UNMET 'UNMET' - Uncovering Mechanisms and Establishing Strategies to Target Vessel Co-Opted Colorectal Cancer Liver Metastases

Summary

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.

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Web resources:	https://cordis.europa.eu/project/id/101155460
Start date:	01-06-2024
End date:	31-05-2026
Total budget - Public funding:	- 214 934,00 Euro

Cordis data

Original description

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/-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.