN12 for induction of DNMDP- and anagrelide-dependent cell death ...... 52

Evaluation of genetic perturbation of SLFN12 in the PDE3A-lacking RVH421 cell line ...... 52

Verification of phenotypic change on treatment with PDE3 modulating agents after SLFN12- KO in RVH421 ...... 56

Monitoring of proliferation after genetic knock-out of SLFN12 ...... 58

Selective impact on expression, PDE3A-interaction and induction of cell death by truncating major domains in SLFN12 protein ...... 61

Discussion ...... 73

Outlook ...... 74

References ...... 77

Acknowledgements ...... 83

Statutory Declaration ...... 84

List of Abbreviations Abbreviation/acronym Full word CCLE Cancer Cell Line Encyclopedia, GTEX Genotype Tissue Expression Project ET Essential thrombocythemia HCC Hepatocellular carcinoma NLS Nuclear localization signal EMT Epithelial-mesenchymal transition AR Allergic rhinitis PBMC peripheral blood mononuclear cell WGS Whole genome sequencing CNS Central nervous system FDA Food and Drug Administration ACT Adoptive cell therapy KO knock-out KD knock-down RNAi RNA interference TALENs transcription activator-like effector nucleases ZFN Zinc-finger nuclease CRISPR clustered regularly interspaced palindromic repeats crRNA CRISPR-RNA tracrRNA Transactivating CRISPR-RNA PAM Protospacer adjacent motif sgRNA single guide RNA NHEJ Non-homologous end joining HDR Homology directed repair HR Homologous recombination Indel Insertion or deletion ORF Open reading frame CRISPRi CRISPR-interference cDNA complementary DNA ATCC American Type Culture Collection PD Population doubling CPD cumulative PD CTG Cell Titer Glo

mRNA messenger RNA qRT-PCR Quantitative real time polymerase chain reaction CT Threshold cycle gDNA genomic DNA CoIP Protein complex immunoprecipitation MITE Mutagenesis by Integrated Tiles

Introduction

Current state on cancer With 14 million new cases and over 8 million deaths in 2012 cancer is the leading cause of human death globally. Data has shown that the highest rates of incidences throughout all cancer types can be found in high-income countries. This fact is attributed to risk-increasing customs and lifestyles such as smoking or unhealthy diet as well as continuous and rapid growth in populations all over the world. Cancer incidences have reportedly increased in low- and middle- income countries as their economies are progressively thriving, which leads to adaptations of habits usually found in countries with higher income. Through prevalence about preventative measures, heightened awareness of risk factors as well as access to early detection and improved treatment options numbers of cancer incidences and mortalities in western countries are being brought under control1. Major advances and progress in technology as well as our understanding about molecular driver pathways have been seen in the last decade. The notion of broad one-size-fit-all therapies have gradually been abandoned as we gain more insight in the biology of tumors. Tailored therapies that target specific molecular drivers in cancer, according to their mutational landscape, have entered center stage of interest. Furthermore, these novel therapeutic options are now considered an integral part of cancer patient care along with chemo- and radiation- therapy as well as surgery, if applicable. Obtainment of further information on biology has suggested that cancer is not a single disease, but rather considered a group of more than 200 genetically distinct types due to significant genetic heterogeneity. Those different types all bear their unique set of genetic features, thus, have to be targeted in an individual way. Recent advances in distinct research fields have opened new opportunities: i) Nanotechnology has made initial successful steps in providing novel drug delivery systems with higher efficiency and less toxicity such as the very promising nano-particle delivery of paclitaxel (Abraxane®) for treatment of non-small cell lung cancer (NSCLC); ii) Epigenetics is considered a promising field of research since tumors possess significant aberrations of their epigenome and epigenetic changes are potentially reversible, thus, development of epigenetic drugs have picked up speed and already demonstrated outstanding results in the treatment of leukemia (Dacogen®), lymphoma (Farydak®) and breast cancer (Entinostat – Phase III)2; iii) after a protracted and initially little-rewarding development process, several types of immune therapy have shown significant clinical relevance due to durable and substantial response with cancer vaccines, adoptive T-cell therapy and checkpoint blockage approaches; iv) preventative 8

measures and cancer diagnostics have been strongly improved, resulting in enhanced imaging techniques along with better imaging agents that facilitate more precise cancer diagnosis, which represents the basis of choosing the appropriate therapy. Said new insights as well as promising technical applications suggest personalized medicine as the future approach for tackling cancer. However, these remarkable advances have not benefited treatment for all cancer types but rather address the needs of certain patient groups. This fact is attributed to several aspects in research and treatment as we still do not completely comprehend the biology in cancer: i) uneven distribution of different types of cancer across population3; ii) tumor heterogeneity is an influencing factor that contributes to de novo resistance to anti-tumor therapies, which dampens scientific efforts4; iii) potential of cancer cells to develop resistance to targeted therapies – several mechanisms of acquired drug resistance have been identified in the past and increased our understanding of evading strategies, which include drug inactivation, target modification, DNA damage repair, cell death inhibition and drug efflux; iv) proteins that confer cancer vulnerabilities, which are considered technically undruggable due to impediments regarding spatial inaccessibility5. The scientific and technological advancements that have emerged in the last years impact the global cancer burden in the favor of patients and researchers. However, the prevalence of cancer remains a medical challenge of utmost importance and demands advancement of new and innovative therapies capable of tackling the deadliest disease worldwide3.

DNMDP is a PDE3 modulating small molecule with selective cancer cell killing activity De Waal et al. have screened 1,924 compounds of the Molecular Libraries Small Molecule Repository validation set with NCI-H1734 cell lines and A549 for selective cytotoxicity. DNMDP (6-(4-(diethylamino)-3-nitrophenyl)-5-methyl-4,5-dihydropyridazin-3(2H)-one) was identified to significantly decrease viability of NCI-H1734 but not A549 cells6 (figure 1).

Figure 1. Chemical structure of (R)-DNMDP6.

Further testing with 766 genetically characterized cell lines identified 22 cell lines that are selectively killed by DNMDP, including several cell lines of the melanoma lineages. Leveraging unbiased chemogenomics analysis by correlating genomic features of the cell lines (Cancer Cell Line Encyclopedia, CCLE)7 and sensitivity to DNMDP, de Waal et al. identified phosphodiesterase 3A (PDE3A) as the putative target of DNMDP6. In physiologically healthy cells PDE3A is responsible for catalyzing the hydrolysis of cyclic adenosine monophosphate (cAMP) to 5 ́adenosine monophosphate (5 ́AMP). PDE3A inhibition plays a key role in the treatment of several cardiovascular diseases. Although DNMDP inhibits the enzyme activity of PDE3A, inhibition of enzyme activity alone was not sufficient, since most PDE3A inhibitors, including the very potent inhibitor trequinsin, do not kill. Notably, requirement of PDE3A to exhibit DNMDP- induced cancer cell killing was confirmed by knocking out PDE3A. Indeed, an IP-iTRAQ/MS identified a second protein, Schlafen 12 (SLFN12), which forms a complex with PDE3A only in the presence of DNMDP but not in the presence of trequinsin. This compound-induced interaction seems to trigger apoptosis in susceptible cell lines, measurable by induction of caspase 3/7 and cleavage of PARP. Just as high expression of PDE3A, high expression of SLFN12 also correlates with DNMDP sensitivity. In fact, simultaneous high expression of both PDE3A and SLFN12 predicts sensitivity to DNMDP. Furthermore, an insensitive cell line, A549 can be turned sensitive upon ectopic overexpression of PDE3A and SLFN12 (X. Wu, H. Greulich and M. Meyerson, unpublished), supporting the crucial role of both proteins in the mode of action of DNMDP. In contrast to cancer cell lines PDE3A and SLFN12 rarely co-express in normal cells/tissues, as revealed by data from the GTEX consortium8. Taken together, PDE3A and SLFN12-mediated DNMDP cancer cell killing points to a potential anti-tumor therapy with a novel mechanism of action6. 10

PDE3A in the context of cardiovascular diseases and cancer Phosphodiesterase 3A is a member of the large and diverse superfamily of cyclic nucleotide phosphodiesterases, which catalyze the hydrolysis of cAMP and cGMP into 5’-AMP and 5’-GMP, thus blocking the signaling pathway of cAMP and cGMP. The phosphodiesterase superfamily is comprised of 11 enzyme families and have been of major interest due to their dysfunctional activity in pathological conditions. The vast complexity of this enzyme family has proven to make research very challenging. The catalytic activity of PDEs is coordinated with adenylyl cyclases and guanylyl cyclases, which in concert regulate and maintain the cellular cAMP and cGMP levels. The PDE superfamily is clustered into two groups based on domain features, namely GAF (cGMP-binding PDEs, Anabaena adenylyl cyclase, and Escherichia coli FhlA)-containing phosphodiesterases (PDE2, 5, 6, 10, 11) and other PDEs (PDE1, 3, 4, 7, 8, 9). Besides lacking a GAF-domain, PDE3 proteins harbor two N-terminal hydrophobic regions (NHR1, NHR2) conferring membrane anchoring and two sites for phosphorylation, both features located at the N-terminus. PDE3A, amongst other phosphodiesterases, has demonstrated the ability to form homodimers using the C-terminal tail of the protein. Evidence was provided that oligomerization of PDE3A enables regulatory mechanisms including phosphorylation, auto- inhibition and ligand binding. PDE3A and PDE3B, encoded by distinct genes, share selective affinity for cAMP (90%) over cGMP (10%). Notably, presence of cGMP can lead to competition of cAMP breakdown at the catalytic site of these PDEs, suggesting an indirect regulatory mechanism by cellular cGMP concentrations. It was reported that different stimuli lead to phosphorylation of Serines in the N-terminus of PDE3A and 3B, which activates catalytic function. For example Hunter et al. discovered phosphorylation of PDE3A on several Serine residues by protein kinase C in human platelets9. Another study reported that activation of phosphatidylinositol 3-kinase (PI3K) and protein kinase B (PKB) by insulin or leptin resulted in phosphorylation of PDE3B. Development of selective PDE inhibitors have been of great interest since the phosphodiesterase superfamily contributes to a broad spectrum of medical conditions such as hypertension, intermittent claudication, heart failure, hypertension, asthma and chronic obstructive pulmonary disease (COPD). Due to broad involvement of the phosphodiesterase superfamily in various diseases the development for selective inhibitors has been of great interest10. In physiological conditions, PDE3A and 3B contribute to cardiac contractility and pacemaking by terminating cyclic nucleotide signaling through enzymatic breakdown of cAMP. Inhibition of PDE3A, as the primary isoform, results in an increase in heart rate and contractility,

representing a positive inotropic effect. Therefore, PDE3A inhibition alleviates symptoms of heart failure with great success. As of 2014, four selective PDE3A inhibitors were FDA-approved for clinical use: milrinone, amrinone and enoximone for the treatment of heart failure and cilostazol for the treatment of intermittent claudication. Besides, anagrelide is an FDA-approved PDE3A inhibitor that is applied for treatment of essential thrombocythemia (ET)11. However, chronic administration of PDE3A inhibitor milrinone in patients with heart failure caused an increase in mortality due to arrhythmias. Therefore, milrinone is applied for acute treatment of decompensated and refractory heart failure12. It was revealed that chronic PDE3 inhibition or PDE3A depletion led to significantly increased apoptosis in neonatal and adult cardiomyocytes, which provides an explanation for dramatic medical complications in these patients13. Cilostazol is a PDE3A inhibitor clinically applied for treatment of intermittent claudication, a pathological condition that affects lower-extremity peripheral arteries, which is characterized by cramping an ischemia-induced leg pain in the legs. Cilostazol inhibits PDE3A, which in turn leads to increased cAMP levels and mediates vasodilation and inhibition of platelet activation. This alleviates clinical symptoms of intermittent claudication12. Initially, PDE3A was considered a target in cardiovascular diseases and their inhibitors as drugs for diseases such as heart failure. However, recent studies in the past few years have reported about PDE3A in the context of cancer. In 2014, Sun et al. discovered that phosphodiesterase 3/4 inhibitor zardaverine exhibits selective cancer cell killing properties. Zardaverine was developed in 1984 for the treatment of asthma for its potent efficacious inhibition of bronchoconstriction. It was however, quickly terminated seven years later due to fast metabolic elimination. The recent study by Sun et al. demonstrates potent antitumor effects in hepatocellular carcinoma cells (HCC) in vitro as well as in vivo. Furthermore, treatment with zardaverine showed cell cycle arrest in G1 along with dysregulations of several cell cycle- associated factors such as cyclin A/E, cyclin-dependent kinase (Cdk) 2, 4, 6 as well as p21. Furthermore, phosphorylation of Rb (encoded by the retinoblastoma susceptibility gene Rb) at serine 780 was suppressed in sensitive cell lines along with a generally low protein expression compared with resistant tumor cells. Hence, low expression of Rb protein was suggested as biomarker for the prediction of cell lines that are sensitive to the treatment of zardaverine. Notably, zardaverine proved to selectively kill sensitive tumor cells and demonstrated that dual- PDE3/4 inhibition alone is not sufficient to achieve this antitumor activity. This mechanism described by Sun et al. shows similarities to the selective cancer-cell killing activity of DNMDP14.

The results obtained by Sun et al. and de Waal et al. raised a novel implication of PDE3A in cancer-therapy related mechanisms, which is in strong contrast to former studies that centered PDE3A and its inhibition in cardiovascular diseases. The importance of PDE3A- inhibitors and PDE3A modulating small molecules in cancer has to be further validated, however, the number of reports remains scarce compared to heart-disease related research. Nevertheless, these discoveries suggest versatile mechanisms related to PDE3A-inhibition in distinct pathological conditions, such as cancer, and highlight the need for deeper research.

The Schlafen gene family SLFN12 is a member of the large Schlafen gene family, located on chromosome 17 in the human genome. First described by Schwartz et al. in 1998, the SLFN gene family was identified as key components for thymocyte maturation in mice. Initially, the described family was comprised of murine SLFN1, 2, 3, 4, 5, 8, 9, 10 and 14. In humans six Schlafen members have been identified, including SLFN5, SLFN11, SLFN12, SLFN12L, SLFN13 and SLFN1415. Upon expansion of the genes that were homologous to murine SLFNs, individual members were categorized in three classes based on the molecular weight of their protein products (figure 2). Specifically, SLFN genes are grouped into class I (short SLFNs), class II (intermediate SLFNs) and class III (long SLFNs). Murine SLFN1 and 2 are found in class I, whereas class II is comprised of mSLFN3 and 4 as well as human SLFN12 and 12-like. Among mouse and human class III includes the most SLFN family members, namely mSLFN5, 8, 9, 10, 11 and 14 as well as hSLFN5, 11, 13 and 14. Interestingly, no human SLFN is found in class I15, 16.

Figure 2. Schematic overview of human SLFNs | SLFN members are separated into class II and III based on molecular weight. All SLFNs feature a common truncated AAA-domain (AAA_4 domain) as well as a SLFN box and SWADL motif (both not shown).

A common feature that is found across all SLFN family members is a truncated AAA_4 ATPase-domain (ATPases associated with diverse cellular activities), which is involved in diverse cellular functions such as protein proteolysis, cell-cycle regulation, organelle biogenesis, intracellular transport and oligomerization. Furthermore, proxies of AAA-domain proteins include molecular chaperones, helicases and transcription factors. Besides a truncated AAA domain all SLFN members harbor a conserved sequence known as “SLFN box”, which is located adjacent to the AAA_4 domain and unique for this specific gene family17.

Class I and II SLFNs do not carry an NLS (nuclear localization signal), hence, are thought to be localized in the cytoplasm. According to past discoveries members of class I and class II mainly contribute to cell proliferation. However, it remains unclear how murine SLFN1, a member of class I, manages to regulate cell cycle without actively translocating into the nucleus15. SLFN gene products of class II and III carry a SWADL domain, which is comprised of Ser-Trp-Ala-Asp-Leu. Class III SLFNs exclusively contain a C-terminal extension, which carries a motif that is conserved in superfamily 1 of RNA helicases18. Additionally, it harbors a nuclear localization signal, RKRRR, which enables active translocation into the nucleus for RNA-helicase related functions. Other than the sequence homology, the cellular function of this domain in physiologically healthy cells is yet to be revealed. Furthermore, murine SLFN5-10, which are members of class III SLFNs, share features that can be observed in proteins with RNA helicase or RNA structure modeling 14

functions. As suggested by Geserick et al. several SLFN members harbor a non-ATPase N- terminal part that is, in general, necessary for substrate recognition in AAA-domain proteins. However, ATPase functions in murine and human SLFN family members are yet to be confirmed. Scientific discoveries within the past decades have demonstrated the involvement of the Schlafen gene family in cell proliferation and differentiation as well as in the context of the immune system18.

SLFN12, the uncharacterized partner of PDE3A Schlafen family member 12 (SLFN12) belongs to the class II SLFNs coding for a 578-amino acid long protein, which carries sequence domains that are common among SLFN family members. These domains include a truncated AAA_4 domain usually found in AAA-domain ATPases, a SWADL domain as well as a unique “SLFN box” (figure 3). SLFN12 lacks a helicase domain, which is only found in class III SLFNs. SLFN12 and the closely related SLFN12-like (SLFN12L, 78% identity) are the only members of the class II SLFNs15.

Figure 3. Schematic depiction of sequence elements in SLFN12 protein | The N-terminus stretches from 1-200 amino acids and includes the unique “SLFN box” (dotted grey rectangle) as well as the two SNPs at position 43aa and 168aa. The internal part of the protein is comprised of the variable AAA_4 domain. The truncated C-terminal end does not harbor significant sequence elements. Ranging from position 35aa to 341aa the highest sequence homology throughout the human SLFN gene family can be observed.

