'UNMET' - Uncovering Mechanisms and Establishing Strategies to Target Vessel Co-...
'UNMET' - Uncovering Mechanisms and Establishing Strategies to Target Vessel Co-Opted Colorectal Cancer Liver Metastases
An estimated 25-50% of colorectal cancer patients will encounter liver metastasis during their illness. Tumor vessel co-option is a non-angiogenic mechanism whereby tumors, rather than forming new blood vessels (a process known as...
An estimated 25-50% of colorectal cancer patients will encounter liver metastasis during their illness. Tumor vessel co-option is a non-angiogenic mechanism whereby tumors, rather than forming new blood vessels (a process known as angiogenic growth), hijack pre-existing blood vessels in the affected organ. Standard anti-angiogenic therapy (AAT) is ineffective against vessel co-optioned tumors. This process has been linked to unfavorable patient outcomes. The exact mechanisms distinguishing vessel co-option remain elusive. Preliminary data suggest that metastatic cancer displaying the vessel co-option phenotype increased in gene expression, regulated by Lymphoid enhancer binding factor 1 or LEF1 protein, which is a key mediator of the Wnt/β-catenin signaling. The dysregulation of the Wnt pathway can activate target genes that promote cell proliferation and survival. In this proposal, I hypothesize, that inhibition of the Wnt signaling (e.g. by blocking LEF1), will change the properties of vessel co-optioned tumors and improve the effectiveness of conventional treatment for liver metastases. Patient-derived organoids, obtained from hospitals, will be used to validate whether the inhibition of LEF1 will impact the vessel co-option phenotype, making it more susceptible to AAT. Advanced microscopy techniques, like Atomic force microscopy and Scanning ion-conductance microscopy, will facilitate monitoring the decreased stiffness of vessel co-opted tumor cells, leading to improved AAT delivery. By using humanized patient-derived organoid xenografts with inhibited Wnt signaling, I will monitor tumor growth, its phenotype, and the response to AAT in vivo. Furthermore, I aim to pinpoint diagnostic markers for vessel co-option tumors using blood tests and computed tomography (CT) scans. Utilizing artificial intelligence tools, I plan to analyze CT scans of liver patients to better predict metastatic tumor subtypes and treatment responses in the future.ver más
Seleccionando "Aceptar todas las cookies" acepta el uso de cookies para ayudarnos a brindarle una mejor experiencia de usuario y para analizar el uso del sitio web. Al hacer clic en "Ajustar tus preferencias" puede elegir qué cookies permitir. Solo las cookies esenciales son necesarias para el correcto funcionamiento de nuestro sitio web y no se pueden rechazar.
Cookie settings
Nuestro sitio web almacena cuatro tipos de cookies. En cualquier momento puede elegir qué cookies acepta y cuáles rechaza. Puede obtener más información sobre qué son las cookies y qué tipos de cookies almacenamos en nuestra Política de cookies.
Son necesarias por razones técnicas. Sin ellas, este sitio web podría no funcionar correctamente.
Son necesarias para una funcionalidad específica en el sitio web. Sin ellos, algunas características pueden estar deshabilitadas.
Nos permite analizar el uso del sitio web y mejorar la experiencia del visitante.
Nos permite personalizar su experiencia y enviarle contenido y ofertas relevantes, en este sitio web y en otros sitios web.