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.
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.
Objective
Clinical trials with immune checkpoint inhibitors (ICI) in adrenocortical carcinoma (ACC) have yielded contradictory results. We aimed to evaluate treatment response and safety of ICI in ACC in a real-life setting.
Design
Retrospective cohort study of 54 patients with advanced ACC receiving ICI as compassionate use at six German reference centres between 2016 and 2022.
Methods
Objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS) and treatment-related adverse events (TRAE) were assessed.
Results
In 52 patients surviving at least 4 weeks after initiation of ICI, ORR was 13.5% (6-26) and DCR 24% (16-41). PFS was 3.0 months (95%CI 2.3-3.7). In all patients, median OS was 10.4 months (3.8-17). 17 TRAE occurred in 15 patients, which was associated with a longer PFS of 5.5 (1.9-9.2) vs. 2.5 (2.0- 3.0) months (HR 0.29, 95%CI 0.13-0.66, p=0.001) and OS of 28.2 (9.5-46.8) vs. 7.0 (4.1-10.2) months (HR 0.34, 95%CI 0.12-0.93). Positive tissue staining for programmed cell death ligand 1 (PD-L1) was associated with a longer PFS of 3.2 (2.6-3.8) vs. 2.3 (1.6-3.0, p<0.05) months. Adjusted for concomitant mitotane use, treatment with nivolumab was associated with lower risk of progression (HR 0.36, 0.15-0.90) and death (HR 0.20, 0.06-0.72) compared to pembrolizumab.
Conclusions
In the real-life setting we observe a response comparable to other second-line therapies and an acceptable safety profile in ACC patients receiving different ICI. The relevance of PD-L1 as a marker of response and the potentially more favourable outcome in nivolumab treated patients require confirmation.
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