Publications by authors named "Jakob N Kather"

28 Publications

  • Page 1 of 1

Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review.

Eur J Cancer 2021 Nov 19. Epub 2021 Nov 19.

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

Background: Over the past decade, the development of molecular high-throughput methods (omics) increased rapidly and provided new insights for cancer research. In parallel, deep learning approaches revealed the enormous potential for medical image analysis, especially in digital pathology. Combining image and omics data with deep learning tools may enable the discovery of new cancer biomarkers and a more precise prediction of patient prognosis. This systematic review addresses different multimodal fusion methods of convolutional neural network-based image analyses with omics data, focussing on the impact of data combination on the classification performance.

Methods: PubMed was screened for peer-reviewed articles published in English between January 2015 and June 2021 by two independent researchers. Search terms related to deep learning, digital pathology, omics, and multimodal fusion were combined.

Results: We identified a total of 11 studies meeting the inclusion criteria, namely studies that used convolutional neural networks for haematoxylin and eosin image analysis of patients with cancer in combination with integrated omics data. Publications were categorised according to their endpoints: 7 studies focused on survival analysis and 4 studies on prediction of cancer subtypes, malignancy or microsatellite instability with spatial analysis.

Conclusions: Image-based classifiers already show high performances in prognostic and predictive cancer diagnostics. The integration of omics data led to improved performance in all studies described here. However, these are very early studies that still require external validation to demonstrate their generalisability and robustness. Further and more comprehensive studies with larger sample sizes are needed to evaluate performance and determine clinical benefits.
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http://dx.doi.org/10.1016/j.ejca.2021.10.007DOI Listing
November 2021

Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study.

Lancet Digit Health 2021 Nov 15. Epub 2021 Nov 15.

Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany; Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Electronic address:

Background: Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection.

Methods: We performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were identified in the archive of the Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Convolutional neural networks (CNNs) were trained to classify allograft biopsies as normal, rejection, or other diseases. Three times cross-validation (1847 patients) and deployment on an external real-world cohort (101 patients) were used for validation. Area under the receiver operating characteristic curve (AUROC) was used as the main performance metric (the primary endpoint to assess CNN performance).

Findings: Serial CNNs, first classifying kidney allograft biopsies as normal (AUROC 0·87 [ten times bootstrapped CI 0·85-0·88]) and disease (0·87 [0·86-0·88]), followed by a second CNN classifying biopsies classified as disease into rejection (0·75 [0·73-0·76]) and other diseases (0·75 [0·72-0·77]), showed similar AUROC in cross-validation and deployment on independent real-world data (first CNN normal AUROC 0·83 [0·80-0·85], disease 0·83 [0·73-0·91]; second CNN rejection 0·61 [0·51-0·70], other diseases 0·61 [0·50-0·74]). A single CNN classifying biopsies as normal, rejection, or other diseases showed similar performance in cross-validation (normal AUROC 0·80 [0·73-0·84], rejection 0·76 [0·66-0·80], other diseases 0·50 [0·36-0·57]) and generalised well for normal and rejection classes in the real-world data. Visualisation techniques highlighted rejection-relevant areas of biopsies in the tubulointerstitium.

Interpretation: This study showed that deep learning-based classification of transplant biopsies could support pathological diagnostics of kidney allograft rejection.

Funding: European Research Council; German Research Foundation; German Federal Ministries of Education and Research, Health, and Economic Affairs and Energy; Dutch Kidney Foundation; Human(e) AI Research Priority Area of the University of Amsterdam; and Max-Eder Programme of German Cancer Aid.
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http://dx.doi.org/10.1016/S2589-7500(21)00211-9DOI Listing
November 2021

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

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

The impact of site-specific digital histology signatures on deep learning model accuracy and bias.

Nat Commun 2021 07 20;12(1):4423. Epub 2021 Jul 20.

Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA.

The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. Site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the image characteristics constituting this site-specific digital histology signature. We demonstrate that these site-specific signatures lead to biased accuracy for prediction of features including survival, genomic mutations, and tumor stage. Furthermore, ethnicity can also be inferred from site-specific signatures, which must be accounted for to ensure equitable application of DL. These site-specific signatures can lead to overoptimistic estimates of model performance, and we propose a quadratic programming method that abrogates this bias by ensuring models are not trained and validated on samples from the same site.
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http://dx.doi.org/10.1038/s41467-021-24698-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292530PMC
July 2021

Artificial Intelligence Can Cut Costs While Maintaining Accuracy in Colorectal Cancer Genotyping.

