Facultad de Medicina

Identification of novel oncogene dependencies in mutant KRAS-driven epithelial tumours: the role of the transcription factor FOSL1

Adrián Vallejo Blanco

Tesis Doctoral

Facultad de Medicina Identification of novel oncogene dependencies in mutant KRAS-driven epithelial tumours: the role of the transcription factor FOSL1

Memoria presentada por D. Adrián Vallejo Blanco para aspirar al grado de Doctor por la Universidad de Navarra

Adrián Vallejo Blanco

El presente trabajo ha sido realizado bajo nuestra dirección en el Programa de Tumores Sólidos (CIMA) y autorizamos su presentación ante el tribunal que lo ha de juzgar. Pamplona, 12 de Septiembre de 2019

Dr. Silvestre Vicent Cambra Dr. Luis Montuenga Badía

El presente trabajo ha sido realizado con las siguientes ayudas:

 Ayudas para el Personal de Investigación en Formación (Asociación de Amigos, Universidad de Navarra)

 Ayudas de movilidad para la obtención de Mención "Doctor Internacional" (Fundación Caja Navarra, Obra Social "La Caixa" y Universidad de Navarra)

A mis abuelos.

A Papá y Mamá.

A Piti.

A Alba.

AGRADECIMIENTOS

Han pasado ya seis años desde que vine por primera vez al Centro de Investigación Médica Aplicada (CIMA) como estudiante de Fin de Grado y, después de todo el esfuerzo, la pasión, la ilusión y las ganas que he invertido en esta etapa de mi vida, ha llegado el final. Muchos me habéis acompañado en esta aventura que llamamos tesis, y por mucho que pase el tiempo, jamás me olvidaré del cariño con el que me habéis cuidado todos estos años. Gracias de todo corazón a todos y cada uno de vosotros que habéis conseguido que tras estas líneas salga un doctor y una mejor persona. Esta tesis es tanto mía como vuestra.

En primer lugar, me gustaría dar las gracias a la Asociación de Amigos (ADA), de la Universidad de Navarra, por confiar en mí y otorgarme la beca predoctoral que ha permitido que este proyecto se materialice. Gracias tambiéna los amigos que colaboran con ADA y que, aunque muchas veces su ayuda se manifieste de forma discreta, tengo muy presente que son ellos quienes realmente confían en que estudiantes como nosotros, puedan algún día contribuir para mejorar la vida de las personas. Gracias también al departamento de Oncología Médica de la Clínica Universidad de Navarra (CUN) y a sus pacientes, y al Departamento de Histología y Anatomía Patológica de la Universidad. Finalmente, me gustaría agradecer al CIMA, y al Programa de Tumores Sólidos y Biomarcadores del mismo, la formación recibida y la posibilidad de desarrollar el proyecto en un centro de investigación de vanguardia.

Gracias de antemano a los miembros que conformarán mi tribunal de tesis, que desinteresadamente han accedido a valorar el proyecto, por su esfuerzo y disposición para evaluar un trabajo de estas características.

Luis, gracias por confiar en mí, por preguntarme siempre “¿qué tal estás?” y asegurarte de que siempre estuviese bien. Por guiarme en lo profesional y en lo personal e inculcarme que la forma en la que desarrollo mi tesis, mi vida y cada proyecto, debería ser siempre un ejemplo de conducta para la sociedad.

Fernando, sin ser mi jefe, has estado siempre pendiente de mi trayectoria y mi desarrollo. Te he escuchado atento en todo momento y he agradecido profundamente tus consejos respecto a la ciencia y a la vida. Te considero una persona íntegra que se preocupa por lo que más le debería importar a un jefe, su equipo. Las comidas extra- laborales que organizas de vez en cuando son las varillas del hormigón armado. Pocos laboratorios hay tan unidos como lo está el nuestro. Nos has cuidado.

Silve, eres el artífice de todo este jaleo. No te considero un jefe sino un mentor y sobre todo un amigo. Aunque estos últimos meses en mi historial de búsquedas aparecen registros sobre “plantas venenosas indetectables” y “muertes que no dejan rastro”, he de reconocer que has sido un guía excepcional desde el día en que puse un pie en tu despacho. Me has enseñado a pensar, a argumentar, a darle "otra vuelta" a eso, y a lo otro. Me has impulsado a ser autónomo y a dejar que me estrellase cuando probaba alguna hipótesis que ya de antemano era estúpida. A veces me has levantado para volverme a empujar, porque hay cosas que se aprenden a la segunda. Creo que siempre has pensado que aún podía mejorar en algo y te has esforzado para que así fuera. Pienso que tienes un fondo y unos valores como la copa de un pino, y que intentas siempre ser ejemplo en todo lo que haces. Has buscado que nuestro “labo” sea una piña, que haya equipo. Desde organizar reuniones de laboratorio con bebidas y pastelitos, hasta comidas extra-laborales con el objetivo de que estuviéramos bien. Sobre todo, me has hecho sentir que la puerta de ese despacho seguirá siempre abierta para mí. Gracias.

Laboratorio 2.01, sois sin lugar a dudas lo mejor de la tesis. Hay equipos por los que se puede pasar sin echar raíces y equipos con los que te quedarías para siempre. Sois de los segundos. Ojalá pudiera llevaros allá donde yo vaya. Me habéis cuidado, mimado, enseñado, aconsejado y ayudado siempre. Allá donde haya un problema, habrá una solución del gallinero. Esta tesis es vuestra.

Inés y Susana, las mamis del laboratorio. Me habéis cuidado como al mayor de vuestros hijos, y yo os he dejado un corazón bien fuerte para afrontar con salud la vejez. Seguiré dándoos mas sustos cuando pase a visitaros. Susana, tú me has enseñado absolutamente todo a nivel técnico. Y tú, Inés, me has enseñado a ser "menos malo". Sois alegría y a mí me habéis alegrado seis años de vida.

Carol, me has dado las claves para ahorrar en casa, creo que ahora dejo todos los aparatos apagados y desconectados durante la noche. Gracias por enseñarme los detalles y técnicas más difíciles, y por aconsejarme y ayudarme siempre. Tienes las mejores manos del laboratorio.

Haritz, eres felicidad e inocencia, eres buena persona. Pienso que eres el técnico más completo que hay en el CIMA, sabes de todo y sabes mucho: animales, estadística, ofimática, informática, química, física, etc. Sabes enseñar y eres capaz de explicarlo todo con su discurso científico y su base molecular. Aprovechando cada hueco de desconocimiento ajeno para “meter la chapa” sobre el fundamento de eso que uno no sabe, y dejar así herida a tu víctima. Tratas siempre a los demás como te gustaría que te tratasen a ti. No cambies nunca.

Karmele, tú eres el muñequito del mal que aparece en mi hombro cuando pienso entre hacer lo correcto o lo divertido, tú me dices siempre que haga lo divertido. Así me va. Mil gracias por tus consejos sobre la vida y también por los de “prueba esto”, “cambia lo otro”, pero sobre todo por el de “este paso te lo puedes saltar”, ese me encanta. Gracias por ser mi postdoc.

Marta, hay mil cosas que te puedo agradecer y mil cosas que jamás podría agradecerte. Hemos vivido juntos muchísimos momentos increíbles, dentro y fuera del laboratorio. Sé que me has ayudado en todo lo que has podido y que te has preocupado por mí siempre que me has visto agobiado. Gracias por montar esas cenas, esos planes, esos viajes. Eres la organización en persona y movilizas a cualquiera solo para pasar un rato agradable. Una amiga para toda la vida, eso es lo que me llevo.

Borja y Rodrigo, se espera mucho de vosotros, pero sin presión ¿eh? Borja, en tres años hemos visto cómo pasabas de ser un chaval con pelo amarillo, a un tío hecho y derecho con el que me he reído siempre de cosas políticamente incorrectas. ¡Qué bien sienta quitar el filtro cuando uno está en confianza! Espero que asustes mucho a las mamis en mi ausencia. Rodrigo, nos conocemos poco pero sé que vas a reventar la tesis estando con Silve. Gracias por tus consejos y tus ánimos en este final de tesis, me has ayudado más de lo que te imaginas. Trabaja duro y disfruta del doctorado ¡que merece la pena!

Oihane, sé que estos momentos los recordarás con gran ilusión toda tu vida, además de por mi tesis, ¡porque enseguida nace tu retoño! Me voy sabiendo que dejo a Silve en buenas manos contigo como postdoc.

Ester, fíjate que tardé en empezar a hacerte bromas del miedo que me dabas. Más fría por fuera que el hielo seco, y tan alegre y divertida por dentro. ¡Muchísima suerte!

Iker, Irati y Camino, a vosotros os he conocido muy poquito pero tengo claro que sois fichajes excepcionales para el laboratorio. Gracias por hacerme sentir tan a gusto.

Itzi, Connor, Andrea y Saioa, gracias por haber estado y seguir estando a mi lado. Esas horas de terapia dentro y fuera del “labo” han sido fundamentales para que hoy pueda estar escribiendo estas líneas. Sois las clásicas personas que solo aportan cosas positivas a una relación, y a las que necesitas tener siempre cerca. ¡Haberos conocido no lo cambio por nada!

Gracias a ti, Naiara que, a pesar de estar en el final de tu tesis, perdiste horas y horas en enseñarme todo lo que habías aprendido durante tu doctorado. Arlet y Bea, me quedo con lo que me he reído con vosotras durante vuestro paso.

A los que no sois de mi laboratorio pero habéis decidido conocerme y pasar buenos momentos a mi lado, Marina, Irati, Fran, Esther, María, Montse, Sergio, etc., he disfrutado de momentos increíbles con vosotros. Mil gracias por seguirme el rollo.

A Rubén, Alfonso, Marta y a vuestros equipos: Dani, Ana, Cris, Cris, Mari José, Jackie, Miriam, Marisol, Marc, Marta, Naiara, Virginia, etc., gracias por hacerme sentir siempre muy querido por todos vosotros. Gracias también al resto de las personas del CIMA que me ha ayudado siempre con una sonrisa cuando bajaba o subía de piso para buscarme la vida. A Patxi y Amaia por acogerme esa semanita en vuestro laboratorio. A Raquel, Paula, Javi, Adri y Silvia, “los prospers”, ¡gracias por echarme siempre un cable! Maite y Marina, gracias por haberme ayudado tanto con el final de la tesis y abrirme vuestro despacho por cuarta vez cada día. A Diego y a Idoia, por acudir al rescate tras mi primera lágrima cuando se atascaba el citómetro. A los que integráis todas las plataformas y sin los que todo esto sería imposible, Laura Guembe, Elisabeth, Erika, etc. Gracias a Elena Bodegas, Marian Burrel, Marina, Javi y Ana por los buenos momentos en el departamento. Gracias a las demás personas que con su trabajo hacen posible que todo funcione en el centro: personal de limpieza, mantenimiento, informáticos, administración y secretaría.

Gracias a dos pedazo de mujeres a las que tengo en un pedestal, Marisol Ripa y Sylvie Blanco. Marisol, gestionas y solucionas todo lo que se pueda solucionar, creo que no conozco a nadie tan resolutivo como a la secretaria de nuestro programa, eres una pasada. Sylvie, te digo desde el corazón que eres de las personas que más me alegro de ver cada mañana. Llevas cuatro años dándome los “Buenos días, Adrián” y cada vez que me lo dices, haces que empiece el día con más alegría que con la que entraba.

David, Amaya, Fonso, Hilary, Niamh, Luis, Lucía, Vero, Shannon, Liu y Estefi, mil gracias por hacerme sentir Irlanda mi segunda casa, sois un grupo genial que acoge a los invitados de forma excepcional. Hicisteis que los seis meses se me pasaran volando, gracias por ser tan abiertos y tan buenas personas conmigo.

Gracias a mis amigos de Burgos de toda la vida. A vosotros no tengo que enumeraros, gracias por preguntarme qué tal voy “con eso que estás haciendo” y por escucharme sin saber qué narices es lo que os estoy contando. Gracias por hacer lo imposible por quedar para vernos, y por haberme acompañado todo el camino. Viajes, cenas, fiestas, confidencias… ¡que nunca nada de esto acabe!

Gracias a Yadi, Oihana, Rete, Oihan, Mireya y Yolanda, habéis hecho lo imposible por seguir viéndonos tras acabar la carrera y, cada momento que he pasado a vuestro lado, ha sido pura diversión y alegría. Viajes, cenas, más viajes y más cenas, todas geniales. Sois la felicidad y la naturalidad en persona. ¡Gracias!

¡Huérfanos por Pamplona! El núcleo duro del Café Bar de Pio, expertos levantajarras y reyes del divertimiento, habéis sido lo mejor de la tesis. No os podéis imaginar lo que me alegro de haberos conocido. A Pai, mi villano favorito, porque tú y yo nacimos sin filtro y, cuando nos juntamos, nos desahogamos. Marcos, porque estás a las duras y a las maduras, Marcos siempre está. Me prometiste que al acabar la tesis me enseñarías el truco de descolgar la cara, se acerca la hora. Diego, a ti quiero darte especialmente las gracias porque, no solo me has tratado como a un amigo de toda la vida, sino que me has hecho sitio con tus mejores amigos, que ahora también son míos, me has incluido en todos tus planes y te has preocupado por ser el mejor anfitrión del que puede presumir Pamplona. Eres un diez como persona.

A Jorge, eres un gran amigo pero aún mejor niñera, gracias por cuidarla en el “labo” y en cada vuelta a casa. Gracias también a Peña, Aure, Oscar, Pedro, Matías y Pirata.

A mis padres, Ángel y Elena, sois los mejores padres que un hijo puede desear. Lo habéis dado todo para darme la mejor educación y los mejores recursos, la mayor felicidad y la mejor calidad de vida. Habéis hecho kilómetros y kilómetros para verme "aunque sea una horita" y siempre con una sonrisa y un brillo en los ojos que tendré presente toda la vida. Gracias por creer en mí, por apoyarme, por escucharme, por aconsejarme, por respetar mis decisiones y darme un beso cuando más lo he necesitado. Gracias a ti, Piti, siempre se me hincha el pecho cuando hablo de mi hermana. Eres mi referente, mi amiga, eres el apoyo que quiero tener al lado toda la vida. A mis abuelos, que no entienden lo que hago pero siempre me desean que me vaya bien en la vida. He tenido que irme fuera para sentir que os quiero tanto. Sois mi familia.

Gracias a los Calvo-Bacaicoa mi "reciente" nueva familia. Una caña de familia. Gracias por hacerme sentir como el cuarto de a bordo. Gracias ti, Alba, por compartir esta locura conmigo, no la tesis sino tu vida. Gracias por confiar en mí y apoyarme siempre con los ojos cerrados. Gracias por escucharme, guiarme y mirarme siempre con admiración. Gracias por sonreírme cada mañana, por tu alegría y tu vitalidad, por esa personalidad arrolladora y ese gran corazón que te hace ser tan buena persona. Eres el reflejo de cómo uno debe ser y cómo debe comportarse. Para mi eres perfecta y por eso te quiero.

Gracias a ti, Django, que jamás leerás esta frase. Gracias por recibirme cada día con un abrazo a dos patas y un lengüetazo. Eres el mejor perro que puede querer un ser humano.

“ Por encima de todo, tienes que crear algo de lo que estés orgulloso”

(Richard Branson)

INDEX

INDEX LIST OF ABBREVIATIONS ...... 5 INTRODUCTION ...... 13 1. THE RAS FAMILY ...... 15 1.1 RAS proteins in cancer ...... 16 1.2 RAS downstream signalling pathways ...... 18 1.2.1 The RAF-MEK-ERK pathway ...... 19 1.2.2. The PI3K-AKT-mTOR pathway ...... 19 1.2.3 RALGEF-RAL pathway ...... 20 1.2.4 The RAC1 pathway ...... 21 1.3 Therapeutic strategies to inhibit RAS oncoproteins ...... 21 1.3.1 Targeting post-translational modifications ...... 21 1.3.2 Targeting RAS directly ...... 22 1.3.3. Targeting upstream pathway activators ...... 22 1.3.4 Targeting canonical downstream effectors ...... 23 1.3.5 Targeting RAS distal effectors or synthetic lethal partners ...... 23 1.4 RAS transcriptome analyses ...... 25 1.5 RAS loss-of-function genetic screens ...... 26 2. LUNG CANCER ...... 29 2.1 Lung cancer introduction ...... 29 2.2 Classification of lung cancer ...... 29 2.3 Genomics of lung adenocarcinoma ...... 30 2.4 Therapies for mutant KRAS LUAD ...... 33 3. CHOLANGIOCARCINOMA ...... 35 3.1 Cholangiocarcinoma introduction ...... 35 3.2 Classification of cholangiocarcinoma ...... 37 3.3 Genomics of cholangiocarcinoma...... 38 3.4 Targeted therapies in CCA ...... 39 4. FOS-LIGAND 1...... 41 4.1 Introduction ...... 41 4.2 FOSL1 activity regulation ...... 42 4.3 Transcriptional regulation ...... 43 4.4 Post-translational regulation ...... 44 4.5. FOSL1 biology...... 46

4.5.1 FOSL1 in normal tissue ...... 46 4.5.2 FOSL1 in cancer ...... 46 4.6 FOSL1 functions in cancer ...... 47 4.6.1 Proliferation ...... 47 4.6.2 Apoptosis ...... 48 4.6.3 Cell motility, invasiveness and EMT ...... 48 4.6.4 Angiogenesis ...... 49 HYPOTHESIS AND OBJECTIVES ...... 51 HYPOTHESIS ...... 53 OBJECTIVES ...... 53 MATERIALS AND METHODS ...... 55 1. CELL CULTURE ...... 57 1.1 Cell lines ...... 57 2. ISOLATION AND CULTURE OF MOUSE CHOLANGIOCYTES ...... 57 3. CONSTITUTIVE AND INDUCIBLE shRNA KNOCKDOWN SYSTEM ...... 59 3.1 Short-hairpin RNA sequences design and cloning ...... 59 3.2 Lentiviral production ...... 61 3.3 Cell infection and selection ...... 61 4. IN VITRO PROLIFERATION ASSAYS ...... 61 4.1 MTS Cell proliferation assay ...... 61 4.2 Population doubling time assay ...... 62 4.3 Clonogenic assay ...... 62 5. FOSL1 OVEREXPRESSION SYSTEM ...... 63 6. GENE EXPRESSION ANALISIS ...... 64 6.1 RNA extraction from cell cultures with TRIzol ...... 64 6.2 RNA extraction from cell cultures with Macherey-Nagel RNA isolation kit ..... 64 6.3 Reverse transcription ...... 65 6.4 Real time quantitative PCR ...... 65 7. WESTERN BLOT ...... 66 7.1 Protein extraction ...... 66 7.2 Protein quantification ...... 67 7.3 Protein electrophoresis and transference ...... 67 8. IMMUNOHISTOCHEMISTRY ...... 68 9. APOPTOSIS ASSAY ...... 69 9.1 CellEvent® cleavage Flow Cytometry Assay...... 69 10. CELL CYCLE ASSAY ...... 70 10.1 Click-iT® EdU Flow Cytometry Assay ...... 70 10.2 Phosphorylated Histone H3 (PHH3) Flow Cytometry Labelling Assay ...... 71 11. DRUG COMBINATION STUDIES ...... 71 12. IN VIVO EXPERIMENTS ...... 72 12.1 Xenograft and allograft models ...... 72 12.1.1 Genotypes ...... 72 12.1.2 Subcutaneous xenograft and allograft injection ...... 72 12.1.3 Pharmacological drug administration ...... 73 12.2 Genetically Engineered Mouse Models (GEMMs) ...... 73 12.2.1 Genotypes ...... 73 12.2.3 Lung tumours induction in GEMMs ...... 74 13. TRANSCRIPTOMIC ANALYSIS ...... 74 13.1 Microarray hybridization and data analysis ...... 74 13.2 Low input 3’ end RNA-Seq ...... 75 14. CHROMATIN IMMUNOPRECIPITATION qPCR (ChIP-qPCR) ...... 80 15. GENE SET ENRICHMENT AND GENE ONTOLOGY ANALYSIS OF DATA SETS ...... 81 16. STATISTICAL ANALYSIS ...... 82 RESULTS I: Lung Adenocarcinoma (LUAD) ...... 85 1. Identification of KRAS-dependent candidate genes by a cross-tumours gene- expression screen ...... 87 2. An 8-gene signature conserved across KRAS tumours predicts status and patient survival ...... 89 3. Upregulation of FOSL1 in KRAS-driven human LUAD ...... 93 4. FOSL1 is overexpressed in a mouse model of advanced LUAD ...... 96 5. Mutant KRAS LUAD cells are preferentially sensitive to FOSL1 inhibition in vitro ..... 100 6. Mutant KRAS LUAD cells are preferentially sensitive to FOSL1 inhibition in in vivo. . 104 7. FOSL1 is required for tumorigenesis in a genetically-engineered mouse model of advanced LUAD ...... 108 8. FOSL1 regulates a gene signature associated with poor survival that includes components of the mitotic machinery ...... 110 9. AURKA depletion recapitulates FOSL1 loss phenotype and its pharmacological inhibition in combination with a MEK inhibitor is synergistic in mutant KRAS cells . 114 RESULTS II: Cholangiocarcinoma (CCA) ...... 119

1. FOSL1 is overexpressed in mutant KRAS CCA and its expression is modulated by oncogenic KRAS ...... 120 2. FOSL1 is overexpressed in a genetically-engineered mouse model of CCA characterized by Kras mutation and Trp53 loss ...... 121 3. Mutant KRAS CCA cell lines are sensitive to FOSL1 inhibition in vitro ...... 124 4. FOSL1 inhibition compromises tumour formation and tumour maintenance in vivo 126 5. FOSL1 abrogation extends overall survival and decreases tumour burden in mice ... 129 6. FOSL1 regulates a cluster of genes involved in cholesterol biosynthesis ...... 131 7. FOSL1 directly binds to genes involved in cholesterol biosynthesis and steroid and lipid regulation ...... 133 8. Genetic inhibition of the FOSL1-direct target HMGCS1 recapitulates FOSL1 loss in a KRAS-mutant CCA cell line ...... 136 9. CCA cells are sensitive to pharmacological AURKA inhibition ...... 138 DISCUSSION ...... 141 1. An integrative analysis to unveil oncogenic-KRAS dependencies ...... 143 2. FOSL1 regulation in KRAS-mutated cells ...... 145 3. Molecular mechanisms regulated by FOSL1 ...... 146 4. Inhibition of FOSL1 and its downstream targets as potential therapies ...... 149 CONCLUSIONS ...... 153 REFERENCES ...... 157 ANNEXES ...... 179 PUBLICATIONS ...... 189

LIST OF ABBREVIATIONS

5

Abbreviations

LIST OF ABBREVIATIONS CCA: Cholangiocarcinoma CCLE: Cancer Cell Line Encyclopedia A CCNB1: Cyclin B1 CD44: CD44 Molecule AKT: Protein Kinase B (PKB)/Akt CDC6: Cell Division Cycle 6 ALK: Anaplastic Lymphoma Kinase CDH1: Cadherin 1 AP-1: Activator Protein-1 CDK4: Cyclin-Dependent Kinase 4 APC: Adenomatous Polyposis Coli CDK6: Cyclin-Dependent Kinase 6 APS: Ammonium Persulfate CDKN2A: Cyclin-Dependent Kinase Inhibitor 2A AREG: Amphiregulin cDNA: Complementary DNA ARID1A: AT-Rich Interaction Domain 1A CDS: Coding sequence ATCC: American Type Culture Collection C-FOS: Fos Proto-Oncogene ATF: Activating Transcription Factor ChIP: Chromatin immunoprecipitation ATP: Adenosine Triphosphate CI: Combination index AURKA: Aurora Kinase A CK19: Cytokeratin 19

CK7: Cytokeratin 7 B C-RAF: c-raf proto-oncogene serine/threonine protein kinase BAP1: BRCA1 Associated Protein-1 CRC: Colorectal cancer BCA: Bicinchoninic Acid CREB: cAMP response element binding protein BCL-XL: B-cell Lymphoma-Extra Large CRISPR: Clustered regularly interspaced short BRAF: v-raf Murine Sarcoma Viral Oncogene palindromic repeats homolog B1 CTNNB1: Catenin Beta 1 BSA: Bovine Serum Albumin bZIP: Basic Leucine-Zipper D

C dCCA: Distal Cholangiocarcinoma DDR2: Discoidin Domain Receptor Tyrosine CA: Clonogenic assay Kinase 2 Cas9: CRISPR associated protein 9

7

Abbreviations

DEPC: Diethylpyrocarbonate FC: Fold Change DMEM: Dulbecco's Modified Eagle Medium FDR: False Discovery Rate DMSO: Dimethyl Sulfoxide FGFR1: Fibroblast Growth Factor Receptor 1 DNA: Deoxyribonucleic Acid FGFR2: Fibroblast Growth Factor Receptor 2 dsRNA: Double-Stranded RNA FOSB: FosB Proto-Oncogene DTT: Dithiothreitol FOSL1: Fos-Ligand 1 DUSP4: Dual Specificity Phosphatase 4 FOSL2: Fos-Ligand 2 DUSP6: Dual Specificity Phosphatase 6 FPP: Farnesyl-Pyrophosphate FRA1: Fos-Related Antigen 1 FTIs: Farnesyl Transferase Inhibitors E EDTA: Ethylenediamine Tetraacetic Acid G EdU: 5-Ethynyl-2´-Deoxyuridine

EGF: Epidermal Growth Factor GAPDH: Glyceraldehyde-3-Phosphate EGFR: Epidermal Growth Factor Receptor Dehydrogenase ELK1: ETS Transcription Factor ELK1 GAPs: GTPase-Activating Proteins ELOVL5: Elongation Of Very Long Chain Fatty GATA2: GATA Binding Protein 2 Acids Protein 5 GBM: Glioblastoma EML4: Chinoderm Microtubule-Associated GDP: Guanosine Diphosphate Protein-Like 4 GEFs: Guanine Nucleotide Exchange-Factors EMT: Epithelial–Mesenchymal Transition GEMMs: Genetically-Engineered Mouse Models ERBB: Erb-B2 Receptor Tyrosine Kinase GEO: Gene Expression Omnibus ERK1: Mitogen-Activated Protein Kinase 3 GGPP: Geranyl-geranyl pyrophosphate ERK2: Mitogen-Activated Protein Kinase 1 GLRX: Glutaredoxin ERK5: Mitogen-Activated Protein Kinase 7 GO: Gene Ontology GPP: Genetic Perturbation Platform F GSEA: Gene Set Enrichment Analysis GTP: Guanosine Triphosphate

FAK: Focal Adhesion Kinase GUSβ: Beta-Glucuronidase FBS: Fetal Bovine Serum

Abbreviations

H J

HCC: Hepatocellular Carcinoma JAK1: Janus Kinase 1 HDAC6: Histone Deacetylase 6 JAK2: Janus Kinase 2 HE: Hematoxylin - eosin JUN: Jun Proto-Oncogene HER2: Erb-B2 Receptor Tyrosine Kinase 2 JUNB: JunB Proto-Oncogene HH3: Histone H3 JUND: JunD Proto-Oncogene HK2: Hexokinase 2 HMGCS1: 3-Hydroxy-3-Methylglutaryl-CoA Synthase K HRAS: Harvey Rat Sarcoma Viral Oncogene KRAS: Kirsten Rat Sarcoma Viral Oncogene Homolog Homolog HRP: Horseradish Peroxidase KEAP1: Kelch-Like ECH Associated Protein 1 HSCc: Hepatic Stem Cells KI-67: Marker Of Proliferation Ki-67

HSP90: Heat Shock Protein 90kDa Alpha L I LAMC2: Laminin Subunit Gamma 2

IBDUs: Intrahepatic Bile Duct Units LCC: Large Cell Carcinoma iCCA: Intrahepatic Cholangiocarcinoma LIMMA: Linear Models for Microarray Data IDH1: Isocitrate Dehydrogenase 1 LKB1: Serine/Threonine Kinase 11 (STK11) IDH2: Isocitrate Dehydrogenase 2 LUAD: Lung Adenocarcinoma IDI1: Isopentenyl-Diphosphate Delta LUSC: Lung Squamous Cell Carcinoma Isomerase 1 IHC: Immunochemistry IL-6: Interleukin 6 M INSIG1: Insulin Induced Gene 1 MAPK: Mitogen-Activated Protein Kinase IP: Immunoprecipitation MEFs: Mouse embryonic Fibroblasts IPBN: Intraductal Papillary Biliary Neoplasms MEK1: Mitogen-Activated Protein Kinase Kinase 1

9

Abbreviations

MEK2: Mitogen-Activated Protein Kinase Kinase 2 NFƙB: Nuclear Factor Kappa B Subunit MEK5: Mitogen-Activated Protein Kinase Kinase 5 NGS: Next Generation Sequencing MET: MET Proto-oncogene NIH: National Institutes of Health MM: Multiple Myeloma NRAS: Neuroblastoma RAS Viral Oncogene MMP1: Matrix Metallopeptidase 9 Homolog MMP-9: Matrix Metallopeptidase 9 NSCLC: Non-small cell lung carcinoma MOX2: Mesenchyme Homeobox 2 NT: Nucleotides mRNA: Messenger RNA NTRK: Neurotrophic Receptor Tyrosine Kinase MSMO1: Methylsterol Monooxygenase 1 MT-ND3: Mitochondrially Encoded NADH Dehydrogenase 3 O

MT-ND4: Mitochondrially Encoded NADH OR: Odds Ratio Dehydrogenase 4 OS: Overall Survival MT-ND5: Mitochondrially Encoded NADH Dehydrogenase 5 mTOR: Mechanistic Target Of Rapamycin Kinase P MTS: 3-(4,5-Dimethylthiazol-2-yl)-5-(3- p53: Tumour Protein P53 (TP53) Carboxymethoxyphenyl)-2-(4-Sulfophenyl)-2H- PBRM1: Polybromo 1 Tetrazolium MVA: Mevalonate PBS: Phosphate Buffered Saline MYC: MYC proto-Oncogene PBS-T: Phosphate Buffered Saline-Tween ΜMI: Unique Molecular Identifier PC: Pancreatic Cancer pCCA: Perihiliar Cholangiocarcinoma PCR: Polymerase Chain Reaction N PD-1: Programmed Cell Death 1 PDAC: Pancreatic Ductal Adenocarcinoma NCBI: National Center for Biotechnology PD-L1: Programmed Cell Death 1 Ligand 1 Information PDX: Patient-Derived Xenografts NCI: National Cancer Institute PFU: Plaque-Forming Unit NEB: New England Biolabs PHLDA1: Pleckstrin Homology Like Domain Family

A Member 1

Abbreviations

PI3K: Phosphoinositide-3-Kinase RGL2: Ral Guanine Nucleotide Dissociation PIM1: Pim-1 Proto-Oncogene, Serine/Threonine Stimulator Like 2 Kinase RMA: Robust Multi-Array Average PKCθ: Protein Kinase C Theta RNA: Ribonucleic Acid PLCε: Phospholipase C Epsilon RNA-seq: Ribonucleic Acid Sequencing PLK1: Polo Like Kinase 1 ROC: Receiver Operator Characteristics Curve PMSF: Phenylmethylsulfonyl Fluoride ROS1: c-ros Oncogene PNK: T4-Polynucleotide Kinase RPM: Revolutions Per Minute PTEN: Phosphatase And Tensin Homolog RQI: RNA Quality Indicator PTGS1: Prostaglandin-Endoperoxide Synthase 1 RT: Room Temperature RT-qPCR: Real Time Quantitative- Polymerase Chain Reaction Q qPCR: Quantitative- Polymerase Chain Reaction S

SCD: Stearoyl-CoA Desaturase R SDS: Sodium Dodecyl Sulfate shRNA: Short Hairpin RNAs RAF: RAF Proto-Oncogene Serine/Threonine- siRNA: Small Inhibitory RNA Protein Kinase SMAD4: Mothers Against Decapentaplegic RALA: RAS-Like Proto-Oncogene A Homolog 4 RALB: RAS-Like Proto-Oncogene B SNAIL2: Snail Family Transcriptional Repressor 2 RalGDS: Ral Guanine Nucleotide Dissociation SPAK1: Ste20-related Proline-alanine-rich kinase Stimulator (STK39) RAS: Rat Sarcoma Virus SPRY4: Sprouty RTK Signaling Antagonist 4 RB1: Retinoblastoma Protein 1 SRE: Serum response element RCE1: Retinoblastoma Control Element SREBPs: Sterol Regulatory Element-binding RET: Ret Proto-Oncogene

Proteins

11

Abbreviations

STK33: Serine/Threonine Kinase 33 V SWI/SNF: SWItch/Sucrose Non Fermentable complex VEGF: Vascular Endothelial Growth Factor VEGFR: Vascular Endothelial Growth Factor Receptor T VMC: Von Meyenburg Complexes

TACC3: Transforming Acidic Coiled-Coil VO: Vanadate Containing Protein 3 TAK1: Mitogen-Activated Protein Kinase Kinase Kinase 7 W

TBK1: TANK-Binding Kinase 1 WB: Western Blot TCF: T-cell transcription Factor Family WHO: World Health Organization TERT: Telomerase Reverse Transcriptase WNT: Wingless-Type MMTV Integration Site TF: Transcription Factor Family TGFβR2: Transforming Growth Factor Beta WT1: Wilms’ Tumor 1 Receptor 2 TIAM1: T Cell Lymphoma Invasion And Metastasis 1 X TKI: Tyrosine kinase Inhibitor XPO1: Exportin 1 TMB: Tumour Mutational Burden

TP53: Tumour Protein P53 TRE: TPA-Response Element Y

U Z

UMI: Unique Molecular Identifier

INTRODUCTION

13

Introduction

INTRODUCTION

1. THE RAS FAMILY

Rat sarcoma viral oncogene homologue (RAS) proteins, including Kirsten RAS (KRAS), Harvey RAS (HRAS) and Neuroblastoma RAS (NRAS), belong to the RAS family of small GTPases that are key players in the signal transduction of eukaryotic organisms. The RAS genes encode for transducer proteins that operate as binary switches that transition between a GDP-bound inactive and a GTP-bound active state [6]. In the active state, RAS proteins are involved in transmitting signals from extracellular growth factor receptors like EGFR to intracellular effectors. This signal transduction results in the control of a plethora of cellular functions such as proliferation, motility, survival, apoptosis and others [7, 8], highlighting the relevance of the RAS family for cell homeostasis.

