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MEMORIA FINAL DEL PROYECTO DE INVESTIGACIÓN FINANCIADO POR LA FUNDACIÓN SANDRA IBARRA DE SOLIDARIDAD FRENTE AL CÁNCER Estudio del gen Snai2/Slug como diana para interferir en el desarrollo del cáncer de mama y su diseminación in vivo. Dr. Jesús Pérez Losada Científico Titular del CSIC Instituto de Biología Molecular y Celular del Cáncer (IBMCC) Centro de Investigación del Cáncer (CIC) Instituto mixto CSIC / Universidad de Salamanca Laboratorio-7 Campus Miguel de Unamuno s/n Salamanca, 37007. Spain Phone: 34-923-294807 Email: jperezlosada@usal.es Índice de la Memoria A. Contexto del proyecto. B. Descripción de los objetivos propuestos en la investigación. C. Concreción de los objetivos logrados. D. Discusión E. Conclusiones del Proyecto F. Difusión de Resultados Manuscrito-1 1. Carolina Vicente-Dueñas, Cesar Cobaleda, JesusPerez-Losada, and Isidro Sanchez-Garcia. The evolution of cancer modeling: the shadow of stem cells. MS ID#: Disease Models & Mechanisms (DMM). DMM/2009/002774 (in press). Se incluye en la memoria Manuscrito-2 2. Climent J (*) , Perez-Losada J (*), Quigley D, DelRosario R, Mao JH , Bosch A, Cardiff R.D., Lluch A. Balmain A. Deletion of the Per3 Circadian Rhythm Gene in ERpositive Tamoxifen-resistant Breast Cancer (en revision). These authors contributed equally to this work. Se incluye en la memoria MEMORIA FINAL DEL PROYECTO DE INVESTIGACIÓN FINANCIADO POR LA FUNDACIÓN SANDRA IBARRA DE SOLIDARIDAD FRENTE AL CÁNCER Título del proyecto: Estudio del gen Snai2/Slug como diana para interferir en el desarrollo del cáncer de mama y su diseminación in vivo. Apellidos y nombre del investigador responsable. Jesús Pérez Losada Centro: Departamento de Medicina. Facultad de Medicina. Institución: Universidad de Salamanca. Dirección: Campus Miguel de Unamuno s/n Salamanca, 37007. Spain Filiación actual: Dr. Jesús Pérez Losada Científico Titular del CSIC Instituto de Biología Molecular y Celular del Cáncer (IBMCC) Centro de Investigación del Cáncer (CIC) Instituto mixto CSIC / Universidad de Salamanca Laboratorio-7 Campus Miguel de Unamuno s/n Salamanca, 37007. Spain Phone: 34-923-294807 Email: jperezlosada@usal.es En SALAMANCA, a 1 de Febrero de 2010 ...................................... Fdo. Dr. Jesús Pérez Losada El Investigador principal del proyecto 1 A. Resumen del proyecto y justificación El cáncer de mama es uno de los más prevalentes y una de las principales causas de mortalidad en el Mundo Occidental. La mortalidad por cáncer, en general, y por el de mama en particular, viene determinada por la diseminación tumoral. Impedir la metástasis permitiría convertir al cáncer en una enfermedad crónica, no mortal. De hecho, la característica fundamental que diferencia tumores benignos de malignos es la capacidad para metastatizar. Se precisa, por tanto, comprender mejor los mecanismos moleculares y celulares que llevan a la diseminación del tumor y que, a su vez, permitan la identificación de nuevas dianas terapéuticas para un tratamiento más eficaz con el que poder bloquear la diseminación tumoral. El gen SNAI2/SLUG se ha implicado en el mal pronóstico y la diseminación de diversos tipos de cáncer, incluidos mama, pulmón, páncreas, melanoma, y otros. La proteína SNAI2/SLUG regula procesos de transición epitelio-mesenquimal (EMT), por los que una célula epitelial adquiere características mesenquimales y capacidad de movimiento, fenómenos que definen la diseminación tumoral. Ello convierte al gen SNAI2/SLUG como un candidato ideal para ser utilizado como diana que pueda impedir la metástasis. Por ello, nuestro objetivo global es clarificar el papel del gen Snai2/Slug en la generación y diseminación del cáncer de mama, estudiando el desarrollo de estos tumores en ratones modificados genéticamente deficientes de dicho gen. Este estudio permitirá establecer el valor del gen SNAI2/SLUG o sus productos como dianas para interferir en el desarrollo del cáncer de mama y su diseminación. B. Contexto del proyecto. B.1. Antecedentes El cáncer de mama es el más frecuente en mujeres del Mundo Occidental y una de las principales causas de muerte. Pero en este caso, como en otros tipos de cáncer, la causa de muerte no es el tumor primario, sino la metástasis. Entre un 10 y un 15% de los pacientes con cáncer de mama presentan una enfermedad agresiva que desarrolla metástasis antes de 3 años del diagnóstico y la manifestación de metástasis a distancia hasta 10 años después del diagnóstico inicial no es algo inusual (1); de hecho los pacientes con cáncer de mama no estarán nunca del todo exentos de presentar alguna metástasis a lo largo de toda su vida, el riesgo nunca llega a ser nulo. No cabe ninguna 2 duda, de que mejorar nuestro conocimiento de los mecanismos moleculares que llevan a la metástasis en el cáncer de mama, permitiría mejorar el manejo clínico de esta enfermedad. Datos recientes parecen indicar que el programa genético que activa la diseminación tumoral ya estaría presente en el tumor primario antes de la diseminación, de modo que los tumores primarios con ese estigma genético tendrían más posibilidades de diseminarse. Este hecho, en teoría, nos permitiría discernir entre aquellos tumores primarios de buen y mal pronóstico. Por ello, se ha hecho un esfuerzo considerable en identificar los patrones de expresión (“signatures”) de genes que definan y predigan aquellos tumores de mala evolución, es decir, que vayan a metastatizar. Así, diversos laboratorios han propuesto una serie de patrones de genes con un fin pronóstico en cáncer de mama, que están aún bajo estudio y comprobación clínica (2), por ejemplo Van’t Veer y cols. describen un patrón de 70 genes (3). Pero un objetivo no resuelto es identificar cuáles de esos genes tienen además interés terapéutico y, por tanto, puedan utilizarse como dianas farmacológicas para evitar la diseminación tumoral. Es decir, del alto número de genes que se han identificado como hipotéticamente formando parte del patrón que define el tumor de mal pronóstico, no se sabe cuáles de ellos son realmente importantes en disparar el proceso de metástasis; muchos de esos genes sólo serían marcadores sin más, contribuirían al fenotipo metástásico pero no serían los iniciadores, con lo que su bloqueo farmacológico no tendría consecuencias drásticas en la inhibición del proceso tumoral. Pensamos que identificar aquellos genes clave responsables de desencadenar el programa génico de mal pronóstico, equivaldría a identificar aquellos genes responsables de desencadenar el proceso de diseminación tumoral. El proceso de metástasis consiste en una pérdida progresiva de la adhesión de las células epiteliales tumorales, lo que las capacita para iniciar el movimiento y alcanzar los vasos sanguíneos y linfáticos colonizando órganos a distancia. Simultáneamente, las células en movimiento adquieren características mesenquimales o fibroblastoides. Este proceso se denomina transición epitelio-mesenquimal (EMT) (4). Aquellos genes que desencadenan el proceso de EMT, serían los principalmente responsables de iniciar la metástasis y el programa génico de mal pronóstico y, por tanto, son candidatos a dianas de tratamiento farmacológico con el fin de inhibir la diseminación tumoral. Estos genes son fundamentalmente factores de transcripción, cuya función principal es inhibir la expresión de moléculas de adhesión (la más importante en este contexto es E-cadherina) y permitir así el movimiento celular. Distintos genes se han visto implicados tanto en la 3 el control de la EMT como en la diseminación del cáncer de mama, como: CBF-A, E12/E47, FOXC2, HOXB7, SIP-1, Snail, Twist, Delta-EF1 y Snai2/Slug, entre otros (1) y que, por tanto, son candidatos a desplegar el programa de metástasis tumoral y de mal pronóstico en el cáncer de mama. Esto hace de ellos dianas ideales con el fin de inhibir el proceso de metástasis tumoral. Por ello, nuestro objetivo será estudiar el efecto de la deficiencia de uno de estos genes, Snai2/Slug, en la diseminación del cáncer de mama y así validar indirectamente su posible utilidad como diana terapéutica si se anulara su función farmacológicamente. 1. Weigelt B, Peterse JL, van 't Veer LJ. Breast cancer metastasis: markers and models.Nat Rev Cancer. 2005 Aug;5(8):591-602. 2. Driouch K, Landemaine T, Sin S, Wang S, Lidereau R. Gene arrays for diagnosis, prognosis and treatment of breast cancer metastasis. Clin Exp Metastasis. 2007 Nov 1; 3. van 't Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002 Jan 31;415(6871):530-6. 4. Berx G, Raspé E, Christofori G, Thiery JP, Sleeman JP. Pre-EMTing metastasis? Recapitulation of morphogenetic processes in cancer. Clin Exp Metastasis. 2007 Nov 3 B.2. Bases que sustentan el estudio del papel del gen SNAI2/SLUG en la metástasis del cáncer de mama El proceso de metástasis parece ser que se inicia mediante una pérdida progresiva de las características epiteliales de la célula (transitoria o permanente), produciéndose lo que se denomina un proceso de EMT, por el que en la célula tumoral disminuye la expresión de genes que codifican proteínas de adhesión como E-cadherina, y alpha, beta y gamma-cateninas, y sobre-expresa otras que son características de células mesenquimales como vimentina, N-cadherina y fibronectina. Estos cambios disminuyen la capacidad de adhesión de la célula, permiten su movilidad y la diseminación a distancia del tumor. Diversos factores de trascripción que reprimen la expresión de proteínas de adhesión han sido implicados en el proceso de EMT, entre ellos Snai2/Slug es unos de los genes principales (1). SNAI2/SLUG se localiza en la región 8q11.21 que se encuentra amplificada en un gran número de tumores humanos, incluido mama, colon, ovario, útero y otros (2). Y 4 se encuentra sobre-expresado en diversos tipos de leucemias, rabdomiosarcomas con la traslocación PAX3-FKHR, cáncer de esófago, mesotelioma y otros. Además se ha implicado claramente en la diseminación de mesotelioma cKit+ (3); y en melanoma (4). La pérdida de E-cadherina conlleva a la pérdida de adhesión y es considerada como marcador de malignidad en múltiples tumores. Y por ellos, genes que inhiben la expresión de E-cadherina son considerados como posibles dianas para drogas con un efecto anti-invasivo. En este sentido, en el cáncer de mama, existe una fuerte correlación entre la expresión de Snai2/Slug y la pérdida de la expresión de E-cadherina (5-8) lo que le convierte en un candidato ideal para estudiar in vivo el efecto de su deficiencia en la diseminación de dicho cáncer. Con ello, pretendemos encontrar una aproximación a priori sobre la pertinencia y utilidad de la inhibición farmacológica del gen Snai2/Slug o sus productos sobre el desarrollo y, especialmente, la diseminación del cáncer de mama. B.3. Bases que sustentan el modelo de estudio utilizado Un gran número de genes se han analizado mediante modelos de ratón con el fin de comprobar si son necesarios o suficientes para producir la diseminación tumoral en cáncer de mama. Estos experimentos, de forma característica, se han llevado a cabo mediante dos estrategias. La primera, mediante células en cultivo en que se sobreexpresa un gen o se disminuye su expresión y, posteriormente, se inyectan a ratones inmunocomprometidos con el fin de testar su habilidad para metastatizar. La segunda, consiste en el uso de ratones genéticamente modificados deficientes en un gen específico que se cruzan con ratones que