Publications by authors named "Christof von Kalle"

237 Publications

Deep learning can predict lymph node status directly from histology in colorectal cancer.

Eur J Cancer 2021 11 11;157:464-473. Epub 2021 Oct 11.

Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address:

Background: Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC).

Objectives: The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM).

Methods: Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set.

Results: On the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage.

Conclusion: Deep learning-based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.
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http://dx.doi.org/10.1016/j.ejca.2021.08.039DOI Listing
November 2021

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.

Eur J Cancer 2021 10 8;156:202-216. Epub 2021 Sep 8.

First Department of Dermatology, School of Medicine, Faculty of Health Sciences, Aristotle University, Thessaloniki, Greece.

Background: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice.

Objective: The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians.

Methods: PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included.

Results: A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images.

Conclusions: All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice.
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http://dx.doi.org/10.1016/j.ejca.2021.06.049DOI Listing
October 2021

SARS-CoV-2 infection and transmission in school settings during the second COVID-19 wave: a cross-sectional study, Berlin, Germany, November 2020.

Euro Surveill 2021 08;26(34)

The members of the BECOSS study group are listed under Investigators.

BackgroundSchool attendance during the COVID-19 pandemic is intensely debated.AimIn November 2020, we assessed SARS-CoV-2 infections and seroreactivity in 24 randomly selected school classes and connected households in Berlin, Germany.MethodsWe collected oro-nasopharyngeal swabs and blood samples, examining SARS-CoV-2 infection and IgG antibodies by RT-PCR and ELISA. Household members self-swabbed. We assessed individual and institutional prevention measures. Classes with SARS-CoV-2 infection and connected households were retested after 1 week.ResultsWe examined 1,119 participants, including 177 primary and 175 secondary school students, 142 staff and 625 household members. SARS-CoV-2 infection occurred in eight classes, affecting each 1-2 individuals. Infection prevalence was 2.7% (95% confidence interval (CI): 1.2-5.0; 9/338), 1.4% (95% CI: 0.2-5.1; 2/140), and 2.3% (95% CI: 1.3-3.8; 14/611) among students, staff and household members. Six of nine infected students were asymptomatic at testing. We detected IgG antibodies in 2.0% (95%CI: 0.8-4.1; 7/347), 1.4% (95% CI: 0.2-5.0; 2/141) and 1.4% (95% CI: 0.6-2.7; 8/576). Prevalence increased with inconsistent facemask-use in school, walking to school, and case-contacts outside school. For three of nine households with infection(s), origin in school seemed possible. After 1 week, no school-related secondary infections appeared in affected classes; the attack rate in connected households was 1.1%.ConclusionSchool attendance under rigorously implemented preventive measures seems reasonable. Balancing risks and benefits of school closures need to consider possible spill-over infection into households. Deeper insight is required into the infection risks due to being a schoolchild vs attending school.
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http://dx.doi.org/10.2807/1560-7917.ES.2021.26.34.2100184DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8393892PMC
August 2021

Safety, reactogenicity, and immunogenicity of homologous and heterologous prime-boost immunisation with ChAdOx1 nCoV-19 and BNT162b2: a prospective cohort study.

Lancet Respir Med 2021 11 13;9(11):1255-1265. Epub 2021 Aug 13.

Department of Infectious Diseases and Respiratory Medicine, Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany. Electronic address:

Background: Heterologous vaccine regimens have been widely discussed as a way to mitigate intermittent supply shortages and to improve immunogenicity and safety of COVID-19 vaccines. We aimed to assess the reactogenicity and immunogenicity of heterologous immunisations with ChAdOx1 nCov-19 (AstraZeneca, Cambridge, UK) and BNT162b2 (Pfizer-BioNtech, Mainz, Germany) compared with homologous BNT162b2 and ChAdOx1 nCov-19 immunisation.

Methods: This is an interim analysis of a prospective observational cohort study enrolling health-care workers in Berlin (Germany) who received either homologous ChAdOx1 nCov-19 or heterologous ChAdOx1 nCov-19-BNT162b2 vaccination with a 10-12-week vaccine interval or homologous BNT162b2 vaccination with a 3-week vaccine interval. We assessed reactogenicity after the first and second vaccination by use of electronic questionnaires on days 1, 3, 5, and 7. Immunogenicity was measured by the presence of SARS-CoV-2-specific antibodies (full spike-IgG, S1-IgG, and RBD-IgG), by an RBD-ACE2 binding inhibition assay (surrogate SARS-CoV-2 virus neutralisation test), a pseudovirus neutralisation assay against two variants of concerns (alpha [B.1.1.7] and beta [B.1.351]), and anti-S1-IgG avidity. T-cell reactivity was measured by IFN-γ release assay.