RAS proteins are normally located in the cytoplasm but relocalize to the intracellular side of the plasma membrane upon addition of farnesyl and geranylgeranyl groups by cytoplasmic transferases [9-11]. At the plasma membrane, the transition of RAS proteins between the inactive and active states is regulated by guanine nucleotide exchange-factors (GEFs) and GTPase-activating proteins (GAPs), which are also recruited to the plasma membrane and anchored next to RAS to activate or repress its activity by direct binding. The RAS active status is regulated by GEFs and GAPs by boosting GTP to GDP exchange, or by rapidly expediting RAS- mediated GTP hydrolysis respectively [12]. In the active state, the RAS-GTP complex binds and activates multiple downstream signalling effectors such as the BRAF-MEK- ERK, the PI3K-AKT-mTOR, RalGDS-RalA/B and TIAM1-RAC1 pathways [13-15] (Figure I1).

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Introduction

Figure I1: RAS signalling downstream pathways and effectors. 1) RAF protein kinases and mitogen-activated protein (MAP) kinase cascade. 2) Type I phosphoinositide 3-kinases (PI3Ks) pathway and target proteins. 3) RALGDS-RAL guanine nucleotide exchange factors (GEFs). 4) MAP kinase kinase kinase pathway (MEKK). 5) Phospholipase Cε (PLCε) and protein kinase C (PKC) calcium pathway.

1.1 RAS proteins in cancer

Ras genes were the first oncogenes identified in human cancer cells [16-19] and constitute the most frequently mutated gene family in human cancer, in approximately one-third of cases. Among RAS proteins, oncogenic mutations are found predominantly in KRAS isoform (85%) compared to NRAS (12%) and HRAS (3%) isoforms [20] (cBioPortal/ COSMICv80). KRAS is present in approximately 25% of non- small cell lung cancer (NSCLC), 90% of pancreatic cancer (PC), 40% of colon cancer (CC) and 20% of biliary tract cancers, a group of tumours including gallbladder cancer and cholangiocarcinomas (CCA) [8, 14, 15, 21]. NRAS is frequently mutated in malignant

Introduction melanoma (20%) and acute myelogenous leukemia (15%), while HRAS mutations predominantly occur in bladder cancer (12%) and seminoma (19%) [14, 22].

Most RAS missense mutations befall at codons 12, 13 or 61 as single amino acid substitutions, impeding GAPs to reach the GTPase site and triggering the subsequent failure of GTP hydrolysis. For decades, it had been thought that this failed inactivation of RAS activity led to a permanent constitutively active GTP-bound “on state” causing an uncontrolled activation of downstream signalling pathways [23-25]. However, recent work by Patricelli and colleagues, using a small molecule (ARS-853) that preferentially binds to the GDP-bound state of KRASG12C and reduces GEFs activity by impeding GTP binding [26], showed that oncogenic RAS proteins undergo nucleotide cycling in cancer cells and proved that its GTPase activity is not disabled in RAS mutants [26, 27] (Figure I2). Other mutations such as A146, T158A, R164Q and K176Q provoke an even more rapid dissociation of GDP resulting in an aberrant accumulation of the RAS-GTP form [20].

The potent cancer-inducing activity of the various RAS members, including that of different types of mutations in RAS genes, has been demonstrated in vivo by numerous genetically-engineered mouse models (GEMMs) of RAS-driven cancers [21, 28-34]. Additionally, consistent with the need for additional genetic alterations to cooperate with mutant RAS for full transforming activity, loss of tumour suppressor function (e.g., TP53, LKB1, APC) together with RAS activation results in enhanced tumour formation and progression [35-38]. Furthermore, genetic inhibition of RAS in established tumours yields almost a complete regression of tumours, indicating that mutant RAS tumours are highly addicted to oncogene activity [31, 39-42].

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Introduction

Figure I2: RAS activation and deactivation under normal and oncogenic conditions. (Left) RAS is activated by GEFs (green) through GTP binding, regulating cellular processes through the controlled signalling of its canonical downstream effectors. Under normal conditions, to guarantee cell homeostasis, RAS-GTP form is rapidly inactivated by GAPs (red) resting in a RAS-GDP bound form. Highlighted in faint blue is the prevalent reaction and RAS-state in cell homeostasis. (Right) RAS is activated by GEFs through GTP binding but remains active if a G12, G13 or Q61 mutation (between others) occur. GAPs-mediated GTP hydrolysis is blocked and a permanent uncontrolled downstream signalling lead cells to promote oncogenesis. Highlighted in faint orange is the prevalent reaction and RAS-state in cell oncogenesis. Adapted figure from Zeitouni, 2016.

1.2 RAS downstream signalling pathways

RAS oncogenes activity is mediated by various downstream signalling cascades that collectively rely on intracellular signals to induce the oncogenic phenotype. This signalling cascades include the mitogen-activated protein kinase MAPK pathway [43, 44], the phosphatidyl inositol 3-kinase PI3K-AKT-mTOR route [45, 46] and the Ras-like

Introduction

(RAL) small GTPases family of guanine nucleotide exchange factors (GEFs) RALGEF-RAL [47] pathways.

1.2.1 The RAF-MEK-ERK pathway

The RAF-MEK-ERK pathway is considered the most critical effector in RAS oncogenesis. In human, mutations in elements of this pathway such as BRAF and MEK have been reported in lung and colorectal cancer, as well as in melanoma [48-51]. Infrequent co-occurrence with RAS mutations, provides compelling evidence for a driver role in RAS-mutant cancers [52-58]. Additionally, gain- and loss-of-functions experiments using GEMMs have demonstrated the implication of pathway members in RAS oncogenesis. On the one hand, RAF-transgenic mice expressing a constitutively active form of Craf or a mutation in B-Raf gene (BRAFV600E) resulted in the development of tumours characterized by increased levels of phosphorylated ERK and high rates of cell proliferation and invasion [59-62]. On the other hand, genetic inhibition of C-Raf, Mek1/Mek2 or Erk1/Erk2 drastically impaired growth of mutant RAS lung and pancreatic tumours [63, 64]. Altogether, these data highlight a central role of this signalling cascade in oncogenic RAS biology.

1.2.2. The PI3K-AKT-mTOR pathway

The PI3K-AKT-mTOR pathway also functions as an important oncogenic signalling cascade to promotes RAS tumorigenesis [45]. Mice expressing a Pi3k/p110α mutant form failed to bind RAS and were almost resistant to the development of lung [65-67] or pancreatic [45] RAS-driven tumours, and even yielded complete regression of mutant RAS lung tumours. The relevance of continued PI3K signalling in tumour maintenance was also demonstrated in RAS-mutant human tumour cells, where activated p110α was able to substitute for the loss of mutant RAS to sustain tumour growth [68]. Despite these exciting results, there is evidence in similar mouse models that pharmacologic inhibition of this pathway in Kras-driven mouse lung cancers did

19

Introduction not effectively block tumour growth [69]. Indeed, in others mutant RAS tumours like colorectal cancer (CRC) the role of this pathway is residual [70, 71], suggesting that the dependency to this signalling arm may also be in some grade tissue specific. Along these lines, RAS mutations often are seen together with mutations in elements of this pathway ((PIK3CA, PTEN), unlike non-overlapping BRAF and RAS mutations seen in human cancers, a pattern that suggests that mutated RAS alone may not potently activate PI3K signalling.

1.2.3 RALGEF-RAL pathway

A third effector pathway largely validated in RAS-driven cancer is the RALGEF- RAL pathway. RalGDS knockout mice were viable but partially refractory to carcinogen- induced mutant Hras-driven skin tumour formation [72]. Another RalGEF, Rgl2, was found overexpressed in KRAS-mutant CRC cell lines, and Rgl2 RNAi-based knock-down reduced Ral-GTP levels and impaired cell growth in vitro [73].

RalGEFs activate two RAL gene isoforms, RALA and RALB. RNA-based silencing of RALA and/or RALB impaired growth in KRAS-mutant pancreatic cancer cell lines. Notably RALA and RALB exhibit very distinct roles in cancer cell behaviour in experiments with cell lines. For example, RALA but not RALB was found to be necessary for the tumourigenic growth of pancreatic cancer cell lines, whereas RALB was important for invasion in vitro and metastasis in vivo [74]. In contrast, in GEMMs with conditional tissue-specific knockout of these genes, both RalA and RalB loss was necessary to decrease Ras-driven lung or skin tumour growth [75]. Collectively, these observations may reflect distinct roles for RAL GTPases in early and late stages of tumour carcinogenesis, or cancer-type differences.

Introduction

1.2.4 The RAC1 pathway

Other most recently identified RAS effectors involve RAC1. Rac1 can be activated by RAS directly via direct effector binding (e.g., Tiam1) [76] or indirectly through PI3K activation (e.g., P-Rex) [77]. The importance of Rac1 as a driver of cancer growth is supported by the identification of mutations activating RAC1 in melanomas [78, 79]. Furthermore, Rac1 ablation in GEM models impaired initiation of mutant RAS- driven cancers of the lung and pancreas [80, 81].

1.3 Therapeutic strategies to inhibit RAS oncoproteins

Multiple inhibition approaches have been attempted to target RAS-mutant tumours since the discovery of RAS oncogenes over 3 decades ago. These inhibitory approaches have ranged from preventing the binding of KRAS to the cell membrane, directly inhibiting KRAS, and blocking upstream activators of KRAS or canonical KRAS effector pathways, to inhibition of tumour-specific vulnerabilities or synthetic lethal interactions. Despite most efforts have yielded limited or no success in the clinic, some of the knowledge obtained along the way may be fruitful in the overall goal of treating cancer patients with RAS mutations (Figure I3).

1.3.1 Targeting post-translational modifications

As explained earlier, RAS proteins require the addition of a farnesyl group to re- localize to the plasma membrane. Farnesyl transferase inhibitors (FTIs), such as salirasib or tipifarnib, were developed to prevent this irreversible process. While this inhibitors have some effect in mutant HRAS tumours, KRAS and NRAS isoforms undergo alternative posttranscriptional modification by geranylgeranyl transferases [82, 83] thus bypassing the effect of FTIs [84] (Figure I3i).

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Introduction

1.3.2 Targeting RAS directly

RAS oncoproteins have been widely perceived as an undruggable target for many years. However, recently there has been a great interest in developing direct inhibitors for this oncogene, particularly instigated by the ‘‘RAS initiative’’ of the National Cancer Institute (NCI) in the US. In the last years different approaches to directly inhibit RAS have been reported. These include: 1) an irreversible allosteric inhibitor of G12C RAS to prevent GTP-KRAS formation [85], 2) compounds targeting the guanine nucleotide binding pocket [86, 87] or allele-specific inhibitors that favour the inactive state, making drug-bound KRAS G12C unsusceptible to nucleotide exchange factors [88] and 3) small molecules targeting the GDP-bound form [26]. In all cases, encouraging results have been found in vitro upon RAS blockade. More recently, a covalent compound with high potency and selectivity for KRAS G12C (ARS- 1620) has also shown promising therapeutic activity in vivo [89], leading to its investigation in clinical trials. Despite the exciting results of those compounds in preclinical models, long-term and in vivo efficacy remain as standing hurdles, mostly due to low binding affinity [90], their elevated toxic effects [91] or even resistance mechanisms arising in those tumours where RAS inhibition may be achievable in the future [92, 93] (Figure I3ii).

1.3.3. Targeting upstream pathway activators

RAS proteins can be activated by upstream tyrosine kinases such as EGFR. Indeed, RAS-mutant cells bring about autocrine growth factors such as EGF, which bind to tyrosine-kinase receptors [94]. Several clinical studies in LUAD and CRC have shown that patients harbouring KRAS mutations are not sensitive to EGFR tyrosine kinase inhibitors (TKIs) [95-97]. In CRC, mutations in codon 12 of KRAS, but not those in codon 13, are behind the lack of response to EGFR inhibitors and serve to exclude patients from treatment. However, recent preclinical studies suggest that EGFR inhibition initiates a rapid resistance mechanism involving non-EGFR ERBB family

Introduction members that can be circumvented by treatment with pan-ERBB inhibitors, either alone or in combination with inhibitors of KRAS downstream effectors [98, 99]. Although these later findings need to be tested in the clinic, they may provide new avenues for patient treatment (Figure I3iii).

1.3.4 Targeting canonical downstream effectors

Extensive efforts targeting elements of canonical RAS pathways such as the RAF-MEK-ERK and the PI3K-AKT-mTOR routes have been undertaken. These inhibitory strategies failed mainly due to the activation of compensatory mechanisms through rewiring of alternative kinase modules [100-102]. For instance, RAF inhibitors led to the paradoxical activation of MEK-ERK proteins in mutant RAS tumours, analogously to what is observed in BRAF-mutant tumours [62, 103-106]. In RAS tumours treated with MEK1/2 inhibitors resistance mechanisms often involve reactivation of ERK [107-114]. Pharmacological inhibition of the PI3K pathway reveals resistance mechanisms involving reactivation of the MEK-ERK module. Based on these observations, efficacy of BRAF and MEK inhibitors or that of PI3K inhibitors could be enhanced by concomitant addition of ERK or MEK inhibitors respectively. Other most recently identified RAS effectors including TIAM, RAC and PLCε pathways are currently being investigated, and their therapeutic value for RAS mutant patients has not yet been proven (Figure I3iv).

1.3.5 Targeting RAS distal effectors or synthetic lethal partners

Upon oncogene activation normal cellular pathways are co-opted, either directly or indirectly, to foster prominent phenotypic changes in cancer cells collectively known as the ‘‘hallmarks of cancer’’ [115]. This often manifests in one of two ways. First, RAS oncogenic transformation leads cells to adapt their signalling pathways to elevated cellular stress (oncogenic stress), which includes DNA damage/replication stress, proteotoxic stress, mitotic stress, metabolic stress or oxidative stress [116-118]. This requires the cancer cell to activate cellular stress-relief

23

Introduction mechanisms and pathways for survival through the expression of distal effectors genes. Second, cancer cells can alter their metabolic flux to support proliferation, what requires compensatory changes in pathways that run orthogonally to the driver oncogene. These “non-oncogene addictions” constitute potential synthetic lethal interactions that may be exploitable for therapeutic intervention [119]. In both cases, studying the complexity of those gene expression changes driven by oncogene signalling rewiring requires approaches beyond the old-fashioned one-gene-at-a-time approach. With the advent of functional genomics approaches, such as transcriptome analyses and genetic screens, this endeavour has been greatly facilitated (Figure I3v).

Figure I3: Schematic representation of therapeutic strategies to inhibit RAS oncoproteins. i)Targeting post-translational modifications by inhibiting RAS membrane association. ii) Targeting RAS molecule directly. iii) Inhibition of upstream tyrosine-kinase receptors. iv) Inhibition of downstream canonical RAS pathways. v) Inhibition of RAS distal effectors or synthetic lethal partners.

Introduction

1.4 RAS transcriptome analyses

Mutations in RAS genes are responsible for activation of a gene expression program that influences the oncogenic phenotype. Thus, gene-expression profiling provides a tractable strategy to expose novel components of the oncogenic RAS network.

RAS signatures from single experimental systems or tumour types that classify patients according to RAS genotype [120, 121] and RAS dependency [122], or that function as markers of survival outcome [123-125] have been reported. The first RAS gene expression signature described was generated by the Nevins’ group by forced HRAS oncogene expression in mouse embryonic fibroblasts (MEFs). The resulting expression changes evoked by HRAS overexpression in normal cells were characteristic of the RAS oncogene phenotype and could predict the activity of in vivo tumour models that result from deregulation of HRAS pathway [126]. A year later, Sweet- Cordero and colleagues implemented a cross-species approach integrating mouse and human data to unveil a gene expression signature of KRAS mutation in human lung cancer, demonstrating that gene-expression data from mice can faithfully infer aspects human biology [120]. Later on, follow-up work from the Nevins’ group generated a novel expression signature by overexpressing oncogenic HRAS in human primary cells. This signature also accurately predicted RAS pathway activation in mouse and human tumours harbouring RAS mutations, but more importantly, its high levels correlated with lower survival in lung cancer [123]. Furthermore, Chang and colleagues developed a slightly different approach deconstructing a gene signature into sections representing smaller components of the RAS pathway. This study reported that specific sections of a RAS gene expression signature were capable to distinguish high- and low- risk survival populations in LUAD better than the entire signature [124], thus demonstrating the advantage of using gene expression signatures for dissecting and better understanding the RAS signalling network.

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Introduction

More recent expression signatures have also been published including the one predicting KRAS-dependence. This ‘‘KRAS-dependency signature’’ revealed regulators of the EMT and tumour cell survival through the establishment of a gene expression signature that defined the actual addiction to KRAS oncogene of lung and pancreatic cancer tumours harbouring KRAS mutations [122].

Altogether, these data demonstrate that methods for using genes expression signatures to analyse and measure RAS pathway activity goes beyond the classical use of single genes to determine RAS activation, which does not always depict pathway activity. Nonetheless, straight comparison of some of these so-called RAS signatures has shown little overlap [121]. This suggests that the transcriptional response is highly dependent on the tissue of origin and raises the question whether a core of genes relevant for mutant KRAS biology is preserved across different tumour types. Integration of multiple laboratory and clinical gene-expression data from different tissues spanning mouse and human species would address this question but has yet not been described. Specifically, this endeavour would be greatly facilitated by using the wealth of publicly-available information from early and recent genomics studies in tumours where RAS mutations are often found.

1.5 RAS loss-of-function genetic screens

Starting a decade ago, a large number of loss-of-function screens have been performed to identify KRAS vulnerabilities based on the concept of synthetic lethality. Two genes are synthetic lethal when the perturbation of either gene alone is viable but the perturbation of both genes simultaneously results in the loss of viability [127]. Loss-of-function genetic screens carried out can vary in 1) the type of gene inhibition technologies, such as small interfering RNA (siRNA), short hairpin RNA (shRNA) or CRISPR/Cas9, 2) the screening platforms (pooled versus arrayed), 3) the number of cell lines and their tissue of origin, 4) the readout methodologies (NGS, Luminex, fluorimetry) and 5) the deconvolution algorithms (Z score, ATARI, REVEALER).

Introduction

RAS loss-of-function screens have unveiled a plethora of genes including PLK1, APC/C [128], TBK1 [129], STK33 [130], WT1 [131], SNAIL2 [132], TAK1-MAP3K7 [133], GATA2 [134], CDC6 [135] and XPO1 [136] which expression was indispensable for cell proliferation and viability in KRAS mutant cancers compared to wild-type in a context- selective manner [137]. Some of the synthetic lethal interactors amenable to pharmacological blockade were inhibited in preclinical or clinical trials (STK33 or TBK1/NFƙB) with no inhibitory effects, while others are demonstrating effectiveness in preliminary data from clinical trials in a tissue specific context (TBK1-i in combination with MEK inhibition). Momelotinib (PAN kinase inhibitor of JAK1/2 and TBK1/NFƙB and IKKe kinases) showed strong preclinical effects in vitro in KRAS-mutant tumours [138] but failed in Phase 1 clinical trials in combination with Trametinib [139].

Alternative screens have also developed to determine the compensatory responses of KRAS-mutant tumours to MEK inhibitors. These studies have identified B- cell lymphoma-extra large (BCL-XL) [140] or FGFR1 [114] expression as potential resistance mechanisms to MEK inhibitors. The combination of Navitoclax (Bcl-XL/Bcl2 inhibitor) and Trametinib (MEK inhibitor) showed synergistic effects in KRAS mutant CRC xenografts [140], and this combination is currently being tested in a Phase I/II clinical trial (NCT02079740) in subjects with KRAS or NRAS mutation-positive advanced or metastatic solid tumours. In summary, loss-of-function screens offer opportunities to unveil new RAS vulnerabilities that could eventually be tested pharmacologically as single or combinatorial inhibitory strategies.

One recurrent theme arising from KRAS loss-of-function screens is their little overlap, although proteasome subunits have been identified in three screens [128, 129, 135]. For one part, the lack of significant overlap can be attributed to technical differences related to screen set-up and data deconvolution analyses. However, the patterns of oncogene and non-oncogene addictions in the presence of RAS oncogenes may be context-dependent. This context dependency can be influenced not only by secondary mutations that could alter the patterns of genetic dependency [141, 142] but also by the high influence of the tissue of origin, which could drive genetic

27

Introduction dependencies in a tissue-specific fashion. If one of the goals in the RAS field is the identification of molecular targets that could be targeted in as many RAS tumours as possible, unveiling oncogene vulnerabilities that operate across RAS tumours is of paramount importance.

Introduction

2. LUNG CANCER

2.1 Lung cancer introduction

Lung cancer is the leading cause of death by cancer worldwide [143, 144]. Despite striving prevention campaigns and early diagnosis efforts, lung cancer is the most common cancer in terms of incidence and mortality, accounting for 18-25 % of all cancer deaths [145] (World Health Organization, 2018). At the time of diagnosis, the vast majority of patients have already developed advanced disease and present a median 5-year survival rate lower than 18% [146]. This markedly poor prognosis is a result of the advanced stage of most diagnosed lung tumours, the lack of universally- implemented population screening programs for early-stage diagnosis, and the absence of effective therapies for a large fraction of patients [147]. Therefore, lung cancer represents a social and economic burden that makes it a public health priority.

2.2 Classification of lung cancer

Lung cancers have been traditionally classified based on histological features. More recently, immunohistochemical information and genomic data have been incorporated to the last edition of the World Health Organization (4th edition, 2015) to complement the histological classification [148]. The WHO histopathological classification defines two main lung cancer groups: non-small cell lung cancer (NSCLC), which accounts for approximately 85% of the cases, and small cell lung cancer (SCLC), accounting for the remaining cases. NSCLC is in turn subdivided in lung adenocarcinoma (LUAD), squamous cell carcinoma (SSC) and large cell carcinoma (LCC). LUAD is the dominant NSCLC subtype (approximately 40% of cases), where mutations in KRAS oncogene as well as other driver genetic alterations are found [149] (Lungevity.org/2019) (Figure I4).

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Introduction

2.3 Genomics of lung adenocarcinoma

With the advent of next generation sequencing (NGS) technologies, a large number of genetic alterations have been described in LUAD in the last decade [150- 152]. These and other studies have allowed a comprehensive analysis of the genetic and epigenetic basis of LUAD, establishing the scientific rationale for the development of novel targeted therapies. LUAD is featured by mutations in KRAS (in over 25% of cases), EGFR (17%), and fusions of anaplastic lymphoma kinase (ALK,7%), as well as other less-prevalently mutations of MET proto-oncogene (MET, 3%), v-raf murine sarcoma viral oncogene homolog B1 (BRAF,3%), RET1 (2%) and PI3CA (1%) and fusions of c-ros oncogene 1 (ROS1, 2%) (Lungevity.org/2019) (Figure I4).

Figure I4: Lung cancer classification based on cell type histology and driver mutations. (Left) Pie chart depicting classification of lung cancer including NSCLC (LUAD, SCC and LCC) and SCLC. (Right) LUAD genomic profile including most frequent genomic alterations in oncogenic drivers. Figure adapted from Lungevity.org/2019.

Introduction

KRAS mutations are the most prevalent in LUAD and are present almost exclusively in smokers [151, 153]. Mutations occur in codons 12, 13 and 61 of KRAS gene, being the ones in codon 12 the most frequently found, especially those with the substitution of glycine with cytosine (G12C) or valine (G12V). Mutations in KRAS may influence overall prognosis and G12C mutations have been associated with the worst prognosis [154, 155]. Furthermore, KRAS-mutated NSCLC patients with concurrent mutations in tumour suppressor genes such as STK11/LKB1 or CDKN2A show a worse prognosis than those harbouring TP53 mutations [142].

LUAD patients harbouring KRAS mutations show a shorter median survival (2.4 years) compared to patients harbouring other actionable drivers such as EGFR (3.97 years) and ALK (4.25 years) [156-158] (Figure I5). KRAS mutations are generally mutually exclusive with other oncogenic drivers frequently found in LUAD such as EGFR, ALK or BRAF, for which existing targeted therapies are showing significant clinical efficacy [156-159]. Notably, KRAS mutations can influence treatment response to some targeted therapies aforementioned [160, 161]. In the particular case of BRAF inhibitors, mutant KRAS-driven tumourigenesis is paradoxically further enhanced through ERK phosphorylation after CRAF (c-raf proto-oncogene serine/threonine protein kinase) activation [105], calling for concomitant MEK and ERK inhibition as a means to inhibit oncogenic KRAS as reported in preclinical models of NSCLC [114, 162]. Lastly, KRAS mutation arise as a mechanism of acquired resistance to therapy with EGFR and EML4-ALK inhibitors in LUAD patients [163-165].

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Introduction

Figure I5: Survival probability of patients harbouring the 5 most frequent oncogenic LUAD oncogenic driver mutations. Median survival (95% CI): EGFR sensitizing (s), 3.97 years; EGFR others (o), 2.70 years; ALK, 4.25 years; KRAS, 2.41 years; doubletons (oncogenic drivers in 2 genes), 2.03 years. Vertical tick marks are censoring events. Figure adapted from Kris, 2006.

The critical role of Kras in LUAD initiation and progression has been demonstrated genetically in mice. In 2001, Johnson and colleagues developed a spontaneous Kras-mutant mouse model by replacing the endogenous Kras allele with a latent mutant KrasG12D allele that developed lung tumours with a 100% penetrance, progressing from hyperplastic lesions to alveolar adenomas and lastly adenocarcinomas. This model strongly recapitulated human LUAD as analysed tumours were positive for alveolar type II markers SPC and SPA [28] and expressed a gene signature common to mutant KRAS LUAD patients [120]. Newer mouse models were developed almost simultaneously by Mariano Barbacid’s and Tyler Jacks’ groups where the expression of KrasG12V or KrasG12D mutation is controlled by loxP sites, allowing control of the timing, location and multiplicity of tumour initiation [29, 30]. These

Introduction models have remained as the standard transgenic models for oncogenic Kras studies of LUAD as well as other KRAS-driven tumours. More complex versions of these conditional models, in combination with additional genetic events concurrently found in human LUAD, such as TP53, LKB1/STK11 or KEAP1 mutations have been also described [36, 166-168], which faithfully recapitulate the behaviour of the distinct tumour subtypes found in human.

2.4 Therapies for mutant KRAS LUAD

Despite remarkable clinical advances regarding targeted therapies in LUAD and the promising results in preclinical models [69, 169], targeted therapies for KRAS- mutant LUAD have proven unsuccessful. Briefly, clinical trials testing the use of inhibitors to EGFR [165, 170], BRAF [171-173], MEK [114, 174-179], TBK1 [180], MET [181-184], mTOR [69, 185-190], FAK [191], HSP90 [192-194], CDK4/CDK6 [195] in monotherapy or in combination regimes have failed, demonstrating no significant clinical benefit for mutant KRAS patients.

The high mutational burden of LUAD tumours with KRAS mutations has been proposed as a potential biomarker of response to immune checkpoint inhibitors [196, 197]. Additionally, regulation of the immune checkpoint inhibitor PD-L1 by KRAS oncogene has been reported in KRAS-mutated NSCLC [198]. These observations provided the rationale for the use of immune checkpoint inhibitors in this LUAD subtype. Notably, a remarkable clinical benefit to PD-1 inhibitors was shown in KRAS or KRAS-TP53 mutant patients [196]. However, other subtypes of mutant KRAS LUAD, primarily represented by those harbouring LKB1/STK11 mutations, are refractory to immune checkpoint inhibitors [199]. Taken together, these observations highlight the need for new therapeutic strategies in mutant KRAS LUAD, an endeavour that will be deeply facilitated by a better knowledge of the underlying molecular mechanisms driving this tumour type.