Although little is known about the physiological function of SLFN12 past research efforts have discovered connections to different cellular functions with emphasis on the immune system. For example, SLFN12 expression was found to be elevated in T-lymphocytes in the presence of ct-CD45. CD45 is a type I transmembrane protein, expressed in all nucleated hematopoietic cells. Upon activation-induced cell death, soluble cytoplasmic tail of CD45 (ct- CD45) is secreted and binds to a receptor located on the surface of T-cells in order to transmit inhibitory signals. It was revealed that ct-CD45 depleted plasma leads to stronger proliferation of T-lymphocytes, whereas in the presence of ct-CD45 inhibited proliferation was observed in

CD4+ T-helper cells and CD8+ cytotoxic T-cells. Furthermore, secretion of certain cytokines was strongly inhibited by ct-CD45. The inhibitory impact in the presence of ct-CD45 on T-cell proliferation and cytokine secretion was attributed to strong overexpression of Krueppel-like- factor 2 (KLF2) and SLFN12, confirmed by global gene expression analysis. Interestingly, upon treatment with IL-2 in the presence of ct-CD45 T-cells respond with decreased expression of SLFN12. Moreover, it was found that overexpression of SLFN12 in the T-cell leukemia cell line Jurkat substantially decreased cell viability and growth rate. The data obtained by Puck et al. suggests that SLFN12 may exert the role of a quiescence factor in T-lymphocytes19. Apart from reports drawing connections of SLFN12 to the immune system, SLFN12 was found to be involved in cancer cell differentiation. A study reports that SLFN12 suppresses the expression of prostate-specific antigen (PSA), which is a characteristic biomarker for prostate epithelial differentiation. Furthermore, it was observed that SLFN12 overexpression led to upregulation of E-cadherin in human prostate epithelial cells, which is an important protein in epithelial-mesenchymal transition (EMT)20, 21. It is known that reduced PSA expression and increased E-cadherin in prostate epithelial cells lead to a rather differentiated and less malignant phenotype. This suggests that SLFN12 may promote cell differentiation in the context of cell transformation21. Besides, SLFN12 was found to be subject of epigenetic silencing in allergy-related medical conditions. A study published earlier this year described epigenetic silencing of SLFN12 upon exposure to certain allergens. Specifically, patients suffering from allergic rhinitis (AR) were exposed to grass pollens and peripheral blood mononuclear cells (PBMCs) were subsequently harvested and the differential genome-wide DNA methylation pattern was analyzed. It was reported that DNA methylation of SLFN12 correlated with symptoms induced by the pollen treatment, which is consistent with SLFN12 functions in T-cell differentiation and activation22. In summary, SLFN12 has been observed in the context of immune cells by several sources and changes in mRNA expression are seen especially in T-cells. However, beyond the reported correlating effects on SLFN12 expression in various cellular settings, the true physiological function of SLFN12 remains to be discovered.

Anagrelide is a potent inhibitor of PDE3A and platelet formation Anagrelide is a cyto-reductive agent that is used for the treatment of essential thrombocythemia, a myeloproliferative disorder characterized by an overabundance of platelets. Patients suffering from this pathological condition are prone to experiencing vascular occlusive events in peripheral and coronary circulation, which can ultimately lead to thrombosis and hemorrhage. Major risks include the occurrence of occlusions in mircrocirculation as well as bleeding, however, thromboses in large arteries may result in neurological/cardiac disturbances or mortality in severe cases23. Anagrelide inhibits phosphodiesterase 3A (PDE3A), which prevents degradation of cyclic adenosine monophosphate (cAMP) in turn blocking differentiation and maturation of megakaryocytes. Although, Espasandin et al discovered that the early onset is due to the repression of proplatelet formation apart from inhibition of megakaryopoiesis and PDE3A, it is still a potent inhibitor of phosphodiesterase 3A24. Besides its effect on pathological cardiovascular conditions it was demonstrated by de Waal et al that treatment of DNMDP-sensitive cells with anagrelide do show a reduction in viability. Moreover, its cytotoxic effect can be competed with trequinsin, another very potent PDE3A inhibitor, leading to a rescue from DNMDP-mediated induction of cell death. This suggests a mode of action similar to DNMDP6.

Melanoma: Subtypes, current medical options and setbacks During screening of 766 genetically characterized cell lines for DNMDP sensitivity by de Waal et al. a substantial amount of cell lines from the melanoma lineage showed responsiveness. Some melanoma cell lines fulfill the criteria of high PDE3A and SLFN12 expression, which serve as predicting factors for sensitivity to DNMDP.

Figure 4. SLFN12 mRNA expression in distinct cancer types | Plot depicts mRNA expression levels of SLFN12 determined by RNAseq, made accessible by CCLE. Number in brackets equals amount of cancer cell lines per lineage used for analysis. Arbitrary units on the y-axis equal magnitude of expression level.

SLFN12 expression varies strongly across distinct lineages of cancer cells. The majority of cancer types do not express SLFN12, whereas a minor fraction of cancer cell types do exhibit relatively high levels of SLFN12, such as melanoma cell lines (figure 4). Melanoma cell lines were found to be amongst the pool of cells that showed sensitivity to DNMDP as reported by de Waal et al6. However, even within the cluster of 63 melanoma cell lines in the CCLE databank, SLFN12 mRNA levels are scattered, ranging from very low to relatively high expression levels. Melanoma is a skin cancer type affecting melanocytes, which are cells that give the skin its characteristic color by producing melanin. In general, melanomas can be separated into three major subtypes: cutaneous, acral (originating from palms, soles or nail beds) and mucosal25. Amongst all cancer types malignant melanomas show the highest prevalence of somatic mutations. The most dominant class of base substitution is the conversion of cytidine to thymidine (C>T), which is attributable to the formation of pyrimidine dimers due to extensive exposure to ultraviolet light26. In a recent whole genome sequencing (WGS) study by Hayward

et al, a substantial amount of melanoma samples was investigated according to their mutational profile25. One of the discoveries demonstrated that cutaneous melanomas show >18 times more mutational burden than acral and mucosal melanomas (mean of 49.17 to 2.64 mutations per megabase, respectively). Whereas, more genomic structural variants (e.g. deletions, duplications, inversions) were observed in mucosal and acral subtypes than in cutaneous cancers. Reportedly, acral and mucosal subtypes showed more clusters of breakpoints, which suggests a higher frequency of complex chromosomal rearrangements such as breakage fusion bridges. Local excision of tumor tissue represents the foundation of medical interventions for non- metastatic melanoma. This procedure aims for preventing local recurrence by surgical removal of wide margins of skin tissue depending on the thickness of the melanoma27. In contrast, the current treatment options for metastatic melanomas include a variety of interventions such as surgery, chemo- and radiation therapy, as well as different immune- and biochemotherapies. Locoregional options such as surgery or radiation therapy serve mainly as alleviation for symptoms that are caused by local tumor growth. Surgeries serve as medical intervention that are used for selected patients in whom a complete resection of tumor tissue is expected. Whereas radiation therapy might not have a beneficial outcome due to the radioresistant properties of malignant melanomas. However, in some patients suffering from formation of metastases within the central nervous system (CNS) radiotherapy, especially whole brain irradiation, can lead to better treatment results. Cytotoxic chemotherapy, sometimes as combination treatment remains a standard procedure for malignant melanomas. Current options include alkylating agents, platinum analogs and microtubular toxins. With an objective response rate of approximately 20% and a median duration of response of 5-6 months, dacarbazine is the gold standard in treatment of metastatic melanomas28. Dacarbazine is a prodrug that is metabolized in vivo to the active alkylating agent monomethyl triazeno imidazole carboxamide (MTIC). MTIC acts on DNA by converting guanine into O6-methylguanine, which in turn allows guanine to pair with thymine during DNA replication. This process inhibits replication and eventually induces apoptosis29. Immunotherapies are considered a promising alternative to chemotherapy in the treatment of metastatic melanomas. Despite the fact that predicting the applicability has proven to be challenging and only a selected group of patients might have a beneficial outcome, immunotherapies bear the potential for durable response. Activation of T- cell proliferation and function result in stimulation of natural killer cell proliferation and subsequent cytotoxic activity resulting in secretion of cytokines including interferon gamma,

tumor necrosis factor (TNF) and other immune-stimulatory agents, which can be achieved by treatment with high dose bolus interleukin-2 (HD IL-2). Approved by the Food and Drug Administration (FDA) in 1998 for the long-term control of micrometastases and durable response (6% and 10% complete and partial response rate, respectively)30. IL-2 achieves substantial remission of micrometastases at a high dose, hence, accompanied by a greater risk for toxicity, which makes this regimen preferentially suitable for younger patients with optimal organ function. In an effort to boost the effectiveness of chemotherapeutic and immuno- therapeutic treatments biochemotherapies are of much scientific interest and have been subject to intensive research. It combines immune-stimulatory agents such as IL-2 with cytotoxic drugs. Biochemotherapies yielded significant improvement regarding overall response in patients, however, it fails to increase overall survival rates and is associated with greater toxicity compared to traditional chemotherapy31. Substantial research work has been invested in advancing existing therapies to yield higher survival rates while reducing side effects. Upon elucidating more molecular drivers, the discovery and development of targeted therapies for specific pathways in metastatic melanomas have been an area of focus. Immune system evasion is one hallmark of cancer cells and remains a major issue. By expressing auto-inhibitory proteins on the surface of T-cells, the overall immune response is adversely affected and immune surveillance mechanisms fail to operate. Cytotoxic T-lymphocyte antigen-4 (CTLA-4) is constantly expressed on the surface of T-cells and limits activation/stimulation as well as proliferation, hence, serving as immune checkpoint32. Additionally, it is localized on the surface of regulatory T-lymphocytes where it dampens immune stimulation and ultimately increases tumor immune evasion. Two fully human monoclonal antibodies, ipilimumab and tremelimumab, are FDA-approved for treatment of metastatic melanoma by inhibition of CTLA-4. In 2012 Topalian et al. discovered Programmed Death-1 (PD- 1) as promising target for immune checkpoint inhibition. PD-1 is a co-repressor located on the surface of T-lymphocytes, which in orchestra with several ligands such as PD-1L, maintains an immunosuppressive tumor microenvironment33. Nivolumab is an inhibitory monoclonal antibody targeting PD-1 in order to enhance immune surveillance and antitumor immune response34. It was reported that treatment with nivolumab yielded in fast and durable response rates (partial and complete) and even exhibited long-lasting effects after discontinuation of therapy35. However, monoclonal antibody therapies targeting CTLA-4 and PD-1 have been associated with greater risk for severe autoimmune side effects36.

Adoptive cell therapy (ACT) has entered the realm as promising option for different types of cancer. During adoptive T-cell therapy, peripheral blood T-lymphocytes or tumor infiltrating lymphocytes of patients are harvested, stimulated/activated and subsequently injected back into the patient in order to efficiently target cancer cells. It is a method that tries to harness the power of the patient’s immune system to overcome the tumor37. Dudley et al. have reported that upon treatment of metastatic melanoma with cyclophosphamide and fludarabine and subsequent transfer of autologous tumor infiltrating lymphocytes with consecutive HD-IL2 56% overall response was achieved in 93 patients38. This highlights a first success in tackling cancer by leveraging the full potential of the immune system, hence, marking the path to obtain therapies with durable overall response rates while maintaining low toxicity for the patient. In summary, treatment of metastatic melanoma remains a challenging venture for research. So far, radiation- and chemotherapy represent measures for palliative treatment, whereas substantial improvement or overall survival and durable response has only been achieved in combination with immune therapies. Inhibition of CTLA-4 and PD-1 plus dacarbazine showed consistent therapeutic success and remains to be a treatment of high interest in cancer research. Nevertheless, the set of reliable and durable therapies for metastatic melanomas is yet to be complemented by new ideas and drugs that are convenient for administration, durable and efficacious in response and circumvent acquired drug resistances. By discovering DNMDP as a cancer selective cytotoxic agent that selectively kills several established melanoma cell lines such as RVH421, A2058 and SKMEL-3, which might be advanced into a suitable treatment option for patients.

CRISPR is a powerful tool for generating site-specific knock-outs In the past, scientific efforts to study gene functions were hampered by the limitations due to extensive labor and imprecise methods, hence, it proved to be a very challenging venture to achieve knock-outs (KOs) or knock-downs (KDs) of target genes. RNA interference (RNAi) was considered a promising technology, even though it only provided incomplete knock-down by inhibition of mRNA translation or enabling mRNA degradation. Additionally, knock-down efficiency varies between experiments, off-target effects are unpredictable and blockage of gene function is only temporarily. Whereas, bona fide genome-editing describes the use of engineered nucleases that are comprised of sequence-specific DNA binding- and nonspecific cleaving-domains. Genome-editing methods such as zinc finger nucleases and transcription activator-like effector nucleases (TALENs) allowed for stable and precise knock-down of genes.

However, since the advent of CRISPR/Cas9 technology for site-specific DNA recognition and double strand cleavage older methods have taken a backseat on genomic engineering, not only due to practical reasons such as generating custom ZFNs or TALENs but also because of the high fidelity and efficacy of CRISPR gene editing39. The successful story of CRISPR began with a rather inconspicuous discovery made by Ishino et al. In 1987 he and his team reported about short direct repeats that are interspaced by short sequences in the genome of Escherichia coli (E. coli), which rapidly led to intensive research on these newly discovered clustered regularly interspaced palindromic repeats (CRISPRs)40. It was further observed that CRISPR-associated (cas) genes encode putative nucleases and helicases. In 2006 it was found that archaea and bacteria are capable of integrating fragments of foreign genes into their own chromosome, which suggests inheritable, yet unstable, defense mechanisms in these organisms, against invading phages. Furthermore, it was discovered that the CRISPR-Cas system shows similar, yet not orthologous, components that are found in eukaryotic RNA interference (RNAi) systems, such as RNA-dependent RNA polymerase, mRNA endonuclease (slicer) and double stranded RNA-specific helicase-nuclease (dicer)41. The mechanism of gene editing by CRISPR/Cas9 is orchestrated by several essential components: Caspase proteins (Cas) bind two types of RNA molecules, CRISPR-RNA (crRNA) and transactivating CRISPR-RNA (tracrRNA), which both fulfill distinctive functions. tracrRNA enables spatial orientation of crRNA, which in turn is essential for recognizing the complementary DNA strand of the target DNA sequence42. The specific target DNA sequence is marked by a PAM (protospacer adjacent motif) sequence that is comprised of a GG dinucleotide and flanks the region to be cut. After successful recognition and binding of the target sequence, a precise double strand lesion is generated. As elucidated by later studies, an artificial single chimeric tacrRA:crRNA molecule is still capable of facilitating Cas9-mediated dsDNA cleavage43. This dual component system (Cas9 plus tracrRNA:crRNA duplex) was considered a breakthrough due to its major advantages to zinc finger nucleases (ZFNs)-technology and TALENs in ease of application. In 2013 Cong et al. successfully applied the CRISPR-Cas9 system in mammalian cells (murine and human) and achieved targeted genome engineering. Moreover, it was demonstrated, that using a single CRISPR/Cas9 array with several sgRNAs (single guide- RNAs), simultaneous editing of multiple genomic loci is possible44. It was discovered that after targeted dsDNA cleavage cell-inherent repair mechanisms either give rise to error-prone non-homologous end joining (NHEJ) or accurate homology directed repair (HDR) using the second uncompromised allele44. Since, DNA double strand breaks

are common events in eukaryotes NHEJ and HDR are highly conserved mechanisms. Homologous recombination (HR) is a type of homology directed repair and, although initially described as repair mechanism during meiosis, it constitutes a key factor in maintenance of genomic stability after dramatic DNA double strand breaks. It is a template-dependent mechanisms that uses sister chromatids for achieving accurate repair45. In contrast, non-homologous end joining is a specialized ligation reaction that is a rather early repair mechanism in evolution, where many organisms were haploid. During NHEJ, DNA gaps are edited by nucleases and subsequently ligated, which leads to insertions or deletions (indels) that affect gene function when located in a coding region46. Targeted genome-engineering approaches such as CRISPR/Cas9 technology aim for non- homologous end joining in order to achieve gene knockout. The resulting indels likely lead to frameshifts in the open reading frame (ORF) of the target gene, causing premature stop codons. This triggers RNA degradation by the nonsense mediated mRNA decay (NMD), a well-conserved mRNA quality surveillance mechanism in eukaryotes47. Besides genetic perturbation by incorporating DNA double strand breaks, CRISPR/Cas9 technology can be adapted to repress or activate target genes at the transcriptional level. CRISPR-interference (CRISPRi) uses a catalytically deactivated Cas9 (dCas9) protein. This allows for target recognition, however, its nuclease activity is lost. Depending on the target site CRISPRi perturbs gene function by: either i) inhibiting transcription initiation by blocking binding of RNA polymerase and necessary transcription factors to the promoter region; or ii) inhibition of transcription elongation48. In contrast, it was reported about Cas9-based transactivators that specifically promote expression of target genes. Based on these observations precise activation of endogenous target genes might lead to promising clinical applications in several medical fields49, 50. In this study, I leverage the power of the CRISPR/Cas9 technology in order to generate SLFN12 knockout cells. Designing single guide RNAs (sgRNA) is an essential step for achieving successful genetic knockouts. The commercial use of CRISPR/Cas9 gave rise to development of various types of bioinformatics software. CHOPCHOP is a widely used computational tool for CRISPR gene editing experiments. The software is a versatile tool, which allows for extensive customization. It offers precise targeting of: i) protein-coding genes; ii) non-protein-coding genes; iii) splice sites; iv) UTRs (untranslated regions); v) individual exons; vi) promoter-regions. Furthermore, it enables intensive off-target effect prediction and designs primers for target site amplification. Moreover, CHOPCHOP offers a scoring tool for sgRNAs based on: i) the likelihood

of a particular sgRNA to cut at the predicted site (cutting efficiency) and ii) the probability that DNA sites are recognized other than the intended target (off-target effects). Besides, CHPOCHOP designs guide RNAs with low probability for self-complementarity. Self-complementarity represents an undesired event and major criterion for binding efficiency and on-target recognition due to inefficient incorporation into the effector complex. To address this, CHOPCHOP includes a self-complementarity score that predicts the formation of 4bp-stems between sgRNA and backbone or within sgRNA. The mentioned features make CHOPCHOP a powerful, versatile and easy-to-use computational tool, which made it the optimal choice for this project51, 52. On-target validation can be achieved in various different ways. Evaluation of on-target gene editing can be done by using the SURVEYOR assay, described by Cong et al44. Here the edited and unedited target sequence is amplified by PCR, denatured and subsequently annealed. Indels cause mis-matches in binding, which are recognized and cleaved by the surveyor nuclease. The SURVEYOR assay is a rapid and simple method that yields reliable results, however, it is not possible to discover frameshift mutations. CRISPRseq is a rather accurate approach for evaluating on-target cleavage. Here, the target sequence is amplified by PCR followed by next generation sequencing (NGS) of the generated amplicon. It enables analysis of edited alleles and precise profiling of generated mutations (indels, frameshifts)53. The overwhelming informational load of next generation sequencing demanded for reliable and convenient analysis tools that cope with the high level of complexity. CRISPResso is a web and python-based computational aid that enables analysis of: i) insertions; ii) deletions; iii) substitutions; iv) frameshift mutations. Furthermore, information about the repair mechanism is provided (NHE, HR). The computational pipeline includes several data processing steps: i) filtering of low-quality reads; ii) trimming of adapters; iii) alignment to a reference amplicon54. The gold standard to confirm on-target gene editing is a rescue experiment for restoration of gene function. Here, a “degenerate” SLFN12 cDNA is incorporated in the cell, which is not targeted by Cas9 due to a silent mutation in the PAM sequence. Redundancy of the genetic code enables expression of a functional SLFN12 protein for restoration of the phenotype and confirms on-target efficiency of the sgRNA53.

