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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
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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
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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
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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
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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,
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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.
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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.
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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
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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
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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.
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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. Research at C.C.´s lab is supported partially by FEDER and
by Fondo de Investigaciones Sanitarias (PI080164) and Junta de Castilla y
León (SA060A09 and proyecto Biomedicina 2009-2010). Research at JPL´s lab
is supported partially by FEDER and by Fondo de Investigaciones Sanitarias
(PI070057), by MICINN (PLE2009-O119), Junta de Castilla y León (SA078A09
and Proyecto Biomedicina 2009-2010) and Sandra Ibarra Foundation.
12
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Zhu, L., Gibson, P., Currle, D. S., Tong, Y., Richardson, R. J., Bayazitov, I. T., Poppleton, H.,
Zakharenko, S., Ellison, D. W. and Gilbertson, R. J. (2009). Prominin 1 marks intestinal stem cells
that are susceptible to neoplastic transformation. Nature 457, 603-607.
15
FIGURE LEGENDS
Figure 1. Main molecular mechanisms of human cancer and traditional
mouse cancer models. A) Human cancer is a genetic disease originated by
several possible types of alterations affecting the structure and/or number of
oncogenes or tumour suppressor genes. 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.
In the DMBA gavage experiment female mice were examined every three days for sickness or symptoms
of tumor development for up to 19.7 months. MMTVneu/Per3 transgenic female mice were examined
weekly for mammary tumor development by palpation for up to 25.8 months. Mice that showed
significant weight loss, morbidity or excessive tumor burden were sacrificed by cervical dislocation after
being anesthetized according to the UCSF Animal Care and Use (IACUC) protocol. Tumors and tissues
were fixed in 4% neutral buffered paraformaldehyde for histological examination. Mice found dead were
censored from the study.
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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.