Findings: Between Dec 27, 2020, and June 14, 2021, 380 participants were enrolled in the study, with 174 receiving homologous BNT162b2 vaccination, 38 receiving homologous ChAdOx1 nCov-19 vaccination, and 104 receiving ChAdOx1 nCov-19-BNT162b2 vaccination. Systemic symptoms were reported by 103 (65%, 95% CI 57·1-71·8) of 159 recipients of homologous BNT162b2, 14 (39%, 24·8-55·1) of 36 recipients of homologous ChAdOx1 nCov-19, and 51 (49%, 39·6-58·5) of 104 recipients of ChAdOx1 nCov-19-BNT162b2 after the booster immunisation. Median anti-RBD IgG levels 3 weeks after boost immunisation were 5·4 signal to cutoff ratio (S/co; IQR 4·8-5·9) in recipients of homologous BNT162b2, 4·9 S/co (4·3-5·6) in recipients of homologous ChAdOx1 nCov-19, and 5·6 S/co (5·1-6·1) in recipients of ChAdOx1 nCov-19- BNT162b2. Geometric mean of 50% inhibitory dose against alpha and beta variants were highest in recipients of ChAdOx1 nCov-19-BNT162b2 (956·6, 95% CI 835·6-1095, against alpha and 417·1, 349·3-498·2, against beta) compared with those in recipients of homologous ChAdOx1 nCov-19 (212·5, 131·2-344·4, against alpha and 48·5, 28·4-82·8, against beta; both p<0·0001) or homologous BNT162b2 (369·2, 310·7-438·6, against alpha and 72·4, 60·5-86·5, against beta; both p<0·0001). SARS-CoV-2 S1 T-cell reactivity 3 weeks after boost immunisation was highest in recipients of ChAdOx1 nCov-19-BNT162b2 (median IFN-γ concentration 4762 mIU/mL, IQR 2723-8403) compared with that in recipients of homologous ChAdOx1 nCov-19 (1061 mIU/mL, 599-2274, p<0·0001) and homologous BNT162b2 (2026 mIU/mL, 1459-4621, p=0·0008) vaccination.

Interpretation: The heterologous ChAdOx1 nCov-19-BNT162b2 immunisation with 10-12-week interval, recommended in Germany, is well tolerated and improves immunogenicity compared with homologous ChAdOx1 nCov-19 vaccination with 10-12-week interval and BNT162b2 vaccination with 3-week interval. Heterologous prime-boost immunisation strategies for COVID-19 might be generally applicable.

Funding: Forschungsnetzwerk der Universitätsmedizin zu COVID-19, the German Ministry of Education and Research, Zalando SE.
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http://dx.doi.org/10.1016/S2213-2600(21)00357-XDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360702PMC
November 2021

Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review.

Eur J Cancer 2021 09 11;155:200-215. Epub 2021 Aug 11.

Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address:

Background: Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown great potential in medical computer vision. In this systematic review, we summarise recent studies reporting CNN-based approaches for digital biomarkers for characterization and prognostication of gastrointestinal cancer pathology.

Methods: Pubmed and Medline were screened for peer-reviewed papers dealing with CNN-based gastrointestinal cancer analyses from histological slides, published between 2015 and 2020.Seven hundred and ninety titles and abstracts were screened, and 58 full-text articles were assessed for eligibility.

Results: Sixteen publications fulfilled our inclusion criteria dealing with tumor or precursor lesion characterization or prognostic and predictive biomarkers: 14 studies on colorectal or rectal cancer, three studies on gastric cancer and none on esophageal cancer. These studies were categorised according to their end-points: polyp characterization, tumor characterization and patient outcome. Regarding the translation into clinical practice, we identified several studies demonstrating generalization of the classifier with external tests and comparisons with pathologists, but none presenting clinical implementation.

Conclusions: Results of recent studies on CNN-based image analysis in gastrointestinal cancer pathology are promising, but studies were conducted in observational and retrospective settings. Large-scale trials are needed to assess performance and predict clinical usefulness. Furthermore, large-scale trials are required for approval of CNN-based prediction models as medical devices.
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http://dx.doi.org/10.1016/j.ejca.2021.07.012DOI Listing
September 2021

A benchmark for neural network robustness in skin cancer classification.

Eur J Cancer 2021 09 11;155:191-199. Epub 2021 Aug 11.

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address:

Background: One prominent application for deep learning-based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask common shortcomings such as susceptibility to confounding factors. To increase clinical applicability, it is necessary to thoroughly evaluate such classifiers on out-of-distribution (OOD) data.

Objective: The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured.

Methods: Using a proprietary dermoscopic image database and a set of image transformations, we create an OOD robustness benchmark and evaluate the robustness of four different convolutional neural network (CNN) architectures on it.

