The EMT-activator Zeb1 is a key factor for cell plasticity and promotes metastasis in pancreatic cancer.

Metastasis is the major cause of cancer-associated death. Partial activation of the epithelial-to-mesenchymal transition program (partial EMT) was considered a major driver of tumour progression from initiation to metastasis. However, the role of EMT in promoting metastasis has recently been challenged, in particular concerning effects of the Snail and Twist EMT transcription factors (EMT-TFs) in pancreatic cancer. In contrast, we show here that in the same pancreatic cancer model, driven by Pdx1-cre-mediated activation of mutant Kras and p53 (KPC model), the EMT-TF Zeb1 is a key factor for the formation of precursor lesions, invasion and notably metastasis. Depletion of Zeb1 suppresses stemness, colonization capacity and in particular phenotypic/metabolic plasticity of tumour cells, probably causing the observed in vivo effects. Accordingly, we conclude that different EMT-TFs have complementary subfunctions in driving pancreatic tumour metastasis. Therapeutic strategies should consider these potential specificities of EMT-TFs to target these factors simultaneously.

Imaging and Selective Elimination of Glioblastoma Stem Cells with Theranostic Near-Infrared-Labeled CD133-Specific Antibodies.

Near-infrared photoimmunotherapy (NIR-PIT), which employs monoclonal antibody (mAb)-phototoxic phthalocyanine dye IR700 conjugates, permits the specific, image-guided and spatiotemporally controlled elimination of tumor cells. Here, we report the highly efficient NIR-PIT of human tumor xenografts initiated from patient-derived cancer stem cells (CSCs). Using glioblastoma stem cells (GBM-SCs) expressing the prototypic CSC marker AC133/CD133, we also demonstrate here for the first time that NIR-PIT is highly effective against brain tumors. The intravenously injected theranostic AC133 mAb conjugate enabled the non-invasive detection of orthotopic gliomas by NIR fluorescence imaging, and reached AC133+ GBM-SCs at the invasive tumor front. AC133-targeted NIR-PIT induced the rapid cell death of AC133+ GBM-SCs and thereby strong shrinkage of both subcutaneous and invasively growing brain tumors. A single round of NIR-PIT extended the overall survival of mice with established orthotopic gliomas by more than a factor of two, even though the harmless NIR light was applied through the intact skull. Humanised versions of this theranostic agent may facilitate intraoperative imaging and histopathological evaluation of tumor borders and enable the highly specific and efficient eradication of CSCs.

Volumetric analysis of MRI data monitoring the treatment of polycystic kidney disease in a mouse model.

OBJECT: The human condition autosomal dominant polycystic kidney disease (ADPKD) is characterized by the growth of cysts in the kidneys that increase renal volume and lead to kidney failure. Mice studies are performed for treatment development monitored with imaging. The analysis of the imaging data is typically manual, which is costly and potentially biased. This paper presents a reliable and reproducible method for the automated segmentation of polycystic mouse kidneys. MATERIALS AND METHODS: Treated and untreated mice have been imaged longitudinally with high field anatomic MRI. The region of interest (ROI) of the kidneys in the images is identified and restored for artifacts. It is then analyzed statistically and geometric models are estimated for each kidney. The statistical and geometric information are provided to the graph cuts algorithm that delineates the kidneys. RESULTS: The accuracy of the analysis has been demonstrated by showing consistency with results obtained with previous methods as well as by comparing with manual segmentations. CONCLUSION: The method developed can accelerate and improve the accuracy of kidney volumetry in preclinical treatment trials for ADPKD.

Analysis of MR images of mice in preclinical treatment monitoring of polycystic kidney disease.

A common cause of kidney failure is autosomal dominant polycystic kidney disease (ADPKD). It is characterized by the growth of cysts in the kidneys and hence the growth of the entire kidneys with eventual failure in most cases by age 50. No preventive treatment for this condition is available. Preclinical drug treatment studies use an in vivo mouse model of the condition. The analysis of mice imaging data for such studies typically requires extensive manual interaction, which is subjective and not reproducible. In this work both untreated and treated mice have been imaged with a high field, 9.4T, MRI animal scanner and a reliable algorithm for the automated segmentation of the mouse kidneys has been developed. The algorithm first detects the region of interest (ROI) in the image surrounding the kidneys. A parameterized geometric shape for a kidney is registered to the ROI of each kidney. The registered shapes are incorporated as priors to the graph cuts algorithm used to extract the kidneys. The accuracy of the automated segmentation has been demonstrated by comparing it with a manual segmentation. The processing results are also consistent with the literature for previous techniques.