Front Oncol 2021 8;11:630953. Epub 2021 Jun 8.

Department of Medicine, University of Chicago, Chicago, IL, United States.

Rising cancer care costs impose financial burdens on health systems. Applying artificial intelligence to diagnostic algorithms may reduce testing costs and avoid wasteful therapy-related expenditures. To evaluate the financial and clinical impact of incorporating artificial intelligence-based determination of mismatch repair/microsatellite instability status into the first-line metastatic colorectal carcinoma setting, we developed a deterministic model to compare eight testing strategies: A) next-generation sequencing alone, B) high-sensitivity polymerase chain reaction or immunohistochemistry panel alone, C) high-specificity panel alone, D) high-specificity artificial intelligence alone, E) high-sensitivity artificial intelligence followed by next generation sequencing, F) high-specificity artificial intelligence followed by next-generation sequencing, G) high-sensitivity artificial intelligence and high-sensitivity panel, and H) high-sensitivity artificial intelligence and high-specificity panel. We used a hypothetical, nationally representative, population-based sample of individuals receiving first-line treatment for metastatic colorectal cancer (N = 32,549) in the United States. Model inputs were derived from secondary research (peer-reviewed literature and Medicare data). We estimated the population-level diagnostic costs and clinical implications for each testing strategy. The testing strategy that resulted in the greatest project cost savings (including testing and first-line drug cost) compared to next-generation sequencing alone in newly-diagnosed metastatic colorectal cancer was using high-sensitivity artificial intelligence followed by confirmatory high-specificity polymerase chain reaction or immunohistochemistry panel for patients testing negative by artificial intelligence ($400 million, 12.9%). The high-specificity artificial intelligence-only strategy resulted in the most favorable clinical impact, with 97% diagnostic accuracy in guiding genotype-directed treatment and average time to treatment initiation of less than one day. Artificial intelligence has the potential to reduce both time to treatment initiation and costs in the metastatic colorectal cancer setting without meaningfully sacrificing diagnostic accuracy. We expect the artificial intelligence value proposition to improve in coming years, with increasing diagnostic accuracy and decreasing costs of processing power. To extract maximal value from the technology, health systems should evaluate integrating diagnostic histopathologic artificial intelligence into institutional protocols, perhaps in place of other genotyping methodologies.
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http://dx.doi.org/10.3389/fonc.2021.630953DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8217761PMC
June 2021

Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2.

Viruses 2021 04 2;13(4). Epub 2021 Apr 2.

Department of Molecular Medicine II, Medical Faculty, Heinrich-Heine-University, 40225 Düsseldorf, Germany.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes COVID-19 and is responsible for the ongoing pandemic. Screening of potential antiviral drugs against SARS-CoV-2 depend on in vitro experiments, which are based on the quantification of the virus titer. Here, we used virus-induced cytopathic effects (CPE) in brightfield microscopy of SARS-CoV-2-infected monolayers to quantify the virus titer. Images were classified using deep transfer learning (DTL) that fine-tune the last layers of a pre-trained Resnet18 (ImageNet). To exclude toxic concentrations of potential drugs, the network was expanded to include a toxic score (TOX) that detected cell death (CPETOXnet). With this analytic tool, the inhibitory effects of chloroquine, hydroxychloroquine, remdesivir, and emetine were validated. Taken together we developed a simple method and provided open access implementation to quantify SARS-CoV-2 titers and drug toxicity in experimental settings, which may be adaptable to assays with other viruses. The quantification of virus titers from brightfield images could accelerate the experimental approach for antiviral testing.
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http://dx.doi.org/10.3390/v13040610DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066066PMC
April 2021

Artificial Intelligence-based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer: A Possible Preselection for Molecular Testing?

Eur Urol Focus 2021 Apr 21. Epub 2021 Apr 21.

Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.

Background: Fibroblast growth factor receptor (FGFR) inhibitor treatment has become the first clinically approved targeted therapy in bladder cancer. However, it requires previous molecular testing of each patient, which is costly and not ubiquitously available.

Objective: To determine whether an artificial intelligence system is able to predict mutations of the FGFR3 gene directly from routine histology slides of bladder cancer.

