Automated detection of the HER2 gene amplification status in Fluorescence in situ hybridization images for the diagnostics of cancer tissues.

The human epidermal growth factor receptor 2 (HER2) gene amplification status is a crucial marker for evaluating clinical therapies of breast or gastric cancer. We propose a deep learning-based pipeline for the detection, localization and classification of interphase nuclei depending on their HER2 gene amplification state in Fluorescence in situ hybridization (FISH) images. Our pipeline combines two RetinaNet-based object localization networks which are trained (1) to detect and classify interphase nuclei into distinct classes normal, low-grade and high-grade and (2) to detect and classify FISH signals into distinct classes HER2 or centromere of chromosome 17 (CEN17). By independently classifying each nucleus twice, the two-step pipeline provides both robustness and interpretability for the automated detection of the HER2 amplification status. The accuracy of our deep learning-based pipeline is on par with that of three pathologists and a set of 57 validation images containing several hundreds of nuclei are accurately classified. The automatic pipeline is a first step towards assisting pathologists in evaluating the HER2 status of tumors using FISH images, for analyzing FISH images in retrospective studies, and for optimizing the documentation of each tumor sample by automatically annotating and reporting of the HER2 gene amplification specificities

Shotgun lipidomics-based characterization of the landscape of lipid metabolism in colorectal cancer.

Solid tumors are characterized by global metabolic alterations which contribute to their growth and progression. Altered gene expression profiles and plasma lipid composition suggested a role for metabolic reprogramming in colorectal cancer (CRC) development. However, a conclusive picture of CRC-associated lipidome alterations in the tumor tissue has not emerged. Here, we determined molar abundances of 342 species from 20 lipid classes in matched biopsies of CRC and adjacent normal mucosa. We demonstrate that in contrast to previous reports, CRC shows a largely preserved lipidome composition that resembles that of normal colonic mucosa. Important exceptions include increased levels of lyso-phosphatidylinositols in CRC and reduced abundance of ether phospholipids in advanced stages of CRC. As such, our observations challenge the concept of widespread alterations in lipid metabolism in CRC and rather suggest changes in the cellular lipid profile that are limited to selected lipids involved in signaling and the scavenging of reactive oxygen species