Results: The benchmark contains three data sets-Skin Archive Munich (SAM), SAM-corrupted (SAM-C) and SAM-perturbed (SAM-P)-and is publicly available for download. To maintain the benchmark's OOD status, ground truth labels are not provided and test results should be sent to us for assessment. The SAM data set contains 319 unmodified and biopsy-verified dermoscopic melanoma (n = 194) and nevus (n = 125) images. SAM-C and SAM-P contain images from SAM which were artificially modified to test a classifier against low-quality inputs and to measure its prediction stability over small image changes, respectively. All four CNNs showed susceptibility to corruptions and perturbations.

Conclusions: This benchmark provides three data sets which allow for OOD testing of binary skin cancer classifiers. Our classifier performance confirms the shortcomings of CNNs and provides a frame of reference. Altogether, this benchmark should facilitate a more thorough evaluation process and thereby enable the development of more robust skin cancer classifiers.
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http://dx.doi.org/10.1016/j.ejca.2021.06.047DOI Listing
September 2021

Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours.

Eur J Cancer 2021 09 20;154:227-234. Epub 2021 Jul 20.

Department of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg, Mannheim, Germany; Department of Dermatology, University Hospital (UKSH), Kiel, Germany.

Aim: Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical excision involves some risks for affected patients. In this study, we therefore aimed to develop a digital biomarker that can predict lymph node metastasis non-invasively from digitised H&E slides of primary melanoma tumours.

Methods: A total of 415 H&E slides from primary melanoma tumours with known sentinel node (SN) status from three German university hospitals and one private pathological practice were digitised (150 SN positive/265 SN negative). Two hundred ninety-one slides were used to train artificial neural networks (ANNs). The remaining 124 slides were used to test the ability of the ANNs to predict sentinel status. ANNs were trained and/or tested on data sets that were matched or not matched between SN-positive and SN-negative cases for patient age, ulceration, and tumour thickness, factors that are known to correlate with lymph node status.

Results: The best accuracy was achieved by an ANN that was trained and tested on unmatched cases (61.8% ± 0.2%) area under the receiver operating characteristic (AUROC). In contrast, ANNs that were trained and/or tested on matched cases achieved (55.0% ± 3.5%) AUROC or less.

Conclusion: Our results indicate that the image classifier can predict lymph node status to some, albeit so far not clinically relevant, extent. It may do so by mostly detecting equivalents of factors on histological slides that are already known to correlate with lymph node status. Our results provide a basis for future research with larger data cohorts.
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http://dx.doi.org/10.1016/j.ejca.2021.05.026DOI Listing
September 2021

MicroRNA-sensitive oncolytic measles virus for chemovirotherapy of pancreatic cancer.

Mol Ther Oncolytics 2021 Jun 5;21:340-355. Epub 2021 May 5.

Clinical Cooperation Unit Virotherapy, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.

Advanced pancreatic cancer is characterized by few treatment options and poor outcomes. Oncolytic virotherapy and chemotherapy involve complementary pharmacodynamics and could synergize to improve therapeutic efficacy. Likewise, multimodality treatment may cause additional toxicity, and new agents have to be safe. Balancing both aims, we generated an oncolytic measles virus for 5-fluorouracil-based chemovirotherapy of pancreatic cancer with enhanced tumor specificity through microRNA-regulated vector tropism. The resulting vector encodes a bacterial prodrug convertase, cytosine deaminase-uracil phosphoribosyl transferase, and carries synthetic miR-148a target sites in the viral gene. Combination of the armed and targeted virus with 5-fluorocytosine, a prodrug of 5-fluorouracil, resulted in cytotoxicity toward both infected and bystander pancreatic cancer cells. In pancreatic cancer xenografts, a single intratumoral injection of the virus induced robust expression of prodrug convertase. Based on intratumoral transgene expression kinetics, we devised a chemovirotherapy regimen to assess treatment efficacy. Concerted multimodality treatment with intratumoral virus and systemic prodrug administration delayed tumor growth and prolonged survival of xenograft-bearing mice. Our results demonstrate that 5-fluorouracil-based chemovirotherapy with microRNA-sensitive measles virus is an effective strategy against pancreatic cancer at a favorable therapeutic index that warrants future clinical translation.
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http://dx.doi.org/10.1016/j.omto.2021.04.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8182383PMC
June 2021

Comprehensive Genomic and Transcriptomic Analysis for Guiding Therapeutic Decisions in Patients with Rare Cancers.

Cancer Discov 2021 Nov 10;11(11):2780-2795. Epub 2021 Jun 10.

Omics IT and Data Management Core Facility, DKFZ, Heidelberg, Germany.