Design, Setting, And Participants: We trained a deep learning network to detect FGFR3 mutations on digitized slides of muscle-invasive bladder cancers stained with hematoxylin and eosin from the Cancer Genome Atlas (TCGA) cohort (n = 327) and validated the algorithm on the "Aachen" cohort (n = 182; n = 121 pT2-4, n = 34 stroma-invasive pT1, and n = 27 noninvasive pTa tumors).

Outcome Measurements And Statistical Analysis: The primary endpoint was the area under the receiver operating curve (AUROC) for mutation detection. Performance of the deep learning system was compared with visual scoring by an uropathologist.

Results And Limitations: In the TCGA cohort, FGFR3 mutations were detected with an AUROC of 0.701 (p < 0.0001). In the Aachen cohort, FGFR3 mutants were found with an AUROC of 0.725 (p < 0.0001). When trained on TCGA, the network generalized to the Aachen cohort, and detected FGFR3 mutants with an AUROC of 0.625 (p = 0.0112). A subgroup analysis and histological evaluation found highest accuracy in papillary growth, luminal gene expression subtypes, females, and American Joint Committee on Cancer (AJCC) stage II tumors. In a head-to-head comparison, the deep learning system outperformed the uropathologist in detecting FGFR3 mutants.

Conclusions: Our computer-based artificial intelligence system was able to detect genetic alterations of the FGFR3 gene of bladder cancer patients directly from histological slides. In the future, this system could be used to preselect patients for further molecular testing. However, analyses of larger, multicenter, muscle-invasive bladder cancer cohorts are now needed in order to validate and extend our findings.

Patient Summary: In this report, a computer-based artificial intelligence (AI) system was applied to histological slides to predict genetic alterations of the FGFR3 gene in bladder cancer. We found that the AI system was able to find the alteration with high accuracy. In the future, this system could be used to preselect patients for further molecular testing.
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http://dx.doi.org/10.1016/j.euf.2021.04.007DOI Listing
April 2021

Serum levels of soluble B and T lymphocyte attenuator predict overall survival in patients undergoing immune checkpoint inhibitor therapy for solid malignancies.

Int J Cancer 2021 09 27;149(5):1189-1198. Epub 2021 May 27.

Clinic for Gastroenterology, Hepatology and Infectious Diseases, University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

Therapy with immune checkpoint inhibitors (ICIs) can lead to durable tumor control in patients with various advanced stage malignancies. However, this is not the case for all patients, leading to an ongoing search for biomarkers predicting response and outcome to ICI. The B and T lymphocyte attenuator (BTLA) is an immune checkpoint expressed on immune cells that was shown to modulate therapeutic responses. Here, we evaluate circulating levels of its soluble form, soluble B and T lymphocyte attenuator (sBTLA), as a biomarker for the prediction of treatment response and outcome to ICI therapy. Serum levels of sBTLA were analyzed by multiplex immunoassay in n = 84 patients receiving ICI therapy for solid malignancies and 32 healthy controls. BTLA expression was evaluated on peripheral blood mononuclear cells in a subset of patients (n = 6) using multicolor flow cytometry. Baseline sBTLA serum levels were significantly higher in cancer patients compared to healthy controls. Importantly, circulating sBTLA levels were an independent prognostic factor for overall survival (OS). As such, patients with initial sBTLA levels above the calculated prognostic cutoff value (311.64 pg/mL) had a median OS of only 138 days compared to 526 for patients with sBTLA levels below this value (P = .001). Uni- and multivariate Cox regression analyses confirmed the prognostic role of sBTLA in the context of ICI therapy. Finally, we observed a significant correlation between sBTLA levels and the frequency of CD3 + CD8 + BTLA+ T cells in peripheral blood. Thus, our data suggest that circulating sBTLA could represent a noninvasive biomarker to predict outcome to ICI therapy, helping to select eligible therapy candidates.
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http://dx.doi.org/10.1002/ijc.33610DOI Listing
September 2021

Serum Levels of Soluble Urokinase Plasminogen Activator Receptor Predict Tumor Response and Outcome to Immune Checkpoint Inhibitor Therapy.

Front Oncol 2021 1;11:646883. Epub 2021 Apr 1.

Clinic for Gastroenterology, Hepatology and Infectious Diseases, University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

Background: Immune checkpoint inhibitors (ICIs) have led to a paradigm shift in cancer therapy, improving outcomes in the treatment of various malignancies. However, not all patients benefit to the same extend from ICI. Reliable tools to predict treatment response and outcome are missing. Soluble urokinase plasminogen activator receptor (suPAR) is a marker of immune activation, whose levels are prognostic in various cancers. We evaluated circulating suPAR levels as a novel predictive and prognostic biomarker in patients receiving ICI therapy for solid tumors.

