Challenges and considerations in designing multidrug combinations for cancer treatment

Thesis

Reference

Challenges and considerations in designing multidrug combinations for cancer treatment

ZOETEMELK, Marloes

Abstract

This study aimed to investigate and improve the current standard of care chemotherapy combinations and optimize new targeted drug combinations for the improved treatment of colorectal carcinoma (CRC). We emphasize the relevance of various pre-clinical and clinical CRC systems for validation and clinical translation of drug combinations and establish a heterotypic 3D cell culture platform that can easily be adapted to improve other cancer cell cultures. We find that optimized low-dose cell-specific chemotherapy combinations did not improve the efficacy or the safety profile. We employed the phenotypically-driven therapeutically guided multidrug optimization (TGMO) technology and identified low-dose high-order cell-specific synergistic drug combinations of tyrosine kinase inhibitors.

Mechanistically, multi-target inhibition orchestrated subtle multi-node regulation of cell signaling which predominantly converged on MAP kinase signaling and cell cycle arrest. We provide scientific evidence in vitro and in vivo of the value of personalized drug combinations to improve effective and selective CRC treatment.

ZOETEMELK, Marloes. Challenges and considerations in designing multidrug

combinations for cancer treatment. Thèse de doctorat : Univ. Genève, 2020, no. Sc. 5445

DOI : 10.13097/archive-ouverte/unige:141170 URN : urn:nbn:ch:unige-1411706

Available at:

http://archive-ouverte.unige.ch/unige:141170

Disclaimer: layout of this document may differ from the published version.

UNIVERSITÉ DE GENÈVE FACULTÉ DES SCIENCES

Institut des Sciences Pharmaceutiques Professeur P. Nowak-Sliwinska de Suisse Occidentale

Pharmacologie Moléculaire

_________________________________________________________________________

Challenges and considerations in designing multidrug combinations for cancer treatment

THÈSE

présenté à la Faculté de sciences de l’Université de Genève

pour obtenir le grade de Docteur ès sciences, mention sciences pharmaceutiques

par

Marloes Zoetemelk de

Rijpwetering (Pays-Bas)

Thèse Nº5445

GENÈVE

28 février 2020

Index

Abstract ... 1

Résumè ... 3

Acknowledgements ... 6

Abbreviations ... 8

Introduction ... 13

Cancer development and common treatment modalities ... 13

Colorectal carcinoma and current treatment options ... 15

Bottlenecks in successful CRC (combination) treatment ... 17

Advantages and considerations of drug combinations ... 18

Drug combination optimization methods ... 20

Considerations of pre-clinical in vitro models ... 21

Thesis outline ... 22

References ... 323

Chapter 1. Understating the in vitro drug-drug interactions of irinotecan, 5- fluorouracil, folinic acid and oxaliplatin for improved colorectal carcinoma treatment ... 29

1.1 Summary ... 30

1.2 Introduction ... 31

1.3 Results ... 33

1.3.1 Optimization of cell-line specific low-dose drug combinations ... 33

1.3.2 The search for optimal drug administration sequence ... 38

1.3.3 Integration of cell line-specific ODCs in clinically used drug administration schedules ... 38

1.3.4 Chronically pretreated colorectal carcinoma cells lose sensitivity to FA/5-FU/OX/SN combinations ... 40

1.4 Discussion ... 41

1.5 Materials and Methods ... 44

1.6 Acknowledgements ... 46

1.7 References ... 47

Chapter 2. Short-term 3D culture systems of various complexity for treatment of colorectal carcinoma ... 51

2.1 Summary ... 52

2.2 Introduction ... 53

2.3 Results ... 54

2.3.1 CRC 3D culture optimization and characterization ... 54

2.3.2 Treatment optimization of CRC 3D cultures... 56

2.3.3 Cell type-specific variations in drug sensitivity in 3D and 2D cultures ... 57

2.3.4 CRC 3D co-cultures with fibroblasts and endothelial cells have unique

characteristics ... 57

2.3.5 Drug sensitivity is culture system and cell line dependent ... 59

2.3.6 Signaling in 3D and 3D-CC upon AKT and MAPK pathway regulation and matrix deposition ... 64

2.4 Discussion ... 66

2.5 Conclusion ... 69

2.6 Acknowledgements ... 69

2.7 Materials and methods ... 69

2.8 Supplementary Material ... 73

2.8.1 Supplementary Methods... 73

2.8.2 Supplementary Figures ... 73

2.8.3 Supplementary Tables ... 85

2.8.4 Supplementary Movies ... 93

2.9 References ... 94

Chapter 3. Optimized high-order low-dose drug mixtures boost selectivity and efficacy of colorectal carcinoma treatment ... 98

3.1 Summary ... 99

3.2 Introduction ... 100

3.3 Results ... 101

3.3.1 Identification of synergistic multidrug combinations using phenotypically-driven therapeutically guided multidrug optimization ... 101

3.3.2 ODCs induce changes in cell cycle and cell morphology and are active in heterotypic 3D co-cultures ... 106

3.3.3 ODCs effectively inhibits tumor growth in subcutaneous models ... 107

3.3.4 Synergistic ODCs increase drug concentrations in blood and in tumor tissues ... 108

3.3.5 ODC reduces tumor cell proliferation, microvessel density and the number of reticular fibroblasts ... 110

3.3.6 ODC synergistically inhibits tumor growth in CRC orthotopic model ... 110

3.3.7 RNA sequencing reveals differentially expressed genes after ODC treatment ... 112

3.3.8 Phosphoproteome profiling suggests the molecular signature of active drug targets in CRC cell lines ... 115

3.3.9 ODCs are active in cells from patient’s CRC liver metastases ... 117

3.4 Discussion ... 118

3.5 Acknowledgements ... 122

3.6 Materials and Methods ... 122

3.7 Supplementary Material ... 127

3.7.1 Supplementary Information ... 127

3.7.2 Supplementary Methods... 131

3.7.3 Supplementary Tables ... 134

3.7.4 Supplementary Figures ... 143

3.8 References ... 4169

Discussion, conclusions & future perspectives ... 175

Chemotherapy combinations for the treatment of CRC ... 175

Targeted drugs for the treatment of CRC ... 175

Multi-drug combination optimization approach ... 176

Targeted multi-drug combinations – efficacy and mechanisms of action ... 178

Multi-drug combinations and side effects ... 179

Translation of optimal drug combinations between various in vitro and in vivo pre-clinical models... 180

Conclusions ... 181

Future perspectives ... 182

References ... 5185

Annex A. Scientific contributions ... 190

Annex B. Press release ... 193

Annex C. Epigenetic approach for angiostatic therapy: promising combinations for cancer treatment ... 196

C.1 Summary ... 197

C.2 Introduction ... 198

C.3 Histone deacetylase inhibitors ... 200

C.4 DNA methyltransferase inhibitors... 206

C.5 Ruthenium-based compounds impacting epigenetics and angiogenesis ... 210

C.6 Links between epigenetic mechanisms and relevance to combination strategies ... 213

