Infection by the new corona virus strain SARS-CoV-2 and its related syndrome COVID-19 has been associated with more than two million deaths worldwide. Patients of higher age and with preexisting chronic health conditions are at an increased risk of fatal disease outcome. However, detailed information on causes of death and the contribution of pre-existing health conditions to death yet is missing, which can be reliably established by autopsy only. We performed full body autopsies on 26 patients that had died after SARS-CoV-2 infection and COVID-19 at the Charité University Hospital Berlin, Germany, or at associated teaching hospitals. We systematically evaluated causes of death and pre-existing health conditions. Additionally, clinical records and death certificates were evaluated. We report findings on causes of death and comorbidities of 26 decedents that had clinically presented with severe COVID-19. We found that septic shock and multi organ failure was the most common immediate cause of death, often due to suppurative pulmonary infection. Respiratory failure due to diffuse alveolar damage presented as immediate cause of death in fewer cases. Several comorbidities, such as hypertension, ischemic heart disease, and obesity were present in the vast majority of patients. Our findings reveal that causes of death were directly related to COVID-19 in the majority of decedents, while they appear not to be an immediate result of preexisting health conditions and comorbidities. We therefore suggest that the majority of patients had died of COVID-19 with only contributory implications of preexisting health conditions to the mechanism of death.

AimsAntibodies targeting the checkpoint molecules programmed cell death 1 (PD-1) and its ligand PD-L1 are emerging cancer therapeutics. We systematically investigated PD-1 and PD-L1 expression patterns in the poor-prognosis tumor entity high-grade serous ovarian carcinoma.MethodsPD-1 and PD-L1 protein expression was determined by immunohistochemistry on tissue microarrays from 215 primary cancers both in cancer cells and in tumor-infiltrating lymphocytes (TILs). mRNA expression was measured by quantitative reverse transcription PCR. An in silico validation of mRNA data was performed in The Cancer Genome Atlas (TCGA) dataset.ResultsPD-1 and PD-L1 expression in cancer cells, CD3+, PD-1+, and PD-L1+ TILs densities as well as PD-1 and PD-L1 mRNA levels were positive prognostic factors for progression-free (PFS) and overall survival (OS), with all factors being significant for PFS (p < 0.035 each), and most being significant for OS. Most factors also had prognostic value that was independent from age, stage, and residual tumor. Moreover, high PD-1+ TILs as well as PD-L1+ TILs densities added prognostic value to CD3+TILs (PD-1+: p = 0.002,; PD-L1+: p = 0.002). The significant positive prognostic impact of PD-1 and PD-L1 mRNA expression could be reproduced in the TCGA gene expression datasets (p = 0.02 and p < 0.0001, respectively).ConclusionsDespite their reported immune-modulatory function, high PD-1 and PD-L1 levels are indicators of a favorable prognosis in ovarian cancer. Our data indicate that PD-1 and PD-L1 molecules are biologically relevant regulators of the immune response in high-grade serous ovarian carcinoma, which is an argument for the evaluation of immune checkpoint inhibiting drugs in this tumor entity.

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Purpose Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix‐assisted laser desorption/ionization (MALDI) imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray. Experimental design Formalin‐fixed‐paraffin‐embedded tissue of 20 patients with ovarian clear‐cell, 14 low‐grade serous, 19 high‐grade serous ovarian carcinomas, and 14 serous borderline tumors are analyzed using MALDI‐Imaging. Classifications are computed by linear discriminant analysis (LDA), support vector machines with linear (SVM‐lin) and radial basis function kernels (SVM‐rbf), a neural network (NN), and a convolutional neural network (CNN). Results MALDI‐Imaging and machine learning methods result in classification of EOC histotypes with mean accuracy of 80% for LDA, 80% SVM‐lin, 74% SVM‐rbf, 83% NN, and 85% CNN. Based on sensitivity (69–100%) and specificity (90–99%), CCN and NN are most suited to EOC classification. Conclusion and clinical relevance The pilot study demonstrates the potential of MALDI‐Imaging derived proteomic classifiers in combination with machine learning algorithms to discriminate EOC histotypes. Applications may support the development of new prognostic parameters in the assessment of EOC.