Methods: A total of n = 87 patients receiving ICI therapy for different solid malignancies as well as 32 healthy controls were included into this study. Serum levels of suPAR were measured by ELISA prior to and sequentially at two time points during ICI therapy.

Results: Baseline suPAR serum levels were significantly higher in solid tumor patients compared to healthy controls. Importantly, patients with low suPAR levels both before or during ICI treatment were more likely to have a favorable response to treatment at three and six months, respectively. This finding was confirmed by multivariate binary logistic regression analysis including several clinicopathological parameters. Moreover, circulating suPAR levels before and during therapy were an independent prognostic factor for overall survival (OS). As such, patients with initial suPAR levels above our ideal prognostic cut-off value (4.86 ng/ml) had a median OS of only 160 days compared to 705 days for patients with suPAR levels below this cut-off value. Finally, low baseline suPAR levels identified a subgroup of patients who experienced ICI-related side effects which in turn were associated with favorable treatment response and outcome.

Conclusion: Our data suggest that measurements of suPAR serum levels are a previously unknown, easily accessible tool to predict individual treatment response and outcome to ICI therapy. Circulating suPAR might therefore be implemented into stratification algorithms to identify the ideal candidates for ICI treatment.
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http://dx.doi.org/10.3389/fonc.2021.646883DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8047604PMC
April 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

Nerve Fibers in the Tumor Microenvironment Are Co-Localized with Lymphoid Aggregates in Pancreatic Cancer.

J Clin Med 2021 Jan 30;10(3). Epub 2021 Jan 30.

Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, 52074 Aachen, Germany.

B cells and tertiary lymphoid structures (TLS) are reported to be important in survival in cancer. Pancreatic Cancer (PDAC) is one of the most lethal cancer types, and currently, it is the seventh leading cause of cancer-related death worldwide. A better understanding of tumor biology is pivotal to improve clinical outcome. The desmoplastic stroma is a complex system in which crosstalk takes place between cancer-associated fibroblasts, immune cells and cancer cells. Indirect and direct cellular interactions within the tumor microenvironment (TME) drive key processes such as tumor progression, metastasis formation and treatment resistance. In order to understand the aggressiveness of PDAC and its resistance to therapeutics, the TME needs to be further unraveled. There are some limited data about the influence of nerve fibers on cancer progression. Here we show that small nerve fibers are located at lymphoid aggregates in PDAC. This unravels future pathways and has potential to improve clinical outcome by a rational development of new therapeutic strategies.
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http://dx.doi.org/10.3390/jcm10030490DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866811PMC
January 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

Completion rate and impact on physician-patient relationship of video consultations in medical oncology: a randomised controlled open-label trial.

ESMO Open 2020 11;5(6):e000912

Department of Medical Oncology, National Center for Tumor Diseases, Heidelberg, Germany.

Background: Mobile phone video call applications generally did not undergo testing in randomised controlled clinical trials prior to their implementation in patient care regarding the rate of successful patient visits and impact on the physician-patient relationship.

Methods: The National Center for Tumour Diseases (NCT) MOBILE trial was a monocentric open-label randomised controlled clinical trial of patients with solid tumours undergoing systemic cancer therapy with need of a follow-up visit with their consulting physician at outpatient clinics. 66 patients were 1:1 randomised to receive either a standard in-person follow-up visit at outpatient clinics or a video call via a mobile phone application. The primary outcome was feasibility defined as the proportion of patients successfully completing the first follow-up visit. Secondary outcomes included success rate of further video calls, time spent by patient and physician, patient satisfaction and quality of physician-patient relationship.

Findings: Success rate of the first follow-up visit in the intention-to-treat cohort was 87.9% (29 of 33) for in-person visits and 78.8% (26 of 33) for video calls (relative risk: RR 0.90, 95% CI 0.70 to 1.13, p=0.51). The most common reasons for failure were software incompatibility in the video call and no-show in the in-person visit arm. The success rate for further video visits was 91.7% (11 of 12). Standardised patient questionnaires showed significantly decreased total time spent and less direct costs for patients (Δmean -170.8 min, 95% CI -246 min to -95.5 min), p<0.0001; Δmean -€14.37, 95% CI -€23.9 to -€4.8, p<0.005) and comparable time spent for physicians in the video call arm (Δmean 0.5 min, 95% CI -5.4 min to 6.4 min, p=0.86). Physician-patient relationship quality mean scores assessed by a validated standardised questionnaire were higher in the video call arm (1.13-fold, p=0.02).