desarrollan cáncer de mama, en particular se ha usado el ratón transgénico que sobreexpresa el oncogén PyMT bajo el la región promotora LTR del Mouse Mammary Tumor Virus (MMTV). Entre los genes testados de esta manera están: Mgat5, CSF1, plasminógeno, urokinasa activadora del plasminógeno, Rho-C, Insulin Receptor Sustrate-2, MEKK-1, Ron, MUC-1, entre otros (revisión en referencia 9). El uso del ratón transgénico MMTV-PyMT se ha extendido debido a la agresividad del cáncer de mama desarrollado por estos ratones, así como por la generación de metástasis pulmonares. En nuestro estudio preferimos usar el ratón transgénico que sobreexpresa el protooncogén ERBB2/cNeu bajo el promotor LTR del MMTV, por diversas razones: (i) Primero, miembros de la familia ERBB2/cNeu inducen el proceso de EMT; (ii) segundo, el anticuerpo monoclonal contra el receptor ERBB2, 5 Trastuzumab (Herceptin) y el inhibitor de la actividad tirosín kinasa (Lapatinib), se han aprobado en el tratamiento del cáncer de mama metástásico; (iii) tercero, la expresión de la proteína ERBB2/Neu en el tumor tiene implicaciones en el pronóstico clínico; y (iv) Además, se prefiere el protooncogén ERBB2/cNeu a la forma oncogénica, porque el protooncogén remeda mejor lo que sucede en humanos al adquirir la mutación durante el proceso tumoral, los ratones viven más tiempo y ello les permite desarrollar metástasis pulmonares con mayor claridad. La agresividad del tumor primario local hace que en la forma oncogénica no da tiempo a que se desarrollen metástasis pulmonares, uno de los lugares característicos de diseminación del cáncer de mama en humanos (10). Por todo ello, pensamos que el modelo de estudio seleccionado permitirá una más fácil extrapolación de los resultados obtenidos a la población humana. 1- Berx G, Raspé E, Christofori G, Thiery JP, Sleeman JP. Pre-EMTing metastasis? Recapitulation of morphogenetic processes in cancer. Clin Exp Metastasis. 2007 Nov 3; 2-Cobaleda C, Pérez-Caro M, Vicente-Dueñas C, Sánchez-García I. Function of Zinc-Finger Transcription Factor SNA12 in Cancer and Development. Annu Rev Genet. 2007 Jun 5; 3-Catalano A, Rodilossi S, Rippo MR, Caprari P, Procopio A. Induction of stem cell factor/c-Kit/slug signal transduction in multidrug-resistant malignant mesothelioma cells. J Biol Chem. 2004 Nov 5;279(45):46706-14. 4- Gupta PB, Kuperwasser C, Brunet JP, Ramaswamy S, Kuo WL, Gray JW, Naber SP, Weinberg RA. The melanocyte differentiation program predisposes to metastasis after neoplastic transformation.Nat Genet. 2005 Oct;37(10):1047-54. 5- Elloul S, Elstrand MB, Nesland JM, Tropé CG, Kvalheim G, Goldberg I, Reich R, Davidson B. Snail, Slug, and Smad-interacting protein 1 as novel parameters of disease aggressiveness in metastatic ovarian and breast carcinoma. Cancer. 2005 Apr 15;103(8):1631-43. 6- Hajra KM, Chen DY, Fearon ER. The SLUG zinc-finger protein represses Ecadherin in breast cancer.Cancer Res. 2002 Mar 15;62(6):1613-8. 7- Tripathi MK, Misra S, Chaudhuri G. Negative regulation of the expressions of cytokeratins 8 and 19 by SLUG repressor protein in human breast cells. Biochem Biophys Res Commun. 2005 Apr 8;329(2):508-15. 6 8-Tripathi MK, Misra S, Khedkar SV, Hamilton N, Irvin-Wilson C, Sharan C, Sealy L, Chaudhuri G. Regulation of BRCA2 gene expression by the SLUG repressor protein in human breast cells. J Biol Chem. 2005 Apr 29;280(17):17163-71. Epub 2005 Feb 24. 9- Vernon AE, Bakewell SJ, Chodosh LA. Deciphering the molecular basis of breast cancer metastasis with mouse models. Rev Endocr Metab Disord. 2007 Sep;8(3):199213. 10- Ursini-Siegel J, Schade B, Cardiff RD, Muller WJ. Insights from transgenic mouse models of ERBB2-induced breast cancer.Nat Rev Cancer. 2007 May;7(5):38997. C. Descripción de los objetivos propuestos en la investigación. El objetivo principal-1 es determinar el papel del gen Snai2/Slug en la diseminación tumoral en el cáncer de mama. El planteamiento de nuestro estudio nos permitirá simultáneamente definir su función en la susceptibilidad y desarrollo local del cáncer de mama. El fin último será establecer el valor de su inhibición como terapia efectiva del desarrollo y diseminación del cáncer de mama. Los objetivos específicos de este proyecto son: -Objetivo-1: Determinación del papel del gen Snai2/Slug en la diseminación del cáncer de mama in vitro. -Objetivo-2: Determinación del papel del gen Snai2/Slug de la susceptibilidad, desarrollo y diseminación del cáncer de mama in vivo. -Objetivo-3: Determinación de los genes y vías moleculares que modifican la función (genes modificadores) del gen Snai2/Slug en la susceptibilidad, desarrollo y, sobre todo, la diseminación del cáncer de mama in vivo. Ello nos permitirá entender la función del gen Snai2/Slug en el cáncer de mama en un contexto génico global. Teniendo para nosotros particular interés su efecto sobre la diseminación tumoral. D. Concreción de los objetivos logrados, Con respecto al objetivo-1 Determinación del papel del gen SNAI2/SLUG en la diseminación del cáncer de mama in vitro. 7 Nos gustaría resaltar los siguientes puntos: 1) Hemos estudiado los niveles del gen SNAI2/SLUG y de ERBB2 en el panel completo de líneas celulares humanas a nivel de RNA (55 líneas celulares). Demostrándose niveles más elevados en los grupos basales que en los luminales; esto es que se encuentra en general más elevado en los grupos más agresivos, y en especial en aquéllos que han sufrido transición epitelio-mesenquimal, como cabría esperar. Ello sugiere que la sobreexpresión del gen SNAI2/SLUG podría ser un marcador de mal pronóstico en cáncer de mama. En el grupo de células ERBB2 positivas los niveles de SNAI2/SLUG son heterogéneos. 2) Hemos determinado los niveles de la proteína SNAI/SLUG mediante western blot, en una representación de líneas celulares pertenecientes a cada grupo: Luminal A, Luminal B, Basal A y Basal B o mesenquimal y ERBB2 positivas disponibles. En este sentido, hemos comprobado como los niveles de la proteína se correlacionan bastante bien con los niveles de mRNA estudiados previamente. 3) Se han obtenido líneas celulares estables de cáncer de mama que sobreexpresan ERBB2 en las que se ha “downregulado” los niveles se SNAI2/SLUG mediante siRNA. El análisis de su capacidad tumoral está en marcha. Aunque previamente estamos interesados en el estudio del comportamiento de diferentes subclones. 4) Se está en proceso de generar líneas celulares de cáncer de mama que sobreexpresan el gen SNAI2/SLUG así como en el análisis de su capacidad tumoral. Con respecto al objetivo-2 Determinación del papel del gen Snai2/Slug en la diseminación del cáncer de mama in vivo. Se ha generado una cohorte de ratones portadores del transgén MMTV-cNeu y con los tres posibles genotipos para el gen Snai2/Slug: “wild type” o salvajes, heterocigotos y “knockouts” o nulos. La cohorte tiene ahora un año de edad, ya que buna parte de los ratones se generaron antes de comenzar el proyecto. Parte de ellos han generado cáncer de mama, no obstante el proceso es lento, primero porque en este modelo hemos utilizado el protooncogén cNeu, estos transgénicos desarrollan tumores más lentamente que la versión oncogénica del mismo, pero pensamos que, aún así, merece la pena usar esta versión normal de cNeu por las razones aducidas anteriormente en el apartado de justificación del modelo. La otra razón por la que los ratones llevan retraso en la producción del tumores es su background genético mixto, provocado porque el 8 knockout de Slug estaba en un fondo híbrido C57/CBA parcialmente resistente al desarrollo del cáncer de mama, aunque previamente habíamos medito estos animales hasta la F4 FVB (fondo en el que se encuentran los ratones MMTV-cNeu), nos vimos forzazos a llevar a cabo dos intercross consecutivos para conseguir el suficiente número de animales para el estudio, dada la mfrtfortalidad perinatal de ratónñ Slug -/-. Aún así, aunque ese fondo genético mixto ralentiza el desarrollo tumoral, es una ventaja a la hora de llevar a cabo estudios de genotipado y ligamiento (ver objetivo-3) Sobre esta cohorte estamos determinando: A) Parámetros de susceptibilidad tumoral, que incluyen: (i) Fecha de aparición del primer tumor, (ii) número de tumores. B) Parámetros de progresión tumoral local: Mediante la determinación del volumen semanal mediante un calibrador digital, mediante la fórmula D x d 2 / 2, donde D es el diámetro mayor del tumor y d es el diámetro menor. Con ello estamos calculando datos de progresión tumoral como son: Volumen final – volumen inicial / número de semanas de enfermedad. Incremento del volumen semanal / número de semanas de enfermedad Promedio del volumen semanal Curva de crecimiento. Peso en la necropsia Peso tumor / peso ratón en la necropsia. Etc. C) Por último, estamos cuantificando el número de metástasis pulmonares (progresión tumoral a distancia) tras la necropsia. Con todo ello, estamos generando los datos preliminares que nos permitan inferir el papel del gen slug en el cáncer de mama generado por sobreexpresión del protooncogén cNeu in vivo. Con respecto al objetivo-3: Determinación de los genes y vías moleculares que modifican la función del gen Snai2/Slug en la susceptibilidad, desarrollo y diseminación del cáncer de mama in vivo. Se ha obtenido el DNA de la cohorte de ratones generado en el objetivo-2 para proceder a su genotipado. Hemos cambiado la estrategia, el genotipado se llevará a cabo al final del experimento, sólo sobre aquellos animales en que se haya realizado un seguimiento completo de la enfermedad (no en aquéllos que hayan fallecido antes de completar el experimento por otra causa distinta al cáncer de mama). Ello nos permitirá reducir costo 9 en el genotipado, al incluir sólo los DNAs de los animales de los que disponemos una información completa. E. Discusión Los datos preliminares obtenidos nos permiten concluir que la expresión del gen SNAI2/SLUG predominante en líneas celulares de cáncer de mama de tipo basal y en particular aquéllas que han sufrido transición epitelio-mesenquimal, nos hace sospechar que el gen SNAI2/SLUG pueda contribuir a la agresividad tumoral de dicho tumor. El objetivo-2 nos está permitiendo la evaluación in vivo del grado de contribución de dicho gen al desarrollo y diseminación tumoral, esto último cuantificando el número de metástasis pulmonares características del modelo utilizado (transgénicos MMTV-cNeu). Por otra parte, el mantener los ratones en un background predominante FVB pero con “contaminación” C56/BL6 y CBA, en grado variable, nos permitirá localizar regiones tras genotipado que interaccionan con el gen Snai2/Slug en la patogenia del cáncer de mama, como hemos establecido en el objetivo-3. F. Conclusiones del Proyecto Los datos obtenidos hasta la fecha, nos permiten establecer las siguientes conclusiones preliminares: 1. El gen SNAI2/SLUG se expresa predominantemente en células de fenotipo basal y en aquéllas que han sufrido transición epitelio-mesenquima, lo que sugiere que dicho gen pudiera tener un papel en la agresividad del cáncer de mama, y en particular en su diseminación. Lo que aún está en estudio 2. La conclusión del objetivo-2 nos permitiría valorar la importancia de ese papel sugerido por el objetivo-1 mediante el análisis de la deficiencia del gen Snai2/Slug in vivo. 10 G. Difusión de Resultados Nos comprometemos a enviar a la fundación Sandra Ibarra los resultados publicados de los experimentos principales aún en marcha. Nos complace también enviar a la Fundación Sandra Ibarra aquéllos trabajos realizados durante el año 2009-2010 en los que figura nuestro agradecimiento a la Fundación por la ayuda recibida, cuya lista se adjunta a continuación 1. Carolina Vicente-Dueñas, Cesar Cobaleda, JesusPerez-Losada, and Isidro Sanchez-Garcia. The evolution of cancer modeling: the shadow of stem cells. MS ID#: Disease Models & Mechanisms (DMM). DMM/2009/002774 (in press). Se incluye en la memoria 2. Climent J (*) , Perez-Losada J (*), Quigley D, DelRosario R, Mao JH , Bosch A, Cardiff R.D., Lluch A. Balmain A. Deletion of the Per3 Circadian Rhythm Gene in ERpositive Tamoxifen-resistant Breast Cancer (en revision). These authors contributed equally to this work. Se incluye en la memoria 3. Andrés Castellanos, Carolina Vicente-Dueñas, Elena Campos-Sánchez, Juan Jesús Cruz, Francisco Javier García-Criado, María Begoña García-Cenador, Pedro A. Lazo, Jesús Pérez-Losada1#, & Isidro Sánchez-García2# Cancer as a Reprogramming-like Disease: Implications in Tumor Development and Treatment. Seminars in Cancer Biology # To whom correspondence should be addressed.E-mail: jperezlosada@usal.es or isg@usal.es (en preparación). 