Patterns of Carbon-Bound Exogenous Compounds Impact Disease Pathophysiology in Lung Cancer Subtypes in Different Ways.

Carbon-bound exogenous compounds, such as polycyclic aromatic hydrocarbons (PAHs), tobacco-specific nitrosamines, aromatic amines, and organohalogens, are known to affect both tumor characteristics and patient outcomes in lung squamous cell carcinoma (LUSC); however, the roles of these compounds in lung adenocarcinoma (LUAD) remain unclear. We analyzed 11 carbon-bound exogenous compounds in LUAD and LUSC samples using in situ high mass-resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging and performed a cluster analysis to compare the patterns of carbon-bound exogenous compounds between these two lung cancer subtypes. Correlation analyses were conducted to investigate associations among exogenous compounds, endogenous metabolites, and clinical data, including patient survival outcomes and smoking behaviors. Additionally, we examined differences in exogenous compound patterns between normal and tumor tissues. Our analyses revealed that PAHs, aromatic amines, and organohalogens were more abundant in LUAD than in LUSC, whereas the tobacco-specific nitrosamine nicotine-derived nitrosamine ketone was more abundant in LUSC. Patients with LUAD and LUSC could be separated according to carbon-bound exogenous compound patterns detected in the tumor compartment. The same compounds had differential impacts on patient outcomes, depending on the cancer subtype. Correlation and network analyses indicated substantial differences between LUAD and LUSC metabolomes, associated with substantial differences in the patterns of the carbon-bound exogenous compounds. These data suggest that the contributions of these carcinogenic compounds to cancer biology may differ according to the cancer subtypes

MALDI mass spectrometry imaging - Diagnostic pathways and metabolites for renal tumor entities

BACKGROUND Correct tumor subtyping of primary renal tumors is essential for treatment decision in daily routine. Most of the tumors can be classified on morphology alone. Nevertheless, some diagnoses are difficult and further investigations are needed for correct tumor subtyping. Beside histochemical investigations high mass resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can detect new diagnostic biomarkers and hence improve the diagnostic. PATIENTS AND METHODS Formalin-fixed paraffin embedded (FFPE) tissue specimens from clear cell renal cell carcinoma (ccRCC, n=552), papillary RCC (pRCC, n=122), chromophobe RCC (chRCC, n=108) and renal Oncocytoma (rO, n=71) were analyzed by high mass resolution matrix-assisted laser desorption/ionization (MALDI) fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometry imaging (MSI). SPACiAL pipeline was executed for automated co-registration of histological and molecular features. Pathway enrichment and pathway topology analysis were performed to determine significant differences between RCC subtypes. RESULTS We discriminated the four histological subtypes (ccRCC, pRCC, chRCC and rO) and established the subtype specific pathways and metabolic profiles. RO showed an enrichment of pentose phosphate, taurine and hypotaurine, glycerophospholipid, amino sugar and nucleotide sugar, fructose and mannose, glycine, serine and threonine pathways. ChRCC is defined by enriched pathways including the amino sugar and nucleotide sugar, fructose and mannose, glycerophospholipid, taurine and hypotaurine, glycine, serine and threonine pathways. Pyrimidine, amino sugar and nucleotide sugar, glycerophospholipid and glutathione pathways are enriched in ccRCC. Furthermore, we detected enriched phosphatidylinositol and glycerophospholipid pathways in pRCC. CONCLUSION In summary, we performed a classification system with a mean accuracy in tumor discrimination of 85,13%. Furthermore, we detected tumor specific biomarkers for the four most common primary renal tumors by MALDI-MSI. This method is a useful tool in differential diagnosis and in biomarker detection

