GALAD Score Detects Early-Stage Hepatocellular Carcinoma in a European Cohort of Chronic Hepatitis B and C Patients.

Despite vaccination programs and direct antiviral treatments, the incidence of virus-related hepatocellular carcinoma (HCC) remains high, while ultrasound-based detection rates for early-stage HCC is continuously low. To address this insufficiency, we set out to characterize whether the GALAD score, which incorporates gender, age, and serum levels of AFP, AFP isoform L3 (AFP-L3), and des-gamma-carboxy-prothrombin (DCP), can improve early-stage HCC detection in a Caucasian HBV/HCV cohort. In a retrospective German single-center study, 182 patients with HBV, 223 with HCV and 168 with other etiology (OE) of chronic liver disease (CLD) were enrolled. HCC was confirmed in 52 HBV, 84 HCV and 60 OE CLD patients. The diagnostic performance of the single biomarkers in HCC detection was compared to the GALAD model. At initial diagnosis, most patients were at (very) early BCLC 0 (n = 14/7%) or A (n = 56/29%) or intermediate stage BCLC B (n = 93/47%) HCC in all three subgroups. In the BCLC 0/A cohort, GALAD exhibited an AUC of 0.94 discriminating HCC from non-HCC, surpassing AFP (AUC 0.86), AFP-L3 (AUC 0.83) and DCP (AUC 0.83). In the HBV population, GALAD achieved an AUC of 0.96, in HCV an AUC of 0.98 and in OE an AUC of 0.99, clearly superior to the biomarkers alone. Furthermore, in HCV patients GALAD showed a significantly higher specificity (89%) versus AFP (64%) alone. In chronic viral hepatitis, the GALAD model showed superior performance in detection of early-stage HCC, while exhibiting higher specificity in HCV patients compared to AFP alone. We conclude that the GALAD score shows potential for HCC surveillance in Caucasian HBV/HCV patients.

Classification of inflammatory bowel diseases by means of Raman spectroscopic imaging of epithelium cells.

We report on a Raman microspectroscopic characterization of the inflammatory bowel diseases (IBD) Crohn's disease (CD) and ulcerative colitis (UC). Therefore, Raman maps of human colon tissue sections were analyzed by utilizing innovative chemometric approaches. First, support vector machines were applied to highlight the tissue morphology (=Raman spectroscopic histopathology). In a second step, the biochemical tissue composition has been studied by analyzing the epithelium Raman spectra of sections of healthy control subjects (n=11), subjects with CD (n=14), and subjects with UC (n=13). These three groups exhibit significantly different molecular specific Raman signatures, allowing establishment of a classifier (support-vector-machine). By utilizing this classifier it was possible to separate between healthy control patients, patients with CD, and patients with UC with an accuracy of 98.90%. The automatic design of both classification steps (visualization of the tissue morphology and molecular classification of IBD) paves the way for an objective clinical diagnosis of IBD by means of Raman spectroscopy in combination with chemometric approaches.

Classification of HER2 receptor status in breast cancer tissues by MALDI imaging mass spectrometry.

Clinical laboratory testing for HER2 status in breast cancer tissues is critically important for therapeutic decision making. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a powerful tool for investigating proteins through the direct and morphology-driven analysis of tissue sections. We hypothesized that MALDI-IMS may determine HER2 status directly from breast cancer tissues. Breast cancer tissues (n = 48) predefined for HER2 status were subjected to MALDI-IMS, and protein profiles were obtained through direct analysis of tissue sections. Protein identification was performed by tissue microextraction and fractionation followed by top-down tandem mass spectrometry. A discovery and an independent validation set were used to predict HER2 status by applying proteomic classification algorithms. We found that specific protein/peptide expression changes strongly correlated with the HER2 overexpression. Among these, we identified m/z 8404 as cysteine-rich intestinal protein 1. The proteomic signature was able to accurately define HER2-positive from HER2-negative tissues, achieving high values for sensitivity of 83%, for specificity of 92%, and an overall accuracy of 89%. Our results underscore the potential of MALDI-IMS proteomic algorithms for morphology-driven tissue diagnostics such as HER2 testing and show that MALDI-IMS can reveal biologically significant molecular details from tissues which are not limited to traditional high-abundance proteins.