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Introduction

3. CHOLANGIOCARCINOMA

3.1 Cholangiocarcinoma introduction

Human cholangiocarcinoma (CCA) is a heterogeneous group of cancers that arise from the biliary tree, whose pathogenesis is poorly understood. CCA represents the second most common type of liver cancer, preceded by hepatocellular carcinoma (HCC), and the median survival of inoperable CCA is only 6-12 months and the 5-year survival rate has remained at 10% for almost four decades. In Europe, CCA mortality has steadily increased in the last years [200]. Furthermore, United States data show that while mortality associated to all malignant cancers has been reduced approximately by a 20% from 1990 to 2009, liver and bile ducts cancers increase of more than 40% in female and 60% in male [201] (Figure I6). Of note, the incidence is increasing for iCCAs, in contrast to extrahepatic CCAs, which show rather a gradually decreasing trend [202].

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Introduction

Figure I6: USA mortality trends of patients harbouring different malignancies between 1990 - 2009. Liver and bile duct malignancies show a dramatic increase in patient mortality (up to 40% in females and 60% in males) compared to the global trend of cancer-associated mortality decrease. Figure adapted from Llovet, 2015.

Most CCAs occur sporadically and are considered to be de novo [203, 204]. Besides liver flukes, which have been recognized as a causative agent for CCA, most of these tumours appear, without any apparent cause. Nevertheless, several risk factors for CCA have been identified, such as chronic biliary and liver diseases, and lifestyle- related aspects causing chronic inflammation and cholestasis [205-209]. Notably, almost all CCA display a common pathogenic feature that occurs in the biliary tract: chronic inflammation [210, 211]. Owing to CCA molecular and pathological heterogeneity, the scarce knowledge of its aetiology, the absence of effective

Introduction detection screens, the lack of early diagnosis, and its refractory nature, CCA represent a global health problem and its treatment an unmet clinical need [202].

3.2 Classification of cholangiocarcinoma

Based on their anatomical location, CCAs are classified as intrahepatic (iCCA), perihiliar (pCCA) and distal (dCCA) (Figure I7). Histologically, conventional perihilar/distal cholangiocarcinomas are mucin-producing adenocarcinomas or papillary tumours; intrahepatic cholangiocarcinomas are more heterogeneous and can be sub-classified according to the level or size of the displayed bile duct. Based on these anatomical differences, the cell of origin of CCA may be intrinsically related to the site of CCA development. In this regard, hepatic stem cells (HSCc), cholangiocytes and peribiliary gland cells have been proposed as the cells of origin in CCA [212]. CCA displays pronounced inter- and intra-tumoural heterogeneity consequence of the interplay between distinct tissues and cells of origin, the underlying diseases, and the associated molecular alterations, that as a whole significantly affect CCA pathogenesis.

Figure I7: Classification of CCAs and representation of hepatic cell types including cholangiocytes. (Left) Based on the anatomical location, CCAs are classified as intrahepatic (iCCA), perihiliar (pCCA) and distal (dCCA). (Right) Schematic representation of an anatomical section of the liver. Cholangiocytes (in green) are the epithelial cells coating the bile ducts that constitute the billiary tree represented in left. Figure adapted from Banales, 2016 and Gordillo, 2015.

37

Introduction

3.3 Genomics of cholangiocarcinoma

CCA is characterized by a plethora of genetic and epigenetic alterations in addition to a dense and complex tumour microenvironment. These features determine the rapid evolution of the disease and its poor response to treatments. In the last years, numerous studies have detailed the molecular landscape of CCA using NGS technologies, thus cataloguing the genetic alterations involved in this liver tumour. Genetic alterations found in CCA affect fundamental signalling networks such as tyrosine kinase receptor signalling (activating mutations in KRAS, BRAF, FGFR2 and SMAD4) [208, 213-220], DNA repair mechanisms (TP53 loss of function mutations) [215, 217, 221], development and differentiation pathways (activating mutations in WNT or CTNNB1) [222] or telomere stability control (TERT mutations) [214]. Mutations in genes modifying epigenetic mechanisms have been also identified in genome maintenance genes (IDH1 and IDH2 mutations) [214, 216, 217, 221, 223-227], in chromatin-remodelling factors such as the SWI/SNF complex (ARID1A, BAP1 and PBRM1 loss of function mutations) [214, 216, 217, 221, 223] and histone deacetylases (HDAC6 overexpression) [228]. To date, practically all genetic studies have been carried out in surgically-resected samples (i.e. early tumour stages), and only two studies have performed genomics studies in advanced-stage tumours [229, 230]. Comparative analyses of genomics studies in early vs advanced tumours revealed that while the co-occurrence of two or more actionable lesions is present only in 30% of early cases, a median of three molecular alterations per patients was detected in 87% of advanced tumours. Consequently, the genomic evolution of CCA may influence the potential treatment of patients based on targeted therapies.

KRAS and TP53 are the oncogene and tumour suppressor gene more frequently mutated in CCA respectively in more than 20% of cases. Both genes are similarly mutated in intrahepatic and extrahepatic CCA. Specifically, mutations in KRAS oncogene are one of the most frequently activating aberrations common to all cholangiocarcinoma subtypes, found in approximately 40% of pCCA and dCCA and in 9- 24% of iCCAs [220, 231, 232]. This fact contrasts with other altered genetic drivers

Introduction such as FGFR2 and IDH1/2, which are rarely found in extrahepatic CCA. The presence of genetic alteration in additional members of the KRAS pathway, such as EGFR or BRAF, highlights the relevance of the KRAS signalling network in CCA. Along these lines, KRAS mutations or gene-expression signatures inferred from KRAS tumours identify

CCA patients with the worst prognosis outcome [231, 233-235].

Genetic experiments in mice have ascertained a role for Kras as an initiating event in CCA development. Introduction of Kras mutations in liver precursor (i.e. hepatoblasts) is sufficient to induce iCCA through a sequence resulted in the formation of invasive iCCAs with low penetrance and long latency. By contrast, combination of oncogenic KRAS activation with TP53 loss shortened latency, widespread local and distal metastasis, and reduced mice mean survival in more than half compared to KRASG12D group (56 weeks vs 19 weeks, respectively) [34]. This model, faithfully recapitulates the histologic and molecular features of multistage progression of human iCCA, including the biliary typical lesions: intraductal papillary neoplasms (IPBN) and biliary hamartomas (VMC -Von Meyenburg complexes). Subsequent iCCAs GEMMs combining KRAS mutations with other oncogenic drivers (FIG-ROS) or tumour supressor genes (PTEN) demonstrated similar histopathological and molecular features, including the development of a stroma-dense tumours [236, 237]. Furthermore ablation of Tgfbr2 and Cdh1 in cooperation with mutant Kras yields the formation of eCCA. These mouse models for distinct CCA types faithfully recapitulate the human disease [238] and, thus, are valuable tools for the study of cholangiocarcinogenesis.

3.4 Targeted therapies in CCA

Current therapies for CCA have modest survival benefits and highlight the need for more efficacious treatments. Based on the genomic and molecular profile of CCA, several targets and/or pathways are under investigation, including the immune environment. However, these emerging treatments are still too premature to change the clinical practice. So far, promising results have been observed in IDH1, FGFR and

39

Introduction

BRAF-mutant patients. In a phase I-II trial, IDH1-mutated CCA patients were treated with the IDH1 inhibitor ivosidenib leading to a significant 12-month progression-free survival (PFS) of 20.7% [239, 240]. Two phase 2 studies with FGFR inhibitors have been reported for FGFR2-altered patients with a similar response rate of 18% [241, 242]. Interestingly, this lower response rate might be explained by the emergence of secondary polyclonal mutations in the FGFR2 kinase domain that provide resistance. The targeting of BRAF-mutant patients with single agent BRAF inhibitors has been associated with modest clinical responses and short duration of disease control. However, in a more recent trial testing the concomitant administration of BRAF and MEK inhibitors in biliary cancer patients, a response rate of 42% was described with a median overall of 11.7 months [243, 244]. Unlike these previous patients’ groups, the effectiveness of EGFR or HER2 inhibitors in patients with EGFR/HER2 amplifications have yielded disappointing results in CCA similarly to inhibitors against other oncogenic drivers (cMET and NTRK) or activated pathways (PI3K) in CCA [245]. Along these lines, mutant KRAS tumours lack efficacious treatment options and inhibition of canonical downstream effectors including the Pi3K-AKT-mTOR and the RAF-MEK-ERK pathways have been unfruitful.

Immunotherapy has raised much attention as an effective therapy in various solid and haematological tumours. Durable responses have been obtained with pembrolizumab in patients with advanced biliary tract cancer expressing PD-L1 [246- 248], and additional study indicates that combining VEGFR inhibitors with PD-1 inhibitors is a promising strategy in advanced CCA with high Tumour Mutational Burden (TMB) [249]. Despite these encouraging results, responses to immunotherapy regimes apply only to subsets of patients for which clear biomarkers of response are still missing. In light of these results, understanding the molecular mechanisms associated with cholangiocarcinogenesis is of paramount importance to provide novel and effective therapies for CCA patients.

Introduction

4. FOS-LIGAND 1

4.1 Introduction

Fos-ligand 1 (FOSL1) or Fos-related antigen 1 (FRA1) is a transcription factor protein located on chromosome 11 in humans. FOSL1 contains a basic leucine-zipper (bZIP) domain essential for its interaction with partners of the JUN family of transcription factors to form the activating protein 1 (AP-1) transcriptional complex. The AP-1 complex is usually composed of dimers from the JUN (C-JUN, JUNB and JUND) and FOS (C-FOS, FOSB, FOSL1 and FOSL2) families, although it can also integrate ATF and MAF family members. AP-1 regulates gene transcription through direct binding to DNA, recognizing a specific TPA-response element (TRE) in the promoter region of specific genes.

FOSL1 contains two critical elements in the promoter region: a TRE (TPA response element) and SRE (serum response element) that allow its transcriptional induction in response to external stimuli including cytokines, mitogens, growth factors, stress signals and transcription factors such as AP-1, ELK1, SRF and ATF/CREB [250- 253]. While JUN proteins can homodimerize, FOS proteins (including FOSL1) do not dimerize which each other to form the AP-1 complex, being unable to bind to TRE promoter regions and, therefore, to function as transcriptional regulators. For that reason, FOSL1 associates with any of the JUN proteins to generate stable heterodimers and control gene transcription [254, 255] (Figure I8).

41

Introduction

Figure I8: Transcriptional regulation of FOSL1: MAPK signalling activates transcriptional factors binding at the human FOSL1 promoter. Binding of FOSL1 and c-JUN to a TRE site of first intron which contains AP-1 binding elements, is required for positive auto-regulation of FOSL1 by the RAS-ERK pathway. TRE: TPA response element, SRE: Serum response element, SRF: serum response factor, ATF: activating transcription factor binding site, RCE: retinoblastoma control element.

4.2 FOSL1 activity regulation

The interaction of FOSL1 with members of the JUN family of transcription factors provides a dynamic platform for integrating multi-pathway signalling events. Furthermore, they can also physically and functionally interact with other factors on chromatin, underscoring their versatility in signal integration during cell fate control [256-258]. For a fine-tuned control of FOSL1, multiple transcriptional and posttranscriptional events can take place.

Introduction

4.3 Transcriptional regulation

An increasing number of studies have determined that activation of some RAS canonical pathways under different external stimuli involving mitogens, carcinogen substances or oncogenic signals lead to an increase in FOSL1 mRNA synthesis [259, 260]. While the MEK-ERK pathway has been reported as the critical effector pathway controlling FOLS1 transcription [261, 262], other RAS pathways have also been described to control FOSL1 expression. For instance, the PI3K/AKT/Sp1 pathway was implicated in FOSL1 promoter control in cells lacking PTEN. In these prostate cancer cells, FOSL1 transcription is stimulated by AKT3 activation through a region that contains a retinoblastoma control element (RCE1) different to the typical TRE or SRE sites of FOSL1 promoter. Under AKT3 control, a heterodimer composed by the transcription factor SP1 and the retinoblastoma protein RB1 is thought to bind the RCE1 region to induce FOSL1 expression [263]. Interestingly, that RCE1 site was previously identified as contributing to the regulation of FOSL1 by serum and the TAX1 protein of human T-cell leukaemia virus [264]. In line with these results, a subsequent study demonstrated that FOSL1 was regulated by the PI3K-AKT pathway through p21- activated kinase 1 (RAC1) under cigarette smoke stimulation [265]. Another RAS- related pathway the JNK pathway, was found to stimulate FOSL1 transcriptional activity in non-transformed alveolar type II epithelial cell after silica exposure [266]. In this case, JNK controls FOSL1 possibly via c‑ JUN dependent induction of the TRE elements in the FOSL1 promoter. Similarly, increased levels of the small ubiquitous protein THIOREDOXIN led to transcriptional upregulation of FOSL1 in lung cancer cells through the MEKK1-MKK4/MKK7-JNK pathway [267].

It has also been suggested that FOSL1 transcription can be regulated by other pathways relevant for cell homeostasis. In vitro studies in colon cancer indicated that c-MYC, Cyclin D1 and FOSL1 are target genes of the β-Catenin/Tcf (T-cell transcription factor family) complex [268]. However, it remains unclear whether the FOSL1 promoter is directly targeted by this complex. Along with the aforementioned pathways, it was shown that Fosl1 transcription can be regulated by other effectors

43

Introduction such as c-FOS or MYC. c-Fos induced Fosl1 transcription under non-oncogenic [260, 269] and oncogenic conditions [270]. Notably, once transcribed, FOSL1 can positively activate its own transcription by binding to the AP-1 site in the first intron [260]. Moreover, this study described Fosl1 as a unique member of the Fos gene family which is positively controlled by AP-1 activity, bringing to light an auto-regulatory mechanism that maintains the constitutive Fosl1 expression [250, 270]. MYC proto-oncogene (MYC) has also been identified as a direct transcriptional regulator of FOSL1 in normal cells stimulated by serum or VEGF. A growth factor-induced MYC-MAX heterodimer has been reported to bound to FOSL1 intronic enhancer and recruit PIM1 kinase to chromatin, resulting in the PIM1-dependent phosphorylation of histone H3 and transcriptional induction of FOSL1 [271].

4.4 Post-translational regulation

Stabilization of FOSL1 protein is essential to ensure its activity. The newly translated FOSL1 protein, which is highly unstable, must to be phosphorylated by ERK to prevent proteasome-mediated degradation [270, 272]. In colon carcinoma RAS mutant cells, different thresholds of ERK activity yielded distinct effects on FOSL1 regulation. Moderate activation of ERK showed to be sufficient to induce FOSL1 gene transcription but not stabilization, while strongly increased ERK signalling prevented FOSL1 proteasome-dependent degradation [272]. Several lines of work have demonstrated that FOSL1 contains a C-terminal destabilizer controlled by ERK1/2 and ERK5-mediated phosphorylation of Ser252 and Ser265 residues, which inhibits the proteasome-dependent degradation of FOSL1 in response to MEK pathway activators [273, 274]. FOSL1 showed abrogated phosphorylation and reduced stabilization when Erk1/2 activity was lowered, as is the case of control non-transformed thyroid cells [270], cells treated with a MEK-inhibitor [272], or upon mitogen withdrawal [275, 276]. Additionally, pharmacological inhibition of the proteasome in colon tumour cells led to a further overaccumulation of the FOSL1 protein. Thus, ERK activity regulates FOSL1 transcription, stabilizes the protein and transactivates it as an AP-1 dimer, which can

Introduction perpetuate increased FOSL1 transcription via a positive feedback mechanism. Nonetheless, a recent study reported an alternative pathway involving sequential activation of the kinases PKCθ and SPAK1 involved in promoting FOSL1 stability in a particular type of ER- breast cancer cells where the contribution of ERK1/2 was weak [277], bringing to light additional means of FOSL1 post-translational regulation. In general, FOSL1 transcription is activated by various mitogen-activated protein kinase (MAPK) pathways, and their protein products are phosphorylated, stabilized and activated by the ERK1/2 [278] and Erk5 [274] cascades (Figure I9).

Figure I9: Schematic representation of FOSL1 transcriptional and posttranslational regulation. Transcriptional induction of FOSL1 in response to the RAF/MEK/ERK pathway is mediated by both an intronic enhancer (containing MYC and AP‑ 1 binding sites), and an upstream enhancer (including TRE, SRE and ATF elements). (RAS*): oncogenic RAS, (P) phosphorylation, (blue squared D) FOSL1 C‑ terminal destabilizer. Degradation by the proteosome is (represented by the dustbin).

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Introduction

4.5. FOSL1 biology

Several studies have described the role of FOSL1 in normal and pathological conditions. A description of these studies is detailed herein.

4.5.1 FOSL1 in normal tissue

Through the use of transgenic models, Schreiber et al. demonstrated that FOSL1 was indispensable for establishing normal vascularisation of the placenta during embryogenesis. Foetuses lacking Fosl1 were severely growth retarded and died in utero owing to defects in extra-embryonic tissues, suggesting a critical role of Fosl1 in angiogenesis in normal cells [279]. To study the impact of FOSL1 on development, Eferl et al. generated a mouse model with a conditional floxed Fosl1 version crossed to Mox2‐cre knock‐in mice (mesenchyme homeobox 2), which leads to deletion during gastrulation, in an attempt to circumvent the embryonic lethality of Fosl1-knockout mice caused by placental defects. Notably, the embryos lacking Fosl1 develop osteopenia which is characterized by low bone mass [280]. Conversely, Jochum et al. showed that ectopic expression of Fosl1 in mice led to osteoclerosis, a bone disorder characterized by the progressive increase in bone mass of the entire skeleton [280, 281]. Additional studies in mice transgenic models constitutively overexpressing FOSL1 reported a role for FOSL1 in liver disease through the induction of progressive cholangitis and liver fibrosis. In this case, mice underwent biliary fibrosis preceded by ductular proliferation and infiltration of inflammatory cells [282, 283].

4.5.2 FOSL1 in cancer

Abnormal expression of FOSL1 has been detected in patients and murine models after continued exposures to toxicants, which contribute to the development of diseases like cancer. Protracted exposure to asbestos, which is a potent lung carcinogen that causes mesothelioma, proved to induce FOSL1 expression levels and

Introduction malignant transformation of rat pleural mesothelial cells, while its silencing reversed the malignant phenotype [284-286]. Other carcinogens such as tobacco smoke, phorbol esters and mitogens have also been identified as potent promoters of FOSL1 induction in bronchial epithelial cells [253, 287]. Also, mutations in genes, such as RAS or EGFR, can recapitulate the effect of the these external stimuli and induce FOSL1 upregulation [261, 288]. Elevated FOSL1 mRNA and protein have been detected in a wide variety of transformed cell lines and tumours, including breast [289, 290], colon [268, 291], lung [284, 292], brain [293], thyroid [294], prostate [295], head and neck [296], oesophagus and stomach [297] among others.

4.6 FOSL1 functions in cancer

Functionally, FOSL1 is not essential in many normal cells but is critical in tumorigenesis, controlling processes which require signal plasticity such as sustaining proliferative signalling [288, 298], promoting EMT [292, 299], activating invasion and metastasis [262, 288], avoiding cell death [272, 298], and inducing angiogenesis [279], etc.

4.6.1 Proliferation

One of the first described genetic programmes in which FOSL1 was involved was cell proliferation. Fosl1 oncogenic potential in cell proliferation was first reported by Bergers et al. (1995) when proved that expression of wild-type Fosl1 was sufficient to transform rat fibroblasts resulting in anchorage-independent growth and tumour development in athymic mice [260]. By contrast, Fosl1 loss-of-function experiments using RNAi significantly reduced tumour cell proliferation in RAS-transformed rat thyrocytes [298], human breast cancer cells [300] and RAS-transformed rat ovarian epithelial cells [24]. This impact in cell proliferation can be explained in part by an observed downregulation of cell cycle controllers cyclin D1 and cyclin A upon Fosl1 inhibition. Furthermore, chromatin immunoprecipitation experiments in RAS- transformed thyroid cells identified cyclin A as a direct transcriptional target of Fosl1

47

Introduction oncoprotein, demonstrating direct binding of Jun-Fosl1 heterodimers to cyclin A promoter [298]. Additionally, Adiseshaiah et al. (2007) proved that ectopic FOSL1 expression promotes tumour growth and lung metastasis in KRAS-mutated LUAD xenografts. Of note, they described that Fosl1 induced malignant phenotypes in normal bronchial epithelial cells in vitro, but highlighted that was not sufficient for tumour formation in vivo [252], suggesting that additional oncogenic events are needed for tumour transformation.

4.6.2 Apoptosis

The role of FOSL1 in cell apoptosis was determined by its functional inhibition in a colon carcinoma cell line carrying both oncogenic BRAF and KRAS alterations and high ERK activity [272]. In this cell system, FOSL1 showed to be critical for protecting cancer cells from anchorage-dependent programmed cell death (anoikis),representing an essential component of the ERK-dependent pathway which controls anchorage- independent cell growth. Interestingly, Fosl1 inhibition did not impair the cell cycle in colon carcinoma cells (unlike in thyroid and breast tumour cells) demonstrating that FOSL1 may have an anti-apoptotic function, independent of its role in cell cycle progression. This survival competences of oncogenic FOSL1 in different tumour cell types in response to apoptotic stimuli can be explained through a recent described link between mutant RAS and p53 pathways. p14ARF (human) and p19ARF (mouse) tumour suppressors are directly controlled by the c-Jun/Fosl1 heterodimer, which binds to their promoter regions leading to growth arrest or apoptosis depending on the p53 status [301]. Based on these data, it seems that FOSL1 might play distinct roles in transformed cells compared to normal cells, and even at different stages of tumour development.

4.6.3 Cell motility, invasiveness and EMT

Mesenchymal properties of aggressive cellular variants expressing high levels of FOSL1 have been observed in human breast cancer cells [302]. In fact, ectopic FOSL1

Introduction expression in non-invasive adenocarcinoma cells was sufficient to promote morphological transformation, cell motility and invasiveness [303]. The molecular mechanism underlying tumour cell motility and invasiveness control was investigated in highly invasive colon cancer cells expressing oncogenic KRASG13D. FOSL1 silencing led to loss of cell polarization, motility, and invasiveness in vitro, producing changes in cell morphology and re-distributing stress fibres and focal adhesions. A strong reactivation of the RhoA/ROCK pathway was observed after FOSL1 knockdown, explained by that removal of the FOSL1-dependent suppression of β1-Integrin signalling to Rho-GTP [262]. In other colon cancer cell line expressing KRASG13 mutation, FOSL1 overexpression was enough to rescue the oncogenic phenotype observed before the targeted deletion of the G13 KRAS mutation which abrogated the tumour cell motility [288]. Furthermore, activation of matrix metalloprotease genes (TIMP1, MMP-9 and MMP-1) that are directly implicated in metastasis processes were found to be strongly regulated by FOSL1 [262, 300, 303]. Despite no direct regulation was mechanistically demonstrated, CD44 and c-MET overexpression was detected after FOSL1 upregulation in asbestos-induced mesotheliomas. Interestingly, CD44 and MET form a protein complex which plays key roles in cancer cell invasion and metastasis, pointing that FOSL1 may promote the transcription of this genes to form the heterodimer and foster cell motility [285, 304]. Additionally, in a mouse breast cancer model, Luo et el. (2010) confirmed that Fosl1 ablation resulted in a marked suppression of tumour cell invasion and metastasis [290]. Lastly, in melanoma FOSL1 directly modulated the transcription of EMT-TF-encoding genes to promote cell transformation and aggressiveness [305].

4.6.4 Angiogenesis

The role of FOSL1 in tumour angiogenesis was investigated in breast tumour cells in support of invasion and progression phenotypes. FOSL1 was shown to remodel tumour micro-enviroment promoting the activation of the IL-6/JAK/Stat3 signalling pathway, which led to increased release of proangiogenic factors MMP-9 and vascular endothelial growth factor (VEGF) and transforming growth factor-beta (TFG-β). Also, subsequent experiments performed in glioblastoma (GBM), a hyper-vascularized highly

49

Introduction progressive brain tumour, and breast cancer cells confirmed previous results showing FOSL1 transcriptional regulation of VEGF-A [293, 300].

Taken together, these data suggest a clear role for FOSL1 in cancer. However, despite FOSL1's pro-oncogenic functions, several questions remain to be asked in the context of mutant KRAS oncogenesis: 1) Is FOSL1 expression directly regulated by endogenous expression of KRAS oncogene?, 2) Can FOSL1 regulate functions beyond cell migration and invasion reported in colorectal cancer?, and 3) does FOSL1 represent an oncogene vulnerability in mutant KRAS tumours? Further work will be required to address all these open questions.

HYPOTHESIS AND OBJECTIVES

51

Hypothesis and Objectives

HYPOTHESIS

KRAS is the most commonly mutated oncogene in human cancer. Since attempts to directly inhibit KRAS activity have been largely fruitless, intense efforts are being focused on unravelling the molecular mechanisms underlying KRAS-driven tumours onset and progression. We hypothesize that genes commonly upregulated across multiple tumours whose expression is dependent on mutant KRAS activation may represent critical elements of the oncogenic phenotype. Thus, they may constitute central oncogene vulnerabilities in tumours harbouring KRAS mutations for which eventual therapeutic strategies could be developed.

OBJECTIVES

The following principal objectives have been established in order to address this hypothesis :

1. To identify a central core of mutant KRAS-dependent candidates across tumours through an integrative gene-expression based approach coupled with clinical information to select candidate genes (FOSL1) for functional follow. 2. To define the mechanisms of FOSL1 regulation in mutant KRAS lung cancer. 3. To determine the functional role of FOSL1 in lung cancer in vitro and in vivo. 4. To dissect the molecular mechanisms elicited by FOSL1 and their functional role in lung cancer. 5. To determine the functional role of FOSL1 in other tumours harbouring KRAS mutations (liver cholangiocarcinoma) in vitro and in vivo.

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MATERIALS AND METHODS

55

Materials and Methods

1. CELL CULTURE

1.1 Cell lines

Mouse lung cancer cell lines LKR10 and LKR13 (a kind gift by Julien Sage), 389N1 and 482N1 (a kind gift by Monte Winslow), LSZ1, LSZ2, LSZ3, LSZ5 and immortalized normal pancreas epithelial cells, KF07, were grown in DMEM supplemented with 10% FBS and 1% Penicillin-Streptomycin. HEK293T cells were grown in DMEM (Gibco) supplemented with 10% FBS (Gibco), 1% Penicillin/Streptomicyn (Gibco) and 2% HEPES (Lonza) and grown in a humidified atmosphere at 37 °C and 5% CO2. Cells at low passages (below passage 16) were used for lentiviral production. Human non-small cell lung cancer and liver cholangiocarcinoma cell lines used had either wild-type RAS alleles (NCI-HCC78, NCI-H322, NCI-H1395, NCI-H1437, NCI-H1568, NCI-H1650, NCI-H1703, NCI-H2126, WITT, HUH28, KMCH,M2CHA, TFK1, OCUG1 and Snu1196) or were mutant for RAS (NCI-H23, NCI-H358, NCI-H441, NCI-H460, NCI-A549, NCI-H1299, NCI-H1792, NCI-H2009, NCI-H2087, NCI-H2347, CFPac1, HPAFII, EGI1, HUCCT1 and KMBC). Human NSCLC cell lines were from ATCC. Human CCA cell lines were obtained through a material transfer agreement with professor Jesús Bañales or professor Diego Calvisi. Human cell lines were authenticated by the Genomics Unit at CIMA using Short Tandem Repeat profiling (AmpFLSTR® Identifiler® Plus PCR Amplification Kit) and grown according to ATCC specifications. All cell lines were tested using the MycoAlert Mycoplasma Detection Kit (LONZA). Only mycoplasma negative cells were used for functional assays. All cell lines were frozen in FBS containing 10% DMSO (Sigma-Aldrich) and stored at -80 °C for short period of time. Cells were then stored in liquid nitrogen (-196 °C).

2. ISOLATION AND CULTURE OF MOUSE CHOLANGIOCYTES

Cultured cholangiocytes were obtained from KrasLSLG12D/+(K), KrasLSLG12D/+, Trp53f/f (KP) and KrasLSLG12D/+, Trp53f/f , Fosl1f/f (KPF) mouse livers, starting with isolation of intrahepatic bile duct units and followed by their seeding and culture on collagen monolayer. Animal interventions to isolate intrahepatic bile duct units were performed by Dr. Bañales’ group following previously-published protocols [306, 307].

57

Materials and Methods

Briefly, liver pieces were digested for 30 min in a shaker bath at 37 °C with 50 mL of DMEM/F-12 medium supplemented with 17 mg pronase, 12.5 mg type IV collagenase, 3 mg DNase (all three from Sigma), 3% fetal calf serum, 0.1% bovine serum albumin (both from Gibco), and 1 mL of penicillin/streptomycin solution. Digested tissue was sequentially filtered through 100 and 30 μm nylon meshes (Millipore). Trapped fragments were collected and incubated again for 30 min with the aforementioned solution, but supplemented with 13 mg hyaluronidase (Sigma) instead of pronase. After a second series of sequential filtrations, intrahepatic bile duct units ranging from 30 to 100 μm were resuspended in fully supplemented DMEM/F-12 medium and seeded on 250 mL flasks (Greiner) coated with a thick (2mm) monolayer of rat-tail collagen. Cholangiocytes, grown as clusters derived from intrahepatic bile duct units (IBDUs) in a monolayer fashion, were passaged at 80-90% confluence, being rendered free of fibroblasts by treatment with dispase (Sigma) at the time of passages (1.66 mg/mL of DMEM/F-12, 1 h at 37 °C) and also between passages (1 mg/mL, for 20 min). Obtained cells were pure differentiated cholangiocytes, strong positive for CK and biliary epithelial markers such as cytokeratin 7 (CK7), cytokeratin 19 (CK19) and E- cadherin (Figure M1).

Materials and Methods

Figure M1: Schematic representation of GEMM cholangiocytes isolation.

3. CONSTITUTIVE AND INDUCIBLE shRNA KNOCKDOWN SYSTEM

3.1 Short-hairpin RNA sequences design and cloning

Short hairpin RNAs (shRNA) were used for targeting the studied genes. Briefly, shRNA silencing system is based on the sequence-specific degradation of target cell mRNA through the delivery of double-stranded RNA (dsRNA) identical to the gene target sequence. shRNAs are single-strand molecules of 40-70 nucleotides able to form structures consisting of a 19-29 base-pair region of double-strand RNA bridged by a single-strand RNA looping and a short 3’ overhang (Materials and Methods, Supplemental information, Figure S2). Sequences were designed using the GPP portal RNAi design tool (https://portals.broadinstitute.org/gpp /public/seq/search) and those

59

Materials and Methods with no or minimum predicted off-target effects were selected. DNA oligonucleotides were ordered and synthesized by SIGMA with a OD scale of 0.05. shRNAs were cloned into the PLKO.1 vector and also into a tetracycline- regulatable version of the same vector (Addgene pLKO.1-puro - TRC #10878 and Tet- pLKO.1-puro#21915). A control shRNA sequence against GFP (shGFP) was used as control. Cloning was performed following the GPP web portal shRNA cloning protocol (https://portals.broadinstitute.org /gpp/public/resources/protocols). Briefly, forward and reverse shRNA oligo sequences flanked by AgeI and EcoRI overhangs were annealed in 50 μl of final volume, denatured for 5 min at 95 °C and cooled to 25 °C at a ramp rate of 0.1 °C per second. Annealed primers were phosphorylated using 1μL of sample, 1 μL of 10X T4 ligase Buffer, 1μL of T4 PNK (New England Biolabs) and 7 μL of water, incubated 30 min at 37 °C and heat inactivated for 15 min at 65 °C. Phosphorylated annealed oligos were ligated into the pLKO.1-puro and Tet-pLKO-puro vectors (previously digested with AgeI and EcoRI following NEB double digestion finder protocol). Ligation was performed using 6.5 μL of phosphorylated annealed shRNAs, 100ng of of EcoRI / AgeI digested vector, 1 μL of 10X T4 ligase buffer and 0.5 μL of T4 DNA ligase (New England Biolabs) and incubated overnight at 16 °C.