C.7 Epigenetic regulation combined with targeted agents ... 215

C.8 Conclusions ... 221

C.8 References ... 225

Annex D. Identification of a Synergistic Multi-Drug Combination Active in Cancer Cells via the Prevention of Spindle Pole Clustering ... 236

D.1 Summary ... 237

D.2 Introduction ... 238

D.3 Results ... 239

D.3.1 Therapeutically Guided Multidrug Optimization Screen Identifies Synergistic Low-Dose Drug Combinations Consisting of HDAC and Kinase Inhibitors ... 239

D.3.2 C2 Activity on Cell Cycle Regulation, Actin Cytoskeleton Reorganization, and Nuclear Structures ... 243

D.3.3 C2 Prevents the Clustering of Multipolar Spindles in 786-O Cells ... 245

D.3.4 C2 Activity Prevents Spindle Pole Clustering in Sunitinib-Resistant 786-O Cells ... 248

D.3.5 C2 Treatment Shows Greater Efficacy in the Cells with Abnormal Centrosome Numbers ... 250

D.4 Discussion ... 253

D.5 Conclusion ... 255

D.6 Materials and methods ... 256

D.7 Supplementary Materials ... 261

D.7.1 Supplementary Information ... 261

D.7.2 Supplementary Figures ... 265

D.8 References ... 273

Abstract

Patients with colorectal carcinoma (CRC) are regularly diagnosed at an advanced stage when the tumor is already inoperable and more aggressive. First-line treatment includes a combination of chemotherapies that are initially effective in inhibiting tumor growth but incite serious adverse events. Moreover, patients with a metastatic disease commonly develop resistance to current treatments, consequently resulting in a reduced 5-year survival rate of only 14%, underlining the need for improved treatment options. The aim of this study was to investigate and improve the current standard of care chemotherapy combinations and optimize new drug combinations for the improved treatment of CRC. We further emphasize especially on the importance of considerations and challenges in the development and optimization of drug combinations, ways to design drug combinations with improved selectivity and the relevance of various pre-clinical and clinical CRC systems for validation and clinical translation of drug combinations.

We investigated the efficacy and drug interactions of the chemotherapy combination composed of folinic acid, 5-fluorouracil, oxaliplatin and/or irinotecan and confirmed a cell-line dependent synergy between folinic acid and 5-fluorouracil which together had a predominant antagonism with SN-38, the active metabolite of irinotecan. We optimized low-dose cell- specific combinations that were comparatively active in concomitant and sequential drug administration strategies. Finally, compared to clinically used doses, the combination did not improve the efficacy or the safety profile, evidencing the need for improved CRC treatment options.

Many drugs in clinical trails fail to obtain approval with 48% due to lack of efficacy, in part by not reflecting the efficacy obtained in pre-clinical studies. Clinical translation of pre-clinically developed drug combinations in vitro is challenged by the complexity of the tumor microenvironment in vivo, partly responsible for the high rates of failure in efficacy and safety.

Compared to traditional cell cultures, three-dimensional (3D) cultures can more realistically mimic the tumor and its microenvironment, influencing drug treatment response. We established robust, low-cost and reproducible short-term homotypic and heterotypic 3D cell culture systems addressing the various complexities of the CRC microenvironment including the extracellular matrix (ECM), fibroblasts and endothelial cells. We observed cell culture system and cell-line dependent differences in drug combination efficacy and drug interactions, favoring increased resistance in heterotypic 3D cell cultures. The composition of the ECM and signaling pathways were differentially regulated in the heterotypic 3D cultures. Together, the

results demonstrate the importance of 3D cell cultures of higher complexity in bridging the gap towards anticipating the clinical outcomes of proposed treatments.

In order to improve multi-drug treatment of CRC, we aimed to identify drug combinations with synergistic, effective and selective properties. Therefore, we employed the phenotypically- driven therapeutically guided multidrug optimization (TGMO) technology for a panel of CRC cell lines. We identified low-dose high-order cell-specific synergistic drug combinations of tyrosine kinase inhibitors (TKIs). Importantly, the drug combinations remained active in heterotypic 3D cell cultures mimicking various characteristics of CRC tumors, thereby confirming pre-clinical relevance of the drug combinations. Transcriptome sequencing and phosphoproteome analyses revealed that multi-drug target inhibition resulted in subtle multi- node regulation of cell signaling which predominantly converged on MAP kinase signaling and cell cycle arrest. Two cell-specific ODCs were translated to murine in vivo models in which they remained effective and significantly outperformed the standard of care chemotherapy combination (FOLFOX) in both safety and efficacy. The drug combinations had unique pharmacokinetic profiles compared to single drugs with most notably significantly enhanced drug bioavailability. Finally, testing the optimized cell-specific ODCs in patient-derived CRC metastasis cell cultures confirmed anti-tumor activity. Taken together, our results indicate that a drug combination identification approach, guiding towards low-dose, synergistic, effective and selective combinations, can be used to identify personalized drug combinations.

Importantly, simultaneous multi-target inhibition of important deregulated signaling pathways has strong therapeutic potential and translational value between tumor types.

To summarize the scientific value of this thesis, the work presented in this thesis includes the establishment of a heterotypic 3D cell culture platform that can easily be adapted to improve other cancer cell cultures. We provide scientific evidence of the value of personalized drug combinations to improve CRC treatment. We validate a drug combination optimization approach that can be applied to the design of optimal drug combinations for treatment of other cancers and diseases in other medical fields.

Résumè

Aujourd’hui, les patients atteints d’un carcinome colorectal (CRC) sont souvent diagnostiqués à un stade avancé lorsque la tumeur est inopérable et plus agressive. Le traitement de première intention consiste en une combinaison de chimiothérapies, qui est efficace initialement pour inhiber la croissance tumorale, mais qui provoque de graves effets secondaires. De plus, les patients atteints de métastases développent fréquemment des résistances aux traitements actuels, entrainant une survie à 5 ans de seulement 14%, ce qui souligne la nécessité d’améliorer les traitements disponibles. Ce projet avait pour objectif d’étudier et d’améliorer le traitement standard utilisant une combinaison de chimiothérapies, ainsi que d’optimiser de nouvelles combinaisons thérapeutiques afin d’améliorer le traitement du CRC. En outre, nous avons relevé d'importants facteurs et défis à prendre en considération lors du développement de combinaisons thérapeutiques, notamment la méthode d’optimisation, les procédés de conception de combinaisons à forte sélectivité, ainsi que la pertinence des différents modèles précliniques et cliniques de CRC utilisés pour la validation des combinaisons de molécules et leur application clinique.

Nous avons étudié l'efficacité et les interactions médicamenteuses de l'association de chimiothérapies composée d'acide folinique, de 5-fluorouracile, d'oxaliplatine et/ou d'irinotécan et avons confirmé une synergie dépendante de la lignée cellulaire entre l'acide folinique et le 5-fluorouracile qui, ensemble, avaient principalement un effet antagoniste avec SN38, le métabolite actif ou irinotécan. Nous avons optimisé des combinaisons spécifiques des cellules et à faible dose, qui étaient actives de façon comparable dans les stratégies d'administration de médicaments concomitantes et séquentielles. Enfin, par rapport aux doses utilisées en clinique, la combinaison n'a pas amélioré l'efficacité ni le profil d’innocuité des molécules, ce qui démontre la nécessité d'améliorer les options de traitement du CRC.

