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The author discusses aspects of internal medicine and oncology with his fascination on stem cell transplantation in treating intractable diseases in humans. He narrates his experiences and opportunities to work on a gene therapy project with Hans-Peter Kiem and Friedrich Schuening in Rainier Storb's division at the Hutch in Seattle, Washington. The author thinks that stable integration of retrovirus vectors from biological inert can help decipher the structure of human hematopoiesis.

Recently, unusual forms of leukemias have developed as complications following retroviral transfer of potentially therapeutic genes into hematopoietic cells. A crucial component in the pathogenesis of these complications was the upregulation of a cellular proto-oncogene by random insertion of the retroviral gene transfer vector. These findings have great implications for the genetic manipulation of somatic stem cells in medicine. This review discusses the extent to which the random oncogene activation may have required disease-specific stimuli of the transgene and the hematopoietic milieu to become leukemogenic. Based on these considerations, we propose approaches to risk prediction and prevention. [Copyright &y& Elsevier]

The article comments on several research articles related to methods for identifying off-target double-strand breaks produced by CRISPR-associated (Cas)9 nucleases and transcription activator?like effector nucleases. Topics discussed include application of method that detects trapping of an integrase-defective lentiviral vector into doublestrand breaks by nonrestrictive linear amplification?mediated polymerase chain reaction, and use of high-throughput, genome-wide, translocation sequencing.

Gene therapy targeting hematopoietic cells has arrived at a new stage of potency. While the potential for curing inherited disorders of the immune system has been demonstrated in clinical trials, we were also confronted with the first serious adverse events related to random insertion of foreign DNA into cellular chromosomes. As it is likely that the manifestation of severe side effects results from a multifactorial process, it will be of crucial importance to define the significance of the individual risk factors involved. The future of the field will depend on our ability to define risk classifications of clinical approaches, to continuously improve transgene technologies, and to introduce new concepts for targeted selection of transgenic cells. Interestingly, correction of genetic disorders by homologous gene repair in defined stem cell clones is on the horizon, but far from being available for clinical use.Copyright © 2003 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

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. 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). 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. 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. 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. • Convolutional neural networks (CNNs) show potential in skin cancer diagnoses. • Minor dermoscopic image changes suffice to influence the CNNs' diagnosis. • CNNs' susceptibility to such changes can be reduced but not eliminated. • Practitioners need to be aware and attempt to minimise this phenomenon. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC). Methods: Digitised haematoxylin and eosin-stained slides from The Cancer Genome Atlas were used as a training set for a vision transformer (ViT) to extract image features with a self-supervised model called DINO (self-distillation with no labels). Extracted features were used in Cox regression models to prognosticate OS and DSS. Kaplan–Meier for univariable evaluation and Cox regression analyses for multivariable evaluation of the DINO-ViT risk groups were performed for prediction of OS and DSS. For validation, a cohort from a tertiary care centre was used. Results: A significant risk stratification was achieved in univariable analysis for OS and DSS in the training (n = 443, log rank test, p < 0.01) and validation set (n = 266, p < 0.01). In multivariable analysis, including age, metastatic status, tumour size and grading, the DINO-ViT risk stratification was a significant predictor for OS (hazard ratio [HR] 3.03; 95%-confidence interval [95%-CI] 2.11–4.35; p < 0.01) and DSS (HR 4.90; 95%-CI 2.78–8.64; p < 0.01) in the training set but only for DSS in the validation set (HR 2.31; 95%-CI 1.15–4.65; p = 0.02). DINO-ViT visualisation showed that features were mainly extracted from nuclei, cytoplasm, and peritumoural stroma, demonstrating good interpretability. Conclusion: The DINO-ViT can identify high-risk patients using histological images of ccRCC. This model might improve individual risk-adapted renal cancer therapy in the future. [ABSTRACT FROM AUTHOR]

Background: Over the last decades numerous initiatives have been set up that aim at translating the best available medical knowledge and treatment into clinical practice. The inherent complexity of the programs and discrepancies in the terminology used make it difficult to appreciate each of them distinctly and compare their specific strengths and weaknesses. To allow comparison and stimulate dialogue between different programs, we in this paper provide an overview of the German Cancer Society certification program for multidisciplinary cancer centers that was established in 2003.Main Body: In the early 2000s the German Cancer Society assessed the available information on quality of cancer care in Germany and concluded that there was a definite need for a comprehensive, transparent and evidence-based system of quality assessment and control. This prompted the development and implementation of a voluntary cancer center certification program that was promoted by scientific societies, health-care providers, and patient advocacy groups and based on guidelines of the highest quality level (S3). The certification system structures the entire process of care from prevention to screening and multidisciplinary treatment of cancer and places multidisciplinary teams at the heart of this program. Within each network of providers, the quality of care is documented using tumor-specific quality indicators. The system started with breast cancer centers in 2003 and colorectal cancer centers in 2006. In 2017, certification systems are established for the majority of cancers. Here we describe the rationale behind the certification program, its history, the development of the certification requirements, the process of data collection, and the certification process as an example for the successful implementation of a voluntary but powerful system to ensure and improve quality of cancer care.Conclusion: Since 2003, over 1 million patients had their primary tumors treated in a certified center. There are now over 1200 sites for different tumor entities in four countries that have been certified in accordance with the program and transparently report their results from multidisciplinary treatment for a substantial proportion of cancers. This led to a fundamental change in the structure of cancer care in Germany and neighboring countries within one decade. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Sustained Correction of X-Linked Severe Combined Immunodeficiency by Ex Vivo Gene Therapy," in the April 18, 2002 issue.