Interpretation: Follow-up visits with the tested mobile phone video call application were feasible but software compatibility should be critically evaluated.

Trial Registration Number: DRKS00015788.
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http://dx.doi.org/10.1136/esmoopen-2020-000912DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674103PMC
November 2020

Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin.

Cancers (Basel) 2020 Nov 11;12(11). Epub 2020 Nov 11.

giCentre, School of Mathematics, Computer Science & Engineering, City, University of London, Northampton Square, London EC1V 0HB, UK.

Histological evaluation plays a major role in cancer diagnosis and treatment. The appearance of H&E-stained images can vary significantly as a consequence of differences in several factors, such as reagents, staining conditions, preparation procedure and image acquisition system. Such potential sources of noise can all have negative effects on computer-assisted classification. To minimize such artefacts and their potentially negative effects several color pre-processing methods have been proposed in the literature-for instance, color augmentation, color constancy, color deconvolution and color transfer. Still, little work has been done to investigate the efficacy of these methods on a quantitative basis. In this paper, we evaluated the effects of color constancy, deconvolution and transfer on automated classification of H&E-stained images representing different types of cancers-specifically breast, prostate, colorectal cancer and malignant lymphoma. Our results indicate that in most cases color pre-processing does not improve the classification accuracy, especially when coupled with color-based image descriptors. Some pre-processing methods, however, can be beneficial when used with some texture-based methods like Gabor filters and Local Binary Patterns.
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http://dx.doi.org/10.3390/cancers12113337DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7697346PMC
November 2020

Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets.

Nat Commun 2020 08 25;11(1):4238. Epub 2020 Aug 25.

Mila - Quebec Artificial Intelligence Institute, Montréal, Québec, Canada.

Recently, deep learning has unlocked unprecedented success in various domains, especially using images, text, and speech. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at available sample sizes. We systematically profiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improves when escalating from linear models to shallow-nonlinear models, and further improves with deep-nonlinear models. In contrast, using structural or functional brain scans, simple linear models perform on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In sum, linear models keep improving as the sample size approaches ~10,000 subjects. Yet, nonlinearities for predicting common phenotypes from typical brain scans remain largely inaccessible to the examined kernel and deep learning methods.
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http://dx.doi.org/10.1038/s41467-020-18037-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447816PMC
August 2020

Skeletal Muscle Composition Predicts Outcome in Critically Ill Patients.

Crit Care Explor 2020 Aug 5;2(8):e0171. Epub 2020 Aug 5.

Department of Hepatology and Gastroenterology, Charité University Medicine Berlin, Berlin, Germany.

Parameters of patients' body composition have been suggested as prognostic markers in several clinical conditions including cancer and liver transplantation, but only limited data on its value in critical illness exist to date. In this study, we aimed at evaluating a potential prognostic value of the skeletal muscle mass and skeletal muscle myosteatosis of critically ill patients at admission to the ICU.

Design: Exploratory observational cohort study.

Setting: An urban, academic medical institution.

Patients: One-hundred fifty-five patients treated for critical illness on a medical ICU.

Interventions: None.

Measurements And Main Results: We used routine CT scans to assess the patients' individual body composition. The skeletal muscle index as a surrogate for sarcopenia was defined as the total skeletal muscle area at the level of the third lumbar vertebra on axial CT scan, normalized for the patient's height. Myosteatosis was evaluated by assessing the mean skeletal muscle attenuation measured in Hounsfield unit at the same sectional plane. The skeletal muscle index and mean skeletal muscle attenuation at admission to the ICU were significantly higher in patients with long-term survival (180-day or 1-year mortality), while both parameters were comparable between short-term survivors and nonsurvivors (ICU mortality or 30-d mortality). Patients with a skeletal muscle index or mean skeletal muscle attenuation below our established ideal cutoff values (74.95 mm/cm and 29 Hounsfield unit) showed a significantly reduced overall survival. These findings were confirmed in univariate and multivariate Cox regression analyses. Furthermore, myosteatosis significantly correlated with the time of mechanical ventilation, the duration of hospital stay, and the presence of sepsis.