Dans 48% des cas, les molécules en phase clinique échouent du fait d’un manque d’efficacité qui ne reflète pas les résultats précliniques. En effet, le passage in vivo de combinaisons thérapeutiques précliniques développées in vitro est difficile en raison de la complexité du microenvironnement tumoral, qui est en partie responsable du fort taux d’échecs thérapeutiques. Comparée à la culture cellulaire traditionnelle, la culture en trois-dimensions (3D) peut refléter de façon plus réaliste la tumeur et son microenvironnement qui influence la réponse aux traitements médicamenteux. Nous avons établi un modèle homotypique et hétérotypique de culture cellulaire 3D qui est robuste, à faibles coûts, et reproductible à court terme. Ce modèle nous permet d’adresser les différentes complexités du microenvironnement

du CRC, notamment la présence de la matrice extracellulaire (MEC), de fibroblastes et de cellules endothéliales. Nous avons observé des différences dans l’efficacité des combinaisons ainsi que dans l’interaction médicamenteuse, qui favorisent l’apparition de résistance dans les cultures cellulaires 3D hétérotypiques. Ces différences étaient dépendantes du modèle de culture ainsi que de la lignée cellulaire. De plus, la composition de la MEC et les voies de signalisation étaient régulées différemment dans les cultures 3D hétérotypiques. Dans l'ensemble, nos résultats montrent l’importance d’un modèle très complexe de culture cellulaire 3D permettant d’anticiper la réponse clinique d’un traitement.

Afin d’améliorer le traitement du CRC, nous souhaitions identifier des combinaisons de médicaments présentant des propriétés synergiques ainsi qu’une efficacité et sélectivité. Par conséquent, nous avons utilisé la technologie d’optimisation de combinaisons multidrogues basée sur l’analyse phénotypique (TGMO). sur un panel de lignées cellulaires de CRC. Nous avons identifier des combinaisons de médicaments, principalement des inhibiteurs de tyrosine kinase (ITK), à la fois à faible dose, de haut niveau, synergiques et spécifiques aux cellules.

Il est important de noter que ces combinaisons restent actives dans les cultures de cellules hétérotypiques 3D ce qui reproduit les diverses caractéristiques des tumeurs CRC, confirmant ainsi la pertinence de ces associations médicamenteuses en pré-clinique. Le séquençage du transcriptome et les analyses des phosphoprotéomes ont révélé que l'inhibition ciblée de la combinaison résulte en une régulation subtile de la signalisation cellulaire qui convergeait principalement sur la voie de signalisation MAP kinase et sur l’arrêt du cycle cellulaire. Deux ODC spécifiques aux cellules ont été transposées à des modèles murins dans lesquels elles sont restées efficaces et ont surpassé de manière significative la combinaison de chimiothérapie standard (FOLFOX) en termes d’innocuité et d'efficacité. Les combinaisons de médicaments avaient des profils pharmacocinétiques distincts comparés aux médicaments seuls, avec une biodisponibilité des médicaments considérablement améliorée. Enfin, les tests des ODC optimisées sur des lignées cellulaires dérivées de patients métastatiques ont confirmé leur activité anti-tumorale. Dans l’ensemble, nos résultats indiquent qu’une démarche d’identification d’association médicamenteuse, orientée vers des combinaisons à faible dose, synergiques, efficaces et sélectives, peut être utilisée pour identifier des combinaisons de médicaments personalisées. Il est important de noter que l'inhibition simultanée d'importantes voies de signalisation cellulaires dérégulées a un fort potentiel thérapeutique et un intérêt particulier pour la recherche translationnelle.

Pour résumer la valeur scientifique de cette thèse, le travail présenté inclut l’établissement d’une plateforme de culture cellulaire 3D hétérotypique qui peut être facilement adaptée pour améliorer la culture d’autres lignées cellulaires cancéreuses. Nous fournissons une preuve

scientifique de l’intérêt des combinaisons de médicaments personnalisées pour améliorer le traitement du CRC. Nous validons une approche d’optimisation des combinaisons de médicaments qui peut être appliquée à la conception d’associations médicamenteuses optimales pour le traitement de différent cancers et de maladies d’autres domaines médicaux.

Acknowledgements

I would like to give a special thank you to Prof. Patrycja Nowak-Sliwinska for not just giving me the opportunity to do a PhD with you, but also for encouraging my personal and professional development during this time. During these three years I encountered many hurdles and setbacks as is natural in science, but I learned from you that with hard work and keeping your goals in sight, one can triumph. Merci!

A big thank you to the members of the thesis committee, Prof. Jules Desmeules, Prof. Hubert van den Bergh, Prof. Paul Dyson and Prof. Arjan Griffioen, for your time and evaluation of my work, and the interesting and energetic discussions I am looking forward to during the thesis defense.

A sincere thank you to all the collaborators contributing to the work published in this thesis.

Their efforts in either teaching me new techniques or experimental analyses have had a great positive influence on the obtained results. Specifically: Dr. Olivier Dormond, Dr. Laetitia Troquier and Adrian Duval for teaching of Western Blotting; Dr. Didier Colin and Olivia Bejuy for immunohistochemistry analysis of 3D cultures and spectrometer bioluminescence imaging support; Dr. Mylène Docquier and Dr. Céline Delucinge-Vivier for transcriptomic analyses; Prof. Connie Jimenez and her team, Prof. Arjan Griffioen and Dr. Judy van Beijnum for the posphoproteome analyses; Prof. Tatiana Petrova and Dr. Simone Ragusa for teaching the orthotopic implantation of tumor cells in vivo; Prof. Youssef Daali, Fabienne Doffey-Lazeyras and Melanie Kuntzinger for pharmacokinetic analysis of samples; the surgeons (Prof. Christian Toso, Dr. Axel Andres), oncologists (Prof. Pierre-Yves Dietrich, Dr. Thibaud Koessler) and pathologists (Prof. Thomas McKee, Prof. Laura Rubbia-Brandt) for providing patient-derived CRC metastasis tissues. A thank you to Prof. Gerrit Borchard and Céline Lemoine for the start of a collaboration on the incorporation of drug combinations into nanoparticles. A special thank you to Morgan Le Roux-Bourdieu, Seimia Chebbi- Mathlouthi, Dr. Judy van Beijnum and Tse Wong for the close collaboration on renal cell carcinoma projects and the many interesting discussions.

A big and special thank you to all of my colleagues: Dr. Andrea Weiss, “Magdi” Magdalena Rausch, Dr. “Gosia” Malgorzata Kucińska, Dr. St. Stefan(o/a) Zweifel, George “little G”

Ramzy and Eloïse Ducrey. Thank you for the many perspective exchanges, productive discussions and moments of learning. You have been a pleasure to work, argue and laugh with and this experience wouldn’t have been the same without the occasional pizza lunch, shoulder rub, dancing and “feed me” moments to brighten my day.