Ligation product was transformed into 50 μl of One Shot Stable3 Chemically competent cells (Invitrogen) and plated in ampicillin 100 μg/μL (Sigma-Aldrich) LB Agar Lennox (Conda) plates and incubated overnight at 37 °C. Transformed clones were grown into 6 ml of LB Broth Lennox (Conda) with 100 μg/μL of ampicillin (Sigma- Aldrich)for 16 h at 37 °C on agitation (220 rpm). DNA plasmid extraction was performed using ATP Plasmid Mini Kit (ATP Biotech) following manufacturers protocol. Purified DNA was Sanger-sequenced in the Genomic Core Facility of CIMA. Maxi-Prep reactions for obtaining high DNA yields from positive clones were carried out using Macherey-Nagel NucleoBond® Xtra Maxi Kit following the corresponding protocol.

Materials and Methods

3.2 Lentiviral production

Lentiviruses were generated using X-tremeGENE HP DNA Transfection Reagent (Roche) and a third generation Lentiviral Packing Mix (Sigma Aldrich). For safety reasons, in this Lentiviral Packing Mix -gag- and -pol- are expressed by one packaging plasmid and -rev- from another. 5x105HEK293T cells were seeded into 6-well plates in complete DMEM medium. Only HEK293T at low passages (below passage 16) were used for lentiviral production. After 24 h, DMEM was replaced by Opti-MEM medium without antibiotics and supplemented with 10% FBS. The following transfection mixture was prepared per well: 300 μL of FBS and antibiotics free Opti-MEM, 2 μg of plasmid DNA, 5 μL of Lentiviral Packing Mix and 9 μL of X-tremeGENE HP DNA Transfection Reagent. The mix was incubated at room temperature for 20 min and added drop wise to the cells. Lentivirus-containing supernatant was harvested 60 h later, centrifuged at 1,000 rpm for 1 min and filtered through 0.45 μm cellulose acetate filters.

3.3 Cell infection and selection

Cells were seeded into 6-well plates 24 h prior infection. 400 μL of lentivirus- containing supernatant were directly added to cells in presence of 8 μg/ml polybrene (Sigma). After 48 h, the medium was replaced with 1.5-4 μg/mL puromycin (Invivogen) supplemented medium and target cell lines selected after 72 to 96 h. Antibiotic resistant cells were expanded in culture for subsequent experiments.

4. IN VITRO PROLIFERATION ASSAYS

4.1 MTS Cell proliferation assay

Cell proliferation rate was determined using MTS reagent [3-(4,5- dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium]

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Materials and Methods according to manufacturer’s instructions (Promega). Briefly, MTS reagent is reduced by cell dehydrogenases to a soluble chromogenic product (formazan) with absorbance properties at 490 nm. Between 400 to 1,500 cells were seeded in 96-well plates in complete medium and measured at days 0 (first day after cell seeding), 3, 5 and 7.Doxycycline supplemented medium was refreshed every two days. Plates were incubated for 2 h at 37°C with 10 µl/well MTS and read at 490 nm and 650 nm (reference) using a spectrophotometer (SpectroStar Nano, BMG Labtech). Absorbance values were normalized based on values from day 0 and presented as mean values of six technical replicates ± SD. Three biological replicates of each experiment were always performed.

4.2 Population doubling time assay

Population doubling time was performed as previously described (Bruckova et al., 2011). Briefly, cells were counted at seeded, allowed to grow to a confluence of 80- 90% and then harvested to determine total cell number. Population doubling time was calculated using the following formula: Number of hours from seeding to harvest/[((log N(t)-logN(t0))/log2]. N(t) is the number of cells at time of passage and N(t0) is the number of cells seeded at previous passage. Twelve (1 mm x 1 mm) square subdivisions from 3 counting grids of 2 Neubauer’s chamber were counted.

4.3 Clonogenic assay

For clonogenic assays, cells were seeded onto 6, 12 or 24 well plates at 400 to 1,500 cells/well, and cultured with complete medium for at least 2 weeks. Clones were fixed in 4% formaldehyde (Panreac) for 1 hand stained with 0,5% crystal violet (in H2O- methanol 1:1) for 15 min. Dried plates were later scanned and quantified. Clonogenic assays were carried out in triplicate and repeated at least three times. Doxycycline was added to cell media of those cell lines transduced with the TET inducible shRNA vector.

Materials and Methods

5. FOSL1 OVEREXPRESSION SYSTEM

FOSL1 overexpressing construct was design with a Flag-tag sequence in the N- terminal position. FOSL1 consensus coding sequence (CDS 8121.1) from isoform 1 was used. To translate the Flag-tag along with FOSL1 CDS, initiation ATG aminoacid (START) was introduced before the Flag-tag peptide sequence (DYKDDDDK) which was spaced from FOSL1 coding sequence using a standard flexible linker (Gly4Ser). To efficiently translate FOSL1-flagged fusion protein, a Kozak sequence (AXXATGG) was added preceding the initiation codon. SpeI, BamHI and EcoRI restriction sites were introduced flanking FOSL1-flagged sequence for a suitable cloning into lentiviral (pSIN-EF2-nanog- puro) and retroviral (pBABE-Neo) vectors. SpeI and EcoRI were used for cloning the construct it into the pSIN-EF2-nanog-puro vector by replacing NANOG gene with FOSL1-flagged sequence. pSIN-EF2-nanog-puro vector was a kind gift from Dr. Rodriguez-Madoz (CIMA, Spain). BamHI and EcoRI were used for cloning the construct into the pBABE-neo vector (Annexes, Figure A4I).

ExPASy translate tool software was used to ensure that a complete Open Reading Frame 5'- 3' of the FOSL1-flagged fusion protein was properly translated (Annexes, Figure A4II). The sequence was chemically synthesized (Genescript) and cloned into a delivery pUC57 vector using EcoRV and SalI restriction sites. All double digestion and ligation reactions were performed following NEB Double Digestion Tool and NEB Ligation Tool software's (New England Biolabs).

Schematic description of FOSL1 overexpression sequence.

EcoRVgctcctSpeIggatatBamHIKOZAKSTARTFLAGLINKERFOSL1STOPEcoRIatattcSal I

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6. GENE EXPRESSION ANALISIS

6.1 RNA extraction from cell cultures with TRIzol

Total RNA was isolated from cells seeded at 80% confluence in 21 cm2 dishes using TRI Reagent® (Sigma) following manufacturer's protocol. Briefly, culture medium was removed and cells were washed 1X with PBS (Gibco). Cells were scraped with 1ml of TRI Reagent and incubated in 1.5 ml tubes for 5 min at room temperature after the addition of 200 μL of chloroform. Samples were centrifuged at 12,000xg for 15 min at 4 °C and the upper Aqueous phase was recovered into a new tube where 100 μL of pure 2-Propanol were added. After 10 min of incubation at room temperature, cells were centrifuged at 10,000xg for 10 min at 4 °C. The supernatant was discarded by decantation and 500 μL of 75% ethanol were added to wash the pellet. Samples were again centrifuged at 10,000xg for 10 min at 4 °C and the supernatant discarded. The pellet was air-dried in the fume hood for 10 min to evaporate ethanol traces and resuspended in 20 μL of DEPC RNAse-free water. RNA concentration was measured using NanoDrop spectrophotometer based on 260 and 280nm absorbance values and purity was determined by the 260/280 nm ratio. Pure RNA samples were stored at -80 °C.

6.2 RNA extraction from cell cultures with Macherey-Nagel RNA isolation kit

Total RNA was isolated from cells seeded at 70-80% confluence using NucleoSpin RNA Kit (Macherey-Nagel). Briefly, culture medium was removed, cells were washed twice with PBS (Gibco) and scrapped in lysis buffer and 2- mercaptoethanol (Sigma-Aldrich) and collected into NucleoSpin Filters. Cell lysates were centrifuged at 10,000xg for 1 min. 70% ethanol was added to the homogenized lysates and transferred to NucleoSpin RNA Columns and centrifuged at 10,000 rpm for 30 sec. Membrane Desalting Buffer and DNAse was used to desalt silica membranes and remove genomic DNA for 15 min at room temperature. Columns were washed

Materials and Methods tree times and RNA was eluted with RNase-free water by centrifugation at 10,000 rpm for 1 min. RNA was measured and stored as previously described.

6.3 Reverse transcription

First-strand cDNA was synthesized using the High Capacity cDNA reverse transcription kit (Applied Biosystems) following manufacturer's instructions. In summary, 1 μg of pure RNA was mixed in 0.1 ml tubes with 2 μL of 10X RT Buffer, 0.8 μL of 25x dNTP Mix (100 mM), 2μL of 10X RT Random Primers, 0.2 μL of RNase Inhibitor and 1 μL of MultiScribe™ Reverse Transcriptase. Nuclease-free water was added up to 20 μL of final volume. Tubes were vigorously mixed and subjected to the following incubation steps in a Peltier Thermal Cycler 200 (MJ Research): 10 min at 25 °C, 120 min at 37 °C, 5 min at 85 °C and maintained at 4 °C until sample collection. cDNA products were stored at -20 °C.

6.4 Real time quantitative PCR

RT-qPCR was performed using SYBR® GreenER™Select Master Mix method (Applied Biosystems) on a 7300 Real-Time PCR machine (Applied Biosystems). 7300 RT- qPCR was later replaced by a QuantStudio 3 Real-Time PCR machine (ThermoScientific). The 2x mix contains SYBR® GreenER™ dye, AmpliTaq® DNA Polymerase UP, dNTPs with dUTP/dTTP blend, heat-labile UDG, ROX passive reference dye and optimized buffer components. PCR reactions were performed with 0.5 μL of cDNA (~60 ng), 5 μL of SYBR Green Master Mix, 0.2 μL of forward primer 10 μM, 0.2 μL reverse primer 10 μM and 4.1 μL of Sigma water. All samples were run in triplicates subjected to the following incubation steps: Step 1: 10 min at 95°C; Step 2: 40 cycles of 15 s at 95 °C and 1 min at 60 °C; Step 3 (melting curve): 15 s at 95 °C and 1 min at 60 °C.

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GAPDH was used as housekeeping gene and shControl as reference sample. Data were analysed using 2-∆∆Ct method for relative gene expression quantification. First normalization was performed using GAPDH gene expression data (∆Ct= Ct target gene-Ct GAPDH). Second normalization was performed to all ∆Ct values related to the Ct value of the reference sample shControl (∆Ct sample - ∆Ct reference sample).

Primers were designed using Primer-BLAST (NCBI - NIH) designing tool and selected based on the following parameters: Primer melting temperatures -Tm- between 58°C to 62°C, GC% between 50 to 55, and a self-complementarity ratio lower than 4 (Annexes, Table A5). Primers quality and specificity was tested performing serial dilutions of the cDNA (1, 1/10, 1/100, 1/1000 and 1/10000) from H358 and EGI.1 cell lines.

7. WESTERN BLOT

7.1 Protein extraction

Cells were harvested at around 70-80% confluence from 35 mm, 60 mm, 100 mm or 150 mm culture dishes depending on the experiment. Cells were firstly washed twice with cold PBS (Gibco) and scraped using 20 to 500 μL of protein lysis buffer (1% NP-40, 150 mM NaCl, 50 mM Tris pH 8, 1 mM EDTA, 1 mM PMSF , 1 mM

VO3, 5 mM NaF,) supplemented with 1X protease inhibitor cocktail (Complete Mini, Roche) and transfered to 1,5 ml tubes. Cells were incubated for 40 min on ice and lysates were centrifuged for 15 min at 13,200 rpm at 4°C. Supernatants containing protein extracts were collected and transferred to new 1.5 ml tubes and stored at - 80°C .

Materials and Methods

7.2 Protein quantification

Protein extracts were quantified using BCA (Pierce BCA Protein Assay Kit, Thermo Fisher Scientific) colorimetric assay that measures the formation of water- soluble complex with bicinchoninic acid (BCA) through the Cu2+ to Cu+ reduction reaction performed by proteins in alkaline medium. The formed complex presents absorbance at 562 nm, which is proportional to protein concentration in a wide concentration range between 20 to 2,000 µg/ml. A standard curve was prepared with BSA for the quantification of 2 μL of Protein extracts as described in manufacturers protocol guide.

7.3 Protein electrophoresis and transference

Between 15 to 30 µg of protein lysates were denatured at 99 °C for 5 min in 1X loading buffer (32 mM Tris-HCl pH 6.8, 12% glycerol, 2% SDS, 0.005% bromophenol blue and 1 mM Dithiothreitol (DTT)). Proteins were loaded into 6 to 12% polyacrylamide gels and electrophoresed under denaturing conditions (SDS-PAGE) in the following buffer: 25 mM Tris, 192 mM glycine and 0.1% SDS for 1h 30min at 120V. After polyacrylamide resolution, proteins were transferred to nitrocellulose membranes (BioRad) in transfer buffer (25 mM Tris, 192 mM glycine, 0.05% SDS and 20% methanol) for 1 h 40 min at 4°Cat 300 mA.

Briefly, gels were elaborated using two different acrylamide concentration percentages. An upper stratum (Stacking gel) was prepared to compress samples into a thin starting band and a lower stratum (Resolving gel) was prepared to resolve and separate individual proteins. Stacking gels were always prepared at 4% acrylamide, while resolving gels were prepared at 6%, 8%, 10% or 12% acrylamide, depending on protein size. 100 μL of ammonium persulfate (APS) and 10 μL of TEMED were added to 10 ml of gel solution to polymerize acrylamide in 20 min at room temperature. Gel solution composition: 2.5 ml of (1.5 M Tris-HCl, pH 8.8 for resolving gels and 0.5 M

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Tris-HCl, pH 6.8 for stacking gels), 0.1 ml of 10% SDS, 30% Acrylamide/Bis, 29:1 (Bio- Rad) and deionized water to 10 ml.

Nitrocellulose membranes were blocked in 5% skimmed milk prepared in 0.05% Tween-TBS for 1 h at room temperature and rolled overnight at 4°C with primary antibodies (Annexes, Table A6). After overnight primary antibody incubation, membranes were washed three times with 0.05% Tween-TBS for 5 min and incubated with horseradish peroxidase (HRP)-linked secondary antibodies at 1:5,000 dilution against rabbit, mouse, goat or rat immunoglobulins (Amershan Biosciences) for 1 h at room temperature. Membranes were again three times washed for 5 min with 0.05% Tween-TBS and incubated for 5 min with the peroxidase substrate Super-Signal West Pico Reagent (Thermo Scientific). Membranes were initially exposed to ECL Hyperfilms (Amershan Biosciences) for 10 sec to 10 min -depending on the experiment- and developed in a Curix 60 processor (AGFA Healthcare). Since 2018, membranes are exposed inside a ChemiDoc MP Imaging System and ECL films have been replaced by digital files. β-tubulin was used as loading control.

7.4 Protein stripping and re-incubation

Protein-bound antibodies were removed from the nitrocellulose membranes after 15 min of incubation at room temperature with Restore Western Blot Stripping Buffer (Thermo Scientific). Membranes were then washed three times in 0.05% Tween-TBS and re-probed with other antibodies of interest.

8. IMMUNOHISTOCHEMISTRY

Prior to immunostaining, tumours were dehydrated in a graded series of ethanol, cleared in xylene and paraffin-embedded. FOSL1, Ki67 and caspase-3 immunostainings were performed at the Morphology Core Facility at CIMA where samples were first longitudinally sectioned in 4 μm slides. Then, tissues were dewaxed with xylene, rehydrated through a graded series of ethanol and incubated in a Pascal

Materials and Methods

Pressure Chamber containing Tris-EDTA (10 mM/1 mM), pH 9, at 95 °C for 30 min. Temperature was monitored using Pascal quality strips (S2800, Dako). Next, endogenous peroxidase was blocked with 3% H2O2 in water for 12 min. Slides were then incubated with a primary antibody to human and mouse FOSL1 (sc-376148, Santa Cruz Biotech), Ki67 at 4°C overnight (human: 1:200; mouse: 1:500) and cleaved caspase-3 (sc-56053, Santa Cruz Biotech) at 4°C overnight (human: 1:200; mouse: 1:400). The reaction was developed using an anti-mouse EnVision kit (K4007, Dako) and tissues counterstained with Harris hematoxylin. Finally, slides were dehydrated in a graded series of ethanol, cleared in xylene and mounted in Cytoseal XYL (Thermo Scientific).

Cleaved caspase-3 and Ki67 expression was analysed for apoptosis and tumour proliferation quantification and FOSL1 validation of mouse antibody for human and mouse samples was performed by western blot using two independent shRNAs against human and mouse FOSL1.

Slides were scanned using Aperio CS2 image capture device (Leica Microsystems) and Aperio ImageScope 12.1.0 software (Leica Microsystems). Two independent observers evaluated the intensity and extensiveness of staining in all of the study samples.

9. APOPTOSIS ASSAY

9.1 CellEvent® cleavage Flow Cytometry Assay

Apoptotic cells were detected by CellEvent® Caspase-3/7 Green Flow Cytometry Assay Kit (Invitrogen). Briefly, the reagent is a cell permeant four amino acid peptide conjugated to a nucleic acid binding dye. The substrate is intrinsically non-fluorescent, due to the peptide inhibits the ability of the dye to bind to DNA. When caspase-3 or caspase-7 are activated in apoptotic cells, the peptide is cleaved,

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Materials and Methods and the dye is now able to bind to DNA and produce a fluorogenic signal with an absorption/emission maxima of 502/530 nm.

After shRNA or inhibitor treatment, cells were harvested, washed and resuspended in PBS 1X containing 2% BSA. Next, cells were incubated with CellEvent® Caspase-3/7 Green Detection Reagent for 30 min at 37 °C in darkness and then stained with 1 mM SYTOX® AADvanced™ dead cell reagent for 5 min. Cells were acquired in a FACSCanto II cytometer (BD Biosciences) and data were analysed using FlowJo® software v9.3.

10. CELL CYCLE ASSAY

10.1 Click-iT® EdU Flow Cytometry Assay

Cell cycle analysis was performed using Click-iT® EdU Flow Cytometry Assay Kit (Invitrogen). EdU (5-ethynyl-2’-deoxyuridine) is a nucleoside analogue to thymidine and is incorporated into DNA during active DNA synthesis. It is detected by Alexa Fluor 647 dye, which is coupled to an azide that reacts with EdU in the presence of Cu 7AAD (BD Biosciences) was used to stain DNA. After incubation with EdU, cells in the S-phase will be Alexa Fluor 647 positive, cells in G0/G1 phase 7AAD positive and cells in G2/M phase will show double 7AAD staining.

Briefly, cells were seeded in 21 cm2 dishes and maintained in culture for 48 h. Then, cells were incubated with 10 µM EdU for 2 h at 37 °C inside. Next, cells were harvested at 60-80% confluence, washed in PBS 1x containing 1% BSA, and fixed in formaldehyde (Click-iT® fixative) for 15 min at room temperature. Cells were washed in PBS 1X containing 1% BSA to remove formaldehyde, and permeabilized in 100 µl of 1X Click-iT® saponin buffer for 15 min at room temperature. Next, Click-iT® reaction cocktail (219 µl of DPBS, 5 µl of CuSO4, 1.25 µl of Alexa Fluor 647-coupled azide and 25 µl of 1X Click-iT® EdU buffer additive) was added per tube to cells and incubated for 30 min at room temperature in darkness. Cells were again washed

Materials and Methods with 1X Click-iT® saponin buffer, resuspended in 250 µl of the same reagent and incubated with 0.2 µg/µl RNase A (Sigma-Aldrich) for 1 h at room temperature in darkness. Finally, 15 µl of 7AAD were added to the tubes and after10 min cells were acquired using a FACSCanto II cytometer (BD Biosciences). Data were later analysed using FlowJo® software v9.3.

10.2 Phosphorylated Histone H3 (pH3) Flow Cytometry Labelling Assay

Cells were maintained in culture with no treatment or stimulated with 0.5 μM taxol (Sigma) for 20 h. Harvested cells were fixed and permeabilized in 70% ethanol at 4°Cfor 2 h. After a washing step, cells were resuspended in PBS + 1% BSA at 106 cells/ml. Cells were incubated with phospho-histone H3 (Ser10) antibody (Cell Signaling) at 1:70 dilution for 20 min, washed and subsequently incubated with Alexa Fluor 488 fluorochrome-coupled secondary antibody (Invitrogen) at 20 μg/ml for 20 min. After washing with PBS + 1% BSA, cells were incubated with 7AAD (BD Biosciences) for 10 min. Cells were acquired in a FACSCalibur cytometer (BD Biosciences) and data were analysed using FlowJo software v9.3.

11. DRUG COMBINATION STUDIES

The combination index (CI) was obtained using the CompuSyn software (www.combosyn.com), which takes advantage of the Chou-Talalay method for drug combination [308]. This method is based on the median-effect equation, derived from the mass-action law principle, which is the unified theory that provides the common link between single entity and multiple entities, and first order and higher order dynamics. The resulting combination index offers quantitative definition for additive effect (CI=1), synergism (CI<1), and antagonism (CI>1) in drug combinations. For the determination of the CI, at least two working concentrations for each drug are required. We used drug concentrations equal or lower than the GI25.

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12. IN VIVO EXPERIMENTS

All experiments in mice were performed according to the institutional Animal Care Committee of the University of Navarra under the protocols CEEA/120-13, CEEA/021-14, approved by the Government of Navarra. Mice sample size was always chosen using the following software: http://www.biomath.info/power/ttest.htm.

12.1 Xenograft and allograft models

12.1.1 Genotypes

Rag2-/- and/or Balb/cnu/nu mice were used for xenografts and allografts experiments. Mice were obtained from Charles River laboratories and bred at the Animal Core Facility at CIMA.

12.1.2 Subcutaneous xenograft and allograft injection

For xenograft and allograft experiments, 25 x 103 (LKR13 and 339) or 2 x 106 (H358, H2347, H1568,H1650, H2009, H1792, EGI1, HUCCT1) cells infected with specified shRNAs were suspended in 200 μl of serum free medium and injected subcutaneously into the two lower flanks of immune deficient 8 to 12-weeks old Rag2- /- or Balb/cnu/nu mice for engraftment of human and mouse cells respectively. Mice with subcutaneous shRNA inducible tumours were fed with 2 mg/ml doxycycline plus 5% sucrose in drinking water to activate gene silencing. For tumour formation experiments, doxycycline was added to water when cells were implanted. Conversely for tumour therapy experiments, doxycycline treatment started at day 14-18 when tumours were established and had an average volume of 80 to 100 mm3.

Mice were randomized by age and one week post-injection, tumour dimensions were measured every three days and tumour volume calculated using the formula:

Materials and Methods

Volume= π/6 x length x width. Investigator analysis was blinded (sample was coded to a random number).

12.1.3 Pharmacological drug administration

For experiments with pharmacological inhibitors, Alisertib (25 mg/kg), Trametinib (1 mg/kg) or dual administration was done by oral gavage daily. Mice were randomized by tumour size to minimize tumour volume differences among the 4 groups. Mutant KRAS tumours were treated for 14 days (H2009) or until control tumours reached the largest volume allowed under the ethical protocol (11 days, H1792).

12.2 Genetically Engineered Mouse Models (GEMMs)

12.2.1 Genotypes

For mouse genetics experiments, KrasLSLG12D/+ [29], Trp53flox/flox [309], Fosl1flox/flox [280] and AlbuminCre/+ [310] mice from129/Sv and C57bl/6 background were used. In KrasLSLG12D/+mice G12D mutation is inserted in exon 1 of the mouse endogenous Kras allele and guarded for transcription by a STOP cassette flanked by LoxP sites. The STOP transcriptional termination signal prevent ubiquitous activation of oncogenic KrasG12D strain and when Cre-mediated excision of the STOP element occur, KrasG12D is transcribed and expressed. In Trp53flox/flox mice exons 2 to 10 of Trp53 gene are flanked by LoxP sites allowing wild type p53 expression under normal conditions. In Fosl1flox/flox mice, Fosl1 exons 3 and 4 containing the dimerization and the DNA-binding domains of the FOSL1 protein are flanked by LoxP sites and followed by a GFP reporter gene. In presence of Cre, exons 3 and 4 are depleted and GFP is spliced to the residual N-terminal part of inactivated Fosl1. In AlbuminCre/+ mice, Cre recombinase expression is controlled by the serum albumin (alb) gene promoter [310], which is expressed in liver progenitors (hepatoblasts).

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Materials and Methods

The aforementioned mice were inter-crossed to generate the following genotypes:

1) KrasLSLG12D/+, Trp53f/f, Fosl1+/+ (KP) 2) KrasLSLG12D/+, Trp53f/f, Fosl1f/f (KPF)

3) AlbuminCre/+, KrasLSLG12D/+, Trp53f/f, Fosl1+/+(aKP)

4) AlbuminCre/+, KrasLSLG12D/+, Trp53f/f, Fosl1f/f (aKPF)

Genotyping of mice was done on DNA extracted from tail or ear clippings as described previously [166, 280].

12.2.3 Lung tumours induction in GEMMs

For induction of lung tumours, mice were intra-tracheally administered a dose of 1 x 107 pfu of AdCre and lungs harvested 12 weeks after infection. Blinding analyses were carried out when tumour burden, tumour number or tumour size was scored in the genetic experiment. Score of immuno-histochemistry analyses was also blinded.

13. TRANSCRIPTOMIC ANALYSIS

13.1 Microarray hybridization and data analysis

Microarray analysis was performed in the mutant KRAS NSCLC cell line H2009 to compare differentially expressed genes between control (H2009 GFPsh), and FOSL1- silenced cells (H2009 FOSL1sh). RNA extraction from in vitro cultures was carried out using the RNA Isolation and Purification Kit (Macherey-Nagel) previously described. RNA quality assessment and hybridization were performed in the Genomics Core Facility at CIMA and data analysis was performed by Dr. Elizabeth Guruceaga at the Bioinformatics Core Facility of CIMA. Specifically, RNA quality was analysed using Experion™ (Bio-Rad) and only RNAs with RQI>7 were included in the experiment. RQI (RNA quality indicator) is a number (on a scale of 1-10) that indicates RNA degradation

Materials and Methods grade, an RQI of 1 being “highly degraded” and an RQI of 10 being “highly intact”. RNAs extracted from 21 cm2 dishes at 80% of confluence ( 2 groups with 3 biological replicates per group) were hybridized to Human Gene ST 2.0 microarrays (Affymetrix) and normalized with RMA (Robust Multi-Array Average) approach. Low expression probes were removed by filtering those that did not exceed a level of expression of 32 in at least one of the samples in each condition analysis. Differentially expressed genes between control (GFPsh) and FOSL1-silenced cells (FOSL1sh) were identified using LIMMA (Linear Models for Microarray Data) method. Genes with B>0 and p<0.05 in FOSL1sh vs GFPsh analysis were identified. Selected genes were analysed using Ingenuity Pathway Analysis (IPA®, QIAGEN Redwood City, www.qiagen.com/ingenuity) to identify functions and pathways differentially activated between control and FOSL1- silenced cells.

13.2 Low input 3’ end RNA-Seq

Low input 3’ end RNA-Seq was performed on EGI1 CCA cells as previously described [311] (Figure M2). Briefly, 100ng/sample of purified RNA using RNeasy Mini Kit (Quiagen) were used to perform the protocol. Three experimental conditions were established (TET GFPsh, TET FOSL1 sh3 and sh10) and three biological replicates per condition were used. For barcoded cDNA generation, polyadenylated RNA was retrotranscribed using Affinity Script cDNA Synthesis Kit (Agilent Technologies) and barcoded primers harbouring also a T7 promoter and a partial Read2 sequence for Illumina sequencing (CGATTGAGGCCGGTAATACGACTCACTATAGGGGCGACGTGTGCTCT TCCGATCTXXXXXXNNNNTTTTTTTTTTTTTTTTTTTT, where XXXXXX is the cell barcode and NNNN is a unique molecular identifier or ΜMI, which represents the same initial RNA molecule for further quantification). Then, samples were assessed by qPCR and grouped in pools (up to 6 samples per pool) at equimolar ratios. Samples were then treated with Exonuclease I (Thermo Fisher Scientific) following a 1.2x positive SPRI- selection.

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Next, second strand cDNA synthesis was performed using NEBNext Ultra II Directional RNA Second Strand Synthesis Module (New England Biolabs) for 2 h at 16 °C, followed by a 1.4X positive SPRI clean-up. The samples were linearly amplified by T7 in vitro transcription using a T7 RNA Polymerase (New England Biolabs) for 16 h at 37 °C. After DNase treatment (TURBO DNse I, Thermo Fisher Scientific) for 15 min at 37 °C to remove dsDNA, samples were cleaned with a positive 1.2X SPRI-selection. RNA size, quality and concentration were determined by Qubit Fluorometer (Termo Fisher Scientific) and TapeStation (Agilent 4200 TapeStation System, Agilent Technologies). RNA was fragmented in 200 to 300 bp fragments using Zn2+ divalent cations followed by a 2X positive SPRI clean-up.

Fragmented RNA was dephosphorylated using FastAP Thermosensitive Alkaline Phosphatase (Thermo Fisher Scientific) for 10 min at 37 °C to enhance RNA/ssRNA ligation yield. In this step a partial Read1 (AGATCGGAAGAGCGTCGTGTAG) sequence for Illumina sequencing was ligated to the 3’ end of the fragmented RNA using a T4 RNA Ligase I (New England Biolabs). After a 1.5x positive SPRI clean-up, the RNA product was retrotranscribed using Affinity Script cDNA Synthesis Kit (Agilent Technologies) and a specific retrotranscription primer (TCTAGCCTTCTCGCAGCACATC). The library was then purified by 1.5X positive SPRI clean-up and the yield of previous steps was determined by qPCR.

