Krankenkassenkommunikation

Reifegerste D, Schiller S, Leu J. Krankenkassenkommunikation. In: Rossmann C, Hastall MR, eds. Handbuch der Gesundheitskommunikation. Kommunikationswissenschaftliche Perspektiven. Wiesbaden: Springer Fachmedien Wiesbaden; 2019: 121-132.Die Kommunikation der gesetzlichen Krankenkassen ist vielfältig, komplex und von den gesetzlichen und strukturellen Rahmenbedingungen im Gesundheitswesen geprägt. Aufgrund ihrer Funktionen als Zahlungsinstanz und Vermittler zwischen Versicherten und Leistungserbringern weisen Krankenkassen Interaktionen mit verschiedenen Akteuren des Gesundheitswesens auf. Der Kontakt mit Versicherten spiegelt Konflikte in den Zielstellungen (wie Wettbewerbsdruck, Kostenmanagement und Leistungsversorgung) wider. Dies wird anhand der Kommunikation zur Mitgliedergewinnung, Kundenbindung und Versorgung aufgezeigt

8-Oxoguanine DNA Glycosylase (OGG1) Cys326 Variant: Increased Risk for Worse Outcome of Patients with Locally Advanced Rectal Cancer after Multimodal Therapy

Simple Summary In Western countries, the lifetime risk for rectal cancer is around 1.5%. Most patients are diagnosed with locally advanced stages. For these patients, multimodal treatment comprising radiotherapy, chemotherapy, and surgery has become the standard of care. Whereas excellent local control is achieved, still about one out of three dies from this disease. Cytotoxicity of radiochemotherapy substantially involves reactive oxygen species (ROS). In ROS-related genes, we selected eight inherited variants, for which the literature reports functional or medical effects and which occur frequently in the general population. These variants were assessed whether they impact the clinical outcome of patients with rectal cancer. We found that the OGG1 Cys326 variant, which affected 37% of the 287 patients in the sample, was strongly linked with a worse outcome, in particular cancer-specific survival. Screening for this variant may identify a particular risk subgroup of patients who may be considered for more intensified therapy and aftercare. Despite excellent loco-regional control by multimodal treatment of locally advanced rectal cancer, a substantial portion of patients succumb to this disease. As many treatment effects are mediated via reactive oxygen species (ROS), we evaluated the effect of single nucleotide polymorphisms (SNPs) in ROS-related genes on clinical outcome. Based on the literature, eight SNPs in seven ROS-related genes were assayed. Eligible patients (n = 287) diagnosed with UICC stage II/III rectal cancer were treated multimodally starting with neoadjuvant radiochemotherapy (N-RCT) according to the clinical trial protocols of CAO/ARO/AIO-94, CAO/ARO/AIO-04, TransValid-A, and TransValid-B. The median follow-up was 64.4 months. The Ser326Cys polymorphism in the human OGG1 gene affected clinical outcome, in particular cancer-specific survival (CSS). This effect was comparable in extent to the ypN status, an already established strong prognosticator for patient outcome. Homozygous and heterozygous carriers of the Cys326 variant (n = 105) encountered a significantly worse CSS (p = 0.0004 according to the log-rank test, p = 0.01 upon multiple testing adjustment). Cox regression elicited a hazard ratio for CSS of 3.64 (95% confidence interval 1.70-7.78) for patients harboring the Cys326 allele. In a multivariable analysis, the effect of Cys326 on CSS was preserved. We propose the genetic polymorphism Ser326Cys as a promising biomarker for outcome in rectal cancer

IDH/MGMT-driven molecular classification of low-grade glioma is a strong predictor for long-term survival