I would like to say thank you to all the master students for having contributed directly or indirectly to the work in the thesis and the opportunity to learn as a supervisor. Specifically:

Nathalie Wunderlin for her work on 3D cell cultures; Devon Weterings for cell morphology stainings; Andrei Rotari for immunohistochemistry stainings; Valentin Mieville for immunohistochemistry stainings and analyses. The latter deserves a special thank you for the fantastic memes that were appearing as surprise decoration on my desk.

I would like to thank the many facilities and platforms which provided technical support and technical assistance, including the histology core facility, the bioimaging facility, the flow cytometry facility and the animal facility.

There is many other people in the CMU I had the pleasure to get to know, work and eat lunch with. Therefore, a thank you to all the people of the pharmacocognosy group, specifically, Dr.

Muriel Cuendet, Frédéric Borlat, Dr. Aymeric Monteillier, Noémie Saraux, Micaela Freitas, Angelica Ferro and Weronika Spaleniak for the technical cell culture support, aperós and escape games. The people of the phytochemistry group, in specific, Dr. Jean-Luc Wolfender, Adriano Rutz, Dr. Davide Righi, Léonie Pellissier, Dr. Pierre-Marie Allard and Luis Quiros Guerroro, thank you, for the many shared lunches, spring and pharma parties and aperós. The “pharma girls” from the 7^th floor, in specific, Dr. Sandra Hocevar, Aristea Massara, Julia Wagner, Betul Taskoparan, Hélène Poinot, Montserrat Alvarez, Dr. Viola Puddinuk and Eloise Dupuychaffray, thank you for providing professional and emotional support, escape game puzzles and aperós. A special thank you to Hélène Poinot and Eloise Dupuychaffray for translating my abstract. Last, but not least, thank you Vassily Vorobiev and Céline Lemoine for the scientific discussions and Mont Blanc expedition!

These acknowledgements would not be complete without mentioning my friends and family.

They have provided unwavering encouragement and support from often so far away. You will forever remind me to stay with both feet on the ground and provide dry humor on sunny and dreary days. You are my pillars to which I return to and for that I am endlessly grateful, thank you. Therefore I have to mention “mam” Marja Zoetemelk-Van der Zwet, “pap” Frans Zoetemelk, “zus” Eefje Zoetemelk, “broer” Daan Zoetemelk, “schoonzus” Margriet Lautenbach and the rest of my huge family. A special thank you to my friend Ellie van Schie for helping to move my cats, my stuff and me; a nerve-wrecking 1000 km drive back to the Netherlands in 4^th gear with car trouble, you are a saint! My other friends, especially, Lisanne Hartog, Pien van der Geest, Marit van den Bos, Giselle Straathof and the “zieke geesten”

Lisette van der Lee, Gemma Dongelmans and Crista van Velzen, and many others, thank you, for your friendship throughout my life and all your support during this PhD.

Abbreviations

2D two-dimensional

3D three-dimensional

3D-CC 3D co-cultures

5FD 5-fluoro-2`-deoxycytidine

5-FU 5-fluorouracil

ABL1 ABL proto-oncogene 1

Akt protein kinase B

ALK ALK receptor tyrosine kinase

AML aute myeloid leukemia

Ang-2 Angiopoietin 2

APC adenomatous polyposis coli

AUC0-24 area under the curve (0-24 hours)

ax axitinib

AZA azacitidine, 5-AZA-CR, 5-azacytidine

AZD AZD-4547

BEZ BEZ-235

bFGF basic fibroblast growth factor

BM basement membrane

BMP4 bone morphogenetic protein 4

BRAF serine/threonine-protein kinase B-Raf CAF cancer-associated fibroblasts

CAM chorioallantoic membrane of the chicken embryo CAPEOX/CAPOX capecitabine and oxaliplatin

CCAT1 colon cancer associated transcript 1

CCND1 cyclin D1

ccRCC cell renal cell carcinoma

CDK cyclin-dependent kinase

CI combination index

CI CI-994

CIN chromosomal instability pathway ClCasp3 cleaved caspase 3

Cmax maximum plasma concentration

CML chronic myelogenous leukaemia

CMS1 consensus MSI immune

CMS2 consensus MSI canonical

CMS3 consensus MSI metabolic

CMS4 consensus MSI mesenchymal

cMYC master regulator of cell cycle entry and proliferative metabolism CRD clinically relevant doses

cre crenolanib

CTCL cutaneous T cell lymphoma

CTG CellTiter-Glo assay

CTRL sham-treated control

CUD clinically used doses

CV coefficient of variation

CX3CL1 C-X3-C motif chemokine ligand 1

CYP cytochrome P450

CYR cysteine-rich angiogenic inducer 61

DAC decitabine, 5-AZA-2’-deoxycytidine, 5-AZA-CdR DAPI 4′,6-diamidino-2-phenylindole

das dasatinib

dMMR deficient mismatch repair

DMSO imethyl sulfoxide

DNMT DNA methyltransferase

DUSP dual-specificity phosphatase

E experiment

EC endothelial cell

E-cadherin epithelial cadherin

ECM extracellular matrix

ED effective dose

EGFR epidermal growth factor receptor

EHS Engelbreth-Holm-Swarm

eNOS endothelial nitric oxide synthase (eNOS)

EPHA2 ephrin receptor A2

ERBB2 Erb-B2 receptor tyrosine kinase 2

ERC European Research Council

ERK extracellular signal-regulated kinase

erl erlotinib

EtHD ethidium homodimer

F2RL3 F2R like thrombin or trypsin receptor 3

FA folinic acid/leucovorin

FAO fatty acid oxidation

FB fibroblasts

FBS fetal bovine serum

FDA U.S. food and drug administration

FF 5-FU/FA

FGF(R) fibroblast growth factor (receptor) FLT2 fibroblast growth factor receptor 1 FOLFIRI 5-fluorouracil, folinic acid and irinotecan

FOLFIRINOX 5-fluorouracil, folinic acid, oxaliplatin and irinotecan FOLFOX 5-fluorouracil, folinic acid and oxaliplatin

FZD7 frizzled class receptor 7

G0 phase gap 0 phase

G1 gap 1 phase

G2 phase gap 2 phase

GDC GDC-0992

GO-BP gene ontology biological process enrichment GPCR G protein-coupled receptor

H&E hematoxylin & eosin

H&N head and neck

HDAC(i histone deacetylase (inhibitor)

HER2 human epidermal growth factor receptor 2

HGF hepatocyte growth factor

HIF-1α hypoxia inducible factor 1 alpha

HSP90 heat shock protein 90

HUVEC human umbilical vein endothelial cell

HYD hydralazine

IC inhibitory concentration

IDO Indoleamine 2,3-Dioxygenase

IF immunofluorescence

IFNγ interferon gamma

IGF1 insulin like growth factor 1

IHC immunohistochemistry

Il interleukin

INKA Integrative Inferred Kinase Activity

IRI irinotecan/CPT-11

KI kinase inhibitor

KP1019 indazolium trans-[tetrachlorobis(1H-indazole)ruthenate(III)