PURPOSE: The combination of whole-genome and transcriptome sequencing (WGTS) is expected to transform diagnosis and treatment for patients with cancer. WGTS is a comprehensive precision diagnostic test that is starting to replace the standard of care for oncology molecular testing in health care systems around the world; however, the implementation and widescale adoption of this best-in-class testing is lacking. METHODS: Here, we address the barriers in integrating WGTS for cancer diagnostics and treatment selection and answer questions regarding utility in different cancer types, cost-effectiveness and affordability, and other practical considerations for WGTS implementation. RESULTS: We review the current studies implementing WGTS in health care systems and provide a synopsis of the clinical evidence and insights into practical considerations for WGTS implementation. We reflect on regulatory, costs, reimbursement, and incidental findings aspects of this test. CONCLUSION: WGTS is an appropriate comprehensive clinical test for many tumor types and can replace multiple, cascade testing approaches currently performed. Decreasing sequencing cost, increasing number of clinically relevant aberrations and discovery of more complex biomarkers of treatment response, should pave the way for health care systems and laboratories in implementing WGTS into clinical practice, to transform diagnosis and treatment for patients with cancer. Barriers to adoption of whole-genome and transcriptome sequencing (WGTS) in routine cancer diagnostics [ABSTRACT FROM AUTHOR]

Image-based cancer classifiers suffer from a variety of problems which negatively affect their performance. For example, variation in image brightness or different cameras can already suffice to diminish performance. Ensemble solutions, where multiple model predictions are combined into one, can improve these problems. However, ensembles are computationally intensive and less transparent to practitioners than single model solutions. Constructing model soups, by averaging the weights of multiple models into a single model, could circumvent these limitations while still improving performance. To investigate the performance of model soups for a dermoscopic melanoma-nevus skin cancer classification task with respect to (1) generalisation to images from other clinics, (2) robustness against small image changes and (3) calibration such that the confidences correspond closely to the actual predictive uncertainties. We construct model soups by fine-tuning pre-trained models on seven different image resolutions and subsequently averaging their weights. Performance is evaluated on a multi-source dataset including holdout and external components. We find that model soups improve generalisation and calibration on the external component while maintaining performance on the holdout component. For robustness, we observe performance improvements for pertubated test images, while the performance on corrupted test images remains on par. Overall, souping for skin cancer classifiers has a positive effect on generalisation, robustness and calibration. It is easy for practitioners to implement and by combining multiple models into a single model, complexity is reduced. This could be an important factor in achieving clinical applicability, as less complexity generally means more transparency. • Artificial intelligence-based skin cancer classifiers suffer from generalisation and robustness issues. • Ensemble solutions improve these issues but are expensive and complex. • Model soups combine multiple models into a single model of equal size and complexity. • Model soups improve classifier performance on images from other clinics. • They also have a positive effect on model robustness and calibration. [ABSTRACT FROM AUTHOR]

For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby risk stratification. Thus, we investigated whether a convolutional neural network (CNN) can extract relevant image features from a representative hematoxylin and eosin-stained slide to predict 5-year overall survival (5y-OS) in ccRCC. The CNN was trained to predict 5y-OS in a binary manner using slides from TCGA and validated using an independent in-house cohort. Multivariable logistic regression was used to combine of the CNNs prediction and clinicopathological parameters. A mean balanced accuracy of 72.0% (standard deviation [SD] = 7.9%), sensitivity of 72.4% (SD = 10.6%), specificity of 71.7% (SD = 11.9%) and area under receiver operating characteristics curve (AUROC) of 0.75 (SD = 0.07) was achieved on the TCGA training set (n = 254 patients / WSIs) using 10-fold cross-validation. On the external validation cohort (n = 99 patients / WSIs), mean accuracy, sensitivity, specificity and AUROC were 65.5% (95%-confidence interval [CI]: 62.9–68.1%), 86.2% (95%-CI: 81.8–90.5%), 44.9% (95%-CI: 40.2–49.6%), and 0.70 (95%-CI: 0.69–0.71). A multivariable model including age, tumor stage and metastasis yielded an AUROC of 0.75 on the TCGA cohort. The inclusion of the CNN-based classification (Odds ratio = 4.86, 95%-CI: 2.70–8.75, p < 0.01) raised the AUROC to 0.81. On the validation cohort, both models showed an AUROC of 0.88. In univariable Cox regression, the CNN showed a hazard ratio of 3.69 (95%-CI: 2.60–5.23, p < 0.01) on TCGA and 2.13 (95%-CI: 0.92–4.94, p = 0.08) on external validation. The results demonstrate that the CNN's image-based prediction of survival is promising and thus this widely applicable technique should be further investigated with the aim of improving existing risk stratification in ccRCC. [ABSTRACT FROM AUTHOR]

Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making by such algorithms is essentially a black-box process that renders it difficult for physicians to judge whether the decisions are reliable. The use of explainable artificial intelligence (XAI) is often suggested as a solution to this problem. We investigate how XAI is used for skin cancer detection: how is it used during the development of new DNNs? What kinds of visualisations are commonly used? Are there systematic evaluations of XAI with dermatologists or dermatopathologists? Google Scholar, PubMed, IEEE Explore, Science Direct and Scopus were searched for peer-reviewed studies published between January 2017 and October 2021 applying XAI to dermatological images: the search terms histopathological image, whole-slide image, clinical image, dermoscopic image, skin, dermatology, explainable, interpretable and XAI were used in various combinations. Only studies concerned with skin cancer were included. 37 publications fulfilled our inclusion criteria. Most studies (19/37) simply applied existing XAI methods to their classifier to interpret its decision-making. Some studies (4/37) proposed new XAI methods or improved upon existing techniques. 14/37 studies addressed specific questions such as bias detection and impact of XAI on man-machine-interactions. However, only three of them evaluated the performance and confidence of humans using CAD systems with XAI. XAI is commonly applied during the development of DNNs for skin cancer detection. However, a systematic and rigorous evaluation of its usefulness in this scenario is lacking. • No evaluation of explainable artificial intelligence (XAI) for skin cancer detection has been conducted to this date. • Overview of 37 studies using XAI on dermatological and dermato histological data. • Analysis of the usage of XAI to inform research on its role as part of computer-aided diagnosis (CAD) systems. [ABSTRACT FROM AUTHOR]

Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC). 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). 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. 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. 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. • We aimed to develop a new biomarker to predict lymph node status of patients with colorectal cancer (CRC). • Convolutional neural network–based image analysis and clinical data were used individually and in combination. • Models were trained and tested on haematoxylin and eosin slides of 3013 primary CRC tumours. • The combined model on the internal test set yielded the best results (area under receiver operating curve of 74.1%). • Studies should address generalisation of the image classifier to external data. [ABSTRACT FROM AUTHOR]