Lentiviral transduction enables efficient gene delivery Cellular delivery of coding DNA has always been of great interest with major emphasis on approaches enabling stable integration. Lentiviruses, which are derived from human immunodeficiency virus (HIV) are considered highly efficient vehicles due to their ability for infecting non-dividing cells. The onset of viral gene delivery systems demanded for higher biosafety standards to prevent unintended distribution. 2nd generation lentiviral transfer uses a three-plasmid system comprised of: i) a transfer plasmid encoding the gene of interest (e.g. cDNA, sgRNA) flanked by long terminal repeat (LTR) sequences; ii) a packaging plasmid coding for gag, pol, rev and tat; iii) an envelope plasmid carrying the env gene. Gag (group-specific- antigen), pol (reverse transcriptase) and env (gp160) are structural viral proteins, which are essential for formation and distribution of viral particles. Whereas, tat (HIV-transactivator) and rev (regulator of expression of virion proteins) are regulatory factors facilitating expression of the structural viral proteins. The transfer plasmid is replication incompetent due to a deletion in the 3’LTR sequence, which render them SIN (self-inactivating) vectors55. The plasmids are delivered to a packaging cell line such as 293T and the harvested viral particles are transferred to the target cell line in which the gene of interest is stably integrated into the host genome56.

Summary of aims and study design I aspire to further characterize the function of SLFN12 in regard to the cancer-selective antitumor activity of DNMDP, based on the discoveries made by de Waal et al. Moreover, I am interested in collecting more conclusive evidence for the interplay between SLFN12 and PDE3A, which enables the cancer cell killing mechanism that is leveraged by PDE3 modulating small molecules, with major emphasis on DNMDP. In an effort to advance knock-down experiments that are done by Luc et al using RNAi and to confirm that SLFN12 is required in the context of cytotoxic activity by PDE3 modulating small molecules I generate genetic SLFN12 knockouts in one cervical cancer and melanoma cell lines. In order to investigate which protein domains are essential for SLFN12 function in DNMDP-mediated cell killing activity I generate deletions of major domains in the SLFN12 protein using truncated cDNA constructs. The deletion mutants are tested for capability of restoring sensitivity to DNMDP treatment, hence mimicking the function of full length SLFN12. Moreover, the potential of the generated deletion mutants for interacting with PDE3A in a DNMDP-dependent mechanism is investigated using protein complex immunoprecipitation (CoIP). Furthermore, the mutation analysis aims for elucidating the domains required for: i) induction of apoptosis by DNMDP as described by de Waal et al., ii)

engaging into a protein complex with PDE3A upon presence of DNMDP. This study is designed to give further and more elaborate evidence for the mechanism of action that leads to DNMDP- induced selective cytotoxic cell killing activity.

Materials and Methods: Cell culture maintenance. Cell lines (see table 1) were cultivated by regular passaging, including one washing step with PBS (Corning™ cellgro™ Cell Culture Phosphate Buffered Saline (1X), Cat No.: MT21040CV, Thermo Fisher Scientific) followed by incubation with trypsin (TrypLE™ Express Enzyme (1X), Cat No.: 12604013, Thermo Fisher Scientific). DMEM (Corning™ Dulbecco´s Modified Eagles Medium with L-Glutamine, 4.5g/L Glucose and Sodium Pyruvate, Cat No.: MT10013CV, Thermo Fisher Scientific) was applied for HeLa (ATCC® CLL-2™) and A2058 (ATCC® CRL-11147™), whereas RPMI (Corning™ cellgro™ RPMI 1640 Medium (Mod.) 1X with L- Glutamine, Cat No.: MT10040CV, Thermo Fisher Scientific) was used for cultivation of RVH421 (Cancer Cell Line Encyclopedia7). Both types of media are supplemented by 10% of FBS (Corning™ Fetal Bovine Serum, Australia Origin, Cat No.: 11-648-647, Thermo Fisher Scientific). Cell counts were conducted by an automated cell counter (VI-CELL™ XR COMPLETE SYSTEM, Cat No.: 731050, Beckman Coulter).

Monitoring of cell proliferation by population doubling. Cell lines (parental and SLFN12- knockout cells) were cultivated such as described in cell culture maintenance. Based on cell line, cells were collected every two to six days, counted and re-seeded. Population doubling (PD) and cumulative population doubling (CPD) was calculated as follows:

푡표푡푎푙 푐푒푙푙 푐표푢푛푡푡=푥 푝표푝푢푙푎푡푖표푛 푑표푢푏푙푖푛푔 (푃퐷)푡=푥 = log2 푡표푡푎푙 푐푒푙푙 푛푢푚푏푒푟 푠푒푒푑푒푑푡=푥−1

푡표푡푎푙 푐푒푙푙 푐표푢푛푡푡=푥+1 푐푢푚푢푙. 푝표푝푢푙푎푡푖표푛 푑표푢푏푙푖푛푔푡=푥+1 = log2 + 푃퐷푡=푥 푡표푡푎푙 푐푒푙푙 푛푢푚푏푒푟 푠푒푒푑푒푑푡=푥

Generation of truncated SLFN12 mutants by site-directed mutagenesis. Primers for generation of major deletions in full length SLFN12 cDNA were designed (see table 3). Site directed mutagenesis was performed according to the recommendations of the vendor (GeneArt™ Site-Directed Mutagenesis System, Cat No.: A13282, Thermo Fisher Scientific). Template DNA (pDONR-SLFN12) was methylated, target regions amplified and the resulting PCR product was subsequently cleared from methylated template. Bacterial transformation into

competent E. coli (One Shot™ Stbl3™ Chemically Competent E. coli, Cat No.: C737303, Thermo Fisher Scientific) and cultivation on LB agar containing spectinomycin (100 µg/ml). The truncated SLFN12 cDNA constructs were then transferred into pLX304 and pLX317, a lentiviral plasmid and an expression vector, respectively, by LR gateway reaction (Gateway™ LR Clonase™ Enzyme mix, Invitrogen, Cat.: 11791019).

Generation of SLFN12 knock-out cell lines by CRISPR/Cas9. Oligos for target sequences were designed by CHOPCHOP, a computational aid for CRISPR approaches. A one-vector lentiviral system (lentiCRISPRv2, figure 5) was applied, which carries two expression cassettes, namely hSpCas9 and the chimeric single guide RNA. Target guide sequence cloning into mentioned lentiCRISPRv2 vector was done according to GeCKO Lentiviral CRISPR Toolbox by the Zhang Lab (LentiCRISPR lentiviral CRISPR/Cas9 and single guide RNA)57, 58. Lentiviral vector was digested by BsmBI and subsequently dephosphorylated. The plasmid was run on an agarose gel and the resulting band, minus filler piece was extracted. Oligos were annealed, phosphorylated and then ligated with the BsmBI-digested lentiviral vector. Ligation products were transformed into Stbl3 chemically competent cells.

Figure 5. Schematic depiction of lentiCRISPRv2 | One-vector system contains two expression cassettes, a chimeric synthetic guide RNA and hSpCas9. Vector enables control-digest by BsmBI restriction sites. Abbreviations: psi+ (psi packaging signal), RRE (response element), cPPT (central polypurine tract), EFS (elongation factor-1α short promoter), SpCas9 (Streptococcus pyogenes Cas9), FLAG epitope tag, P2A (2A self-cleaving peptide), Puro (Puromycin resistance cassette), WPRE (posttranscriptional regulatory element).

Viral transduction. Virus production was carried out in 6-well plates (Corning™ BioCoat™ Collagen I Multiwell Plates, Cat No.: 08-772-69, Thermo Fisher Scientific). 293T packaging cells (ATCC® CRL-3216™) were seeded at 0.5x106/ml (2ml per well) in DMEM + 10% FBS. The cells were incubated for approximately 24 hours and then transfected: packaging plasmid (pCMV- R8.74psPAX2, 1 µg/well), envelope plasmid (pMD2.G, 100 ng/well) and pLX304 gene transferplasmid (1 µg/well) were combined with transfection medium (Gibco™ Opti-MEM™ I Reduced Serum Medium, Cat No.: 31-985-062, Thermo Fisher Scientific) up to 25µl total volume.

Then transfection reagent (FuGENE® 6 Transfection Reagent, Cat No.: E2691, Promega) was diluted in transfection medium at 4:1 ratio to total plasmid DNA mixture (all plasmids see table 4). Complete transfection mix (transfection reagent and plasmid mix) was added to packaging cells. At 18 hours after transfection, the transfection medium was replaced by DMEM + 30% FBS and virus-containing supernatant was harvested. Target cell lines were seeded for lentiviral infection in 12 well plate with individual media (0.5x105/ml, cell line-dependent). 24 hours later medium was replaced by medium supplemented by polybrene (8 µg/ml final concentration, Cat No.: TR-1003-G, Sigma). Viral particle-containing supernatant of 293T packaging cells was harvested, filtered and added to target cells. After another 24 hours, antibiotic selection was performed using puromycin (1 µg/ml) or blasticidin (15 µg/ml) depending on resistance cassette on plasmid.

Compound sensitivity testing. Cells (see table 1) were seeded in a 384 well plate at 1x104/ml and spun down for approximately five minutes at 1500 rpm. After 24 hours of incubation, compounds (DNMDP, anagrelide and trequinsin) were distributed using a drug printer (D300e Digital Dispenser, Hewlett-Packard) and disposable dispensehead cassettes (T8+, Cat No.: 30097370, Hewlett-Packard). Stock concentrations of 1mM was used for all compounds. Final concentrations of indicated compounds: 3µM – 0.3nM (DNMDP, anagrelide), 50nM (trequinsin).

Detection of mRNA expression levels. RNA extraction: Cells were cultivated in a 10cm petri dish until a confluency of 80-90% and subsequently harvested. Cell pellets were either frozen at -20°C or further processed right away. The cell pellets were lysed, RNA was extracted and purified according to the manufacturer’s recommendations (RNeasy plus mini kit (50), Cat No.: 74134, Qiagen). Quantity and level of purification of extracted RNA was measured by UV- Vis spectrophotometer (NanoDrop™ 8000, Cat No.: ND-8000-G, Thermo Fisher Scientific). cDNA synthesis: 1µg of total purified RNA sample was used for oligo-dT primer annealing and subsequent reverse transcription (Super Script™ III Reverse Transcriptase, Cat No.: 18080093, Invitrogen). qRT-PCR: Synthesized cDNA is diluted 1:10 with nuclease-free water and distributed to individual wells on a transparent 384w microtiter plate. Specific TaqMan gene expression assay probes for individual target and reference genes were applied to the microtiter plate. Afterwards the microtiter plate was spun down shortly. The run was performed by QuantStudio™ 6 Flex Real-Time PCR system (Applied Biosystems™) and the corresponding software. Individual samples (cell lines) were assayed in four duplicates. The outcome, the 28

average threshold cycle number (CTmean) for each target gene in individual samples, was then normalized with the CTmean of the reference gene (ACTB, β-actin) resulting in ∆CT. Calculated ∆CTs for specific target genes in the reference sample (parental cell line) were subtracted from the ∆CTs of the same target gene in the sample that was analyzed (SLFN12-KO cell line) yielding ∆∆CT.

Validation of SLFN12-antibodies. A panel of SLFN12-expressing and –non-expressing cell lines (see table 1) was chosen based on open source CCLE data7 an unpublished RNAseq data (L. de Waal, X. Wu, H. Greulich and M. Meyerson). Sample collection: Cells were seeded until 90% confluency, washed with cold PBS and collected using a cell scraper. Harvested samples were then spun down, the supernatant was aspirated and the formed cell pellet was stored at -20°C to -80°C. Cell lysis and protein quantification: Lysis buffer (ModRIPA lysis and extraction buffer) supplemented with protease- (cOmplete™ EDTA-free protease-inhibitor cocktail, Cat No.: 11873580001, Roche) and phosphatase-inhibitors (PhosSTOP™, Cat No.: 04906837001, Roche) was added to the pellet (ca. 10µL per 1g of pellet) and incubated at 4°C for 20-30 min with gentle orbital shaking. The cell lysate was centrifuged and the resulting supernatant was saved. Protein concentration was measured by visual determination using a multiplate reader (SpectraMax M5, Molecular Devices) and in accordance to the manufacturer of the protein quantification kit (Pierce™ BCA Protein Assay Kit, Cat No.: 23227, Thermo Fisher Scientific). Protein lysate was mixed with loading dye solution (NuPage™ LDS Sample Buffer 4X, Cat No.: NP0008, Invitrogen), reducing agent (NuPage™, Sample Reducing Agent 10X, Cat No.: NP0009, Invitrogen), and lysis buffer for a final volume of 24µl. Heat inactivation at ~90°C for 10-15 min. Protein transfer and detection: Sample was transferred to a precast polyacrylamide gel (NuPage™ 4-12% Bis-Tris Midi Protein Gels 20w, Cat No.: WG1402BOX, Invitrogen) for dry electro blotting (iBlot™ 2 Dry Blotting System, Cat No.: IB21001, Invitrogen). After transfer, membrane was blocked (Odyssey® Blocking Buffer PBS, Cat No.: 927-40100, LI-COR) and then incubated with primary antibody to recognize SLFN12 (see table 2) and vinculin (V9264, Sigma-Aldrich) overnight and secondary antibody for mouse (see table 2) and rabbit (see table 2) for one to two hours, respectively. Antibodies were visualized using an infrared laser imaging system (Odyssey® CLx Laser Imaging System, LI-COR).

Evaluation of protein-protein interaction using Co-immunoprecipitation. Transient transfection: HeLa cells were seeded at 3x106 per 15cm petri dish (in DMEM +10% FCS). 15µg of

purified expression plasmid (see table 4) were incubated with 60µl (equals 4x of amount of vector) of transfection reagent (FuGENE® 6 Transfection Reagent, Cat.: E2691) and supplement with transfection medium (Gibco™ Opti-MEM™ I Reduced Serum Medium, Cat.: 31-985-062) up to 1.25 ml final volume. The transfection mix was added to the target cells and incubated for 72 hours at 37°C. Compound treatment: Depending on the sample condition, after approximately 64 hours, depleted DMEM +10% FCS was exchanged by 15 ml fresh medium supplemented with 15µl of compound: i) 10mM DNMDP stock (equals final concentration of 10µM per petri dish); or ii) 100% DMSO (negative control). Sample collection and protein quantification: Same procedure as for SLFN12-antibody validation. Co-immunoprecipitation: The volume equivalent to 2mg of total protein was incubated with magnetic beads conjugated to an anti-V5 IgG (see table 2). Magnetic beads undergo washing with lysis buffer and elution with 30µl elution mix comprised of loading dye solution (NuPage™ LDS Sample Buffer 4X, Cat No.: NP0008, Invitrogen), reducing agent (NuPage™, Sample Reducing Agent 10X, Cat No.: NP0009, Invitrogen), and lysis buffer at ~90°C for 10-15 min. Protein transfer and detection: Same procedure as for SLFN12- antibody validation was applied to supernatant of previous step. Incubation was performed with primary antibody to recognize V5 (see table 2) and PDE3A (see table 2). Secondary antibodies and imaging system was identical to SLFN12-antibody validation.

Verifying SLFN12 knock-out efficiency by CRISPRseq. Genomic DNA extraction: SLFN12- KO cell lines were seeded on a 10cm petri dish until a confluency of ~90% and harvested. Cell suspension was spun down and resulting supernatant was discarded. Cells were lysed, genomic DNA (gDNA) was extracted and subsequently purified according to the manufacturer’s recommendations (PureLink™ Genomic DNA Mini Kit, Cat No.: K182001, Thermo Fisher Scientific). Amplification of target region to confirm amplicon size: The specific region on the purified gDNA, which was targeted by sgRNA was amplified by PCR (AccuPrime Taq DNA Polymerase high fidelity, Cat No.: 12346086, Thermo Fisher Scientific) using individual primers flanking the target sequence (see table 3). A small aliquot of the gained PCR products was used in order to determine specific and efficient amplification by agarose gel. Sample preparation for next generation amplicon sequencing: The remaining aliquot was cleaned up using a PCR purification kit (QIAquick PCR Purification Kit 50, Cat No.: 28104, Qiagen) and sent to next generation sequencing (DNA Core Facility, Center for Computational and Integrative Biology, Massachusetts General Hospital). Quantification and statistical data analysis: Sequencing results (in FASTQ format) were analyzed by CRISPResso (python-based computational tool) in order to

evaluate sgRNA performance.