Metabolic heterogeneity in adrenocortical carcinoma impacts patient outcomes

Spatially resolved metabolomics enables the investigation of tumoral metabolites in situ. Inter- and intratumor heterogeneity are key factors associated with patient outcomes. Adrenocortical carcinoma (ACC) is an exceedingly rare tumor associated with poor survival. Its clinical prognosis is highly variable, but the contributions of tumor metabolic heterogeneity have not been investigated thus far to our knowledge. An in-depth understanding of tumor heterogeneity requires molecular feature-based identification of tumor subpopulations associated with tumor aggressiveness. Here, using spatial metabolomics by high-mass resolution MALDI Fourier transform ion cyclotron resonance mass spectrometry imaging, we assessed metabolic heterogeneity by de novo discovery of metabolic subpopulations and Simpson's diversity index. After identification of tumor subpopulations in 72 patients with ACC, we additionally performed a comparison with 25 tissue sections of normal adrenal cortex to identify their common and unique metabolic subpopulations. We observed variability of ACC tumor heterogeneity and correlation of high metabolic heterogeneity with worse clinical outcome. Moreover, we identified tumor subpopulations that served as independent prognostic factors and, furthermore, discovered 4 associated anticancer drug action pathways. Our research may facilitate comprehensive understanding of the biological implications of tumor subpopulations in ACC and showed that metabolic heterogeneity might impact chemotherapy

Pharmako- und/oder Psychotherapie bei posttraumatischer Belastungsstörung: Systematischer Überblick zu Therapieansätzen und ihrer Effektivität

Die psychotherapeutischen Behandlungsansätze der posttraumatischen Belastungsstörung („posttraumatic stress disorder“, PTSD) wurden in den letzten 20 Jahren beständig weiterentwickelt und überprüft. Ihre positiven Effekte sind mittlerweile weitgehend gut und übereinstimmend belegt. Der Nutzen pharmakologischer Behandlungsmöglichkeiten ist weniger gut evaluiert, und die Datenlage ist widersprüchlicher. Der vorliegende Beitrag präsentiert einen Überblick über den gegenwärtigen Forschungsstand der störungsorientierten psychotherapeutischen und pharmakologischen Ansätze der PTSD und deren Effektivität. Zunächst erfolgt ein Überblick über die Metaanalysen der letzten 10 Jahre zur Effektivität der Psychotherapie und der Pharmakotherapie der PTSD. Im Weiteren wird die aktuelle Datenlage aus Vergleichsstudien zu Pharmako- und Psychotherapie vorgestellt. Die präsentierten Studien zeigen durchgängig relativ geringe Effektstärken bei Pharmakotherapie als Monotherapie (zumeist placebokontrolliert) und relativ geringe Effektstärkengewinne in Verbindung mit Psychotherapie gegenüber Psychotherapie allein. Beim Vergleich Psychotherapie vs. Pharmakotherapie bestätigt die aktuelle Studienlage keine Äquivalenz bezüglich der Effekte. Auf der Basis der aktuellen Studienlage erscheint es angezeigt, dass Fachgesellschaften eventuell noch fortbestehende Empfehlungen der Pharmakotherapie als Behandlung erster Wahl einer Revision unterziehen und generell pharmakotherapeutische Optionen mit größerer Zurückhaltung vertreten. Abstract Over the last 20 years psychotherapeutic treatment approaches for posttraumatic stress disorder (PTSD) have constantly been refined and reviewed. Nowadays, the positive effects of psychotherapy in PTSD are consistent across studies and well-documented; however, the benefits of pharmacological treatment approaches in PTSD are less well evaluated and the available data are more inconsistent. This article provides an overview over the current state of research on the disorder-oriented psychotherapeutic and pharmacological approaches in PTSD along with their effectiveness. Firstly, an overview of meta-analyses published in the last 10 years on the effectiveness of psychotherapy and pharmacotherapy of PTSD is presented. Furthermore, the currently available literature on comparative studies regarding pharmacotherapy and psychotherapy in PTSD is summarized. All of the presented studies only show relatively small effect sizes for pharmacotherapy as monotherapy (mostly placebo controlled) and relatively small gains in effect size when combined with psychotherapy as opposed to psychotherapy alone. The current study situation confirms that there is no equivalency with respect to the effects when comparing psychotherapy versus pharmacotherapy in PTSD. On the basis of the current state of research, the potentially still persisting recommendation by various expert associations to consider the pharmacotherapy of PTSD as first-line treatment should be revised. In general, pharmacotherapeutic options in PTSD should be advocated with restraint

MET as resistance factor for afatinib therapy and motility driver in gastric cancer cells.