Retroviral vectors are unique in their ability to integrate their genome into the host genome of transduced cells. Several members of the retrovirus family show distinct pattern for preferential integration into the host genome. Despite many years of investigation, precise mechanisms of target site selection and the fundamental interplay of viral integrase and host cell proteins are still unknown. Improved methods to detect retroviral integrations genome-wide as well as recent advances on the retroviral integrase structure and integrase interacting proteins may lead to further uncover the process of retroviral target site selection. A better knowledge of these mechanisms and interactions will allow further improving safety of retroviral vectors for gene therapy by providing an opportunity to retarget retroviral integration into non-harmful genomic positions. [ABSTRACT FROM AUTHOR]

The curative potential of retroviral vectors for somatic gene therapy has been demonstrated impressively in several clinical trials leading to sustained long-term correction of the underlying genetic defect. Preclinical studies and clinical monitoring of gene modified hematopoietic stem and progenitor cells in patients have shown that biologically relevant vector induced side effects, ranging from in vitro immortalization to clonal dominance and oncogenesis in vivo, accompany therapeutic efficiency of integrating retroviral gene transfer systems. Most importantly, it has been demonstrated that the genotoxic potential is not identical among all retroviral vector systems designed for clinical application. Large scale viral integration site determination has uncovered significant differences in the target site selection of retrovirus subfamilies influencing the propensity for inducing genetic alterations in the host genome. In this review we will summarize recent insights gained on the mechanisms of insertional mutagenesis based on intrinsic target site selection of different retrovirus families. We will also discuss examples of side effects occurring in ongoing human gene therapy trials and future prospectives in the field. [ABSTRACT FROM AUTHOR]

The potential of in vivo lentivirus-mediated bone marrow stem cell gene transfer by bone cavity injection, which could take full advantage of any source of stem cells present there, has not been previously explored. Such an approach may avoid several difficulties encountered by ex vivo hematopoietic stem cell (HSC) gene transfer. We sought to determine if efficient gene transfer could be achieved in HSC and mesenchymal stem/progenitor cells (MSC) by intrafemoral injection of a lentivirus vector in mice. Four months after injection, up to 12% GFP-expressing cells were observed in myeloid and lymphoid subpopulations. Significant transduction efficiencies were seen in Lin−c-kit+Sca1+ HSC/progenitors and CFU with multilineage potential, which were also confirmed by duplex PCR analysis of progenitor-derived colonies. Four months after secondary BMT, we observed 8.1 to 15% vector+ CFU in all recipients. Integration analysis by LAM-PCR demonstrated that multiple transduced clones contributed to hematopoiesis in these animals. We also showed that GFP-expressing MSC retained multilineage differentiation potential, with 2.9 to 8.8% GFP-containing CFU-fibroblasts detected in both injected and BMT recipients. Our data provide evidence that adult stem cells in bone marrow can be efficiently transduced “in situ” by in vivo vector administration without preconditioning. This approach could lead to a novel application for treatment of human diseases.Molecular Therapy (2006) 14, 514–524; doi: 10.1016/j.ymthe.2006.05.014 [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. • Computational medical image analysis is rising in gastrointestinal cancer pathology. • Several studies deal with CNN–based identification of biomarkers on HE slides. • Results are promising but studies are at early stages. • Further studies are needed to assess performance and potential clinical usefulness. [ABSTRACT FROM AUTHOR]

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. The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured. 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. 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. 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. • AI-based skin cancer classifier testing is often limited to holdout data. • However, holdout testing can mask severe susceptibilities and shortcomings. • Therefore, testing on out-of-distribution (OOD) data should become standard practice. • We release a dermoscopic skin cancer image benchmark designed for OOD testing. • This benchmark incorporates various distribution shifts such as corrupted images. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. • Lymph node analysis conveys relevant prognostic information. • Surgical removal of lymph nodes can be associated with considerable morbidity. • Thus, biomarkers that allow prediction of lymph node status are needed. • We trained a CNN on H&E slides of node positive and -negative primary melanomas. • This classifier could predict sentinel node status to some extent. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses. 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. 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. 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%. 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. • Pathologists incorporate patient data in addition to clinical examination. • They put more emphasis on patient data if they are uncertain. • We investigated fusing histologic image/patient data within CNN-based classifiers. • State-of-the-art fusing approaches in general did not yield a performance benefit. • Mimicking humans by fusing patient data only if CNN was uncertain raised accuracy. [ABSTRACT FROM AUTHOR]

Historically, cancer diagnoses have been made by pathologists using two-dimensional histological slides. However, with the advent of digital pathology and artificial intelligence, slides are being digitised, providing new opportunities to integrate their information. Since nature is 3-dimensional (3D), it seems intuitive to digitally reassemble the 3D structure for diagnosis. To develop the first human-3D-melanoma-histology-model with full data and code availability. Further, to evaluate the 3D-simulation together with experienced pathologists in the field and discuss the implications of digital 3D-models for the future of digital pathology. A malignant melanoma of the skin was digitised via 3 µm cuts by a slide scanner; an open-source software was then leveraged to construct the 3D model. A total of nine pathologists from four different countries with at least 10 years of experience in the histologic diagnosis of melanoma tested the model and discussed their experiences as well as implications for future pathology. We successfully constructed and tested the first 3D-model of human melanoma. Based on testing, 88.9% of pathologists believe that the technology is likely to enter routine pathology within the next 10 years; advantages include a better reflectance of anatomy, 3D assessment of symmetry and the opportunity to simultaneously evaluate different tissue levels at the same time; limitations include the high consumption of tissue and a yet inferior resolution due to computational limitations. 3D-histology-models are promising for digital pathology of cancer and melanoma specifically, however, there are yet limitations which need to be carefully addressed. • Presentation of the first human 3D-melanoma-histology-model. • Nine pathologists from four different countries evaluated the model. • Implications, limitations and the likelihood of a 2D to 3D transition are discussed. • Further development is ensured by complete data and code availability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. • This article describes the first experiment on combining human and artificial intelligence for the classification of images suspicious of skin cancer. • The combination achieved a superior accuracy of 82.95% (compared to 81.59%/42.94% achieved by artificial/human intelligence alone). • The combination of human and artificial intelligence indicates superiority over a separated approach. [ABSTRACT FROM AUTHOR]

Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30–50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. Despite its high relevance for melanoma screening, neither clinicians nor computers are yet able to reliably predict a nevus' oncologic transformation. The cause of this lies in the static nature of lesion presentation in the current standard of care, both for clinicians and algorithms. The status quo makes it difficult to train algorithms (and clinicians) to precisely assess the likelihood of a benign skin lesion to transform into melanoma. In addition, it inhibits the precision of current algorithms since 'evolution' image features may not be part of their decision. The current literature reveals certain types of melanocytic nevi (i.e. 'spitzoid' or 'dysplastic' nevi) and criteria (i.e. visible vasculature) that, in general, appear to have a higher chance to transform into melanoma. However, owing to the cumulative nature of oncogenic mutations in melanoma, a more fine-grained early morphologic footprint is likely to be detectable by an algorithm. In this perspective article, the concept of melanoma prediction is further explored by the discussion of the evolution of melanoma, the concept for training of such a nevi classifier and the implications of early melanoma prediction for clinical practice. In conclusion, the authors believe that artificial intelligence trained on prospective image data could be transformative for skin cancer diagnostics by (a) predicting melanoma before it occurs (i.e. pre-in situ) and (b) further enhancing the accuracy of current melanoma classifiers. Necessary prospective images for this research are obtained via free mole-monitoring mobile apps. • Thirty percent to 50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. • Neither clinicians nor artificial intelligence (AI) algorithms are yet able to reliably predict a nevus' oncologic transformation. • AI trained on prospective image data could predict melanoma before it occurs and enhance the accuracy of diagnoses. [ABSTRACT FROM AUTHOR]

Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level precision. However, a statistically significant improvement compared with dermatologist classification has not been reported to date. For this comparative study, 4204 biopsy-proven images of melanoma and nevi (1:1) were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated. For the experiment, an additional 804 biopsy-proven dermoscopic images of melanoma and nevi (1:1) were randomly presented to dermatologists of nine German university hospitals, who evaluated the quality of each image and stated their recommended treatment (19,296 recommendations in total). Three McNemar's tests comparing the results of the CNN's test runs in terms of sensitivity, specificity and overall correctness were predefined as the main outcomes. The respective sensitivity and specificity of lesion classification by the dermatologists were 67.2% (95% confidence interval [CI]: 62.6%–71.7%) and 62.2% (95% CI: 57.6%–66.9%). In comparison, the trained CNN achieved a higher sensitivity of 82.3% (95% CI: 78.3%–85.7%) and a higher specificity of 77.9% (95% CI: 73.8%–81.8%). The three McNemar's tests in 2 × 2 tables all reached a significance level of p < 0.001. This significance level was sustained for both subgroups. For the first time, automated dermoscopic melanoma image classification was shown to be significantly superior to both junior and board-certified dermatologists (p < 0.001). • Recent publications demonstrated that deep learning is capable to classify images of benign nevi and melanoma with dermatologist-level precision. • A systematic outperformance of dermatologists was not demonstrated to date. • This study shows the first systematic (p < 0.001) outperformance of board-certified dermatologists in dermoscopic melanoma image classification. [ABSTRACT FROM AUTHOR]

The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25–26% of discordance for classifying a benign nevus versus malignant melanoma. A recent study indicated the potential of deep learning to lower these discordances. However, the performance of deep learning in classifying histopathologic melanoma images was never compared directly to human experts. The aim of this study is to perform such a first direct comparison. A total of 695 lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides of these lesions were digitalised via a slide scanner and then randomly cropped. A total of 595 of the resulting images were used to train a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison to 11 histopathologists. Three combined McNemar tests comparing the results of the CNNs test runs in terms of sensitivity, specificity and accuracy were predefined to test for significance (p < 0.05). The CNN achieved a mean sensitivity/specificity/accuracy of 76%/60%/68% over 11 test runs. In comparison, the 11 pathologists achieved a mean sensitivity/specificity/accuracy of 51.8%/66.5%/59.2%. Thus, the CNN was significantly (p = 0.016) superior in classifying the cropped images. With limited image information available, a CNN was able to outperform 11 histopathologists in the classification of histopathological melanoma images and thus shows promise to assist human melanoma diagnoses. • A convolutional neural network (CNN) was trained with 595 histopathologic images of melanoma and nevi. • In a direct comparison, the CNN and 11 histopathologists classified a test set of 100 additional histopathologic images (1:1 melanoma/nevi). • The CNN systematically outperformed the 11 histopathologists in terms of overall accuracy, sensitivity and specificity (p = 0.016). [ABSTRACT FROM AUTHOR]

The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports 25–26% of discordance for classifying a benign nevus versus malignant melanoma. Deep learning was successfully implemented to enhance the precision of lung and breast cancer diagnoses. The aim of this study is to illustrate the potential of deep learning to assist human assessment for a histopathologic melanoma diagnosis. Six hundred ninety-five lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi and 345 melanomas). Only the haematoxylin and eosin stained (H&E) slides of these lesions were digitalised using a slide scanner and then randomly cropped. Five hundred ninety-five of the resulting images were used for the training of a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison with the original class labels. The total discordance with the histopathologist was 18% for melanoma (95% confidence interval [CI]: 7.4–28.6%), 20% for nevi (95% CI: 8.9–31.1%) and 19% for the full set of images (95% CI: 11.3–26.7%). Even in the worst case, the discordance of the CNN was about the same compared with the discordance between human pathologists as reported in the literature. Despite the vastly reduced amount of data, time necessary for diagnosis and cost compared with the pathologist, our CNN archived on-par performance. Conclusively, CNNs indicate to be a valuable tool to assist human melanoma diagnoses. • A convolutional neural network (CNN) was trained with 595 histopathologic images of melanomas and nevi that were classified by an expert dermatohistopathologist. • The CNN was then tested with 100 additional images (melanoma/nevi = 1:1) and revealed a discordance of only 19% to the histopathologist. • Thus, even in the worst case, the discordance of the CNN is about the same compared with the discordance between human pathologists as reported in the literature (25–26%). [ABSTRACT FROM AUTHOR]