KRAS KRAS Proto-Oncogene, GTPase

LBH LBH-589

LD low dose

M phase mitotic phase

(m)CRC (metastatic) colorectal carcinoma

MAPK 5-fluorouracil, folinic acid, oxaliplatin and irinotecan

MAX MYC associated fctor X

MEK mitogen activated protein kinase

MET MET proto-oncogene

miRNA noncoding microRNA

MMP matrix metalloproteinase

MPAS MAPK pathway activity score MPC maximal plasma concentrations MRM multiple reaction monitoring

MSI-H microsatellite instability-high-frequency MSS microsatellite stability

MTA1 metastasis-associated protein 1 mTOR mammalian target of rapamycin

MVD microvessel density

MXD MAX dimerization protein

NAMI-A trans-[tetrachloro(dimethylsulfoxide)(imidazole)ruthenate(III)]

NEBD nuclear envelope breakdown

NFkβ nuclear factor NF-kabba β

NOW Netherlands Organization for Scientific Research

NOX4 NADPH oxidase 4

NSCLC non-small cell lung cancer

NuRD nucleosome remodeling and deacetylase Oatp solute carrier organic anion transporter OACD orthogonal array composite design

ODC optimal drug combination

OS overall survival

OX oxaliplatin

p300-HAT p300 histone acetyltransferase

PBA phenylbutyrate

PBST-X PBS supplemented with 0.2% Triton-X PCL plasma concentration limit

PCM pericentriolar material

PD-1 programmed cell death protein 1

PDGF(R) platelet-derived growth factor (receptor) PD-L1 programmed death-ligand 1

PFS progression free survival

PHLD pleckstrin homology like domain PI3K phosphatidylinositol 3-kinase

PIK3CA fosfatidylinositol-4,5-bisfosfaat 3-kinase, katalytische subeenheid alfa

PTEN phosphatase and tensin homolog

PUMA p53-upregulated modulator of apoptosis PVRL2 poliovirus receptor-related 2

R² multiple determination

RAPTA-C Ru(η6-p-cymene)(pta)Cl2

RAPTA-T Ru(η6-toluene)(pta)Cl2

RAS rat sarcoma

RCC renal cell carcinoma

reg regorafenib

RFU relative fluorescence unit

RGS16 regulator Of G protein signaling 16

RTK receptor tyrosine kinase

S phase synthesis phase

SAHA vorinostat

SAM S-adenosyl methionine

SD standard deviation

sel selumetinib

SEM standard error of the mean

s-FSC streamlined-Feedback System Control siRNA short-interference RNA

SIRT histone deacetylase sirtuins

SMAD caenorhabditis elegans Sma genes and the Drosophila Mad, mothers against decapentaplegic

SN SN-38

SN-38 7-ethyl-10-hydroxycamptothecin

sor sorafenib

SPRED1 sprouty related EVH1 domain containing 1 SPRY sprouty RTK signaling antagonist

STAT signal transducer and activator of Transcription

sun sunitinib

TGFβ transforming growth factor beta 1

TGMO therapeutically guided multidrug optimization Tie-2 angiopotin 1 receptor

TIMP3 tissue inhibitor of matrix metalloproteinase 3 TKI tyrosine kinase inhibitor

TLE3 TLE family member 3

TNFRSF10A TNF receptor superfamily member 10a

TNT tunneling nanotubule

TP53 tumor protein 53

TSA trichostatin A

TSG tumor suppressor gene

TSN4 rranslin 4

TSP-1 thrombospondin 1

tub tubacin

TW therapeutic window

UBASH3B ubiquitin associated and SH3 domain containing B UGT/Ugt UDP glucuronosyltransferase

vat vatalanib

VE-cadherin vascular endothelial cadherin

VEGF(R) vascular endothelial growth factor (receptor)

vem vemurafenib

VHL Von Hippel-Lindau

VPA valproic acid

VX VX-680

WEE1 WEE1 G2 checkpoint kinase

WIF-1 wnt inhibitory factor 1

Wnt wingless-related integration site

zal zaltrap

ZEB zebularine

Introduction

Cancer development and common treatment modalities

Cancer finds its etymological origin in the Greek word karkinos (Καρκιυός, or ‘crab’), so-called for its physical similarities and associated adversary bite and grab characteristics ¹. The modern definition of cancer is “uncontrolled division of abnormal cells that can invade and destroy surrounding normal tissues and spread to distant locations” and is now the second- leading cause of death worldwide. Incidence estimates in 2018, were over 18 million new cancer cases and nearly 10 million cancer-related deaths worldwide ².

The reviews of cancer hallmarks, published by Hanahan and Weinberg in 2000 and updated in 2011, define genome instability and mutation, as well as tumor-promoting inflammation, as two enabling traits in cancer development and progression ^3,4. First, the acquisition of alterations occurs not only in the genome but also in other “omics” such as the epigenome, transcriptome, proteome and metabolome. Those key alterations provide a selective advantage over neighboring cells and enable their outgrowth in a succession of clonal expansions ⁵. Cancer cells have often increasing rates of mutation through higher concentrations of mutagenic agents in their surroundings, higher sensitivity to mutagenic agents and compromised genome maintenance systems ⁶. Moreover, tumor-promoting inflammation defines the heterogeneous quantities of immune infiltrate that while initially generate anti-tumor responses, can paradoxically enhance tumorigenesis and tumor progression. Tumor cell-induced immune evasion decreases the effectiveness of anti-tumor immune responses while simultaneously cross-talk induces an inflammatory environment rich in biomolecules contributing to the various hallmarks of cancer ⁷.

The hallmarks of cancer include sustained proliferative signaling, evading growth suppressors, resisting cell death, reprogramming energy metabolism, enabling replicative immortality, inducing angiogenesis, evading immune responses and tissue invasion and metastasis ^3,4. First, “sustained proliferation” is enabled by a deregulated cell cycle, growth factors provided by surrounding tumor and stromal cells (i.e. immune cells), and growth factor independent signaling through constitutive activation of downstream components. Second,

“evading growth suppressors” predominantly refers to the circumvention of negative regulators of proliferation, termed tumor suppressor genes. Cancer cells commonly harbor one or more key alterations driving growth ⁸ in the form of activating/deactivating mutations and deregulated negative feedback loops that mediate homeostasis of proliferative signaling ^3,9. The hallmarks “resisting cell death” and “reprogramming energy metabolism”, can be attributed to an altered stress response favoring survival, thereby affecting autophagy (i.e. the

metabolic recycling process of intracellular components), senescence (i.e. the metabolic and proliferation inactive cell state), apoptosis (programmed cell death) or cell death in general ^3,10. Furthermore, the “angiogenic switch” is a key ability of tumor cells to induce continuous development of new vessels and to support the expansion of tumor growth by providing nutrients and oxygen, and draining metabolic waste and CO2. The angiogenic switch is orchestrated by endothelial cells, tumor cells, endothelium supporting cells (i.e. pericytes) and endothelium mimicking cells (e.g. stem cells). Angiogenesis induction by these cells is dependent on various regulators such as stimulation of the angiogenic signaling pathways (e.g. via vascular endothelial growth factors, VEGFs; VEGF receptors). The imbalanced activation of tumor angiogenesis is often marked by high capillary sprouting (neo- vascularization), extensive sprouting and distorted enlarged vessels ^3,11.