BACKGROUND Low-grade gliomas (LGGs) are rare brain neoplasms, with survival spanning up to a few decades. Thus, accurate evaluations on how biomarkers impact survival among patients with LGG require long-term studies on samples prospectively collected over a long period. METHODS The 210 adult LGGs collected in our databank were screened for IDH1 and IDH2 mutations (IDHmut), MGMT gene promoter methylation (MGMTmet), 1p/19q loss of heterozygosity (1p19qloh), and nuclear TP53 immunopositivity (TP53pos). Multivariate survival analyses with multiple imputation of missing data were performed using either histopathology or molecular markers. Both models were compared using Akaike's information criterion (AIC). The molecular model was reduced by stepwise model selection to filter out the most critical predictors. A third model was generated to assess for various marker combinations. RESULTS Molecular parameters were better survival predictors than histology (ΔAIC = 12.5, P< .001). Forty-five percent of studied patients died. MGMTmet was positively associated with IDHmut (P< .001). In the molecular model with marker combinations, IDHmut/MGMTmet combined status had a favorable impact on overall survival, compared with IDHwt (hazard ratio [HR] = 0.33, P< .01), and even more so the triple combination, IDHmut/MGMTmet/1p19qloh (HR = 0.18, P< .001). Furthermore, IDHmut/MGMTmet/TP53pos triple combination was a significant risk factor for malignant transformation (HR = 2.75, P< .05). CONCLUSION By integrating networks of activated molecular glioma pathways, the model based on genotype better predicts prognosis than histology and, therefore, provides a more reliable tool for standardizing future treatment strategies

IDH/MGMT-driven molecular classification of low-grade glioma is a strong predictor for long-term survival

BACKGROUND Low-grade gliomas (LGGs) are rare brain neoplasms, with survival spanning up to a few decades. Thus, accurate evaluations on how biomarkers impact survival among patients with LGG require long-term studies on samples prospectively collected over a long period. METHODS The 210 adult LGGs collected in our databank were screened for IDH1 and IDH2 mutations (IDHmut), MGMT gene promoter methylation (MGMTmet), 1p/19q loss of heterozygosity (1p19qloh), and nuclear TP53 immunopositivity (TP53pos). Multivariate survival analyses with multiple imputation of missing data were performed using either histopathology or molecular markers. Both models were compared using Akaike's information criterion (AIC). The molecular model was reduced by stepwise model selection to filter out the most critical predictors. A third model was generated to assess for various marker combinations. RESULTS Molecular parameters were better survival predictors than histology (ΔAIC = 12.5, P< .001). Forty-five percent of studied patients died. MGMTmet was positively associated with IDHmut (P< .001). In the molecular model with marker combinations, IDHmut/MGMTmet combined status had a favorable impact on overall survival, compared with IDHwt (hazard ratio [HR] = 0.33, P< .01), and even more so the triple combination, IDHmut/MGMTmet/1p19qloh (HR = 0.18, P< .001). Furthermore, IDHmut/MGMTmet/TP53pos triple combination was a significant risk factor for malignant transformation (HR = 2.75, P< .05). CONCLUSION By integrating networks of activated molecular glioma pathways, the model based on genotype better predicts prognosis than histology and, therefore, provides a more reliable tool for standardizing future treatment strategies

Tumour class prediction and discovery by microarray-based DNA methylation analysis

Aberrant DNA methylation of CpG sites is among the earliest and most frequent alterations in cancer. Several studies suggest that aberrant methylation occurs in a tumour type-specific manner. However, large-scale analysis of candidate genes has so far been hampered by the lack of high throughput assays for methylation detection. We have developed the first microarray-based technique which allows genome-wide assessment of selected CpG dinucleotides as well as quantification of methylation at each site. Several hundred CpG sites were screened in 76 samples from four different human tumour types and corresponding healthy controls. Discriminative CpG dinucleotides were identified for different tissue type distinctions and used to predict the tumour class of as yet unknown samples with high accuracy using machine learning techniques. Some CpG dinucleotides correlate with progression to malignancy, whereas others are methylated in a tissue-specific manner independent of malignancy. Our results demonstrate that genome-wide analysis of methylation patterns combined with supervised and unsupervised machine learning techniques constitute a powerful novel tool to classify human cancers