Background: In recent months, multiple publications have demonstrated the use of convolutional neural networks (CNN) to classify images of skin cancer as precisely as dermatologists. However, these CNNs failed to outperform the International Symposium on Biomedical Imaging (ISBI) 2016 challenge which ranked the average precision for classification of dermoscopic melanoma images. Accordingly, the technical progress represented by these studies is limited. In addition, the available reports are impossible to reproduce, due to incomplete descriptions of training procedures and the use of proprietary image databases or non-disclosure of used images. These factors prevent the comparison of various CNN classifiers in equal terms. Objective: To demonstrate the training of an image-classifier CNN that outperforms the winner of the ISBI 2016 CNNs challenge by using open source images exclusively. Methods: A detailed description of the training procedure is reported while the used images and test sets are disclosed fully, to insure the reproducibility of our work. Results: Our CNN classifier outperforms all recent attempts to classify the original ISBI 2016 challenge test data (full set of 379 test images), with an average precision of 0.709 (vs. 0.637 of the ISBI winner) and with an area under the receiver operating curve of 0.85. Conclusion: This work illustrates the potential for improving skin cancer classification with enhanced training procedures for CNNs, while avoiding the use of costly equipment or proprietary image data. [ABSTRACT FROM AUTHOR]

Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%–100%) and 60% (range 21.3%–91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%–91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%–95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. • A convolutional neural network (CNN) received enhanced training with 12,378 open-source dermoscopic images. • In a head-to-head comparison, the CNN outperformed 136 of 157 participating dermatologists. • The CNN was capable to outperform dermatologists of all hierarchical subgroups (from junior to chief physicians) in dermoscopic melanoma image classification. [ABSTRACT FROM AUTHOR]

Abstract Background Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comparability of the performance of these algorithms and thereby the technical progress in this field. Methods An electronic questionnaire was sent to dermatologists at 12 German university hospitals. Each questionnaire comprised 100 dermoscopic and 100 clinical images (80 nevi images and 20 biopsy-verified melanoma images, each), all open-source. The questionnaire recorded factors such as the years of experience in dermatology, performed skin checks, age, sex and the rank within the university hospital or the status as resident physician. For each image, the dermatologists were asked to provide a management decision (treat/biopsy lesion or reassure the patient). Main outcome measures were sensitivity, specificity and the receiver operating characteristics (ROC). Results Total 157 dermatologists assessed all 100 dermoscopic images with an overall sensitivity of 74.1%, specificity of 60.0% and an ROC of 0.67 (range = 0.538–0.769); 145 dermatologists assessed all 100 clinical images with an overall sensitivity of 89.4%, specificity of 64.4% and an ROC of 0.769 (range = 0.613–0.9). Results between test-sets were significantly different (P < 0.05) confirming the need for a standardised benchmark. Conclusions We present the first public melanoma classification benchmark for both non-dermoscopic and dermoscopic images for comparing artificial intelligence algorithms with diagnostic performance of 145 or 157 dermatologists. Melanoma Classification Benchmark should be considered as a reference standard for white-skinned Western populations in the field of binary algorithmic melanoma classification. Highlights • This paper provides the first open access melanoma classification benchmark for both non-dermoscopic and dermoscopic images. • Algorithms can now be easily compared to the performance of dermatologists in terms of sensitivity, specificity and ROC. • The melanoma benchmark allows comparability between algorithms of different publications and provides a new reference standard. [ABSTRACT FROM AUTHOR]

Abstract Background Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%–100%) and 64.4% (range: 22.5%–92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%–86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. Highlights • A convolutional neural network (CNN) received enhanced training with 12,378 open-source dermoscopic images but was compared with dermatologists for classification of clinical images. • The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% and 64.4%. At the same sensitivity, the CNN exhibited a mean specificity of 68.2%. • For the first time, a CNN performed on par with dermatologists on a clinical image classification task without training on clinical images. [ABSTRACT FROM AUTHOR]

Data-sharing among genomic researchers is promoted for its potential to accelerate our understanding of the molecular basis of cancer. However, with genomic data sharing the risks of re-identifying study participants, revealing personal genomic information and data misuse might increase. This study aims at exploring perceptions of patients and physicians in Oncology regarding their assessment of the informational risks resulting from participating in whole genomic research studies in order to improve the informed consent process. For this purpose, we conducted a qualitative focus group study at the National Center for Tumor Diseases (NCT). Patients and oncologists assessed the informational risks either as minimal or as greater than minimal, depending on the context factors of occupational status, age, and patients' prognosis. Interestingly, even patients who assumed a greater risk did not refrain from participating in genomic research, provided that certain informational and institutional safeguards are implemented. Moreover, they expected comprehensive disclosure of the risks resulting from genomic data sharing. These results suggest (1) comprehensive disclosure of the risks of genomic research to potential study participants in genomic research to facilitate risk assessment and sound decision making, (2) establishing independent governance entities in order to minimize the informational risks of genomic research, and (3) implementing data sharing policies which offer guidance for physicians and researchers involved in genomic research. [ABSTRACT FROM AUTHOR]

Lentiviral vectors hold great promise for the genetic correction of various inherited diseases. However, lentiviral vector biology is still not completely understood and warrants the precise decoding of molecular mechanisms underlying integration and post-translational modification. This study investigated a series of self-inactivating (SIN) and full long terminal repeat (LTR) lentiviral vectors that contained different types of promoters with or without a transgene to gain deeper insights in lentiviral target site selection and potential perturbation of cellular gene expression. Using an optimized nonrestrictive linear amplification-mediated polymerase chain reaction (nrLAM-PCR) protocol, vector structure-dependent integration site profiles were observed upon transduction of mouse lin- hematopoietic progenitors in vitro. Initial target site selection mainly depended on the presence of the promoter while being independent of its nature. Despite the increased propensity for read-through transcription of SIN lentiviral vectors, the incidence of viral-cellular fusion transcript formation involving the canonical viral splice donor or cryptic splice sites was reduced in both unselected primary lin- cells and transformed 32D cells. Moreover, the strength of the internal promoter in vectors with SIN LTRs is decisive for in vitro selection and for the abundance of chimeric transcripts, which are decreased by moderately active promoters. These results will help to better understand vector biology and to optimize therapeutic vectors for future gene therapy applications. [ABSTRACT FROM AUTHOR]

