Interference of tumour mutational burden with outcome of patients with head and neck cancer treated with definitive chemoradiation: a multicentre retrospective study of the German Cancer Consortium Radiation Oncology Group.

BACKGROUND: Tumour mutational burden (TMB) estimated from whole exome sequencing or comprehensive gene panels has previously been established as predictive factor of response to immune checkpoint inhibitors (ICIs). Its predictive value for the efficacy of concurrent chemoradiation (cCRTX), a potential combination partner of ICI, remains unknown. METHODS: The accuracy of TMB estimation by an in-house 327-gene panel was established in the Cancer Genome Atlas (TCGA) head and neck squamous cell carcinoma (HNSCC) data set. Interference of TMB with outcome after cCRTX was determined in a multicentre cohort of patients with locally advanced HNSCC uniformly treated with cCRTX. Targeted next-generation sequencing was successfully applied in 101 formalin-fixed, paraffin-embedded pretreatment tumour samples. In a subset of cases (n = 40), tumour RNA was used for immune-related gene expression profiling by the nanoString platform. TMB was correlated with TP53 genotype, human papilloma virus (HPV) status, immune expression signatures and survival parameters. Results were validated in the TCGA HNSCC cohort. RESULTS: A high accuracy of TMB estimation by the 327-gene panel was established. High TMB was significantly associated with an increased prevalence of TP53 mutations and immune gene expression patterns unrelated to T cell-inflamed gene expression profiles. Kaplan-Meier analysis revealed significantly reduced overall survival in the patient group with high TMB (hazard ratio for death: 1.79, 95% confidence interval: 1.02-3.14; P = 0.042) which remained significant after correcting for confounding factors in the multivariate model. The prognostic value of TMB was confirmed in the TCGA HNSCC cohort. CONCLUSION: High TMB identifies HNSCC patients with poor outcome after cCRTX who might preferentially benefit from CRTX-ICI combinations.

Functional connectivity analysis using whole brain and regional network metrics in MS patients.

In the present study we investigated brain network connectivity differences between patients with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC) as derived from functional resonance magnetic imaging (fMRI) using graph theory. Resting state fMRI data of 18 RRMS patients (12 female, mean age ± SD: 42 ± 12.06 years) and 25 HC (8 female, 29.2 ± 5.38 years) were analyzed. In order to obtain information of differences in entire brain network, we focused on both, local and global network connectivity parameters. And the regional connectivity differences were assessed using regional network parameters. RRMS patients presented a significant increase of modularity in comparison to HC, pointing towards a network structure with densely interconnected nodes within one module, while the number of connections with other modules outside decreases. This higher decomposable network favours cost-efficient local information processing and promotes long-range disconnection. In addition, at the regional anatomical level, the network parameters clustering coefficient and local efficiency were increased in the insula, the superior parietal gyrus and the temporal pole. Our study indicates that modularity as derived from fMRI can be seen as a characteristic connectivity feature that is increased in MS patients compared to HC. Furthermore, specific anatomical regions linked to perception, motor function and cognition were mainly involved in the enhanced local information processing.

Network effects and pathways in Deep brain stimulation in Parkinson's disease.

Deep brain stimulation of subthalamic nucleus (STN-DBS) became a standard therapeutic option in Parkinson's disease (PD), even though the underlying modulated network of STN-DBS is still poorly described. Probabilistic tractography and connectivity analysis as derived from diffusion tensor imaging (DTI) were performed together with modelling of implanted electrode positions and linked postoperative clinical outcome. Fifteen patients with idiopathic PD without dementia were selected for DBS treatment. After pre-processing, probabilistic tractography was run from cortical and subcortical seeds of the hypothesized network to targets represented by the positions of the active DBS contacts. The performed analysis showed that the projections of the stimulation site to supplementary motor area (SMA) and primary motor cortex (M1) are mainly involved in the network effects of STN-DBS. An involvement of the "hyperdirected pathway" and a clear delimitation of the cortico-spinal tract were demonstrated. This study shows the effects of STN-DBS in PD distinctly rely on the network connections of the stimulated region to M1 and SMA, motor and premotor regions.

[Current aspects of therapy conversion for multiple sclerosis]. / Aktuelles zur Therapieumstellung bei Multipler Sklerose.

In recent years the approval of new substances has led to a substantial increase in the number of course-modifying immunotherapies available for multiple sclerosis. Therapy conversion therefore represents an increasing challenge. The treatment options sometimes show complex adverse effect profiles and necessitate a long-term and comprehensive monitoring. This article presents an overview of therapy conversion of immunotherapies for multiple sclerosis in accordance with the recommendations of the Disease-Related Competence Network for Multiple Sclerosis and the German Multiple Sclerosis Society as well as the guidelines on diagnostics and therapy for multiple sclerosis of the German Society of Neurology and the latest research results. At the present point in time it should be noted that no studies have been carried out for most of the approaches for therapy conversion given here; however, the recommendations are based on theoretical considerations and therefore correspond to recommendations at the level of expert consensus, which is currently essential for the clinical daily routine.

Testing different ICA algorithms and connectivity analyses on MS patients.

Multiple sclerosis (MS) is a progressive neurological disorder that affects the central nervous system. Functional magnetic resonance imaging (fMRI) has been employed to track the course and disease progression in patients with MS. The two main aims of this study were to apply in a data-driven approach the independent component analysis (ICA) in the spatial domain to depict the active sources and to look at the effective connectivity between the identified spatial sources. Several ICA algorithms have been proposed for fMRI data analysis. In this study, we aimed to test two well characterized algorithms, namely, the fast ICA and the complex infomax algorithms, followed by two effective connectivity algorithms, namely, Granger causality (GC) and generalized partial directed coherence (GPDC), to illustrate the connections between the spatial sources in patients with MS. The results obtained from the ICA analyses showed the involvement of the default mode network sources. The connectivity analyses depicted significant changes between the two applied algorithms. The significance of this study was to demonstrate the robustness of the analyzed algorithms in patients with MS and to validate them before applying them on larger datasets of patients with MS.