Table 1. Panel of SLFN12-expressing and -non-expressing cell lines. SLFN12 PDE3A PDE3B cell line cell type; origin tissue expression expression expression epithelial cells from HeLa + + + adenocarcinoma; cervix epithelial cells from HeLa resistant adenocarcinoma; cervix; acquired - + + DNMDP resistance 293T human embryonic kidney cells - - - derivative of 293T with stable 293T+pPDE3A - + - integration of pLX304 PDE3A lymphoblast from H2122 + + + adenocarcinoma; lung lymphoblast from H2122 adenocarcinoma; lung; acquired - + + resistant DNMDP-resistance SK-MEL3 malignant melanoma; skin + + + epithelial cells from A549 - - - adenocarcinoma, lung derivative of A549 with stable A549+pPDE3A - + - integration of pLX304 PDE3A lymph node derived metastatic A2058 + + + melanoma; skin RVH421 malignant melanoma, skin + - +

Table 2. Antibodies. Antibody/Cat Target Vendor Origin Epitope Application No. synthetic peptide corresponding to a Western SLFN12 ab99200 Abcam rabbit region within N- blot terminus (35-84aa) Synthetic peptide Western SLFN12 ab173515 Abcam rabbit within Human SLFN12 blot (191-220aa) Thermo fragment from 327- Western SLFN12 PA5-54595 Fisher rabbit 392aa blot Scientific Sigma- Western SLFN12 SAB2107246 rabbit 528-577aa Aldrich blot Thermo 12aa peptide at the C- Western SLFN12 PA5-20867 Fisher rabbit terminus blot Scientific

anti- IRDye 680LT Western LI-COR goat - Mouse (925-68020) blot anti- IRDye 680LT Western LI-COR goat - Rabbit (925-68021) blot anti-V5 Western R960-25 Invitrogen mouse - tag blot anti- Western A302-741A Bethyl rabbit - PDE3A blot mAb-Magnetic anti-V5- Immuno- Beads (M167- MBL mouse - tag precipitation 11)

Table 3. Primers for diverse applications used in this study. Primer ID Sequence (5’-3’) Application GAAAAATTGACCTTTACTCATG SLFN12_AAA_deletion_F Mutagenesis TGAAAGATAAC GTTATCTTTCACATGAGTAAAG SLFN12_AAA_deletion_R Mutagenesis GTCAATTTTTC AAATAAGTTGGCATGAAGACT SLFN12_Nterm_deletion_F Mutagenesis AGAGGGAG CTCCCTCTAGTCTTCATGCCAA SLFN12_Nterm_deletion_R Mutagenesis CTTATTT GAATGGATCCAGTTCATGTGC SLFN12_Cterm_deletion_F Mutagenesis CCAACTTTCTTGTAC GTACAAGAAAGTTGGGCACAT SLFN12_Cterm_deletion_R Mutagenesis GAACTGGATCCATTC SLFN12_AAA+Cterm_deletion AAATTGACCTTTACTTGCCCAA Mutagenesis _F CTTTCTTG SLFN12_AAA+Cterm_deletion CAAGAAAGTTGGGCAAGTAAA Mutagenesis _R GGTCAATTTT AAATAAGTTGGCATGCATGTG SLFN12_N+AAA_deletion_F Mutagenesis AAAGATAAC GTTATCTTTCACATGCATGCCA SLFN12_N+AAA_deletion_R Mutagenesis ACTTATTTG pDONR_nonmutagenic_3500_ GAGGCGCTAAATGAAACCTTA Mutagenesis F ACGCTATGG pDONR_nonmutagenic_3500_ CCATAGCGTTAAGGTTTCATTT Mutagenesis R AGCGCCTC SLFN12_2F AAGTTGGCATGAACATCAGTG Sequence verification SLFN12_428F CTGCTGCACTGGAGTTCC Sequence verification SLFN12_922F CTCAGAGTGGAGCGCTTCT Sequence verification SLFN12_1420F ACACAAACTGCCCTAACCTT Sequence verification SLFN12_1325R TTGTGGTTCTCTTGCAAGCC Sequence verification SLFN12_1743R GGTGAGCCTTCGACAAGATT Sequence verification

CCCAGTCACGACGTTGTAAAA pDONR223 M13/pUC Fwd Sequence verification CG pDONR223 M13 Rev CAGGAAACAGCTATGAC Sequence verification CGCAAATGGGCGGTAGGCGT pLX304 CMV-F Sequence verification G pLX304 WPRE-R CATAGCGTAAAAGGAGCAACA Sequence verification pLEX317 EF-1a Fwd TCAAGCCTCAGACAGTGGTTC Sequence verification pLEX317 WPRE-R CATAGCGTAAAAGGAGCAACA Sequence verification CACCGGGTGGTAATCCGCAGA SLFN12sg1_F generating sgRNA duplex CCAG AAACCTGGTCTGCGGATTACC SLFN12sg1_R generating sgRNA duplex ACCC CACCGGCTTGAACACCTCTGG SLFN12sg2_F generating sgRNA duplex TCTG AAACCAGACCAGAGGTGTTCA SLFN12sg2_R generating sgRNA duplex AGCC CACCGTGTTCTATCAAAAAAA SLFN12sg3_F generating sgRNA duplex ACCC AAACGGGTTTTTTTTGATAGA SLFN12sg3_R generating sgRNA duplex ACAC CACCGTCCTGGTCAACTGCATC SLFN12sg4_F generating sgRNA duplex ACA AAACTGTGATGCAGTTGACCA SLFN12sg4_R generating sgRNA duplex GGAC CACCGTGCCGTTGCCATTCCAA SLFN12sg5_F generating sgRNA duplex AAG AAACCTTTTGGAATGGCAACG SLFN12sg5_R generating sgRNA duplex GCAC CACCGGGCCTCTTTTGGAATG SLFN12sg6_F generating sgRNA duplex GCAA AAACTTGCCATTCCAAAAGAG SLFN12sg6_R generating sgRNA duplex GCCC GGAGCTTGAACACTTCTGGTC generating SLFN12 rescue SLFN12_sg1rescue_T114T_F TGCGGAT cDNA CCGCAGACCAGAAGTGTTCAA generating SLFN12 rescue SLFN12_sg1rescue_T114T_R GCTCCAT cDNA CCTCTGGTCTGCGAATTACCAC generating SLFN12 rescue SLFN12_sg2rescue_R118R_F CTTGAG cDNA CTCAAGGTGGTAATTCGCAGA generating SLFN12 rescue SLFN12_sg2rescue_R118R_R CCAGAGG cDNA CATGAAGGCCTTGGCAGGGGT generating SLFN12 rescue SLFN12_sg3rescue_A181A_F TTTTTTTG cDNA CAAAAAAAACCCCTGCCAAGG generating SLFN12 rescue SLFN12_sg3rescue_A181A_R CCTTCATG cDNA CAAGGCTGAAATTGAGAATGA amplification of sgRNA SLFN12_187_F A target sequence

amplification of sgRNA SLFN12_438_R TTCATGTCTTTGAGGAACTCCA target sequence TGGAGTTCCTCAAAGACATGA amplification of sgRNA gSLFN12_437_F A target sequence TGCAAATGCAGAAACATATTG amplification of sgRNA gSLFN12_679_R A target sequence CAGTGTTTGCTAAAGAGCCTG amplification of sgRNA gSLFN12_948_F A target sequence amplification of sgRNA gSLFN12_1164_I_R TTGATTGTATCCACCAGGGATT target sequence CCCTTCTGCGAAATTAAGAAT amplification of sgRNA gSLFN12_2617_I_F G target sequence CAAGACTGAGCTGGTTTGAAG amplification of sgRNA gSLFN12_2823_I_R A target sequence

Table 4. Plasmids for various applications. Plasmid (Cat No.) Vendor Application pLX304 (25890) Addgene viral transduction pLX307 Addgene/GPP Broad Institute transfections pLEX317 Addgene/GPP Broad Institute transfections pDONR221 (12536017) Thermo Fisher Scientific gateway cloning 2nd generation lentiviral pCMV-R8.74 (22036) Addgene packaging plasmid 2nd generation lentiviral pMD2.G (12259) Addgene envelope plasmid

Results:

SLFN12 is a key protein and the limiting factor to confer sensitivity to PDE3 modulating agents In 2015, de Waal et al. discovered SLFN12 to be engaging into a DNMDP-dependent protein complex with PDE3A, which leads to selective induction of apoptosis. This requires that the cells have high co-expression of both SLFN12 and PDE3A. Knock-down of SLFN12 using short hairpin RNA (shRNA) partially rescued the cytotoxic effect of DNMDP, hence, providing evidence for the requirement of SLFN12 in DNMDP-induced cell killing. The rationale of generating KO- cell lines using CRISPR/Cas9 technology included the confirmation of the requirement of SLFN12 for DNMDP-induced cancer cell killing, not only in cervical cancer cell line HeLa but also in two malignant melanoma cell lines A2058 and RVH421. Additionally, I was interested in establishing cellular systems serving as essential tools to enable further mode of action experiments.

Design of distinct sgRNAs by CHOPCHOP to attain genetic knockout of SLFN12 In an effort to fully investigate the requirement of SLFN12 in DNMDP-sensitivity, I leveraged the CRISPR/Cas9 technology to generate complete genetic knockout (KO) of SLFN12 in selected cancer cell lines. Small guide RNAs (sgRNAs) targeting SLFN12 were designed by the web-based bioinformatic tool CHOPCHOP and ranked by a scoring system that considers cutting efficiency, target sequence position and predicted off-target potential (figure 6). As illustrated in figure 6, 6 sgRNAs were chosen (SG1 to SG6), with SG1 to SG3 targeting the N-terminal half of the gene while SG4 to SG6 targeting the C-terminal half of the gene.

Figure 6. Schematic depiction of target sites of distinct sgRNAs | Indicated guide RNAs (colored arrows) target specific sites on the SLFN12 gene (depiction shows gene product). sgRNA target sequences are located in the N-terminal, C-terminal and center region of the gene product. Dashed arrows indicate possibility for a knock-out phenotype, which can be rescued.

In order to address the specificity of any CRISPR-KO induced phenotype, I generated “degenerate” SLFN12 ORFs to perform rescue experiments. In these experiments the SLFN12 cDNA is comprised of a synonymous open reading frame that carries a mutated PAM recognition sequence, which changes the codon combination but maintains amino acid identity due to redundancy of the genetic code. To this end, four out of the six chosen sgRNAs carry a PAM sequence located on amino acid codons that allow synonymous codon changes. By mutating the PAM sequence, target site recognition by Cas9 is abolished, hence, genetic knockout is prevented. Therefore, generating such ORF expression constructs that were deprived of the PAM recognition sequence while maintaining correct amino acid sequence enables rescue of CRISPR-induced phenotypes. Individual sgRNAs were cloned into the lentiviral expression vector lentiCRISPRv2, packaged into lentiviral particles and introduced into target cancer cell lines (cervical cancer cell line HeLa and the melanoma cell line A2058) using viral transduction. Infected cells that stably integrated the lentiviral plasmid into the host genome were subsequently selected using Puromycin. The performance of the sgRNAs was then evaluated using two assays: qRT-PCR to measure SLFN12 expression and CRISPRseq to assess on-target gene editing.

Evaluation of α-SLFN12 antibodies In 2015 de Waal et al used ectopically expressed V5-tagged SLFN12 in order to confirm DNMDP-induced protein-protein interaction with PDE3A in HeLa. This was done due to lack of a reliable α-SLFN12 antibody. Here, I evaluated the performance of five publicly available primary antibodies in order to confirm the loss of SLFN12 gene expression after genetic knockout. Each antibody recognizes a different epitope on the SLFN12 protein: i) 35-84aa; ii) 191-220aa; iii) 327- 392aa; iv) 528-577aa and v) 12aa at C-terminus. Monitoring of gene expression by protein detection provides direct information about gene expression compared to analysis of mRNA levels. Based on previously gained RNA sequencing data (X. Wu, H. Greulich and M. Meyerson, unpublished) a panel of SLFN12-expressing and –non-expressing cell lines was chosen. Therefore, in SLFN12-expressing cell lines a specific band at 67kDa was anticipated.

Figure 7. SLFN12 antibody validation | a), b), c), d) and e) depict evaluation of individual SLFN12 antibodies with unique epitope at 35-84aa, 191-220aa, 327-392aa, 528-577aa and 12aa at C-terminus. Based on unpublished RNA sequencing data, SLFN12-positive (+) and –negative (-) cell lines were used for controls. 293T SLFN12 and 293T GFP represent transiently transfected parental 293T cells. Western blots were immunoblotted for vinculin (housekeeping gene, 116kDa) and either endogenous or transfected SLFN12 (67kDa) by individual antibodies in the upper and lower panel, respectively.

The indicated SLFN12 antibodies in figure 7 did not achieve protein detection of SLFN12. All five antibodies showed nonspecific binding affinity. Furthermore, no difference in the band profile comparing SLFN12-positive and –negative cell lines was observed. SLFN12-antibody validation revealed no reliable reagent for monitoring of gene expression, hence, detection of mRNA levels by qRT-PCR is used to confirm genetic SLFN12-knockouts.

Validation of SLFN12 knock-out in HeLa and A2058 In order to evaluate loss of gene function after genetic perturbation of SLFN12 mRNA expression levels were analyzed. Here, we relied on qRT-PCR due to lack of an accurate SLFN12 antibody, which might not provide as significant evidence for gene expression than western blotting. However, decrease in mRNA level does confirm loss of gene function, which is caused by nonsense-mediated mRNA decay (NMD) through generating premature stop codons. Therefore, in a desired outcome SLFN12 mRNA expression levels of SLFN12-KO cell lines are significantly reduced compared to their parental cell lines. In general, unaltered mRNA levels do not necessarily indicate ineffective knock-out, but might point out that genetic editing presumably did not affect mRNA expression, yet impeded protein expression. mRNA was isolated and synthesized into cDNA for detection of expression level of SLFN12 in the generated KO-cell lines using qRT-PCR analysis (figure 8a). mRNA levels of SLFN12 in HeLa SG1-4 were significantly reduced compared to parental HeLa. Whereas for HeLa SG5 and 6 only a slight and no decrease, respectively, in SLFN12 expression was observed. These results gained from RT-qPCR suggested an inefficient knock-out of SLFN12 for SG5 and 6 using CRISPR/Cas9 technology based on mRNA levels. Notably, also a slight reduction in PDE3A expression was observed in HeLa SG1, 5 and 6. These variations might be due to inaccuracy of the method. Additionally, SLFN12 expression was determined in A2058 (figure 8b). Here, SG1-6 exhibited a mediocre drop in SLFN12 mRNA levels compared to the parental cell line. Based on the obtained results from qRT-PCR, HeLa SG1-4 as well as A2058 SG1-6 indicated impact on SLFN12 gene expression by decreased mRNA levels. In contrast, HeLa SG5 and SG6 demonstrated no change in SLFN12 mRNA expression, thus, qRT-PCR did not provide evidence for efficient knock-out.

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Figure 8. Validation of CRISPR/Cas9 SLFN12 knock-out in HeLa and A2058 | Evaluation of change in relative gene expression of SLFN12 by qRT-PCR. a) and b) show specific sgRNAs (SG1-6) targeting distinct locations on endogenous SLFN12 gene in HeLa and A2058, respectively. Bar chart plot represents mRNA level after SLFN12-KO. qRT-PCR was performed by target gene-specific Taqman probes. mRNA level of SLFN12 is plotted as relative gene expression compared to HeLa parental cell line. Expression levels represent four technical replicates, resulting mean is normalized to mRNA level of ACTB housekeeping gene and further used for calculation of relative gene expression.

CRISPRseq reveals on-target efficacy on endogenous SLFN12 in HeLa and A2058. Amplicon sequencing of amplified CRISPR/Cas9 target sequences (CRISPRseq) poses a powerful technology for detecting RNA-guided dsDNA cleavage. Here, CRISPRseq was applied to enable direct evaluation of cutting efficiency and on-target specificity, which are essential parameters for successful genetic knock-out. In this case computational analysis of genetic editing of alleles, indel size distribution and frame-shift mutation events was conducted. Analysis and visualization of genetic editing events such as non-homologous end joining (NHEJ) as well as frame-shift mutations are key factors that indicate efficient knock-out. NHEJ describes a genetic event that is induced by CRISPR/Cas and causes inefficient repair of double strand DNA breaks, which might impair gene function. Distribution of insertions and deletions provides information about the precision of the RNA-guided cut. Generated indels, as consequence of NHEJ, may lead to frame-shift mutations that can be grouped either in out-of-frame or in-frame. For genetic perturbation major allele-fractions that are subject to NHEJ and high degree of out-of-frame mutations are favorable. In-frame mutations possibly indicate microdeletions, which might also impair gene function. In contrast, high allele populations with no genetic editing (termed as unmodified) indicate poor knock-out efficacy, which is an undesirable outcome. SLFN12-KO cell lines were collected, genomic DNA was extracted, target sequence was amplified by PCR, purified and subsequently sequenced using next generation sequencing. The data gained from this experiment was analyzed by CRISPResso, a python-based computational tool for visualization and processing of amplicon sequencing results. Applying CRISPResso enables quantification of editing frequency, meaning a visualization of the fraction of alleles that was subject to repair mechanisms, such as homologous recombination or non-homologous end joining, due to RNA-guided dsDNA cleavage. Visualization of genetic editing events in HeLa- and A2058-SLFN12 KO cell lines revealed high fraction of alleles that were subject to genetic alterations (figure 9). Greater than 99% of all reads from amplicon sequencing showed non-homologous end joining as result of genetic perturbation in endogenous SLFN12. Significant sgRNA on-target efficiency was verified in HeLa SG1-4, which was consistent with the RT-qPCR analysis. However, SG5 and 6 merely showed slight or no reduction in mRNA levels, yet exhibited substantial DNA editing of >99%. sgRNAs in A2058 scored DNA modifications (NHEJ) greater than 98%. The overall DNA editing frequency confirmed substantial on-target efficacy of SLFN12 sgRNAs in both HeLa and A2058.

Figure 9. Quantification and visualization of genetic editing of endogenous SLFN12 due to CRISPR- targeted knock-out in HeLa | a) and b) show quantification of allele editing frequency in HeLa and A2058, respectively. Pie charts represent percentage and numbers of sequenced reads after genomic editing, such as NHEJ that was caused by RNA-guided dsDNA cleavage by each individual SLFN12 SG. Populations are split in NHEJ and unmodified indicated by purple and light pink, respectively. NHEJ: non-homologous end joining.