Individual self-reactive T cells have been discovered in both humans and mice. It is difficult to assess the entire contained selfreactive peripheral T cell repertoire in healthy individuals because regulatory T cells (Tregs) can render these cells anergic and, therefore, functionally indistinguishable. We addressed this issue by removing regulatory T cells, thereby allowing us to characterize the exposed self-reactive T cells. This resulted in activation of approximately 4% of both CD4+ and CD8+ T cells. Activation and division of these cells was not a bystander product of Ag-independent signals but required TCR stimulation. Analysis of TCR sequences showed that these responding cells were polyclonal and encompassed a broad range of structural TCR diversity. Adoptive transfer of naive and effector/memory T cell populations showed that even the naive T cell pool contained selfreactive T cell precursors. In addition, transfer of mature thymocytes showed that this response was an intrinsic T cell property rather than a peripheral adaptation. Finally, we found that the unexpectedly strong contribution of the naive CD5low T cell pool showed that the overall self-reactive response has not only a diverse polyclonal TCR repertoire, but also comprises a broad range of affinities for self. [ABSTRACT FROM AUTHOR]

Hairy cell leukemia (HCL) is marked by near 100% mutational frequency of BRAFV600E mutations. Recurrent cooperating genetic events that may contribute to HCL pathogenesis or affect the clinical course of HCL are currently not described. Therefore, we performed whole exome sequencing to explore the mutational landscape of purine analog refractory HCL. In addition to the disease-defining BRAFV600E mutations, we identified mutations in EZH2, ARID1A, and recurrent inactivating mutations of the cell cycle inhibitor CDKN1B (p27). Targeted deep sequencing of CDKN1B in a larger cohort of HCL patients identify deleterious CDKN1B mutations in 16% of patients with HCL (n = 13 of 81). In 11 of 13 patients the CDKN1B mutation was clonal, implying an early role of CDKN1B mutations in the pathogenesis of HCL. CDKN1B mutations were not found to impact clinical characteristics or outcome in this cohort. These data identify HCL as having the highest frequency of CDKN1B mutations among cancers and identify CDNK1B as the second most common mutated gene in HCL. Moreover, given the known function of CDNK1B, these data suggest a novel role for alterations in regulation of cell cycle and senescence in HCL with CDKN1B mutations. [ABSTRACT FROM AUTHOR]

Background: Reactivation of latent viruses such as human cytomegalovirus (HCMV) after allogeneic hematopoietic stem cell transplantation (HSCT) results in high morbidity and mortality. Effective immunization against HCMV shortly after allo-HSCT is an unmet clinical need due to delayed adaptive T cell development. Donor-derived dendritic cells (DCs) have a critical participation in stimulation of naïve T cells and immune reconstitution, and therefore adoptive DC therapy could be used to protect patients after HSCT. However, previous methods for ex vivo generation of adoptive donor-derived DCs were complex and inconsistent, particularly regarding cell viability and potency after thawing. We have previously demonstrated in humanized mouse models of HSCT the proof-of-concept of a novel modality of lentivirus-induced DCs ("SmyleDCpp65") that accelerated antigen-specific T cell development. Methods: Here we demonstrate the feasibility of good manufacturing practices (GMP) for production of donorderived DCs consisting of monocytes from peripheral blood transduced with an integrase-defective lentiviral vector (IDLV, co-expressing GM-CSF, IFN-α and the cytomegalovirus antigen pp65) that were cryopreserved and thawed. Results: Upscaling and standardized production of one lot of IDLV and three lots of SmyleDCpp65 under GMP-compliant conditions were feasible. Analytical parameters for quality control of SmyleDCpp65 identity after thawing and potency after culture were defined. Cell recovery, uniformity, efficacy of gene transfer, purity and viability were high and consistent. SmyleDCpp65 showed only residual and polyclonal IDLV integration, unbiased to proto-oncogenic hot-spots. Stimulation of autologous T cells by GMP-grade SmyleDCpp65 was validated. Conclusion: These results underscore further developments of this individualized donor-derived cell vaccine to accelerate immune reconstitution against HCMV after HSCT in clinical trials. [ABSTRACT FROM AUTHOR]

Incidental findings are the subject of intense ethical debate in medical genomic research. Every human genome contains a number of potentially disease-causing alterations that may be detected during comprehensive genetic analyses to investigate a specific condition. Yet available evidence shows that the frequency of incidental findings in research is much lower than expected. In this Opinion, we argue that the reason for the low level of incidental findings is that the filtering techniques and methods that are applied during the routine handling of genomic data remove these alterations. As incidental findings are systematically filtered out, it is now time to evaluate whether the ethical debate is focused on the right issues. We conclude that the key question is whether to deliberately target and search for disease-causing variations outside the indication that has originally led to the genetic analysis, for instance by using positive lists and algorithms. [ABSTRACT FROM AUTHOR]