11 1 Commentary The evolution of cancer modelling: the shadow of stem cells Carolina Vicente-Dueñas1, César Cobaleda2, Jesús Pérez-Losada3, & Isidro Sánchez-García1# 1 Experimental Therapeutics and Translational Oncology Program, Instituto de Biología Molecular y Celular del Cáncer, CSIC/ Universidad de Salamanca, Campus M. Unamuno s/n, 37007-SALAMANCA, (SPAIN). 2 Centro de Biología Molecular Severo Ochoa, CSIC/Universidad Autónoma de Madrid, c/Nicolás Cabrera, nº 1, Campus de Cantoblanco, 28049, Madrid, SPAIN. 3 Instituto de Biología Molecular y Celular del Cáncer, CSIC/ Universidad de Salamanca, Campus M. Unamuno s/n, 37007-SALAMANCA, (SPAIN). # To whom correspondence should be addressed. Phone: (923) 238403 Fax: (923) 294813 E-mail: isg@usal.es Running Title: mouse cancer modelling. Keywords: cancer, mouse models, stem cells, cancer stem cells. 2 Summary Cancer is a complex and highly dynamic process. Genetically engineered mouse models (GEMs) to develop cancer are essential systems to dissect the processes that lead to human cancer. These animal models provide a means to determine the causes of malignancy and to develop new treatments, thus representing a resource of immense potential for medical oncology. The sophistication of modelling cancer in mice has increased to the extent that now we can induce, study and manipulate the cancer disease process in a manner impossible to perform in human patients. However, all GEMs described so far have diverse shortcomings in mimicking the hierarchical structure of human cancer tissues. In recent years, a more detailed picture of the cellular and molecular mechanisms determining the formation of the cancer has emerged. This commentary addresses new experimental approaches toward a better understanding of carcinogenesis and discusses the impact of new animal models. 3 Introduction: The need to reproduce human cancer in the mouse The dilemma of current cancer therapies is that although most cancer patients respond to therapy, only few are definitely cured (Etzioni et al., 2003). Current cancer therapies are designed to target proliferating tumor cells. While such strategies eliminate the visible portion of the tumor, namely the tumor mass, they mostly fail to eliminate the unseen root of cancer (Sanchez-Garcia et al., 2007). In order to study and accurately solve the complex host-tumor interactions that occur during tumor development, it is necessary to perform experiments in an in vivo setting in which neoplasm emerges in the appropriate microenvironment. Research in mice integrates the complexity of the organs and their different cell types within the context of the global physiological status of the organism. Certain strain of mice develop cancer spontaneously (Hardisty, 1985). However, such models develop a restricted subset of tumor types that do not reflect the common forms of human cancer and do not allow the systematic investigation of tumor genetics and gene-environment interactions. Since the discovery that human tumors contain activated oncogenes (Fig. 1A), many efforts have been made to develop organ-specific cancer mouse models where tumors arise from normal cells resident in their natural tissue microenvironments in the context of intact immune systems. The ultimate goal is to be able to mimic in the mouse the entire molecular, cellular, tisular and organic features of human cancers, including their initiation, progression, evolution, response to therapy and eventual cure or relapse. Of course, this is a vicious circle, since there are many things about human cancer that we still do not understand so, how can we possibly try to reproduce them? However, we believe that it will be precisely in the quest for the best animal models, where 4 many of the unsolved questions about cancer will find an answer, and the vicious circle will become a virtuous one, since animal models will provide an invaluable feedback to our understanding of cancer in the human. Transgenic mice as model systems: the beginnings The introduction of transgenic methodology in the cancer field showed that human oncogenes produce tumors when introduced into the mouse genomic DNA from the germline onwards (Steward et al., 1982; Stewart et al., 1984; Adams et al., 1985; Hanahan, 1985; Leder et al., 1986). These seminal works showed that oncogene expression is not only required for the initiation of cancer, but also for the maintenance of the disease, which disappears again when the inducing stimulus is switched off (Chin et al., 1999; Huettner et al., 2000; Boxer et al., 2004; Perez-Caro et al., 2007). This has kept oncogenes firmly in focus as therapeutic targets (Fig. 1). However, in these early transgenic experiments, the phenotype was highly influenced by the choice of the attached expression cassette that regulates when and where the transgene is going to be expressed. Specifically, in the case of tissue-directed cassettes, they are used under the assumption that the main bulk cellular population that forms the tumor mass is also the relevant population in terms of tumour origin. This intuitive observation does not need to be true: erythrocytes are the most abundant cells in the blood, but they do not contribute at all to blood regeneration, neither they carry anymore any genetic information relevant to their function or to their origin. So, targeting oncogenes to specific differentiated cell types just because these cell types are the most abundant ones in the tumor mass does not need to recapitulate the ontogeny or even the structure of the tumor (Fig. 1B). Another 5 technical artefact is due to the fact that, unlike the human oncogenes, which occur sporadically in single cells during prenatal or postnatal development, these transgenic mice express the oncogene in all developing and/or adult cells in which the expression cassette is active. Introducing oncogenes into embryonic stem (ES) cells to generate dominant mouse mutants: Knock-in mouse models One possibility, in order to express the initial oncogenic event in the correct cell type, would be to introduce the alteration in the specific locus of the genome where the wild-type version of the protooncogene or suppressor gene is located, using homologus recombination in embryonic stem (ES) cells followed by blastocyst injections to create chimeric mice (Fig. 2). In this way, only a single copy of the oncogene is expressed and a non-directed restriction for genome alteration is obtained, so that a limited number of cells in the organism undergo the genomic alteration, but they can be any type of cell. In this way, if the mutant ES cells have a biased contribution to the embryo or animal, it can be very informative about the nature of the defect caused by the cancer gene. Chimera studies have been also useful in answering the question of whether the initial oncogenic mutation is sufficient in nature to induce the tumor phenotype (Castilla et al., 1996; Castellanos et al., 1997; Yergeau et al., 1997; Okuda et al., 1998; Castilla et al., 1999; Dobson et al., 1999). A chimera approach was used to investigate the biological role of Bcr-ABLp190 and MllAF9 oncogenes (Fig. 2B) (Corral et al., 1996; Castellanos et al., 1997). Both studies demonstrated oncogenicity and lineage specificity in the chimeric mice. Despite the activity of the Bcr and Mll endogenous promoters in a variety of 6 lineages, these mice only developed leukemias, the specific pathologies that these fusion genes are associated with in humans (Corral et al., 1996; Castellanos et al., 1997). Thus, these findings indicated that Bcr-ABLp190 and Mll-AF9 were sufficient to induce the tumor phenotype when expressed from the right endogenous promoters. Similar studies were carried out with the Aml1ETO and Cbfb-MYH11 fusions associated with myeloid leukemia (Castilla et al., 1996; Yergeau et al., 1997; Okuda et al., 1998; Castilla et al., 1999). However, in this case, the modified ES cells could not contribute to the hematopoietic lineages and leukemia did not develop in the chimeric mice (Castilla et al., 1996; Castilla et al., 1999). Furthermore, all these knock-in models also presented other complications, mainly the fact that only the chimeras are viable, and the attempts to obtain heterozygous descendants through chimera germline transmission systematically failed. Altogether, these data demonstrated that the oncogenicity of some of these fusion genes is restricted to the context of sporadically acquired mutations and cannot be reproduced through inherited germline events. Conditional knock-in mouse models Overall these studies suggested that the leukemia-initiating genetic events might regularly occur at the stem cell/progenitor level, irrespective of the phenotypic makeup of the bulk population of leukemic blasts. An explanation could be that the oncogene itself determines the differentiation program of the affected cell clone, which contrasts with the opinion that the leukemic phenotype is a reflection of the level of the hematopoietic hierarchy at which the genetic defect occurs. However, as previously mentioned, germline mutations 7 do not allow the correct modelling of sporadic cancer. A solution could be to restrict the genome alteration, either by limiting the type and/or number of cells that carry it, or by introducing the genetic alteration in a silent way that can be activated in a spatial- or temporal-specific manner. One way to achieve such a model is the use of an inducible and lineage-specific recombinase (Fig. 2C). The Cre recombinase of the P1 bacteriophage or the FLP recombinase of yeast have been the systems of choice for experiments in mammalian systems. Also, the recently developed Dre-rox system adds another set of efficient tools that will enable the generation of more sophisticated mouse models (Anastassiadis et al., 2009). Using these recombinase-based systems, recombination/excision results in creation of specific inter- or intra-chromosomal rearrangements (Fig. 2C). Thus, completely normal mice carrying this altered allele in heterozygous form can be established. If a transgene expressing the recombinase under the control of a tissue/cell type-specific promoter is introduced into this homozygous animal, it will rearrange both genes in the specifically