Moreover, evidence for the length of DNA sequence modifications, such as insertions and deletions (indels) and how they were distributed in each individual SLFN12 knock-out cell line was provided (figure 10). Except for SG3, small deletions of 10-20 bases represented the majority of modifications, ranging from 25% to greater than 40% of sequences throughout all different gRNAs in HeLa. Whereas, HeLa SG3 showed significant variation in size of deletions as well as a striking proportion of base substitutions. Additionally, DNA deletions demonstrated wide distribution in size to a great proportion of sequences, meaning that several small sequence fractions harbored a strongly fluctuating pattern of deletions. Similar results were obtained in A2058, however, SG5 showed a rather variable distribution of deletions compared to the rest of

the sgRNAs. Overall, distribution analysis revealed small deletions, as anticipated by the effect of CRISPR-targeted knock-out, which indicates precise cutting efficiency.

a)b) HeLa SG1 HeLa SG2 HeLa SG3

HeLa SG4 HeLa SG5 HeLa SG6

b)b) A2058 SG1 A2058 SG2 A2058 SG3

A2058 SG5 A2058 SG6

Figure 10. Depiction of indel size distributions by RNA-guided dsDNA cleavage | Visualization separated by individual gRNAs in HeLa (a) and A2058 (b). Size of the insertions and deletions (indels) that were caused by genetic editing are plotted versus the fraction of sequences that corresponds to these indels. Purple color indicates insertions (positive values on x-axis) and deletions (negative values on x-axis). Red color indicates nucleotide substitutions.

In HeLa frame-shift mutation analysis revealed approximately 82%-85% of DNA alterations that cause a shift in the open reading frame of the coding sequence for SG1, 2 and 4 (figure 11a). Whereas, circa 35%, 23% and 46% of in-frame mutations, hence DNA modifications with a multiplicity of three bases, were observed in HeLa SG3, 5 and 6, respectively. Moreover, frame-shift mutation analysis showed that A2058 SG1, 2 and 5 feature in-frame alterations of approximately 16-23% of reads (figure 11b). Whereas, sgRNA 3 and 6 yielded significantly higher fractions of reads that were subject to in-frame mutations, namely 27% and 44%, respectively. In conclusion, high frequency of in-frame mutations in HeLa SG3 as well as in HeLa and A2058 SG6 indicate undesirable genetic editing, which might not impair gene function of SLFN12.

Figure 11. Visualization of frame-shift mutations analysis| Analysis of frame-shift mutations in coding sequence upon genetic perturbation of the target gene in HeLa (a) and A2058 (b). Red color shows proportion of reads that harbor frame-shift alterations. Light orange color indicates the fraction of reads with mutations that are in-frame, meaning insertions and deletions with a multiplicity of three bases.

In summary, based on the obtained qRT-PCR data, in HeLa SG1-4 managed to significantly reduce SLFN12 mRNA levels. Whereas in A2058, SG5 and SG6 achieved substantial decrease in SLFN12 mRNA level. Moreover, analysis of genetic editing frequency revealed that SG1-4 attained high fractions of alleles with NHEJ (>98%) in both HeLa and A2058 as consequence of DNA editing. In HeLa and A2058 low distribution of indels were observed in SG1, 2, 4 and 6, representing precise genetic modification as expected by Cas9-mediated dsDNA cleavage. Besides, SG3 (HeLa and A2058) and SG5 (A2058) showed wider distribution of deletions and major fractions of base substitutions. Furthermore, frame-shift mutation analysis showed high fractions of in-frame mutations in SG3 (HeLa) and SG6 (HeLa and A2058), indicating weaker knock-out efficiency compared to other SGs. However, SG3 in HeLa achieved substantial decrease of SLFN12 mRNA expression, which might suggest that generated deletions at this 46

specific target location yet caused impairment of gene function. In contrast, no significant reduction of SLFN12 mRNA levels was observed for SG6 in HeLa and A2058. Along with the data obtained by frame-shift analysis it indicates that SG6 did not achieve efficient knock-out of SLFN12. Overall, SLFN12-KO assessment provided indications that incorporation of SG1-4 resulted in a successful knock-out of endogenous SLFN12, which ultimately poses the base for functional phenotypic evaluation.

Genetic perturbation of SLFN12 by CRISPR resulted in loss of sensitivity to PDE3 modulating small molecules Functional phenotypic evaluation was conducted in order to assess the requirement of SLFN12 in DNMDP-induced cancer cell killing. Evaluation of on-target efficacy was performed for illuminating the potential of individual sgRNAs for efficient knock-out of SLFN12. By assessment, four sgRNAs (SG1-4) demonstrated strong performance regarding reduction of expression level, genetic editing, indel size distribution and frame-shift mutation profile, suggesting promising candidates to reliably evaluate the functional effect of knocking out SLFN12. A Cell Titer Glo viability test was performed in order to evaluate the effect of genetic perturbation of SLFN12 in HeLa and A2058 (figure 12). Four of the sgRNAs, SG1-4, were able to completely abolish sensitivity to PDE modulating agents, DNMDP and anagrelide, in HeLa. Whereas for two sgRNAs, SG5 and 6, partial sensitivity was observed resulting in a reduction of cell viability to approximately 75% and 50%, respectively. The remaining sensitivity in SG5 and 6 to DNMDP and anagrelide is consistent with lower efficacy of genetic knockout, as demonstrated by slight/no reduction of SLFN12 mRNA levels and in-frame mutation events ranging from 23.3% to 45.5% of alleles. In contrast, parallel infection with an empty CRISPR vector that does not contain a sgRNA had no effect on compound sensitivity, demonstrating that the loss of sensitivity in SLFN12-KO cell lines is due to loss of SLFN12 gene function, rather than an artefact of introducing CRISPR/Cas9. For A2058, all five SLFN12-KO cell lines (SG1-6) completely abolished sensitivity to DNMDP and anagrelide, while parallel SG-empty cell line had maintained similar sensitivity as the parental A2058 cells. Notably, A2058 was found to be partially sensitive to DNMDP and anagrelide, yielding maximum killing of ~80% and ~40% of cells, respectively. This observation contrasts the sensitivity behavior of HeLa, which might indicate differences in the biological mechanism that is leveraged in both cell lines to achieve induction of apoptosis. Although

fluctuations for SG1-6 were observed, no trend for sensitivity could be detected.

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Figure 12. Evaluating loss of sensitivity to PDE3 modulating small molecules in HeLa and A2058 | a), b) and c), d) show evaluation of loss of sensitivity to DNMDP and anagrelide in HeLa and A2058, respectively, by genetic perturbation of endogenous SLFN12. Plot represents logarithmic compound concentration versus percentage of cell viability. Individual colors indicate six individual sgRNAs (SG1-6) targeting distinct positions on the SLFN12 gene. A curve fit model was used to highlight a decrease in viability. Incubation of compound was performed for 72 hours. Compound concentration ranges from 0.3nM-3M.

Restoration of DNMDP-sensitivity phenotype in SLFN12-KO cancer cell lines by integrating non-CRISPR-targeted rescue constructs. The phenotype conferred by DNMDP-sensitivity was abolished by generating SLFN12- knockout cancer cell lines using CRISPR/Cas9 technology. Genetic editing, indel size distribution and frame-shift mutations caused by sgRNAs was verified by CRISPRseq. Another method that remains gold standard in order to evaluate on-target specificity is a rescue-experiment. Here, the abolished phenotype is rescued by incorporating a “degenerate” SLFN12 ORF expression construct that is not recognized by Cas9 due to a single base exchange in the PAM sequence. Figure 13 indicates the results of the rescue experiments that were performed in the SLFN12-KO cancer cell lines. Slight decrease of viability is rather attributable to fluctuations of the assay, hence, no rescue of the DNMDP-mediated phenotype was observed. However, the hypothesis of this assay was the complete restoration of sensitivity to DNMPD by incorporating a SLFN12 expression ORF that is not targeted by Cas9 due to mutations in the PAM recognition sequence. Indeed, partial restoration of sensitivity to DNMDP was achieved in A2058, however, complete rescue of the phenotype was not possible. This may be due to the following reasons: i) mixed plasmid populations that arose during cloning procedure; ii) incomplete selection process during viral transduction.

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Figure 13. Restoration of sensitivity to DNMDP in SLFN12-KO cancer cell lines | a) and b) depict viability assay showing restoration of sensitive phenotype in HeLa and A2058, respectively. Graph indicates logarithmic compound concentration versus cell viability in percent. Colors indicate three individual sgRNAs (SG1-3) and their “rescue-equivalent”, which carries a synonymous open reading frame, but whose PAM recognition sequence has been mutated. In order to highlight a decrease in cell viability a curve fit model was applied. Compound was incubated for 72 hours. Compound concentration titrated from 0.3nM-3M.

Overall, in HeLa and A2058 SLFN12 SG1-4 demonstrated to be very efficient in abolishing sensitivity to PDE3 modulating small molecules, whereas SG5 and 6 only achieved partial reduction of responsiveness in HeLa, already indicated by results obtained by qRT-PCR and evaluation of knock-out efficacy by CRISPRseq. Notably, treatment with PDE modulating small molecules in A2058 did not manage to achieve complete cell killing compared to HeLa. In contrast, cell number was diminished down to 20% and 60% by DNMDP and anagrelide, respectively. This raises the question which molecular settings in the cell enables complete cell killing in HeLa opposed to attainment of partial cell death in A2058, while presumably leveraging the same mechanism of action of PDE3 modulating small molecules. The observations made here, highlight the evidence that SLFN12, as PDE3A, is indeed a required factor in the selective cancer cell killing mechanism that is exhibited by PDE3 modulating small molecules in HeLa and A2058.

RVH421 is a PDE3A-lacking melanoma cell line that requires SLFN12 for induction of DNMDP- and anagrelide-dependent cell death

Evaluation of genetic perturbation of SLFN12 in PDE3A-lacking RVH421 RVH421 is another DNMDP sensitive melanoma cell line identified in the National Cancer Institute’s Cancer Target Discovery and Development (CTD2) network59. However, RVH421 does not express PDE3A but has significant expression levels of PDE3B based on RNAseq analysis. Unpublished results (X.Wu, K. Williamson, H. Greulich, M. Meyerson) indicate that the sensitivity in RVH421 is indeed mediated by PDE3B. However, it is not known whether this PDE3B-mediated sensitivity also depends on SLFN12. Therefore, I set out to test the effect of SLFN12-KO on the sensitivity in RVH421. After lentiviral infection and positive antibiotic selection sgRNA-guided genetic perturbation of SLFN12 was verified using qRT-PCR in order to monitor mRNA expression and CRISPRseq was applied for evaluation of on-target efficacy in RVH421.

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Figure 14. Evaluating mRNA expression levels in RVH421 by qRT-PCR | Bar chart plots demonstrate mRNA expression after genetic SLFN12 perturbation. qRT-PCR was conducted using target gene-specific Taqman probes. Individual mRNA level of SLFN12 is plotted as relative gene expression compared to RVH421 parental cell line. Expression levels represent four technical replicates, resulting mean is normalized to mRNA level of ACTB housekeeping gene and subsequently used for calculation of relative gene expression.

Across all different SGs (1-6) no clear indication for a significant decrease in relative SLFN12-mRNA levels was observed after CRISPR-targeted knock-out in RVH421 (figure 14). Nevertheless, no change in mRNA expression levels does not necessarily indicate no decrease in expression. It might suggest that the genetic knock-out of SLFN12 did not induce nonsense- mediated mRNA decay, but impaired protein folding resulting in loss of gene function. CRISPRseq analysis in RVH421 SLFN12 KO-cell lines enabled to quantify the frequency of genomic editing. Targeted genetic perturbation using distinct sgRNAs resulted in NHEJ events in approximately 99% of alleles throughout all individual SLFN12 SGs, which represents highly efficient on-target genetic cutting (figure 15). Distribution analysis of indel sizes revealed high abundance of short insertions and deletions for SG1, 2, 5 and 6, hence, demonstrating precise cutting efficiency. Whereas, for RVH421 SG3 base substitutions for about 10% of sequences were observed, besides strong distribution of deletion sizes almost up to 50 bases. Notably, the indel size distribution of RVH421 SG3 showed a similar pattern in HeLa and A2058. Moreover, RVH421 SG1, 4 and 5 exhibited approximately 11-12% of in-frame DNA alterations, as evaluated by frame-shift mutation analysis. In contrast, SG2, 3 and 6 featured significantly higher fractions of in-frame alterations with 21%, 26% and 32% of all reads, respectively. Summarizing, genetic editing of at least 99% of alleles confirms high on-target efficiency of each individual SLFN12 sgRNA. Moreover, size distribution of indels verified highly precise DNA editing for SG1, 2, 5 and 6. Whereas, distribution of indels and the abundance of substitutions was consistent with the distribution pattern in HeLa and A2058. Therefore, the results based on evaluation of SLFN12- KO efficacy using qRT-PCR and CRISPRseq suggest promising genetic knock-out for SG1-5 in PDE3A-lacking melanoma cell line RVH421sgRNA. Moreover, size distribution of indels verified highly precise DNA editing for SG1, 2, 5 and 6. Whereas, distribution of indels and the abundance of substitutions was consistent with the distribution pattern in HeLa and A2058. Therefore, the results based on evaluation of SLFN12-KO efficacy using qRT-PCR and CRISPRseq suggest promising genetic knock-out for SG1-5 in PDE3A-lacking melanoma cell line RVH421.

b)b) RVH421 SG1 RVH421 SG2 RVH421 SG3

RVH421 SG4 RVH421 SG5 RVH421 SG6

Figure 15. Quantification and visualization of genetic editing of endogenous SLFN12 due to CRISPR- targeted knock-out in RVH421 | a) Quantification of genomic editing frequency. Pie charts represent numbers and percentage of sequenced reads after CRISPR/Cas9 editing by individual SLFN12 gRNA. Populations are separated in a fraction subject to NHEJ indicated by the purple and unmodified fraction labelled in light pink. b) Indel size distributions for individual gRNAs. Size of the insertions and deletions (indels) that were caused by genetic editing are plotted versus the fraction of sequences that corresponds to these indels. Purple color indicates insertions (positive values on x-axis) and deletions (negative values on x-axis). c) Analysis of frame-shift mutations in coding sequence due to targeted gene perturbation. Red color indicates proportion of reads that are subject to frame-shift alterations. Light orange color visualizes the fraction of reads with mutations that are in-frame, meaning insertions and deletions with a multiplicity of three bases. NHEJ: non-homologous end joining

Verification of phenotypic change on treatment with PDE3 modulating small molecules after SLFN12-KO in RVH421 The results of the viability assay demonstrated that all six sgRNAs (SG1-6) were capable of abolishing sensitivity to PDE3 modulating agents after targeted SLFN12-KO (figure 16). RVH421 SG6 showed a decrease in cell viability yielding about 40% cell survival, demonstrating partial loss of sensitivity to DNMDP compared to parental RVH421. Response of incomplete cell killing was similar as observed in A2058, which indicates different influential factors that presumably

interfere with PDE3 modulating cell killing activity. For RVH421 parental cell line and SG empty similar sensitivity was observed with EC50 of approximately 250nM. Furthermore, anagrelide exhibited a partial cell killing effect in the parental and SG empty condition of RVH421. Again, indicating that the loss of DNMDP- and anagrelide-sensitivity is due to targeted genetic knock- out and not a result of an artefact.

RVH421 a) SLFN12 SG1 SLFN12 SG2 100 SLFN12 SG3

l SLFN12 SG4

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V 50 % SLFN12 SG6 RVH421 RVH421 SG empty 0 0.0001 0.001 0.01 0.1 1 10 log [DNMDP] µM

RVH421 b) SLFN12 SG1 SLFN12 SG2 100 SLFN12 SG3

i SLFN12 SG4

a i SLFN12 SG5

V 50 % SLFN12 SG6 RVH421 RVH421 SG empty 0 0.0001 0.001 0.01 0.1 1 10 log [Anagrelide] µM

Figure 16. Verification of loss of sensitivity to PDE3 modulating small molecules in RVH421 | a) and b) demonstrate evaluation of loss of sensitivity to DNMDP and anagrelide after knocking out SLFN12. Plot represents logarithmic compound concentration versus percentage of cell viability. Individual colors indicate six specific sgRNAs (SG1-6) targeting different positions on the SLFN12 gene. A curve fit model was used to highlight a decrease in viability. Compound incubation was performed for 72 hours. Concentration of compounds ranges from 0.3nM-3M.

In summary, SLFN12 knock-out in metastatic melanoma cell line RVH421 resulted in successful genetic editing and high frequency of deletions. Frame-shift mutation analysis revealed approximately 88-89% of out-of-frame DNA alterations for SG1, 4 and 5, which is consistent with the results gained for cell viability assays with PDE3A modulating agents and qRT-PCR. Surprisingly, RVH421 SG2, 3 and 6 featured significantly higher number of in-frame mutations. Which leads to the suggestion that the deletions that were a result of the genetic perturbation of endogenous SLFN12 caused a substantial mutational load that disturbed either mRNA transcription or impaired protein expression and ultimately resulted in loss of sensitivity to DNMDP and anagrelide. Genetic perturbation of SLFN12 using CRISPR/Cas9 resulted in abolishment of sensitivity to PDE3 modulating small molecules in RVH421 for SG1-5. Furthermore, the obtained data suggests that SLFN12 is a required and limiting factor for conferring sensitivity to anagrelide and DNMDP. Further, it is evident that cells lacking PDE3A, yet expressing PDE3B, respond to treatment with PDE3 modulating small molecules with partial sensitivity. Since partial response was observed in A2058, the impact of lacking PDE3A may be negligible. This raises the question whether other isoforms of the phosphodiesterase family, such as PDE3B might be capable of compensating for the lack of PDE3A in order to fulfill DNMDP- and anagrelide-mediated cytotoxic activity.