Background: Liver Transplantation (LT) is treatment of choice for patients with hepatocellular carcinoma (HCC) within MILAN Criteria. Tumour progression and subsequent dropout from waiting list have significant impact on the survival. Transarterial chemoembolization (TACE) controls tumour growth in the treated HCC nodule, however, the risk of tumour development in the untreated liver is increased by simultaneous release of neo-angiogenic factors. Due to its anti-angiogenic effects, Sorafenib delays the progression of HCC. Aim of this study was to determine whether combination of TACE and Sorafenib improves tumour control in HCC patients on waiting list for LT. Methods: Fifty patients were randomly assigned on a 1:1 ratio in double-blinded fashion at four centers in Germany and treated with TACE plus either Sorafenib (n = 24) or placebo (n = 26). The end of treatment was development of progressive disease according to mRECIST criteria or LT. The primary endpoint of the trial was the Time-to-Progression (TTP). Other efficacy endpoints were Tumour Response, Progression-free Survival (PFS), and Time-to-LT (TTLT). Results: The median time of treatment was 125 days with Sorafenib and 171 days with the placebo. Fourteen patients (seven from each group) developed tumour progression during the course of the study period. The Hazard Ratio of TTP was 1.106 (95% CI: 0.387, 3.162). The results of the Objective Response Rate, Disease Control Rate, PFS, and TTLT were comparable in both groups. The incidence of AEs was comparable in the placebo group (n = 23, 92%) and in the Sorafenib group (n = 23, 96%). Twelve patients (50%) on Sorafenib and four patients (16%) on placebo experienced severe treatment-related AEs. Conclusion: The TTP is similar after neo-adjuvant treatment with TACE and Sorafenib before LT compared to TACE and placebo. The Tumour Response, PFS, and TTLT were comparable. The safety profile of the Sorafenib group was similar to that of the placebo group. Trial registration: ISRCTN24081794. [ABSTRACT FROM AUTHOR]

Background: Liver Transplantation (LT) is treatment of choice for patients with hepatocellular carcinoma (HCC) within MILAN Criteria. Tumour progression and subsequent dropout from waiting list have significant impact on the survival. Transarterial chemoembolization (TACE) controls tumour growth in the treated HCC nodule, however, the risk of tumour development in the untreated liver is increased by simultaneous release of neo-angiogenic factors. Due to its anti-angiogenic effects, Sorafenib delays the progression of HCC. Aim of this study was to determine whether combination of TACE and Sorafenib improves tumour control in HCC patients on waiting list for LT. Methods: Fifty patients were randomly assigned on a 1:1 ratio in double-blinded fashion at four centers in Germany and treated with TACE plus either Sorafenib (n = 24) or placebo (n = 26). The end of treatment was development of progressive disease according to mRECIST criteria or LT. The primary endpoint of the trial was the Time-to-Progression (TTP). Other efficacy endpoints were Tumour Response, Progression-free Survival (PFS), and Time-to-LT (TTLT). Results: The median time of treatment was 125 days with Sorafenib and 171 days with the placebo. Fourteen patients (seven from each group) developed tumour progression during the course of the study period. The Hazard Ratio of TTP was 1.106 (95% CI: 0.387, 3.162). The results of the Objective Response Rate, Disease Control Rate, PFS, and TTLT were comparable in both groups. The incidence of AEs was comparable in the placebo group (n = 23, 92%) and in the Sorafenib group (n = 23, 96%). Twelve patients (50%) on Sorafenib and four patients (16%) on placebo experienced severe treatment-related AEs. Conclusion: The TTP is similar after neo-adjuvant treatment with TACE and Sorafenib before LT compared to TACE and placebo. The Tumour Response, PFS, and TTLT were comparable. The safety profile of the Sorafenib group was similar to that of the placebo group. Trial registration: ISRCTN24081794 [ABSTRACT FROM AUTHOR]

The article focuses on the genome editing technologies. The genomic editing technologies have scope for becoming powerful tools for gene therapy as they inactivate genes, correct mutated sequences or insert intact genes. The genomic editing field is advancing and are issues regarding development of appropriate preclinical assays to evaluate.

Gene therapy utilizing lentiviral-vectors (LVs) is postulated as a dynamic therapeutic alternative for monogenic diseases. However, retroviral gene transfer may cause insertional mutagenesis. Although, such risks had been originally estimated as extremely low, several reports of leukemias or clonal dominance, have led to a re-evaluation of the mechanisms operating in insertional mutagenesis. Therefore, unraveling the mechanism of retroviral integration is mandatory toward safer gene therapy applications. In the present study, we undertook an experimental approach which enabled direct correlation of the cell cycle stage of the target cell with the integration profile of LVs. CD34+ cells arrested at different stages of cell cycle, were transduced with a GFP-LV. LAM-PCR was employed for integration site detection, followed by microarray analysis to correlate transcribed genes with integration sites. The results indicate that ~10% of integration events occurred in actively transcribed genes and that the cell cycle stage of target cells affects integration pattern. Specifically, use of thymine promoted a safer profile, since it significantly reduced integration within cell cycle-related genes, while we observed increased possibility for integration into genes related to development, and decreased possibility for integration within cell cycle and cancer-related genes, when transduction occurs during mitosis. [ABSTRACT FROM AUTHOR]

Genome engineering with designer nucleases is a rapidly progressing field, and the ability to correct human gene mutations in situ is highly desirable. We employed fibroblasts derived from a patient with Fanconi anemia as a model to test the ability of the clustered regularly interspaced short palindromic repeats/Cas9 nuclease system to mediate gene correction. We show that the Cas9 nuclease and nickase each resulted in gene correction, but the nickase, because of its ability to preferentially mediate homology-directed repair, resulted in a higher frequency of corrected clonal isolates. To assess the off-target effects, we used both a predictive software platform to identify intragenic sequences of homology as well as a genome-wide screen utilizing linear amplification-mediated PCR. We observed no off-target activity and show RNA-guided endonuclease candidate sites that do not possess low sequence complexity function in a highly specific manner. Collectively, we provide proof of principle for precision genome editing in Fanconi anemia, a DNA repair-deficient human disorder. [ABSTRACT FROM AUTHOR]