Conclusions: Our data suggest that sarcopenia and myosteatosis represent important prognostic factors in critically ill patients that can be easily obtained from routine CT scans. Both parameters at admission to the ICU yield important information on the patients' long-term outcome and might be used for early clinical decision-making in these patients.
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http://dx.doi.org/10.1097/CCE.0000000000000171DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418902PMC
August 2020

Development of AI-based pathology biomarkers in gastrointestinal and liver cancer.

Nat Rev Gastroenterol Hepatol 2020 10;17(10):591-592

Department of Pathology, Henri Mondor University Hospital, Créteil, France.

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http://dx.doi.org/10.1038/s41575-020-0343-3DOI Listing
October 2020

Effects of Label Noise on Deep Learning-Based Skin Cancer Classification.

Front Med (Lausanne) 2020 6;7:177. Epub 2020 May 6.

National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.

Recent studies have shown that deep learning is capable of classifying dermatoscopic images at least as well as dermatologists. However, many studies in skin cancer classification utilize non-biopsy-verified training images. This imperfect ground truth introduces a systematic error, but the effects on classifier performance are currently unknown. Here, we systematically examine the effects of label noise by training and evaluating convolutional neural networks (CNN) with 804 images of melanoma and nevi labeled either by dermatologists or by biopsy. The CNNs are evaluated on a test set of 384 images by means of 4-fold cross validation comparing the outputs with either the corresponding dermatological or the biopsy-verified diagnosis. With identical ground truths of training and test labels, high accuracies with 75.03% (95% CI: 74.39-75.66%) for dermatological and 73.80% (95% CI: 73.10-74.51%) for biopsy-verified labels can be achieved. However, if the CNN is trained and tested with different ground truths, accuracy drops significantly to 64.53% (95% CI: 63.12-65.94%, < 0.01) on a non-biopsy-verified and to 64.24% (95% CI: 62.66-65.83%, < 0.01) on a biopsy-verified test set. In conclusion, deep learning methods for skin cancer classification are highly sensitive to label noise and future work should use biopsy-verified training images to mitigate this problem.
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http://dx.doi.org/10.3389/fmed.2020.00177DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218064PMC
May 2020

Circulating levels of soluble urokinase plasminogen activator receptor predict outcome after resection of biliary tract cancer.

JHEP Rep 2020 Apr 31;2(2):100080. Epub 2020 Jan 31.

Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074 Aachen, Germany.

Background & Aims: Surgical resection is the only potentially curative therapy for patients with biliary tract cancer (BTC), but 5-year survival rates after tumor resection have remained below 30%, corroborating the need for better stratification tools to identify the ideal surgical candidates. The soluble urokinase plasminogen activator receptor (suPAR) represents a mediator of inflammation and has been associated with distinct types of cancer. In this study, we evaluated a potential role of suPAR as a novel biomarker in patients undergoing BTC resection.

Methods: Tumor expression of uPAR was analyzed by immunohistochemistry in 108 BTC samples. Serum levels of suPAR were analyzed by ELISA in a training and validation cohort comprising a total of 117 patients with BTC and 76 healthy controls.

Results: High tumoral uPAR expression was associated with an adverse outcome after BTC resection. Accordingly, circulating levels of suPAR were significantly elevated in patients with BTC compared to healthy controls, as well as in patients with primary sclerosing cholangitis. Using a small training set, we established an optimal prognostic suPAR cut-off value of 3.72 ng/ml for patients with BTC. Importantly, preoperative suPAR serum levels above this cut-off value were associated with significantly impaired overall survival in both the training and validation cohort. Multivariate Cox-regression analysis including various clinicopathological parameters such as tumor stage, markers of inflammation and organ dysfunction, as well as tumor markers, revealed circulating suPAR levels as an independent prognostic marker following BTC resection. Finally, high preoperative suPAR levels were indicative of acute kidney injury after tumor resection.

Conclusion: Circulating suPAR represents a previously unrecognized biomarker in patients with resectable BTC, which might help to preoperatively identify the ideal candidates for liver surgery.