Monitoring of proliferation after genetic knock-out of SLFN12 In order to monitor cell growth of parental cells and genetically perturbed cells, a population doubling experiment was conducted. This allowed for comparison of proliferation behavior over a time course of approximately two to three weeks. Due to successful results obtained from evaluating SLFN12 knock-out efficiency throughout all three cell lines, SG1 was used to perform this experiment. Cells were seeded at fixed cell numbers, counted after several days and subsequently re-seeded. The impact on proliferation behavior and overall viability by knocking out SLFN12 in HeLa, A2058 and RVH421 was evaluated due to the following factors: i) recent studies proposed a differentiation-promoting molecular role for SLFN12 with major

emphasis on T-lymphocyte quiescence; ii) genetic perturbation might cause a disadvantage in proliferation by deprivation of essential genes. Population doubling experiments performed with SG1 in HeLa, A2058 and RVH421 compared to each parental cell line revealed no difference in the proliferation behavior (figure 17). Knock-out cells neither exhibited any growth advantage due to genetic perturbation of SLFN12 nor was there any obvious detrimental impact on overall cell viability.

a) b)

Figure 17. Population doubling experiment comparing proliferation behavior of SLFN12 SG1 in HeLa, A2058 and RVH421 with their parental cell lines | Plots depict proliferation rates of cell lines as population doubling versus time. a), b) and c) visualize proliferation of HeLa, A2058 and RVH421, respectively, compared to their SLFN12 SG1 KO cell lines. Cell counts carried out in duplicates. Vertical lines indicate +/- standard deviation.

In conclusion, knock-out of SLFN12 represented an advancement of the knock-down approach using RNAi performed by de Waal et al. to show the involvement of Schlafen family member 12 in the selective cancer cell killing mechanism exhibited by PDE3 modulating small molecules such as DNMDP and anagrelide.

In this study, I demonstrated the generation of cell lines with targeted genetic perturbations of endogenous SLFN12. Six distinct sgRNAs were used to achieve a precise and accurate knock-out in order to investigate the impact on sensitivity to DNMDP and anagrelide (table 5).

Notably, unlike cervical cancer cell line HeLa, it was observed that A2058 and RVH421 respond to PDE modulating small molecules with partial sensitivity, meaning no complete cell killing was achieved. Changes in the cellular response to DNMDP and anagrelide in HeLa and A2058 allows for the following hypothesis: heterogeneous cell populations might vary in the expression levels of SLFN12, which causes an expression threshold necessary for induction of cell death that is only given in a subset of cells. However, more scientific effort in elucidating the cause for partial sensitivity occurring in A2058 and RVH421 has to be taken in order to utterly understand this novel mechanism among differently responding cell lineages.

The findings of these experiments confirm the requirement and the limiting role of SLFN12 in the selective cancer cell killing mechanism leveraged by PDE3 modulating small molecules in HeLa, A2058 and PDE3A-lacking RVH421.

SG1 SG2 SG3 SG4 SG5 SG6 CHOPCHOP 1 3 23 11 8 2 ranking decrease of significant/low/ significant/low/ significant/low/ significant no/significant/ no/significant/ SLFN12 significant significant significant decrease no no mRNA level DNA editing >99% >99% >98% 100% >99% 100% efficacy in-frame 17.6%/22.7%/ 15.5%/16.3%/ 35.1%/27.1%/ 23.3%/20.6%/ 45.5%/43.7%/ 18.3%/11.6% mutations 10.7% 21.3% 26.1% 11.8% 32.2% loss of 75%/100%/ 50%/100%/ sensitivity to 100% 100% 100% 100% 100% 80% DNMDP loss of 75%/100%/ 50%/100%/ sensitivity to 100% 100% 100% 100% 100% 100% anagrelide

Table 5. Overview of evaluation of individual SLFN12 sgRNA performances in HeLa, A2058 and RVH421 | Evaluation based on individual performance regarding reduction of SLFN12 expression monitored by RT- qPCR, achievement of high on-target efficacy elucidated by CRISPRseq and verification of phenotype using CTG. CHOPCHOP ranking indicates a computational scoring system based on prediction of high on-target efficacy and low off-target effects due to genomic location and target sequence. Values and assessments of sgRNAs are attributable for individual cell lines separated by slash, i.e. HeLa/A2058/RVH421 (except

SG4, which was not used in A2058).

Selective impact on expression, PDE3A-interaction and induction of cell death by truncating major domains in SLFN12 protein.

The physiological cellular function of SLFN12 is largely unknown, which made it difficult to speculate what SLFN12 does to help trigger apoptosis in the presence of PDE3A modulating compounds. I sought to understand this question by querying structural functional relationship for individual SLFN12 domains. To this end, I generated truncation or internal deletion mutants of SLFN12 cDNA expression constructs and characterized these mutants for protein expression, their ability to complex with PDE3A in the presence of DNMDP and mediate DNMDP-sensitivity (figure 18).

Given the lack of structural information of SLFN12 or homologous proteins within and outside the Schlafen family, those truncations were based on sequence features found in SLFN12 such as a divergent AAA-domain and a SLFN box. The rest of the domains was separated into an N-terminal and C-terminal end.

Figure 18. Schematic depiction of truncations of major domains in SLFN12 protein | SLFN12 mutants feature deletions of N-terminus (aa 151-578), C-terminus (aa 1-341), AAA-domain (aa 1-199; 323-578) as well as AAA-domain plus C-terminus (aa 1-199), N- plus C-terminus (aa 151-341) and N-terminus plus AAA- domain (aa 323-578). Numbers indicate amount of amino acids for each individual protein based on the truncated cDNA. Full length SLFN12 WT (wild type) is a 578-amino acid long protein. The white box on the very left side represents the N-terminus, the solid grey rectangle represents SLFN box, black box indicates a naturally truncated AAA domain and the dashed element on the very right side is the C-terminal end of the SLFN12 protein.

Truncations of N-terminus, internal AAA-domain and C-terminal end as well as loss of combined domains were generated by site-directed mutagenesis and subsequent gateway cloning step into lentiviral expression vectors. The set of truncated SLFN12 proteins include: i) 151-578aa (deleting N-terminus), ii) 1-341aa (deleting C-terminus), iii) 1-199; 323-578aa (deleting internal AAA-domain), iv) 1-199aa (deleting internal AAA domain plus C-terminus), v) 151-341aa (deleting N- and C-terminus) and vi) 323-578aa (deleting N-terminus plus internal AAA domain). Deletion mutants were introduced into target cells by viral transduction and stably integrated into host cell lines in which sensitivity to DNMDP solely depends on functional SLFN12 expression.

Two such SLFN12-dependent host cell lines were used. The first, “HeLa-res”, is a derivative of the cervical cancer cell line HeLa, which acquired resistance to DNMDP after continuous treatment and has lost the expression of SLFN12 as revealed by RNAseq analysis (X. Wu, H. Greulich, M. Meyerson, unpublished). Sensitivity in HeLa-res cells can be restored by reintroducing a SLFN12 overexpression plasmid. The second host cell line is a derivative of the 62

lung adenocarcinoma cell line A549. A549 is not sensitive to DNMDP, and has medium levels of PDE3A but no SLFN12 expression. After stable integration of a PDE3A expression construct, hence A549+pLX317-PDE3A, sensitivity to DNMDP can be induced by ectopic expression of functional SLFN12 (X. Wu, H. Greulich, M. Meyerson, unpublished). As anticipated, cell viability assays in HeLa-res as well as in A549+pLX317-PDE3A cells demonstrated lack of DNMDP-sensitivity after mock-infection (pGFP). Whereas, integration of full length functional SLFN12 (pSLFN12) showed restoration of sensitivity (figure 19). As depicted, none of the truncation and deletion mutants of SLFN12 can restore sensitivity to DNMDP in either cell lines, suggesting that all these truncations and deletions are detrimental to the function of SLFN12. a) HeLa res. pSLFN12_151-578aa 100 pSLFN12_1-341aa pSLFN12_1-199; 323-578aa

b pSLFN12_1-199aa

V 50 pSLFN12_151-341aa

% pSLFN12_323-578aa pGFP 0 pSLFN12 0.0001 0.001 0.01 0.1 1 10 log [DNMDP] µM

A549 + pLX317 PDE3A b) pSLFN12_151-578aa 100 pSLFN12_1-341aa pSLFN12_1-199; 323-578aa

i pSLFN12_1-199aa

V pSLFN12_151-341aa

50 % pSLFN12_323-578aa pGFP

0 pSLFN12 0.0001 0.001 0.01 0.1 1 10 log [DNMDP] µM

c) S L F N 1 2 m R N A in H e L a -r e s

s s

e 3

e 2 n

e S L F N 1 2

e v

i 1

e r 0

a a a a a a 2 P a a a a a a 1 8 1 8 9 1 8 F 7 4 7 9 4 7 N G F p -5 -3 -5 -1 -3 -5 L 1 1 3 1 1 3 S 5 _ 2 _ 5 2 p 1 2 3 2 1 3 _ 1 ; 1 _ _ 2 N 9 N 2 2 1 F 9 F 1 1 N L -1 L N N F S 1 S F F L p _ p L L S 2 S S p 1 p p N F L S p

Figure 19. Evaluating impact on DNMDP-mediated cell killing effect by truncating domains in SLFN12 | a) and b) Elucidating the requirement of specific domains of SLFN12 for conferring sensitivity to DNMPD in HeLa resistant and A549+pLX317 PDE3A, respectively by genetically stable incorporation of truncated SLFN12 cDNA. Graphs depict the logarithm of compound concentration plotted against viability of cells in percent. Individual colors signalize individual SLFN12 mutants. pSLFN12 represents full length cDNA. A curve fit model was applied in order to highlight a decrease in viability. DNMDP was incubated for 72 hours. Concentration of compound ranges from 0.3nM- expression level by qRT-PCR in HeLa resistant cell line. Bar chart plots demonstrate mRNA expression. qRT- PCR analysis was conducted after CTG viability assay using target gene-specific Taqman probes. mRNA level of SLFN12 is plotted as gene expression relative to HeLa resistant with a stably integrated full length SLFN12 cDNA. Expression levels represent four technical replicates, resulting mean is normalized to mRNA level of ACTB housekeeping gene.

Expression levels of SLFN12 in those cell lines were determined by qRT-PCR (figure 19c). Overexpression of SLFN12 full-length cDNA construct was used as a reference point for evaluating mRNA levels of each individual SLFN12 deletion mutant. If compared to full length SLFN12 construct, the mutant SLFN12 constructs express the same or greater SLFN12 mRNA, hence the loss of function for individual mutants is unlikely a result of weak expression.

Interestingly, compared to full length SLFN12 construct, four truncation mutants 151- 578aa, 1-199aa, 151-341aa and 323-578aa showed substantial increased SLFN12 mRNA levels. Whereas two mutants, namely 1-341aa and 1-199; 323-578aa showed slightly reduced or comparable expression levels, respectively. However, this is a very minor decrease, and thus unlikely to explain the complete lack of function for mutant 1-341aa. In conclusion, the results of the CTG viability assay in DNMDP-resistant HeLa-res and A549+pLX317 PDE3A together with the determination of mRNA expression levels confirmed the following: no deletion mutant was capable of mimicking the function of functional full-length SLFN12, namely induction of cell death upon treatment with DNMDP. Noteworthy, the obtained results suggest that no single domain of SLFN12 can be deleted, hence, every protein domain is required for DNMDP-induced function. Cell viability assays represented a first effort in elucidating domains that are required for mediating DNMDP-induced cell death. However, the lack of cell killing ability might be due to two scenarios: i) the mutants are completely non-functional and are incapable to form a complex with PDE3A in the presence of DNMDP; ii) or, the mutants still retain the ability to interact with PDE3A but have lost the ability to further induce cell death. In order to differentiate between those two opportunities, I evaluate the ability of these SLFN12 domain-truncation mutants to form a complex with PDE3A, using protein co-immunoprecipitation (CoIP). De Waal et al. demonstrated protein interaction upon DNMDP-treatment using ectopically expressed and V5-tagged SLFN12, performing anti-PDE3A immunoprecipitation and then detecting the co-complexed SLFN12-V5 fusion protein by anti-V5 Western. Here, I used a similar co-IP Western approach, but in the reverse direction. That is, I performed anti-V5 immunoprecipitation for SLFN12-V5 fusion protein, and detected co-complexed PDE3A using anti-PDE3A Western. Specifically, HeLa cells were transfected with an overexpression construct for SLFN12-V5 fusion protein, or a construct expressing eGFP-V5 fusion protein (pGFP-V5) as control. Cells were then treated with DNMDP or DMSO, harvested and lysed. After quantification and adjusting for protein quantity, whole cell lysates were incubated with V5-antibody- conjugated magnetic agarose beads. Immuno-precipitated proteins were subsequently analyzed by Western blots for SLFN12-V5 and PDE3A (figure 20). Consistent with discoveries made by de Waal et al., DNMDP-induced SLFN12-PDE3A complex formation was also detected in our experiment (figure 20). First, only weak and ambiguous signal was detected for V5-tagged SLFN12, in the lysate or in the elution.In particular, while two weak bands were present in pSLFN12-V5 transfected samples, two bands with similar

molecular weight were also present in pGFP-V5 transfected samples. Therefore, it remains to be analyzed whether one of these bands are truly SLFN12-V5. Thus, despite being carried on an overexpression vector, the expression levels of SLFN12-V5 was rather low, possibly due to a cytotoxic phenotype that was observed 24-48 hours after transfection. Despite the ambiguity in detecting SLFN12-V5 fusion, DNMDP-treated cells, but not DMSO-treated cells, showed substantial PDE3A co-immunoprecipitation when pulling down SLFN12-V5 using anti-V5 antibody. Notably, the amount of PDE3A pulled down represented a substantially smaller fraction of what was observed in the unbound protein condition, which can be attributable to the lowly expressed SLFN12-V5 fusion protein.

Figure 10. Evaluating DNMDP-induced protein-protein-interaction of SLFN12 and PDE3A by V5-co- immunoprecipitation | Graphs are separated into IP-elution, unbound fraction and whole cell lysate. Numbers on the very left side indicate molecular weight of dual precision protein ladder. Transient transfection of HeLa cells was performed for 72 hours. Compound treatment was carried out for 8 hours. IgG heavy and light chain are observed in the elution panels at approximately 50kDa and 25kDa. Elution was performed by loading dye buffer and reducing agent. IP and detection conducted with same anti-V5 antibody. Upper and lower panels represent detection with rabbit anti-PDEA and mouse anti-V5, respectively.

After confirming the detection of DNMDP-induced interaction between PDE3A and full length SLFN12, the six generated deletion mutants were tested for their ability to interact with PDE3A (figure 21).

Figure 21. Co-immunoprecipitation of PDE3A using ectopically expressed V5-tagged SLFN12 deletion mutants | a), b) and c) Graphs are separated into IP-elution, unbound fraction and whole cell lysate, respectively. The numbers on the very left side indicate molecular weight of dual precision protein ladder. 72 hours of transient transfection. Treatment with DNMDP was conducted for 8 hours. IgG heavy and light chain are observed in the elution panels at approximately 50kDa and 25kDa. Elution was performed by loading dye buffer and reducing agent. IP and detection conducted with same anti-V5 antibody. Upper and lower panels represent detection with rabbit anti-PDEA and mouse anti-V5, respectively.

Similar to the previous co-immunoprecipitation experiment no clear band for V5-tagged full length SLFN12 could be detected, and yet PDE3A could be consistently detected in the SLFN12-V5 IP (figure za). Interestingly, two out of six deletion mutants, namely aa 151-578 (50.25 kDa) and 323-578 (30.11 kDa) were detectable in IP- and unbound-fraction as well as in whole cell lysate (figure za, b, c), suggesting substantially increased expression compared to the full length SLFN12-V5 protein. The high expression may suggest that those two mutants are less toxic to the host cells and can be accumulated to relatively high level. Therefore, this suggested that the other four mutants did not achieve efficient protein folding, hence, could not be

detected by Western blot. Interestingly, aa 323-578 showed much weaker signal compared to aa 151-578, indicating indeed an impact on expression efficiency due to substantial truncation. Out of the six deletion mutants, only mutant 151-578aa was able to interact with PDE3A after DNMDP treatment. However, given the vast increase in protein levels for this mutant compared to the full length SLFN12-V5, and yet the reduced level of PDE3A CoIP, the overall complex formation ability is weaker for this mutant. This suggests that the N-terminus of SLFN12 may not be completely expendable for the interaction with PDE3A. Therefore, the obtained results provided evidence that in fact the whole SLFN12 protein is more or less required for interacting with PDE3A and potentially loss of N-terminal domain negatively affected the binding affinity to PDE3A. Given the strong impact on SLFN12 activity by large domain deletion, I changed to generating small but progressive deletions from the C-terminus of SLFN12. Two new truncated versions of SLFN12 were made, carrying a 10aa and a 30aa deletion starting from the very C- terminal end, resulting in aa 1-568 and 1-548, respectively (figure 22). These two mutants were tested for SLFN12 function using the SLFN12-dependent cell lines, HeLa-res and A549+pLX317- PDE3A.

Figure 22. Schematic visualization of smaller and consecutive C-terminal deletions in SLFN12 | SLFN12 mutants aa 1-568 and 1-548 exhibit minor truncations of the C-terminal domain by 10 and 30 amino acids, respectively. Numbers indicate amount of amino acids for each individual protein based on the truncated cDNA. Full length SLFN12 WT (wild type) is a 578-amino acid long protein. The white box on the very left side represents the N-terminus, the solid grey rectangle represents SLFN box, black box indicates a naturally truncated AAA domain and the dashed element on the very right side is the C-terminal end of the SLFN12 protein.