Finally, a PCR using KAPA HiFi HotStart Ready Mix (Kapa Byosistems) was performed for second strand cDNA synthesis and Illumina primers were added (Read1- P5: AATGATACGGCGACCACCGAGATCTACAC TCTTTCCCTACACGACGCTCTTCCGATCT and Read2-P7: CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCG ATCT) for library amplification, using the recommended cycle numbers to minimize over-amplification. The product was purified by a 0.7X positive SPRI clean-up. The quality of the final library was determined by qPCR (based on the threshold cycle, or Ct, obtained for GAPDH (Fw: CCAGCAAGAGCACAAGAGGAA, Rv: GATTCAGTGTGGTGGGGG) or GUSβ (Fw: CAAGTGCCTCCTGGACTGTT, Rv: TCCACCTTTAGTGTTCCCTGC) housekeeping genes calibrated per individual set of

Materials and Methods samples), Qubit Materials and Methods Fluorometer (Termo Fisher Scientific) and TapeStation (Agilent 4200 TapeStation System, Agilent Technologies). All libraries were pooled at equimolar ratios and sequenced on a NextSeq500 (Illumina) using 60 bp single-end reads, obtaining a mean of 11 million reads per sample.

Figure M2: Schematic representation of the experimental procedure for RNA tagging, pooling, amplification, fragmentation and library setting-up. RNA molecules are coloured in blue, and DNA in black. Second synthesized strains are represented with a dashed line. NT20: 20-mer poly-T oligonucleotide; UMI: unique molecular identifier; rd2: Illumina primer sequence Read2; rd1: Illumina primer sequence Read1; rev: reverse; P5 and P7: Illumina adapters P5 and P7. (Image reproduced from Jaitin et al., 2014).

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13.3 Next-generation Chromatin immunoprecipitation sequencing (ChIP-seq)

Next-generation chromatin immunoprecipitation sequencing (ChIPseq) and chromatin immunoprecipitation PCR (ChIP-qPCR) were performed on EGI.1 CCA cell line transfected with the pSIN-EF1-FOSL1-FLAG overexpressing vector. Twelve P150 dishes with 107cells were seeded prior harvesting. Next day, cells were washed twice with cold 1X PBS and double cross-linked in gentle agitation first with 2 mM DSG (Pierce, pre-dissolved in DMSO in a 50 mM stock solution) in 1X PBS for 30 min at RT, and secondly with 1% formaldehyde in 1X PBS for 10 min at RT. The formaldehyde was quenched using glycine 0.125 M final concentration in gentle agitation for 5 min at RT. Cells were washed three times in cold 1x PBS and scraped on ice with 500 μL of cold 1X PBS complemented with 1X protease inhibitor (cOmplete, Mini, EDTA-free Protease Inhibitor Cocktail, Roche) with a plastic scraper.

For lysis, cells were resuspended in 10 ml of LB1 buffer (50 mM Hepes-KOH, pH 7.5, 140 mM NaCl , 1 mM EDTA pH 8.0, 10% glycerol, 1 mM EDTA pH 8.0, 0.5% NP-40 (Sigma-Aldrich), 0.25% Triton X-100 (Sigma-Aldrich) ) and complemented with 1x protease inhibitor. The sample was loaded into a Dounce tissue grinder (Kontes™) and homogenized using two different pestles: an A large pestle for initial sample reduction and a B small pestle to form the final lysate in which the nucleus remains intact after homogenization. 10X up and down movements were performed with each pestle. The homogenate was rotated for 15 min at 4 °C and centrifuged at 4,500xg for 5 min at 4 °C.

Nuclei pellet was resuspended in 1 mL of LB2 buffer (10 mM Tris–HCl pH 8.0, 200 mM NaCl, 1 mM EDTA pH 8.0, 0.5 mM EGTA pH 8.0) complemented with 1x protease inhibitor , rotated for 5 min at 4 °C and centrifuged at 4500xg for 5 min at 4 °C. The new pellet was resuspended in 300 μL of LB3 buffer (10 mM Tris–HCl pH 8.0, 100 mM NaCl, 1 mM EDTA pH 8.0, 0.5 mM EGTA pH 8.0, 0.1 % Na-Deoxycholate, 0.5 % N-Lauroylsarcosine) complemented with 1x protease inhibitor and frozen at -80 °C for 2 h to facilitate the following sonication. Sonication was performed in LB3 buffer using

Materials and Methods a bioruptor Pico (Diagenode) with high power mode (H position) for 30 cycles (sonication cycle: 30 sec ON, 30 sec OFF). 5μL of sonicated sample were reverse crosslinked and analysed in a 1% agarose gel to ensure chromatin fragments between 150-600 bp size.

1/10 of 10% Triton X-100 (in LB3) was added to the chromatin and centrifuged at 14,000xg for 10 min at 4 °C to pellet debris and store the supernatant. 10% of the supernatant was separately stored as IP input for sequencing. Per IP, 100μL of paramagnetic protein G-coated beads (Dynabeads, Invitrogen) were pre-blocked 3x with 1 mL of blocking solution (0.5% BSA in 1xPBS) and incubated overnight on rotation in 400 μL of blocking solution with 10 μg of specific antibody. Mouse α- FlagM2 (F1804 Sigma-Aldrich) and mouse α-IgG (632136) antibodies were used for the immunoprecipitation. Next day, beads were washed seven times with 1mL of RIPA buffer (50 mM Hepes-KOH, pH 7.5, 500 mM LiCl, 1 mM EDTA, 1.0 NP-40, 0.7% Na- Deoxycholate) complemented with 1X protease inhibitor and once with 1mL of TBS (20mM Tris-HCL, pH 7.6, 150 mM NaCl). Beads were resuspended in 100 μL of Elution buffer (50 mM Tris–HCl pH 8.0, 10mM EDTA, 1% SDS) complemented with 1X protease inhibitor and incubated ON at 65 °C to reverse formaldehyde crosslinking. Inputs were also de-crosslinked at this point after the addition of 70 μL of Elution buffer. Samples were diluted in 100 μL of TE and 3 μL of 1 mg/ml RNase A (Fermentas) and 3 μL of 1mg/ml Proteinase K (Fermentas) were added to remove RNAs and proteins. Samples were purified and eluted in 12 μL of water using ChIP DNA Clean & Concentrator purification kit (Zymo Research) and quantified using the Qubit dsDNA HS Assay Kit on a Qubit 2.0 Fluorometer (Thermo Scientific).

Library preparation was performed with the NEBNext® Ultra™ II DNA Library Prep Kit which provides a versatile, streamlined protocol for the construction of libraries for Illumina sequencing from fragmented, double-stranded DNA (dsDNA) using 5 ng of ChIP DNA and 100 ng of input DNA. Briefly, ChIP-seq workflow includes and "end repair and phosphorylate" step followed by and "A-tailing" of the DNA fragments and a later adapter ligation. Libraries were amplified by PCR using the

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Materials and Methods recommended cycles to reduce over-amplification and quantified using Qubit 2.0 Fluorometer (Thermo Scientific). Size selection was performed with TapeStation (Agilent 4200 TapeStation System, Agilent Technologies) for the 200- to 400-bp fragment range selection. Libraries were pooled in equimolar ratios and sequenced on a HiSeq 2500 (Illumina), using 60 bp single-end reads, obtaining a mean of 25 million reads per sample.

14. CHROMATIN IMMUNOPRECIPITATION qPCR (ChIP-qPCR)

Samples for were processed following precisely the protocol previously described. Unlike the ChIP-seq elution step, ChIP-qPCR samples were purified and eluted in 24 μL of water using ChIP DNA Clean & Concentrator purification kit (Zymo Research). Eight pairs of primers were designed for each gene using Primer-BLAST (NCBI - NIH) designing tool and selected based on the following parameters: Primer melting temperatures -Tm- between 58 °C to 62 °C , GC% between 50 to 55, a self- complementarity ratio lower than 4 and a PCR product size between 70 and 120 bp.

ChIP-qPCR data were normalized for sources of variability, including amount of chromatin, efficiency of immunoprecipitation, and DNA recovery. Two methods were used to normalize ChIP-qPCR data: 1) the Percent Input Method and 2) the Fold Enrichment Method. ChIP experiments were run in triplicate and results presented together with the background signal. The Percent Input Method includes normalization for both background levels and input chromatin going into the ChIP. Signals obtained from the ChIP are divided by signals obtained from an input sample. This input sample represents the amount of chromatin used in the ChIP. Typically, 1% of starting chromatin is used as input. With The Fold Enrichment Method the ChIP signals are divided by the no-antibody signals, representing the ChIP signal as the fold increase in signal relative to the background signal. The assumption of this method is that the level of background signal is reproducible between different primer sets, samples, and replicate experiments. Background signal levels do vary between primer sets, samples,

Materials and Methods and experiments. Source: https://www.thermofisher.com/es/es/home/life-science/ epigenetics-noncoding-rna-research/chromatin-remodeling/chromatin- immunoprecipitation-chip/chip-analysis.html

15. GENE SET ENRICHMENT AND GENE ONTOLOGY ANALYSIS OF DATA SETS

The molecular component, processes, functions and signalling networks were analysed studding the differentially expressed genes through Tool (GSEA, http://www.broad.mit.edu/gsea/index.html). Public datasets used for this analysis were downloaded from Gene Expression Omnibus (GEO) data repository (http://www.ncbi.nlm.nih.gov/geo) or The Cancer Genome Atlas (TCGA) Data Portal (https://tcga-data.nci.nih.gov/tcga/tcgaHome2.jsp). TCGA processed data for RNA-Seq experiments of lung adenocarcinoma samples were downloaded for the logFC calculation of the comparison KRAS_mut vs KRAS_wt. Microarray raw data was also downloaded from GEO, normalized with RMA9 and analyzed for the logFC calculation using R [312]. In the case of the identification of FOSL1 transcriptional targets in mutant KRAS lung adenocarcinoma cells, microarrays were normalized with RMA [313]. Quality assessment, outlier detection and filtering process of probe sets with an expression value lower than 4 in more than the 50% of the samples were performed with R [313]. LIMMA [314] was used to identify the probe sets with significant differential (B>0). Data of microarray analysis on H2009 transduced with FOSL1 shRNA are publicly available in GEO database with the accession number GSE76290. Hierarchical clustering of microarray data was performed with R [313] and functional enrichment analysis of Gene Ontology categories [315] was performed using the hypergeometric distribution in R [313].

For KRAS status predictor, a machine learning algorithm based on logistic regression [316] was applied to public datasets (GSE12667, GSE16515, GSE26566, GSE26939 and Battacharjee et al. [317]) in order to distinguish patients with mutant KRAS from patients with wild-type KRAS with the selected 8-gene signature summation

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[318]. The performance of each classifier was evaluated using Receiver Operator Characteristics Curve (ROC) analysis [319]. Both the classifiers and the corresponding ROC analyses were performed using R [312]. The same methodology was applied to patients or cell lines with mutated EGFR (GSE31210, Nguyen et al. [4] and GSE36133) and LUAD patients of the TCGA dataset with mutations in EGFR, BRAF or DDR2, EML- ALK4 fusions, or MYC amplification plus no KRAS mutation (https://tcga-data.nci. nih.gov/tcga/tcgaHome2.jsp).

Survival analysis was conducted on both gene sets and individual genes using TCGA LUAD data (https://tcga-data.nci.nih.gov/tcga/tcgaHome2.jsp). Log-rank test was used to calculate the statistical significance of differences observed among Kaplan- Meier curves [320]. In the case of the gene sets, a summation of all the genes for a particular sample was calculated as previously described [318]. Multivariate Cox proportional hazards analysis was also performed considering the age, gender, tumour stage and the expression of each gene set or individual gene as covariates [321]. All the survival analyses were performed with R [312] and p-values<0.05 were considered statistically significant. Survival analysis on KRAS 8-gene signature and FOSL1 47-gene signature using TCGA lung squamous cell carcinoma (LUSC) RNA-seq data (https://tcga- data.nci.nih.gov/tcga/tcgaHome2.jsp) was performed as described for the TCGA LUAD data.

16. STATISTICAL ANALYSIS

Statistical analysis was performed using STATA software for Windows 10. Prior to all analyses, normality and homoscedasticity were assessed by Shapiro-Wilk and Levene tests, respectively. When data showed homoscedasticity, pairwise Student’s t test and Mann-Whitney U test were respectively used for normally and non-normally distributed variables. When data did not showed homoscedasticity, Welch and Median tests were respectively used for normally and non-normally distributed variables. ANOVA and posterior Bonferroni tests were used for multiple comparisons of normally

Materials and Methods distributed variables. Kruskal-Wallis and posterior Bonferroni adjusted-Mann-Whitney U tests were used for multiple comparisons of non-normally distributed variables. Survival curves were compared with long-rank test. Statistical significance was defined as significant (p < 0.05, *), very significant (p < 0.01, **) and highly significant (p < 0.001, ***).

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RESULTS I: Lung Adenocarcinoma (LUAD)

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1. Identification of KRAS-dependent candidate genes by a cross-tumours gene- expression screen

To uncover a core of genes consistently regulated by KRAS across mouse and human tumours, we followed a two-tiered approach. First, we screened gene expression data from in vitro and in vivo experimental systems of epithelial and mesenchymal origin with either wild-type or mutant KRAS allele (human immortalized broncho-epithelial cells [129], KrasLSLG12D/+ mouse embryo fibroblasts -MEFs- [131] and a mouse model of Kras-driven lung cancer [120]). Only 19 genes were consistently overexpressed in the mutant KRAS phenotype in at least 2 of the 3 studies, indicating that the transcriptional response to oncogenic KRAS is markedly influenced by the tissue or species of origin (Figure R1 and Annexes Table 1). Only 3 genes (DUSP6, GLRX and PHLDA1) overlapped with a previous cross-species KRAS signature [322], underscoring the validity of our approach to uncover novel KRAS-regulated genes.

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Figure R1: Identification of KRAS-dependent genes. (A) Workflow of the gene expression strategy for the identification of mutant KRAS-regulated genes. Venn’s diagram summarizing the cross-species meta-analysis. In white are genes over-expressed in mutant KRAS cells or tumours over wild-type KRAS controls (n=19). Blue: Human lung immortalized broncho-epithelial cells (AALE cells) expressing exogenous KRASG12D compared to wild-type KRAS-expressing cells. Yellow: KrasLA2 mouse lung adenocarcinoma tumours compared to normal lung tissue. Red: Kras-activated mouse embryo fibroblasts (MEFs) compared to wild-type Kras MEFs. (B) Bar graph representing the expression levels of the 19 indicated genes in the microarrays analyses comparing human lung immortalized broncho-epithelial cells (AALE cells) expressing exogenous KRASG12D over wild-type KRAS-expressing cell (black), mutant KRAS MEFs over wild-type MEFs (dark grey) and KrasLA2 mouse lung adenocarcinoma tumours over normal lung tissue (light grey).

Next, GSEA of publicly-available data from mouse and human tumours was performed to determine the biological relevance of the 19 KRAS-regulated candidate genes (inter-species KRAS signature) in human cancer. In the mouse datasets, a negative enrichment of the inter-species KRAS signature was observed in mouse lung adenocarcinoma (LUAD) and pancreatic ductal adenocarcinoma (PDAC) where mutant KRAS expression was depleted [28, 41] (Figure R2). In the human cancer data sets, a positive enrichment was observed in mutant KRAS patients compared to wild-type in

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LUAD [1, 2, 323], PDAC [324], colorectal cancer (CRC) [325], cholangiocarcinoma (CCA) [326] and multiple myeloma (MM). These observations were recapitulated in human LUAD and CRC cell lines from the Cancer Cell Line Encyclopaedia [3], suggesting that the 19 genes are regulated through cell-autonomous mechanisms (Figure R2).

Figure R2: Relevance of KRAS-dependent genes in mouse and human cancer. (Blue circle) Gene set enrichment analysis of mouse LUAD and PDAC cancer data sets comparing active mutant KRAS cells to KRAS-inhibited cells. (Red circle) Gene set enrichment analysis of human cancer data sets comparing mutant KRAS patients to wild-type KRAS patients.

2. An 8-gene signature conserved across mutant KRAS tumours predicts status and patient survival

To refine the value of the identified KRAS-regulated genes to human cancer, those genes recurrently present in >50% of the leading edges of the mouse and human cancer GSEAs were identified (Supplementary Table 2). This analysis across different mutant KRAS-driven tumours unveiled a core of 8 genes including AREG, DUSP4, DUSP6, FOSL1, LAMB3, LAMC2, PHLDA1 and SPRY4. Notably, LAMB3 has been

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Results reported as a synthetic-lethal interaction with oncogenic KRAS in colon and lung cancer [135, 327], suggesting that other co-identified genes could also display a functional role in mutant KRAS cancer.

Next, we interrogated independent data sets of KRAS-driven cancers to determine whether the 8-gene signature (cross-tumours KRAS signature) would be a predictor for KRAS status. Interestingly, a higher geometric mean was representative of LUAD [152, 317, 328], PDAC [329], and CCA [234] tumours harbouring KRAS mutations compared to wild-type ones (Figure R3).

Figure R3: The 8-gene expression signature predicts KRAS status in LUAD patients. (A) Box and whiskers classification analysis plots summarizing the geometric mean based on the expression of the 8-gene cross-tumours signature in lung adenocarcinoma (LUAD), pancreatic ductal adenocarcinoma (PDAC) and cholangiocarcinoma (CCA) data sets. Each dot is a sample. P values were obtained using Student’s t test. (B) Performance of a univariate classification model for predicting mutant vs. wild-type KRAS of LUAD, PDAC and CCA patients based on gene expression of the 8-gene signature measured by area under receiver operator characteristics (AUC).

Since KRAS mutations are mutually exclusive with other dominant oncogenic drivers in LUAD [323] we reasoned that the cross-tumours KRAS signature would be exclusive of the KRAS phenotype. To verify this, we then determined if other oncogenic

Results drivers were mutated or amplified in patient tumours. The geometric mean of the 8 genes did not discriminate mutant EGFR, BRAF, AML4-ALK and DDR2 tumours or MYC amplified tumours from wild-type patients (Figure R4).

Figure R4: The 8-gene expression signature did not predict EGFR, BRAF, EML4-ALK, DDR2 or MYC status in LUAD patients. (A) Box and whiskers plots summarizing the geometric mean of the 8- gene signature in mutant and wild-type EGFR LUAD. Four published data sets [1-4] were used for EGFR analysis. P values obtained using Student’s t test. P. (B) Box and whiskers plots summarizing the geometric mean of the 8-gene signature in mutant BRAF, EML4-ALK, DDR2 or amplified MYC and wild- type LUAD. TCGA LUAD data set was used for the remaining oncogenic drivers [1]. P values obtained using Student’s t test.

Next we sought to investigate whether the cross-tumours KRAS signature could be used as a prognosis marker. To do this, the TCGA LUAD data set was used as it is large enough to stratify patients based on KRAS status. Mutant KRAS patients expressing high levels of the 8-gene signature had the worst outcome with a significant decreased survival compared to wild-type patients (p=0.009)(Figure R5A). The 8-gene signature was also associated with poor survival in PDAC patients (Figure R5B) [330] whereas no association with patient survival was observed in other tumours

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Results where KRAS mutations are rarely found or absent such as squamous cell lung carcinoma (Figure R5C).

Figure R5: The cross-tumours KRAS signature predicts survival in LUAD and PDAC patients. Kaplan-Meier plot of LUAD (A) and PDAC (B) patients for the expression of the cross- tumours KRAS signature taking into account KRAS status. Wild type group excluded patients with genetic alterations in non-KRAS oncogene drivers to prevent any bias in survival due to administration of targeted therapies. P values obtained using the log-rank test (Mantel-Cox), *p<0.05. (C) Kaplan-Meier plot of lung squamous carcinoma patients (TCGA data set) stratified by the mean expression of the cross-tumours KRAS signature. P values obtained using log-rank test (Mantel-Cox).

For the LUAD data set, multivariate analysis of mutant KRAS patients expressing high levels of the cross-tumours KRAS signature showed that the effect of gene expression on patient survival is irrespective of stage, age and gender (p=0.003, HR=1.599 (1.177-2.172)), conversely to what was found in wild-type KRAS patients

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(p=0.883, HR=1.024 (0.748-1.402)). All together these results argue that high expression of the cross-tumours KRAS signature is representative of mutant KRAS cancers and presents clinical relevance.

3. Upregulation of FOSL1 in KRAS-driven human LUAD

To further delineate the contribution of the 8 candidate genes, we carried out single gene survival analysis to discriminate those genes involved in the clinical outcome of mutant KRAS patients. We focused on LUAD where patients can be stratified based on KRAS genotype. Only high FOSL1 expression identified a group of patients bearing KRAS mutations with the worst survival outcome and the differences between wild-type and mutant KRAS patients expressing high FOSL1 levels were statistically significant (p=0.016) (Figure R6).

Figure R6: High levels of FOSL1 confer the worst survival outcome to LUAD patients harbouring KRAS mutations. Kaplan-Meier plot of the LUAD TCGA data set for FOSL1 expression based on KRAS status. The wild-type group excluded patients with genetic alterations in non-KRAS oncogene drivers. P values obtained using the log-rank test (Mantel-Cox), * p<0.05.

Multivariate analysis including stage, age and sex showed that FOSL1 expression was an independent survival marker in LUAD patients with KRAS mutations (p=0.006; HR=5.072 (1.61-18.98)). No association was observed between FOSL1

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Results expression and survival of squamous lung carcinoma where KRAS mutations are rarely found. These results suggest that high FOSL1 expression identifies the group of patients harbouring KRAS mutations with the worst survival outcome mutations. To determine if the patient data translated to cell samples derived from human LUAD, a panel of cell lines with known KRAS status was utilized. FOSL1 mRNA and protein levels were upregulated in mutant KRAS cell lines compared to wild-type (Figure R7A). Likewise, phosphorylation of FOSL1, previously suggested to be a mechanism of FOSL1 activation [331], was preferentially observed in mutant cells. Given that FOSL1 heterodimerizes with members of the JUN and FOS family to form the active AP1 transcriptional complex, all members of the AP1 complex were studied. Of note, analysis of other AP1 members of the JUN and FOS families revealed no obvious genotype-specific expression (Figure R7B).

Figure R7: FOSL1 is upregulated in KRAS-mutant LUAD human cell lines. (A) mRNA and protein analyses by qPCR and Western blot respectively for the indicated genes and proteins in a panel of mutant (n=7) and wild-type (n=7) KRAS cell lines. Image is representative of 3 independent qPCR and Western blots with different cell lysates. mRNA levels were compared by Student's t test. Error bars correspond to s.d. (B) Western blot analysis for members of the FOS and JUN families in a panel of mutant (n=7) and wild-type (n=7) KRAS cell lines.

Additionally, analysis of patient-derived xenografts (PDX) also revealed upregulation of FOSL1 expression in tumours with KRAS mutations compared to wild type (Figure R8).

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Figure R8: FOSL1 is upregulated in human KRAS-mutant LUAD PDX. Immunochemistry of FOSL1 protein in mutant (n=2)(top row) and wild-type (n=2) (lower row) KRAS patient-derived xenografts. Scale bar: 50µm.

To explore whether FOSL1 was downstream of mutant KRAS, KRAS inhibition was performed in 2 different mutant LUAD cell lines. KRAS knock-down lowered FOSL1 protein levels (Figure 9A). Conversely, in gain of function experiments in a wild-type KRAS cell line, mutant KRAS overexpression upregulated FOSL1 expression levels (Figure R9B). These results corroborate that FOSL1 expression is modulated by oncogenic KRAS.

Figure R9: FOSL1 expression is dependent on mutant KRAS expression. (A) Western blot showing FOSL1 expression levels after KRAS inhibition with a specific shRNA in mutant KRAS cells (H2009 and H358). (B) Western blot to detect FOSL1 protein expression upon KRAS induced expression in wild-type KRAS cells (H1568). Image is representative of 3 independent Western blots with different cell lysates.

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So far, it had been reported that ERK1/2 acts as the main KRAS effector controlling FOSL1 expression in KRAS-driven cancer [262]. To dissect the canonical downstream pathways controlling FOSL1 expression under endogenous mutant KRAS signalling, pharmacological inhibitors against known KRAS effectors were used. U0126, BIX02189, SP600125, LY294002 and SB203580 pharmacological inhibitors were used to respectively block MEK, MEK5, JNK, AKT, and p38. The results showed that in addition to ERK1/2, other KRAS effectors such as ERK5, JNK and p38, the later to a lower extent, also consistently regulated FOSL1 expression in mutant KRAS cell lines (Figure R10). Thus, FOSL1 is regulated by, and integrates signals from, different kinases downstream of KRAS.

Figure R10: FOSL1 is regulated by various KRAS downstream effectors. Western blot of H2009 and H358 cells treated with U0126 (MEKi, 10μM), BIX02189 (MEK5i, 10μM), SP600125 (JNKi, 20μM), LY294002 (AKTi, 10μM) and SB203580 (p38i, 20μM) and probed with indicated antibodies. The experiment was carried out 3 times.

4. FOSL1 is overexpressed in a mouse model of advanced LUAD

To study FOSL1 expression in a more physiologically-relevant system, a genetically-engineered mouse model of KRAS-driven LUAD, KRASLSL-G12D; Trp53f/f (KP) was used. First, immunochemistry (IHC) analysis was performed in normal and tumour tissue after antibody validation by Western blot using specific shRNAs to FOSL1. KP mice were then intratracheally administered adenoviral Cre (AdCre) to induce Kras mutation and Trp53 loss. No FOSL1 expression was detected in normal alveolar and bronchiolar epithelia of KP mice (Figure R11A, a and b). Conversely, around 60% of lung tumours expressed FOSL1 to some extent (Figure R11A, c-f). Of note, FOSL1

Results expression was also found in advanced tumour lesions such as lymph node metastases (n=4) (Figure R11B).

Figure R11: FOSL1 is expressed in GEMMs neoplasms upon oncogenic KRAS activation and p53 loss. (A) Expression of FOSL1 by immunohistochemistry in KrasLSLG12D, Trp53f/f mice -KP- (n=5; (a): normal alveolar epithelium; (b): normal bronchiolar epithelium; (c) and (d): adenoma; e: adenocarcinoma; (f): lymph node tumour metastasis -Met-). Scale bar 50μm. (B) Quantification of FOSL1 staining in lung tumours of KrasLSLG12D, p53f/f (KP) mice.

Interestingly, only a small fraction of cells expressing FOSL1 was positive for Ki67 (7.25%), suggesting that FOSL1 does not specifically label a population of proliferative cells (Figure R12).

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Figure R12: Immunofluorescence in tumours from KP mice. (A) Expression of FOSL1 (red) and LSL-G12D f/f Ki67 (green) by immunofluorescence in tumours from Kras , Trp53 mice. Scale bar 50μm. (B) Bar graph indicating the percentage of double FOSL1 and Ki67 positive cells in FOSL1-positive tumours.

To rule out the possibility that FOSL1 upregulation was mediated by a non-cell autonomous mechanism, its expression was assessed in lung cancer cell lines derived from different mouse models [28, 29, 332, 333]. FOSL1 expression was detected at mRNA and protein level in mutant Kras LUAD cell lines but not in normal lung tissue or wild-type Kras squamous cell carcinoma cells (Figure R13). Notably, FOSL1 expression was preserved in cell lines derived from metastatic sites [333].

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Figure R13: FOSL1 is upregulated in KRAS-mutant LUAD murine cell lines. (A) FOSL1 protein expression in mouse LUAD cells derived from KrasLA2 (LKR10, LKR13), KrasLSLG12D (LSZ1-5) and KrasLSLG12D, p53f/f (389N1, 482N1), squamous cell lung carcinoma cells (UNSCC680) and normal lung. Asterisk indicates metastatic cell lines isolated from the lymph nodes. Image is representative of 3 independent western blots with different cell lysates. (B) qRT-PCR on mutant Kras LUAD cells, wild-type Kras squamous carcinoma cells (UNSCC680) and normal lung. Error bars correspond to s.d. mRNA was obtained from 3 independent isolates per sample.

These results indicate that FOSL1 is upregulated by a cell-autonomous mechanism in an autochthonous model of KRAS-driven LUAD and suggest that it may be important in lung tumorigenesis, including advanced metastatic stage.

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5. Mutant KRAS LUAD cells are preferentially sensitive to FOSL1 inhibition in vitro

To test the functional relevance of FOSL1 in KRAS-driven LUAD, FOSL1 expression was depleted by interference short hairpin RNAs (shRNA) in mutant and wild-type KRAS cell lines (Figure R14). Three independent shRNAs targeting FOSL1 were selected from a pool of 5 due to its adequate protein inhibition and low predicted off-target effects.

Figure R14: FOSL1 is effectively silenced with 3 independent constitutive shRNAs in KRAS- mutated and wild-type human LUAD cell lines. Western blot for FOSL1 in mutant (A) and wild- type (B) KRAS LUAD cell lines after infection with 3 independent FOSL1shRNAs. Image is representative of 3 independent western blots with different cell lysates.

LUAD cell lines (6 KRAS-mutated and 7 KRAS wild-type) were infected with the selected shRNAs for FOSL1 silencing and subjected to proliferation analysis. Mutant KRAS cells were significantly more sensitive to FOSL1 inhibition than wild-type cells for the 3 independent shRNAs, suggesting a specific on-target effect (Figure R15A). Importantly, this result was consistent among mutant and wild-type KRAS cell lines with similar population doubling (Figure R15B), suggesting that sensitivity to FOSL1 loss is specific to KRAS mutations and not due to potential changes in cell proliferation capacity between the two genotype groups.

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Figure R15: FOSL1 inhibition preferentially impairs cell proliferation of LUAD human KRAS- mutated cells in vitro. (A) Percent cell viability of human wild-type (HCC78, H1437, H1568, H1650, H1703 and H2126) and mutant (H23, H358, H441, A549, H1792, H2009 and H2347) KRAS LUAD cell lines with 3 independent FOSL1 shRNAs. Data is relative to number of cells expressing a GFP shRNA. Relative cell number was assessed by an MTS assay for 4 days and repeated 3 times. P values obtained using Student's t test. **, p<0.01; ***, p<0.001. (B) Cumulative (left) and average (right) population doubling time of mutant (H358 and H2347) and wild-type (H1568 and H1650) KRAS cells. These four cell lines are also represented in top row graphs as open circles. All experiments were run in biological triplicates and repeated 3 times. Population doubling time was calculated using the following formula: Number of hours from seeding to harvest/[((log N(t)-logN (t0))/log2]. N(t) is the number of cells at time of passage and N(t0) is the number of cells seeded at previous passage [5].

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Likewise, FOSL1 depletion in two mouse LUAD cell lines derived from a Kras- driven mouse model led to a significant decrease in cell viability (Figure R16).

Figure R16: Fosl1 inhibition impairs cell proliferation of LUAD murine KRAS-mutated cells in vitro. Relative cell number of mutant Kras, mouse LUAD cell lines (LSZ2 and LSZ3) expressing a control GFP shRNA or two independent shRNAs against Fosl1 assessed by MTS. Experiment was done 3 times. P values obtained using Student's t test.

Next, the cellular mechanisms underlying the effects of FOSL1 loss were analysed using an inducible shRNA that compromised proliferation of mutant KRAS cells in vitro as seen for the same constitutive shRNA (Figure R17).