We hypothesized that the combination of oncolytic virotherapy with immune checkpoint modulators would reduce tumor burden by direct cell lysis and stimulate antitumor immunity. In this study, we have generated attenuated Measles virus (MV) vectors encoding antibodies against CTLA-4 and PD-L1 (MV-aCTLA-4 and MV-aPD-L1). We characterized the vectors in terms of growth kinetics, antibody expression, and cytotoxicity in vitro. Immunotherapeutic effects were assessed in a newly established, fully immunocompetent murine model of malignant melanoma, B16-CD20. Analyses of tumor-infiltrating lymphocytes and restimulation experiments indicated a favorable immune profile after MV-mediated checkpoint modulation. Therapeutic benefits in terms of delayed tumor progression and prolonged median overall survival were observed for animals treated with vectors encoding anti-CTLA-4 and anti-PD-L1, respectively. Combining systemic administration of antibodies with MV treatment also improved therapeutic outcome. In vivo oncolytic efficacy against human tumors was studied in melanoma xenografts. MV-aCTLA-4 and MV-aPD-L1 were equally efficient as parental MV in this model, with high rates of complete tumor remission (> 80%). Furthermore, we could demonstrate lysis of tumor cells and transgene expression in primary tissue from melanoma patients. The current results suggest rapid translation of combining immune checkpoint modulation with oncolytic viruses into clinical application. [ABSTRACT FROM AUTHOR]

Standard integration-proficient lentiviral vectors (IPLVs) are effective at much lower doses than other vector systems and have shown promise for gene therapy of Parkinson's disease (PD). Their main drawback is the risk of insertional mutagenesis. The novel biosafety-enhanced integration-deficient lentiviral vectors (IDLVs) may offer a significant enhancement in biosafety, but have not been previously tested in a model of a major disease. We have assessed biosafety and transduction efficiency of IDLVs in a rat model of PD, using IPLVs as a reference. Genomic insertion of lentivectors injected into the lesioned striatum was studied by linear amplification-mediated polymerase chain reaction (PCR), followed by deep sequencing and insertion site analysis, demonstrating lack of significant IDLV integration. Reporter gene expression studies showed efficient, long-lived, and transcriptionally targeted expression from IDLVs injected ahead of lesioning in the rat striatum, although at somewhat lower expression levels than from IPLVs. Transgenic human glial cell line-derived neurotrophic factor ( hGDNF) expression from IDLVs was used for a long-term investigation of lentivector-mediated, transcriptionally targeted neuroprotection in this PD rat model. Vectors were injected before striatal lesioning, and the results showed improvements in nigral dopaminergic neuron survival and behavioral tests regardless of lentiviral integration proficiency, although they confirmed lower expression levels of hGDNF from IDLVs. These data demonstrate the effectiveness of IDLVs in a model of a major disease and indicate that these vectors could provide long-term PD treatment at low dose, combining efficacy and biosafety for targeted central nervous system applications. [ABSTRACT FROM AUTHOR]

Self-inactivating (SIN) lentiviral vectors (LV) have an excellent therapeutic potential as demonstrated in preclinical studies and clinical trials. However, weaker mechanisms of insertional mutagenesis could still pose a significant risk in clinical applications. Taking advantage of novel in vivo genotoxicity assays, we tested a battery of LV constructs, including some with clinically relevant designs, and found that oncogene activation by promoter insertion is the most powerful mechanism of early vector-induced oncogenesis. SIN LVs disabled in their capacity to activate oncogenes by promoter insertion were less genotoxic and induced tumors by enhancer-mediated activation of oncogenes with efficiency that was proportional to the strength of the promoter used. On the other hand, when enhancer activity was reduced by using moderate promoters, oncogenesis by inactivation of tumor suppressor gene was revealed. This mechanism becomes predominant when the enhancer activity of the internal promoter is shielded by the presence of a synthetic chromatin insulator cassette. Our data provide both mechanistic insights and quantitative readouts of vector-mediated genotoxicity, allowing a relative ranking of different vectors according to these features, and inform current and future choices of vector design with increasing biosafety. [ABSTRACT FROM AUTHOR]

Integration-deficient lentiviral vectors (IDLVs) have been shown to transduce a wide spectrum of target cells and organs in vitro and in vivo and to maintain long-term transgene expression in nondividing cells. However, epigenetic silencing of episomal vector genomes reduces IDLV transgene expression levels and renders these safe vectors less efficient. In this article, we describe for the first time a complete correction of factor IX (FIX) deficiency in hemophilia B mice by IDLVs carrying a novel, highly potent human FIX cDNA. A 50-fold increase in human FIX cDNA potency was achieved by combining two mechanistically independent yet synergistic strategies: (i) optimization of the human FIX cDNA codon usage to increase human FIX protein production per vector genome and (ii) generation of a highly catalytic mutant human FIX protein in which the arginine residue at position 338 was substituted with leucine. The enhanced human FIX activity was not associated with liver damage or with the formation of human FIX-directed inhibitory antibodies and rendered IDLV-treated FIX-knockout mice resistant to a challenging tail-clipping assay. A novel S1 nuclease-based B1-quantitative polymerase chain reaction assay showed low levels of IDLV integration in mouse liver. Overall, this study demonstrates that IDLVs carrying an improved human FIX cDNA safely and efficiently cure hemophilia B in a mouse model. [ABSTRACT FROM AUTHOR]

Acute intermittent porphyria (AIP) results from haplo-insufficient activity of porphobilinogen deaminase (PBGD) and is characterized clinically by life-threatening, acute neurovisceral attacks. To date, liver transplantation is the only curative option for AIP. The aim of the present preclinical nonhuman primate study was to determine the safety and transduction efficacy of an adeno-associated viral vector encoding PBGD (recombinant AAV serotype 5-codon-optimized human porphobilinogen deaminase, rAAV5- cohPBGD) administered intravenously as part of a safety program to start a clinical study in patients with AIP. Macaques injected with either 1×1013 or 5×1013 vector genomes/kg of clinical-grade rAAV5- cohPBGD were monitored by standardized clinical parameters, and vector shedding was analyzed. Liver transduction efficacy, biodistribution, vector integration, and histopathology at day 30 postvector administration were determined. There was no evidence of acute toxicity, and no adverse effects were observed. The vector achieved efficient and homogenous hepatocellular transduction, reaching transgenic PBGD expression levels equivalent to 50% of the naturally expressed PBGD mRNA. No cellular immune response was detected against the human PBGD or AAV capsid proteins. Integration site analysis in transduced liver cells revealed an almost random integration pattern supporting the good safety profile of rAAV5- cohPBGD. Together, data obtained in nonhuman primates indicate that rAAV5- cohPBGD represents a safe therapy to correct the metabolic defect present in AIP patients. [ABSTRACT FROM AUTHOR]