Lay Summary: Surgical resection represents the only curative treatment option for patients with biliary tract cancer, but not all patients benefit to the same extent in terms of overall survival. Here, we provide evidence that serum levels of an inflammatory mediator (suPAR) are indicative of a patient's postoperative outcome and might thus help to identify the ideal surgical candidates.
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http://dx.doi.org/10.1016/j.jhepr.2020.100080DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7049662PMC
April 2020

Aryl hydrocarbon receptor nuclear translocator-like (ARNTL/BMAL1) is associated with bevacizumab resistance in colorectal cancer via regulation of vascular endothelial growth factor A.

EBioMedicine 2019 Jul 9;45:139-154. Epub 2019 Jul 9.

Department of Medicine II, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.

Background: The identification of new biomarkers and the development of novel, targetable contexts of vulnerability are of urgent clinical need in drug-resistant metastatic colorectal cancer (mCRC). Aryl-Hydrocarbon-Receptor-Nuclear-Translocator-Like (ARNTL/BMAL1) is a circadian clock-regulated transcription factor promoting expression of genes involved in angiogenesis and tumour progression. We hypothesised that BMAL1 increases expression of the vascular endothelial growth factor A VEGFA gene and, thereby, confers resistance to anti-angiogenic therapy with bevacizumab (Beva), a clinically used antibody for neutralization of VEGFA.

Methods: PCR and immunohistochemistry were employed to assess BMAL1 expression in mice (C57BL/6 J ; BALB/c xenografts) and CRC patients under combination chemotherapy with Beva. BMAL1 single nucleotide gene polymorphisms (SNPs) were analysed by DNA-microarray in clinical samples. BMAL1 functions were studied in human CRC cell lines using colorimetric growth, DNA-binding and reporter assays.

Findings: In murine CRCs, high BMAL1 expression correlated with poor preclinical response to Beva treatment. In CRC patients' tumours (n = 74), high BMAL1 expression was associated with clinical non-response to combination chemotherapy with Beva (*p = .0061) and reduced progression-free survival (PFS) [*p = .0223, Hazard Ratio (HR) = 1.69]. BMAL1 SNPs also correlated with shorter PFS (rs7396943, rs7938307, rs2279287) and overall survival (OS) [rs11022780, *p = .014, HR = 1.61]. Mechanistically, Nuclear-Receptor-Subfamily-1-Group-D-Member-1 (NR1D1/REVERBA) bound a - 672 bp Retinoic-Acid-Receptor-Related-Orphan-Receptor-Alpha-responsive-element (RORE) adjacent to a BMAL1 DNA-binding motif (E-box) in the VEGFA gene promoter, resulting in increased VEGFA synthesis and proliferation of human CRC cell lines.

Interpretation: BMAL1 was associated with Beva resistance in CRC. Inhibition of REVERBA-BMAL1 signalling may prevent resistance to anti-angiogenic therapy. FUND: This work was in part supported by the European Commission Seventh Framework Programme (Contract No. 278981 [ANGIOPREDICT]).
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http://dx.doi.org/10.1016/j.ebiom.2019.07.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642438PMC
July 2019

High baseline soluble urokinase plasminogen activator receptor (suPAR) serum levels indicate adverse outcome after resection of pancreatic adenocarcinoma.

Carcinogenesis 2019 08;40(8):947-955

Department of Medicine III, Hepatology and Hepatobiliary Oncology, Pauwelsstrasse, Aachen, Germany.

Surgical resection represents the only potentially curative therapy for patients with pancreatic adenocarcinoma (PDAC), an aggressive malignancy with a very limited 5-year survival rate. However, even after complete tumor resection, many patients are still facing an unfavorable prognosis underlining the need for better preoperative stratification algorithms. Here, we explored the role of the secreted glycoprotein soluble urokinase plasminogen activator receptor (suPAR) as a novel circulating biomarker for patients undergoing resection of PDAC. Serum levels of suPAR were measured by enzyme-linked immunosorbent assay (ELISA) in an exploratory as well as a validation cohort comprising a total of 127 PDAC patients and 75 healthy controls. Correlating with a cytoplasmic immunohistochemical expression of uPAR in PDAC tumor cells, serum levels of suPAR were significantly elevated in PDAC patients compared to healthy controls and patient with PDAC precursor lesions. Importantly, patients with high preoperative suPAR levels above a calculated cutoff value of 5.956 ng/ml showed a significantly reduced overall survival after tumor resection. The prognostic role of suPAR was further corroborated by uni- and multivariate Cox-regression analyses including parameters of systemic inflammation, liver and kidney function as well as clinico-pathological patients' characteristics. Moreover, high baseline suPAR levels identified those patients particularly susceptible to acute kidney injury and surgical complications after surgery. In conclusion, our data suggest that circulating suPAR represents a novel prognostic marker in PDAC patients undergoing tumor resection that might be a useful addition to existing preoperative stratification algorithms for identifying patients that particularly benefit from extended tumor resection.
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http://dx.doi.org/10.1093/carcin/bgz033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6735890PMC
August 2019

Serum levels of miR-29, miR-122, miR-155 and miR-192 are elevated in patients with cholangiocarcinoma.