Figure R17: A doxycyline inducible FOSL1 shRNA recapitulates the effect of constitutive shRNAs (A) Western blot analysis for indicated antibodies in mutant and wild-type KRAS cell lines carrying an inducible FOSL1 shRNA (TET_FOSL1 sh1). Cells were treated with 1 µg/ml doxycycline for 96 h prior to protein collection. Results were similar between 2 different protein isolates. (B) Relative cell number of H358 cell line expressing an inducible FOSL1 shRNA. Error bars correspond to s.d.

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Apoptosis and cell cycle analysis were carried out by flow cytometry in order to unveil the cellular mechanism underlying the effects of FOSL1 loss. A significant deregulation of cell cycle was observed upon FOSL1 inhibition only in KRAS-mutated cell lines, with a significant shortened S-phase and an increased G2/M phase (Figure R18A). Significant induction of the apoptotic rate was also found upon FOSL1 loss in KRAS-mutated cells compared to KRAS wild-type (Figure R18B), suggesting that FOSL1 is involved in various cell processes in those cells.

Figure R18: FOSL1 inhibition preferentially arrests cell cycle and increases apoptosis of KRAS-mutated LUAD cell lines in vitro. (A) Cell cycle analyses using an EdU incorporation assay of mutant (H358 and H2347) and wild-type (H1568 and H1650) KRAS cell lines expressing an inducible shRNA against GFP or FOSL1. shRNA expression was induced by doxycycline (1μg/ml) for 3 days and then cells plated for 24 h before analysis. Data is normalized to cells expressing a GFP shRNA. Result is average of 3 independent experiments. Error bars correspond to s.d. P values obtained using Student’s t test. *, p<0.05;***, p<0.001. (B) Analysis of active caspase 3-7 in the same cells as in left. Data is normalized to cells expressing a GFP shRNA. Result is average of 3 independent experiments. Error bars correspond to s.d. P values obtained using Student’s t test. **, p<0.01; ***, p<0.001.

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6. Mutant KRAS LUAD cells are preferentially sensitive to FOSL1 inhibition in vivo.

The in vitro experiments prompted us to test whether FOSL1 would be required for LUAD tumorigenesis. Injection of human mutant KRAS cells infected with the inducible FOSL1 shRNA into immunodeficient mice yielded tumours of significant smaller volume than control cells (Figure R19 and Figure R20).

Figure R19: In vivo FOSL1 inhibition impairs tumour growth in KRAS-mutant LUAD xenografts. (A) Average tumour volume of xenografts from KRAS-mutant (H358 and H2347) cells expressing an inducible GFP or FOSL1 shRNA (n=8-12 per group). Mice were fed 2 mg/ml doxycycline plus 5% sucrose in drinking water when cells were implanted. Error bars correspond to s.e.m. P values obtained using Student’s t test. **, p<0.01; ***, p<0.001. (B) Representative images of xenografted tumours derived from mice in A.

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Conversely, average tumour volume of wild-type KRAS cells with FOSL1 inhibition did not change compared to the control group (Figure R20).

Figure R20: In vivo FOSL1 inhibition do not alter tumour growth in KRAS-wild-type LUAD xenografts. (A) Average tumour volume of xenografts from wild-type KRAS (H1650 and H1568) cells expressing an inducible GFP or FOSL1 shRNA (n=8-12 per group). Mice were fed 2 mg/ml doxycycline plus 5% sucrose in drinking water when cells were implanted. Error bars correspond to s.e.m. P values obtained using Student’s t test. **, p<0.01; ***, p<0.001. (B) Representative images of xenografted tumours derived from mice in A.

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As observed in vitro, the differences after FOSL1 loss were due to a significant decrease in the number of proliferating cells and an increase of apoptosis in concordance with reduced FOSL1 expression compared to the GFPsh (Figure R21).

Figure R21: In vivo FOSL1 inhibition impairs tumour cell proliferation and increases apoptosis in LUAD xenografts. (A) Analysis of Ki67 and cleaved caspase 3 positive cells in representative areas (n=15) and (n=10) respectively, from H358-derived tumours in R19. Error bars correspond to s.e.m. P values obtained using Student’s t test. **, p<0.01; ***, p<0.001. (B) Immunohistochemistry to detect FOSL1 expression in representative sections from tumours in R19. Scale bar 50 µm.

The human data were recapitulated in a mouse model, where Fosl1 depletion in mouse Kras-driven lung cancer cells allografted in mice also decreased tumour volume

(Figure R22).

Figure R22: In vivo Fosl1 inhibition is detrimental for tumour growth in Kras-mutant LUAD allografts. (A) Average tumour volume of allografts from mouse LUAD LSZ3 cells transduced with a control GPF shRNA and 2 shRNAs against Fosl1 (n=8 per group). Error bars correspond to s.e.m. P values obtained using Student’s t test. ***, p<0.001. (B) Representative images of allografted tumours derived from mouse mutant Kras LUAD cells (LSZ3) expressing a Fosl1 or a control GFP shRNA.

Results

We then investigated if FOSL1 expression in KRAS-driven LUAD was also important in tumour maintenance. Mutant KRAS cells were engrafted and grown until average tumour diameter reached 80-100 mm3 prior to doxycycline administration. FOSL1 inhibition significantly decreased tumour fold change compared to control shRNA (Figure R23A and B). These changes correlated with a decrease in the number of proliferative cells and an increase of apoptotic cells in FOSL1-depleted tumours (Figure R23C-E). All in all, these data show that FOSL1 plays an important role in KRAS- driven tumorigenesis and tumour progression.

Figure R23: In vivo FOSL1 inhibition decreases tumour growth in established tumours of KRAS-mutant LUAD xenografts, impairing cell proliferation and inducing apoptosis. (A) Average tumour volume change of xenografts from H358 cells with inducible GFP and FOSL1 shRNAs (n=12 per group) relative to start of doxycycline treatment. Doxycycline treatment started at day 18 when tumours had an average volume of 80 to 100 mm3. Error bars correspond to s.e.m. P value obtained using Student’s t test. ***, p<0.001. (B) Representative images of xenografted tumours from A. (C) Analysis of Ki67 positive cells in representative areas (n=15 per group) of tumours in A. Error bars correspond to s.e.m. P value obtained using Student’s t test. ***, p<0.001. (D) Analysis of cleaved caspase 3 positive cells in representative areas (n=10 per group) of tumours in A. Error bars correspond to s.e.m. P value obtained using a t-test. **, p<0.01. (E) Immunohistochemistry to detect FOSL1 expression in representative sections of the same tumours as in A. Scale bar 50μm.

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7. FOSL1 is required for tumorigenesis in a genetically-engineered mouse model of advanced LUAD

To investigate the role of FOSL1 in a model of LUAD initiation by oncogenic Kras, KrasLSL-G12D/+; Trp53f/f; Fosl1+/+ (KP) and KrasLSL-G12D/+; Trp53f/f; Fosl1f/f (KPF) mice were used. Adenoviral Cre recombinase (AdCre) administration induced KrasG12D expression and Trp53 loss in KP mice. A notable decrease in tumour burden was observed in KPF mice compared to KP control mice by micro-computed tomography and histology after 12 weeks of AdCre administration (Figure R24).

Figure R24: In vivo Fosl1 ablation decreases tumour burden and tumour area in GEMMs of advanced LUAD. (A) Representative microCT scans of tumours in KrasLSLG12D/+; Trp53f/f; Fosl1+/+ (KP) and KrasLSL-G12D/+; Trp53f/f; Fosl1f/f (KPF) and treated with AdCre and allowed to develop tumours for 12 weeks. (B) Representative Haematoxylin and Eosin stained sections of KP and KPF lungs. Scale is 6mm. (C) Dot plot of the quantification of tumour area in KP (n=16) and KPF (n=16) groups (n, number of mice per group). Error bars correspond to s.e.m. P value obtained using a Mann-Whitney test. ***, p<0.001.

Detailed image analysis of haematoxylin-eosin sections revealed a significant decrease of tumour area in KPF compared to KP mice (Figure R24), despite the presence of large KPF tumours with incomplete recombination of Fosl1 floxed alleles (Figure R25).

Results

Figure R25: In vivo ablation of Fosl1 in LUAD GEMMs decreases tumour area despite Fosl1 recombination is not fully achieved in tumours. PCR to assess the degree of Fosl1 recombined allele in micro-dissected tumours from KPF mice.

Quantitative analysis of total tumour number in KP versus KPF mice showed a significant decrease in the number of tumours initiated in lung epithelial cells depleted of Fosl1 (Figure R26A). Likewise, also analysis of tumour size revealed that tumours in KPF mice were significantly smaller compared to KP (Figure R26B). In this regard, binning of the total number of tumours into quartiles by size revealed that median differences at each quartile were statistically significant between the two groups (Figure R26C).

Figure R26: Reduced tumour number and size was found in Fosl1 knocked out GEMMs of LUAD. (A) Dot plot showing mean number of tumour per mouse in KP and KPF mice. Error bars correspond to s.e.m. P value obtained using Student’s t test. *, p<0.05. (B) Bar graph of the average of tumour size in KP and KPF groups. Error bars correspond to s.e.m. P values obtained using Student’s t test. ***, p<0.001. (C) Tumour size analysis of KP and KPF mice by quartiles. Error bars correspond to s.e.m. P value obtained using Student’s t test. ***, p<0.001.

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Lastly, Fosl1 deletion in KPF mice prolonged overall survival compared to control mice (Figure R27).

Figure R27: Extended overall survival was observed in Fosl1 knocked out GEMMs of LUAD. Kaplan-Meier plot of KP (n=20) and KPF (n=16) mice. P value obtained using the log rank test (Mantel- Cox). Student’s t test. *, p<0.05.

These results suggest that Fosl1 functions in initiation and progression of mutant Kras tumours, and demonstrate an important role of FOSL1 in KRAS-driven LUAD oncogenesis in vivo.

8. FOSL1 regulates a gene signature associated with poor survival that includes components of the mitotic machinery

To explore the molecular mechanisms elicited by FOSL1, transcriptomic profiling was performed on mutant KRAS LUAD cells where FOSL1 expression was depleted with a specific shRNA and compared with a control GFPsh. Among differentially expressed genes, forty-five genes were downregulated upon FOSL1 inhibition (FC<0.5 and B>0)(Figure R28A). This list included genes or pathways whose inhibition had been shown to impair Kras-driven tumorigenesis such as HK2 [334], FOXM1 [335] or PI3K/mTOR [69]. To investigate if the FOSL1 transcriptional signature was representative of mutant KRAS tumours, GSEA were carried out in LUAD showing a significant enrichment in mutant KRAS tumours across several human and mouse different data sets (Figure R28B). Specifically, a positive enrichment (blue circle) was

Results found in human KRAS-mutant LUAD datasets compared to KRAS-wild type while a negative enrichment (red circle) was found in a murine cell line data set where Kras was knocked-out.

Figure R28: Transcriptomic analysis of FOSL1-depleted cells unveiled a FOSL1-gene signature positively enriched in KRAS mutant patients of LUAD compared to wild-type. (A) Heat map of 45 down-regulated genes upon inhibition of FOSL1 in mutant KRAS cells (H2009) by a specific shRNA. (B) Gene set enrichment analysis of human LUAD data sets comparing mutant KRAS patients to wild- type KRAS patients as well as a murine mutant Kras cell line dataset compared to a Kras knocked- down counterpart.

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Results

Then, we sought to investigate the clinical relevance of the FOSL1-down signature in LUAD patients. High expression of the FOSL1 signature was found in LUAD patients harbouring KRAS mutations, and was associated with the poorest survival outcome (Figure R29).

Figure R29: High levels of FOSL1-signature confer the worst survival outcome to LUAD patients harbouring KRAS mutations. Survival analysis of LUAD patients (TCGA data set) stratified by KRAS status and expression of a FOSL1 gene signature. P values obtained using the log-rank test (Mantel-Cox). ***,p<0.001.

Given FOSL1’s strong clinical role, we explored the biological processes associated with the FOSL1 signature. The top biological processes identified by Gene Ontology analysis (GO) with a significant enrichment in FOSL1 genes were microtubule- based process, mitotic cell cycle, regulation of response to stress, negative regulation of biological process and cell cycle phase (Figure R30).

Results

Figure R30: FOSL1-signature is enriched in genes implicated in mitotic processes and cell cycle control. Bar graph representing the Gene Ontology analysis of the biological pathways enriched in genes down-regulated after FOSL1 inhibition.

Protein expression of genes involved in mitosis-related processes such as AURKA, CCNB1, FOXM1 [336-338] and AURKA coactivator TACC3 [339] was decreased upon FOSL1 inhibition in a mutant KRAS LUAD cell line but not in a wild-type cells with similar proliferation rate in vitro and in vivo (Figure R31A). Interestingly, the AURKA target PLK1, previously identified as an synthetic lethal interaction with KRAS [337], was also downregulated upon FOSL1 inhibition. These results strongly suggest that KRAS oncogene intersects with the mitosis machinery through FOSL1 in human cancer.

The above results suggested that the phenotype originated by FOSL1 loss involves components of the mitotic machinery. Genetic inhibition of these mitotic genes was performed, and only AURKA inhibition recapitulated FOSL1 loss effects in a mutant KRAS LUAD cell line using two independent shRNAs (Figure R31B). Phosphorylation of histone H3 (HH3) by AURKA is a central event in mitosis [339]. Using taxol, which interferes with mitotic spindle function and blocks cells in mitosis, we found that FOSL1 inhibition led to decreased phospho-HH3 levels in mutant KRAS

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LUAD cells compared to control cells, in concordance with decrease expression of mitotic genes (Figure R31C and D). These findings argue that the phenotype observed in FOSL1-silenced cells may involve the regulation of components of the mitotic machinery.

Figure R31: Mitosis-related genes from the FOSL1-siganture are preferentially decreased in KRAS mutant cells upon FOSL1 genetic inhibition. (A) Western blot analysis on the indicated mitotic genes in mutant (H358) and wild-type (H1650) KRAS LUAD cells after FOSL1 inhibition by two independent shRNAs. (B) Western blot analysis on the indicated mitotic genes in mutant H358 KRAS LUAD cells after AURKA inhibition by two independent shRNAs. (C) Cell cycle analysis of pHH3 positive cells in mutant KRAS LUAD (H358 and A549) cell lines expressing an inducible GFP or FOSL1 shRNA (1 μg/ml doxycycline) upon treatment with taxol (0.5 μM). (D) Western blot analysis for indicated antibodies in mutant KRAS LUAD cells (H358 and A549) carrying an inducible FOSL1 or GFP shRNA. Expression of the shRNA was induced with 1 μg/ml doxycycline for 96h prior to protein collection. Western blot are representative of 3 independent Western blots with different lysates.

9. AURKA depletion recapitulates FOSL1 loss phenotype and its pharmacological inhibition in combination with a MEK inhibitor is synergistic in mutant KRAS cells

Mutant KRAS tumours undergo a high degree of mitotic stress and, thus, genes contributing to this phenotype may represent cancer vulnerabilities [119]. We focused on AURKA, for which pharmacological inhibitors are currently being investigated in clinical trials, to test its role in LUAD. Survival analyses in LUAD cohorts showed that the poorest survival outcome corresponded to mutant KRAS patients expressing high levels of AURKA in LUAD (Figure R32A). At the functional level, genetic inhibition of

Results

AURKA preferentially decreased the relative cell number of mutant KRAS cell lines compared to wild-type, including those with similar population doubling time (Figure R32B). Notably, also genetic inhibition of AURKA coactivator TACC3 was preferentially detrimental to proliferation of mutant KRAS LUAD cells (Figure R32C).

Figure R32: Protein expression analysis of FOSL1 regulated mitotic genes upon FOSL1 loss. (A) Survival analysis of LUAD patients stratified by KRAS status and expression of AURKA. P values obtained using log-rank test (Mantel-Cox). (B) MTS assay of wild-type (HCC78, H1437, H1568, H1650 and H2126) and mutant KRAS (H23, H358, A539, H1792, H2009 and H2347) treated with two independent shRNAs targeting AURKA. Error bars correspond to s.d. Assay is average of three independent experiments. P value obtained using Student’s t test. *, p< 0.05. Western blot for AURKA in H358 cells expressing two different AURKA shRNAs. (C) MTS assay of wild-type (HCC78, H1437, H1568, H1650 and H2126) and mutant KRAS (H23, H358, A539, H1792, H2009 and H2347) treated with two independent shRNAs targeting TACC3. Error bars correspond to s.d. Assay is average of 3 independent experiments. P value obtained using Student’s t test. *, p< 0.05. Western blot for TACC3 in H358 cells expressing two different TACC3 shRNAs.

Based on the AURKA knock-down results and the fact that FOSL1 is regulated by various KRAS effectors including the MEK-ERK pathway, we posited that combined inactivation of AURKA and MEK would have a larger effect on mutant KRAS cells than single protein inactivation. Pharmacological inhibition of AURKA (Alisertib) and MEK1/2 (Trametinib) was carried out in mutant and wild-type LUAD cell lines using drug concentrations equal or lower than the GI25 (1 µM and 0.5 µM). Analysis of the dual treatment effect revealed a synergistic effect in 3 out of 4 (rectangle) mutant KRAS cell

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Results lines (combination index -CI- lower than 1) for all possible drug combinations, but no synergy was observed in wild-type KRAS cell lines (Figure R33A).

Apoptosis analyses were carried out by flow cytometry in order to determine the mechanisms underlying the synergistic effect observed in the KRAS mutant cell lines after the pharmacological combined inhibition of AURKA and MEK1/2 . The synergistic effect correlated with a significantly higher induction of active caspase 3-7 positive cells in the inhibitor combination compared to single treatments (Figure R33B).

Figure R33: In vitro drug inhibition of AURKA in combination with a MEK1/2 inhibitor reveals synergistic effects in KRAS mutant LUAD cell lines. (A) MTS analysis of mutant (H2009, H1792, H23 and A549) and wild-type (H1650, H2126, H1703, HCC78, H322 and H1437) KRAS cells lines treated with alisertib (1 μM), trametinib (1 μM) or both. CI: combination index. CI<1 in bold and with a spotted red line. Results are average of 4 different independent treatment experiments performed in triplicate. (B) Analysis of active caspase 3-7 in H2009, H1792 and H23 cells treated with vehicle, Alisertib (1μM), Trametinib (1μM) or both for 72 h. P values obtained using Student’s t test. *, p<0.05; **, p<0.01; ***, p<0.001.

Next, to test the effect of Alisertib and Trametinib combination in vivo, established mutant KRAS tumours were treated for 14 days (H2009) or until control tumours reached the largest volume allowed under the ethical protocol (11 days, H1792). Alisertib or trametinib induced significant reduction of tumour volume in two xenograft models. However, concomitant treatment induced a more significant reduction than each single drug (Figure R34A). Interestingly, the drug combination led

Results to regression of 15 out of 16 tumours in the two models while Alisertib and Trametinib alone to 1 and 5 tumours respectively (Figure R34B).

Figure R34: In vivo drug combination of Alisertib (AURKA-i) and Trametinib (MEK1/2-i) reduced tumour growth and induced tumour regression in KRAS mutant LUAD xenografts. (A) Tumour volume analysis of mice injected with H2009 or H1792 cells and orally administered vehicle, Alisertib (25 mg/kg), Trametinib (1 mg/kg) or both. Tumours grew until average tumour volume ranged from 80 to 100mm3 and randomized before treatment start. Error bars correspond to s.e.m (n=8 per group). Comparisons to control group:*, p<0.05;**, p<0.01;***, p<0.001. Comparisons to combo group: ^^, p<0.01; ^^^, p<0.001. P values obtained using Student’s t test. (B) Analysis of tumour change from samples from H2009 or H1792 vivo drug combination experiments (n=8 per group). <-30%: partial response.

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Notably, the in vivo treatments did not impact mice weight over the course of the experiment (Figure R35).

Figure R35: In vivo drug combination experiments did not impair mice weight of LUAD xenografts. Average tumour weight of mice injected with H2009 or H1792 cell lines and orally administered vehicle, alisertib (25 mg/kg), trametinib (1 mg/kg) or both. S: start of experiment; E: end of experiment.

Collectively, these results support a functional role of the FOSL1 target AURKA in KRAS-driven tumours and unveil the dual inhibition of AURKA and MEK activation as a potential strategy to treat patients with KRAS mutations.

RESULTS II: Cholangiocarcinoma (CCA)

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1. FOSL1 is overexpressed in mutant KRAS CCA and its expression is modulated by oncogenic KRAS

The enrichment analyses described in R2 and R3 suggested that the cross- tumours KRAS signature may also be representative of other tumours harbouring KRAS mutations, including liver CCA. These results prompted us to test the relevance of FOSL1 in this type of liver tumour where KRAS mutations are often found [208].

First, FOSL1 expression was characterized in normal and immortalized cholangiocytes, as well as in CCA cell lines with known KRAS status. In general, FOSL1 protein levels were upregulated in CCA cell lines compared to cholangiocytes. Interestingly, FOSL1 expression levels were preferentially overexpressed in mutant KRAS cell lines compared to wild-type ones even though a noticeable FOSL1 expression was also detected in KRAS wild-type cells (Figure R36A).

To explore whether the increased FOSL1 expression found in mutant KRAS cells was due to activation of KRAS oncogene, KRAS inhibition and overexpression experiments were carried out. Gain-of-function experiments were performed by transducing a KRAS mutant G12D CDS in normal cholangiocytes with no endogenous expression levels of FOSL1. Interestingly, mutant KRAS overexpression up-regulated FOSL1 expression levels (Figure R36B). Conversely, loss-of-function experiments performed in a mutant KRAS CCA cell line transduced with an inducible shRNA against KRAS itself showed decreased FOSL1 protein levels upon KRAS inhibition (Figure R36C).

Then, to dissect the canonical downstream pathways controlling FOSL1 expression under endogenous mutant KRAS signalling in CCA, pharmacological inhibitors against known KRAS effectors were used. The results showed that with the exception of p38 pathway, FOSL1 is regulated by, and integrates signals from, different kinases downstream of KRAS (Figure R36D).

Results

Figure R36: FOSL1 is predominantly upregulated in KRAS-mutant CCA cell lines and its expression is modulated by oncogenic KRAS. (A) Western blot for FOSL1 in a panel of mutant (n=3), wild-type (n=5) KRAS and non-tumorigenic cholangiocytes (n=2) cell lines. (B) Protein analyses by Western blot for FOSL1 after wild-type (wt) and oncogenic KRAS (G12D) overexpression in a normal cholangiocyte cell line (H69). Gain of function experiments. (C) Protein analyses by Western blot for FOSL1 after mutant KRAS inhibition with a specific shRNA in a KRAS-mutant CCA cell line (EGI1). All images are representative of three independent Western blots with different cell lysates. (D) Western blot of EGI.1 cells treated for 20 h with U0126 (MEKi, 10 μM), BIX02189 (MEK5i, 10 μM), SP600125 (JNKi, 20 μM), GSK2126458 (Pi3Ki, 0.1 μM) SB203580 (p38i, 20 μM), IKK-16 (IKKi, 1 μM) and LY294002 (AKTi, 10 μM) and probed with indicated antibodies. The experiment was carried out three times.

2. FOSL1 is overexpressed in a genetically-engineered mouse model of CCA characterized by Kras mutation and Trp53 loss

To study FOSL1 expression in a more physiologically-relevant system, a genetically-engineered mouse model of KRAS-driven CCA (AlbuminCre/+; KRASLSL-G12D/+; Trp53f/f -aKP-) was used [34]. In this GEM expression of Cre recombinase is controlled by an albumin promoter. Albumin expressed by normal hepatoblasts, which can give rise to hepatocytes and cholangiocytes, allows Cre expression and spontaneous recombination of the modified alleles, favouring CCA development under the expression of mutant Kras and Trp53 loss [34]. First, IHC analysis was performed in normal and CCA tumour tissue after antibody validation. No nuclear FOSL1 expression was detected in normal hepatocytes or cholangiocytes, the latter characterized by a positive staining for CK19 representative of bile ducts (R37A). Conversely CCA lesions,

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characterized by Masson's trichrome and CK19 positivity, exhibited nuclear expression of FOSL1 (Figure R37B).

Figure R37: FOSL1 expression is induced in cholangiocarcinoma lesions of aKP GEMMs after oncogenic Kras expression and Trp53 loss. (A) Masson’s Trichrome (left), CK19 (middle) and Fosl1 (right) staining of normal liver tissue of aKP mice. Unspecific intercellular staining is observed for Fosl1 due to a mouse-synthesized antibody was used against mouse tissue. (B) Masson’s trichrome (left), CK19 (middle) and Fosl1 (right) staining of tumour tissue of aKP GEMMs after Cre-recombination of Cre/+ LSL-G12D/+ f/f Albumin ; Kras ; Trp53 alleles and CCA development.

Next, to discard the possibility that FOSL1 upregulation was mediated by a non- cell autonomous mechanism, its expression was assessed in normal mouse cholangiocytes and CCA cell lines derived from aKP mice cultured in vitro. Similarly to what was observed in the human setting, Fosl1 expression was preferentially observed in CCA cell lines compared to normal cholangiocytes (Figure R38).

Figure R38: Fosl1 is expressed in a panel of cholangiocarcinoma cells derived from aKP mice. Fosl1 protein expression of mouse cholangiocarcinoma cells derived from Cre-recombinated AlbuminCre/+; KrasLSL-G12D/+; Trp53f/f mice -aKP- (335, 338, 339, 344, 449, 476 and 518) compared to mouse normal cholangiocytes (mCh). Image is representative of three independent western blots with different cell lysates.

Results

Moreover, we tested whether Fosl1 expression is also directly regulated by mutant Kras in mouse cholangiocytes. To do this, KRASLSL-G12D/+ (K) and KRASLSL-G12D/+; Trp53f/f (KP) cholangiocytes were isolated using previously-published protocols [306, 307] and treated with AdCre or AdEmpty to induce recombination of the floxed alleles. Fosl1 expression was overtly increased in K and KP mice-derived cholangiocytes compared to wild-type cholangiocytes (Figure R39A), suggesting that Fosl1 expression is indeed dependent on oncogenic Kras activation. Interestingly, expression of mutant Kras alone or in the absence of Trp53 increased the proliferative and clonogenic potential of mouse cholangiocytes (Figure R39B and C), suggesting that mutant Kras expression confers a growth advantage to these cells.

Figure R39: Fosl1 is expressed after in vitro induction of Kras mutation and Trp53 knock-out in normal cholangiocytes derived from K and KP mice. (A) mRNA and protein analyses by qPCR and Western blot respectively for Fosl1 in normal cholangiocyte cell lines (S655 and S658) derived respectively from KrasLSL-G12D/+ (K) and KrasLSL-G12D/+; Trp53f/f (KP) GEMMs and treated in vitro with Ad- Cre or Ad-Empty to respectively induce expression of the KRAS mutation alone or in combination with Trp53 loss. Image is representative of three independent qPCR and Western blots with different cell lysates. mRNA levels were compared by Student's t test. Error bars correspond to s.d. (B) Relative cell number of cells described in A. (Relative cell number was assessed by an MTS assay for seven days and repeated three times. P values obtained using Student's t test. *, p<0.05; ***, p<0.001. (C) Clonogenic Assay (CA) of cells from B cultured for two weeks and stained with crystal violet. Image is representative of three independent CAs.

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3. Mutant KRAS CCA cell lines are sensitive to FOSL1 inhibition in vitro

To test the functional relevance of FOSL1 in KRAS-driven CCA, FOSL1 expression was depleted by interference short hairpin RNAs (shRNA) in mutant KRAS cell lines. Two independent doxycycine inducible shRNAs targeting FOSL1 were selected. shRNAs were cloned into the inducible vector system TET PLKO.1 and transduced into two CCA cell lines with KRAS mutations. After a 4 day-treatment with doxycycline, FOSL1 inhibition was achieved by both shRNAs (Figure R40A). Interestingly, in EGI.1 and HUCCT1 FOSL1 inhibition produced a deleterious effect compared to control associated with decreased cell proliferation (Figure R40B). Additionally, clonogenic assays were performed to test the impact of FOSL1 silencing in their colony forming efficiency. FOSL1 inhibition led to the formation of less and smaller colonies compared to control in both EGI.1 and HUCCT1 CCA KRAS-mutated cell lines (Figure R40C).

Results

Figure R40: In vitro FOSL1 inhibition impairs cell proliferation in two KRAS-mutant human CCA cell lines. (A) Western blot for FOSL1 antibody in mutant KRAS CCA cell lines (EGI1 and HUCCT1) transduced with two independent inducible shRNAs (one against the CDS and other against the 3' UTR region). Image is representative of three independent western blots with different cell lysates. (B) MTS assay of EGI1 and HUCCT1 cell lines after infection with two independent shRNAs. (Relative cell number was assessed by an MTS assay for 6-7 days and repeated three times. P values obtained using Student's t test. **, p<0.01; ***, p<0.001. (C) Clonogenic Assay (CA) of EGI1 and HUCCT1 cell lines after infection with two independent shRNAs cultured for two weeks and stained with crystal violet. Image is representative of three independent CAs.

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Likewise, FOSL1 depletion in mouse CCA cell lines derived from the aKP mouse model led to a significant decrease in cell proliferation and colony formation ability (Figure R41).

Figure R41: In vitro Fosl1 inhibition of murine KRAS-mutated CCA cells derived from a GEMM aKP mice reduced cell proliferation. (A) Western blot for Fosl1 antibody for 339 murine CCA cell line derived from Alb-Cre; KrasLSLG12D/+; p53f/f (AKP) mice. Image is representative of three independent western blots with different cell lysates. (B) Relative cell number of aKP mouse CCA cells (339) expressing a control GFP shRNA or two independent shRNAs against Fosl1 assessed by MTS. Experiment was done three times. P values obtained using Bonferroni's multiple comparison test. ***, p<0.001. (C) Clonogenic Assay (CA) of 339 murine cells after infection with two independent murine shRNAs cultured for two weeks and stained with crystal violet. Image is representative of three independent CAs.

4. FOSL1 inhibition compromises tumour formation and tumour maintenance in vivo

Once the in vitro functional relevance of FOSL1 in KRAS-driven CCA cell lines was determined, we then asked whether FOSL1 would be required for CCA tumorigenesis. Human KRAS-mutated CCA cells (EGI.1 and HUCCT1) infected with 2 independent inducible shRNAs for FOSL1 were inoculated into immuno-deficient mice and treated with 2 mg/ml of doxycycline from the day of cell injection to the end of the experiment. FOSL1-silenced cell lines yielded tumours of significant smaller volume and weight than those derived from control cells carrying a shRNA targeting GFP (Figure R42).

Results

Figure R42: In vivo FOSL1 inhibition impairs tumour growth in two KRAS-mutated CCA xenografts. (A) Average tumour volume of xenografts from KRAS-mutated (EGI.1 and HUCCT1) CCA cells expressing an inducible GFP or two FOSL1 shRNA (n=8-12 per group). Mice were fed 2 mg/ml doxycycline plus 5% sucrose in drinking water when cells were implanted. Error bars correspond to s.e.m. P values obtained using Bonferroni's multiple comparison test. ***, p<0.001. (B) Representative images of xenografted tumours derived at end of experiment. (C) Average tumour weight at end of experiment of xenografts from R42, left. Error bars correspond to s.e.m. P values obtained using Bonferroni's multiple comparison test. ***, p<0.001.