designed tissue, rendering the cancer-inducing alteration functional. But once again, the final cancer phenotype in these conditional knock-in mouse models (Johnson et al., 2001; Forster et al., 2003; Grippo et al., 2003; Coste et al., 2007; Guerra et al., 2007) is influenced by the tissue-specific nature of the cassette expressing the recombinase (Fig. 2C). Stem cells as the cancer-initiating/propagating population So clearly, until recently, the main weight of the efforts attempting to mimic cancer in the mouse has been put on the oncogene’s side, greatly overlooking the cellular origin of the tumor. This aspect has been largely taken for granted, 8 always assuming that the phenotype of the mature tumor cells already implied that the closest non-pathological relatives to them would be the cells of origin. It is well established that cancer is a clonal disease that initiates in a single cell whose progeny makes up the tumour. However, the nature of the cell in which the initiating mutation occurred in human cancer has received little attention during the last decades. In recent years, there is growing evidence that the stem cells are the cells of origin for several types of cancer (Bonnet and Dick, 1997; Cobaleda et al., 2000; Reya et al., 2001; Weissman, 2005; Tan et al., 2006; Ailles and Weissman, 2007; Sanchez-Garcia et al., 2007; Cobaleda et al., 2008; Vicente-Duenas et al., 2009). An example is provided by chronic myelogenous leukaemia, a granulocytic disease. However, the BCR-ABL translocation, pathognomonic of this disease, does not arise in a granulocyte, but rather in a cell at the top of the hematopoietic differentiation tree (Jamieson et al., 2004). In agreement with this idea, recent findings suggest that a stem cell constitutes the target cell in an increasing number of human solid tumours (Al-Hajj et al., 2003; Singh et al., 2004; Wang et al., 2009). Much of our current conceptualization of how tumorigenesis occurs in humans is strongly influenced by mouse models of cancer development (Sanchez-Martin et al., 2002; Quigley et al., 2009). Therefore, studies in mice in which the oncogenic alteration(s) is not directed to the specific cells of origin, as it normally occurs in most current mouse models, should be interpreted cautiously. The genetic alterations found in human cancer seem to occur during specific periods of time and restricted to a few specific cells. In several cases, like in the case of CML, the cancer cell-of-origin is a stem/progenitor cell, and this explains the stem properties that allow the cancer stem cells to maintain the 9 tumour mass. However there are also many cancers where most probably the cancer cell-of-origin is a differentiated cell (Cobaleda et al., 2007). In these cases, the combination of the reprogramming capabilities of the oncogenic alteration and the intrinsic plasticity of the target cell (i.e., its susceptibility to the reprogramming) determine the final outcome of a cancer stem cell. Since not all the cells present the same susceptibility to reprogramming, and not all the oncogenes posses the same reprogramming capacities (i.e., the ability to confer stem cell features to the target cell), the targeting of the oncogenic alteration to the wrong cellular compartment is a likely cause of failure in the generation of accurate mouse models of human cancer. Potential solutions: stem-activated conditional knock-in mouse models Considering these facts, three independent groups have already shown that the genotype-phenotype correlations found in human cancer can be established in mice by specific targeting of the stem cells (Barker et al., 2009; Perez-Caro et al., 2009; Zhu et al., 2009). Further to this, it has also been shown in the haematopoietic (Eminli et al., 2009) and nervous (Kim et al., 2009) systems that the susceptibility of cells to reprogramming is inversely proportional to their degree of differentiation, and that hematopoietic stem cells (HSC) are 300 times more prone to be reprogrammed than B or T cells (Eminli et al., 2009). This stem cell reprogramming is indeed possible in the case of BCR-ABL-induced CML, showing that cancer stem cells arise through a reprogramming-like mechanism and suggesting that the oncogenes that initiate tumor formation might be dispensable for tumor progression (Perez-Caro et al., 2009) (Fig. 3A). Using the Sca-1 promoter as a stem-cell restricted transgenic 10 expression system, the expression of the oncogene in the reprogrammingprone stem cells and progenitors allows the development of all the cells that compose the tumor mass by a “hands-off” mechanism. The modified gene is present in all the mouse cells but the oncogene expression is limited to the stem/progenitor compartment. This model is very informative with respect to the fact that the oncogenic mutations can have different roles in cancer stem cells versus differentiated cancer cells, and explains why targeted therapies like imatinib can eliminate the latter without affecting the former. However, once again these GEMS differ form the real human situation in the fact that, in the human, all the tumoral cells carry the oncogenic alteration (independently of the role that this alteration is playing at every stage, Fig. 1A). So clearly, refinements are required. In order to express cancer-initiating genetic defects randomly in the same target stem/progenitor cells in which the cancer-mutations take place in humans we should take advantage once more of conditional gene targeting approaches but in this case, in combination with stem-cell specific promoters (Fig. 3B). Using different conditional modifications of oncogenes or tumour suppressor genes in combination with a stem/progenitor-restricted recombinase, the oncogenic anomaly is initiated in stem cells and maintained in all their descendants, in a manner very similar to how it happens in humans. However, we should be cautious in interpreting the data as a mimicking of human disease as mouse cells are more prone of transformation than human cells and thus one mutation can lead to full blown cancer in the mouse transgenic model but not in human. Furthermore, the regulation of certain genes/pathways might differ between mouse and human. 11 Outlook The recent discoveries that critical genetic events take place within somatic primitive cells in some human cancers have led to enthusiasm within the scientific community for generating cancer mouse models that accurately reflect the genotype-phenotype correlation seen in human cancer. These future mouse cancer models are needed as a source for dissecting the genomic pathways that feed these cancers and for the discovery of new therapeutic leads. The challenge of the next decade is to define cancers according to their unique molecular alterations and to treat them accordingly. These recent discoveries will allow the translation from modern genetic laboratory tools to advances that will improve the lives of cancer patients. ACKNOWLEDGMENTS We thank all members of lab 13 at IBMCC for their helpful comments and constructive discussions on this project. Research in ISG group is supported partially by FEDER and by MICINN (SAF2006-03726 and SAF2009-08803), Junta de Castilla y León (CSI13A08 and proyecto Biomedicina 2009-2010), MEC OncoBIO Consolider-Ingenio 2010 (Ref. CSD2007-0017), NIH grant (2R01 CA109335-04A1) and by Group of Excellence Grant (GR15) from Junta de Castilla y Leon. 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Independently of the nature of the oncogenic insult, all human tumour cells carry the oncogenic alteration, from the cell-of-origin to the more differentiated cancer cells, although the role of this oncogene may be different at different stages of tumor differentiation, and these mutations might become carrier mutations rather than driving ones depending on the cellular context. B) The classical transgenic mouse models of cancer, the oncogene is expressed under the control of a gene that can be either constitutively expressed or, alternatively, tissue-restricted. In both cases all the cells in the mouse are genetically modified. In the first case, also all the cells express the oncogene. In the second case, the oncogene is expressed in all the cells of a certain chosen tissue. This rather uncontrolled oncogenic expression leads to the appearance of tumours that not necessarily reproduce the hierarchical structure of human cancers. Figure 2. Example of the mimicking of a complex human oncogenic alteration in the mouse. A) Molecular mechanism of a human chromosomal translocation resulting in a chimeric oncogene. B) Knock-in mouse model of chromosomal translocation. By homologous recombination in ES cells one allele of gene A is modified to introduce the 3´elements of gene B in order to mimic the rearrangement seen in humans. These ES cells are injected into WT blastocysts to generate chimeric mice composed by WT and genetically 16 modified cells. So a percentage of the cells in every organ of the chimera carries the oncogenic alteration, which is expressed under the regulatory sequences of gene A, thus generating a model that very closely mimics the human case where tumoral cells are mixed in a background of normal cells. Unfortunately, most of these chimeric mice cannot produce viable knock-in offspring, indicating that fusion proteins are toxic for development. C) Conditional, Cre-inducible translocation model: genes A and B are modified separately by homologous recombination in ES cells and loxP sites (or the recently developed Dre/rox sites) are introduced at the precise points where chromosomal translocation happens in humans. F1 mice heterozygous for these modified genes are generated and crossed with a tissue specific Cre (or Dre) recombinase. These mice carry the modified alleles in all cells and have no phenotype in the absence of recombinase. The oncogene is expressed under the regulatory sequences of gene A in all the cells expressing recombinase and their potential descendants. [The expression of the recombinase under differentiated cell-promoters in differentiated cells leads to the appearance of tumours that not necessarily reproduce the hierarchical structure of human cancers]. Figure 3. New approaches to reproduce the hierarchical structure of human cancer in the mouse. A) Based on the reprogramming nature of oncogenes, it has been proven that restricting the expression of the oncogenic alterations to the stem cell compartment is all what is needed to recapitulate all the tumoral heterogeneity. Using a stem-cell restricted transgenic expression system, the expression of the oncogene in the reprogramming-prone stem cells 17 and progenitors allows the development of all the cells that compose the tumor mass by a “hands-off” mechanism. The modified gene is present in all the mouse cells but the oncogene expression is limited to the stem/progenitor compartment. B) Conditional activation of an oncogenic alteration from the stem cell onwards: B1) by using a recombinase-activatable conventional transgene with the regulatory sequences of a constitutive or tissue-restricted gene; B2) by modifying the locus of an oncogene introducing a recombinase-inducible activating mutation or, B3) by modifying the locus of a tumor suppressor to achieve a recombinase-mediated deletion. In these three cases, in combination with a stem/progenitor-restricted recombinase, the oncogenic anomaly is initiated in stem cells and