Charakterisierung des HLA-Klasse-I Ligandoms in Primärtumoren beim humanen Nierenzellkarzinom

T-Zellen können tumorassoziierte HLA-präsentierte Peptide auf Tumorzellen des Nierenzellkarzinoms erkennen, und diese dann eliminieren. Gelingt es, diese Fähigkeit des Immunsystems mit einer Impfung geziehlt anzuregen kann eine Peptid-basierte Immuntherapie ein Verfahren zur Behandlung des Nierenkrebeses darstellen. Aus diesem Grund wurden durch massenspektrometrische Untersuchungen HLA-Klasse-I Ligandome maligner transformierter Zellen des soliden Nierenzellkarzinoms isoliert. Mit Hilfe von vergleichenden Genexpressionsanalysen wurden diejenigen Gene identifiziert, die aufgrund ihres Expressionsmusters als tumorassoziiert angesehen werden können. Diese aus HLA-Liganden abgeleiteten Tumorantigene stellen potentielle T-Zellepitope für eine Peptid-basierte Immuntherapie dar

New pathogen-specific immunoPET/MR tracer for molecular imaging of a systemic bacterial infection

PublishedArticleThe specific and rapid detection of Enterobacteriaceae, the most frequent cause of gram-negative bacterial infections in humans, remains a major challenge. We developed a non-invasive method to rapidly detect systemic Yersinia enterocolitica infections using immunoPET (antibody-targeted positron emission tomography) with [64Cu]NODAGA-labeled Yersinia-specific polyclonal antibodies targeting the outer membrane protein YadA. In contrast to the tracer [18F]FDG, [64Cu]NODAGA-YadA uptake co-localized in a dose dependent manner with bacterial lesions of Yersinia-infected mice, as detected by magnetic resonance (MR) imaging. This was accompanied by elevated uptake of [64Cu]NODAGA-YadA in infected tissues, in ex vivo biodistribution studies, whereas reduced uptake was observed following blocking with unlabeled anti-YadA antibody. We show, for the first time, a bacteria-specific, antibody-based, in vivo imaging method for the diagnosis of a Gram-negative enterobacterial infection as a proof of concept, which may provide new insights into pathogen-host interactions.The research leading to these results has received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement n°602820, from the European Social Fund Baden-Württemberg (to SEA), and from the Deutsche Forschungsgemeinschaft (grant WI 3777/1-2; to SW)

PiggyBac transposon tools for recessive screening identify B-cell lymphoma drivers in mice.

B-cell lymphoma (BCL) is the most common hematologic malignancy. While sequencing studies gave insights into BCL genetics, identification of non-mutated cancer genes remains challenging. Here, we describe PiggyBac transposon tools and mouse models for recessive screening and show their application to study clonal B-cell lymphomagenesis. In a genome-wide screen, we discover BCL genes related to diverse molecular processes, including signaling, transcriptional regulation, chromatin regulation, or RNA metabolism. Cross-species analyses show the efficiency of the screen to pinpoint human cancer drivers altered by non-genetic mechanisms, including clinically relevant genes dysregulated epigenetically, transcriptionally, or post-transcriptionally in human BCL. We also describe a CRISPR/Cas9-based in vivo platform for BCL functional genomics, and validate discovered genes, such as Rfx7, a transcription factor, and Phip, a chromatin regulator, which suppress lymphomagenesis in mice. Our study gives comprehensive insights into the molecular landscapes of BCL and underlines the power of genome-scale screening to inform biology

A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

PURPOSE: We aimed to precisely estimate intra-tumoral heterogeneity using spatially regularized spectral clustering (SRSC) on multiparametric MRI data and compare the efficacy of SRSC with the previously reported segmentation techniques in MRI studies. PROCEDURES: Six NMRI nu/nu mice bearing subcutaneous human glioblastoma U87 MG tumors were scanned using a dedicated small animal 7T magnetic resonance imaging (MRI) scanner. The data consisted of T2 weighted images, apparent diffusion coefficient maps, and pre- and post-contrast T2 and T2* maps. Following each scan, the tumors were excised into 2–3-mm thin slices parallel to the axial field of view and processed for histological staining. The MRI data were segmented using SRSC, K-means, fuzzy C-means, and Gaussian mixture modeling to estimate the fractional population of necrotic, peri-necrotic, and viable regions and validated with the fractional population obtained from histology. RESULTS: While the aforementioned methods overestimated peri-necrotic and underestimated viable fractions, SRSC accurately predicted the fractional population of all three tumor tissue types and exhibited strong correlations (r(necrotic) = 0.92, r(peri-necrotic) = 0.82 and r(viable) = 0.98) with the histology. CONCLUSIONS: The precise identification of necrotic, peri-necrotic and viable areas using SRSC may greatly assist in cancer treatment planning and add a new dimension to MRI-guided tumor biopsy procedures. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11307-016-1009-y) contains supplementary material, which is available to authorized users