In addition to the functional role of FOSL1 in tumour formation, its functional relevance in established CCA KRAS-driven tumours was also addressed. To do this, a similar approach to the previous in vivo experiments was carried out, but in this instance doxycycline was administered to mice once tumours reached an average tumour volume of 80-100 mm3. FOSL1 inhibition significantly impaired tumour volume

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and weight growth in FOSL1-silenced cells compared to control shRNA in the two cell lines investigated (Figure R43).

Figure R43: In vivo FOSL1 inhibition reduced tumour growth of established tumours in two KRAS-mutated CCA xenografts. (A) Average tumour volume change of xenografts from EGI.1 and HUCCT1 cells with inducible GFP and two shRNAs targeting FOSL1 (n=8-12 per group). Doxycycline treatment (2 mg/ml doxycycline plus 5% sucrose in drinking water) started at day 18 when tumours had an average volume of 80 to 100 mm3. Error bars correspond to s.e.m. P value obtained using Bonferroni's multiple comparison test. ***, p<0.001. (B) Average tumour weight at end of experiment of xenografts from R43, left. Error bars correspond to s.e.m. P values obtained using Bonferroni's multiple comparison test. ***, p<0.001. (C) Representative images of xenografted tumours at end of experiment.

All in all, these data show that FOSL1 plays a significant role in human KRAS- driven CCA formation and tumour progression.

Results

5. FOSL1 abrogation extends overall survival and decreases tumour burden in mice

To study FOSL1 function in a more physiologically-relevant system, we bred the aKP mice to the Fosl1f/f mouse strain to obtain AlbuminCre/+; KrasLSL-G12D/+; Trp53f/f (aKPF) mice. To assess Fosl1 role in these GEMs, survival analyses of aKP (n=19) and aKPF (n=23) mice were carried out). We observed that aKPF mice lacking Fosl1 expression presented a significant higher overall survival compared to aKP mice expressing Fosl1 native alleles (p< 0.0001)(Figure R44A). Interestingly, the survival differences remained significant even when mice were stratified by male (p = 0.0102) or female (p = 0.0053) (Figure R44B and C).

Figure R44: In vivo depletion of Fosl1 significantly increased overall survival of aKPF mice compared to aKP mice. (A) Kaplan-Meier survival analysis of AlbuminCre/+; KRASLSLG12D/+; Trp53f/f (aKP; n: 19 and AlbuminCre/+; KrasLSLG12D/+; Trp53f/f, Fosl1f/f (aKPF; n: 23) mice. P values obtained using the log- rank test (Mantel-Cox), *** p<0.001. (B) Kaplan-Meier survival analysis of male mice (aKP: 12 mice; aKPF: 13 mice). P values obtained using the log-rank test (Mantel-Cox), * p<0.05. (C) Kaplan-Meier survival analysis of female mice (aKP: 7 mice; aKPF: 10 mice). P values obtained using the log-rank test (Mantel-Cox), ** p<0.01.

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Recent data points at adult cholangiocytes as the main contributors to CCA formation upon expression of mutant Kras and Trp53, while adult hepatocytes seem to be refractory to these mutations [340]. In tune with this observation, we have found that injection of adult mouse cholangiocytes with mutant Kras and Trp53 loss induces the formation of CCA-like lesions that express the lineage marker CK19. To test if Fosl1 had any role on tumour formation by mouse cholangiocytes transformed by simultaneous Kras mutation and Trp53 ablation, we isolated cholangiocytes from KP and KPF mice. These cholangiocytes were treated with AdCre in vitro to flox the corresponding alleles and injected into the lower flank of immunodeficient mice. Analyses of tumour volume from KP and KPF cholangiocytes revealed that tumours derived from Fosl1-depleted cells had a smaller volume compared to Fosl1 proficient ones (Figure R45). Also, tumour weight was reduced in those tumours lacking Fosl1.

Figure R45: Fosl1 depletion in xenografted mice with transformed cholangiocites from KPF model significantly reduced tumour growth compared to xenografted KP mice. (A) Average tumour volume of xenografted mice with transformed cholangiocytes isolated from KrasLSL-G12D/+; Trp53f/f (KP) and KrasLSL-G12D/+; Trp53f/f, Fosl1f/f (KPF) GEMMs. Cells were treated with Ad-Cre to respectively induce expression of the Kras mutation and Trp53 loss alone or in combination with Fosl1 depletion. Mice (n=5 per group). Error bars correspond to s.e.m. P value obtained using Student’s t test. *, p<0.05. (B) Representative images of xenografted tumours derived at end of experiment. (C) Average tumour weight at end of experiment of xenografts from A. Error bars correspond to s.e.m. P value obtained using Student’s t test. **, p<0.01

Results

Taken together, these data demonstrate that Fosl1 displays a critical role in cholangiocarcinogenesis that impacts mouse survival, probably by regulating the oncogenic potential of transformed mouse cholangiocytes.

6. FOSL1 regulates a cluster of genes involved in cholesterol biosynthesis

Given the transcriptional role of FOSL1, we queried the transcriptome of FOSL1- depleted CCA cells to explore the molecular mechanisms elicited by this transcription factor. 3' Low Input RNA sequencing was performed to unveil the transcriptomic profiling regulated by FOSL1 in a KRAS mutated CCA cell line where FOSL1 was depleted with 2 specific inducible shRNAs and compared with control GFPsh (FC<0.5 and B>0) (Figure R46A). This analysis yielded a list of 56 down-regulated and 73 up- regulated genes in FOSL1-inhibited cells compared to controls (Annex Results Table 3 and 4). Gene Ontology analysis using the down-regulated gene list revealed an enrichment of biological processes involving cholesterol, steroid and lipid biosynthesis (false discovery rate -FDR-below 0.05) (Figure R46B).

Additionally, protein-protein interaction analyses performed on the 56 downregulated genes identified three main independent clusters of genes. One cluster (red circle) was composed of various genes implicated in cholesterol biosynthesis and regulation of steroids and lipids (HMGCS1, IDI1, INSIG1, MSMO1, ELOVL5 and SCD), the top biological pathways found to be enriched in the FOSL1-signature (Figure R46). The second cluster (blue circle) was characterized by genes involved in mitotic progression, including AURKA which was described in Part 1 of the Results section as a FOSL1 downstream effector whose inhibition recapitulated FOSL1-inhibitionin mutant-KRAS LUAD. A third cluster (green circle) was composed by a group of mitochondrial genes with NADH dehydrogenase and oxido-reductase activity (MT-ND4L, MT-ND5 and MT- ND3) and the membrane gene PTGS1, which synthesizes the formation of thromboxanes and prostaglandins.

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Figure R46: Transcriptomic analysis of FOSL1-depleted cells unveiled a downregulated cluster of genes predominantly involved in cholesterol synthesis. (A) STRING protein-protein network interaction analysis of the 56 downregulated genes after FOSL1 silencing with 2 independent shRNAs and compared to control (GFPsh). Network edges are based on experimental evidences with a high confidence interaction score (>0.700). The red circle identifies the cluster of genes predominantly involved in cholesterol and terpenoid synthesis with predicted binding pikes for FOSL1. (B) Functional enrichments obtained by Gene Ontology analysis (GO) of the 56 identified genes from the RNA-seq (FDR<0.05). STRING GO analysis is powered by PANTHER software.

To validate the RNA-seq results obtained from the EGI.1 cell line after FOSL1 inhibition with 2 different shRNAs, mRNA expression of those genes involved in cholesterol synthesis and of a FOSL1 target identified in the LUAD studies, AURKA, was analysed by qPCR in EGI.1 and HUCCT1 KRAS-mutated CCA cell lines. A significant decrease in mRNA expression for all genes was found in the two cell lines when FOSL1 was inhibited (Figure R47).

Results

Figure R47: Expression levels of cholesterol-pathway genes are downregulated after FOSL1 silencing. mRNA analyses of FOSL1, HMGCS1, INSIG1, IDI1, MSMO and AURKA expression levels by qPCR from mutant KRAS CCA cell lines EGI.1 (A) and HUCCT1 (B) after FOSL1 silencing with 2 different shRNAs for the validation of the RNA-seq results. Images are representative of 3 independent qPCRs with different biological cell lysates. mRNA levels were compared using Bonferroni's multiple comparison test. P values *, p<0.05; ***, p<0.001.

Other genes including EEPD1, CBL11, PRR11, KLHL4, TXNDC17 and DDAH1 which showed to be downregulated in the RNA-seq analysis after FOSL1 silencing were additionally validated by qPCR in EGI.1 cells. All of these genes were downregulated after FOSL1 inhibition compared to control (data not shown).

7. FOSL1 directly binds to genes involved in cholesterol biosynthesis and steroid and lipid regulation

To determine if FOSL1 was directly regulating this cluster of genes in CCA cell lines, Chromatin immunoprecipitation (ChIP-seq) analyses were performed in EGI.1 cells transduced with a Flag-tagged FOSL1 overexpression construct and compared with its own input samples. Among all identified peaks, HMGCS1 promoter sequence was the most enriched region after FOSL1 immunoprecipitation and subsequent genome sequencing in both independent IPs performed (FC=15.5 (IP1) FC=13.9 (IP2)

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and p.value= 2.3x10-6 and B>5 in both cases) (Figure R48). Notably, there was no enrichment in FOSL1 binding to AURKA promoter, suggesting an indirect transcriptional regulation of this gene.

Figure R48: HMGCS1 enrichment signals were found after FOSL1 pull down. Enrichment peaks for HMGCS1 promoter region are shown for two independent replicates. Cells were collected from different dishes and processed independently. -Dashed red line- Pikes in HMGCS1 promoter were detected after aligning the obtained sequences of the FOSL1 pull-down samples (IP1 and IP2) and Inputs samples (Input 1 and Input 2) with the whole genome. -Dashed green line- Exon 1 of HMGCS1 indicating the starting point of transcription (from right to left of the image).

Since FOSL1 regulates transcription bound to JUN members into the AP-1 complex and may not directly bind to gene promoters, we tried to determine which sequence motifs these peaks were enriched in. We examined the genomic sequences of all observed peaks from the IP samples and significant enrichment compared to their own Input controls, to determine if there were known binding motifs for transcription factors. Indeed, the top ranking sequence motif identified was the corresponding to FOSL1 gene showing the smallest E-value (Motifs with small E-values (e.g., less than 0.001) are very unlikely to be random sequence artefacts) (Figure R49).

Results

Figure R49: FOSL1 motif was the top scoring sequence identified in ChIP-seq samples peaks. MEME and DREME software's were used for motif discovery. DREME (Discriminative Regular Expression Motif Elicitation) discovers short (up to 8 positions), ungapped motifs relatively enriched in a sequences compared with its control sequence. MEME (Multiple EM for Motif Elicitation) discovers novel, ungapped motifs splitting variable-length patterns into two or more separate motifs.

Finally, to validate ChIP-seq results, ChIP-qPCR analyses were carried out on independent samples using specifically designed HMGCS1 primers. A 9-fold enrichment signal was observed for HMGCS1 gene compared to control (Figure R50A and B). Of note, FOSL1 silencing using two independent shRNAs proved to decrease HMGCS1 protein levels compared to control (Figure R50C).

Figure R50: HMGCS1 enrichment was found by ChIP-qPCR analysis after FOSL1 pull down. HMGCS1 qPCR amplification levels in FOSL1 pull down samples (Flag) compared to control (IgG), normalized with their respective input amplification signal. ChIP-qPCR was performed in EGI.1 CCA FOSL1 over-expressing cells and analysed by two different methods (A: Percent Input and B: Fold Enrichment method). Samples were run in duplicates using different biological cell lysates. (C) Western blot for HMGCS1 antibody in mutant KRAS EGI.1 CCA cell line transduced with two independent constitutive shRNAs targeting FOSL1. Image is representative of three independent western blots.

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All together these results demonstrate that FOSL1 is tightly regulating HMGCS1 expression through direct binding to its promoter region in KRAS-mutated CCA cells.

8. Genetic inhibition of the FOSL1-direct target HMGCS1 recapitulates FOSL1 loss in a KRAS-mutant CCA cell line

HMGCS1 (3-Hydroxy-3-Methylglutaryl-CoA Synthase) condenses acetyl-CoA with acetoacetyl-CoA to produce HMG-CoA, which is the substrate for HMG-CoA reductase (HMGCR). This step is indispensable for mevalonate production, which is an intermediary product for cholesterol and terpenoid biosynthesis (Figure R51).

Results

Figure R51: HMGCS1, IDI1, MSMO1 and INSIG1 are key enzymatic components of the cholesterol biosynthesis pathway. HMGCS1 (3-Hydroxy-3-Methylglutaryl-CoA Synthase) catalyzes the production of HMG-CoA, which is an essential product for the cholesterol biosynthesis intermediary, mevalonate (MVA). IDI1 (Isopentenyl-Diphosphate Delta Isomerase 1) encodes a peroxisomally-localized enzyme that catalyzes the interconversion of isopentenyl diphosphate (IPP) to dimethylallyl diphosphate (DMAPP), which are the substrates for squalene production for cholesterol biosynthesis. MSMO1 (Methylsterol Monooxygenase 1) is localized to the endoplasmic reticulum membrane and is believed to function in cholesterol biosynthesis converting lanosterol products to cholesterol substrates. INSIG1 (Insulin Induced Gene 1) is an endoplasmic reticulum membrane protein that regulates cholesterol metabolism by blocking the processing of sterol regulatory element- binding proteins (SREBPs). INSIG1 mediates feedback control of cholesterol synthesis by controlling HMGCR.

HMGCS1 is an indispensable enzyme of the cholesterol synthesis pathway (Figure R51) and also responsible of the generation of farnesyl-pyrophosphate (FPP) and geranyl-geranyl-pyrophosphate (GGPP) forms (in blue in Figure R51). Since pieces of

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evidence point at cholesterol levels having an important role un CCA [341] and FPP and GPP forms are used for protein prenylation, including the oncoproteins of the RAS superfamily [342, 343], we proceeded to inhibit HMGCS1. Genetic inhibition of HMGCS1 decreased EGI.1 cell viability compared to control (GFPsh) (Figure R52B).

Figure R52: In vitro genetic inhibition of HMGCS1 reduced cell proliferation of human CCA KRAS-mutated EGI.1 cells. (A) Relative cell number of EGI.1 cells expressing a control GFP shRNA or two independent constitutive shRNAs against HMGCS1 assessed by MTS for 5 days. Experiment was done 3 times. P values obtained using Student's t test ***, p<0.001. (B) mRNA analyses of HMGCS1 expression levels by qPCR from mutant KRAS CCA EG.1 cell lines in A. Images are representative of 3 independent qPCRs. mRNA levels were compared using Bonferroni's multiple comparison test. P values ***, p<0.001.

These results suggest that the relevant role of FOSL1 in CCA may in part be mediated by its downstream target HMGCS1 , whose inhibition is detrimental for cell proliferation of CCA cells.

9. CCA cells are sensitive to pharmacological AURKA inhibition

In addition to targets directly bound by FOSL1, we investigated the functional role of AURKA, which was also identified as a FOSL1 downstream effector in LUAD, in CCA. Pharmacological inhibition of AURKA was carried out in two mutant CCA cell lines (EGI.1 and HUCCT1) using alisertib concentrations equal (0.5 μM) or 5 fold lower (0.1 μM) to the ones used to treat LUAD cell lines. Treatment with both drug concentrations significantly impaired cell proliferation in the two cell lines tested. On

Results average, the percent decrease in cell viability CCA cell lines was 17% higher compared to LUAD cell lines (35% vs 18% respectively) even at low doses of alisertib (Figure R53), suggesting that mutant KRAS CCA cells are more sensitive to this treatment than LUAD counterparts.

Figure R53: In vitro pharmacologic inhibition of AURKA (Alisertib) al low doses in KRAS- mutated CCA cell lines. MTS analysis of EGI.1 (A) and HUCCT1 (B) mutant KRAS CCA cells lines treated with low doses of Alisertib (0,1 μM and 0,5 μM) for 72 h. Results are average of two different independent treatment experiments performed in sextuplicates. P values obtained using Bonferroni's multiple comparison test. P values ***, p<0.001.

All together, these results demonstrate that targeting FOSL1 through direct and indirect transcriptional targets significantly impairs cell proliferation of mutant KRAS CCA cell lines, and open up the possibility of developing combinatorial inhibitory strategies targeting FOSL1 effectors in this type of cancer.

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DISCUSSION

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Discussion

DISCUSSION

In this study we provide a substantial body of evidence supporting the role of the transcription factor FOSL1as an oncogene dependency in mutant KRAS-driven tumours and unveil FOSL1 transcriptional targets amenable to pharmacological intervention that may yield novel therapeutic strategies for tumours where KRAS is mutated.

1. An integrative analysis to unveil oncogenic-KRAS dependencies

Solid tumours accumulate different alterations during cell transformation and progression, generating a wide range of genomic programmes and gene expression patterns uniquely characteristic of a biological phenotype that support tumourigenesis [344]. Those early events arising in the phylogeny of a tumour and sustained through tumour evolution may yield novel oncogene vulnerabilities and their inhibition could attenuate tumour growth and relapse [345]. However, intra-tumour heterogeneity arises as a hurdle for the identification of such events, what consequently favours tumour progression and relapse. One potential strategy to unveil those early events relies on the study of initial stages of tumorigenesis by using experimental systems representing initial stages of oncogene-induced cell transformation and tumour progression.

Based on this concept, we described an integrative gene expression-based screen to unveiling KRAS oncogene dependencies. Our approach followed a two-tiered “zoom-in” strategy to first identify KRAS-regulated candidate genes by a cross-species meta-analysis of laboratory data and, second, to select genes frequently upregulated across human KRAS-driven cancers by querying the KRAS candidate genes against a panel of five different mouse and human tumour types. The identified cross-tumours KRAS gene signature was able to classify KRAS patients according to mutational status and predicted poor outcome of patients harbouring KRAS mutations, highlighting its

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Discussion clinical relevance. Furthermore, selection of the candidate genes within the cross- tumours KRAS signature for follow-up studies relied on the incorporation of patient outcome information, what led our subsequent efforts to study FOSL1. Using a large compendium of experimental models we demonstrated a significant functional role of FOSL1 in various mutant KRAS tumours such as LUAD, CCA and PDAC, the findings for the latter tumour type reported elsewhere [346]. Moreover, inhibitory experiments in established tumours unveiled FOSL1 as a potential molecular target in LUAD and CCA. Our strategy using experimental systems featuring initial stages of KRAS-induced cell transformation and tumour progression is in part coincidental with recent work by the Barbacid’s group. In this case, a gene expression signature derived from mouse lung hyperplasias, lesions found early in the development of lung adenocarcinomas driven by oncogenic Kras, served to uncover DDR1, which was eventually validated as a therapeutic target in KRAS-driven LUAD [347]. Taken together, our findings argue that the overall approach integrating gene-expression and survival data is a successful strategy to identify genes with a relevant role in oncogenic KRAS-driven cancer.

The relevance of FOSL1 in mutant KRAS tumours is further supported by additional data from two independent recently-published studies. First, the Tamayo’s group reported the identification of gene-expression components that are representative of different transcriptional states of mutant KRAS cancers. One of such components, spanning approximately one third of mutant KRAS tumours of the Cancer Cell Line Encyclopedia (CCLE) and the PanCan-TCGA data sets, was significantly associated with FOSL1 mRNA and protein expression, and genetic inhibition of the transcription factor led to attenuation of such gene-expression component. Second, a recent study also identified FOSL1 as a vulnerability in mutant KRAS LUAD using a GEM model. Notably, through gene knock-down and reconstitution experiments, the authors also found that another member of the cross-tumours KRAS signature, AREG, is a direct transcriptional target and a functional mediator of FOSL1 activity. Thus, FOSL1 consistently holds a functional role in oncogenic KRAS tumours.

Discussion

Bibliographic query of other genes belonging to the cross-tumours KRAS signature also confirmed the sturdiness of the approach for selecting potential oncogene vulnerabilities in oncogenic KRAS-driven tumours. For example, DUSP4 upregulation was significantly correlated with KRAS mutations in pre-treated biopsies from patients with locally advanced rectal carcinomas [348]. A second study focused on the transforming potential of gain-of-function mutations in KRAS, demonstrated that oncogenic KRAS activation in normal intestinal epithelial crypt cells resulted in the rapid expression of DUSP4 [349]. Furthermore, two independent groups uncovered LAMB3 as a synthetic lethality in KRAS-mutated colon cancer cells [135] and in LUAD [327]. Lastly, high expression levels of LAMC2 were prognostic of poor survival outcome in PDAC patients [350], while in NSCLC driven by oncogenic KRAS expression LAMC2 played a role as a metastasis promoting gene [351].

2. FOSL1 regulation in KRAS-mutated cells

In the process of RAS transformation, ERK was reported to be sufficient to induce FOSL1 gene transcription and prevent proteasome-dependent degradation [270, 298]. Overexpression of FOSL1 in oncogenic RAS-driven cells could be explained by a FOSL1-mediated positive feedback mechanism of transcriptional autoregulation and post-translational stabilization of the protein. This feedback mechanism involved FOSL1 transcriptional activation by an AP-1 heterodimer containing FOSL1 and phosphorylation of FOSL1 by ERK [270]. Additionally, mechanistic work by Gillies and colleagues demonstrated that FOSL1 is expressed in proportion to the amplitude and duration of ERK activity, correlating its trajectories with ERK kinetics across different pharmacological conditions [352]. Indeed, FOSL1 regulation by ERK was also found even in colon cancer cell lines under constitutive expression of KRASG13D [272]. These data points at the ERK as the principal kinase regulating FOSL1 expression. Our work expands the previous knowledge on the regulation of FOSL1 in RAS-driven tumours to show that, under endogenous expression of KRAS oncogene, FOSL1 is as well regulated by other downstream kinases such as ERK5, JNK or p38. This data suggests that FOSL1

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Discussion is a central transcriptional node in mutant KRAS tumours, what may in part explain the dependency of these tumours to its genetic inhibition.

3. Molecular mechanisms regulated by FOSL1

In mouse cells transformed by oncogenic RAS, FOSL1 was originally considered a gene involved in controlling G1/S phase transition by upregulating CCND1 [353]. However, FOSL1 inhibition experiments in human colon cancer cells expressing oncogenic KRASG13D unveiled a role in the control of a motility and invasion program in vitro [262, 354]. These findings bring up the question as to whether FOSL1 functions under endogenous expression of mutant KRAS may be different to those elicited by oncogene over-expression. Alternatively, tissue-specific differences may account for the diverse FOSL1-regulated outcomes. Our studies shed new light on the molecular mechanisms triggered by FOSL1 in tumours driven by endogenous oncogenic KRAS expression. Indeed, we provide evidence that FOSL1 can as well regulate a set of genes implicated in mitosis progression, a cellular function previously proposed to work orthogonally to KRAS oncogene in human cancer [128, 355] but for which a direct link to the KRAS network was missing. Among mitotic genes, AURKA represents a central FOSL1 transcriptional effector across all KRAS-mutated tumours investigated, although its regulation is unlikely to occur by FOSL1 direct binding to its promoter. AURKA had been found upregulated in PDAC [356, 357], intrahepatic CCA [358] and malignant peripheral nerve sheath sarcomas with abnormal RAS signaling [359], and reported to phosphorylate the KRAS effectors RalGDS and RalA when ectopically expressed in mouse and human cells [360, 361]. However, the molecular mechanisms whereby oncogenic KRAS regulated AURKA expression and the clinical and functional role of this mitotic kinase in KRAS-driven tumours had not been formally addressed. Our observations indicate that FOSL1 links KRAS oncogene to AURKA across different tumour types such as LUAD, PDAC and CCA, and show that AURKA partially mediates the “synthetic sensitivity” of mutant KRAS tumours to FOSL1 loss.

Discussion

Additionally, we found that the mitosis kinase PLK1 is also regulated by FOSL1, providing evidence about the molecular mechanisms regulating PLK1 in the context of KRAS oncogene. This observation contrasts with the original study leading to the nomination of PLK1 as a synthetic lethality in RAS-mutant tumour where a KRAS- independent regulation of PLK1 had been proposed [128]. More importantly, the specific regulation of PLK1 by FOSL1 exclusively in mutant KRAS cell lines, at least in LUAD, provides compelling evidence regarding the anti-tumour effect of FOSL1 loss in oncogenic KRAS tumours.

Although FOSL1 inhibition involved the regulation of mitotic genes, sensitivity of mutant KRAS cells to FOSL1 loss seems to occur irrespectively of the proliferative rate of tumour cells. This is supported by the fact that both in vitro and in vivo mutant KRAS cells showed a higher sensitivity to FOSL1 knock-down than wild-type cell lines with similar population doubling. A possible explanation of the “synthetic sensitivity” to FOSL1 loss maybe the heightened mitotic stress reported in mutant KRAS cells [128]. However, despite the central contribution of elements of the mitotic machinery to the oncogenic KRAS phenotype, the contribution of other members of the FOSL1 signature to the phenotype induced by FOSL1 loss remains unexplored and may also help to explain the impact of FOSL1 inhibition in homeostasis of KRAS-mutated tumours.

As formulated earlier, FOSL1 may regulate different functions under KRAS oncogene expression in a tissue-dependent manner. In this regard, gene-expression analyses in CCA exposed cholesterol biosynthesis and steroid and lipid regulation as the main biological processes related to FOSL1 activity. These biological processes were represented by a subset of genes composed of HMGCS1, IDI1, MSMO1 and INSIG1 that clustered together by protein-protein interaction analyses. It is well known that cancer cells undergo drastic metabolic re-programming to meet their increased demand for energy and macromolecules, and increased cholesterol synthesis trough mevalonate (MVA) pathway is one of those adaptations occurring in cancer [362]. MVA pathway does not only lead to the production of cholesterol, but also results in important non-sterol end products including farnesyl and geranylgeranyl isoprenoids,

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Discussion which are important post-translational modulators of RAS activity [363]. The fact that HMGCS1, a central gene in the MVA pathway whose expression is directly regulated by FOSL1, is necessary for proliferation of mutant KRAS CCA cells supports the idea that the MVA pathway fuels the oncogenic KRAS phenotype. Notably, HMGCS1 has been recently reported to be a synthetic lethal partner of BRAFV600E in a panel of human melanoma and colon cancer cells [364], supporting the role of this enzyme in oncogene-induced homeostasis. Moreover, in an attempt to unveil novel sensitizers that could be combined to maximize anti-cancer efficacy of statins, a family of inhibitors that act downstream of HMGCS1 in the MVA pathway, HMGCS1 was identified as a top scoring gene of a genome-wide RNAi analysis required for cell survival in a mutant KRAS LUAD cell line treated with statins [365].

In tune with the regulation of cholesterol genes by FOSL1, other components of the AP-1 complex such as c-fos have been recently identified as hepatic cholesterol deregulators in HCCs. In this context, liver-specific c-fos expression in GEMMs proved to trigger reversible premalignant hepatocyte transformation by decreasing the expression and activity of the nuclear oxysterols Liver X Receptor-Alpha (Lxrα), leading to increased hepatic cholesterol and accumulation of bile acids [366]. Interestingly, statins treatment partially reversed the c-fos dependent hepatic phenotype, similarly to our FOSL1 findings in CCA. Therefore, the AP-1 transcriptional complex may require the presence of distinct members of the FOS family to execute similar functions in different liver tumours such as HCC and CCA.

One limitation of our CCA study is that, although we have shown that cholangiocyte-driven CCA depends on FOSL1 expression, we have not completely resolved whether this transcription factor plays a role in CCA originated from hepatocytes. A previous study using transgenic Fosl1 mice suggested that cholangiocytes may be more tolerant to FOSL1 upregulation. In 2011, Kireva et al investigated the functional relevance of Fosl1 in chronic inflammatory disease of the biliary system, which is considered a putative pathogenic mechanism of CCA. Mice overexpressing Fosl1 spontaneously developed biliary fibrosis with an increased in

Discussion hepatic collagen content, preceded by ductular proliferation and infiltration of inflammatory cells [282]. Furthermore, progressive liver fibrosis observed in Fosl1- overexpressing mice was associated with up-regulation of profibrotic cytokines (Tgf-β and Pdgf-d) mainly confined to the cholangiocytes, where Fosl1 was also found to be expressed. Neither hepatocytes nor other resident liver cells were found to express Fosl1, suggesting that cholangiocytes expressing high levels of Fosl1 are crucial for cholestatic liver disease and potential drivers of the fibrotic response.

Another limitation of our CCA studies relies on the fact that the functional in vitro and in vivo experiments have been performed exclusively in cell lines harbouring KRAS mutations. Indeed, the GEMM deployed in this study is built upon oncogenic manipulation of Kras and Tp53, an in vivo model that represents only a fraction of all the driver mutations in CCA [367]. Therefore, it remains to be tested whether other oncogenic drivers such as NOTCH, EGFR, BRAF or IDH1 regulate FOSL1 in additional human and mouse models.

4. Inhibition of FOSL1 and its downstream targets as potential therapies

As addressed in the introduction section, different approaches to target KRAS are currently being tested with limited success. Some of them include inhibition of KRAS downstream effectors, especially the RAF-MEK-ERK pathway or, even more recently, KRAS itself. Given the mounting evidence from our group and others on the role of FOSL1 in mutant KRAS cancer [24, 298, 368], the consideration of FOSL1 as therapeutic target would be justified. However, based on FOSL1 regulation experiments in vitro, inhibitors against the RAS-RAF-MEK-ERK module are expected to decrease expression levels of the transcription factor and, thus, one would anticipate that targeting FOSL1 is a meaningless endeavour as inhibitors against upstream kinases are already available. Two key findings from our group and others argue against this interpretation. First, we have observed across all KRAS-driven tumours investigated that FOSL1 expression levels are absent or barely detectable in normal tissues. These

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Discussion observations suggest that FOSL1 inhibitors may have a better therapeutic window (i.e. less toxicity) compared to MEK or ERK inhibitors, whose target activation is not just restricted to tumour tissues but also extensively found under normal physiological conditions, and, thus, translates in a high toxicity in patients. Second, experiments to characterize the resistance mechanisms to the KRAS G12C inhibitor ARS-1620 using CRISPR libraries have recently revealed that concomitant KRAS inhibition and FOSL1 ablation significantly increased the effect of the drug [369]. Thus, one could anticipate that FOSL1 inhibitors in combination with KRAS inhibitors would yield a more efficacious anti-tumour response in patients. Lastly, it is plausible that FOSL1 inhibitors could also be applied to other tumours where its activity is deregulated, expanding the utility of potential inhibitors and warranting future efforts towards FOSL1 inhibition. Despite transcription factors have been historically difficult to target, in part explained by the fact that their activity is ignited via protein‐protein and/or protein‐DNA interactions, inhibition of key transcription factors in KRAS-driven tumours such as MYC, long-thought to be undruggable, have been recently demonstrated [370]. Thus, one would hope that the development of newly blocking strategies would facilitate the challenging endeavour of FOSL1 inhibition.