The therapeutic options for advanced gastric cancer are still limited. Several drugs targeting the epidermal growth factor receptor family have been developed. So far, the HER2 antibody trastuzumab is the only drug targeting the HER-family that is available to gastric cancer patients. The pan-HER inhibitor afatinib is currently investigated in clinical trials and shows promising results in cell culture experiments and patient-derived xenograft (PDX) models. However, some cell lines do not respond to afatinib treatment. The determination of resistance factors in these cell lines can help to find the best treatment option for gastric cancer patients. In this study, we analyzed the role of MET as a resistance factor for afatinib therapy in a gastric cancer cell line. MET expression in afatinib-resistant MET-amplified Hs746T cells was reduced by means of siRNA transfection. The effects of MET knockdown on signal transduction, cell proliferation and motility were examined. In addition to the manual assessment of cell motility, a computational motility analysis involving parameters such as (approximate) average speed, displacement entropy or radial effectiveness was realized. Moreover, the impact of afatinib was compared between MET knockdown cells and control cells. MET knockdown in Hs746T cells resulted in impaired signal transduction and reduced cell proliferation and motility. Moreover, the afatinib resistance of Hs746T cells was reversed after MET knockdown. Therefore, the amplification of MET is confirmed as a resistance factor in gastric cancer cells. Whether MET is a useful resistance marker for afatinib therapy or other HER-targeting drugs in patients should be investigated in clinical trials

Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients.

The response to neoadjuvant therapy can vary widely between individual patients. Histopathological tumor regression grading (TRG) is a strong factor for treatment response and survival prognosis of esophageal adenocarcinoma (EAC) patients following neoadjuvant treatment and surgery. However, TRG systems are usually based on the estimation of residual tumor but do not consider stromal or metabolic changes after treatment. Spatial metabolomics analysis is a powerful tool for molecular tissue phenotyping but has not been used so far in the context of neoadjuvant treatment of esophageal cancer. We used imaging mass spectrometry to assess the potential of spatial metabolomics on tumor and stroma tissue for evaluating therapy response of neoadjuvant-treated EAC patients. With an accuracy of 89.7%, the binary classifier trained on spatial tumor metabolite data proved to be superior for stratifying patients when compared with histopathological response assessment, which had an accuracy of 70.5%. Sensitivities and specificities for the poor and favorable survival patient groups ranged from 84.9% to 93.3% using the metabolic classifier and from 62.2% to 78.1% using TRG. The tumor classifier was the only significant prognostic factor (HR 3.38, 95% CI 1.40-8.12, p = 0.007) when adjusted for clinicopathological parameters such as TRG (HR 1.01, 95% CI 0.67-1.53, p = 0.968) or stromal classifier (HR 1.86, 95% CI 0.81-4.25, p = 0.143). The classifier even allowed us to further stratify patients within the TRG1-3 categories. The underlying mechanisms of response to treatment have been figured out through network analysis. In summary, metabolic response evaluation outperformed histopathological response evaluation in our study with regard to prognostic stratification. This finding indicates that the metabolic constitution of the tumor may have a greater impact on patient survival than the quantity of residual tumor cells or the stroma. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland

Multimodal analysis of formalin-fixed and paraffin-embedded tissue by MALDI imaging and fluorescence in situ hybridization for combined genetic and metabolic analysis.

Multimodal tissue analyses that combine two or more detection technologies provide synergistic value compared to single methods and are employed increasingly in the field of tissue-based diagnostics and research. Here, we report a technical pipeline that describes a combined approach of HER2/CEP17 fluorescence in situ hybridization (FISH) analysis with MALDI imaging on the very same section of formalin-fixed and paraffin-embedded (FFPE) tissue. FFPE biopsies and a tissue microarray of human gastroesophageal adenocarcinoma were analyzed by MALDI imaging. Subsequently, the very same section was hybridized by HER2/CEP17 FISH. We found that tissue morphology of both, the biopsies and the tissue microarray, was unaffected by MALDI imaging and the HER2 and CEP17 FISH signals were analyzable. In comparison with FISH analysis of samples without MALDI imaging, we observed no difference in terms of fluorescence signal intensity and gene copy number. Our combined approach revealed adenosine monophosphate, measured by MALDI imaging, as a prognostic marker. HER2 amplification, which was detected by FISH, is a stratifier between good and poor patient prognosis. By integrating both stratification parameters on the basis of our combined approach, we were able to strikingly improve the prognostic effect. Combining molecules detected by MALDI imaging with the gene copy number detected by HER2/CEP17 FISH, we found a synergistic effect, which enhances patient prognosis. This study shows that our combined approach allows the detection of genetic and metabolic properties from one very same FFPE tissue section, which are specific for HER2 and hence suitable for prognosis. Furthermore, this synergism might be useful for response prediction in tumors