ITIH5 as a multifaceted player in pancreatic cancer suppression, impairing tyrosine kinase signaling, cell adhesion and migration

Inter-alpha-trypsin inhibitor heavy chain 5 (ITIH5) has been identified as a metastasis suppressor gene in pancreatic cancer. Here, we analyzed ITIH5 promoter methylation and protein expression in The Cancer Genome Atlas (TCGA) dataset and three tissue microarray cohorts (n = 618), respectively. Cellular effects, including cell migration, focal adhesion formation and protein tyrosine kinase activity, induced by forced ITIH5 expression in pancreatic cancer cell lines were studied in stable transfectants. ITIH5 promoter hypermethylation was associated with unfavorable prognosis, while immunohistochemistry demonstrated loss of ITIH5 in the metastatic setting and worsened overall survival. Gain-of-function models showed a significant reduction in migration capacity, but no alteration in proliferation. Focal adhesions in cells re-expressing ITIH5 exhibited a smaller and more rounded phenotype, typical for slow-moving cells. An impressive increase of acetylated alpha-tubulin was observed in ITIH5-positive cells, indicating more stable microtubules. In addition, we found significantly decreased activities of kinases related to focal adhesion. Our results indicate that loss of ITIH5 in pancreatic cancer profoundly affects its molecular profile: ITIH5 potentially interferes with a variety of oncogenic signaling pathways, including the PI3K/AKT pathway. This may lead to altered cell migration and focal adhesion formation. These cellular alterations may contribute to the metastasis-inhibiting properties of ITIH5 in pancreatic cancer.</p

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images in 11,671 patients across nine cancer types. We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology

Syndromale Ziliopathien : molekulargenetische Aufklärung als methodische Herausforderung

The autosomal recessive primary ciliopathies Meckel syndrome (MKS), Joubert syndrome (JS), Joubert syndrome related disorders (JSRD) and Bardet-Biedl syndrome are in the focus of scientific research because of their complex genetics. As a common feature all involved gene products are found in the primary cilia or associated organelles. In respect to the diagnosis of the different entities, their clinical and genetic overlap hampers a distinct clinical diagnosis. Therefore an efficient molecular genetic algorithm to identify the underlying gene defects is of high practical relevance in the clinical daily routine. Knowing the genetic cause has implications for therapeutic management of the patients and opens up the possibility of prenatal diagnosis. To assess the gene specific and overall detection rates for the so far known involved disease genes the clinical and molecular genetic data of the literature were reviewed. Additionally the data of 215 collected patients of the Institute of Human Genetics RWTH Aachen were ascertained and evaluated, as a first step to develop a diagnostic algorithm. The current development of high throughput and high resolution molecular genetic methods offers new possibilities to analyze the involved genes. Therefore the use of SNP arrays in consanguineous families and of benchtop instrument based next generation sequencing (NGS) approaches was tested in a subset of DNA samples to improve a Sanger sequencing based gene by gene analysis. A comprehensive diagnostic algorithm including the NGS and array observations is presented. Furthermore as part of this work the clinical and genetic data of the patients were evaluated to uncover new genotype phenotype correlations. Quantitative conclusions regarding the frequency of the single entities in relation to the molecular genetic findings are hardly possible because of the rareness of the particular observations. However, it can be concluded from the own studies that the spectrum of genes involved in the so called Meckel-like phenotype has to be extended to BBS1 and BBS12. Additionally MKS1 mutations – so far only related to an MKS phenotype – could be demonstrated as cause of JS/JSRD

Mutation analysis of multiple pilomatricomas in a patient with myotonic dystrophy type 1 suggests a DM1-associated hypermutation phenotype.

Myotonic dystrophy type 1 (DM1) is an inherited neuromuscular disease which results from an expansion of repetitive DNA elements within the 3' untranslated region of the DMPK gene. Some patients develop multiple pilomatricomas as well as malignant tumors in other tissues. Mutations of the catenin-β gene (CTNNB1) could be demonstrated in most non-syndromic pilomatricomas. In order to gain insight into the molecular mechanisms which might be responsible for the occurrence of multiple pilomatricomas and cancers in patients with DM1, we have sequenced the CTNNB1 gene of four pilomatricomas and of one pilomatrical carcinoma which developed in one patient with molecularly proven DM1 within 4 years. We further analyzed the pilomatrical tumors for microsatellite instability as well as by NGS for mutations in 161 cancer-associated genes. Somatic and independent point-mutations were detected at typical hotspot regions of CTNNB1 (S33C, S33F, G34V, T41I) while one mutation within CTNNB1 represented a duplication mutation (G34dup.). Pilomatricoma samples were analyzed for microsatellite instability and expression of mismatch repair proteins but no mutated microsatellites could be detected and expression of mismatch repair proteins MLH1, MSH2, MSH6, PMS2 was not perturbed. NGS analysis only revealed one heterozygous germline mutation c.8494C>T; p.(Arg2832Cys) within the ataxia telangiectasia mutated gene (ATM) which remained heterozygous in the pilomatrical tumors. The detection of different somatic mutations in different pilomatricomas and in the pilomatrical carcinoma as well as the observation that the patient developed multiple pilomatricomas and one pilomatrical carcinoma over a short time period strongly suggest that the patient displays a hypermutation phenotype. This hypermutability seems to be tissue and gene restricted. Simultaneous transcription of the mutated DMPK gene and the CTNNB1 gene in cycling hair follicles might constitute an explanation for the observed tissue and gene specificity of hypermutability observed in DM1 patients. Elucidation of putative mechanisms responsible for hypermutability in DM1 patients requires further research