The clinical relevance of comprehensive molecular analysis in rare cancers is not established. We analyzed the molecular profiles and clinical outcomes of 1,310 patients (rare cancers, 75.5%) enrolled in a prospective observational study by the German Cancer Consortium that applies whole-genome/exome and RNA sequencing to inform the care of adults with incurable cancers. On the basis of 472 single and six composite biomarkers, a cross-institutional molecular tumor board provided evidence-based management recommendations, including diagnostic reevaluation, genetic counseling, and experimental treatment, in 88% of cases. Recommended therapies were administered in 362 of 1,138 patients (31.8%) and resulted in significantly improved overall response and disease control rates (23.9% and 55.3%) compared with previous therapies, translating into a progression-free survival ratio >1.3 in 35.7% of patients. These data demonstrate the benefit of molecular stratification in rare cancers and represent a resource that may promote clinical trial access and drug approvals in this underserved patient population. SIGNIFICANCE: Rare cancers are difficult to treat; in particular, molecular pathogenesis-oriented medical therapies are often lacking. This study shows that whole-genome/exome and RNA sequencing enables molecularly informed treatments that lead to clinical benefit in a substantial proportion of patients with advanced rare cancers and paves the way for future clinical trials...
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http://dx.doi.org/10.1158/2159-8290.CD-21-0126DOI Listing
November 2021

Delayed Antibody and T-Cell Response to BNT162b2 Vaccination in the Elderly, Germany.

Emerg Infect Dis 2021 08 8;27(8):2174-2178. Epub 2021 Jun 8.

We detected delayed and reduced antibody and T-cell responses after BNT162b2 vaccination in 71 elderly persons (median age 81 years) compared with 123 healthcare workers (median age 34 years) in Germany. These data emphasize that nonpharmaceutical interventions for coronavirus disease remain crucial and that additional immunizations for the elderly might become necessary.
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http://dx.doi.org/10.3201/eid2708.211145DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8314803PMC
August 2021

Deutschland krempelt die Ärmel hoch.

MMW Fortschr Med 2021 06;163(11):45

Institute of Health and Charité, Berlin, Deutschland.

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http://dx.doi.org/10.1007/s15006-021-9978-4DOI Listing
June 2021

The balance between the intronic miR-342 and its host gene Evl determines hematopoietic cell fate decision.

Leukemia 2021 Oct 21;35(10):2948-2963. Epub 2021 May 21.

Translational Functional Cancer Genomics, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.

Protein-coding and non-coding genes like miRNAs tightly control hematopoietic differentiation programs. Although miRNAs are frequently located within introns of protein-coding genes, the molecular interplay between intronic miRNAs and their host genes is unclear. By genomic integration site mapping of gamma-retroviral vectors in genetically corrected peripheral blood from gene therapy patients, we identified the EVL/MIR342 gene locus as a hotspot for therapeutic vector insertions indicating its accessibility and expression in human hematopoietic stem and progenitor cells. We therefore asked if and how EVL and its intronic miRNA-342 regulate hematopoiesis. Here we demonstrate that overexpression (OE) of Evl in murine primary Lin Sca1 cKit cells drives lymphopoiesis whereas miR-342 OE increases myeloid colony formation in vitro and in vivo, going along with a profound upregulation of canonical pathways essential for B-cell development or myelopoietic functions upon Evl or miR-342 OE, respectively. Strikingly, miR-342 counteracts its host gene by targeting lymphoid signaling pathways, resulting in reduced pre-B-cell output. Moreover, EVL overexpression is associated with lymphoid leukemia in patients. In summary, our data show that one common gene locus regulates distinct hematopoietic differentiation programs depending on the gene product expressed, and that the balance between both may determine hematopoietic cell fate decision.
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http://dx.doi.org/10.1038/s41375-021-01267-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478659PMC
October 2021

SARS-CoV-2 infections in kindergartens and associated households at the start of the second wave in Berlin, Germany-a cross-sectional study.

Eur J Public Health 2021 10;31(5):1105-1107

Institute of Tropical Medicine and International Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany.

Actual surveys in kindergartens on SARS-CoV-2 infections are rare. At the beginning of the second pandemic wave, we screened 12 randomly selected kindergartens in Berlin, Germany. A total of 720 participants (pre-school children, staff and connected household members) were briefly examined and interviewed, and SARS-CoV-2 infections and anti-SARS-Cov-2 IgG antibodies were assessed. About a quarter of the participants showed common cold-resembling symptoms. However, no SARS-CoV-2 infection was detected, and only one childcare worker showed IgG seroreactivity. Against a backdrop of increased pandemic activity in the community, this cross-sectional study does not suggest that kindergartens are silent transmission reservoirs.
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http://dx.doi.org/10.1093/eurpub/ckab079DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8135989PMC
October 2021

Clinical and virological characteristics of hospitalised COVID-19 patients in a German tertiary care centre during the first wave of the SARS-CoV-2 pandemic: a prospective observational study.

Infection 2021 Aug 22;49(4):703-714. Epub 2021 Apr 22.

