0000000000241512
AUTHOR
Sebastian Foersch
Programming of Intestinal Epithelial Differentiation by IL-33 Derived from Pericryptal Fibroblasts in Response to Systemic Infection.
SummaryThe intestinal epithelium constitutes an efficient barrier against the microbial flora. Here, we demonstrate an unexpected function of IL-33 as a regulator of epithelial barrier functions. Mice lacking IL-33 showed decreased Paneth cell numbers and lethal systemic infection in response to Salmonella typhimurium. IL-33 was produced upon microbial challenge by a distinct population of pericryptal fibroblasts neighboring the intestinal stem cell niche. IL-33 programmed the differentiation of epithelial progenitors toward secretory IEC including Paneth and goblet cells. Finally, IL-33 suppressed Notch signaling in epithelial cells and induced expression of transcription factors governing…
Molecular imaging of VEGF in gastrointestinal cancer in vivo using confocal laser endomicroscopy
Vascular endothelial growth factor (VEGF) is a therapeutic target in gastrointestinal cancer (GiC). However, its in vivo visualisation could not be achieved to date with endoscopic techniques. Confocal laser endomicroscopy (CLE) is a novel imaging technique for gastrointestinal endoscopy providing in vivo microscopy at subcellular resolution. The aim of the study was to evaluate CLE for in vivo molecular imaging of VEGF in GiC.Molecular imaging of tumours in APCmin mice, in xenograft models and in surgical specimens of patients with colorectal cancer (CRC) was achieved after application of labelled antibodies. The tumour sites were scanned with the probe for the strongest specific fluoresce…
13P Comparison study of different programmed death-ligand 1 (PD-L1) assays, readers and scoring methods in triple-negative breast cancer (TNBC)
Tumor Lipids of Pediatric Papillary Renal Cell Carcinoma Stimulate Unconventional T Cells
Papillary renal cell carcinoma (PRCC) is a rare entity in children with no established therapy protocols for advanced diseases. Immunotherapy is emerging as an important therapeutic tool for childhood cancer. Tumor cells can be recognized and killed by conventional and unconventional T cells. Unconventional T cells are able to recognize lipid antigens presented via CD1 molecules independently from major histocompatibility complex, which offers new alternatives for cancer immunotherapies. The nature of those lipids is largely unknown and α-galactosylceramide is currently used as a synthetic model antigen. In this work, we analyzed infiltrating lymphocytes of two pediatric PRCCs using flow cy…
Multimodal Deep Learning for Prognosis Prediction in Renal Cancer
BackgroundClear-cell renal cell carcinoma (ccRCC) is common and associated with substantial mortality. TNM stage and histopathological grading have been the sole determinants of a patient’s prognosis for decades and there are few prognostic biomarkers used in clinical routine. Management of ccRCC involves multiple disciplines such as urology, radiology, oncology, and pathology and each of these specialties generates highly complex medical data. Here, artificial intelligence (AI) could prove extremely powerful to extract meaningful information to benefit patients.ObjectiveIn the study, we developed and evaluated a multimodal deep learning model (MMDLM) for prognosis prediction in ccRCC.Desig…
Deep learning for diagnosis and survival prediction in soft tissue sarcoma.
Background Clinical management of soft tissue sarcoma (STS) is particularly challenging. Here, we used digital pathology and deep learning (DL) for diagnosis and prognosis prediction of STS. Patients and methods Our retrospective, multicenter study included a total of 506 histopathological slides from 291 patients with STS. The Cancer Genome Atlas cohort (240 patients) served as training and validation set. A second, multicenter cohort (51 patients) served as an additional test set. The use of the DL model (DLM) as a clinical decision support system was evaluated by nine pathologists with different levels of expertise. For prognosis prediction, 139 slides from 85 patients with leiomyosarcom…
Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides.
Abstract Background Muscle-invasive bladder cancer (MIBC) is the second most common genitourinary malignancy, and is associated with high morbidity and mortality. Recently, molecular subtypes of MIBC have been identified, which have important clinical implications. Objective In the current study, we tried to predict the molecular subtype of MIBC samples from conventional histomorphology alone using deep learning. Design, setting, and participants Two cohorts of patients with MIBC were used: (1) The Cancer Genome Atlas Urothelial Bladder Carcinoma dataset including 407 patients and (2) our own cohort including 16 patients with treatment-naive, primary resected MIBC. This resulted in a total …
Interassay and interobserver comparability study of four programmed death-ligand 1 (PD-L1) immunohistochemistry assays in triple-negative breast cancer
Different immunohistochemical programmed death-ligand 1 (PD-L1) assays and scorings have been reported to yield variable results in triple-negative breast cancer (TNBC). We compared the analytical concordance and reproducibility of four clinically relevant PD-L1 assays assessing immune cell (IC) score, tumor proportion score (TPS), and combined positive score (CPS) in TNBC. Primary TNBC resection specimens (n = 104) were stained for PD-L1 using VENTANA SP142, VENTANA SP263, DAKO 22C3, and DAKO 28–8. PD-L1 expression was scored according to guidelines on virtual whole slide images by four trained readers. The mean PD-L1 positivity at IC-score ≥1% and CPS ≥1 ranged between 53% and 75% with th…