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Background & aims: Current guidelines recommend immunosuppressive treatment (IT) in patients with primary sclerosing cholangitis (PSC) and elevated aminotransferase levels more than five times the upper limit of normal and elevated serum IgG-levels above twice the upper limit of normal. Since there is no evidence to support this recommendation, we aimed to assess the criteria that guided clinicians in clinical practice to initiate IT in patients with previously diagnosed PSC.
Methods: This is a retrospective analysis of 196 PSC patients from seven German hepatology centers, of whom 36 patients had received IT solely for their liver disease during the course of PSC. Analyses were carried out using methods for competing risks.
Results: A simplified autoimmune hepatitis (AIH) score >5 (HR of 36, p<0.0001) and a modified histological activity index (mHAI) greater than 3/18 points (HR 3.6, p = 0.0274) were associated with the initiation of IT during the course of PSC. Of note, PSC patients who subsequently received IT differed already at the time of PSC diagnosis from those patients, who did not receive IT during follow-up: they presented with increased levels of IgG (p = 0.004) and more frequently had clinical signs of cirrhosis (p = 0.0002).
Conclusions: This is the first study which investigates the parameters associated with IT in patients with PSC in clinical practice. A simplified AIH score >5 and a mHAI score >3, suggesting concomitant features of AIH, influenced the decision to introduce IT during the course of PSC. In German clinical practice, the cutoffs used to guide IT may be lower than recommended by current guidelines.
The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen’s quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model’s Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.
Formalin‐fixed, paraffin‐embedded (FFPE ), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here, we developed a pressure cycling technology (PCT )‐SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PC a) and diffuse large B‐cell lymphoma (DLBCL ) samples. We show that the proteome patterns of FFPE PC a tissue samples and their analogous fresh‐frozen (FF ) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PC a tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between 1 and 15 years in a biobank and show a high degree of the proteome pattern similarity between two types of histological regions in small FFPE samples, that is, punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of a certain degree of biological variations. Applying the method to two independent DLBCL cohorts, we identified myeloperoxidase, a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PC a and DLBCL have been discovered.