Department of Infectious Diseases and Respiratory Medicine Berlin, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.

Purpose: Adequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories. The purpose of this work was to identify risk factors associated with need for invasive mechanical ventilation (IMV), to analyse viral kinetics in patients with and without IMV and to provide a comprehensive description of clinical course.

Methods: A cohort of 168 hospitalised adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care centre was analysed.

Results: Forty-four per cent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95% CI 1.10-1.37, p < 0.01) and history of hypertension (aOR 5.55, 95% CI 2.00-16.82, p < 0.01) were associated with need for IMV. Patients on IMV had higher maximal concentrations, slower decline rates, and longer shedding of SARS-CoV-2 than non-IMV patients (33 days, IQR 26-46.75, vs 18 days, IQR 16-46.75, respectively, p < 0.01). Median duration of hospitalisation was 9 days (IQR 6-15.5) for non-IMV and 49.5 days (IQR 36.8-82.5) for IMV patients.

Conclusions: Our results indicate a short duration of symptoms before admission as a risk factor for severe disease that merits further investigation and different viral load kinetics in severely affected patients. Median duration of hospitalisation of IMV patients was longer than described for acute respiratory distress syndrome unrelated to COVID-19.
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http://dx.doi.org/10.1007/s15010-021-01594-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8061715PMC
August 2021

Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification.

Eur J Cancer 2021 05 7;149:94-101. Epub 2021 Apr 7.

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.

Background: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses.

Objectives: We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone.

Methods: We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques. Furthermore, we tested a naive combination of patient data and an image classifier: for cases interpreted as 'uncertain' (CNN output score <0.7), the decision of the CNN was replaced by the decision of the patient data classifier.

Results: The CNN on its own achieved the best performance (mean ± standard deviation of five individual runs) with AUROC of 92.30% ± 0.23% and balanced accuracy of 83.17% ± 0.38%. While the classification performance was not significantly improved in general by any of the tested fusions, naive strategy of replacing the image classifier with the patient data classifier on slides with low output scores improved balanced accuracy to 86.72% ± 0.36%.

Conclusion: In most cases, the CNN on its own was so accurate that patient data integration did not provide any benefit. However, incorporating patient data for lesions that were classified by the CNN with low 'confidence' improved balanced accuracy.
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http://dx.doi.org/10.1016/j.ejca.2021.02.032DOI Listing
May 2021

Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer.

BJU Int 2021 09 5;128(3):352-360. Epub 2021 May 5.

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.

Objective: To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors.

Patients And Methods: Haematoxylin and eosin (H&E) stained primary tumour slides from 218 patients (102 N+; 116 N0), matched for Gleason score, tumour size, venous invasion, perineural invasion and age, who underwent radical prostatectomy were selected to train a CNN and evaluate its ability to predict LN status.

Results: With 10 models trained with the same data, a mean area under the receiver operating characteristic curve (AUROC) of 0.68 (95% confidence interval [CI] 0.678-0.682) and a mean balanced accuracy of 61.37% (95% CI 60.05-62.69%) was achieved. The mean sensitivity and specificity was 53.09% (95% CI 49.77-56.41%) and 69.65% (95% CI 68.21-71.1%), respectively. These results were confirmed via cross-validation. The probability score for LNM prediction was significantly higher on image sections from N+ samples (mean [SD] N+ probability score 0.58 [0.17] vs 0.47 [0.15] N0 probability score, P = 0.002). In multivariable analysis, the probability score of the CNN (odds ratio [OR] 1.04 per percentage probability, 95% CI 1.02-1.08; P = 0.04) and lymphovascular invasion (OR 11.73, 95% CI 3.96-35.7; P < 0.001) proved to be independent predictors for LNM.

Conclusion: In our present study, CNN-based image analyses showed promising results as a potential novel low-cost method to extract relevant prognostic information directly from H&E histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.
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http://dx.doi.org/10.1111/bju.15386DOI Listing
September 2021

Renewed Absence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infections in the Day Care Context in Berlin, January 2021.

Clin Infect Dis 2021 11;73(10):1944-1945

Institute of Tropical Medicine and International Health, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.

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http://dx.doi.org/10.1093/cid/ciab199DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989586PMC
November 2021

Common clonal origin of conventional T cells and induced regulatory T cells in breast cancer patients.

Nat Commun 2021 02 18;12(1):1119. Epub 2021 Feb 18.

RCI Regensburg Centre for Interventional Immunology, University and Department of Hematology/Oncology, University Medical Centre of Regensburg, Regensburg, Germany.