maintained in all their descendants, in a manner very similar to how it happens in humans. Human Cancer A Normal Gene DNA Normal Protein Normal Amounts PROTEIN DELETIONS or POINT MUTATIONS CHROMOSOME RERRANGEMENTS GENE AMPLIFICATIONS DNA or Abnormal Protein Normal Amounts Abormal Protein Normal Protein Normal or Excessive Amounts Abnormal Amounts Normal Protein Excessive Amounts or PROTEIN or CSC Oncogenic alteration in all cancer cells Normal genome in all non-cancer cells B Conventional Transgenic GEMs Constitutive or Tissue-restricted mouse gene (Transgenic Vector) Stop ATG 1 2 3 4 n Oncogene CONSTITUTIVELY EXPRESSING VECTOR Modified allele in all mouse cells, expressed in all cells Oncogenic alteration expressed in all cells, and in all cancer cells TISSUE-SPECIFIC VECTOR OR TISSUE-SPECIFIC ACTIVATING CRE Modified allele in all mouse cells, expressed only in the targeted tissue Oncogenic alteration expression in the targeted organ, and in all cancer cells Figure 1 Vicente-Dueñas et al. A B Constitutive translocation-carrying GEM Translocation in Human Cancer Gene A Gene B ATG 1 2 Stop 1 2 Chimeric Oncogene Knock-in into Gene A Chromosomal Translocation ATG 4 3 1 2 n 5 3 Stop 5 n ATG 2 n 5 3 1 3 ATG n 5 Stop 2 Stop 2 Chimeric mRNA 1 Stop ATG 1 ATG Chimeric Oncogene n 3 3 Stop 5 n CSC Chimeric Mouse: Oncogenic alteration in a % of all types of mouse cells CSC Expression under the control of Gene A endogenous promoter Oncogenic alteration in all cancer cells Normal genome in all non-cancer cells C Conditional translocation-carrying GEM Gene A Gene B ATG 1 2 Chimeric Oncogene n CRE-mediated recombination ATG 1 Stop loxP 3 2 3 1 2 4 5 3 5 n ATG Chimeric mRNA Stop ATG 1 2 3 n Stop 5 n CRE/DRE Modified alleles in all mouse cells Stop Stage-specific CRE/DRE + Gene A endogenous promoter ? ? ? ? Oncogenic alteration restricted to Cre-expressing-cells and their descendants, expressed under the control of Gene A endogenous promoter Figure 2 Vicente-Dueñas et al. A Constitutive Stem-Cell Restricted Oncogene Expression Mouse Stem-cell-restricted gene (Transgenic Vector) ATG 1 2 Stop n 3 Oncogene CSC Oncogenic alteration restricted to Stem/Progenitor Cells Modified allele in all mouse cells B Conditional Stem Cell-Initiated Oncogene Activation 1) Constitutive or Tissue-restricted gene (Transgenic Vector) ATG 1 2 Stop n 3 Conditional STOP Cassette + Oncogene loxP STOP loxP STEM-SPECIFIC CRE-mediated recombination 2) Knock-in of conditional activating mutation into endogenous oncogene locus loxP Stop loxP CRE Stop loxP STOP 3) Knock-in: conditional deletion of endogenous tumour supressor locus loxP loxP Stop CRE Modified alleles in all mouse cells CRE loxP Stop CSC STEM-specific CRE Oncogenic alteration is initiated in Stem/progenitor cells and expressed in Stem cells and their descendants Figure 3 Vicente-Dueñas et al. Deletion of the hPER3 gene on chromosome 1p36 in recurrent ER-positive breast cancer. Joan Climent1,7, Jesus Perez-Losada2,7, David A. Quigley1, Il-Jin Kim1, Reyno Delrosario1, Kuang-Yu Jen3, Ana Bosch4 , Robert D. Cardiff5, Ana Lluch4, Jian-Hua Mao1,6, Allan Balmain1. 1.- From UCSF Helen Diller Family Cancer Center, Cancer Research Institute. 2.- From Departamento de Medicina y Centro de Investigación del Cáncer. Universidad de SalamancaCSIC. 3.- From UCSF. Deparment of Pathology. 4.- From Hospital Clínic Universitari. Universitat de València, Department of Haematology and Clinical Oncology. 5.- From University of California at Davis, Center for Comparative Medicine. 6.- From Life Sciences Division, Lawrence Berkeley National Laboratory, University of California, Berkeley) 7.- Both authors contributed equally to this work. Correspondence should be addressed to AB at abalmain@cc.ucsf.edu We thank. YH Fu and LJ Ptáček for providing Per3 knockout mice and Z Werb for providing FVBMMTV-neu mice. We also thank MD To for helpful discussion of the manuscript. These studies were supported by grants from National Cancer Institute (U01 CA84244) to A. Balmain, from Spanish Ministry of Education and Culture (EX-2005-1059) and Department of Defense (BC063443) to J. Climent, from “Ramon y Cajal” Program, Fondo de Investigaciones Sanitarias (PI070057), “Junta de Castilla y León” and Sandra Ibarra Foundation to J. Perez-Losada and from California Breast Cancer Research Program (I5FB-0099) to KY. Jen. A. Balmain acknowledges support from the Barbara Bass Bakar Chair of Cancer Genetics. The authors declare that they have no competing financial interest. ABSTRACT The PER3 gene is a member of a conserved family of genes linked to control of the circadian cycle in flies, mice and humans. We show that deletion of the PER3 gene located on human chromosome 1p36 is directly related to tumor recurrence in patients with estrogen receptor (ER) positive breast cancers treated with Tamoxifen. Low expression of PER3 mRNA is associated with poor prognosis, particularly in a subset of tumors that are ER-positive, and either luminal-A type or ERBB2-positive tumors. Mice deficient in Per3 showed increased susceptibility to breast cancer induced by carcinogen treatment or by over-expression of Erbb2. Epidemiological evidence suggests that disruption of sleep patterns plays a significant role in susceptibility to breast cancer, and inherited genetic variants in PER3 have previously been associated with both phenotypes. Disruption of PER3 function could provide a link between deregulation of sleep homeostasis and breast tumorigenesis, and may serve as an indicator of probability of tumor recurrence in patients with ER-positive tumors. INTRODUCTION Chromosomal region 1p36 is among the most commonly deleted regions in human cancers. Deletion of 1p36 is especially frequent in breast tumors and is associated with progression and lymph node metastasis1, poor prognosis2 higher rate of recurrence3, larger tumor size and DNA aneuploidy4. However, no direct relationship between breast carcinogenesis or prognosis and any specific tumor suppressor gene on 1p36 has been established. Recent elegant studies have identified CHD55 and more recently KIF1B6 as candidate tumor suppressor genes in this region, but no specific roles for these genes in breast cancer development have been demonstrated. The human PER3 gene is located within 1.5Mb of CHD5, and the mouse homologue is a member of the Period gene family that controls circadian rhythms7,8. Members of the Period family of circadian rhythm genes (Per1 and Per2) have been implicated in cell cycle control, DNA damage responses and tumor progression9-13. Although inactivation of mPer3 in the mouse germline has only subtle effects on circadian clock function14, it has been shown that mPer3 transcripts exhibit a clear circadian rhythm both in the suprachiasmatic nucleus (SCN)7 and in mouse peripheral tissues15. Similar data have been shown in human peripheral blood cells, where circadian oscillations were more robust for PER3 expression than for other clock genes including PER1 and PER216,17. The possible functions of PER3 in tumor development have not been explored, but links to breast cancer are supported by biochemical studies demonstrating the existence of complexes including proteins of the PER family together with the estrogen receptor18,19, and by reports of association between a polymorphism in the human PER3 gene and breast cancer susceptibility20. The location of the PER3 gene within a region that is commonly deleted in breast cancers suggested a possible link to epidemiological studies showing an association between disrupted sleep cycles and higher risk higher risk of developing breast cancer21,22. We used a combination of human breast tumor analysis and mouse models to show that disruption of PER3 may serve as a prognostic biomarker of tumor recurrence in patients with ER+, Luminal A and/or ERBB2+ tumors. RESULTS Deletion of 1p36 and loss of PER3 genetic variants in breast cancers. We previously reported genome-wide array CGH profiles of 185 lymph node negative breast cancers from a Spanish cohort23, of whom 85 received anthracycline chemotherapy (Chemo group), and 95 received no chemotherapy (non-Chemo group). To search for genetic events related to resistance to hormonal (Tamoxifen) therapy, we divided the non-Chemo group into two subgroups based on whether they had received hormonal treatment. Of the 95 patients in the non-Chemo group, 59 patients with ER and/or PgR positive tumors received Tamoxifen, whereas 36 did not receive any treatment. Analysis of CGH profiles for these patients revealed that deletion of chromosome 1p was associated with recurrence in this subgroup of ER+ Tamoxifen treated patients (p < 0.05 after multiple testing correction using method of Benjamini & Hoffberg) (Supplementary Fig. 1). The chromosome 1p36 locus is frequently deleted in many human tumors, but the region of deletion is large, and separate, non-overlapping chromosome fragments have been implicated24-26. This suggests that multiple tumor suppressor genes are involved. We considered PER3 to be a good candidate for involvement in breast cancer because of its location within one of the minimal deletion regions on 1p36.2 (Refs. 5,6), as well as the epidemiological20 and mechanistic18 data linking circadian rhythm genes to hormone status and breast cancer. We therefore examined the copy number status of PER3 by quantitative TaqMan analysis in DNA samples from 180 breast cancer patients. The relationship between the frequency of deletion or copy number gain and clinico-pathological characteristics of the patients is shown in Supplementary Table 1. The number of copies of PER3 showed a significant gene dosage association with recurrence-free survival at 10 years (Fig. 1a, p= 0.01). The proportion of disease free surviving patients after 10 years was lowest in patients with single copy PER3 deletion (56% ± 8.6; red line) , compared to those with two (75% ± 4.0; blue line) or more (89% ± 5.6; green line) copies of the PER3 gene (Fig. 1a). Further analysis showed that the effect of PER3 deletion was most pronounced in the Tamoxifen treated group, with no significant association in the non-treated or chemotherapy-treated groups (Figs.1b-d). Among the 59 patients who only received Tamoxifen treatment (Fig. 1d), patients with single copy PER3 deletions had a significantly lower disease-free survival rate at 10 years (47% ±12) than those with normal PER3 (84%±6) or copy number gains (100% survival) (p=0.007). To look for potential inactivating mutations in PER3 in breast cancers, we initially sequenced the complete coding region of PER3 in a panel of 35 breast cancer cell lines. No clear pathogenic (nonsense or missense) mutation was identified. However many known27 and some other unknown polymorphisms and alternative splicing isoforms were found (see online supplementary data for full detailed description). One of the polymorphic variants identified by sequencing had been associated in other studies with breast cancer susceptibility20 and also with disruption of sleep homeostasis28-30 Low expression of PER3 is associated with reduced survival We next examined PER3 gene expression in 413 breast tumor expression arrays taken from two publicly available data sets (Van de Vijver31 2002, n=295 and Chin32 2007, n=118). A full description of the stratification of the patients into different subgroups according to PER3 expression together with disease-free survival curves for all patients in