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Biology subjects

Bruns, C. J.

Publications and source records attributed to Bruns, C. J..

3 recordsLinked to original sources

Gene expression adaptation of metastases to their host tissue

The adaptation of metastatic cells to their host tissue critically determines the pathogenicity of a cancer and therefore patient survival. Yet, it remains elusive to what extent the host environment drives gene expression programs in metastatic cells. Here we identify adaptive mechanisms that enable metastases to establish themselves in a novel tissue context. We performed single-cell RNA-sequencing on malignant and benign tissue samples from untreated donors with colorectal adenocarcinoma and liver metastasis to deduce tissue adaptive expression patterns. A novel computational approach identified genes and pathways that consistently adapted to the host tissue at the transition from the primary tumor to the paired metastasis across donors. This analysis revealed that the majority of expression changes in the metastasis reflect an expression signature reminiscent of benign liver epithelial cells. Cellular processes adapting to the liver environment include basic cellular functions such as energy metabolism, as well as tissue-specific pathways such as the regulation of lipid metabolism by PPAR-. These adaptations potentially increase the pathogenicity of the metastatic cells and may provide new therapeutic strategies.

cancer biology↗

Single cell analysis of Barrett's esophagus and carcinoma reveals cell types conferring risk via genetic predisposition

Inherited genetic variants contribute to Barretts esophagus (BE) and esophageal adenocarcinoma (EAC) but it is unknown which cell types are involved in this process. We performed single cell RNA-sequencing of BE, EAC and paired normal tissues and integrated data of a genome-wide association study to determine cell type-specific genetic risk and cellular processes that contribute to BE and EAC. The analysis revealed that EAC development is driven to a greater extent by local cellular processes than BE development. One cell type of BE origin (BE-EAC) and cellular processes that control the differentiation of columnar cells are of particular relevance for EAC development. Further, specific subtypes of fibroblasts and endothelial cells contribute to BE and EAC development, while dendritic cells and CD4+ memory T cells contribute exclusively to BE development. The diagnostic use of markers characterizing the identified cell types and cellular processes should be explored in future for EAC prediction.

cancer biology↗

Predicting the HER2 status in esophageal cancer from tissue microarrays using convolutional neural networks

BackgroundFast and accurate diagnostics are key for personalized medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted therapies which can prolong lives. In this work we focus on the automatic identification of gastroesophageal adenocarcinoma (GEA) patients that qualify for a personalized therapy targeting epidermal growth factor receptor 2 (HER2). We present a deep learning method for scoring microscopy images of GEA for the presence of HER2 overexpression. MethodsOur method is based on convolutional neural networks (CNNs) trained on a rich dataset of 1,602 patient samples and tested on an independent set of 307 patient samples. We incorporated an attention mechanism in the CNN architecture to identify the tissue regions in these patient cases which the network has detected as important for the prediction outcome. Our solution allows for direct automated detection of HER2 in immunohistochemistry-stained tissue slides without the need for manual assessment and additional costly in situ hybridization (ISH) tests. ResultsWe show accuracy of 0.94, precision of 0.97, and recall of 0.95. Importantly, our approach offers accurate predictions in cases that pathologists cannot resolve, requiring additional ISH testing. We confirmed our findings in an independent dataset collected in a different clinical center. ConclusionsWe demonstrate that our approach not only automates an important diagnostic process for GEA patients but also paves the way for the discovery of new morphological features that were previously unknown for GEA pathology.

bioinformatics↗