As alternative strategies to FOSL1 direct inhibition, characterization of the gene-expression changes dependent on FOSL1 activity may expose molecular targets amenable to pharmacological inhibition. Our observations indicate that disruption of AURKA and the MVA pathway, the latter through the action of statins that act downstream of HMGCS1, arise as two therapeutic options to treat mutant KRAS tumours. Furthermore, combinatorial strategies including AURKA inhibitors or statins may be proven effective in KRAS-driven tumours but remain to be tested. Other pharmacological approaches involving AURKA or statins in combination with other targeted therapies for KRAS-driven tumours may also be efficacious. As an example, we reported that concomitant inhibition of the FOSL1 target AURKA and the KRAS canonical effector MEK is more effective than single treatments. In this regard, clinical trials combining AURKA inhibitors (AURKAi) and targeted therapies such as the EGFRi erlotinib, (NCT01471964), the pan-RAFi MLN0128 (NCT02327169), the mTORi

Discussion

MLN1117 (NCT02551055) and sapanisertib (NCT02719691) are being conducted in solid tumours, thus paving the path for combination studies in KRAS-driven tumours. The development of novel therapeutic opportunities becomes of paramount importance in the context of lung cancer and cholangiocarcinoma, two of the deadliest tumours where KRAS mutations are present in a large percentage of patients.

In summary, this work provides functional and mechanistic data that could be translated for the eventual treatment of LUAD and CCA patients harbouring KRAS mutations.

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CONCLUSIONS

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Conclusions

CONCLUSIONS

1. A gene-expression based approach integrating experimental and clinical data- uncovers an 8-gene KRAS signature recurrently upregulated across various tumours. High expression levels of this ‘’cross-tumours KRAS signature’’ predict KRAS mutational status and identify the group of patients harbouring mutations in this oncogene with the worst survival outcome. 2. Elevated FOSL1 expression levels are associated with a shorter overall survival in patients with KRAS mutations compared to KRAS-wild type ones. 3. FOSL1 is upregulated in human and mouse cell lines of LUAD and CCA, as well as in GEMMs representative of both tumour types. 4. FOSL1 expression is directly regulated by KRAS oncogene in mutant KRAS- driven LUAD and CCA. Regulation of FOSL1 in LUAD and CCA involves several canonical effectors of the KRAS signalling network such as ERK, ERK5 and JNK, while other kinases such as PI3K/AKT and IKK participate in FOSL1 regulation primarily in CCA. 5. In vitro FOSL1 silencing preferentially impairs cell proliferation of KRAS-mutated LUAD and CCA. In LUAD, this anti-proliferative effect is associated with a cell cycle arrest in G2/M and an increase in apoptosis. 6. FOSL1 depletion in in vivo xenografts of LUAD and CCA delays tumour formation and restricts progression of established tumours. 7. FOSL1 abrogation in GEMMs of LUAD and CCA extends mice overall survival and decreases tumour burden. 8. FOSL1 transcriptionally regulates genes implicated in mitotic progression in LUAD and CCA KRAS-driven tumours such as AURKA, whose genetic silencing recapitulates FOSL1 loss phenotype. 9. FOSL1 transcriptionally regulates the expression of genes involved in the mevalonate and cholesterol biosynthesis pathway exclusively in CCA. Transcription of one of these genes, HMGCS1, is regulated by FOSL1 through direct binding.

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Conclusions

10. Mutant KRAS LUAD cells are moderately sensitive to pharmacologic inhibition of AURKA in vitro while sensitivity of KRAS-mutated CCA cells is more profoundly affected. 11. Concomitant pharmacological inactivation of AURKA and MEK in mutant KRAS LUAD cells in vitro presents a synergistic effect. Combination of AURKA and MEK inhibitors in two xenograft models of mutant KRAS LUAD induced a more significant reduction of tumour volume than each single drug. 12. Pharmacological inhibitory approaches involving FOSL1 downstream targets may represent novel therapeutic strategies for mutant KRAS LUAD and CCA.

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179

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ANNEXES

Annex 1: List of the 19 upregulated genes dependent on mutant KRAS expression. Data were obtained from a meta-analysis of mouse and human data sets.

N ame Pro b eset . A A LE. A A LE. Probeset.z M EF s. M EF s. Pro b eset . LA 2 . LA 2 . x M ut vsW t M ut vsW t M ut vsW t M ut vsW t d M ut vsN M ut vsN . .lo g F C .B .lo g F C .B .lo g F C B ADAM 19 209765_at 1.314.214 12.896.262 1418402_at 13.268.062 -3.223.111 103554_at 1.006.791 1.958.707

AOX1 205083_at 1.111.810 17.897.119 1419435_at 23.390.785 73.497.663 104011_at 0.663907 2.161.026

AREG 205239_at 0.413419 -38.888.733 1421134_at 26.554.295 0.1657574 99915_at 2.606.357 3.533.778

CLU 222043_at 0.035780 -65.953.168 1454849_x_at 23.587.853 0.485963 95286_at 3.066.041 3.290.870

DLK1 209560_s_a -0.116535 -62.018.752 1449939_s_at 39.086.399 99.167.482 101975_at 2.326.707 4.179.971 t DOCK4 205003_at 1.505.532 29.350.510 1431114_at 25.459.064 36.226.476 NA NA NA

DUSP4 204014_at 14.410.569 30.640.729 1428834_at 24.310.458 0.3348953 NA NA NA

DUSP6 208891_at 14.296.436 30.038.907 1415834_at 23.512.166 38.157.829 93285_at 1.300.014 1.625.728

FOSL1 204420_at 10.751.076 29.652.077 1417488_at 12.904.244 0.8214591 99835_at -0.03760 -7.458.848

GLRX 209276_s_a 11.101.230 31.659.668 1416592_at 0.6201431 -0.377596 95722_at 1.358.422 2.607.477 t HDAC9 205659_at 14.071.775 50.132.903 1434572_at 19.009.038 0.579016 NA NA NA

LAM B3 209270_at 10.528.667 0.8707975 1417812_a_at 0.5310281 -3.691.679 92759_at 1.854.269 3.885.552

LAM C2 207517_at 13.824.770 18.991.025 1421279_at 0.3573144 -5.382.765 100428_at 1.259.546 4.234.499

NAV3 204823_at 16.810.849 43.220.719 1456144_at 10.934.242 14.325.169 NA NA NA

PHLDA1 217997_at 0.9767986 22.372.975 1418835_at 12.189.158 0.9143330 160829_at 1.434.815 1.737.798

SPRY2 204011_at 10.868.965 0.2485686 1436584_at 15.251.486 69.222.766 NA NA NA

SPRY4 221489_s_at 18.767.318 56.101.826 1445669_at 23.802.114 68.172.489 98278_at -0.018891 -7.671.968

STC1 204597_x_at 11.527.248 13.944.385 1450448_at 10.745.275 0.2166546 NA NA NA

M T1 NA NA NA 1451612_at 20.513.138 0.3885645 93573_at 26.124.376 54.670.217

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Annex 2: List of downregulated genes in EGI.1 FOSL1-silenced cells compared to control cells expressing a GFP shRNA.

gene_name chr DB logFC AveExpr t P.Value adj.P.Val B RP11-389K14.3 chr9 HAVANA -2,57883 -1,00443545 -5,759 3,75E-05 0,011514522 1,153 TESC chr12 HAVANA -2,16997 -0,65119234 -5,148 0,00012 0,02554647 0,612 RP11-764E9.1 chr12 HAVANA -2,0964 0,81559082 -6,024 2,31E-05 0,008404147 2,488 FOSL1 chr11 HAVANA -1,94462 6,61998944 -10,92 1,53E-08 7,04E-05 9,957 RGS2 chr1 HAVANA -1,89982 3,72466025 -7,124 3,45E-06 0,003249633 4,732 HMGCS1 chr5 HAVANA -1,87201 4,37045313 -7,354 2,37E-06 0,002940453 5,112 ST3GAL6 chr3 HAVANA -1,78829 0,90844118 -4,59 0,00035 0,044983248 0,292 ONECUT3 chr19 HAVANA -1,72356 1,42557206 -5,658 4,52E-05 0,012767148 2,125 KLHL4 chrX HAVANA -1,70363 0,79968371 -4,44 0,00048 0,050942229 0,023 RP11-799O21.1 chr10 HAVANA -1,66729 4,31503332 -7,993 8,61E-07 0,001320541 6,066 IL1RN chr2 HAVANA -1,58836 4,99165349 -11,14 1,16E-08 7,04E-05 10,09 BARX2 chr11 HAVANA -1,58082 2,01669488 -6,63 7,94E-06 0,004692843 3,747 TMEM158 chr3 HAVANA -1,39218 4,03395228 -4,851 0,00021 0,034007052 0,801 DDAH1 chr1 HAVANA -1,31974 1,56453347 -4,572 0,00037 0,044983248 0,332 POLE2 chr14 HAVANA -1,26845 3,86493437 -5,201 0,00011 0,024200285 1,466 MT-ND4L chrM ENSEMBL -1,2209 4,95224487 -6,712 6,90E-06 0,004692843 4,085 DPP4 chr2 HAVANA -1,21573 3,58832074 -4,473 0,00044 0,049517169 0,115 CEACAM6 chr19 HAVANA -1,20309 5,58146284 -5,935 2,72E-05 0,00937814 2,728 RP11-813I20.3 chr14 HAVANA -1,1782 2,91244901 -4,442 0,00047 0,050942229 0,098 C11orf86 chr11 HAVANA -1,10893 2,61326985 -4,681 0,00029 0,042366713 0,55 C16orf87 chr16 HAVANA -1,10281 3,34489414 -5,86 3,11E-05 0,01024182 2,65 INSIG1 chr7 HAVANA -1,08599 5,368429 -4,569 0,00037 0,044983248 0,172 ADSS chr1 HAVANA -1,08137 3,84148522 -4,921 0,00018 0,031754908 0,947 XXbac-BPG308K3.5chr6 HAVANA -1,07636 3,83973392 -6,627 7,99E-06 0,004692843 3,943 OLFML2A chr9 HAVANA -1,07222 2,8348802 -5,157 0,00012 0,02554647 1,411 EEPD1 chr7 HAVANA -1,0639 3,09006066 -4,541 0,00039 0,046696962 0,275 PTGS1 chr9 HAVANA -1,00346 3,35658516 -4,941 0,00018 0,031386951 1,01 CMB9-22P13.1 chr11 HAVANA -0,99727 5,2915076 -6,076 2,11E-05 0,008087621 2,987 AC087886.1 chr12 HAVANA -0,9618 2,46782004 -4,511 0,00041 0,04707609 0,241 MROH8 chr20 HAVANA -0,95436 3,89534076 -4,978 0,00016 0,030699394 1,053 MT-ND5 chrM ENSEMBL -0,95168 9,30965701 -7,307 2,55E-06 0,002940453 5,025 SPC25 chr2 HAVANA -0,94263 5,3526966 -5,498 6,09E-05 0,015579212 1,943 SAP30 chr4 HAVANA -0,93044 4,96785026 -5,002 0,00016 0,030117407 1,033 MT-ND3 chrM ENSEMBL -0,89261 5,84737463 -5,273 9,32E-05 0,021817376 1,503 PRSS23 chr11 HAVANA -0,85764 7,93402301 -7,108 3,54E-06 0,003249633 4,701 CEP55 chr10 HAVANA -0,85725 4,42450296 -4,591 0,00035 0,044983248 0,283 HMMR chr5 HAVANA -0,85229 3,87826513 -4,6 0,00035 0,044983248 0,341 FST chr5 HAVANA -0,84184 5,78607102 -4,962 0,00017 0,031052393 0,914 MSMO1 chr4 HAVANA -0,8279 6,90892408 -6,245 1,56E-05 0,006518024 3,246 CBLL1 chr7 HAVANA -0,82707 3,94442563 -5,4 7,33E-05 0,017801689 1,829 CFL2 chr14 HAVANA -0,81959 4,25528356 -4,52 0,00041 0,04707609 0,159 FAM108C1 chr15 HAVANA -0,80542 3,94686442 -4,426 0,00049 0,051300925 0,002 DPY19L1 chr7 HAVANA -0,80305 5,02458063 -4,609 0,00034 0,044983248 0,274 PIR chrX HAVANA -0,77273 3,88047893 -4,838 0,00022 0,034200823 0,794

Annexes

gene_name chr DB logFC AveExpr t P.Value adj.P.Val B IDI1 chr10 HAVANA -0,76189 7,58557678 -4,688 0,00029 0,042169637 0,326 RPS24 chr10 HAVANA -0,76011 8,47251983 -4,747 0,00026 0,039073681 0,434 UCA1 chr19 HAVANA -0,75465 7,77055784 -5,538 5,65E-05 0,014726889 1,951 SCD chr10 HAVANA -0,74798 7,37144326 -5,062 0,00014 0,027681177 1,056 C17orf63 chr17 HAVANA -0,74609 4,9521332 -5,304 8,78E-05 0,020910565 1,605 LMNB1 chr5 HAVANA -0,72441 5,48112475 -4,669 0,0003 0,042894485 0,364 ELOVL5 chr6 HAVANA -0,70988 4,66911544 -4,464 0,00045 0,050054581 0,02 PRR11 chr17 HAVANA -0,66838 7,99341802 -4,958 0,00017 0,031052393 0,847 CENPA chr2 HAVANA -0,65262 5,95501452 -4,869 0,0002 0,033815168 0,73 RNF145 chr5 HAVANA -0,61268 6,26159965 -4,588 0,00035 0,044983248 0,168 TXNDC17 chr17 HAVANA -0,59463 9,8875841 -4,602 0,00034 0,044983248 0,155 AURKA chr20 HAVANA -0,59135 6,01962435 -4,564 0,00037 0,044983782 0,132

Table A2: Selected genes include those with a Log FC<0,5 and B>0.

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Annex 3: List of upregulated genes in EGI.1 FOSL1-silenced cells compared to control cells expressing a GFP shRNA. gene_name chr DB logFC AveExpr t P.Value adj.P.Val B GPX4 chr19 HAVANA 0,55943 9,8751295 5,08139 0,00013464 0,027345657 1,086 SLC7A5 chr16 HAVANA 0,57749 7,7320801 4,61088 0,00033819 0,044983248 0,179 SMAD3 chr15 HAVANA 0,6131 6,1497291 4,92189 0,00018345 0,031754908 0,84 ATP5G1 chr17 HAVANA 0,62552 8,9735024 5,11066 0,00012725 0,026628826 1,138 ICT1 chr17 HAVANA 0,6275 7,263329 4,51913 0,00040587 0,04707609 0,012 FAAP20 chr1 HAVANA 0,63224 5,6734702 4,98259 0,00016302 0,030699394 0,982 GADD45B chr19 HAVANA 0,63792 5,660259 4,51971 0,00040541 0,04707609 0,088 SPINT1 chr15 HAVANA 0,63814 6,8778014 4,66233 0,00030542 0,043042306 0,307 CIB1 chr15 HAVANA 0,67167 7,6073002 5,72771 3,97E-05 0,011928862 2,311 IFITM3 chr11 HAVANA 0,71686 9,2617658 4,56828 0,00036805 0,044983248 0,082 CEBPB chr20 HAVANA 0,77454 7,9512791 6,04345 2,23E-05 0,008337501 2,879 HMHA1 chr19 HAVANA 0,77794 5,5544831 4,51151 0,00041209 0,04707609 0,083 HLA-B chr6 HAVANA 0,77834 8,7576851 5,59057 5,12E-05 0,013802102 2,045 PLAC8 chr4 HAVANA 0,78216 8,7989779 6,61494 8,15E-06 0,004692843 3,874 ANGPTL4 chr19 HAVANA 0,78252 6,8020248 6,2859 1,45E-05 0,006518024 3,33 CNNM4 chr2 HAVANA 0,7872 4,3789059 4,58035 0,00035932 0,044983248 0,306 MYOF chr10 HAVANA 0,816 7,8312967 6,09534 2,03E-05 0,008028407 2,974 CPTP chr1 HAVANA 0,82159 5,1628217 5,59864 5,05E-05 0,013802102 2,161 DDAH2 chr6 HAVANA 0,82324 5,7497519 4,59987 0,00034566 0,044983248 0,244 KRT17 chr17 HAVANA 0,83532 7,7433378 6,16001 1,81E-05 0,007360715 3,09 MXD4 chr4 HAVANA 0,84771 5,5081725 4,74335 0,00026028 0,039073681 0,539 JADE2 chr5 HAVANA 0,84815 5,2964374 6,2457 1,56E-05 0,006518024 3,301 ISG15 chr1 HAVANA 0,84952 9,0226603 5,39856 7,35E-05 0,017801689 1,686 TUFT1 chr1 HAVANA 0,8625 4,8271628 5,65687 4,53E-05 0,012767148 2,281 S100A2 chr1 HAVANA 0,86665 7,0393904 4,88779 0,00019607 0,03321707 0,744 WFDC2 chr20 HAVANA 0,87788 7,9123052 6,26062 1,52E-05 0,006518024 3,266 TRMT6 chr20 HAVANA 0,88378 5,9736105 6,44558 1,09E-05 0,005596617 3,627 IFITM2 chr11 HAVANA 0,8962 6,3774524 4,85266 0,00021001 0,034007052 0,702 PDGFB chr22 HAVANA 0,92173 4,5131968 5,78384 3,58E-05 0,011455929 2,516 TGM2 chr20 HAVANA 0,92561 8,6105103 5,7738 3,65E-05 0,011455929 2,384 LEPROT chr1 HAVANA 0,94084 7,3748297 7,07159 3,76E-06 0,003249633 4,651 RP6-65G23.3 chr14 HAVANA 0,94469 5,2367299 6,34338 1,31E-05 0,006233722 3,471 ZC3H12A chr1 HAVANA 0,9615 3,0435784 4,84843 0,00021176 0,034007052 0,854 SUSD2 chr22 HAVANA 0,97563 3,8388375 4,7137 0,00027595 0,040979991 0,594 SEC14L2 chr22 HAVANA 1,00201 4,7396234 6,65619 7,60E-06 0,004692843 3,995 MCAM chr11 HAVANA 1,00358 6,7507802 5,91484 2,82E-05 0,00949774 2,676 B4GALT1 chr9 HAVANA 1,0196 7,2395909 5,67566 4,37E-05 0,012767148 2,228 HEG1 chr3 HAVANA 1,01987 3,1474609 4,3814 0,00053458 0,052735907 0,001 PTPRF chr1 HAVANA 1,02956 7,0945359 6,81743 5,77E-06 0,004686222 4,235 TCEA2 chr20 HAVANA 1,04092 4,3442407 5,02334 0,00015063 0,029301147 1,148 TPRG1L chr1 HAVANA 1,05033 5,4746171 5,22343 0,00010249 0,02359139 1,46

Annexes

gene_name chr DB logFC AveExpr t P.Value adj.P.Val B CORO2A chr9 HAVANA 1,06978 3,8976252 4,58634 0,00035507 0,044983248 0,355 FBXO32 chr8 HAVANA 1,07172 3,967622 4,76506 0,0002494 0,038701312 0,686 PLAT chr8 HAVANA 1,07399 7,2616514 8,11889 7,10E-07 0,001320541 6,287 NAT14 chr19 HAVANA 1,08093 4,414791 6,40257 1,18E-05 0,005818619 3,571 C3 chr19 HAVANA 1,10083 8,1018405 7,14115 3,35E-06 0,003249633 4,759 CYBRD1 chr2 HAVANA 1,11523 4,3149029 6,64579 7,73E-06 0,004692843 3,964 LCN2 chr9 HAVANA 1,11666 7,6798937 6,54481 9,21E-06 0,005085704 3,765 FCMR chr1 HAVANA 1,12649 3,459696 5,15033 0,0001179 0,02554647 1,399 IGFL1 chr19 HAVANA 1,13373 4,6448964 5,99393 2,44E-05 0,008649802 2,881 ZBED6CL chr7 HAVANA 1,13856 3,2553849 4,49307 0,00042753 0,048398694 0,207 TXNIP chr1 HAVANA 1,15177 6,1779568 9,10423 1,67E-07 0,000511316 7,663 TNFRSF18 chr1 HAVANA 1,16588 2,2101194 4,61557 0,00033506 0,044983248 0,395 CPNE7 chr16 HAVANA 1,18786 3,7865136 4,70231 0,00028222 0,041465387 0,579 VAMP3 chr1 HAVANA 1,2182 6,79634 9,03153 1,85E-07 0,000511316 7,581 ATP2B4 chr1 HAVANA 1,22831 3,1695758 4,8848 0,00019722 0,03321707 0,92 GRINA chr8 HAVANA 1,23063 6,3047256 5,11843 0,00012536 0,026628826 1,222 KRT86 chr12 HAVANA 1,23158 4,6547333 4,45493 0,00046137 0,050571048 0,06 TMEM261 chr9 HAVANA 1,23457 6,9808021 8,20544 6,22E-07 0,001320541 6,415 CTD-3128G10.7 chr19 HAVANA 1,25954 3,2912595 5,58302 5,20E-05 0,013802102 2,142 RP1-92O14.3 chr1 HAVANA 1,27046 2,2151321 4,40238 0,00051255 0,051300925 0,021 APBB3 chr5 HAVANA 1,27244 2,5853055 4,4871 0,00043265 0,048580407 0,191 RP11-356J5.12 chr11 HAVANA 1,28789 3,8062227 4,59472 0,00034921 0,044983248 0,379 U47924.27 chr12 HAVANA 1,31137 3,0713473 5,41909 7,07E-05 0,017747588 1,845 OSCP1 chr1 HAVANA 1,31594 2,8707361 5,09443 0,0001313 0,027064482 1,273 MB chr22 HAVANA 1,32643 4,2305834 6,66671 7,46E-06 0,004692843 3,987 RP3-412A9.11 chr22 HAVANA 1,45759 4,3307476 8,05172 7,86E-07 0,001320541 6,052 CALML5 chr10 HAVANA 1,56157 3,7729217 6,45694 1,07E-05 0,005596617 3,606 NNMT chr11 HAVANA 1,60742 8,6432153 4,93951 0,00017726 0,031386951 0,816 L1CAM chrX HAVANA 1,62197 3,8329415 7,45952 2,00E-06 0,00275629 5,119 ITPK1 chr14 HAVANA 1,63241 6,6258238 11,2484 1,02E-08 7,04E-05 10,27 FBXO2 chr1 HAVANA 1,83559 1,400478 4,74736 0,00025824 0,039073681 0,381 KRT6B chr12 HAVANA 2,00571 3,1585159 4,40042 0,00051456 0,051300925 0,041

Table A3: Selected genes include those with a Log FC>0,5 and B>0.

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Annexes

Annex 4: FOSL1 overexpression system

Construct description:

EcoRVgctcctSpe IggataaBamHI KOZAK START FLAG LINKERFOSL1STOPEcoRIatattcSal I

Synthesized sequence:

GATATCgctcctACTAGTggataaGGATCCGCCGCCACCATGGATTACAAGGATGACGACGATAAGGGTGGAGGCGGTAGC ATGTTCCGAGACTTCGGGGAACCCGGCCCGAGCTCCGGGAACGGCGGCGGGTACGGCGGCCCCGCGCAGCCCCCGGCC GCAGCGCAGGCAGCCCAGCAGAAGTTCCACCTGGTGCCAAGCATCAACACCATGAGTGGCAGTCAGGAGCTGCAGTGG ATGGTACAGCCTCATTTCCTGGGGCCCAGCAGTTACCCCAGGCCTCTGACCTACCCTCAGTACAGCCCCCCACAACCCCG GCCAGGAGTCATCCGGGCCCTGGGGCCGCCTCCAGGGGTACGTCGAAGGCCTTGTGAACAGATCAGCCCGGAGGAAGA GGAGCGCCGCCGAGTAAGGCGCGAGCGGAACAAGCTGGCTGCGGCCAAGTGCAGGAACCGGAGGAAGGAACTGACC GACTTCCTGCAGGCGGAGACTGACAAACTGGAAGATGAGAAATCTGGGCTGCAGCGAGAGATTGAGGAGCTGCAGAA GCAGAAGGAGCGCCTAGAGCTGGTGCTGGAAGCCCACCGACCCATCTGCAAAATCCCGGAAGGAGCCAAGGAGGGGG ACACAGGCAGTACCAGTGGCACCAGCAGCCCACCAGCCCCCTGCCGCCCTGTACCTTGTATCTCCCTTTCCCCAGGGCCT GTGCTTGAACCTGAGGCACTGCACACCCCCACACTCATGACCACACCCTCCCTAACTCCTTTCACCCCCAGCCTGGTCTTC ACCTACCCCAGCACTCCTGAGCCTTGTGCCTCAGCTCATCGCAAGAGTAGCAGCAGCAGCGGAGACCCATCCTCTGACCC CCTTGGCTCTCCAACCCTCCTCGCTTTGTGAGAATTCatattcGTCGAC

Figure A4I: FOSL1 Synthesized sequence: (Gray) EcoRV and SalI restriction sites for cloning the sequence into a delivery pUC57 vector. (Pink) SpeI, BamHI and EcoRI restriction sites for cloning the construct into the lentiviral pSIN-EF2-NANOG-puro vector (SpeI-EcoRI cut) and retroviral pBABE-Neo vector (BamHI-EcoRI cut). (Blue) Kozak sequence to efficiently translate FOSL1-flagged fusion protein. (Olive green) ATG translation initiation sequence. (Teal blue) Flag-tag N-terminal sequence preceding FOSL1. (Lime green) FOSL1 coding sequence, CDS: 816 nt. (Red) TGA translation stop sequence.

Translated sequence:

DIAPTSG/GSAATMDYKDDDDKGGGGSMFRDFGEPGPSSGNGGGYGGPAQPPAAAQAAQQKFHLVPSINTM SGSQELQWMVQPHFLGPSSYPRPLTYPQYSPPQPRPGVIRALGPPPGVRRRPCEQISPEEEERRRVRRERN KLAAAKCRNRRKELTDFLQAETDKLEDEKSGLQREIEELQKQKERLELVLEAHRPICKIPEGAKEGDTGST SGTSSPPAPCRPVPCISLSPGPVLEPEALHTPTLMTTPSLTPFTPSLVFTYPSTPEPCASAHRKSSSSSGD PSSDPLGSPTLLAL/EFIFVD Figure A4II: FOSL11-flagged fusion protein amino acid sequence. 5' 3' Open reading frame is highlighted in red. (Obtained using ExPASy translation software).

Annexes

Annex 5: Real time quantitative PCR information

GENE FORWARD REVERSE SPRY4 cggcttcaggatttacacaga ggtcctggactgtacggaga DUSP6 cgactggaacgagaatacgg ttggaacttactgaagccacct DOCK4 tttatatcaccgtgcacattatcc ggtactggacactacaggcattct LAMC2 ctacttcggggacccattg ggttacagttgcaagctcgac AOX1 ctgagtgttgtaaaccatgcatataa aatgtccttcacctgggcta SPRY2 tttgcacatcgcagaaagaa tcaggtcttggaagtgtggtc FOSL1 ggcctctgacctaccctca cttcctccgggctgatct LAMB3 aagatgtggttgggaacctg ctgtcctggataagccgaag PHLDA1 cctccaactctgcctgaaag tcgtcccacttcctcaagtc DLK1 gacggggagctctgtgatag tcatagaggccatcgtcca AREG tgatcctcacagctgttgct tccattctcttgtcgaagtttct CLU gggaccagacggtctcag cgtacttacttccctgattggac MT1E tgcttgttcgtctcactggt gcatttgcactctttgcact STC1 aagccatcactgaggtcgtc caggcttcggacaagtctgt ADAM19 aggggcgagaactgatcc tgggtttctgtgtaggaagga DUSP4 tgcatcccagtggaagataa gcagtccttcacggcatc GLRX ggcttctggaatttgtcgat tttaccaataaagactcgagg HDAC9 gcgaatgtttgaggtgacag ggcccattgtttggtgaa NAV3 tgcaaatggaaacgaaaaaga gcacttggagaagggatgg AURKA gcagattttgggtggtcagt tagtccagggtgccacaga FOXM1 aaaacctgcagctagggatg cctgctgcctcaccatct BORA ccttgtgaaagcagtaacattcag tgtgcatcactttctttgcagt KIF20A cggcgactaggtgtgagtaag ggatcccttgcgacatga CCNB1 acatggtgcactttcctcct aggtaatgttgtagagttggtgtcc SGOL2 caatatatgtggacatccaaaagg gaagaatgaagggagagaaaacg HURP cggcgactaggtgtgagtaag ggatcccttgcgacatga TACC3 agagaccccgctgaggtt ccttaaacgaggaagttccaaa HMGCS1 tctgtctactgcaaaaagatccat gccaaaatcattcaaggtaaaatc INSIG1 atcttttcctccgcctggt ggggtacagtaggccaacaa IDI1 gctaggaattcccttggaaga gttcaccccagataccatcag MSMO1 catgggtgaccattcgtttat tgaagcatagtttccaatgaagtt ELOVL5 acttcttctgtcagggcacac atcttttcctccgcctggt

Table A5: RT-qPCR primer sequences.

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Annexes

Annex 6: Western Blot information.

PROTEIN PRIMARY MANUFACTURER SECONDARY ANTIBODY ANTIBODY GAPDH 1:5000 Abcam (ab9484) Anti-Mouse β-TUBULIN 1:5000 Santa Cruz (sc-9104) Anti-Rabbit FOSL1 1:2000 Cell Signalling (5281) Anti-Rabbit FOSL1 1:500 Santa Cruz (sc-376148) Anti-Mouse p-FOSL1 1:1000 Cell Signalling (5841) Anti-Rabbit AURKA 1:500 Santa Cruz (sc-56881) Anti-Rabbit p-AURKA 1:1000 Cell Signalling (2914) Anti-Rabbit HURP 1:500 Santa Cruz (sc-377004) Anti-Mouse TACC3 1:1000 Santa Cruz (sc-376900) Anti-Mouse CCNB1 1:500 Santa Cruz (sc-245) Anti-Mouse PLK1 1:500 Santa Cruz (sc-17783) Anti-Mouse KRAS 1:500 Santa Cruz (sc-30) Anti-Mouse ERK 1/2 1:1000 Cell Signalling (9102) Anti-Rabbit p-ERK 1/2 1:1000 Cell Signalling (9101) Anti-Rabbit cJUN 1:1000 Cell Signalling (9165) Anti-Rabbit p-cJUN 1:1000 Cell Signalling (3270) Anti-Rabbit cFOS 1:1000 Cell Signalling (2250) Anti-Rabbit FOSB 1:1000 Cell Signalling (2251) Anti-Rabbit FOSL2 1:1000 Sigma (WH0002355M1) Anti-Mouse JUNB 1:1000 Cell Signalling (3753) Anti-Rabbit JUND 1:1000 Cell Signalling (5000) Anti-Rabbit FOXM1 1:500 Santa Cruz (sc-376471) Anti-Mouse KIF20A 1:500 Santa Cruz (sc-374508) Anti-Mouse HMGCS1 1:2000 Invitrogen (PA5-29488) Anti-Rabbit

Table A6: Primary and secondary antibodies used for protein detection by Western blotting.

PUBLICATIONS

189