Regulatory CD4 T cells (Treg) prevent tumor clearance by conventional T cells (Tconv) comprising a major obstacle of cancer immune-surveillance. Hitherto, the mechanisms of Treg repertoire formation in human cancers remain largely unclear. Here, we analyze Treg clonal origin in breast cancer patients using T-Cell Receptor and single-cell transcriptome sequencing. While Treg in peripheral blood and breast tumors are clonally distinct, Tconv clones, including tumor-antigen reactive effectors (Teff), are detected in both compartments. Tumor-infiltrating CD4 cells accumulate into distinct transcriptome clusters, including early activated Tconv, uncommitted Teff, Th1 Teff, suppressive Treg and pro-tumorigenic Treg. Trajectory analysis suggests early activated Tconv differentiation either into Th1 Teff or into suppressive and pro-tumorigenic Treg. Importantly, Tconv, activated Tconv and Treg share highly-expanded clones contributing up to 65% of intratumoral Treg. Here we show that Treg in human breast cancer may considerably stem from antigen-experienced Tconv converting into secondary induced Treg through intratumoral activation.
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http://dx.doi.org/10.1038/s41467-021-21297-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7893042PMC
February 2021

Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study.

J Med Internet Res 2021 02 2;23(2):e23436. Epub 2021 Feb 2.

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Background: An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can also introduce hidden variables. If neural networks are able to distinguish/learn hidden variables, these variables can introduce batch effects that compromise the accuracy of classification systems.

Objective: The objective of the study was to analyze the learnability of an exemplary selection of hidden variables (patient age, slide preparation date, slide origin, and scanner type) that are commonly found in whole slide image data sets in digital pathology and could create batch effects.

Methods: We trained four separate convolutional neural networks (CNNs) to learn four variables using a data set of digitized whole slide melanoma images from five different institutes. For robustness, each CNN training and evaluation run was repeated multiple times, and a variable was only considered learnable if the lower bound of the 95% confidence interval of its mean balanced accuracy was above 50.0%.

Results: A mean balanced accuracy above 50.0% was achieved for all four tasks, even when considering the lower bound of the 95% confidence interval. Performance between tasks showed wide variation, ranging from 56.1% (slide preparation date) to 100% (slide origin).

Conclusions: Because all of the analyzed hidden variables are learnable, they have the potential to create batch effects in dermatopathology data sets, which negatively affect AI-based classification systems. Practitioners should be aware of these and similar pitfalls when developing and evaluating such systems and address these and potentially other batch effect variables in their data sets through sufficient data set stratification.
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http://dx.doi.org/10.2196/23436DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886613PMC
February 2021

Robustness of convolutional neural networks in recognition of pigmented skin lesions.

Eur J Cancer 2021 03 7;145:81-91. Epub 2021 Jan 7.

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address:

Background: A basic requirement for artificial intelligence (AI)-based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin cancer screening, should not change the diagnosis of such assistance systems.

Objective: To quantify to what extent minor image perturbations affect the convolutional neural network (CNN)-mediated skin lesion classification and to evaluate three possible solutions for this problem (additional data augmentation, test-time augmentation, anti-aliasing).

Methods: We trained three commonly used CNN architectures to differentiate between dermoscopic melanoma and nevus images. Subsequently, their performance and susceptibility to minor changes ('brittleness') was tested on two distinct test sets with multiple images per lesion. For the first set, image changes, such as rotations or zooms, were generated artificially. The second set contained natural changes that stemmed from multiple photographs taken of the same lesions.

Results: All architectures exhibited brittleness on the artificial and natural test set. The three reviewed methods were able to decrease brittleness to varying degrees while still maintaining performance. The observed improvement was greater for the artificial than for the natural test set, where enhancements were minor.

Conclusions: Minor image changes, relatively inconspicuous for humans, can have an effect on the robustness of CNNs differentiating skin lesions. By the methods tested here, this effect can be reduced, but not fully eliminated. Thus, further research to sustain the performance of AI classifiers is needed to facilitate the translation of such systems into the clinic.
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http://dx.doi.org/10.1016/j.ejca.2020.11.020DOI Listing
March 2021

The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond.

BMC Med Inform Decis Mak 2020 12 21;20(1):341. Epub 2020 Dec 21.

Berlin Institute of Health (BIH), Berlin, Germany.

Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the "German Corona Consensus Dataset" (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine.

Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.

Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.

Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.
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http://dx.doi.org/10.1186/s12911-020-01374-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751265PMC
December 2020

Überdiagnose von Melanomen - Ursachen, Konsequenzen und Lösungsansätze.

J Dtsch Dermatol Ges 2020 Nov;18(11):1236-1244

Nachwuchsgruppe Digitale Biomarker für die Onkologie (DBO), Nationales Centrum für Tumorerkrankungen (NCT), Deutsches Krebsforschungszentrum (DKFZ), Heidelberg.

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http://dx.doi.org/10.1111/ddg.14233_gDOI Listing
November 2020

Integrating proteomics into precision oncology.