A549 + pLX317 PDE3A pSLFN12_1-568aa 100 pSLFN12_1-568aa + Trequ.

t pSLFN12_1-548aa

a pSLFN12

V 50

% pSLFN12 + Trequ. pGFP

0 0.0001 0.001 0.01 0.1 1 10 log [DNMDP] µM

Figure 2. Evaluating impact on DNMDP-mediated cell killing effect by deleting minor fractions of the C- terminal tail of SLFN12 | a) and b) Elucidating the impact of two consecutive deletions in the C-terminal end of SLFN12 on conferring sensitivity to DNMPD in A549+pLX304 PDE3A. Graphs depict the logarithm of compound concentration plotted against viability of cells in percent. Individual colors signalize individual SLFN12 mutants. pSLFN12 represents full length cDNA. A curve fit model was applied in order to highlight a decrease in viability. Compound incubation was performed for 72 hours. Concentration of compound ranges from 0.3nM-3M.

The results of the viability assay showed that 1-548aa was not able to restore sensitivity in A549+pLX304 PDE3A (figure 23). Whereas, 10 amino acids truncation at the C-terminal end of SLFN12 (1-568aa) demonstrated restoration of sensitivity to DNMDP in a similar fashion as full length SLFN12 (pSLFN12). Moreover, sensitivity conferred by mutant 1-568aa was completely abolished in the presence of trequinsin co-treatment, suggesting the same mode of action as for full length SLFN12. In contrast, mutant 1-548, a 30 amino acid truncation at the C-terminus, had no activity in conferring DNMDP sensitivity, suggesting detrimental loss of protein function. Whether this is due to the loss of complex formation with PDE3A, or disruption of protein folding remains to be determined. In conclusion, the small C-terminal truncations in SLFN12 revealed that by truncating the C-terminal end by 10 amino acids, the function in context of compound-dependent induction of cell death is majorly preserved. However, further truncations to up to 30 amino acids resulted in loss of sensitivity to DNMDP. For more thorough investigation of these smaller deletion mutants, the mRNA levels have to be determined by qRT-PCR in order to ensure a significant expression of these mutants. Furthermore, to examine the potential for interacting with PDE3A

in the presence of DNMDP, co-immunoprecipitation experiments, such as for the SLFN12 mutations carrying major deletions, have to be conducted. In summary, the study of different truncated variants of SLFN12 aimed for the elucidation of domains that are required for induction of cell death upon treatment with DNMDP. Regarding the lack of a protein X-ray crystal structure of SLFN12 or homologues, those domains were based on sequence features such as a truncated AAA-domain and a SLFN box. It turned out that deletions of major domains (N-terminus, C-terminus and AAA-domain) strongly affect the function of SLFN12 and led to loss of sensitivity to DNMDP. Further investigation on the protein- protein-interaction with PDE3A was performed by protein complex-immunoprecipitation using V5-tagged SLFN12 truncation mutants to pull down PDE3A in the presence of DNMDP. One of the six truncation mutants, 151-578aa, retained the capability to bind PDE3A compared to the DMSO-treated condition. However, binding affinity to PDE3A was significantly weaker compared to full length SLFN12. In order to follow up on truncating SLFN12, I generated mutants with consecutive truncations of 10 and 30 amino acids starting from the very C-terminal end. Cell viability assays revealed that mutant 1-568aa was capable of mimicking the function of full length SLFN12 by conferring sensitivity to DNMDP. Nevertheless, the potential for DNMDP- dependent interaction with PDE3A has yet to be examined by CoIP. Moreover, the obtained results point out that the entire SLFN12 protein, rather than isolated domains, exhibits structural and/or functional features that are required for any of the following properties: i) ensuring viable and correct protein folding, ii) enabling interaction with PDE3A in order to engage in a bona fide compound-dependent protein complex, iii) further transduction of signals in the pathway that eventually induces cell death upon treatment with DNMDP.

Discussion: Essential requirement of a reliable antibody. The aim of this study was the verification and confirmation of SLFN12 as essential factor in the DNMDP-dependent mechanism of selective cancer cell killing. The SLFN12 knock-outs generated by CRISPR/Cas9 in one cervical cancer and two malignant melanoma cell lines have proven that SLFN12, like PDE3A, has indeed a limiting role in exhibiting anti-tumor activity in the presence of DNMDP. Since to date no effective anti- SLFN12 antibody is available, I had to rely on determination of mRNA levels in order to evaluate loss or decrease in expression of endogenous SLFN12. All commercially advertised anti-SLFN12 antibodies were tested for their ability and specificity of detecting SLFN12 protein, however, none of them validated to detect SLFN12 from cell lysates.

Cytotoxicity of SLFN12 overexpression. In order to further study the protein-interaction between SLFN12 and PDE3A, CoIP experiments were performed in this project, as described. However, due to generally low expression levels and a lack of a reliable antibody, as described above, SLFN12 protein detection proved to be a challenging venture. De Waal et al. carried out co-immunoprecipitation using HeLa cells transiently transfected with ectopically expressed V5- tagged SLFN126. Notably, protein interaction with PDE3A was achieved, however, clear visualization of SLFN12 remained inconclusive. This might be due to a cytotoxic phenotype that was observed by artificial overexpression also described by Puck et al19. He and his team reported about significantly decreased viability and rapid cell death within three days upon transduction of SLFN12 in HeLa Ohio. It was hypothesized that inhibition of cell growth was mediated by SLFN12 overexpression, which is consistent with other publications on describing its role in promoting cell cycle arrest and especially T-lymphocyte differentiation19. Interestingly the cytotoxic phenotype through SLFN12 overexpression was not rescued by co-treatment with trequinsin, which blocks the target site of DNMDP (data not shown). Furthermore, this killing mechanism was unrelated to the C-terminal V5-tag, since pGFP-V5-transfected HeLa cells did not undergo cell death. This suggested a killing mechanism that is unrelated from the mode of action of DNMDP described by de Waal and implied that this lethal phenotype might be attributable to the physiological function of SLFN12. In order to increase specificity of detection I generated SLFN12 cDNA flanked by a 3xFLAG epitope tag at the C-terminus (data not shown). However, CoIP experiments using anti-FLAG antibody did not allow improved detection of SLFN12, or visualization of DNMDP-dependent interaction with PDE3A. Impact of N- and C-terminal truncations in SLFN12. To address the question whether specific domains are necessary for mediating DNMDP-induced cell death or are necessary for 73

PDE3A-binding, I generated truncated mutants of SLFN12 cDNA. These deletion mutants were tested for retention of DNMDP-related functionality and PDE3A-interaction compared to full length SLFN12. Results show that only one mutant, the deletion of the whole N-terminus, still retains weaker interaction with PDE3A, all other domain truncation mutants are inactive. Therefore, it seems that the whole protein, rather than any isolated domain, is required to efficiently complex with PDE3A and execute DNMDP-induced cancer cell killing..

Future outlook: Other SLFN homologues to substitute SLFN12 in DNMDP-induced cell killing. In this project I attempted to further increase our knowledge about SLFN12 in the context of the selective cell killing mechanism that is induced by DNMDP. The study by de Waal et al. revealed SLFN12 as protein interaction partner of PDE3A, which engages in a compound-dependent complex and ultimately triggers apoptosis in sensitive cell lines6. However, to date the capability of other SLFN family members to mimic the function of SLFN12 in DNMDP-induced antitumor activity due to conserved protein regions has not been addressed. By using SLFN12-dependent cell lines, such as HeLa-res and A549+pLX317-PDE3A, the ability of other SLFNs to substitute SLFN12 and possibly enable induction of cell death upon treatment with DNMDP can be tested. Upon further investigation, SLFN family members that are able to restore sensitivity might be candidates for follow-up experiments, such as CoIP experiments to address whether or not interaction to PDE3A is established17. Cellular localization of SLFN12. To date, subcellular localization of SLFN12 has never been investigated. Although, according to published data SLFN12 does not harbor a nuclear localization signal, suggesting a cytoplasmic localization, visualization of SLFN12 has never been published, which might be due to the lack of a reliable antibody15. In order to determine the cellular localization of SLFN12, immunofluorescence approaches using SLFN12 cDNA fused to an epitope tag (i.e. 3xFLAG) can be performed. Ideally, co-localization of PDE3A and SLFN12 upon incubation with DNMDP enables visual detection of both proteins engaging in a drug-dependent complex. Further investigation on partial sensitivity to DNMDP. Our unpublished results indicate that DNMDP can cause partial sensitivity in some cell lines, such as the melanoma cell lines A2058 and RVH421 (X. Wu, H. Greulich and M. Meyerson, unpublished). DNMDP achieves complete cell killing in HeLa, however, in A2058 and RVH421 only approximately 90% and 70%

of maximum killing, respectively. In order to investigate the impact of elevated expression levels of SLFN12, I suggest overexpressing SLFN12 in partial sensitive cell lines to determine whether the magnitude of cell killing is correlated with expression. This might pose a rather challenging experiment, since overexpression of SLFN12 is accompanied by a cytotoxic phenotype, as described by Puck et al 19. Structural protein analysis of SLFN12. In this study, truncation and deletion mutants of SLFN12 were generated in order to elucidate essential protein domains for induction of cell death by a DNMDP. Some truncations proved to have a substantial impact on protein expression, which might be caused by disturbance of highly structured domains, especially internal domains that ultimately resulted in non-viable protein folding60. Here, I suggest to perform protein folding studies as well as expression analysis in order to further advance our knowledge about the protein science of SLFN1261, 62. Moreover, to ultimately understand the interaction between PDE3A and SLFN12, protein crystallization studies can be conducted. Due to homologous regions between PDE3A and 3B structural analysis might be performed on basis of crystallization approaches of PDE3B in 200463. Co-crystallization experiments with PDE3A binding DNMDP might provide further information about drug-target interaction, whereas analyzing the x-ray structure of SLFN12 may reveal its folding behavior. Deep analysis of functional domains in SLFN12. In order to intensify investigations on the function of SLFN12 a MITE (Mutagenesis by Integrated TilEs) approach can be performed. Here, each individual amino acid residue is modified to discover impactful mutations that might confer a gain or loss of function in regards to DNMDP-dependent cancer cell killing activity64, 65. This technology uses separated cDNA regions (tiles) coding for the protein of interest, which are subject to site-directed mutagenesis. By subsequent ligation of these tiles, a high number of individually generated point mutations can be designed posing a significant advantage over other mutagenesis techniques. Functionality of the modified SLFN12 protein in regards to DNMDP-dependent sensitivity can be monitored by CTG viability assay using SLFN12-dependent cancer cell lines such as HeLa-res and A549+pLX317-PDE3A. Additionally, by transiently transfecting HeLa cells with mentioned MITE-generated mutants, the capability for interaction with PDE3A can be determined using CoIP experiments. These proposed experiments may lead to further discoveries, which will greatly advance our understanding of the biological function of SLFN12 as well as the compound-dependent protein-interaction with PDE3A in the DNMDP-driven selective cancer cell killing activity.

Eventually, this will strengthen the efforts to develop PDE3A modulating small molecules, such as DNMDP, into anti-tumor therapy.

References:

1. Torre, Lindsey A., Rebecca L. Siegel, Elizabeth M. Ward, und Ahmedin Jemal. „Global Cancer Incidence and Mortality Rates and Trends—An Update“. Cancer Epidemiology and Prevention Biomarkers, December 14th, 2015. https://doi.org/10.1158/1055-9965.EPI-15-0578. 2. Jones, Peter A., Jean-Pierre J. Issa, und Stephen Baylin. „Targeting the Cancer Epigenome for Therapy“. Nature Reviews Genetics 17, Nr. 10 (October 2016): 630. https://doi.org/10.1038/nrg.2016.93. 3. Biemar, Frédéric, und Margaret Foti. „Global Progress against Cancer—challenges and Opportunities“. Cancer Biology & Medicine 10, Nr. 4 (December 30th, 2013): 183–86. 4. Housman, Genevieve, Shannon Byler, Sarah Heerboth, Karolina Lapinska, Mckenna Longacre, Nicole Snyder, und Sibaji Sarkar. „Drug Resistance in Cancer: An Overview“. Cancers 6, Nr. 3 (September 5th, 2014): 1769–92. https://doi.org/10.3390/cancers6031769. 5. Verdine, Gregory L., und Loren D. Walensky. „The Challenge of Drugging Undruggable Targets in Cancer: Lessons Learned from Targeting BCL-2 Family Members“. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 13, Nr. 24 (December 15th, 2007): 7264–70. https://doi.org/10.1158/1078-0432.CCR-07-2184. 6. Waal, Luc de, Timothy A. Lewis, Matthew G. Rees, Aviad Tsherniak, Xiaoyun Wu, Peter S. Choi, Lara Gechijian, u. a. „Identification of Cancer-Cytotoxic Modulators of PDE3A by Predictive Chemogenomics“. Nature Chemical Biology 12, Nr. 2 (February 2016): 102–8. https://doi.org/10.1038/nchembio.1984. 7. Barretina, Jordi, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, Adam A. Margolin, Sungjoon Kim, Christopher J. Wilson, u. a. „The Cancer Cell Line Encyclopedia Enables Predictive Modelling of Anticancer Drug Sensitivity“. Nature 483, Nr. 7391 (March 28th, 2012): 603–7. https://doi.org/10.1038/nature11003. 8. Pierson, Emma, Daphne Koller, Alexis Battle, und Sara Mostafavi. „Sharing and Specificity of Co- expression Networks across 35 Human Tissues“. PLoS Computational Biology 11, Nr. 5 (May 13th, 2015). https://doi.org/10.1371/journal.pcbi.1004220. 9. Hunter, Roger W., Carol Mackintosh, und Ingeborg Hers. „Protein Kinase C-Mediated Phosphorylation and Activation of PDE3A Regulate CAMP Levels in Human Platelets“. The Journal of Biological Chemistry 284, Nr. 18 (May 1st, 2009): 12339–48. https://doi.org/10.1074/jbc.M807536200. 10. Francis, Sharron H., Mitsi A. Blount, und Jackie D. Corbin. „Mammalian Cyclic Nucleotide Phosphodiesterases: Molecular Mechanisms and Physiological Functions“. Physiological Reviews 91, Nr. 2 (April 2011): 651–90. https://doi.org/10.1152/physrev.00030.2010. 11. Tefferi, A., M. N. Silverstein, R. M. Petitt, R. A. Mesa, und L. A. Solberg. „Anagrelide as a New Platelet- Lowering Agent in Essential Thrombocythemia: Mechanism of Actin, Efficacy, Toxicity, Current Indications“. Seminars in Thrombosis and Hemostasis 23, Nr. 4 (1997): 379–83. https://doi.org/10.1055/s-2007-996112. 77

12. Maurice, Donald H., Hengming Ke, Faiyaz Ahmad, Yousheng Wang, Jay Chung, und Vincent C. Manganiello. „Advances in Targeting Cyclic Nucleotide Phosphodiesterases“. Nature Reviews. Drug Discovery 13, Nr. 4 (April 2014): 290–314. https://doi.org/10.1038/nrd4228. 13. Knight, Walter, und Chen Yan. „Therapeutic potential of PDE modulation in treating heart disease“. Future medicinal chemistry 5, Nr. 14 (September 2013): 1607–20. https://doi.org/10.4155/fmc.13.127. 14. Sun, Liping, Haitian Quan, Chengying Xie, Lei Wang, Youhong Hu, und Liguang Lou. „Phosphodiesterase 3/4 Inhibitor Zardaverine Exhibits Potent and Selective Antitumor Activity against Hepatocellular Carcinoma Both in Vitro and in Vivo Independently of Phosphodiesterase Inhibition“. PloS One 9, Nr. 3 (2014): e90627. https://doi.org/10.1371/journal.pone.0090627. 15. Mavrommatis, Evangelos, Eleanor N. Fish, und Leonidas C. Platanias. „The Schlafen Family of Proteins and Their Regulation by Interferons“. Journal of Interferon & Cytokine Research 33, Nr. 4 (April 2013): 206–10. https://doi.org/10.1089/jir.2012.0133. 16. Bustos, Olivia, Saijal Naik, Gayle Ayers, Claudio Casola, Maria A. Perez-Lamigueiro, Paul T. Chippindale, Ellen J. Pritham, und Elena de la Casa-Esperón. „Evolution of the Schlafen Genes, a Gene Family Associated with Embryonic Lethality, Meiotic Drive, Immune Processes and Orthopoxvirus Virulence“. Gene 447, Nr. 1 (November 1st, 2009): 1–11. https://doi.org/10.1016/j.gene.2009.07.006. 17. Casa-Esperón, Elena de la. „From Mammals to Viruses: The Schlafen Genes in Developmental, Proliferative and Immune Processes“. Biomolecular Concepts 2, Nr. 3 (June 1st, 2011): 159–69. https://doi.org/10.1515/bmc.2011.018. 18. Geserick, Peter, Frank Kaiser, Uwe Klemm, Stefan H. E. Kaufmann, und Jens Zerrahn. „Modulation of T Cell Development and Activation by Novel Members of the Schlafen (Slfn) Gene Family Harbouring an RNA Helicase-like Motif“. International Immunology 16, Nr. 10 (October 2004): 1535–48. https://doi.org/10.1093/intimm/dxh155. 19. Puck, Alexander, Stefan Hopf, Madhura Modak, Otto Majdic, Petra Cejka, Stephan Blüml, Klaus Schmetterer, u. a. „The soluble cytoplasmic tail of CD45 (ct‐CD45) in human plasma contributes to keep T cells in a quiescent state“. European Journal of Immunology 47, Nr. 1 (January 2017): 193–205. https://doi.org/10.1002/eji.201646405. 20. Kovalenko, Pavlo L., und Marc D. Basson. „Schlafen 12 Expression Modulates Prostate Cancer Cell Differentiation“. The Journal of Surgical Research 190, Nr. 1 (July 2014): 177–84. https://doi.org/10.1016/j.jss.2014.03.069. 21. Gheldof, Alexander, und Geert Berx. „Cadherins and Epithelial-to-Mesenchymal Transition“. Progress in Molecular Biology and Translational Science 116 (2013): 317–36. https://doi.org/10.1016/B978-0- 12-394311-8.00014-5. 22. North, M. L., M. J. Jones, J. L. MacIsaac, A. M. Morin, L. M. Steacy, A. Gregor, M. S. Kobor, und A. K. Ellis. „Blood and Nasal Epigenetics Correlate with Allergic Rhinitis Symptom Development in the Environmental Exposure Unit“. Allergy, July 29th, 2017. https://doi.org/10.1111/all.13263.