PLoS One 2019 17;14(1):e0210944. Epub 2019 Jan 17.

Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.

Objectives: Cholangiocarcinoma (CCA) represents the second most common primary hepatic malignancy. Despite tremendous research activities, the prognosis for the majority of patients is still poor. Only in case of early diagnosis, liver resection might potentially lead to long-term survival. However, it is still unclear which patients benefit most from extensive liver surgery, highlighting the need for new diagnostic and prognostic stratification strategies.

Methods: Serum concentrations of a 4 miRNA panel (miR-122, miR-192, miR-29b and miR-155) were analyzed using semi-quantitative reverse-transcriptase PCR in serum samples from 94 patients with cholangiocarcinoma undergoing tumour resection and 40 healthy controls. Results were correlated with clinical data.

Results: Serum concentrations of miR-122, miR-192, miR-29b and miR-155 were significantly elevated in patients with CCA compared to healthy controls or patients with primary sclerosing cholangitis without malignant transformation. Although preoperative levels of these miRNAs were unsuitable as a prognostic marker of survival, a strong postoperative decline of miR-122 serum levels was significantly associated with a favorable patients' prognosis.

Conclusions: Analysis of circulating miRNAs represents a promising tool for the diagnosis of even early stage CCA. A postoperative decline in miRNA serum concentrations might be indicative for a favorable patients' outcome and helpful to identify patients with a good prognosis after extended liver surgery.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210944PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6336320PMC
October 2019

Angiopoietin-1 is regulated by miR-204 and contributes to corneal neovascularization in KLEIP-deficient mice.

Invest Ophthalmol Vis Sci 2014 Jun 10;55(7):4295-303. Epub 2014 Jun 10.

Department of Vascular Biology and Tumor Angiogenesis, Center for Biomedicine and Medical Technology Mannheim (CBTM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany Division of Vascular Oncology and Metastasis, German Cancer Research Center (DKFZ-ZMBH Alliance), Heidelberg, Germany.

Purpose: Corneal neovascularization can cause loss of vision. The introduction of anti-VEGF therapy has been a major improvement in therapeutic options. Recently, we established Kelch-like Ect2-interacting protein (KLEIP/KLHL20) knockout mice as a model of spontaneous corneal neovascular dystrophy. The aim of the present study was to characterize corneal neovascularization in progressive corneal dystrophy in KLEIP(-/-) mice, to evaluate the efficacy of anti-VEGF therapy, and to identify novel molecular regulators in this experimental model.

Methods: Corneal neovascularization was assessed by immunohistochemistry. Vascular endothelial growth factor signaling was inhibited by injection of a blocking antibody. Microarrays were used to measure expression of mRNA and microRNA (miRNA) in dystrophic corneae. Results were validated by immunohistochemistry and Western blotting.

Results: Blood vessels and lymphatics grew from the limbus toward the dystrophic epithelium in corneae of KLEIP(-/-) mice. Blocking VEGF signaling did not reduce phenotype progression. Correspondingly, microarray analysis revealed no upregulation of canonical vascular growth factors in late dystrophy. During phenotype progression, angiopoietin-1 expression increased while miR-204 expression decreased. Bioinformatic analysis identified a binding site for miR-204 in the angiopoietin-1 gene. Validation by in vitro experiments confirmed regulation of angiopoietin-1 by miR-204.

Conclusions: Vascular endothelial growth factor does not act as a major player in corneal neovascularization in KLEIP(-/-) mice. However, the proangiogenic factor angiopoietin-1 was strongly upregulated in late-stage phenotype, correlating with loss of miR-204 expression. Correspondingly, we identified miR-204 as a novel regulator of angiopoietin-1 in vitro. These findings may explain the incomplete efficacy of anti-VEGF therapy in the clinic and may provide new candidates for pharmaceutical intervention.
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http://dx.doi.org/10.1167/iovs.13-13619DOI Listing
June 2014
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