The hallmark “immune evasion” is the capacity of tumor cells to suppress the anti- tumor immune response, partly through secretion of immunosuppressive molecules, or via recruitment and modulation of immune-suppressive regulators ^3,12. The immune cells, angiogenesis-promoting cells and inflammatory regulators (i.e. fibroblasts, macrophages, various types of T cells) become part of the tumor microenvironment. The tumor microenvironment is continually adapting with tumor growth and is intra- and interpatient heterogeneous in composition and cell crosstalk. It has an important role in the induction of

“tumor invasion and metastasis” ¹³. The latter is a multi-step process involving local invasion through alterations in for example the ECM, loss of tumor cell adhesion, induction of migration, intravasation of tumor cells in nearby blood or lymphatic vessels and extravasation into local or distant sites to form metastatic lesions ^3,14. At this point, cancer becomes more complicated to eradicate, especially since metastases are known to develop different mutations compared to primary tumors under selective pressure ^15-17.

The most common clinical cancer treatments are surgery, chemotherapy and radiotherapy.

Surgical resection is a fundamental modality employed for most cancers at early stages for the regional control of primary and metastatic tumors. However, while it reduces tumor burden it does not change the course of the disease ¹⁸. Radiotherapy originated from the discovery of radium in 1898 by Marie Curie-Sklodowska and has since been utilized for radiation of tumors to directly, or indirectly, induce cell death by causing DNA damage ^19,20. Chemotherapy, coined by the German scientist Paul Ehrlich for the chemical treatment of a disease, has become renowned for the successful treatment of cancer after the Second World War where the use of mustard gas was discovered to enable lymphoid cancer repression ^21,22. Surgery, radiotherapy and chemotherapy alone or combined can be curative for early-stage or low- grade cancers but frequently lead to resistance and important side effects.

The increasing knowledge of cancer biology and underlying molecular alterations preceded the targeted therapy revolution. The first targeted therapy developed was tamoxifen, a failed post-coital contraceptive preventing the growth factor estrogen to bind to its receptor.

Tamoxifen was shown to have anti-cancer properties and was first employed in the treatment of breast cancer in the 1970s ²³. The success of tamoxifen heralded a wave of development in targeted treatments and has led to the development of small molecules. The first small molecule to undergo clinical evaluation was imatinib, an inhibitor of the constitutively active BCR-ABL1 kinase. The gene fusion between BCR and ABL1 promotes dimerization or tetramerization, which in turn facilitates autophosphorylation of BCR, ultimately stimulating proliferation and survival. Imatinib obtained complete responses in 76% of the chronic myelogenous leukemia (CML) patients treated and is thereby more effective and better tolerated compared to the previous first-line therapy of INF-α and cytarabine ²⁴. Since then, numerous targeted agents have been developed targeting specific mutations and mechanisms contributing to cancer progression and have been combined with existing treatments for improved treatment efficacy. However, clinical successes are still low; the probability for an anti-cancer drug entering phase I clinical trials to be approved has remained relatively static over the past 15 years and is currently only 9% due to lack of efficacy, durability or intolerable toxicities ^25,26. Therefore, the development of new drugs or combinations of treatments is in high demand.

Colorectal carcinoma and current treatment options

With an incidence of 1.8 million new cases and a mortality rate of 550.000 people in 2018 only, colorectal carcinoma (CRC) is the third most common cancer worldwide ^2,27. Development of CRC is associated with the acquisition of key mutations occurring with age, lifestyle (i.e. diet, smoking, physical activity) and chronic diseases (e.g. inflammatory bowel disease), but can also derive from hereditary mutations and familial history of adenomatous polyps ²⁸. Depending on the cause, CRC can be sporadic (70%), familial (25%) or inherited (5%) ²⁹. Screening for colorectal cancer with colonoscopy, colonography and stool samples has improved early-stage detection ³⁰ and consequently has decreased cancer-related deaths as the treatment of early-stage results in a high survival rate of 65% ²⁷. However, despite improved detection methods, early-stage CRC is usually asymptomatic and many patients are still diagnosed at an advanced stage, in which the tumor is inoperable and more aggressive, resulting in a low survival rate of only 14% at this stage ²⁷.

Colorectal cancer develops typically from polyp outgrowths protruding from the intestinal lumen and are still classified as benign at this point. However, further accumulation of mutations facilitates polyp growth and expansion, featured as dysplasia, and finally invasion

and spread into local and distant regions ³¹. Common mutations and alterations that function as predictive markers for patient outcomes include APC, Wnt, TP53, TGF-β, c-MYC, KRAS, BRAF, PIK3CA, PTEN, SMAD2/4 ²⁹.

Current treatment options consist of resection of the primary tumor followed by chemotherapy which can be curative for early-stage CRC. Chemotherapy combinations are composed of 5- fluorouracil, folinic acid, and oxaliplatin (FOLFOX) or irinotecan (FOLFIRI), or a combination of all four (FOLFIRINOX). However, these chemotherapy regimens have a high incidence of adverse effects ^32-34 and usually induce drug resistance in advanced CRC ^32-34. Therefore, chemotherapies are often combined with targeted treatments.

On molecular level, common pathways deregulated in CRC include the RAS/MAPK or MEK/ERK (rat sarcoma/mitogen-activated protein kinase/extracellular signal-regulated kinase) and the PI3K/Akt/mTOR (phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin) cascades and their interactions with a variety of major players upstream such as receptor tyrosine kinases (RTKs) epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (HER2) and hepatocyte growth factor (HGF) ³⁵. The tumor microenvironment, in specific cells contributing to tumor angiogenesis, is also important for CRC progression ³⁶. Patients have benefited from inhibition of these pathways through therapies targeting vascular endothelial growth factor (VEGF; bevacizumab/Avastin^), EGFR (cetuximab/Erbitux^ or panitumumab/Vectibix^) ^37-40 and VEGF receptor 1-3 (VEGFR)/platelet-derived growth factor receptor β (PDGFRβ; multi-kinase inhibitor regorafenib/Stivarga^) ^41,42. Although these targeted therapies are positively correlated with survival, side effects and treatment resistance remain bottlenecks ⁴³.

Currently, neoplasm or cancer classification is based on the tissue of origin, which logic is found in the similarities they retain with their roots, even after metastasizing to distant sites ⁴⁴. However, neoplasms derived from the same organ or tissue can be vastly different phenotypically and vary in treatment response. Paradoxically, recent evidence shows that neoplasms of different tissue origin can share more similarities on a genetic and molecular level ^45,46 and respond to treatment alike ⁴⁷. Notorious examples include EGFR and serine/threonine-protein kinase B-Raf (BRAF) targeting drugs ⁴⁸. A notable recent U.S. food and drug administration (FDA) approval was obtained for pembrolizumab for the treatment of dMMR and MSI-H (deficient mismatch repair; high-frequency microsatellite instability) tumors, regardless of tumor location ⁴⁹. As such, it is not surprising that more repurposed drugs make their debut for new applications ^50,51. Importantly, a key step in the development of new treatments is the development of anti-PD-L1 and anti-PD-1 antibodies (anti-programmed

death-ligand 1 and anti-programmed cell death protein 1; pembrolizumab or nivolumab, respectively) which have been approved in 2017 for the treatment of immunogenic dMMR or MSI-H tumors, including CRCs ^49,52. Furthermore, the first two-drug targeted combination, ipilimumab and nivolumab, has been approved by the FDA in 2018 for the treatment of CRC refractory to the standard of care treatments This approval is based on the results of the Checkmate-142 clinical trial where the combination demonstrated high response rates, progression-free and overall survival of 12 months and a manageable safety profile ⁵³. This combination was previously approved for the treatment of metastatic melanoma ⁵⁴ and renal cell carcinoma ⁵⁵ and as such is an excellent example of drug repurposing and efficacy of immunotherapy for CRC treatment.