Infection by the new corona virus strain SARS-CoV-2 and its related syndrome COVID-19 has caused several hundreds of thousands of deaths worldwide. Patients of higher age and with preexisting chronic health conditions are at an increased risk of fatal disease outcome. However, detailed information on causes of death and the contribution of comorbidities to death yet is missing. Here, we report autopsy findings on causes of death and comorbidities of 26 decedents that had clinically presented with severe COVID-19. We found that septic shock and multi organ failure was the most common immediate cause of death, often due to suppurative pulmonary infection. Respiratory failure due to diffuse alveolar damage presented as the most immediate cause of death in fewer cases. Several comorbidities, such as hypertension, ischemic heart disease, and obesity were present in the vast majority of patients. Our findings reveal that causes of death were directly related to COVID-19 in the majority of decedents, while they appear not to be an immediate result of preexisting health conditions and comorbidities. We therefore suggest that the majority of patients had died of COVID-19 with only contributory implications of preexisting health conditions to the mechanism of death.

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for the Randomized European Celecoxib Trial (REACT) Trial Management Group and Investigators IMPORTANCE Patients with breast cancer remain at risk of relapse after adjuvant therapy. Celecoxib has shown antitumor effects in preclinical models of human breast cancer, but clinical evidence is lacking.OBJECTIVE To evaluate the role of celecoxib as an addition to conventional therapy for women with ERBB2 (formerly HER2)-negative primary breast cancer. DESIGN, SETTING, AND PARTICIPANTSThe Randomized European Celecoxib Trial (REACT) was a phase 3, randomized, double-blind study conducted in 160 centers across the UK and Germany testing 2 years of adjuvant celecoxib vs placebo among 2639 patients recruited between January 19, 2007, and November 1, 2012, with follow-up 10 years after treatment completion. Eligible patients had completely resected breast cancer with local and systemic therapy according to local practice. Patients with ERBB2-positive or node-negative and T1, grade 1 tumors were not eligible. Randomization was in a 2:1 ratio between celecoxib or placebo. Statistical analysis was performed from May 5, 2019, to March 5, 2020.INTERVENTIONS Patients received celecoxib, 400 mg, or placebo once daily for 2 years. MAIN OUTCOMES AND MEASURESThe primary end point was disease-free survival (DFS), analyzed in the intention-to-treat population using Cox proportional hazards regression and log-rank analysis. Follow-up is complete.RESULTS A total of 2639 patients (median age, 55.2 years [range, 26.8-86.0 years]) were recruited; 1763 received celecoxib, and 876 received placebo. Most patients' tumors (1930 [73%]) were estrogen receptor positive or progesterone receptor positive and ERBB2 negative. A total of 1265 patients (48%) had node-positive disease, and 1111 (42%) had grade 3 tumors. At a median follow-up of 74.3 months (interquartile range, 61.4-93.6 years), DFS events had been reported for 487 patients (19%): 18% for those who received celecoxib (n = 323; 5-year DFS rate = 84%) vs 19% for those who received placebo (n = 164; 5-year DFS rate = 83%); the unadjusted hazard ratio was 0.97 (95% CI, 0.80-1.17; log-rank P = .75). Rates of toxic effects were low across both treatment groups, with no evidence of a difference. CONCLUSIONS AND RELEVANCEIn this randomized clinical trial, patients showed no evidence of a DFS benefit for 2 years' treatment with celecoxib compared with placebo as adjuvant treatment of ERBB2-negative breast cancer. Longer-term treatment or use of a higher dose of celecoxib may lead to a DFS benefit, but further studies would be required to test this possibility.