each sub-group is shown in Figures 2 and 3. Patients with lower PER3 expression (“PER3 low”, n=122) were significantly more likely to recur than those with normal or higher expression (“PER3 normal/high”, n=291) (Fig. 2a; p=0.013). Disease-free survival analysis showed that PER3 low patients had significantly worse survival rates than PER3 normal/high patients (p<0.001). ER status is an important predictor of recurrence and greatly influences treatment regimes33,34. If low expression of PER3 segregates with ER status, any effect of low PER3 expression could be confounded with the effect of ER status. We therefore performed a subset analysis of PER3 in ER+ and ER- tumors. Low PER3 levels were significantly associated with recurrence (p= 0.01) and shorter disease-free survival times (p<0.001) in patients with ER+, but not ER- tumors (Fig. 2b). We conclude that the association between low PER3 expression and recurrence in the complete patient sample set was driven by the ER+ tumors, with no effect being detected in the ER- tumors. These data are in agreement with the independent association between deletion of PER3 and recurrence specifically in the Tamoxifen-treated (ER positive) patients in Figure 1d. We next asked whether stratifying tumors according to their molecular subtype35,36 could reveal additional information. The tumors were labeled using a nearest centroid classifier and a label was only assigned if correlation with a target class was above 0.1 (Refs. 31,32). This resulted in samples labeled Luminal A (n=90), Luminal B (n=68), ERBB2 (n=56), Normal-like (n=17), Basal (n=73), or Unclassified (n=109) (Fig. 3 and supplementary Fig 4). Of these groups, low PER3 expression had significant association with recurrence only in Luminal A-type (p=0.007) or ERBB2-type tumors (p=0.03) (Fig. 3b). Disease-free survival analysis for Luminal A and ERBB2-type tumors indicated that PER3 low patients had lower disease free survival rates at 10 years than those patients with PER3 normal/high (28%± 10 vs 84%±4) for Luminal A (p<0.001) and (30%± 8 vs 68%±8) for ERBB2-type (p= 0.004). There was also a striking effect on overall survival rate at 10 years in all the patients and in the subgroups of ER positive, Luminal A and ERBB2 patients (Fig. 4): The ten year overall survival rate for ER+ patients with low PER3 was 55% ± 6 vs. 79% ± 3 for normal/high patients (p < 0.001) (Fig. 4b). The overall survival rate was 25% ± 8 for ERBB2 patients with low PER3, vs. 70% ± 7 for ERBB2 patients with normal/high PER3 (p<0.001) (Fig. 4f). The overall Survival rate at 10 years in Luminal-A patients with low PER3 was 34% ± 11 vs. 83% ± 3 for patients with normal/high PER3 (p<0.001) (Fig. 4g). Importantly, multivariate analysis showed that PER3 expression is significant independently from all the prognostic factors tested both for Disease Free Survival (p<0.001) and Overall survival (p=0.001) (Table 1). We next evaluated possible links between expression levels and probability of tumor recurrence for all 54 annotated genes in the 1p36.31-1p36.22 (chr1:6,084,440-9,512,808 (3.5 Mb in size)) region. Gene expression was discretized as described for PER3 and log rank p values were calculated using the survival library for R. This analysis showed that PER3 was the only gene with an uncorrected p < 0.05 in all data sets analyzed. Although chromosome engineering studies have previously identified CHD5 as a candidate tumor suppressor gene within the minimal deletion region on 1p36.2 (Ref. 5), no association of CHD5 expression levels with recurrence or survival was found in any of the subgroups of breast cancer patients analyzed (Supplementary Figs. 5 and 6). These data do not exclude the possibility that CHD5 plays an important role as a tumor suppressor in other tumor types. Inactivation of Per3 increases breast tumor susceptibility in mouse models. In order to investigate a possible causal association between loss of Per3 function and breast tumor development, we performed two studies involving mouse models of breast cancer. A total of 86 mice carrying normal or inactivated alleles of the Per3 gene (17 wild-type Per3+/+, 35 heterozygous Per3+/- and 34 null Per3-/-) were treated by oral gavage with 7, 12-dimethylbenz[a]anthracene (DMBA), a protocol known to induce breast cancer in sensitive strains of mice37. Eight mice (two heterozygous and six null) were found dead before the end point and no tissues were collected from them. The median follow-up of the remaining 78 mice included in the study was 8.3 months (range 3.8 – 15.0). All of the mice treated with DMBA developed tumors of various kinds including lymphoma and solid tumors of the lung, ovary, and skin (Supplementary table 5). However, development of breast tumors was specifically associated with Per3 deficiency. Thirty-six percent of Per3-/- mice treated with DMBA developed breast tumors, while 12% of the Per3+/- mice developed breast tumors. In striking contrast, none of the control Per3+/+ mice developed a breast tumor (p= 0.005) (Fig. 3a). A group of 65 mice (19 wild-type, 25 heterozygous, and 21 null) were used as controls with no DMBA gavage treatment. Two of the Per3-/control mice developed sporadic breast tumors, but none of the remaining mice were found sick or developed any other class of tumor during the time course of this experiment (24 months). The second mouse model was based on the observation that low levels of Per3 expression were strongly associated with recurrence in ERBB2-type human breast cancers. MMTV-Neu mice overexpress ErbB2 in the mammary gland, and spontaneously develop breast tumors38. We generated a total of 79 MMTV-Neu positive mice of which 30 (38%) were Per3+/+, 35 (44%) were Per3+/-, and 14 (18%) were Per3-/-. The median follow-up of all mice was 14.9 months (range 6.3 – 25.8). All Per3-/- mice developed breast tumors, whereas 25 (71%) of the Per3+/-and 14 (47%) of the Per3+/+ mice developed breast tumors. The proportion of Per3-/-null mice free of tumors at 15 months (21% ± 8) was significantly lower than the proportion in the heterozygous and the wild-type mice (63% ±6 in both Per3+/- and Per3+/+, p = 0.003). Histological analysis of tumors from both models of breast cancer showed that loss of Per3 did not affect the tumor class or morphology, since both DMBA-induced and MMTV-Neu-induced tumors in Per3-/mice resembled equivalent tumors from Per3 wild type animals (data not shown). We also evaluated the possible loss of the wild type Per3 allele in tumors from the Per3 heterozygous mice. No loss was observed suggesting that homozygous loss is not essential in this mouse model. DISCUSSION Our data indicate that deletion and/or reduced expression of the PER3 gene on human chromosome 1p36 is associated with breast cancer recurrence, particularly in ER+ patients treated with Tamoxifen who did not receive chemotherapy. No effect of deletion was seen in patients with basal type ER- breast tumors. Within the ER+ category, the effect was primarily in tumors classified as Luminal A or ERBB2, but not in the Luminal B type which share some expression features with basal tumors35,36. Direct evidence for a causal role for loss of PER3, rather than an alternative gene in this commonly deleted region of the genome5,6, comes from analysis of two different mouse models of breast cancer. Both chemically-induced and Neu(ErbB2)-induced breast cancers are increased in frequency and/or reduced in latency in mice carrying inactivated Per3 alleles. Although these data do not prove that Per3 is the only functional tumor suppressor gene in this chromosome interval, they indicate that Per3 is a bona fide tumor suppressor in these mouse models, with a key role in breast tissue. While disruption of the mouse Period gene family members Per1 and Per2 by gene targeting induces biological clock phenotypes39, loss of Per3 function induces only subtle effects on circadian rhythm14,40 . Nevertheless, evidence in favor of PER3 involvement both in sleep disruption and in breast cancer comes from studies of a human structural polymorphism in the PER3 coding sequence that has been associated with delayed sleep phase syndrome, diurnal preference and waking performance28,41,42, but also with increased breast cancer risk20, particularly in premenopausal women. Although the specific molecular mechanisms remain to be elucidated, increasing evidence points to a role for circadian rhythm genes in cell cycle control and DNA damage responses11,43 as well as in hormonal control of gene expression18,19. PER2 has been identified as an estrogen-inducible ER corepressor that forms heterodimers with PER3 to enter the nucleus. Deletion of PER3 prevents nuclear import, and instead promotes accumulation of PER2 in the cytoplasm44. Whether coordinated functional deregulation of all PERIOD family genes occurs in breast cancers remains to be determined. Elucidation of the relationship between control of sleep homeostasis and circadian rhythms, PER gene expression and DNA damage responses may help in understanding the epidemiological data linking sleep disruption to breast cancer susceptibility18,21,22, but further detailed studies will be required to elucidate the exact mechanisms involved. METHODS SAMPLE SELECTION We used three previously published breast cancer data sets that included clinical, gene expression and/or array Comparative Genomic Hybridization (CGH) data31,32. Data on disease-free survival (defined as the time to a first event) and overall survival were available for all the patients in the three data sets except one patient in the Chin et al.32 samples. COPY NUMBER ANALYSIS OF PER3 All tumor DNA samples were obtained from frozen breast tumors with >50% tumor cells23. The genomic sequence of PER3 (GenBank accession NM_016831.1) was used to design a set of primers and probe specific to the PER3 gene (Primer Express software version 1.0 (Applied Biosystems)). The primers for PER3 GCCCGCAGCCTGCTT were -3’ 5’- GGAGTGAGAAACCGGTGTCTGT-3’ (reverse). The probe for PER3 (forward) was and 5’-(6-FAM) 5’- CTGACTGCAAAGTGAG-(TAMRA)-3’, where FAM is 6-carboxyfluorescein and TAMRA is 6carboxytetramethylrhodamine. The primers and probe for RNase P used as an endogenous control gene were obtained from Applied Biosystems. The RNase P probe was labeled at 5’ end with VIC (Applied Biosystems) instead of FAM. PER3 copy number was determined by relative quantification using the ΔΔCt method normalized to the RNase P copy number of two45. To analyze the results from the copy number experiment we used the TaqMan® Gene Copy Number Assays Macro File (Applied Biosystems). ISOLATION and SEQUENCING OF PER3 cDNA. We analyzed the sequence of PER3 cDNA in 35 breast cancer cell lines (see supplementary Tables 2 and 3, and Supplementary Fig. 2). No evidence for the presence of any non-conservative tumor- specific structural changes was detected, although several known polymorphisms were found in this analysis. PER3 GENE EXPRESSION ANALYSIS We examined PER3 expression in 413 breast tumor expression arrays taken from Van de Vijver31 2002 (n=295) and Chin32 2007 (n=118). In each dataset a sample si in the set S was labeled as “PER3 Low”, “PER3 normal”, or “PER3 high” using the rule: If si ≤ ( mean[S] - ½ *standard deviation[S] ), assign LOW If si ≥ (mean[S] + ½ *standard deviation[S]), assign HIGH Otherwise, assign NORMAL. This method allowed us to compare relative PER3 expression levels across both data sets fused as a single group of patients. STATISTICAL ANALYSIS The association between PER3 deletion or PER3 expression and clinical-pathological parameters was analyzed using Fisher’s Exact test. All reported P values were two tailed. Significant differences in disease-free and overall survival time were calculated using the Cox proportional hazard (log-rank) test. Multivariate Cox Regression Analysis was used to prove statistical independence of PER3 from other known prognostic factors. Statistical analysis was performed using SPSS version 12.0. MICE AND TUMOR INDUCTION Wild-type (Per3+/+) and Per3 knockout (Per3-/-) 129/sv mice (provided by Drs. YH Fu and LJ Ptáček, UCSF) were bred and treated according to Laboratory Animal Resource Center (LARC) regulations. 