Bottlenecks in successful CRC (combination) treatment

To improve CRC treatment, new drugs continue to be developed, but also combining different drugs or treatment modalities are potential approaches. Clinical development of promising new treatments has been gaining attention during the past decades but is facing challenges in terms of efficacy, selectivity or durable responses in the large patient population. Drug resistance is the main cause of long-term loss of effectiveness and is usually due to intrinsic or acquired resistance mechanisms ⁵⁶. Firstly, mutations and alterations in cancer cells can confer resistance, i.e. EGFR resistance in CRC can occur from both pre-existing mutations and mutations acquired during treatment ^57,58. In general, resistance can occur at different levels; upstream, downstream or directly interfering with drug activity or the drug target ^59-61. Moreover, a small population of cells can enter a drug-tolerant state in which they can obtain diverse and unpredicted resistance mechanisms and repopulate the tumor and metastasis ⁶². In general, these de novo acquired mutations highlight that although resistance mechanisms are genetically heterogeneous, they converge on key signaling pathways ⁶³.

Tumor heterogeneity has also been proved to be implicated in treatment resistance. Genomic instability aided by destabilization of key cellular processes represents important key features for tumor heterogeneity. Datta et al. investigated the clonal evolution of genetic instability and demonstrated that mutations gained can be advantageous, deleterious or mutation enhancers termed mutator mutations. The acquisition of selective advantageous driver mutations results in clonal expansion of tumors ⁵. Asatryan & Komarova developed mathematical models that indicate that mutator mutations function as passengers: each expanding clone with driver mutations undergoes its own evolutionary wave that may be stable or unstable depending on the mutation rate and selective advantage ⁶⁴. Indeed, many studies have previously shown

that there are lesion-specific responses to treatment, linking tumor heterogeneity to treatment resistance ^65-67.

Bhang et al. investigated the origin of clonal resistance to treatment in cells and found that the majority of the resistant clones were a part of small pre-existing cell subpopulations with selective advantages in survival ⁶⁸. His finding was further supported by Mullighen et al.

who reported that the cell clones responsible for a relapse of leukemia were often present as minor subpopulations at diagnosis ⁶⁹. This suggests that the most heterogeneous tumors would be the most resistant to treatment. Interestingly, while resistance is considered to be stable, in some cases, the patients have responded to the retreatment with the same therapy, to which they were previously resistant, termed “drug rechallenge” applied after a “drug holiday” ^70-72. The theory behind this suggests that some treatment heterogeneity is not per se defined by genomic alterations ⁷³, instead, it can be transient with resistance due to the tumor micro-environment ⁷⁴ or epigenetic reprogramming ⁷⁵. As such, tumor heterogeneity is not only defined by intrinsic genomic alterations but also dependent on external factors and both can influence treatment resistance. Moreover, some advantageous mutations conferring resistance have a lower proliferation rate and growth-related pressure can result in the partial repopulation of the tumor with treatment-sensitive tumor cells ⁶⁴.

Bozic et al. used a predictive mathematical model to demonstrate that with a chance of 1 in 6.6 billion base pairs of developing cross-resistance, patients with large tumor burdens need a three-drug combination to achieve long term treatment responses ⁷⁶. Moreover, intermittent administration of the combination was shown to beneficial in obtaining maximal tumor growth inhibition with minimal toxicity, whereas administration as sequential monotherapies, resulted in the selection and clonal expansion of resistant populations, decreasing treatment efficacy ^76,77. Together, these studies indicate the potential of drug combinations to improve CRC treatment.

Advantages and considerations of drug combinations

Since the first drug combination used to treat cancer originating as far back as the 1960s, the most curative anti-cancer treatments have been established from drug combinations ⁷⁸. Most of these drug mixtures were established using “trial and error” or “marriage of convenience”

approaches with limited pre-clinical data supporting the combination design. The rational selection is not easy due to the complexity of tumor characteristics and the large space of combinatorial possibilities. Current strategies aim for synthetic lethality (when only the perturbation of two proteins leads to loss of viability) ⁷⁹, drug combinations with additive properties (when the effect is the sum of its parts) or synergistic drug interactions (when the effect of the whole is greater than the sum of its parts) on the same, non-overlapping or partly

overlapping targets, or to overcome resistance mechanisms ⁸⁰. Thus, drug combinations are employed as in general they have potential in increasing overall treatment efficacy while not compromising on safety and decreasing the chance of developing or selecting for resistance.

Overall drug combination efficacy depends on individual drug efficacy and drug-drug interactions. Globally, when the effect is greater than, equal to or less than the sum of their separate effects, drug-drug interactions can be classified as synergistic, additive or antagonistic, respectively. Various equations and methods have been used to describe and quantify those interactions. The Bliss Independence model is based on the probability of the contribution of each drug with independent mechanisms of actions contributing to a common result (e.g. cell death) ⁸¹. This model requires only one drug concentration and determines the additive activity of a drug combination based on each single drug effect. Subsequently, drug combinations performing below, equal or above the additive prediction indicate antagonistic, additive or synergistic drug interactions. Another model is Loewe Additivity, which functions on the principle of dose-effect isobolograms. They show varying concentrations of two drugs that together give a constant enzyme activity, indicated by a diagonal dose-effect line. In this model the additive combined dose-effect interaction depends on individual dose-response curves ^82,83. Drug-drug interactions are quantified with a combination index (CI), describing synergy (CI<1), additivity (CI=1) and antagonism (CI>1). Similarly to Bliss Independence, this relation indicates synergy or antagonism if a drug combination effect is higher or lower, respectively, than the expected additive activity. In contrast to Bliss Independence, Loewe Additivity allows investigation and quantification of drug interactions over multiple doses.