Int J Cancer 2021 03 25;148(6):1438-1451. Epub 2020 Sep 25.

Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.

DNA sequencing and RNA sequencing are increasingly applied in precision oncology, where molecular tumor boards evaluate the actionability of genetic events in individual tumors to guide targeted treatment. To work toward an additional level of patient characterization, we assessed the abundance and activity of 27 proteins in 134 patients whose tumors had previously undergone whole-exome and RNA sequencing within the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) program of National Center for Tumor Diseases, Heidelberg. Proteomic and phosphoproteomic targets were selected to reflect the most relevant therapeutic baskets in MASTER. Among six different therapeutic baskets, the proteomic data supported treatment recommendations that were based on DNA and RNA analyses in 10% to 57% and frequently suggested alternative treatment options. In several cases, protein activities explained the patients' clinical course and provided potential explanations for treatment failure. Our study indicates that the integrative analysis of DNA, RNA and protein data may refine therapeutic stratification of individual patients and, thus, holds potential to increase the success rate of precision cancer therapy. Prospective validation studies are needed to advance the integration of proteomic analysis into precision oncology.
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http://dx.doi.org/10.1002/ijc.33301DOI Listing
March 2021

Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.

J Med Internet Res 2020 09 11;22(9):e18091. Epub 2020 Sep 11.

Digital Biomarkers for Oncology Group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.

Background: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that these algorithms should assist a dermatologist's diagnoses.

Objective: The aim of this study was to investigate whether AI support improves the accuracy and overall diagnostic performance of dermatologists in the dichotomous image-based discrimination between melanoma and nevus.

Methods: Twelve board-certified dermatologists were presented disjoint sets of 100 unique dermoscopic images of melanomas and nevi (total of 1200 unique images), and they had to classify the images based on personal experience alone (part I) and with the support of a trained convolutional neural network (CNN, part II). Additionally, dermatologists were asked to rate their confidence in their final decision for each image.

Results: While the mean specificity of the dermatologists based on personal experience alone remained almost unchanged (70.6% vs 72.4%; P=.54) with AI support, the mean sensitivity and mean accuracy increased significantly (59.4% vs 74.6%; P=.003 and 65.0% vs 73.6%; P=.002, respectively) with AI support. Out of the 10% (10/94; 95% CI 8.4%-11.8%) of cases where dermatologists were correct and AI was incorrect, dermatologists on average changed to the incorrect answer for 39% (4/10; 95% CI 23.2%-55.6%) of cases. When dermatologists were incorrect and AI was correct (25/94, 27%; 95% CI 24.0%-30.1%), dermatologists changed their answers to the correct answer for 46% (11/25; 95% CI 33.1%-58.4%) of cases. Additionally, the dermatologists' average confidence in their decisions increased when the CNN confirmed their decision and decreased when the CNN disagreed, even when the dermatologists were correct. Reported values are based on the mean of all participants. Whenever absolute values are shown, the denominator and numerator are approximations as every dermatologist ended up rating a varying number of images due to a quality control step.

Conclusions: The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image-based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions.
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http://dx.doi.org/10.2196/18091DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7519424PMC
September 2020

Overdiagnosis of melanoma - causes, consequences and solutions.

J Dtsch Dermatol Ges 2020 Nov 25;18(11):1236-1243. Epub 2020 Aug 25.

Digital Biomarkers for Oncology group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.

Malignant melanoma is the skin tumor that causes most deaths in Germany. At an early stage, melanoma is well treatable, so early detection is essential. However, the skin cancer screening program in Germany has been criticized because although melanomas have been diagnosed more frequently since introduction of the program, the mortality from malignant melanoma has not decreased. This indicates that the observed increase in melanoma diagnoses be due to overdiagnosis, i.e. to the detection of lesions that would never have created serious health problems for the patients. One of the reasons is the challenging distinction between some benign and malignant lesions. In addition, there may be lesions that are biologically equivocal, and other lesions that are classified as malignant according to current criteria, but that grow so slowly that they would never have posed a threat to patient's life. So far, these "indolent" melanomas cannot be identified reliably due to a lack of biomarkers. Moreover, the likelihood that an in-situ melanoma will progress to an invasive tumor still cannot be determined with any certainty. When benign lesions are diagnosed as melanoma, the consequences are unnecessary psychological and physical stress for the affected patients and incurred therapy costs. Vice versa, underdiagnoses in the sense of overlooked melanomas can adversely affect patients' prognoses and may necessitate more intense therapies. Novel diagnostic options could reduce the number of over- and underdiagnoses and contribute to more objective diagnoses in borderline cases. One strategy that has yielded promising results in pilot studies is the use of artificial intelligence-based diagnostic tools. However, these applications still await translation into clinical and pathological routine.
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http://dx.doi.org/10.1111/ddg.14233DOI Listing
November 2020

Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment.