7-week-old female mice from the F2 intercross population (Per3+/+, Per3+/- and Per3-/- ) were treated with 6 doses of 1 mg of 7, 12-dimethylbenz[a]anthracene (DMBA) diluted in corn oil by weekly oral gavage. A second group of mice was treated only with corn oil as a group control. In a second experiment, male Per3-/- mice were crossed with female FVB mice expressing the Neu (ErbB2) protooncogene under control of the MMTV 3’-LTR promoter38 (provided by Dr. Z Werb, UCSF) to generate F1 transgenic mice heterozygous for Per3 (Neu/Per3+/-). F1 males and females were intercrossed to produce the F2 generation consisting of Neu/ Per3+/+, Neu/ Per3+/- and Neu/ Per3-/- animals. Identification of animal genotypes is described in the Supplementary Data. 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PER3 polymorphism predicts sleep structure and waking performance. Curr Biol. 2007; 17(7):613-8 42. Groeger JA, Viola AU, Lo JC, et al. Early morning executive functioning during sleep deprivation is compromised by a PERIOD3 polymorphism. Sleep. 2008; 31(8):1159-67. 43. Collis SJ, Boulton SJ.Emerging links between the biological clock and the DNA damage response. Chromosoma. 2007;116(4):331-9 44. Yagita K, Yamaguchi S, Tamanini F, van Der Horst GT, Hoeijmakers JH, Yasui A, Loros JJ, Dunlap JC, Okamura H. Dimerization and nuclear entry of mPER proteins in mammalian cells.Genes Dev. 2000; 14(11):1353-63. 45. Mao JH, Wu D, Perez-Losada J, et al. Crosstalk between Aurora-A and p53: frequent deletion or downregulation of Aurora-A in tumors from p53 null mice. Cancer Cell. 2007;11(2):161-73. Figure. 1.- Association between Per3 deletion and disease-free survival in breast cancer patients. (a) TaqMan copy number analysis of PER3 in 180 lymph node negative breast cancer tumors (top left panel), showing decreased survival of patients with PER3 deletions. Patients who received no treatment (36 patients, (b)) or were treated with anthracycline chemotherapy (85 patients, (c)) showed no effect of PER3 deletion. (d) A subset of 59 patients that were ER and/or PGR positive and were treated only with tamoxifen showed strong association between survival and low PER3 copy number. Figure. 2.- Association between PER3 gene expression and survival of breast cancer patients. (a) PER3 low expression (red) was found in 122 (30%) patients from both data sets. Kaplan-Meier analysis for all patients indicates that those patients with tumors with low expression of PER3 (red) have lower disease free survival rates at 10 years than those patients with normal/high expression of PER3 (blue). (b) Comparison of PER3 expression with Estrogen Receptor (ER) status. Low expression of PER3 was less common in ER+ tumors, however those patients with ER+ tumors and low PER3 expression show a higher risk of recurrence (lower left panel). No effect was seen in patients with ER- tumors. (right panel) Figure. 3.- Effect of PER3 expression levels on survival according to molecular subtypes. Kaplan– Meier estimates of Disease-Free Survival among the 413 patients, according to the Per3 expression. Patients were stratified using the Sorlie et al.33,36 tumor classification. (a) In the Basal Tumors, the low expression of PER3 gene had no effect in patient recurrence however in the Non Basal tumors those patients whose tumors had low expression of PER3 showed a significant increase of recurrence. (b) The increase in recurrence was observed mainly in the Luminal A and ERBB2+ subgroup of tumors whereas no significant difference was observed in the Luminal B subgroup. P values were obtained using the logrank test. Figure. 4.- Kaplan-Meier Estimates of Overall Survival. The different expression levels of Per3 were evaluated in all the patients (a) and the different subgroups of patients based on (b) ER positive (c) ER negative, and based on the different molecular subtypes using Sorlie et al35,36 classification, (d) Basal, (e) Non Basal, (f) ERBB2+, (g) Luminal A and (h) Luminal B tumors. P values were obtained using the log-rank test. Figure. 5.- Effect of loss of Per3 on tumor susceptibility in two different mouse models. (a) Breast cancer incidence in a group of mice treated with 7,12-dimethyl-benz[a]anthracene (DMBA) based in the different genotypes (WT +/+, HET +/-, Null -/-) (b) Kaplan-Meier estimates of probability of Tumor Free Survival in the group of MMTVneu-PER3 mice. P values were obtained using the log-rank test. a Variable Disease Free Survival Overall Survival Hazard ratio (95% IC) P-value Hazard ratio (95% IC) P-value PER3 2.13 ( 1.40 - 3.24 ) <0.001 2.04 ( 1.34 - 3.10 ) 0.001 Tumor Size Age (< 40 years) 1.72 ( 1.13 - 2.63 ) 0.012 2.02 ( 1.31 - 3.12 ) 0.002 0.49 ( 0.32 - 0.74 ) 0.001 0.54 ( 0.35 - 0.83 ) 0.005 ER 0.75 ( 0.49 - 1.15 ) 0.19 0.53 ( 0.35 - 0.80 ) 0.003 Lymph Node 1.36 ( 0.90 - 2.06 ) 0.14 1.85 (1.18 - 2.77 ) 0.007 Tumor Grade b good 0.93 ( 0.55 - 1.60 ) 0.8 1.05 (0.61 - 1.80 ) 0.87 intermediate 1.18 ( 0.74 - 1.89 ) 0.48 1.38 ( 0.87 - 2.20 ) 0.17 Variable Disease Free Survival Overall Survival Hazard ratio (95% IC) P-value Hazard ratio (95% IC) P-value PER3 2.92 ( 1.71 – 4.97 ) <0.001 2.63 ( 1.49 – 4.63 ) 0.001 Tumor Size Age (< 40 years) 1.62 ( 0.96 - 2.63 ) 0.072 1.87 ( 1.05 – 3.32 ) 0.03 0.58 ( 0.33 - 0.99 ) 0.047 0.57 ( 0.32 – 1.04 ) 0.06 ER All tumors are ER positive Lymph Node 1.40 ( 0.83 - 2.39 ) All tumors are ER positive 0.21 2.07 (1.18 - 2.77 ) 0.02 Tumor Grade good 1.14 ( 0.59 – 2.23 ) 0.69 1.09 (0.54 – 2.24 ) 0.8 intermediate 1.34 ( 0.73 – 2.46 ) 0.34 1.32 ( 0.70 – 2.49 ) 0.38 Table 1. a.- Cox proportional hazard ratio multivariate analysis. Risk of distant recurrence or death among patients with breast cancer. The analysis included the 413 patients from two different data bases 31,32 b.- Cox proportional hazard ratio multivariate analysis for ER positive samples. Risk of distant recurrence or death among patients with breast cancer. The analysis included the 302 patients with ER positive breast tumors from two different data bases 31,32 FIGURE 1 A B All patients (n= 180) 1.0 Probability of Disease Free Survival Probability of Disease Free Survival 1.0 No treatment (n= 36) 0.8 0.6 0.4 p= 0.01 0.0 0.8 0.6 p= 0.93 0.4 0.0 0.00 50 100 150 200 0.00 250 50 100 D Probability of Disease Free Survival 1.0 Probability of Disease Free Survival 200 Time (Months) Time (Months) C 150 0.8 0.6 0.4 p= 0.18 0.0 1.0 0.8 0.6 0.4 p= 0.007 0.0 0.00 50 100 150 200 250 Time (Months) Chemotherapy (n= 85) Deletion. 1 copy of per3 0.00 50 100 150 200 Time (Months) Only Tamoxifen (n= 59) Normal. 2 copies of per3 Gain. 3 or more copies of per3 A 1.0 122 (30%) 291 (70%) per3 low per3 normal/high 48 Probability of Disease Free Survival All patients n= 413 78 p= 0.013 39% 0.8 0.6 0.4 0.2 27% 0.0 5 0 Recurrence Recurrence p<0.001 10 15 20 Time (Years) DFS at 10 years ± SE (%) B 69 ± 3 vs. 56 ± 5 Expression of per3 Normal/High ER status 302 (73%) ER negative ER positive 69 (23%) 233 (77%) 53 (48%) per3 low per3 normal/high per3 low 28 p= 0.009 55 58 (52%) per3 normal/high 23 29 p= 0.13 41% 24% 38% 40% Recurrence Recurrence Recurrence Recurrence 1.0 1.0 0.8 Probability of Disease Free Survival Probability of Disease Free Survival Expression of per3 Low 111 (27%) 0.6 0.4 p<0.001 0.2 0.8 0.6 0.4 p= 0.9 0.2 0.0 0.0 0 DFS at 10 years ± SE (%) 5 10 15 Time (Years) 20 74 ± 3 vs. 54 ± 5 0 5 10 15 Time (Years) 20 56 ± 5 vs. 57 ± 6 FIGURE 2 A Tumor Class 73 (18%) 231 (56%) Non Basal Basal 34 (47%) 39 (53%) 66 (29%) 165 (71%) per3 low per3 normal/high per3 low per3 normal/high 9 15 32 p= 0.32 26% 49% 39% Recurrence Recurrence 27% Recurrence 1.0 Recurrence 1.0 Probability of Disease Free Survival Probability of Disease Free Survival 37 P< 0.001 0.8 0.6 0.4 0.2 p= 0.27 0.8 Expression of per3 Normal/High 0.6 0.4 0.0 0.0 5 0 10 5 0 15 Time (Years) DFS at 10 years ± SE (%) B 52 ± 10 vs. 70 ± 9 90 (39%) 73 (81%) 56 (24%) per3 low per3 low 8 10 p= 0.007 ERBB2 25 (37%) per3 normal/high 20 74 ± 3 vs. 43 ± 6 Luminal B 17 (19%) 43 (63%) 24 (43%) per3 normal/high per3 normal/high per3 low 16 p= 0.9 32 (57%) 15 10 p= 0.03 41% 11% 40% 38% 69% 38% Recurrence Recurrence Recurrence Recurrence Recurrence Recurrence 1.0 1.0 0.8 0.6 0.4 0.2 1.0 Probability of Disease Free Survival Probability of Disease Free Survival Probability of Disease Free Survival 10 15 Time (Years) 68 (29%) Luminal A 7 Expression of per3 Low p< 0.001 0.2 0.8 0.6 0.4 0.2 p<0.001 10 15 Time (Years) DFS at 10 years ± SE (%) 0.4 0.2 p= 0.0043 0.0 5 0.6 p= 0.58 0.0 0 0.8 84 ± 4 vs. 28 ± 10 0.0 0 5 10 15 Time (Years) 59 ± 9 vs. 58 ± 10 20 0 5 10 15 Time (Years) 20 68 ± 8 vs. 30 ± 8 FIGURE 3 B ER positive 0.8 0.6 0.4 0.2 p< 0.001 0.8 0.6 0.4 0.2 p<0.001 5 10 15 20 Time (Years) 0 5 10 15 Probability of Overall Survival 0.6 0.4 0.2 p= 0.58 5 OS at 10 years ± SE (%) 0.8 0.6 0.4 0.2 p< 0.001 0 5 10 15 20 Time (Years) 77 ± 3 vs. 39 ± 5 Probability of Overall Survival 0.6 0.4 p<0.001 0.0 1.0 0.8 0.6 0.4 0.2 p<0.001 0 5 Overall Survival 0.8 Expression of per3 Normal/High 0.6 0.4 0.2 10 15 Time (Years) 83 ± 3 vs. 34 ± 11 Expression of per3 Low p= 0.28 0 5 10 15 10 15 20 Time (Years) 70 ± 7 vs. 25 ± 8 1.0 0.0 OS at 10 years ± SE (%) 10 15 20 Time (Years) H luminal B 0.8 5 5 0.0 45 ± 8 vs. 57 ± 10 1.0 0 p= 0.47 F ERBB2 1.0 10 15 Time (Years) G luminal A 0.2 0.2 54 ± 7 vs. 49 ± 7 0.0 0.0 0 0.4 0 E non basal 0.8 0.6 20 79 ± 3 vs. 55 ± 6 D basal 1.0 0.8 Time (Years) 74 ± 3 vs. 53 ± 4 OS at 10 years ± SE (%) 1.0 0.0 Probability of Overall Survival 0 Probability of Overall Survival 1.0 0.0 0.0 Probability of Overall Survival C ER negative Probability of Overall Survival 1.0 Probability of Overall Survival Probability of Overall Survival A all patients 20 Time (Years) 70 ± 7 vs. 54 ± 9 FIGURE 4 Variable Disease Free Survival Overall Survival Hazard ratio (95% IC) P-value PER3 2.13 ( 1.40 - 3.24 ) <0.001 2.04 ( 1.34 - 3.10 ) 0.001 Tumor Size 1.72 ( 1.13 - 2.63 ) 0.012 2.02 ( 1.31 - 3.12 ) 0.002 Age (< 40 years) 0.49 ( 0.32 - 0.74 ) 0.001 0.54 ( 0.35 - 0.83 ) 0.005 ER 0.75 ( 0.49 - 1.15 ) 0.19 0.53 ( 0.35 - 0.80 ) 0.003 Lymph Node 1.36 ( 0.90 - 2.06 ) 0.14 1.85 (1.18 - 2.77 ) 0.007 Tumor Grade good intermediate 0.93 ( 0.55 - 1.60 ) 1.18 ( 0.74 - 1.89 ) 0.8 0.48 1.05 (0.61 - 1.80 ) 1.38 ( 0.87 - 2.20 ) 0.87 0.17 Variable Disease Free Survival Hazard ratio (95% IC) P-value Overall Survival Hazard ratio (95% IC) P-value PER3 2.92 ( 1.71 – 4.97 ) <0.001 2.63 ( 1.49 – 4.63 ) 0.001 Tumor Size 1.62 ( 0.96 - 2.63 ) 0.072 1.87 ( 1.05 – 3.32 ) 0.03 Age (< 40 years) 0.58 ( 0.33 - 0.99 ) 0.047 0.57 ( 0.32 – 1.04 ) 0.06 ER Hazard ratio (95% IC) P-value All tumors are ER positive All tumors are ER positive Lymph Node 1.40 ( 0.83 - 2.39 ) 0.21 2.07 (1.18 - 2.77 ) 0.02 Tumor Grade good intermediate 1.14 ( 0.59 – 2.23 ) 1.34 ( 0.73 – 2.46 ) 0.69 0.34 1.09 (0.54 – 2.24 ) 1.32 ( 0.70 – 2.49 ) 0.8 0.38 Table 1. A Cox proportional hazard ratio multivariate analysis. Risk of distant recurrence or death among patients with breast cancer. The analysis included the 412 patients from two different data bases *Chin et al 2006* and *Van de Vijver et al 2002* B.