DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

Combined In Situ Illumination‐NMR‐UV/Vis Spectroscopy: A New Mechanistic Tool in Photochemistry

Synthetic applications in photochemistry are booming. Despite great progress in the development of new reactions, mechanistic investigations are still challenging. Therefore, we present a fully automated insitu combination of NMR spectroscopy, UV/Vis spectroscopy, and illumination to allow simultaneous and time-resolved detection of paramagnetic and diamagnetic species. This optical fiber-based setup enables the first acquisition of combined UV/Vis and NMR spectra in photocatalysis, as demonstrated on a conPET process. Furthermore, the broad applicability of combined UVNMR spectroscopy for light-induced processes is demonstrated on a structural and quantitative analysis of a photoswitch, including rate modulation and stabilization of transient species by temperature variation. Owing to the flexibility regarding the NMR hardware, temperature, and light sources, we expect wide-ranging applications of this setup in various research fields

Remote-Stereocontrol in Dienamine Catalysis: Z-Dienamine Preferences and Electrophile–Catalyst Interaction Revealed by NMR and Computational Studies

Catalysis with remote-stereocontrol provides special challenges in design and comprehension. One famous example is the dienamine catalysis, for which high ee values are reported despite insufficient shielding of the second double bond. Especially for dienamines with variable Z/E-ratios of the second double bond, no correlations to the ee values are found. Therefore, the structures, thermodynamics, and kinetics of dienamine intermediates in S-N-type reactions are investigated. The NMR studies show that the preferred dienamine conformation provides an effective shielding if large electrophiles are used. Calculations at SCS-MP2/CBS-level of theory and experimental data of the dienamine formation show kinetic preference for the Z-isomer of the second double bond and a slow isomerization toward the thermodynamically preferred E-isomer. Modulations of the rate-determining step, by variation of the concentration of the electrophile, allow the conversion of dienamines to be observed. With electrophiles, a faster reaction of Z- than of E-isomers is observed experimentally. Calculations corroborate these results by correlating ee values of three catalysts with the kinetics of the electrophilic attack and reveal the significance of CH-pi and stacking interactions in the transition states. Thus,, for the first time a comprehensive understanding of the remote stereocontrol in gamma-functionalization reactions of dienamines and an explanation to the "Z/E-dilemma" are presented. The combination of bulky catalyst subsystems and large electrophiles provides a shielding of one face and causes different reactivities of E/Z-dienamines in nucleophilic attacks from the other face. Kinetic preferences for the formation of Z-dienamines and their unfavorable thermodynamics support high ee values

iNNvestigate Neural Networks!

In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand and analyze actions and predictions, even of the most complex neural network architectures. Despite these arguments neural networks are often treated as black boxes. In the attempt to alleviate this short-coming many analysis methods were proposed, yet the lack of reference implementations often makes a systematic comparison between the methods a major effort. The presented library iNNvestigate addresses this by providing a common interface and out-of-thebox implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods. To demonstrate the versatility of iNNvestigate, we provide an analysis of image classifications for variety of state-of-the-art neural network architectures

Scoring of tumor-infiltrating lymphocytes:From visual estimation to machine learning

The extent of tumor-infiltrating lymphocytes (TILs), along with immunomodulatory ligands, tumor-mutational burden and other biomarkers, has been demonstrated to be a marker of response to immune-checkpoint therapy in several cancers. Pathologists have therefore started to devise standardized visual approaches to quantify TILs for therapy prediction. However, despite successful standardization efforts visual TIL estimation is slow, with limited precision and lacks the ability to evaluate more complex properties such as TIL distribution patterns. Therefore, computational image analysis approaches are needed to provide standardized and efficient TIL quantification. Here, we discuss different automated TIL scoring approaches ranging from classical image segmentation, where cell boundaries are identified and the resulting objects classified according to shape properties, to machine learning-based approaches that directly classify cells without segmentation but rely on large amounts of training data. In contrast to conventional machine learning (ML) approaches that are often criticized for their “black-box” characteristics, we also discuss explainable machine learning. Such approaches render ML results interpretable and explain the computational decision-making process through high-resolution heatmaps that highlight TILs and cancer cells and therefore allow for quantification and plausibility checks in biomedical research and diagnostics.SCOPUS: re.jinfo:eu-repo/semantics/publishe

LEF1 supports metastatic brain colonization by regulating glutathione metabolism and increasing ROS resistance in breast cancer

More than half of all brain metastases show infiltrating rather than displacing growth at the macro-metastasis/brain parenchyma interface (MMPI), a finding associated with shorter survival. The Lymphoid Enhancer-binding Factor-1 (LEF1) is an epithelial-mesenchymal transition (EMT) transcription factor that is commonly overexpressed in brain-colonizing cancer cells. Here, we overexpressed LEF1 in an in vivo breast cancer brain colonization model. It shortened survival, albeit without engaging EMT at the MMPI. By differential proteome analysis, we identified a novel function of LEF1 as a regulator of the glutathione (GSH) system, the principal cellular redox buffer. LEF1 overexpression also conferred resistance against therapeutic GSH depletion during brain colonization and improved management of intracellular ROS. We conclude that besides EMT, LEF1 facilitates metastasis by improving the anti-oxidative capacity of epithelial breast cancer cells, in particular during colonization of the brain parenchyma