Synergistic, additive and antagonistic drug interactions do not refer to direct binding of one drug to another drug. Instead, when biologically targeting drugs bind a specific biomolecule, which directly or indirectly influences the pharmacokinetics or pharmacodynamics of another drug, this relation is defined as a drug-drug interaction ⁸⁴. Pharmacokinetically, drug-drug interactions indicate modulation of drug absorption, distribution or metabolism of one drug, thereby potentiating or reducing the effect of another drug. Pharmacodynamically, drug-drug interactions indicate modulation of drug targets or connected signaling pathway components, thereby influencing the drug efficacy of the overall combination. Jia et al. investigated the mechanistic potential of drug combinations and observed that among 53 pharmacodynamic synergistic drug combinations, more than a half consisted of complementary regulation of a target or a signaling pathway by simultaneously affecting multiple target sites or signaling pathway components. Interestingly, the remaining drug combinations functioned by reduction of counteractive feedback loops of one drug in therapeutic response to another drug ^84,85. The drugs acting on counteractive measures did so through various mechanisms, which included

affecting the same target on different sites, modulation of different targets in connected pathways, and affecting cross-talking signaling pathways. An example of a synergistic drug combination in their study is the co-administration of the EGFR TKI gefitinib and MEK inhibitor PD98059, which synergistically inhibited cell proliferation of breast cancer cells ⁸⁶. Upon EGFR inhibition, an autocrine loop of downstream MEK and ERK upregulates growth ligands and by simultaneous inhibition of MEK, the feedback loop is interrupted ⁸⁷. Moreover, synergistic drug interactions are attractive as they can enhance the overall efficacy of treatment at potentially lower doses.

Furthermore, additive interactions were shown to function through a variety of mechanisms, which included acting on the same target or pathway, or had completely independent mechanisms of action ⁸⁴. An example of an additive interaction is the combination of DNA alkylating agent doxorubicin with trabectedin forming DNA guanine adducts, both bind DNA and affect the DNA repair system ⁸⁸. Antagonistic interactions can involve targeting related pathways that regulate the same component, mutual modulation at the same target or target site or induction of counteractive actions of one drug on the drug target or signaling pathway of the other drug ⁸⁴. For example, the drugs cytarabine (DNA binding) and 17-AAG, inhibiting heat shock protein 90 (HSP90) and nuclear factor Kabba β (NFkβ), interact antagonistically through 17-AAG induction of gap 1 (G1) arrest, which prevented the incorporation of cytarabine into DNA in leukemic cells ⁸⁹. Note that while in general drug interactions can affect different mechanisms or signaling pathways, the rational design can help in drug selection, but the overall mechanism may also be very specific and require studies investigating the mechanistic potential before clinical consideration.

Finally, clinical development of a drug combination should also be an important consideration from the start and should consider the drugs of interest ⁹⁰, the concentrations/dose ratios of each compound to be applied (affecting both efficacy and potential drug toxicities), the administration route and treatment schedule (i.e. concurrent or sequential drug administration), disease characteristics (bacterial strain, mutational status, disease stage, etc.) and a patients’ treatment history.

Drug combination optimization methods

To a large extent, the rational design of combination therapies can be guided by two approaches: (i) ‘bottom-up’, based on the interpretation of ‘omic’ data in an attempt to rationally design combination therapies based on known drug mechanisms/targets or disease/patient background, or (ii) ‘top-down’, based on a phenotypic approach, measuring drug effect on its ability to alter a cell phenotype such as viability, to link drug combination composition with

treatment efficacy. The bottom-up approach encompasses the accumulation of large quantities of genomic or phosphoproteomic data and the use of systems biology-derived network topology models visualizing network representation with connections between the individual components. They can be used to mathematically predict nodes (major hubs of connections) in a signaling pathway that may be susceptible to pharmacological intervention

91-93. Topological network models can also be trained with phenotypic data. However, those models are frequently very complex, requiring many assumptions, as well as considerable experimental data to estimate model parameters, limiting their applicability and their inference potential. The top-down approach disregards mechanistic information and investigates a drug combination potential on the phenotypic outcome. Various mathematical and statistical methodologies have been applied to reduce the search space in the phenotypic optimization of drug combinations and include data modeling techniques ^94-98, search algorithms ^99-106 which vary in experimental rounds and the amount and type of information acquired during the screening ¹⁰⁷. Such methods have successfully optimized drug combinations for several applications including cancer ^108-110, liver transplantation ⁹⁸, tuberculosis ^111,112, virus infections

100 and Caenorhabditis elegans nematodes ¹¹³.

Considerations of pre-clinical in vitro models

It is important to emphasize that relevant in vitro and in vivo models should be used to increase the chance of successful translation of a drug (combination) to a clinical setting. Tumors develop a complex microenvironment with multi-directional interactions between tumor cells and their surroundings and it has become well established that the tumor microenvironment plays important role in treatment response and resistance ¹¹⁴. Pre-clinical in vitro cancer models only partially simulate reality and in vivo models have cross-species differences with humans. It is therefore not surprising that treatment response is various between pre-clinical models and patient response and ultimately 48% of drugs fail in phase II due to lack of efficacy.

In vitro, two-dimensional (2D) homotypic cultures lose many key characteristics; 2D cultures have altered proliferation, differentiation, and biochemical profiles compared to their in vivo counterparts ^115,116. 3D homotypic cultures more closely mimic reality due to multi- directional cell-cell contact and gradients of exchanging waste and CO2 with nutrients and oxygen throughout the spheroid, creating regional differences in 3D cell cultures. The cells in outer layers are more proliferative as they are readily exposed to oxygen and nutrients, whereas in the core where oxygen and nutrient levels are reduced cells are more quiescent or even necrotic ^117,118. This results in differences in cell proliferation and cell death rates impact the overall 3D culture growth and response to administered treatments ^119,120. The incorporation of a matrix into the 3D cultures more closely resembles the ECM surrounding

cells in vivo, providing structure and signals modulating polarity and growth ^121,122. Including additional cell types found in the tumor microenvironment, thereby creating heterotypic 3D cultures, further mimics the in vivo or clinical situation. Indeed, heterotypic 3D cultures differ in morphology, growth and response to treatment ^119,123.

In vivo, subcutaneous xenografts are one of the most commonly used models, but they do not adequately represent the host and patient immune environment, metastatic development and the tumor heterogeneity and correlation to resistance ¹²⁴. The selection of appropriate models to facilitate translation of treatment efficacy from pre-clinical models to the clinics is therefore important for the improvement of treatment success.

Thesis outline

Chapter 1 of this thesis describes the investigation and optimization of the chemotherapy combination currently serving as the standard of care for CRC treatment. Chapter 2 describes the establishment and characteristics of cell cultures of various complexity replicating important features of the CRC microenvironment. Further, the effect on drug combination treatment response and drug interactions compared to traditional 2D cell cultures is addressed. Annex B presents a relevant press release featuring these result. Chapter 3 describes the identification and optimization of drug combinations for the improved treatment of CRC, with special considerations for synergy, efficacy and selectivity of the drug mixtures.

Furthermore, this chapter also includes the validation of their effect on heterotypic 3D cell cultures and patient-derived CRC metastatic cell cultures. Finally, this chapter describes the investigation of the drug combination mechanism of action and the translation of two optimal drug combinations to murine in vivo models and their pharmacokinetics.

Drug combinations were also investigated for the treatment of renal cell carcinoma. First, Annex C contains the review publication describing promising results with epigenetic drug combinations. Next, Annex D presents the identification of a synergistic multi-drug combination composed of epigenetic drugs and TKIs, and its mechanism of action causing cell mitosis retardation and cell death in renal cell carcinoma and melanoma cells.

Scientific contributions including this work, in publications, oral presentations and poster presentations, including awarded scientific recognition, is presented in Annex A.