23. Brière, Jean B. „Essential thrombocythemia“. Orphanet Journal of Rare Diseases 2 (January 8th, 2007): 3. https://doi.org/10.1186/1750-1172-2-3. 24. Espasandin, Y. R., A. C. Glembotsky, M. Grodzielski, P. R. Lev, N. P. Goette, F. C. Molinas, R. F. Marta, und P. G. Heller. „Anagrelide Platelet-Lowering Effect Is Due to Inhibition of Both Megakaryocyte Maturation and Proplatelet Formation: Insight into Potential Mechanisms“. Journal of Thrombosis and Haemostasis: JTH 13, Nr. 4 (April 2015): 631–42. https://doi.org/10.1111/jth.12850. 25. Hayward, Nicholas K., James S. Wilmott, Nicola Waddell, Peter A. Johansson, Matthew A. Field, Katia Nones, Ann-Marie Patch, u. a. „Whole-Genome Landscapes of Major Melanoma Subtypes“. Nature 545, Nr. 7653 (11 2017): 175–80. https://doi.org/10.1038/nature22071. 26. Alexandrov, Ludmil B., Serena Nik-Zainal, David C. Wedge, Samuel A. J. R. Aparicio, Sam Behjati, Andrew V. Biankin, Graham R. Bignell, u. a. „Signatures of Mutational Processes in Human Cancer“. Nature 500, Nr. 7463 (August 22nd, 2013): 415–21. https://doi.org/10.1038/nature12477. 27. Moncrieff, Marc D., John F. Thompson, Michael J. Quinn, und Jonathan R. Stretch. „Reconstruction after Wide Excision of Primary Cutaneous Melanomas: Part I-the Head and Neck“. The Lancet. Oncology 10, Nr. 7 (July 2009): 700–708. https://doi.org/10.1016/S1470-2045(09)70116-0. 28. Serrone, L., M. Zeuli, F. M. Sega, und F. Cognetti. „Dacarbazine-Based Chemotherapy for Metastatic Melanoma: Thirty-Year Experience Overview“. Journal of Experimental & Clinical Cancer Research: CR 19, Nr. 1 (March 2000): 21–34. 29. Kewitz, Stefanie, Martina Stiefel, Christof M. Kramm, und Martin S. Staege. „Impact of O6- Methylguanine-DNA Methyltransferase (MGMT) Promoter Methylation and MGMT Expression on Dacarbazine Resistance of Hodgkin’s Lymphoma Cells“. Leukemia Research 38, Nr. 1 (January 2014): 138–43. https://doi.org/10.1016/j.leukres.2013.11.001. 30. Atkins, M. B., L. Kunkel, M. Sznol, und S. A. Rosenberg. „High-Dose Recombinant Interleukin-2 Therapy in Patients with Metastatic Melanoma: Long-Term Survival Update“. The Cancer Journal from Scientific American 6 Suppl 1 (February 2000): S11-14. 31. Ives, Natalie J., Rebecca L. Stowe, Paul Lorigan, und Keith Wheatley. „Chemotherapy Compared with Biochemotherapy for the Treatment of Metastatic Melanoma: A Meta-Analysis of 18 Trials Involving 2,621 Patients“. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 25, Nr. 34 (December 1st, 2007): 5426–34. https://doi.org/10.1200/JCO.2007.12.0253. 32. Pardoll, Drew M. „The Blockade of Immune Checkpoints in Cancer Immunotherapy“. Nature Reviews. Cancer 12, Nr. 4 (March 22nd, 2012): 252–64. https://doi.org/10.1038/nrc3239. 33. Topalian, Suzanne L., Charles G. Drake, und Drew M. Pardoll. „Targeting the PD-1/B7-H1(PD-L1) Pathway to Activate Anti-Tumor Immunity“. Current Opinion in Immunology 24, Nr. 2 (April 2012): 207– 12. https://doi.org/10.1016/j.coi.2011.12.009. 34. Topalian, Suzanne L., Mario Sznol, David F. McDermott, Harriet M. Kluger, Richard D. Carvajal, William H. Sharfman, Julie R. Brahmer, u. a. „Survival, Durable Tumor Remission, and Long-Term Safety in

Patients With Advanced Melanoma Receiving Nivolumab“. Journal of Clinical Oncology 32, Nr. 10 (April 1st, 2014): 1020–30. https://doi.org/10.1200/JCO.2013.53.0105. 35. Lipson, Evan J., William H. Sharfman, Charles G. Drake, Ira Wollner, Janis M. Taube, Robert A. Anders, Haiying Xu, u. a. „Durable Cancer Regression Off-Treatment and Effective Reinduction Therapy with an Anti-PD-1 Antibody“. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 19, Nr. 2 (January 15th, 2013): 462–68. https://doi.org/10.1158/1078-0432.CCR-12- 2625. 36. Weber, Jeffrey S., Katharina C. Kähler, und Axel Hauschild. „Management of Immune-Related Adverse Events and Kinetics of Response with Ipilimumab“. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 30, Nr. 21 (July 20th, 2012): 2691–97. https://doi.org/10.1200/JCO.2012.41.6750. 37. Perica, Karlo, Juan Carlos Varela, Mathias Oelke, und Jonathan Schneck. „Adoptive T Cell Immunotherapy for Cancer“. Rambam Maimonides Medical Journal 6, Nr. 1 (January 29th, 2015). https://doi.org/10.5041/RMMJ.10179. 38. Dudley, Mark E., James C. Yang, Richard Sherry, Marybeth S. Hughes, Richard Royal, Udai Kammula, Paul F. Robbins, u. a. „Adoptive Cell Therapy for Patients with Metastatic Melanoma: Evaluation of Intensive Myeloablative Chemoradiation Preparative Regimens“. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 26, Nr. 32 (November 10th, 2008): 5233–39. https://doi.org/10.1200/JCO.2008.16.5449. 39. Gaj, Thomas, Charles A. Gersbach, und Carlos F. Barbas. „ZFN, TALEN, and CRISPR/Cas-Based Methods for Genome Engineering“. Trends in Biotechnology 31, Nr. 7 (July 2013): 397–405. https://doi.org/10.1016/j.tibtech.2013.04.004. 40. Ishino, Y, H Shinagawa, K Makino, M Amemura, und A Nakata. „Nucleotide sequence of the iap gene, responsible for alkaline phosphatase isozyme conversion in Escherichia coli, and identification of the gene product.“ Journal of Bacteriology 169, Nr. 12 (December 1987): 5429–33. 41. Makarova, Kira S., Nick V. Grishin, Svetlana A. Shabalina, Yuri I. Wolf, und Eugene V. Koonin. „A Putative RNA-Interference-Based Immune System in Prokaryotes: Computational Analysis of the Predicted Enzymatic Machinery, Functional Analogies with Eukaryotic RNAi, and Hypothetical Mechanisms of Action“. Biology Direct 1 (March 16th, 2006): 7. https://doi.org/10.1186/1745-6150-1-7. 42. Brouns, Stan J. J., Matthijs M. Jore, Magnus Lundgren, Edze R. Westra, Rik J. H. Slijkhuis, Ambrosius P. L. Snijders, Mark J. Dickman, Kira S. Makarova, Eugene V. Koonin, und John van der Oost. „Small CRISPR RNAs Guide Antiviral Defense in Prokaryotes“. Science (New York, N.Y.) 321, Nr. 5891 (August 15th, 2008): 960–64. https://doi.org/10.1126/science.1159689. 43. Jinek, Martin, Krzysztof Chylinski, Ines Fonfara, Michael Hauer, Jennifer A. Doudna, und Emmanuelle Charpentier. „A Programmable Dual-RNA-Guided DNA Endonuclease in Adaptive Bacterial Immunity“.

Science (New York, N.Y.) 337, Nr. 6096 (August 17th, 2012): 816–21. https://doi.org/10.1126/science.1225829. 44. Cong, Le, F. Ann Ran, David Cox, Shuailiang Lin, Robert Barretto, Naomi Habib, Patrick D. Hsu, u. a. „Multiplex Genome Engineering Using CRISPR/Cas Systems“. Science (New York, N.Y.) 339, Nr. 6121 (February 15th, 2013): 819–23. https://doi.org/10.1126/science.1231143. 45. Heyer, Wolf-Dietrich, Kirk T. Ehmsen, und Jie Liu. „Regulation of Homologous Recombination in Eukaryotes“. Annual Review of Genetics 44 (2010): 113–39. https://doi.org/10.1146/annurev-genet- 051710-150955. 46. Lieber, Michael R. „The Mechanism of Double-Strand DNA Break Repair by the Nonhomologous DNA End-Joining Pathway“. Annual Review of Biochemistry 79 (2010): 181–211. https://doi.org/10.1146/annurev.biochem.052308.093131. 47. Hug, Nele, Dasa Longman, und Javier F. Cáceres. „Mechanism and Regulation of the Nonsense- Mediated Decay Pathway“. Nucleic Acids Research 44, Nr. 4 (February 29th, 2016): 1483–95. https://doi.org/10.1093/nar/gkw010. 48. Qi, Lei S., Matthew H. Larson, Luke A. Gilbert, Jennifer A. Doudna, Jonathan S. Weissman, Adam P. Arkin, und Wendell A. Lim. „Repurposing CRISPR as an RNA-Guided Platform for Sequence-Specific Control of Gene Expression“. Cell 152, Nr. 5 (February 28th, 2013): 1173–83. https://doi.org/10.1016/j.cell.2013.02.022. 49. Perez-Pinera, Pablo, D. Dewran Kocak, Christopher M. Vockley, Andrew F. Adler, Ami M. Kabadi, Lauren R. Polstein, Pratiksha I. Thakore, u. a. „RNA-Guided Gene Activation by CRISPR-Cas9-Based Transcription Factors“. Nature Methods 10, Nr. 10 (October 2013): 973–76. https://doi.org/10.1038/nmeth.2600. 50. Doudna, Jennifer A., und Emmanuelle Charpentier. „Genome Editing. The New Frontier of Genome Engineering with CRISPR-Cas9“. Science (New York, N.Y.) 346, Nr. 6213 (November 28th, 2014): 1258096. https://doi.org/10.1126/science.1258096. 51. Montague, Tessa G., José M. Cruz, James A. Gagnon, George M. Church, und Eivind Valen. „CHOPCHOP: A CRISPR/Cas9 and TALEN Web Tool for Genome Editing“. Nucleic Acids Research 42, Nr. Web Server issue (July 2014): W401-407. https://doi.org/10.1093/nar/gku410. 52. Labun, Kornel, Tessa G. Montague, James A. Gagnon, Summer B. Thyme, und Eivind Valen. „CHOPCHOP v2: A Web Tool for the next Generation of CRISPR Genome Engineering“. Nucleic Acids Research 44, Nr. W1 (July 8th, 2016): W272-276. https://doi.org/10.1093/nar/gkw398. 53. Graham, Daniel B., und David E. Root. „Resources for the design of CRISPR gene editing experiments“. Genome Biology 16 (2015). https://doi.org/10.1186/s13059-015-0823-x. 54. Pinello, Luca, Matthew C. Canver, Megan D. Hoban, Stuart H. Orkin, Donald B. Kohn, Daniel E. Bauer, und Guo-Cheng Yuan. „Analyzing CRISPR genome editing experiments with CRISPResso“. Nature biotechnology 34, Nr. 7 (July 12th, 2016): 695–97. https://doi.org/10.1038/nbt.3583.

55. „Addgene: Lentiviral Guide“. Accessed December 11th, 2017. https://www.addgene.org/viral- vectors/lentivirus/lenti-guide/. 56. Dull, T., R. Zufferey, M. Kelly, R. J. Mandel, M. Nguyen, D. Trono, und L. Naldini. „A Third-Generation Lentivirus Vector with a Conditional Packaging System“. Journal of Virology 72, Nr. 11 (November 1998): 8463–71. 57. Sanjana, Neville E., Ophir Shalem, und Feng Zhang. „Improved vectors and genome-wide libraries for CRISPR screening“. Nature methods 11, Nr. 8 (August 2014): 783–84. https://doi.org/10.1038/nmeth.3047. 58. Shalem, Ophir, Neville E. Sanjana, Ella Hartenian, Xi Shi, David A. Scott, Tarjei Mikkelson, Dirk Heckl, u. a. „Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells“. Science (New York, N.Y.) 343, Nr. 6166 (January 3rd, 2014): 84–87. https://doi.org/10.1126/science.1247005. 59. Aksoy, Bülent Arman, Vlado Dančík, Kenneth Smith, Jessica N. Mazerik, Zhou Ji, Benjamin Gross, Olga Nikolova, u. a. „CTD2 Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network“. Database: The Journal of Biological Databases and Curation 2017 (August 8th, 2017). https://doi.org/10.1093/database/bax054. 60. Flores-Ramírez, Gabriela, Manuel Rivera, Alfredo Morales-Pablos, Joel Osuna, Xavier Soberón, und Paul Gaytán. „The Effect of Amino Acid Deletions and Substitutions in the Longest Loop of GFP“. BMC Chemical Biology 7 (June 26th, 2007): 1. https://doi.org/10.1186/1472-6769-7-1. 61. Borgia, Alessandro, Philip M. Williams, und Jane Clarke. „Single-Molecule Studies of Protein Folding“. Annual Review of Biochemistry 77 (2008): 101–25. https://doi.org/10.1146/annurev.biochem.77.060706.093102. 62. Nienhaus, G. Ulrich. „Single-Molecule Fluorescence Studies of Protein Folding“. Methods in Molecular Biology (Clifton, N.J.) 490 (2009): 311–37. https://doi.org/10.1007/978-1-59745-367-7_13. 63. Scapin, Giovanna, Sangita B. Patel, Christine Chung, Jeffrey P. Varnerin, Scott D. Edmondson, Anthony Mastracchio, Emma R. Parmee, u. a. „Crystal Structure of Human Phosphodiesterase 3B: Atomic Basis for Substrate and Inhibitor Specificity“. Biochemistry 43, Nr. 20 (May 25th, 2004): 6091–6100. https://doi.org/10.1021/bi049868i. 64. Melnikov, Alexandre, Peter Rogov, Li Wang, Andreas Gnirke, und Tarjei S. Mikkelsen. „Comprehensive Mutational Scanning of a Kinase in Vivo Reveals Substrate-Dependent Fitness Landscapes“. Nucleic Acids Research 42, Nr. 14 (August 2014): e112. https://doi.org/10.1093/nar/gku511. 65. Brenan, Lisa, Aleksandr Andreev, Ofir Cohen, Sasha Pantel, Atanas Kamburov, Davide Cacchiarelli, Nicole S. Persky, u. a. „Phenotypic Characterization of a Comprehensive Set of MAPK1/ERK2 Missense Mutants“. Cell Reports 17, Nr. 4 (2016): 1171–83. https://doi.org/10.1016/j.celrep.2016.09.061.

Acknowledgements: Writing my Master thesis in the USA, especially at the very renowned Broad Institute in Boston, was an amazing experience. Spending a semester abroad is always a tough step, however I am really glad for taking the leap in the dark and engaging myself into this extraordinary adventure. Therefore, there are a lot of things I am grateful for. First of all I would like to thank Xiaoyun Wu for guiding me through this very exciting, educational and fascinating internship as my utterly supportive, patient and kind supervisor. She taught me how to change perspectives on certain scientific subjects and she helped me to improve my scientific writing skills as well as my analytical thinking. In the same line, I want to thank Matthew Meyerson and Heidi Greulich who gave me this inexplicably huge opportunity to contribute to such a fascinating project and for giving me valuable advice in challenging moments. I also want to thank the whole Meyerson lab (including Broadies and members of the Dana Farber Cancer Institute) for helping me out with any kind of reagents in the lab, computational advice as well as productive input during presentations. Special thanks to Douglas Wheeler, Jacqueline Watson, Craig Strathdee, Srinivas Viswanathan, Marina Nogueira, Maria Baco, Gavin Schnitzler, Galen Gao, Ashton Berger and Kaylyn Williamson. A special shout-out goes to Andrew Baker, who provided me with steady support on several experiments, especially with overwhelming numbers of sequencing reactions. But more importantly, he created an incredibly comfortable working atmosphere, which made it an even bigger pleasure to work at the Broad Institute. It makes me really glad that a supportive occupational relationship turned into an appreciative friendship. Moreover, I would like to thank some amazing people who accompanied me on this journey: Pierre, Hadrien, Julie, Isabelle, Mario and Lydia, my wonderful roommates who gave me a welcoming and appreciating feeling right from the beginning. Additionally, I want to express a big thanks to Andreas Hoff, Rémi Marenco and Josephine Lee, who made life at the Broad an unforgettable experience. And I am confident that this friendship will survive every country border. Lastly, I want to express my dearest gratitude to my family, friends and a very special person in my life who supported me before, during and after this extraordinary venture. Thank you for all your help and advice.

The Broad Institute of MIT and Harvard provided me with any kind of scientific resource, a thriving and supportive work atmosphere and I was blessed with nice and “wicked smaht” colleagues. It was a pleasure and privilege to conduct my Master thesis project here. Thank you.

Erklärung: Ich erkläre, dass die vorliegende Diplomarbeit/Masterarbeit von mir selbst verfasst wurde und ich keine anderen als die angeführten Behelfe verwendet bzw. mich auch sonst keiner unerlaubter Hilfe bedient habe. Ich versichere, dass ich diese Diplomarbeit/Masterarbeit bisher weder im In- noch im Ausland (einer Beurteilerin/einem Beurteiler zur Begutachtung) in irgendeiner Form als Prüfungsarbeit vorgelegt habe. Weiters versichere ich, dass die von mir eingereichten Exemplare (ausgedruckt und elektronisch) identisch sind.

Statutory Declaration: I hereby declare that the submitted Master thesis was written by myself and that I did not use any aids other than those indicated, none of which are unauthorised. I assure that I have not previously submitted this Master thesis or its contents in any form for assessment as part of an examination either in Austria or abroad. Furthermore, I assure that all copies submitted by myself (electronic and printed) are identical. date: 20.12.2017 signature: ……………………………