Cell 2020 09 5;182(6):1419-1440.e23. Epub 2020 Aug 5.

Department of Infectious Diseases and Respiratory Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany; German Center for Lung Research (DZL).

Coronavirus disease 2019 (COVID-19) is a mild to moderate respiratory tract infection, however, a subset of patients progress to severe disease and respiratory failure. The mechanism of protective immunity in mild forms and the pathogenesis of severe COVID-19 associated with increased neutrophil counts and dysregulated immune responses remain unclear. In a dual-center, two-cohort study, we combined single-cell RNA-sequencing and single-cell proteomics of whole-blood and peripheral-blood mononuclear cells to determine changes in immune cell composition and activation in mild versus severe COVID-19 (242 samples from 109 individuals) over time. HLA-DRCD11c inflammatory monocytes with an interferon-stimulated gene signature were elevated in mild COVID-19. Severe COVID-19 was marked by occurrence of neutrophil precursors, as evidence of emergency myelopoiesis, dysfunctional mature neutrophils, and HLA-DR monocytes. Our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and reveals profound alterations in the myeloid cell compartment associated with severe COVID-19.
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http://dx.doi.org/10.1016/j.cell.2020.08.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7405822PMC
September 2020

[External scientific evaluation of the first teledermatology app without direct patient contact in Germany (Online Dermatologist-AppDoc)].

Hautarzt 2020 Nov;71(11):887-897

Nationales Centrum für Tumorerkrankungen (NCT), Deutsches Krebsforschungszentrum (DKFZ), 69120, Heidelberg, Deutschland.

Background: Teledermatology addresses the problems associated with the lack of specialists and the often long waiting time for an appointment with a dermatologist. The research project Online Dermatologist-AppDoc enables a fast anonymous expert opinion and was approved on 22 October 2018 by the Landesärztekammer Baden-Württemberg for 2 years as a model project.

Objectives: The aim of the present work is the presentation of the first real healthcare data for German teledematology within the framework of the external quality assurance of the model project Online Dermatologist-AppDoc.

Materials And Methods: Anonymous data records submitted to Online Dermatologist-AppDoc between 21 November 2018 and 1 August 2019 were analyzed qualitatively and quantitatively at the Department of Dermatology of the University Hospital Essen. In addition to the evaluation of the data records submitted so far, 100 cases submitted underwent a second assessment by a board-certified dermatologist to assess concordance.

Results: A total of 1364 cases (60.4% men, 39.6% women) were included in the current first external scientific evaluation. In 90.3% of the cases, remote diagnosis was possible. The two most frequent diagnoses were different forms of eczema (n = 270) and nevi (n = 163). Almost two thirds of the patients (64.3%) could be treated teledermatologically only. The random second examination of 100 cases resulted in an agreement of the diagnosis including the differential diagnosis/diagnoses in 97% of the cases.

Conclusions: The first external scientific evaluation of the teledermatological model project Online Dermatologist-AppDoc indicates that the reduction of spatial and temporal barriers of a dermatological examination as well as the teledermatological triage have been so far successful.
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http://dx.doi.org/10.1007/s00105-020-04660-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7387809PMC
November 2020

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection.

Cell Syst 2020 07 2;11(1):11-24.e4. Epub 2020 Jun 2.

Charité Universitätsmedizin, Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany.

The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.
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http://dx.doi.org/10.1016/j.cels.2020.05.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264033PMC
July 2020

COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis.

Nat Biotechnol 2020 08 26;38(8):970-979. Epub 2020 Jun 26.

Institute of Virology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health (BIH), Berlin, Germany.

To investigate the immune response and mechanisms associated with severe coronavirus disease 2019 (COVID-19), we performed single-cell RNA sequencing on nasopharyngeal and bronchial samples from 19 clinically well-characterized patients with moderate or critical disease and from five healthy controls. We identified airway epithelial cell types and states vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In patients with COVID-19, epithelial cells showed an average three-fold increase in expression of the SARS-CoV-2 entry receptor ACE2, which correlated with interferon signals by immune cells. Compared to moderate cases, critical cases exhibited stronger interactions between epithelial and immune cells, as indicated by ligand-receptor expression profiles, and activated immune cells, including inflammatory macrophages expressing CCL2, CCL3, CCL20, CXCL1, CXCL3, CXCL10, IL8, IL1B and TNF. The transcriptional differences in critical cases compared to moderate cases likely contribute to clinical observations of heightened inflammatory tissue damage, lung injury and respiratory failure. Our data suggest that pharmacologic inhibition of the CCR1 and/or CCR5 pathways might suppress immune hyperactivation in critical COVID-19.
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http://dx.doi.org/10.1038/s41587-020-0602-4DOI Listing
August 2020
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