- Cox proportional hazard ratio multivariate analysis for ER positive samples. Risk of distant recurrence or death among patients with breast cancer. The analysis included the 302 patients with ER positive breast tumors from two different data bases *Chin et al 2006* and *Van de Vijver et al 2002* Breast cancer incidence (%) A 40 36% 35 30 p= 0.005 25 20 15 12% 10 5 0% 0 WT (n=17) Het (n=33) Null (n=28) Genotype B Probability of Tumor Free Survival 1.0 0.8 0.6 0.4 0.2 p= 0.003 0.0 5 0 10 15 20 25 Time (Months) n Median follow-up Months (range) Tumor Free Rate at 15 months ± SE (%) Wild-type 30 16 ( 7.5 - 26.4 ) 63% ± 6 Heterozygous 35 16 ( 6.3 - 26.5 ) 63 % ± 6 Null 14 13 ( 9.8 - 22.5 ) 21 % ± 8 Per3 mice FIGURE 5 Genotyping Per3 KO. Shearman LP, Jin X, Lee C, Reppert SM, Weaver DR. Targeted disruption of the mPer3 gene: subtle effects on circadian clock function. Mol Cell Biol. 2000 Sep; 20(17):6269-75. Genotypes were determined by PCR analysis of tail biopsy DNA). The PCR method was done using three different primers, a forward primer in intron 3 (3-43; 5' TCTGTGAGTTCTTCCGTGTCTGTTll) (present only in the wild-type [WT] allele), a primer located in the NEO cassette (Neo6-2; 5'TGCCCCAAAGGCCTACCCGCTTCC), and a common reverse primer in exon 4 (3-41; 5' GTCTTGAGGGGCAAGCAGGTCGAC). The presence of the WT allele led to the amplification of a ca. 200-bp band from primers 3-43 and 3-41, while the presence of the targeted allele was detected by amplification of a ca. 400-bp band with primers Neo6-2 and 3-41 The PCR protocol consisted of 3 min at 95°C, 30 cycles of amplification (each consisting of 30 s at 94°C, 30 s at 60°C, and 90 s at 72°C), and a final extension phase (10 min at 72°C). Products were separated on 1.5% agarose gels and viewed by UV transillumination with ethidium bromide. WT WT WT WT WT Het Het ko Het Het Het ko MMTV-Neu Mice Genotyping. Li B, Rosen JM, McMenamin-Balano J, Muller WJ, Perkins AS. neu/ERBB2 cooperates with p53-172H during mammary tumorigenesis in transgenic mice. Mol Cell Biol. 1997 Jun;17(6):3155-63 Primers Neu1: GGAAGTACCCGGATGAGGAGGGCATATG Neu2: CCGGGCAGCCAGGTCCCTGTGTACAAGCCG PCR reacction: Hotstart PCR buffer Neu1 primer 10 µM Neu2 primer 10 µM 10 mM dNTP Hotstart Taq polymerase DNA H2O 5 µl 1 µl 1 µl 1 µl 0. 5 µl 2 µl 39. 5 µl Program: MMTV-Neu 94°C 15 min; 35 cycles of: 94°C 30 sec, 60°C 1 min, 72°C 1 min; 72°C 2 min; 20°C (o/n) Expectation: MMTV-Neu 660 bp band, wt: no band wt wt + TABLE 1. Frequency of copy number of PER3 related with the clinical data of 180 lymph node negative breast cancer patients from Climent et al 2007. a b Censored/No Recurrence (TREATMENT Non Anthracycline) Censored/No Recurrence (TREATMENT Tamoxifen) Recurrence (TREATMENT Non Anthracycline) Recurrence (TREATMENT: Tamoxifen) No Recurrence vs Recurrence (TREATMENT Non Anthracycline) No Recurrence vs Recurrence (TREATMENT Tamoxifen) Chromosome 1 1.0 1.0 Adjusted p-value 0.6 0.6 0.01 0.2 0.2 - 0.2 - 0.2 - 0.6 -1.0 0.05 0. 1 - 0.6 1p 1q Non Recurrence -1.0 1p 1q Recurrence Statistical difference Figure 1.- Copy number analysis by array-CGH . (A) In 95 lymph node negative breast cancer patients who did not received systemic chemotherapy, BAC clones showing deletion and corresponding to chromosomal region 11q21-q25, were strongly associated with patient relapse (data previously published in Climent et al 2007). (B) In 59 patients from the previous group (A) who were ER and/or PGR positive and were treated only with tamoxifen, additional clones showing deletion and corresponding to chromosomal region 1p were strongly associated with patient recurrence. Genome-wide analysis of DNA-copy number changes of tumor samples was performed using array CGH on a microchip with ∼2.460 BAC and P1 clones printed in triplicate (UCSF Hum Array 2.0) with a resolution of 1.4 Mb across the genome. Methods and analytical procedures have been described previously in detail. Climent J, Dimitrow P, Fridlyand J, et al. Deletion of chromosome 11q predicts response to anthracyclinebased chemotherapy in early breast cancer. Cancer Res. 2007 ;67(2):818-26 Snijders AM, Nowak N, Segraves R, et al. Assembly of microarrays for genome-wide measurement of DNA copy number. Nat Genet 2001;29(3):263-4. PER3 sequencing We performed a mutation screening covering whole coding region of PER3 by direct Sanger sequencing in 35 breast cancer cell lines. We synthesized cDNA from 35 breast cancer cell lines and did RT-PCR using designed 7 forward and reverse primers (Table 2S) for PER3 coding region. PCR reactions were carried out in a volume of 25 ul containing 100 ng cDNA, 10 pmol of each primer, 250 mM each dNTP, 0.5 U of Taq polymerase and the reaction buffer provided by the supplier (Qiagen, Hilden, Germany). Whole PER3 coding regions were sequenced using the Taq dideoxy terminator cycle sequencing kit and an ABI 3730 DNA sequencer (Applied Biosystems). We could identify several single nucleotide polymorphisms and silent mutations (V419M, S445S, I606I, V639G, L697L, T725T, P745P, L827P, P856A, S864S, T1010T, M1028T, and H1149R). No clear pathogenic mutations like nonsense and missense mutations were identified (Table 3S) Table 2. Primer sequences of PER3 mutation screening Fragment Forward primer Sequence Reverse primer Sequence Size (bp) 1 PER3_RT1F gaaaagctcctcggagatga PER3_RT1R tcatgtcttgaggtgcaagc 704 2 PER3_RT2F aacaggctgctttgatcctg PER3_RT2R gtgggctcgttcgaacttta 696 3 PER3_RT3F cagttggtccagctttgtga PER3_RT3R tcatctgccttgtggttctg 681 4 PER3_RT4F ggatttgaggaacgatgagc PER3_RT4R gtgttcgagctgctgctgt 696 5 PER3_RT5F gcaagaaagcaggagcaaag PER3_RT5R tggagattcagagggtctgg 700 6 PER3_RT6F gtcgtcagcaatgagtccaa PER3_RT6R gagaatgcgctcaggtgtct 700 7 PER3_RT7F aaaatgggcagcaatctcag PER3_RT7R ggtttggggctcattctagc 702 Table 3. Polymorphisms and silent mutations of PER3 in 37 breast cancer cell lines Name Fragment Codon Nucleotide change Aminoacid change V419M 3 419 GTG-->ATG Val-->Met S445S 3 445 AGT -->AGC Ser-->Ser I606I 4 606 ATA-->ATT Ile-->Ile V639G 4 639 GTC-->GGC Val-->Gly L697L 4 697 AAG-->AAA Lys-->Lys T725T 4 725 ACT-->ACA Thr-->Thr P745P 4 745 CCG-->CCA Pro-->Pro L827P 5 827 CTG-->CCG Leu-->Pro P856A 5 856 CCT-->GCT Pro-->Ala S864S 5 864 TCG-->TCA Ser-->Ser M1028T 6 1028 ATG-->ACG Met-->Thr T1010T 6 1010 ACA-->ACG Thr-->Thr H1149R 7 1149 CAT-->CGT His-->Arg (a) (b) (c) Allele 1:GATACCTTTGTGGCAGTATTTT Allele 2:TTCCAATACCTACTACTTCAAG (d) Allel 1: AACCGAATGGTGGTGgtgagtcagcgaatggtggtggtgag ……….…….tctgtttcagGTGAATG Allel 2: AACCGAATGGTGGTGgtgagtcagcgaatggtggtggtg……………. tctgtttcagGTGAATG Figure 2 Four alternative splicing isoforms were identified. (a) exon 3 skipping isoform was found in three breast cell lines (b) Differentially expressed two isoforms were identified in intron 4. The major isoform contains one more amino acid (Alanine, GCA) than minor form in the beginning of exon 5. All 35 breast cancer cell lines showed higher expression of the major isoform (containing one more Alanine) allele by RT-PCR and sequencing analysis. (c-d) Additional two alternative splicing were found in fragment 1 and 3, respectively TABLE 4. Relationship between Per3 expression levels and clinical-pathological data of the 413 patients from Van de Vijver et al 2002, and Chin et al. 2006 26% p= 0.007 11% Recurrence 41% Recurrence 40% 8 7 Recurrence per3 low per3 low 10 25 (37%) per3 normal/high p= 0.9 Luminal B 73 (81%) 68 (29%) Luminal A p= 0.32 p= 0.0003 27% Recurrence 38% 16 per3 normal/high 43 (63%) Recurrence 69% 15 per3 low 24 (43%) Recurrence 49% Recurrence 39% Recurrence 90 (39%) Recurrence 37 per3 normal/high 165 (71%) 32 per3 low Non Basal 231 (56%) p= 0.03 ERBB2 56 (24%) p= 0.009 Recurrence 38% 10 per3 normal/high 32 (57%) Recurrence 41% 28 per3 low 69 (23%) ER positive 302 (73%) Recurrence p= 0.013 27% Recurrence 78 per3 normal/high per3 low 48 291 (70%) 122 (30%) All patients n= 413 39% 15 per3 low per3 normal/high 9 66 (29%) 39 (53%) Basal Unclassified Tumor Class 34 (47%) 73 (18%) 109 (26%) 17 (19%) FIGURE 3 Recurrence per3 low none Recurrence 24% 55 per3 normal/high 233 (77%) Normal like 17 (7%) ER status Recurrence 18% 9 per3 normal/high 17 (100%) Recurrence 38% 29 per3 low 53 (48%) p= 0.13 ER negative 111 (27%) Recurrence 40% 23 per3 normal/high 58 (52%) 1.0 0.8 0.6 0.4 0.2 p< 0.0001 1.0 Expression of Per3 / Chd5 Low 0.8 0.6 0.4 0.2 All patients p= 0.40 0.0 5 10 15 0 20 5 10 15 20 Time (Years) Probability of Overall Survival Probability of Overall Survival Time (Years) 1.0 0.8 0.6 0.4 0.2 p= 0.0001 1.0 0.8 0.6 0.4 0.2 ER positive p= 0.73 0.0 0.0 0 5 10 15 0 20 5 10 Time (Years) Probability of Overall Survival 1.0 0.8 0.6 0.4 0.2 p= 0.47 0.0 0 5 15 20 Time (Years) 1.0 0.8 0.6 0.4 0.2 ER negative p= 0.23 0.0 0 10 15 20 Time (Years) 5 10 15 20 Time (Years) Probability of Overall Survival Probability of Overall Survival Expression of Per3 / Chd5 Normal/High 0.0 0 Probability of Overall Survival Overall Survival CHD5 Probability of Overall Survival Probability of Overall Survival PER3 1.0 0.8 0.6 0.4 0.2 p= 0.58 1.0 0.8 0.6 0.4 Basal 0.2 p= 0.82 0.0 0.0 0 5 10 Time (Years) FIGURE 4.- 15 0 5 10 15 Time (Years) Differences between Kaplan Meier Estimates for Overall Survival according the expression levels of Per3 (left column) and Chd5 (right column) in all patients (top) and three different subgroups of patients based on ER positive, ER negative and basal type tumors. P-values were calculated using log-rank test. 1.0 0.8 0.6 0.4 0.2 p< 0.0001 5 1.0 Expression of Per3 / Chd5 Low 0.8 0.6 0.4 0.2 Non Basal p= 0.35 0 10 15 20 Time (Years) 1.0 0.8 0.6 0.4 0.2 p= 0.0007 5 10 15 20 Time (Years) Probability of Overall Survival Probability of Overall Survival 0 1.0 0.8 0.6 0.4 0.2 ERBB2 p= 0.12 0.0 0.0 5 1.0 0.8 0.6 0.4 0.2 p= 0.0007 0.0 0 5 0 10 15 20 Time (Years) 10 5 0.6 0.4 0.2 5 Probability of Overall Survival 0.4 p= 0.28 0.0 15 Time (Years) FIGURE 5.- 10 15 Time (Years) 0.6 10 Luminal A p= 0.75 0.0 0 0.8 5 20 0.8 15 1.0 0 15 1.0 Time (Years) 0.2 10 Time (Years) Probability of Overall Survival 0 Probability of Overall Survival Expression of Per3 / Chd5 Normal/High 0.0 0.0 Probability of Overall Survival Overall Survival CHD5 Probability of Overall Survival Probability of Overall Survival PER3 20 1.0 0.8 0.6 0.4 0.2 Luminal B p= 0.91 0.0 0 5 10 15 Time (Years) Differences between Kaplan Meier Estimates for Overall Survival according the expression levels of Per3 (left column) and Chd5 (right column) in 4 different subgroups of patients based on Non basal, ERBB2, Luminal A and Luminal B tumor subtypes. P-values were calculated using log-rank test. Tumor type n (%) Genotype HET 33 n (%) Lymphoma or leukemia Lung Ovary Breast hyperplasia Breast Skin Ca Liver /kidney Ca in rectal prolapsus Utherus Subcutaneous sarcoma 7 7 5 1 0 3 0 0 1 0 41 41 29 6 0 18 0 0 6 0 18 12 4 1 4 3 0 1 0 2 WT 17 Number of PER3 mice 55 36 12 3 12 9 0 3 0 6 n (%) Total 78 n 9 10 7 2 10 1 3 1 0 0 32 36 25 7 36 4 11 4 0 0 34 29 16 4 14 7 3 2 1 2 NULL 28 TABLE 5. Number of tumors generated by the treatment with DMBA (7, 12-dimethylbenz[a]anthracene ) by oral gavage in PER3 mice.